diff --git a/ldm/__pycache__/util.cpython-38.pyc b/ldm/__pycache__/util.cpython-38.pyc new file mode 100644 index 0000000000000000000000000000000000000000..45e84b0d6457f5514d4d5fc838b80cdb8ff7a49d Binary files /dev/null and b/ldm/__pycache__/util.cpython-38.pyc differ diff --git a/ldm/__pycache__/util.cpython-39.pyc b/ldm/__pycache__/util.cpython-39.pyc new file mode 100644 index 0000000000000000000000000000000000000000..575abe7cd7dd4427af15b8595ab346f5fb62c6a2 Binary files /dev/null and b/ldm/__pycache__/util.cpython-39.pyc differ diff --git a/ldm/data/__pycache__/extract_mel_spectrogram.cpython-38.pyc b/ldm/data/__pycache__/extract_mel_spectrogram.cpython-38.pyc new file mode 100644 index 0000000000000000000000000000000000000000..3066e382b98828ad3ac5884d7aabccba47f46330 Binary files /dev/null and b/ldm/data/__pycache__/extract_mel_spectrogram.cpython-38.pyc differ diff --git a/ldm/data/__pycache__/extract_mel_spectrogram.cpython-39.pyc b/ldm/data/__pycache__/extract_mel_spectrogram.cpython-39.pyc new file mode 100644 index 0000000000000000000000000000000000000000..649dbe8a8131ab6ddfaf3d56e415c0ea48ea163b Binary files /dev/null and b/ldm/data/__pycache__/extract_mel_spectrogram.cpython-39.pyc differ diff --git a/ldm/data/extract_mel_spectrogram.py b/ldm/data/extract_mel_spectrogram.py new file mode 100644 index 0000000000000000000000000000000000000000..42cade483a576f7166011a25d7e4d4bb0ae0f55c --- /dev/null +++ b/ldm/data/extract_mel_spectrogram.py @@ -0,0 +1,151 @@ +import argparse +import os +import os.path as P +from copy import deepcopy +from functools import partial +from glob import glob +from multiprocessing import Pool +from pathlib import Path + +import librosa +import numpy as np +import torchvision + + +class MelSpectrogram(object): + def __init__(self, sr, nfft, fmin, fmax, nmels, hoplen, spec_power, inverse=False): + self.sr = sr + self.nfft = nfft + self.fmin = fmin + self.fmax = fmax + self.nmels = nmels + self.hoplen = hoplen + self.spec_power = spec_power + self.inverse = inverse + + self.mel_basis = librosa.filters.mel(sr=sr, n_fft=nfft, fmin=fmin, fmax=fmax, n_mels=nmels) + + def __call__(self, x): + if self.inverse: + spec = librosa.feature.inverse.mel_to_stft( + x, sr=self.sr, n_fft=self.nfft, fmin=self.fmin, fmax=self.fmax, power=self.spec_power + ) + wav = librosa.griffinlim(spec, hop_length=self.hoplen) + return wav + else: + spec = np.abs(librosa.stft(x, n_fft=self.nfft, hop_length=self.hoplen)) ** self.spec_power + mel_spec = np.dot(self.mel_basis, spec) + return mel_spec + +class LowerThresh(object): + def __init__(self, min_val, inverse=False): + self.min_val = min_val + self.inverse = inverse + + def __call__(self, x): + if self.inverse: + return x + else: + return np.maximum(self.min_val, x) + +class Add(object): + def __init__(self, val, inverse=False): + self.inverse = inverse + self.val = val + + def __call__(self, x): + if self.inverse: + return x - self.val + else: + return x + self.val + +class Subtract(Add): + def __init__(self, val, inverse=False): + self.inverse = inverse + self.val = val + + def __call__(self, x): + if self.inverse: + return x + self.val + else: + return x - self.val + +class Multiply(object): + def __init__(self, val, inverse=False) -> None: + self.val = val + self.inverse = inverse + + def __call__(self, x): + if self.inverse: + return x / self.val + else: + return x * self.val + +class Divide(Multiply): + def __init__(self, val, inverse=False): + self.inverse = inverse + self.val = val + + def __call__(self, x): + if self.inverse: + return x * self.val + else: + return x / self.val + +class Log10(object): + def __init__(self, inverse=False): + self.inverse = inverse + + def __call__(self, x): + if self.inverse: + return 10 ** x + else: + return np.log10(x) + +class Clip(object): + def __init__(self, min_val, max_val, inverse=False): + self.min_val = min_val + self.max_val = max_val + self.inverse = inverse + + def __call__(self, x): + if self.inverse: + return x + else: + return np.clip(x, self.min_val, self.max_val) + +class TrimSpec(object): + def __init__(self, max_len, inverse=False): + self.max_len = max_len + self.inverse = inverse + + def __call__(self, x): + if self.inverse: + return x + else: + return x[:, :self.max_len] + +class MaxNorm(object): + def __init__(self, inverse=False): + self.inverse = inverse + self.eps = 1e-10 + + def __call__(self, x): + if self.inverse: + return x + else: + return x / (x.max() + self.eps) + + +TRANSFORMS_16000 = torchvision.transforms.Compose([ + MelSpectrogram(sr=16000, nfft=1024, fmin=125, fmax=7600, nmels=80, hoplen=1024//4, spec_power=1), + LowerThresh(1e-5), + Log10(), + Multiply(20), + Subtract(20), + Add(100), + Divide(100), + Clip(0, 1.0) + # TrimSpec(860) +]) + diff --git a/ldm/lr_scheduler.py b/ldm/lr_scheduler.py new file mode 100644 index 0000000000000000000000000000000000000000..be39da9ca6dacc22bf3df9c7389bbb403a4a3ade --- /dev/null +++ b/ldm/lr_scheduler.py @@ -0,0 +1,98 @@ +import numpy as np + + +class LambdaWarmUpCosineScheduler: + """ + note: use with a base_lr of 1.0 + """ + def __init__(self, warm_up_steps, lr_min, lr_max, lr_start, max_decay_steps, verbosity_interval=0): + self.lr_warm_up_steps = warm_up_steps + self.lr_start = lr_start + self.lr_min = lr_min + self.lr_max = lr_max + self.lr_max_decay_steps = max_decay_steps + self.last_lr = 0. + self.verbosity_interval = verbosity_interval + + def schedule(self, n, **kwargs): + if self.verbosity_interval > 0: + if n % self.verbosity_interval == 0: print(f"current step: {n}, recent lr-multiplier: {self.last_lr}") + if n < self.lr_warm_up_steps: + lr = (self.lr_max - self.lr_start) / self.lr_warm_up_steps * n + self.lr_start + self.last_lr = lr + return lr + else: + t = (n - self.lr_warm_up_steps) / (self.lr_max_decay_steps - self.lr_warm_up_steps) + t = min(t, 1.0) + lr = self.lr_min + 0.5 * (self.lr_max - self.lr_min) * ( + 1 + np.cos(t * np.pi)) + self.last_lr = lr + return lr + + def __call__(self, n, **kwargs): + return self.schedule(n,**kwargs) + + +class LambdaWarmUpCosineScheduler2: + """ + supports repeated iterations, configurable via lists + note: use with a base_lr of 1.0. + """ + def __init__(self, warm_up_steps, f_min, f_max, f_start, cycle_lengths, verbosity_interval=0): + assert len(warm_up_steps) == len(f_min) == len(f_max) == len(f_start) == len(cycle_lengths) + self.lr_warm_up_steps = warm_up_steps + self.f_start = f_start + self.f_min = f_min + self.f_max = f_max + self.cycle_lengths = cycle_lengths + self.cum_cycles = np.cumsum([0] + list(self.cycle_lengths)) + self.last_f = 0. + self.verbosity_interval = verbosity_interval + + def find_in_interval(self, n): + interval = 0 + for cl in self.cum_cycles[1:]: + if n <= cl: + return interval + interval += 1 + + def schedule(self, n, **kwargs): + cycle = self.find_in_interval(n) + n = n - self.cum_cycles[cycle] + if self.verbosity_interval > 0: + if n % self.verbosity_interval == 0: print(f"current step: {n}, recent lr-multiplier: {self.last_f}, " + f"current cycle {cycle}") + if n < self.lr_warm_up_steps[cycle]: + f = (self.f_max[cycle] - self.f_start[cycle]) / self.lr_warm_up_steps[cycle] * n + self.f_start[cycle] + self.last_f = f + return f + else: + t = (n - self.lr_warm_up_steps[cycle]) / (self.cycle_lengths[cycle] - self.lr_warm_up_steps[cycle]) + t = min(t, 1.0) + f = self.f_min[cycle] + 0.5 * (self.f_max[cycle] - self.f_min[cycle]) * ( + 1 + np.cos(t * np.pi)) + self.last_f = f + return f + + def __call__(self, n, **kwargs): + return self.schedule(n, **kwargs) + + +class LambdaLinearScheduler(LambdaWarmUpCosineScheduler2): + + def schedule(self, n, **kwargs): + cycle = self.find_in_interval(n) + n = n - self.cum_cycles[cycle] + if self.verbosity_interval > 0: + if n % self.verbosity_interval == 0: print(f"current step: {n}, recent lr-multiplier: {self.last_f}, " + f"current cycle {cycle}") + + if n < self.lr_warm_up_steps[cycle]: + f = (self.f_max[cycle] - self.f_start[cycle]) / self.lr_warm_up_steps[cycle] * n + self.f_start[cycle] + self.last_f = f + return f + else: + f = self.f_min[cycle] + (self.f_max[cycle] - self.f_min[cycle]) * (self.cycle_lengths[cycle] - n) / (self.cycle_lengths[cycle]) + self.last_f = f + return f + diff --git a/ldm/models/__pycache__/autoencoder.cpython-38.pyc b/ldm/models/__pycache__/autoencoder.cpython-38.pyc new file mode 100644 index 0000000000000000000000000000000000000000..8cdb769b35ee952347d5658d10b60588ff01fd9f Binary files /dev/null and b/ldm/models/__pycache__/autoencoder.cpython-38.pyc differ diff --git a/ldm/models/__pycache__/autoencoder.cpython-39.pyc b/ldm/models/__pycache__/autoencoder.cpython-39.pyc new file mode 100644 index 0000000000000000000000000000000000000000..a2b5da02572b40320828d278f585d7309df5a238 Binary files /dev/null and b/ldm/models/__pycache__/autoencoder.cpython-39.pyc differ diff --git a/ldm/models/__pycache__/autoencoder_multi.cpython-38.pyc b/ldm/models/__pycache__/autoencoder_multi.cpython-38.pyc new file mode 100644 index 0000000000000000000000000000000000000000..431b8752a4b51e48809bd1e849ea3c7493470232 Binary files /dev/null and b/ldm/models/__pycache__/autoencoder_multi.cpython-38.pyc differ diff --git a/ldm/models/autoencoder.py b/ldm/models/autoencoder.py new file mode 100644 index 0000000000000000000000000000000000000000..cd4529dfcc23562632fa9b059a87fa5af50f014d --- /dev/null +++ b/ldm/models/autoencoder.py @@ -0,0 +1,474 @@ +import os +import torch +import pytorch_lightning as pl +import torch.nn.functional as F +from contextlib import contextmanager +from packaging import version +import numpy as np +from ldm.modules.diffusionmodules.model import Encoder, Decoder +from ldm.modules.distributions.distributions import DiagonalGaussianDistribution +from torch.optim.lr_scheduler import LambdaLR +from ldm.util import instantiate_from_config +# from icecream import ic + +class VQModel(pl.LightningModule): + def __init__(self, + ddconfig, + lossconfig, + n_embed, + embed_dim, + ckpt_path=None, + ignore_keys=[], + image_key="image", + colorize_nlabels=None, + monitor=None, + batch_resize_range=None, + scheduler_config=None, + lr_g_factor=1.0, + remap=None, + sane_index_shape=False, # tell vector quantizer to return indices as bhw + use_ema=False + ): + super().__init__() + self.embed_dim = embed_dim + self.n_embed = n_embed + self.image_key = image_key + self.encoder = Encoder(**ddconfig) + self.decoder = Decoder(**ddconfig) + self.loss = instantiate_from_config(lossconfig) + self.quantize = VectorQuantizer(n_embed, embed_dim, beta=0.25, + remap=remap, + sane_index_shape=sane_index_shape) + self.quant_conv = torch.nn.Conv2d(ddconfig["z_channels"], embed_dim, 1) + self.post_quant_conv = torch.nn.Conv2d(embed_dim, ddconfig["z_channels"], 1) + if colorize_nlabels is not None: + assert type(colorize_nlabels)==int + self.register_buffer("colorize", torch.randn(3, colorize_nlabels, 1, 1)) + if monitor is not None: + self.monitor = monitor + self.batch_resize_range = batch_resize_range + if self.batch_resize_range is not None: + print(f"{self.__class__.__name__}: Using per-batch resizing in range {batch_resize_range}.") + + self.use_ema = use_ema + if self.use_ema: + self.model_ema = LitEma(self) + print(f"Keeping EMAs of {len(list(self.model_ema.buffers()))}.") + + if ckpt_path is not None: + self.init_from_ckpt(ckpt_path, ignore_keys=ignore_keys) + self.scheduler_config = scheduler_config + self.lr_g_factor = lr_g_factor + + @contextmanager + def ema_scope(self, context=None): + if self.use_ema: + self.model_ema.store(self.parameters()) + self.model_ema.copy_to(self) + if context is not None: + print(f"{context}: Switched to EMA weights") + try: + yield None + finally: + if self.use_ema: + self.model_ema.restore(self.parameters()) + if context is not None: + print(f"{context}: Restored training weights") + + def init_from_ckpt(self, path, ignore_keys=list()): + sd = torch.load(path, map_location="cpu")["state_dict"] + keys = list(sd.keys()) + for k in keys: + for ik in ignore_keys: + if k.startswith(ik): + print("Deleting key {} from state_dict.".format(k)) + del sd[k] + missing, unexpected = self.load_state_dict(sd, strict=False) + print(f"Restored from {path} with {len(missing)} missing and {len(unexpected)} unexpected keys") + if len(missing) > 0: + print(f"Missing Keys: {missing}") + print(f"Unexpected Keys: {unexpected}") + + def on_train_batch_end(self, *args, **kwargs): + if self.use_ema: + self.model_ema(self) + + def encode(self, x): + h = self.encoder(x) + h = self.quant_conv(h) + quant, emb_loss, info = self.quantize(h) + return quant, emb_loss, info + + def encode_to_prequant(self, x): + h = self.encoder(x) + h = self.quant_conv(h) + return h + + def decode(self, quant): + quant = self.post_quant_conv(quant) + dec = self.decoder(quant) + return dec + + def decode_code(self, code_b): + quant_b = self.quantize.embed_code(code_b) + dec = self.decode(quant_b) + return dec + + def forward(self, input, return_pred_indices=False): + quant, diff, (_,_,ind) = self.encode(input) + dec = self.decode(quant) + if return_pred_indices: + return dec, diff, ind + return dec, diff + + def get_input(self, batch, k): + x = batch[k] + if len(x.shape) == 3: + x = x[..., None] + x = x.permute(0, 3, 1, 2).to(memory_format=torch.contiguous_format).float() + if self.batch_resize_range is not None: + lower_size = self.batch_resize_range[0] + upper_size = self.batch_resize_range[1] + if self.global_step <= 4: + # do the first few batches with max size to avoid later oom + new_resize = upper_size + else: + new_resize = np.random.choice(np.arange(lower_size, upper_size+16, 16)) + if new_resize != x.shape[2]: + x = F.interpolate(x, size=new_resize, mode="bicubic") + x = x.detach() + return x + + def training_step(self, batch, batch_idx, optimizer_idx): + # https://github.com/pytorch/pytorch/issues/37142 + # try not to fool the heuristics + x = self.get_input(batch, self.image_key) + xrec, qloss, ind = self(x, return_pred_indices=True) + + if optimizer_idx == 0: + # autoencode + aeloss, log_dict_ae = self.loss(qloss, x, xrec, optimizer_idx, self.global_step, + last_layer=self.get_last_layer(), split="train", + predicted_indices=ind) + + self.log_dict(log_dict_ae, prog_bar=False, logger=True, on_step=True, on_epoch=True) + return aeloss + + if optimizer_idx == 1: + # discriminator + discloss, log_dict_disc = self.loss(qloss, x, xrec, optimizer_idx, self.global_step, + last_layer=self.get_last_layer(), split="train") + self.log_dict(log_dict_disc, prog_bar=False, logger=True, on_step=True, on_epoch=True) + return discloss + + def validation_step(self, batch, batch_idx): + log_dict = self._validation_step(batch, batch_idx) + with self.ema_scope(): + log_dict_ema = self._validation_step(batch, batch_idx, suffix="_ema") + return log_dict + + def _validation_step(self, batch, batch_idx, suffix=""): + x = self.get_input(batch, self.image_key) + xrec, qloss, ind = self(x, return_pred_indices=True) + aeloss, log_dict_ae = self.loss(qloss, x, xrec, 0, + self.global_step, + last_layer=self.get_last_layer(), + split="val"+suffix, + predicted_indices=ind + ) + + discloss, log_dict_disc = self.loss(qloss, x, xrec, 1, + self.global_step, + last_layer=self.get_last_layer(), + split="val"+suffix, + predicted_indices=ind + ) + rec_loss = log_dict_ae[f"val{suffix}/rec_loss"] + self.log(f"val{suffix}/rec_loss", rec_loss, + prog_bar=True, logger=True, on_step=False, on_epoch=True, sync_dist=True) + self.log(f"val{suffix}/aeloss", aeloss, + prog_bar=True, logger=True, on_step=False, on_epoch=True, sync_dist=True) + if version.parse(pl.__version__) >= version.parse('1.4.0'): + del log_dict_ae[f"val{suffix}/rec_loss"] + self.log_dict(log_dict_ae) + self.log_dict(log_dict_disc) + return self.log_dict + + def test_step(self, batch, batch_idx): + x = self.get_input(batch, self.image_key) + xrec, qloss, ind = self(x, return_pred_indices=True) + reconstructions = (xrec + 1)/2 # to mel scale + test_ckpt_path = os.path.basename(self.trainer.tested_ckpt_path) + savedir = os.path.join(self.trainer.log_dir,f'output_imgs_{test_ckpt_path}','fake_class') + if not os.path.exists(savedir): + os.makedirs(savedir) + + file_names = batch['f_name'] + # print(f"reconstructions.shape:{reconstructions.shape}",file_names) + reconstructions = reconstructions.cpu().numpy().squeeze(1) # squuze channel dim + for b in range(reconstructions.shape[0]): + vname_num_split_index = file_names[b].rfind('_')# file_names[b]:video_name+'_'+num + v_n,num = file_names[b][:vname_num_split_index],file_names[b][vname_num_split_index+1:] + save_img_path = os.path.join(savedir,f'{v_n}_sample_{num}.npy') + np.save(save_img_path,reconstructions[b]) + + return None + + def configure_optimizers(self): + lr_d = self.learning_rate + lr_g = self.lr_g_factor*self.learning_rate + print("lr_d", lr_d) + print("lr_g", lr_g) + opt_ae = torch.optim.Adam(list(self.encoder.parameters())+ + list(self.decoder.parameters())+ + list(self.quantize.parameters())+ + list(self.quant_conv.parameters())+ + list(self.post_quant_conv.parameters()), + lr=lr_g, betas=(0.5, 0.9)) + opt_disc = torch.optim.Adam(self.loss.discriminator.parameters(), + lr=lr_d, betas=(0.5, 0.9)) + + if self.scheduler_config is not None: + scheduler = instantiate_from_config(self.scheduler_config) + + print("Setting up LambdaLR scheduler...") + scheduler = [ + { + 'scheduler': LambdaLR(opt_ae, lr_lambda=scheduler.schedule), + 'interval': 'step', + 'frequency': 1 + }, + { + 'scheduler': LambdaLR(opt_disc, lr_lambda=scheduler.schedule), + 'interval': 'step', + 'frequency': 1 + }, + ] + return [opt_ae, opt_disc], scheduler + return [opt_ae, opt_disc], [] + + def get_last_layer(self): + return self.decoder.conv_out.weight + + def log_images(self, batch, only_inputs=False, plot_ema=False, **kwargs): + log = dict() + x = self.get_input(batch, self.image_key) + x = x.to(self.device) + if only_inputs: + log["inputs"] = x + return log + xrec, _ = self(x) + if x.shape[1] > 3: + # colorize with random projection + assert xrec.shape[1] > 3 + x = self.to_rgb(x) + xrec = self.to_rgb(xrec) + log["inputs"] = x + log["reconstructions"] = xrec + if plot_ema: + with self.ema_scope(): + xrec_ema, _ = self(x) + if x.shape[1] > 3: xrec_ema = self.to_rgb(xrec_ema) + log["reconstructions_ema"] = xrec_ema + return log + + def to_rgb(self, x): + assert self.image_key == "segmentation" + if not hasattr(self, "colorize"): + self.register_buffer("colorize", torch.randn(3, x.shape[1], 1, 1).to(x)) + x = F.conv2d(x, weight=self.colorize) + x = 2.*(x-x.min())/(x.max()-x.min()) - 1. + return x + + +class VQModelInterface(VQModel): + def __init__(self, embed_dim, *args, **kwargs): + super().__init__(embed_dim=embed_dim, *args, **kwargs) + self.embed_dim = embed_dim + + def encode(self, x):# VQModel的quantize写在encoder里,VQModelInterface则将其写在decoder里 + h = self.encoder(x) + h = self.quant_conv(h) + return h + + def decode(self, h, force_not_quantize=False): + # also go through quantization layer + if not force_not_quantize: + quant, emb_loss, info = self.quantize(h) + else: + quant = h + quant = self.post_quant_conv(quant) + dec = self.decoder(quant) + return dec + + +class AutoencoderKL(pl.LightningModule): + def __init__(self, + ddconfig, + lossconfig, + embed_dim, + ckpt_path=None, + ignore_keys=[], + image_key="image", + colorize_nlabels=None, + monitor=None, + ): + super().__init__() + self.image_key = image_key + self.encoder = Encoder(**ddconfig) + self.decoder = Decoder(**ddconfig) + self.loss = instantiate_from_config(lossconfig) + assert ddconfig["double_z"] + self.quant_conv = torch.nn.Conv2d(2*ddconfig["z_channels"], 2*embed_dim, 1) + self.post_quant_conv = torch.nn.Conv2d(embed_dim, ddconfig["z_channels"], 1) + self.embed_dim = embed_dim + if colorize_nlabels is not None: + assert type(colorize_nlabels)==int + self.register_buffer("colorize", torch.randn(3, colorize_nlabels, 1, 1)) + if monitor is not None: + self.monitor = monitor + if ckpt_path is not None: + self.init_from_ckpt(ckpt_path, ignore_keys=ignore_keys) + # self.automatic_optimization = False # hjw for debug + + def init_from_ckpt(self, path, ignore_keys=list()): + sd = torch.load(path, map_location="cpu")["state_dict"] + keys = list(sd.keys()) + for k in keys: + for ik in ignore_keys: + if k.startswith(ik): + print("Deleting key {} from state_dict.".format(k)) + del sd[k] + self.load_state_dict(sd, strict=False) + print(f"Restored from {path}") + + def encode(self, x): + h = self.encoder(x) + moments = self.quant_conv(h) + posterior = DiagonalGaussianDistribution(moments) + return posterior + + def decode(self, z): + z = self.post_quant_conv(z) + dec = self.decoder(z) + return dec + + def forward(self, input, sample_posterior=True): + posterior = self.encode(input) + if sample_posterior: + z = posterior.sample() + else: + z = posterior.mode() + dec = self.decode(z) + return dec, posterior + + def get_input(self, batch, k): + x = batch[k] + if len(x.shape) == 3: + x = x[..., None] + x = x.permute(0, 3, 1, 2).to(memory_format=torch.contiguous_format).float() + return x + + def training_step(self, batch, batch_idx, optimizer_idx): + inputs = self.get_input(batch, self.image_key) + reconstructions, posterior = self(inputs) + + if optimizer_idx == 0: + # train encoder+decoder+logvar + aeloss, log_dict_ae = self.loss(inputs, reconstructions, posterior, optimizer_idx, self.global_step, + last_layer=self.get_last_layer(), split="train") + self.log("aeloss", aeloss, prog_bar=True, logger=True, on_step=True, on_epoch=True) + self.log_dict(log_dict_ae, prog_bar=False, logger=True, on_step=True, on_epoch=False) + return aeloss + + if optimizer_idx == 1: + # train the discriminator + discloss, log_dict_disc = self.loss(inputs, reconstructions, posterior, optimizer_idx, self.global_step, + last_layer=self.get_last_layer(), split="train") + + self.log("discloss", discloss, prog_bar=True, logger=True, on_step=True, on_epoch=True) + self.log_dict(log_dict_disc, prog_bar=False, logger=True, on_step=True, on_epoch=False) + return discloss + + def validation_step(self, batch, batch_idx): + # self.log_images(batch,only_inputs=False,save_dir='mel_result_ae13_26/fake_class') + return self.log_dict + + def test_step(self, batch, batch_idx): + test_ckpt_path = os.path.basename(self.trainer.tested_ckpt_path) + savedir = os.path.join(self.trainer.log_dir,f'output_imgs_{test_ckpt_path}','fake_class') + os.makedirs(savedir,exist_ok=True) + inputs = self.get_input(batch, self.image_key)# inputs shape:(b,c,mel_len,T) or (b,c,h,w) + # ic(inputs.shape) + # inputs = inputs[...,:624] + # ic(inputs.shape) + xrec, posterior = self(inputs)# reconstructions:(b,c,mel_len,T) or (b,c,h,w) + file_names = batch['f_name'] + # print(f"reconstructions.shape:{reconstructions.shape}",file_names) + for b in range(len(file_names)): + rcon = (xrec[b].squeeze().detach().cpu().numpy() + 1) / 2 # to mel scale,squeeze channel dim + vname_num_split_index = file_names[b].rfind('_')# file_names[b]:video_name+'_'+num + v_n,num = file_names[b][:vname_num_split_index],file_names[b][vname_num_split_index+1:] + save_img_path = os.path.join(savedir,f'{v_n}_sample_{num}.npy') + np.save(save_img_path,rcon) + + return None + + def configure_optimizers(self): + lr = self.learning_rate + opt_ae = torch.optim.Adam(list(self.encoder.parameters())+ + list(self.decoder.parameters())+ + list(self.quant_conv.parameters())+ + list(self.post_quant_conv.parameters()), + lr=lr, betas=(0.5, 0.9)) + opt_disc = torch.optim.Adam(self.loss.discriminator.parameters(), + lr=lr, betas=(0.5, 0.9)) + return [opt_ae, opt_disc], [] + + def get_last_layer(self): + return self.decoder.conv_out.weight + + @torch.no_grad() + def log_images(self, batch, only_inputs=False,save_dir = 'mel_result_ae13_26_debug/fake_class', **kwargs): # 在main.py的on_validation_batch_end中调用 + log = dict() + x = self.get_input(batch, self.image_key) + x = x.to(self.device) + if not only_inputs: + xrec, posterior = self(x) + if x.shape[1] > 3: + # colorize with random projection + assert xrec.shape[1] > 3 + x = self.to_rgb(x) + xrec = self.to_rgb(xrec) + log["samples"] = self.decode(torch.randn_like(posterior.sample())) + log["reconstructions"] = xrec + log["inputs"] = x + return log + + def to_rgb(self, x): + assert self.image_key == "segmentation" + if not hasattr(self, "colorize"): + self.register_buffer("colorize", torch.randn(3, x.shape[1], 1, 1).to(x)) + x = F.conv2d(x, weight=self.colorize) + x = 2.*(x-x.min())/(x.max()-x.min()) - 1. + return x + + +class IdentityFirstStage(torch.nn.Module): + def __init__(self, *args, vq_interface=False, **kwargs): + self.vq_interface = vq_interface # TODO: Should be true by default but check to not break older stuff + super().__init__() + + def encode(self, x, *args, **kwargs): + return x + + def decode(self, x, *args, **kwargs): + return x + + def quantize(self, x, *args, **kwargs): + if self.vq_interface: + return x, None, [None, None, None] + return x + + def forward(self, x, *args, **kwargs): + return x diff --git a/ldm/models/autoencoder_multi.py b/ldm/models/autoencoder_multi.py new file mode 100644 index 0000000000000000000000000000000000000000..cc4f830e24e99950f5ff412e8c5776e6a3489bf2 --- /dev/null +++ b/ldm/models/autoencoder_multi.py @@ -0,0 +1,201 @@ +""" +与autoencoder.py的区别在于，autoencoder.py计算loss时只有一个discriminator，而此处又多了个multiwindowDiscriminator，所以优化器 +优化的参数改为： +opt_disc = torch.optim.Adam(list(self.loss.discriminator.parameters()) + list(self.loss.discriminator_multi.parameters()), + lr=lr, betas=(0.5, 0.9)) +""" + +import os +import torch +import pytorch_lightning as pl +import torch.nn.functional as F +from contextlib import contextmanager + +from packaging import version +import numpy as np +from ldm.modules.diffusionmodules.model import Encoder, Decoder +from ldm.modules.distributions.distributions import DiagonalGaussianDistribution +from torch.optim.lr_scheduler import LambdaLR +from ldm.util import instantiate_from_config + + + +class AutoencoderKL(pl.LightningModule): + def __init__(self, + ddconfig, + lossconfig, + embed_dim, + ckpt_path=None, + ignore_keys=[], + image_key="image", + colorize_nlabels=None, + monitor=None, + ): + super().__init__() + self.image_key = image_key + self.encoder = Encoder(**ddconfig) + self.decoder = Decoder(**ddconfig) + self.loss = instantiate_from_config(lossconfig) + assert ddconfig["double_z"] + self.quant_conv = torch.nn.Conv2d(2*ddconfig["z_channels"], 2*embed_dim, 1) + self.post_quant_conv = torch.nn.Conv2d(embed_dim, ddconfig["z_channels"], 1) + self.embed_dim = embed_dim + if colorize_nlabels is not None: + assert type(colorize_nlabels)==int + self.register_buffer("colorize", torch.randn(3, colorize_nlabels, 1, 1)) + if monitor is not None: + self.monitor = monitor + if ckpt_path is not None: + self.init_from_ckpt(ckpt_path, ignore_keys=ignore_keys) + + def init_from_ckpt(self, path, ignore_keys=list()): + sd = torch.load(path, map_location="cpu")["state_dict"] + keys = list(sd.keys()) + for k in keys: + for ik in ignore_keys: + if k.startswith(ik): + print("Deleting key {} from state_dict.".format(k)) + del sd[k] + self.load_state_dict(sd, strict=False) + print(f"Restored from {path}") + + def encode(self, x): + h = self.encoder(x) + moments = self.quant_conv(h) + posterior = DiagonalGaussianDistribution(moments) + return posterior + + def decode(self, z): + z = self.post_quant_conv(z) + dec = self.decoder(z) + return dec + + def forward(self, input, sample_posterior=True): + posterior = self.encode(input) + if sample_posterior: + z = posterior.sample() + else: + z = posterior.mode() + dec = self.decode(z) + return dec, posterior + + def get_input(self, batch, k): + x = batch[k] + if len(x.shape) == 3: + x = x[..., None] + x = x.permute(0, 3, 1, 2).to(memory_format=torch.contiguous_format).float() + return x + + def training_step(self, batch, batch_idx, optimizer_idx): + inputs = self.get_input(batch, self.image_key) + reconstructions, posterior = self(inputs) + + if optimizer_idx == 0: + # train encoder+decoder+logvar + aeloss, log_dict_ae = self.loss(inputs, reconstructions, posterior, optimizer_idx, self.global_step, + last_layer=self.get_last_layer(), split="train") + self.log("aeloss", aeloss, prog_bar=True, logger=True, on_step=True, on_epoch=True) + self.log_dict(log_dict_ae, prog_bar=False, logger=True, on_step=True, on_epoch=False) + return aeloss + + if optimizer_idx == 1: + # train the discriminator + discloss, log_dict_disc = self.loss(inputs, reconstructions, posterior, optimizer_idx, self.global_step, + last_layer=self.get_last_layer(), split="train") + + self.log("discloss", discloss, prog_bar=True, logger=True, on_step=True, on_epoch=True) + self.log_dict(log_dict_disc, prog_bar=False, logger=True, on_step=True, on_epoch=False) + return discloss + + def validation_step(self, batch, batch_idx): + inputs = self.get_input(batch, self.image_key) + reconstructions, posterior = self(inputs) + aeloss, log_dict_ae = self.loss(inputs, reconstructions, posterior, 0, self.global_step, + last_layer=self.get_last_layer(), split="val") + + discloss, log_dict_disc = self.loss(inputs, reconstructions, posterior, 1, self.global_step, + last_layer=self.get_last_layer(), split="val") + + self.log("val/rec_loss", log_dict_ae["val/rec_loss"]) + self.log_dict(log_dict_ae) + self.log_dict(log_dict_disc) + return self.log_dict + + def test_step(self, batch, batch_idx): + inputs = self.get_input(batch, self.image_key)# inputs shape:(b,c,mel_len,T) or (b,c,h,w) + reconstructions, posterior = self(inputs)# reconstructions:(b,c,mel_len,T) or (b,c,h,w) + reconstructions = (reconstructions + 1)/2 # to mel scale + test_ckpt_path = os.path.basename(self.trainer.tested_ckpt_path) + savedir = os.path.join(self.trainer.log_dir,f'output_imgs_{test_ckpt_path}','fake_class') + if not os.path.exists(savedir): + os.makedirs(savedir) + + file_names = batch['f_name'] + # print(f"reconstructions.shape:{reconstructions.shape}",file_names) + reconstructions = reconstructions.cpu().numpy().squeeze(1) # squuze channel dim + for b in range(reconstructions.shape[0]): + vname_num_split_index = file_names[b].rfind('_')# file_names[b]:video_name+'_'+num + v_n,num = file_names[b][:vname_num_split_index],file_names[b][vname_num_split_index+1:] + save_img_path = os.path.join(savedir,f'{v_n}_sample_{num}.npy') + np.save(save_img_path,reconstructions[b]) + + return None + + def configure_optimizers(self): + lr = self.learning_rate + opt_ae = torch.optim.Adam(list(self.encoder.parameters())+ + list(self.decoder.parameters())+ + list(self.quant_conv.parameters())+ + list(self.post_quant_conv.parameters()), + lr=lr, betas=(0.5, 0.9)) + opt_disc = torch.optim.Adam(list(self.loss.discriminator.parameters()) + list(self.loss.discriminator_multi.parameters()), + lr=lr, betas=(0.5, 0.9)) + return [opt_ae, opt_disc], [] + + def get_last_layer(self): + return self.decoder.conv_out.weight + + @torch.no_grad() + def log_images(self, batch, only_inputs=False, **kwargs): + log = dict() + x = self.get_input(batch, self.image_key) + x = x.to(self.device) + if not only_inputs: + xrec, posterior = self(x) + if x.shape[1] > 3: + # colorize with random projection + assert xrec.shape[1] > 3 + x = self.to_rgb(x) + xrec = self.to_rgb(xrec) + log["samples"] = self.decode(torch.randn_like(posterior.sample())) + log["reconstructions"] = xrec + log["inputs"] = x + return log + + def to_rgb(self, x): + assert self.image_key == "segmentation" + if not hasattr(self, "colorize"): + self.register_buffer("colorize", torch.randn(3, x.shape[1], 1, 1).to(x)) + x = F.conv2d(x, weight=self.colorize) + x = 2.*(x-x.min())/(x.max()-x.min()) - 1. + return x + + +class IdentityFirstStage(torch.nn.Module): + def __init__(self, *args, vq_interface=False, **kwargs): + self.vq_interface = vq_interface # TODO: Should be true by default but check to not break older stuff + super().__init__() + + def encode(self, x, *args, **kwargs): + return x + + def decode(self, x, *args, **kwargs): + return x + + def quantize(self, x, *args, **kwargs): + if self.vq_interface: + return x, None, [None, None, None] + return x + + def forward(self, x, *args, **kwargs): + return x \ No newline at end of file diff --git a/ldm/models/diffusion/__init__.py b/ldm/models/diffusion/__init__.py new file mode 100644 index 0000000000000000000000000000000000000000..e69de29bb2d1d6434b8b29ae775ad8c2e48c5391 diff --git a/ldm/models/diffusion/__pycache__/__init__.cpython-38.pyc b/ldm/models/diffusion/__pycache__/__init__.cpython-38.pyc new file mode 100644 index 0000000000000000000000000000000000000000..b4446592b223fccb2938664f1d72877188365153 Binary files /dev/null and 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+from ldm.modules.diffusionmodules.openaimodel import EncoderUNetModel, UNetModel +from ldm.util import log_txt_as_img, default, ismap, instantiate_from_config + +__models__ = { + 'class_label': EncoderUNetModel, + 'segmentation': UNetModel +} + + +def disabled_train(self, mode=True): + """Overwrite model.train with this function to make sure train/eval mode + does not change anymore.""" + return self + + +class NoisyLatentImageClassifier(pl.LightningModule): + + def __init__(self, + diffusion_path, + num_classes, + ckpt_path=None, + pool='attention', + label_key=None, + diffusion_ckpt_path=None, + scheduler_config=None, + weight_decay=1.e-2, + log_steps=10, + monitor='val/loss', + *args, + **kwargs): + super().__init__(*args, **kwargs) + self.num_classes = num_classes + # get latest config of diffusion model + diffusion_config = natsorted(glob(os.path.join(diffusion_path, 'configs', '*-project.yaml')))[-1] + self.diffusion_config = OmegaConf.load(diffusion_config).model + self.diffusion_config.params.ckpt_path = diffusion_ckpt_path + self.load_diffusion() + + self.monitor = monitor + self.numd = self.diffusion_model.first_stage_model.encoder.num_resolutions - 1 + self.log_time_interval = self.diffusion_model.num_timesteps // log_steps + self.log_steps = log_steps + + self.label_key = label_key if not hasattr(self.diffusion_model, 'cond_stage_key') \ + else self.diffusion_model.cond_stage_key + + assert self.label_key is not None, 'label_key neither in diffusion model nor in model.params' + + if self.label_key not in __models__: + raise NotImplementedError() + + self.load_classifier(ckpt_path, pool) + + self.scheduler_config = scheduler_config + self.use_scheduler = self.scheduler_config is not None + self.weight_decay = weight_decay + + def init_from_ckpt(self, path, ignore_keys=list(), only_model=False): + sd = torch.load(path, map_location="cpu") + if "state_dict" in list(sd.keys()): + sd = sd["state_dict"] + keys = list(sd.keys()) + for k in keys: + for ik in ignore_keys: + if k.startswith(ik): + print("Deleting key {} from state_dict.".format(k)) + del sd[k] + missing, unexpected = self.load_state_dict(sd, strict=False) if not only_model else self.model.load_state_dict( + sd, strict=False) + print(f"Restored from {path} with {len(missing)} missing and {len(unexpected)} unexpected keys") + if len(missing) > 0: + print(f"Missing Keys: {missing}") + if len(unexpected) > 0: + print(f"Unexpected Keys: {unexpected}") + + def load_diffusion(self): + model = instantiate_from_config(self.diffusion_config) + self.diffusion_model = model.eval() + self.diffusion_model.train = disabled_train + for param in self.diffusion_model.parameters(): + param.requires_grad = False + + def load_classifier(self, ckpt_path, pool): + model_config = deepcopy(self.diffusion_config.params.unet_config.params) + model_config.in_channels = self.diffusion_config.params.unet_config.params.out_channels + model_config.out_channels = self.num_classes + if self.label_key == 'class_label': + model_config.pool = pool + + self.model = __models__[self.label_key](**model_config) + if ckpt_path is not None: + print('#####################################################################') + print(f'load from ckpt "{ckpt_path}"') + print('#####################################################################') + self.init_from_ckpt(ckpt_path) + + @torch.no_grad() + def get_x_noisy(self, x, t, noise=None): + noise = default(noise, lambda: torch.randn_like(x)) + continuous_sqrt_alpha_cumprod = None + if self.diffusion_model.use_continuous_noise: + continuous_sqrt_alpha_cumprod = self.diffusion_model.sample_continuous_noise_level(x.shape[0], t + 1) + # todo: make sure t+1 is correct here + + return self.diffusion_model.q_sample(x_start=x, t=t, noise=noise, + continuous_sqrt_alpha_cumprod=continuous_sqrt_alpha_cumprod) + + def forward(self, x_noisy, t, *args, **kwargs): + return self.model(x_noisy, t) + + @torch.no_grad() + def get_input(self, batch, k): + x = batch[k] + if len(x.shape) == 3: + x = x[..., None] + x = rearrange(x, 'b h w c -> b c h w') + x = x.to(memory_format=torch.contiguous_format).float() + return x + + @torch.no_grad() + def get_conditioning(self, batch, k=None): + if k is None: + k = self.label_key + assert k is not None, 'Needs to provide label key' + + targets = batch[k].to(self.device) + + if self.label_key == 'segmentation': + targets = rearrange(targets, 'b h w c -> b c h w') + for down in range(self.numd): + h, w = targets.shape[-2:] + targets = F.interpolate(targets, size=(h // 2, w // 2), mode='nearest') + + # targets = rearrange(targets,'b c h w -> b h w c') + + return targets + + def compute_top_k(self, logits, labels, k, reduction="mean"): + _, top_ks = torch.topk(logits, k, dim=1) + if reduction == "mean": + return (top_ks == labels[:, None]).float().sum(dim=-1).mean().item() + elif reduction == "none": + return (top_ks == labels[:, None]).float().sum(dim=-1) + + def on_train_epoch_start(self): + # save some memory + self.diffusion_model.model.to('cpu') + + @torch.no_grad() + def write_logs(self, loss, logits, targets): + log_prefix = 'train' if self.training else 'val' + log = {} + log[f"{log_prefix}/loss"] = loss.mean() + log[f"{log_prefix}/acc@1"] = self.compute_top_k( + logits, targets, k=1, reduction="mean" + ) + log[f"{log_prefix}/acc@5"] = self.compute_top_k( + logits, targets, k=5, reduction="mean" + ) + + self.log_dict(log, prog_bar=False, logger=True, on_step=self.training, on_epoch=True) + self.log('loss', log[f"{log_prefix}/loss"], prog_bar=True, logger=False) + self.log('global_step', self.global_step, logger=False, on_epoch=False, prog_bar=True) + lr = self.optimizers().param_groups[0]['lr'] + self.log('lr_abs', lr, on_step=True, logger=True, on_epoch=False, prog_bar=True) + + def shared_step(self, batch, t=None): + x, *_ = self.diffusion_model.get_input(batch, k=self.diffusion_model.first_stage_key) + targets = self.get_conditioning(batch) + if targets.dim() == 4: + targets = targets.argmax(dim=1) + if t is None: + t = torch.randint(0, self.diffusion_model.num_timesteps, (x.shape[0],), device=self.device).long() + else: + t = torch.full(size=(x.shape[0],), fill_value=t, device=self.device).long() + x_noisy = self.get_x_noisy(x, t) + logits = self(x_noisy, t) + + loss = F.cross_entropy(logits, targets, reduction='none') + + self.write_logs(loss.detach(), logits.detach(), targets.detach()) + + loss = loss.mean() + return loss, logits, x_noisy, targets + + def training_step(self, batch, batch_idx): + loss, *_ = self.shared_step(batch) + return loss + + def reset_noise_accs(self): + self.noisy_acc = {t: {'acc@1': [], 'acc@5': []} for t in + range(0, self.diffusion_model.num_timesteps, self.diffusion_model.log_every_t)} + + def on_validation_start(self): + self.reset_noise_accs() + + @torch.no_grad() + def validation_step(self, batch, batch_idx): + loss, *_ = self.shared_step(batch) + + for t in self.noisy_acc: + _, logits, _, targets = self.shared_step(batch, t) + self.noisy_acc[t]['acc@1'].append(self.compute_top_k(logits, targets, k=1, reduction='mean')) + self.noisy_acc[t]['acc@5'].append(self.compute_top_k(logits, targets, k=5, reduction='mean')) + + return loss + + def configure_optimizers(self): + optimizer = AdamW(self.model.parameters(), lr=self.learning_rate, weight_decay=self.weight_decay) + + if self.use_scheduler: + scheduler = instantiate_from_config(self.scheduler_config) + + print("Setting up LambdaLR scheduler...") + scheduler = [ + { + 'scheduler': LambdaLR(optimizer, lr_lambda=scheduler.schedule), + 'interval': 'step', + 'frequency': 1 + }] + return [optimizer], scheduler + + return optimizer + + @torch.no_grad() + def log_images(self, batch, N=8, *args, **kwargs): + log = dict() + x = self.get_input(batch, self.diffusion_model.first_stage_key) + log['inputs'] = x + + y = self.get_conditioning(batch) + + if self.label_key == 'class_label': + y = log_txt_as_img((x.shape[2], x.shape[3]), batch["human_label"]) + log['labels'] = y + + if ismap(y): + log['labels'] = self.diffusion_model.to_rgb(y) + + for step in range(self.log_steps): + current_time = step * self.log_time_interval + + _, logits, x_noisy, _ = self.shared_step(batch, t=current_time) + + log[f'inputs@t{current_time}'] = x_noisy + + pred = F.one_hot(logits.argmax(dim=1), num_classes=self.num_classes) + pred = rearrange(pred, 'b h w c -> b c h w') + + log[f'pred@t{current_time}'] = self.diffusion_model.to_rgb(pred) + + for key in log: + log[key] = log[key][:N] + + return log diff --git a/ldm/models/diffusion/ddim.py b/ldm/models/diffusion/ddim.py new file mode 100644 index 0000000000000000000000000000000000000000..6d6e9d396c799ce386fd1fa4262f46ac8fceaacf --- /dev/null +++ b/ldm/models/diffusion/ddim.py @@ -0,0 +1,262 @@ +"""SAMPLING ONLY.""" + +import torch +import numpy as np +from tqdm import tqdm +from functools import partial + +from ldm.modules.diffusionmodules.util import make_ddim_sampling_parameters, make_ddim_timesteps, noise_like, \ + extract_into_tensor + + +class DDIMSampler(object): + def __init__(self, model, schedule="linear", **kwargs): + super().__init__() + self.model = model + self.device = torch.device("cuda") if torch.cuda.is_available() else torch.device("cpu") + self.ddpm_num_timesteps = model.num_timesteps + self.schedule = schedule + + def register_buffer(self, name, attr): + if type(attr) == torch.Tensor: + # if attr.device != torch.device("cuda"): + # attr = attr.to(torch.device("cuda")) + attr = attr.to(self.device) + setattr(self, name, attr) + + def make_schedule(self, ddim_num_steps, ddim_discretize="uniform", ddim_eta=0., verbose=True): + self.ddim_timesteps = make_ddim_timesteps(ddim_discr_method=ddim_discretize, num_ddim_timesteps=ddim_num_steps, + num_ddpm_timesteps=self.ddpm_num_timesteps,verbose=verbose) + alphas_cumprod = self.model.alphas_cumprod + assert alphas_cumprod.shape[0] == self.ddpm_num_timesteps, 'alphas have to be defined for each timestep' + to_torch = lambda x: x.clone().detach().to(torch.float32).to(self.model.device) + + self.register_buffer('betas', to_torch(self.model.betas)) + self.register_buffer('alphas_cumprod', to_torch(alphas_cumprod)) + self.register_buffer('alphas_cumprod_prev', to_torch(self.model.alphas_cumprod_prev)) + + # calculations for diffusion q(x_t | x_{t-1}) and others + self.register_buffer('sqrt_alphas_cumprod', to_torch(np.sqrt(alphas_cumprod.cpu()))) + self.register_buffer('sqrt_one_minus_alphas_cumprod', to_torch(np.sqrt(1. - alphas_cumprod.cpu()))) + self.register_buffer('log_one_minus_alphas_cumprod', to_torch(np.log(1. - alphas_cumprod.cpu()))) + self.register_buffer('sqrt_recip_alphas_cumprod', to_torch(np.sqrt(1. / alphas_cumprod.cpu()))) + self.register_buffer('sqrt_recipm1_alphas_cumprod', to_torch(np.sqrt(1. / alphas_cumprod.cpu() - 1))) + + # ddim sampling parameters + ddim_sigmas, ddim_alphas, ddim_alphas_prev = make_ddim_sampling_parameters(alphacums=alphas_cumprod.cpu(), + ddim_timesteps=self.ddim_timesteps, + eta=ddim_eta,verbose=verbose) + self.register_buffer('ddim_sigmas', ddim_sigmas) + self.register_buffer('ddim_alphas', ddim_alphas) + self.register_buffer('ddim_alphas_prev', ddim_alphas_prev) + self.register_buffer('ddim_sqrt_one_minus_alphas', np.sqrt(1. - ddim_alphas)) + sigmas_for_original_sampling_steps = ddim_eta * torch.sqrt( + (1 - self.alphas_cumprod_prev) / (1 - self.alphas_cumprod) * ( + 1 - self.alphas_cumprod / self.alphas_cumprod_prev)) + self.register_buffer('ddim_sigmas_for_original_num_steps', sigmas_for_original_sampling_steps) + + @torch.no_grad() + def sample(self, + S, + batch_size, + shape, + conditioning=None, + callback=None, + normals_sequence=None, + img_callback=None, + quantize_x0=False, + eta=0., + mask=None, + x0=None, + temperature=1., + noise_dropout=0., + score_corrector=None, + corrector_kwargs=None, + verbose=True, + x_T=None, + log_every_t=100, + unconditional_guidance_scale=1., + unconditional_conditioning=None, + # this has to come in the same format as the conditioning, # e.g. as encoded tokens, ... + **kwargs + ): + if conditioning is not None: + if isinstance(conditioning, dict): + ctmp = conditioning[list(conditioning.keys())[0]] + while isinstance(ctmp, list): ctmp = ctmp[0] + cbs = ctmp.shape[0] + if cbs != batch_size: + print(f"Warning: Got {cbs} conditionings but batch-size is {batch_size}") + else: + if conditioning.shape[0] != batch_size: + print(f"Warning: Got {conditioning.shape[0]} conditionings but batch-size is {batch_size}") + + self.make_schedule(ddim_num_steps=S, ddim_eta=eta, verbose=verbose) + # sampling + C, H, W = shape + size = (batch_size, C, H, W) + # print(f'Data shape for DDIM sampling is {size}, eta {eta}') + + samples, intermediates = self.ddim_sampling(conditioning, size, + callback=callback, + img_callback=img_callback, + quantize_denoised=quantize_x0, + mask=mask, x0=x0, + ddim_use_original_steps=False, + noise_dropout=noise_dropout, + temperature=temperature, + score_corrector=score_corrector, + corrector_kwargs=corrector_kwargs, + x_T=x_T, + log_every_t=log_every_t, + unconditional_guidance_scale=unconditional_guidance_scale, + unconditional_conditioning=unconditional_conditioning, + ) + return samples, intermediates + + @torch.no_grad() + def ddim_sampling(self, cond, shape, + x_T=None, ddim_use_original_steps=False, + callback=None, timesteps=None, quantize_denoised=False, + mask=None, x0=None, img_callback=None, log_every_t=100, + temperature=1., noise_dropout=0., score_corrector=None, corrector_kwargs=None, + unconditional_guidance_scale=1., unconditional_conditioning=None,): + device = self.model.betas.device + b = shape[0] + if x_T is None: + img = torch.randn(shape, device=device) + else: + img = x_T + + if timesteps is None: + timesteps = self.ddpm_num_timesteps if ddim_use_original_steps else self.ddim_timesteps + elif timesteps is not None and not ddim_use_original_steps: + subset_end = int(min(timesteps / self.ddim_timesteps.shape[0], 1) * self.ddim_timesteps.shape[0]) - 1 + timesteps = self.ddim_timesteps[:subset_end] + + intermediates = {'x_inter': [img], 'pred_x0': [img]} + time_range = reversed(range(0,timesteps)) if ddim_use_original_steps else np.flip(timesteps) + total_steps = timesteps if ddim_use_original_steps else timesteps.shape[0] + + # iterator = tqdm(time_range, desc='DDIM Sampler', total=total_steps) + + for i, step in enumerate(time_range): + index = total_steps - i - 1 + ts = torch.full((b,), step, device=device, dtype=torch.long) + + if mask is not None: + assert x0 is not None + img_orig = self.model.q_sample(x0, ts) # TODO: deterministic forward pass? + img = img_orig * mask + (1. - mask) * img + + outs = self.p_sample_ddim(img, cond, ts, index=index, use_original_steps=ddim_use_original_steps, + quantize_denoised=quantize_denoised, temperature=temperature, + noise_dropout=noise_dropout, score_corrector=score_corrector, + corrector_kwargs=corrector_kwargs, + unconditional_guidance_scale=unconditional_guidance_scale, + unconditional_conditioning=unconditional_conditioning) + img, pred_x0 = outs + if callback: callback(i) + if img_callback: img_callback(pred_x0, i) + + if index % log_every_t == 0 or index == total_steps - 1: + intermediates['x_inter'].append(img) + intermediates['pred_x0'].append(pred_x0) + + return img, intermediates + + @torch.no_grad() + def p_sample_ddim(self, x, c, t, index, repeat_noise=False, use_original_steps=False, quantize_denoised=False, + temperature=1., noise_dropout=0., score_corrector=None, corrector_kwargs=None, + unconditional_guidance_scale=1., unconditional_conditioning=None): + b, *_, device = *x.shape, x.device + + if unconditional_conditioning is None or unconditional_guidance_scale == 1.: + e_t = self.model.apply_model(x, t, c) + else: + x_in = torch.cat([x] * 2) + t_in = torch.cat([t] * 2) + if isinstance(c, dict): + assert isinstance(unconditional_conditioning, dict) + c_in = dict() + for k in c: + if isinstance(c[k], list): + c_in[k] = [torch.cat([ + unconditional_conditioning[k][i], + c[k][i]]) for i in range(len(c[k]))] + else: + c_in[k] = torch.cat([ + unconditional_conditioning[k], + c[k]]) + elif isinstance(c, list): + c_in = list() + assert isinstance(unconditional_conditioning, list) + for i in range(len(c)): + c_in.append(torch.cat([unconditional_conditioning[i], c[i]])) + else: + c_in = torch.cat([unconditional_conditioning, c])# c/uc shape [b,seq_len=77,dim=1024],c_in shape [b*2,seq_len,dim] + e_t_uncond, e_t = self.model.apply_model(x_in, t_in, c_in).chunk(2) + e_t = e_t_uncond + unconditional_guidance_scale * (e_t - e_t_uncond) + + if score_corrector is not None: + assert self.model.parameterization == "eps" + e_t = score_corrector.modify_score(self.model, e_t, x, t, c, **corrector_kwargs) + + alphas = self.model.alphas_cumprod if use_original_steps else self.ddim_alphas + alphas_prev = self.model.alphas_cumprod_prev if use_original_steps else self.ddim_alphas_prev + sqrt_one_minus_alphas = self.model.sqrt_one_minus_alphas_cumprod if use_original_steps else self.ddim_sqrt_one_minus_alphas + sigmas = self.model.ddim_sigmas_for_original_num_steps if use_original_steps else self.ddim_sigmas + # select parameters corresponding to the currently considered timestep + a_t = torch.full((b, 1, 1, 1), alphas[index], device=device) + a_prev = torch.full((b, 1, 1, 1), alphas_prev[index], device=device) + sigma_t = torch.full((b, 1, 1, 1), sigmas[index], device=device) + sqrt_one_minus_at = torch.full((b, 1, 1, 1), sqrt_one_minus_alphas[index],device=device) + + # current prediction for x_0 + pred_x0 = (x - sqrt_one_minus_at * e_t) / a_t.sqrt() + if quantize_denoised: + pred_x0, _, *_ = self.model.first_stage_model.quantize(pred_x0) + # direction pointing to x_t + dir_xt = (1. - a_prev - sigma_t**2).sqrt() * e_t + noise = sigma_t * noise_like(x.shape, device, repeat_noise) * temperature + if noise_dropout > 0.: + noise = torch.nn.functional.dropout(noise, p=noise_dropout) + x_prev = a_prev.sqrt() * pred_x0 + dir_xt + noise + return x_prev, pred_x0 + + @torch.no_grad() + def stochastic_encode(self, x0, t, use_original_steps=False, noise=None): + # fast, but does not allow for exact reconstruction + # t serves as an index to gather the correct alphas + if use_original_steps: + sqrt_alphas_cumprod = self.sqrt_alphas_cumprod + sqrt_one_minus_alphas_cumprod = self.sqrt_one_minus_alphas_cumprod + else: + sqrt_alphas_cumprod = torch.sqrt(self.ddim_alphas) + sqrt_one_minus_alphas_cumprod = self.ddim_sqrt_one_minus_alphas + + if noise is None: + noise = torch.randn_like(x0) + return (extract_into_tensor(sqrt_alphas_cumprod, t, x0.shape) * x0 + + extract_into_tensor(sqrt_one_minus_alphas_cumprod, t, x0.shape) * noise) + + @torch.no_grad() + def decode(self, x_latent, cond, t_start, unconditional_guidance_scale=1.0, unconditional_conditioning=None, + use_original_steps=False): + + timesteps = np.arange(self.ddpm_num_timesteps) if use_original_steps else self.ddim_timesteps + timesteps = timesteps[:t_start] + + time_range = np.flip(timesteps) + total_steps = timesteps.shape[0] + # print(f"Running DDIM Sampling with {total_steps} timesteps") + + # iterator = tqdm(time_range, desc='Decoding image', total=total_steps) + x_dec = x_latent + for i, step in enumerate(time_range): + index = total_steps - i - 1 + ts = torch.full((x_latent.shape[0],), step, device=x_latent.device, dtype=torch.long) + x_dec, _ = self.p_sample_ddim(x_dec, cond, ts, index=index, use_original_steps=use_original_steps, + unconditional_guidance_scale=unconditional_guidance_scale, + unconditional_conditioning=unconditional_conditioning) + return x_dec \ No newline at end of file diff --git a/ldm/models/diffusion/ddpm.py b/ldm/models/diffusion/ddpm.py new file mode 100644 index 0000000000000000000000000000000000000000..d6927503659e3aeb3a88965d8574d4435874516f --- /dev/null +++ b/ldm/models/diffusion/ddpm.py @@ -0,0 +1,1444 @@ +""" +wild mixture of +https://github.com/lucidrains/denoising-diffusion-pytorch/blob/7706bdfc6f527f58d33f84b7b522e61e6e3164b3/denoising_diffusion_pytorch/denoising_diffusion_pytorch.py +https://github.com/openai/improved-diffusion/blob/e94489283bb876ac1477d5dd7709bbbd2d9902ce/improved_diffusion/gaussian_diffusion.py +https://github.com/CompVis/taming-transformers +-- merci +""" +import torch +import torch.nn as nn +import numpy as np +import pytorch_lightning as pl +from torch.optim.lr_scheduler import LambdaLR +from einops import rearrange, repeat +from contextlib import contextmanager +from functools import partial +from tqdm import tqdm +from torchvision.utils import make_grid +from pytorch_lightning.utilities.distributed import rank_zero_only + +from ldm.util import log_txt_as_img, exists, default, ismap, isimage, mean_flat, count_params, instantiate_from_config +from ldm.modules.ema import LitEma +from ldm.modules.distributions.distributions import normal_kl, DiagonalGaussianDistribution +from ldm.models.autoencoder import IdentityFirstStage, AutoencoderKL +from ldm.modules.diffusionmodules.util import make_beta_schedule, extract_into_tensor, noise_like +from ldm.models.diffusion.ddim import DDIMSampler + + +__conditioning_keys__ = {'concat': 'c_concat', + 'crossattn': 'c_crossattn', + 'adm': 'y'} + + +def disabled_train(self, mode=True): + """Overwrite model.train with this function to make sure train/eval mode + does not change anymore.""" + return self + + +def uniform_on_device(r1, r2, shape, device): + return (r1 - r2) * torch.rand(*shape, device=device) + r2 + + +class DDPM(pl.LightningModule): + # classic DDPM with Gaussian diffusion, in image space + def __init__(self, + unet_config, + timesteps=1000, + beta_schedule="linear", + loss_type="l2", + ckpt_path=None, + ignore_keys=[], + load_only_unet=False, + monitor="val/loss", + use_ema=True, + first_stage_key="image", + image_size=256, + channels=3, + log_every_t=100, + clip_denoised=True, + linear_start=1e-4, + linear_end=2e-2, + cosine_s=8e-3, + given_betas=None, + original_elbo_weight=0., + v_posterior=0., # weight for choosing posterior variance as sigma = (1-v) * beta_tilde + v * beta + l_simple_weight=1., + conditioning_key=None, + parameterization="eps", # all config files uses "eps" + scheduler_config=None, + use_positional_encodings=False, + learn_logvar=False, + logvar_init=0., + ): + super().__init__() + assert parameterization in ["eps", "x0"], 'currently only supporting "eps" and "x0"' + self.parameterization = parameterization + print(f"{self.__class__.__name__}: Running in {self.parameterization}-prediction mode") + self.cond_stage_model = None + self.clip_denoised = clip_denoised + self.log_every_t = log_every_t + self.first_stage_key = first_stage_key + self.image_size = image_size # try conv? + self.channels = channels + self.use_positional_encodings = use_positional_encodings + self.model = DiffusionWrapper(unet_config, conditioning_key) + count_params(self.model, verbose=True) + self.use_ema = use_ema + if self.use_ema: + self.model_ema = LitEma(self.model) + print(f"Keeping EMAs of {len(list(self.model_ema.buffers()))}.") + + self.use_scheduler = scheduler_config is not None + if self.use_scheduler: + self.scheduler_config = scheduler_config + + self.v_posterior = v_posterior + self.original_elbo_weight = original_elbo_weight + self.l_simple_weight = l_simple_weight + + if monitor is not None: + self.monitor = monitor + if ckpt_path is not None: + self.init_from_ckpt(ckpt_path, ignore_keys=ignore_keys, only_model=load_only_unet) + + self.register_schedule(given_betas=given_betas, beta_schedule=beta_schedule, timesteps=timesteps, + linear_start=linear_start, linear_end=linear_end, cosine_s=cosine_s) + + self.loss_type = loss_type + + self.learn_logvar = learn_logvar + self.logvar = torch.full(fill_value=logvar_init, size=(self.num_timesteps,)) + if self.learn_logvar: + self.logvar = nn.Parameter(self.logvar, requires_grad=True) + + def register_schedule(self, given_betas=None, beta_schedule="linear", timesteps=1000, + linear_start=1e-4, linear_end=2e-2, cosine_s=8e-3): + if exists(given_betas): + betas = given_betas + else: + betas = make_beta_schedule(beta_schedule, timesteps, linear_start=linear_start, linear_end=linear_end, + cosine_s=cosine_s) + alphas = 1. - betas + alphas_cumprod = np.cumprod(alphas, axis=0) + alphas_cumprod_prev = np.append(1., alphas_cumprod[:-1]) + + timesteps, = betas.shape + self.num_timesteps = int(timesteps) + self.linear_start = linear_start + self.linear_end = linear_end + assert alphas_cumprod.shape[0] == self.num_timesteps, 'alphas have to be defined for each timestep' + + to_torch = partial(torch.tensor, dtype=torch.float32) + + self.register_buffer('betas', to_torch(betas)) + self.register_buffer('alphas_cumprod', to_torch(alphas_cumprod)) + self.register_buffer('alphas_cumprod_prev', to_torch(alphas_cumprod_prev)) + + # calculations for diffusion q(x_t | x_{t-1}) and others + self.register_buffer('sqrt_alphas_cumprod', to_torch(np.sqrt(alphas_cumprod))) + self.register_buffer('sqrt_one_minus_alphas_cumprod', to_torch(np.sqrt(1. - alphas_cumprod))) + self.register_buffer('log_one_minus_alphas_cumprod', to_torch(np.log(1. - alphas_cumprod))) + self.register_buffer('sqrt_recip_alphas_cumprod', to_torch(np.sqrt(1. / alphas_cumprod))) + self.register_buffer('sqrt_recipm1_alphas_cumprod', to_torch(np.sqrt(1. / alphas_cumprod - 1))) + + # calculations for posterior q(x_{t-1} | x_t, x_0) + posterior_variance = (1 - self.v_posterior) * betas * (1. - alphas_cumprod_prev) / ( + 1. - alphas_cumprod) + self.v_posterior * betas + # above: equal to 1. / (1. / (1. - alpha_cumprod_tm1) + alpha_t / beta_t) + self.register_buffer('posterior_variance', to_torch(posterior_variance)) + # below: log calculation clipped because the posterior variance is 0 at the beginning of the diffusion chain + self.register_buffer('posterior_log_variance_clipped', to_torch(np.log(np.maximum(posterior_variance, 1e-20)))) + self.register_buffer('posterior_mean_coef1', to_torch( + betas * np.sqrt(alphas_cumprod_prev) / (1. - alphas_cumprod))) + self.register_buffer('posterior_mean_coef2', to_torch( + (1. - alphas_cumprod_prev) * np.sqrt(alphas) / (1. - alphas_cumprod))) + + if self.parameterization == "eps": + lvlb_weights = self.betas ** 2 / ( + 2 * self.posterior_variance * to_torch(alphas) * (1 - self.alphas_cumprod)) + elif self.parameterization == "x0": + lvlb_weights = 0.5 * np.sqrt(torch.Tensor(alphas_cumprod)) / (2. * 1 - torch.Tensor(alphas_cumprod)) + else: + raise NotImplementedError("mu not supported") + # TODO how to choose this term + lvlb_weights[0] = lvlb_weights[1] + self.register_buffer('lvlb_weights', lvlb_weights, persistent=False) + assert not torch.isnan(self.lvlb_weights).all() + + @contextmanager + def ema_scope(self, context=None): + if self.use_ema: + self.model_ema.store(self.model.parameters()) + self.model_ema.copy_to(self.model) + if context is not None: + print(f"{context}: Switched to EMA weights") + try: + yield None + finally: + if self.use_ema: + self.model_ema.restore(self.model.parameters()) + if context is not None: + print(f"{context}: Restored training weights") + + def init_from_ckpt(self, path, ignore_keys=list(), only_model=False): + sd = torch.load(path, map_location="cpu") + if "state_dict" in list(sd.keys()): + sd = sd["state_dict"] + keys = list(sd.keys()) + for k in keys: + for ik in ignore_keys: + if k.startswith(ik): + print("Deleting key {} from state_dict.".format(k)) + del sd[k] + missing, unexpected = self.load_state_dict(sd, strict=False) if not only_model else self.model.load_state_dict( + sd, strict=False) + print(f"Restored from {path} with {len(missing)} missing and {len(unexpected)} unexpected keys") + if len(missing) > 0: + print(f"Missing Keys: {missing}") + if len(unexpected) > 0: + print(f"Unexpected Keys: {unexpected}") + + def q_mean_variance(self, x_start, t): + """ + Get the distribution q(x_t | x_0). + :param x_start: the [N x C x ...] tensor of noiseless inputs. + :param t: the number of diffusion steps (minus 1). Here, 0 means one step. + :return: A tuple (mean, variance, log_variance), all of x_start's shape. + """ + mean = (extract_into_tensor(self.sqrt_alphas_cumprod, t, x_start.shape) * x_start) + variance = extract_into_tensor(1.0 - self.alphas_cumprod, t, x_start.shape) + log_variance = extract_into_tensor(self.log_one_minus_alphas_cumprod, t, x_start.shape) + return mean, variance, log_variance + + def predict_start_from_noise(self, x_t, t, noise): + return ( + extract_into_tensor(self.sqrt_recip_alphas_cumprod, t, x_t.shape) * x_t - + extract_into_tensor(self.sqrt_recipm1_alphas_cumprod, t, x_t.shape) * noise + ) + + def q_posterior(self, x_start, x_t, t): + posterior_mean = ( + extract_into_tensor(self.posterior_mean_coef1, t, x_t.shape) * x_start + + extract_into_tensor(self.posterior_mean_coef2, t, x_t.shape) * x_t + ) + posterior_variance = extract_into_tensor(self.posterior_variance, t, x_t.shape) + posterior_log_variance_clipped = extract_into_tensor(self.posterior_log_variance_clipped, t, x_t.shape) + return posterior_mean, posterior_variance, posterior_log_variance_clipped + + def p_mean_variance(self, x, t, clip_denoised: bool): + model_out = self.model(x, t) + if self.parameterization == "eps": + x_recon = self.predict_start_from_noise(x, t=t, noise=model_out) + elif self.parameterization == "x0": + x_recon = model_out + if clip_denoised: + x_recon.clamp_(-1., 1.) + + model_mean, posterior_variance, posterior_log_variance = self.q_posterior(x_start=x_recon, x_t=x, t=t) + return model_mean, posterior_variance, posterior_log_variance + + @torch.no_grad() + def p_sample(self, x, t, clip_denoised=True, repeat_noise=False): + b, *_, device = *x.shape, x.device + model_mean, _, model_log_variance = self.p_mean_variance(x=x, t=t, clip_denoised=clip_denoised) + noise = noise_like(x.shape, device, repeat_noise) + # no noise when t == 0 + nonzero_mask = (1 - (t == 0).float()).reshape(b, *((1,) * (len(x.shape) - 1))) + return model_mean + nonzero_mask * (0.5 * model_log_variance).exp() * noise + + @torch.no_grad() + def p_sample_loop(self, shape, return_intermediates=False): + device = self.betas.device + b = shape[0] + img = torch.randn(shape, device=device) + intermediates = [img] + for i in tqdm(reversed(range(0, self.num_timesteps)), desc='Sampling t', total=self.num_timesteps): + img = self.p_sample(img, torch.full((b,), i, device=device, dtype=torch.long), + clip_denoised=self.clip_denoised) + if i % self.log_every_t == 0 or i == self.num_timesteps - 1: + intermediates.append(img) + if return_intermediates: + return img, intermediates + return img + + @torch.no_grad() + def sample(self, batch_size=16, return_intermediates=False): + image_size = self.image_size + channels = self.channels + return self.p_sample_loop((batch_size, channels, image_size, image_size), + return_intermediates=return_intermediates) + + def q_sample(self, x_start, t, noise=None): + noise = default(noise, lambda: torch.randn_like(x_start)) + return (extract_into_tensor(self.sqrt_alphas_cumprod, t, x_start.shape) * x_start + + extract_into_tensor(self.sqrt_one_minus_alphas_cumprod, t, x_start.shape) * noise) + + def get_loss(self, pred, target, mean=True): + if self.loss_type == 'l1': + loss = (target - pred).abs() + if mean: + loss = loss.mean() + elif self.loss_type == 'l2': + if mean: + loss = torch.nn.functional.mse_loss(target, pred) + else: + loss = torch.nn.functional.mse_loss(target, pred, reduction='none') + else: + raise NotImplementedError("unknown loss type '{loss_type}'") + + return loss + + def p_losses(self, x_start, t, noise=None): + noise = default(noise, lambda: torch.randn_like(x_start)) + x_noisy = self.q_sample(x_start=x_start, t=t, noise=noise) + model_out = self.model(x_noisy, t) + + loss_dict = {} + if self.parameterization == "eps": + target = noise + elif self.parameterization == "x0": + target = x_start + else: + raise NotImplementedError(f"Paramterization {self.parameterization} not yet supported") + + loss = self.get_loss(model_out, target, mean=False).mean(dim=[1, 2, 3]) + + log_prefix = 'train' if self.training else 'val' + + loss_dict.update({f'{log_prefix}/loss_simple': loss.mean()}) + loss_simple = loss.mean() * self.l_simple_weight + + loss_vlb = (self.lvlb_weights[t] * loss).mean() + loss_dict.update({f'{log_prefix}/loss_vlb': loss_vlb}) + + loss = loss_simple + self.original_elbo_weight * loss_vlb + + loss_dict.update({f'{log_prefix}/loss': loss}) + + return loss, loss_dict + + def forward(self, x, *args, **kwargs): + # b, c, h, w, device, img_size, = *x.shape, x.device, self.image_size + # assert h == img_size and w == img_size, f'height and width of image must be {img_size}' + t = torch.randint(0, self.num_timesteps, (x.shape[0],), device=self.device).long() + return self.p_losses(x, t, *args, **kwargs) + + def get_input(self, batch, k): + x = batch[k] + if len(x.shape) == 3: + x = x[..., None] + x = rearrange(x, 'b h w c -> b c h w') + x = x.to(memory_format=torch.contiguous_format).float() + return x + + def shared_step(self, batch): + x = self.get_input(batch, self.first_stage_key) + loss, loss_dict = self(x) + return loss, loss_dict + + def training_step(self, batch, batch_idx): + loss, loss_dict = self.shared_step(batch) + + self.log_dict(loss_dict, prog_bar=True, + logger=True, on_step=True, on_epoch=True) + + self.log("global_step", self.global_step, + prog_bar=True, logger=True, on_step=True, on_epoch=False) + + if self.use_scheduler: + lr = self.optimizers().param_groups[0]['lr'] + self.log('lr_abs', lr, prog_bar=True, logger=True, on_step=True, on_epoch=False) + + return loss + + @torch.no_grad() + def validation_step(self, batch, batch_idx): + _, loss_dict_no_ema = self.shared_step(batch) + with self.ema_scope(): + _, loss_dict_ema = self.shared_step(batch) + loss_dict_ema = {key + '_ema': loss_dict_ema[key] for key in loss_dict_ema} + self.log_dict(loss_dict_no_ema, prog_bar=False, logger=True, on_step=False, on_epoch=True) + self.log_dict(loss_dict_ema, prog_bar=False, logger=True, on_step=False, on_epoch=True) + + def on_train_batch_end(self, *args, **kwargs): + if self.use_ema: + self.model_ema(self.model) + + def _get_rows_from_list(self, samples): + n_imgs_per_row = len(samples) + denoise_grid = rearrange(samples, 'n b c h w -> b n c h w') + denoise_grid = rearrange(denoise_grid, 'b n c h w -> (b n) c h w') + denoise_grid = make_grid(denoise_grid, nrow=n_imgs_per_row) + return denoise_grid + + @torch.no_grad() + def log_images(self, batch, N=8, n_row=2, sample=True, return_keys=None, **kwargs): + log = dict() + x = self.get_input(batch, self.first_stage_key) + N = min(x.shape[0], N) + n_row = min(x.shape[0], n_row) + x = x.to(self.device)[:N] + log["inputs"] = x + + # get diffusion row + diffusion_row = list() + x_start = x[:n_row] + + for t in range(self.num_timesteps): + if t % self.log_every_t == 0 or t == self.num_timesteps - 1: + t = repeat(torch.tensor([t]), '1 -> b', b=n_row) + t = t.to(self.device).long() + noise = torch.randn_like(x_start) + x_noisy = self.q_sample(x_start=x_start, t=t, noise=noise) + diffusion_row.append(x_noisy) + + log["diffusion_row"] = self._get_rows_from_list(diffusion_row) + + if sample: + # get denoise row + with self.ema_scope("Plotting"): + samples, denoise_row = self.sample(batch_size=N, return_intermediates=True) + + log["samples"] = samples + log["denoise_row"] = self._get_rows_from_list(denoise_row) + + if return_keys: + if np.intersect1d(list(log.keys()), return_keys).shape[0] == 0: + return log + else: + return {key: log[key] for key in return_keys} + return log + + def configure_optimizers(self): + lr = self.learning_rate + params = list(self.model.parameters()) + if self.learn_logvar: + params = params + [self.logvar] + opt = torch.optim.AdamW(params, lr=lr) + return opt + + +class LatentDiffusion(DDPM): + """main class""" + def __init__(self, + first_stage_config, + cond_stage_config, + num_timesteps_cond=None, + cond_stage_key="image",# 'caption' for txt2image, 'masked_image' for inpainting + cond_stage_trainable=False, + concat_mode=True,# true for inpainting + cond_stage_forward=None, + conditioning_key=None, # 'crossattn' for txt2image, None for inpainting + scale_factor=1.0, + scale_by_std=False, + *args, **kwargs): + self.num_timesteps_cond = default(num_timesteps_cond, 1) + self.scale_by_std = scale_by_std + assert self.num_timesteps_cond <= kwargs['timesteps'] + # for backwards compatibility after implementation of DiffusionWrapper + if conditioning_key is None: + conditioning_key = 'concat' if concat_mode else 'crossattn' + if cond_stage_config == '__is_unconditional__': + conditioning_key = None + ckpt_path = kwargs.pop("ckpt_path", None) + ignore_keys = kwargs.pop("ignore_keys", []) + super().__init__(conditioning_key=conditioning_key, *args, **kwargs) + self.concat_mode = concat_mode + self.cond_stage_trainable = cond_stage_trainable + self.cond_stage_key = cond_stage_key + try: + self.num_downs = len(first_stage_config.params.ddconfig.ch_mult) - 1 + except: + self.num_downs = 0 + if not scale_by_std: + self.scale_factor = scale_factor + else: + self.register_buffer('scale_factor', torch.tensor(scale_factor)) + self.instantiate_first_stage(first_stage_config) + self.instantiate_cond_stage(cond_stage_config) + self.cond_stage_forward = cond_stage_forward + self.clip_denoised = False + self.bbox_tokenizer = None + + self.restarted_from_ckpt = False + if ckpt_path is not None: + self.init_from_ckpt(ckpt_path, ignore_keys) + self.restarted_from_ckpt = True + + def make_cond_schedule(self, ): + self.cond_ids = torch.full(size=(self.num_timesteps,), fill_value=self.num_timesteps - 1, dtype=torch.long) + ids = torch.round(torch.linspace(0, self.num_timesteps - 1, self.num_timesteps_cond)).long() + self.cond_ids[:self.num_timesteps_cond] = ids + + @rank_zero_only + @torch.no_grad() + def on_train_batch_start(self, batch, batch_idx, dataloader_idx): + # only for very first batch + if self.scale_by_std and self.current_epoch == 0 and self.global_step == 0 and batch_idx == 0 and not self.restarted_from_ckpt: + assert self.scale_factor == 1., 'rather not use custom rescaling and std-rescaling simultaneously' + # set rescale weight to 1./std of encodings + print("### USING STD-RESCALING ###") + x = super().get_input(batch, self.first_stage_key) + x = x.to(self.device) + encoder_posterior = self.encode_first_stage(x) + z = self.get_first_stage_encoding(encoder_posterior).detach() + del self.scale_factor + self.register_buffer('scale_factor', 1. / z.flatten().std()) + print(f"setting self.scale_factor to {self.scale_factor}") + print("### USING STD-RESCALING ###") + + def register_schedule(self, + given_betas=None, beta_schedule="linear", timesteps=1000, + linear_start=1e-4, linear_end=2e-2, cosine_s=8e-3): + super().register_schedule(given_betas, beta_schedule, timesteps, linear_start, linear_end, cosine_s) + + self.shorten_cond_schedule = self.num_timesteps_cond > 1 + if self.shorten_cond_schedule: + self.make_cond_schedule() + + def instantiate_first_stage(self, config): + model = instantiate_from_config(config) + self.first_stage_model = model.eval() + self.first_stage_model.train = disabled_train + for param in self.first_stage_model.parameters(): + param.requires_grad = False + + def instantiate_cond_stage(self, config): + if not self.cond_stage_trainable: + if config == "__is_first_stage__":# inpaint + print("Using first stage also as cond stage.") + self.cond_stage_model = self.first_stage_model + elif config == "__is_unconditional__": + print(f"Training {self.__class__.__name__} as an unconditional model.") + self.cond_stage_model = None + # self.be_unconditional = True + else: + model = instantiate_from_config(config) + self.cond_stage_model = model.eval() + self.cond_stage_model.train = disabled_train + for param in self.cond_stage_model.parameters(): + param.requires_grad = False + else: + assert config != '__is_first_stage__' + assert config != '__is_unconditional__' + model = instantiate_from_config(config) + self.cond_stage_model = model + + def _get_denoise_row_from_list(self, samples, desc='', force_no_decoder_quantization=False): + denoise_row = [] + for zd in tqdm(samples, desc=desc): + denoise_row.append(self.decode_first_stage(zd.to(self.device), + force_not_quantize=force_no_decoder_quantization)) + n_imgs_per_row = len(denoise_row) + denoise_row = torch.stack(denoise_row) # n_log_step, n_row, C, H, W + denoise_grid = rearrange(denoise_row, 'n b c h w -> b n c h w') + denoise_grid = rearrange(denoise_grid, 'b n c h w -> (b n) c h w') + denoise_grid = make_grid(denoise_grid, nrow=n_imgs_per_row) + return denoise_grid + + def get_first_stage_encoding(self, encoder_posterior): + if isinstance(encoder_posterior, DiagonalGaussianDistribution): + z = encoder_posterior.sample() + elif isinstance(encoder_posterior, torch.Tensor): + z = encoder_posterior + else: + raise NotImplementedError(f"encoder_posterior of type '{type(encoder_posterior)}' not yet implemented") + return self.scale_factor * z + + def get_learned_conditioning(self, c): + if self.cond_stage_forward is None: + if hasattr(self.cond_stage_model, 'encode') and callable(self.cond_stage_model.encode): + c = self.cond_stage_model.encode(c) + if isinstance(c, DiagonalGaussianDistribution): + c = c.mode() + else: + c = self.cond_stage_model(c) + else: + assert hasattr(self.cond_stage_model, self.cond_stage_forward) + c = getattr(self.cond_stage_model, self.cond_stage_forward)(c) + return c + + def meshgrid(self, h, w): + y = torch.arange(0, h).view(h, 1, 1).repeat(1, w, 1) + x = torch.arange(0, w).view(1, w, 1).repeat(h, 1, 1) + + arr = torch.cat([y, x], dim=-1) + return arr + + def delta_border(self, h, w): + """ + :param h: height + :param w: width + :return: normalized distance to image border, + wtith min distance = 0 at border and max dist = 0.5 at image center + """ + lower_right_corner = torch.tensor([h - 1, w - 1]).view(1, 1, 2) + arr = self.meshgrid(h, w) / lower_right_corner + dist_left_up = torch.min(arr, dim=-1, keepdims=True)[0] + dist_right_down = torch.min(1 - arr, dim=-1, keepdims=True)[0] + edge_dist = torch.min(torch.cat([dist_left_up, dist_right_down], dim=-1), dim=-1)[0] + return edge_dist + + def get_weighting(self, h, w, Ly, Lx, device): + weighting = self.delta_border(h, w) + weighting = torch.clip(weighting, self.split_input_params["clip_min_weight"], + self.split_input_params["clip_max_weight"], ) + weighting = weighting.view(1, h * w, 1).repeat(1, 1, Ly * Lx).to(device) + + if self.split_input_params["tie_braker"]: + L_weighting = self.delta_border(Ly, Lx) + L_weighting = torch.clip(L_weighting, + self.split_input_params["clip_min_tie_weight"], + self.split_input_params["clip_max_tie_weight"]) + + L_weighting = L_weighting.view(1, 1, Ly * Lx).to(device) + weighting = weighting * L_weighting + return weighting + + def get_fold_unfold(self, x, kernel_size, stride, uf=1, df=1): # todo load once not every time, shorten code + """ + :param x: img of size (bs, c, h, w) + :return: n img crops of size (n, bs, c, kernel_size[0], kernel_size[1]) + """ + bs, nc, h, w = x.shape + + # number of crops in image + Ly = (h - kernel_size[0]) // stride[0] + 1 + Lx = (w - kernel_size[1]) // stride[1] + 1 + + if uf == 1 and df == 1: + fold_params = dict(kernel_size=kernel_size, dilation=1, padding=0, stride=stride) + unfold = torch.nn.Unfold(**fold_params) + + fold = torch.nn.Fold(output_size=x.shape[2:], **fold_params) + + weighting = self.get_weighting(kernel_size[0], kernel_size[1], Ly, Lx, x.device).to(x.dtype) + normalization = fold(weighting).view(1, 1, h, w) # normalizes the overlap + weighting = weighting.view((1, 1, kernel_size[0], kernel_size[1], Ly * Lx)) + + elif uf > 1 and df == 1: + fold_params = dict(kernel_size=kernel_size, dilation=1, padding=0, stride=stride) + unfold = torch.nn.Unfold(**fold_params) + + fold_params2 = dict(kernel_size=(kernel_size[0] * uf, kernel_size[0] * uf), + dilation=1, padding=0, + stride=(stride[0] * uf, stride[1] * uf)) + fold = torch.nn.Fold(output_size=(x.shape[2] * uf, x.shape[3] * uf), **fold_params2) + + weighting = self.get_weighting(kernel_size[0] * uf, kernel_size[1] * uf, Ly, Lx, x.device).to(x.dtype) + normalization = fold(weighting).view(1, 1, h * uf, w * uf) # normalizes the overlap + weighting = weighting.view((1, 1, kernel_size[0] * uf, kernel_size[1] * uf, Ly * Lx)) + + elif df > 1 and uf == 1: + fold_params = dict(kernel_size=kernel_size, dilation=1, padding=0, stride=stride) + unfold = torch.nn.Unfold(**fold_params) + + fold_params2 = dict(kernel_size=(kernel_size[0] // df, kernel_size[0] // df), + dilation=1, padding=0, + stride=(stride[0] // df, stride[1] // df)) + fold = torch.nn.Fold(output_size=(x.shape[2] // df, x.shape[3] // df), **fold_params2) + + weighting = self.get_weighting(kernel_size[0] // df, kernel_size[1] // df, Ly, Lx, x.device).to(x.dtype) + normalization = fold(weighting).view(1, 1, h // df, w // df) # normalizes the overlap + weighting = weighting.view((1, 1, kernel_size[0] // df, kernel_size[1] // df, Ly * Lx)) + + else: + raise NotImplementedError + + return fold, unfold, normalization, weighting + + @torch.no_grad() + def get_input(self, batch, k, return_first_stage_outputs=False, force_c_encode=False, + cond_key=None, return_original_cond=False, bs=None): + x = super().get_input(batch, k) + if bs is not None: + x = x[:bs] + x = x.to(self.device) + encoder_posterior = self.encode_first_stage(x) + z = self.get_first_stage_encoding(encoder_posterior).detach() + + if self.model.conditioning_key is not None: + if cond_key is None: + cond_key = self.cond_stage_key + if cond_key != self.first_stage_key:# cond_key is not image. for inapint it's masked_img + if cond_key in ['caption', 'coordinates_bbox']: + xc = batch[cond_key] + elif cond_key == 'class_label': + xc = batch + else: + xc = super().get_input(batch, cond_key).to(self.device) + else: + xc = x + if not self.cond_stage_trainable or force_c_encode: + if isinstance(xc, dict) or isinstance(xc, list): + # import pudb; pudb.set_trace() + c = self.get_learned_conditioning(xc) + else: + c = self.get_learned_conditioning(xc.to(self.device)) + else: + c = xc + if bs is not None: + c = c[:bs] + + if self.use_positional_encodings: + pos_x, pos_y = self.compute_latent_shifts(batch) + ckey = __conditioning_keys__[self.model.conditioning_key] + c = {ckey: c, 'pos_x': pos_x, 'pos_y': pos_y} + + else: + c = None + xc = None + if self.use_positional_encodings: + pos_x, pos_y = self.compute_latent_shifts(batch) + c = {'pos_x': pos_x, 'pos_y': pos_y} + out = [z, c] + if return_first_stage_outputs: + xrec = self.decode_first_stage(z) + out.extend([x, xrec]) + if return_original_cond: + out.append(xc) + return out + + @torch.no_grad() + def decode_first_stage(self, z, predict_cids=False, force_not_quantize=False): + if predict_cids: + if z.dim() == 4: + z = torch.argmax(z.exp(), dim=1).long() + z = self.first_stage_model.quantize.get_codebook_entry(z, shape=None) + z = rearrange(z, 'b h w c -> b c h w').contiguous() + + z = 1. / self.scale_factor * z + + if hasattr(self, "split_input_params"): + if self.split_input_params["patch_distributed_vq"]: + ks = self.split_input_params["ks"] # eg. (128, 128) + stride = self.split_input_params["stride"] # eg. (64, 64) + uf = self.split_input_params["vqf"] + bs, nc, h, w = z.shape + if ks[0] > h or ks[1] > w: + ks = (min(ks[0], h), min(ks[1], w)) + print("reducing Kernel") + + if stride[0] > h or stride[1] > w: + stride = (min(stride[0], h), min(stride[1], w)) + print("reducing stride") + + fold, unfold, normalization, weighting = self.get_fold_unfold(z, ks, stride, uf=uf) + + z = unfold(z) # (bn, nc * prod(**ks), L) + # 1. Reshape to img shape + z = z.view((z.shape[0], -1, ks[0], ks[1], z.shape[-1])) # (bn, nc, ks[0], ks[1], L ) + + # 2. apply model loop over last dim + if isinstance(self.first_stage_model, VQModelInterface): + output_list = [self.first_stage_model.decode(z[:, :, :, :, i], + force_not_quantize=predict_cids or force_not_quantize) + for i in range(z.shape[-1])] + else: + + output_list = [self.first_stage_model.decode(z[:, :, :, :, i]) + for i in range(z.shape[-1])] + + o = torch.stack(output_list, axis=-1) # # (bn, nc, ks[0], ks[1], L) + o = o * weighting + # Reverse 1. reshape to img shape + o = o.view((o.shape[0], -1, o.shape[-1])) # (bn, nc * ks[0] * ks[1], L) + # stitch crops together + decoded = fold(o) + decoded = decoded / normalization # norm is shape (1, 1, h, w) + return decoded + else: + if isinstance(self.first_stage_model, VQModelInterface): + return self.first_stage_model.decode(z, force_not_quantize=predict_cids or force_not_quantize) + else: + return self.first_stage_model.decode(z) + + else: + if isinstance(self.first_stage_model, VQModelInterface): + return self.first_stage_model.decode(z, force_not_quantize=predict_cids or force_not_quantize) + else: + return self.first_stage_model.decode(z) + + # same as above but without decorator + def differentiable_decode_first_stage(self, z, predict_cids=False, force_not_quantize=False): + if predict_cids: + if z.dim() == 4: + z = torch.argmax(z.exp(), dim=1).long() + z = self.first_stage_model.quantize.get_codebook_entry(z, shape=None) + z = rearrange(z, 'b h w c -> b c h w').contiguous() + + z = 1. / self.scale_factor * z + + if hasattr(self, "split_input_params"): + if self.split_input_params["patch_distributed_vq"]: + ks = self.split_input_params["ks"] # eg. (128, 128) + stride = self.split_input_params["stride"] # eg. (64, 64) + uf = self.split_input_params["vqf"] + bs, nc, h, w = z.shape + if ks[0] > h or ks[1] > w: + ks = (min(ks[0], h), min(ks[1], w)) + print("reducing Kernel") + + if stride[0] > h or stride[1] > w: + stride = (min(stride[0], h), min(stride[1], w)) + print("reducing stride") + + fold, unfold, normalization, weighting = self.get_fold_unfold(z, ks, stride, uf=uf) + + z = unfold(z) # (bn, nc * prod(**ks), L) + # 1. Reshape to img shape + z = z.view((z.shape[0], -1, ks[0], ks[1], z.shape[-1])) # (bn, nc, ks[0], ks[1], L ) + + # 2. apply model loop over last dim + if isinstance(self.first_stage_model, VQModelInterface): + output_list = [self.first_stage_model.decode(z[:, :, :, :, i], + force_not_quantize=predict_cids or force_not_quantize) + for i in range(z.shape[-1])] + else: + + output_list = [self.first_stage_model.decode(z[:, :, :, :, i]) + for i in range(z.shape[-1])] + + o = torch.stack(output_list, axis=-1) # # (bn, nc, ks[0], ks[1], L) + o = o * weighting + # Reverse 1. reshape to img shape + o = o.view((o.shape[0], -1, o.shape[-1])) # (bn, nc * ks[0] * ks[1], L) + # stitch crops together + decoded = fold(o) + decoded = decoded / normalization # norm is shape (1, 1, h, w) + return decoded + else: + if isinstance(self.first_stage_model, VQModelInterface): + return self.first_stage_model.decode(z, force_not_quantize=predict_cids or force_not_quantize) + else: + return self.first_stage_model.decode(z) + + else: + if isinstance(self.first_stage_model, VQModelInterface): + return self.first_stage_model.decode(z, force_not_quantize=predict_cids or force_not_quantize) + else: + return self.first_stage_model.decode(z) + + @torch.no_grad() + def encode_first_stage(self, x): + if hasattr(self, "split_input_params"): + if self.split_input_params["patch_distributed_vq"]: + ks = self.split_input_params["ks"] # eg. (128, 128) + stride = self.split_input_params["stride"] # eg. (64, 64) + df = self.split_input_params["vqf"] + self.split_input_params['original_image_size'] = x.shape[-2:] + bs, nc, h, w = x.shape + if ks[0] > h or ks[1] > w: + ks = (min(ks[0], h), min(ks[1], w)) + print("reducing Kernel") + + if stride[0] > h or stride[1] > w: + stride = (min(stride[0], h), min(stride[1], w)) + print("reducing stride") + + fold, unfold, normalization, weighting = self.get_fold_unfold(x, ks, stride, df=df) + z = unfold(x) # (bn, nc * prod(**ks), L) + # Reshape to img shape + z = z.view((z.shape[0], -1, ks[0], ks[1], z.shape[-1])) # (bn, nc, ks[0], ks[1], L ) + + output_list = [self.first_stage_model.encode(z[:, :, :, :, i]) + for i in range(z.shape[-1])] + + o = torch.stack(output_list, axis=-1) + o = o * weighting + + # Reverse reshape to img shape + o = o.view((o.shape[0], -1, o.shape[-1])) # (bn, nc * ks[0] * ks[1], L) + # stitch crops together + decoded = fold(o) + decoded = decoded / normalization + return decoded + + else: + return self.first_stage_model.encode(x) + else: + return self.first_stage_model.encode(x) + + def shared_step(self, batch, **kwargs): + x, c = self.get_input(batch, self.first_stage_key) + loss = self(x, c) + return loss + + def forward(self, x, c, *args, **kwargs): + t = torch.randint(0, self.num_timesteps, (x.shape[0],), device=self.device).long() + if self.model.conditioning_key is not None: + assert c is not None + if self.cond_stage_trainable:# true when use text + c = self.get_learned_conditioning(c) # c: string list -> [B, T, Context_dim] + if self.shorten_cond_schedule: # TODO: drop this option + tc = self.cond_ids[t].to(self.device) + c = self.q_sample(x_start=c, t=tc, noise=torch.randn_like(c.float())) + return self.p_losses(x, c, t, *args, **kwargs) + + def _rescale_annotations(self, bboxes, crop_coordinates): # TODO: move to dataset + def rescale_bbox(bbox): + x0 = clamp((bbox[0] - crop_coordinates[0]) / crop_coordinates[2]) + y0 = clamp((bbox[1] - crop_coordinates[1]) / crop_coordinates[3]) + w = min(bbox[2] / crop_coordinates[2], 1 - x0) + h = min(bbox[3] / crop_coordinates[3], 1 - y0) + return x0, y0, w, h + + return [rescale_bbox(b) for b in bboxes] + + def apply_model(self, x_noisy, t, cond, return_ids=False): + + if isinstance(cond, dict): + # hybrid case, cond is exptected to be a dict + pass + else: + if not isinstance(cond, list): + cond = [cond] + key = 'c_concat' if self.model.conditioning_key == 'concat' else 'c_crossattn' + cond = {key: cond} + + if hasattr(self, "split_input_params"): + assert len(cond) == 1 # todo can only deal with one conditioning atm + assert not return_ids + ks = self.split_input_params["ks"] # eg. (128, 128) + stride = self.split_input_params["stride"] # eg. (64, 64) + + h, w = x_noisy.shape[-2:] + + fold, unfold, normalization, weighting = self.get_fold_unfold(x_noisy, ks, stride) + + z = unfold(x_noisy) # (bn, nc * prod(**ks), L) + # Reshape to img shape + z = z.view((z.shape[0], -1, ks[0], ks[1], z.shape[-1])) # (bn, nc, ks[0], ks[1], L ) + z_list = [z[:, :, :, :, i] for i in range(z.shape[-1])] + + if self.cond_stage_key in ["image", "LR_image", "segmentation", + 'bbox_img'] and self.model.conditioning_key: # todo check for completeness + c_key = next(iter(cond.keys())) # get key + c = next(iter(cond.values())) # get value + assert (len(c) == 1) # todo extend to list with more than one elem + c = c[0] # get element + + c = unfold(c) + c = c.view((c.shape[0], -1, ks[0], ks[1], c.shape[-1])) # (bn, nc, ks[0], ks[1], L ) + + cond_list = [{c_key: [c[:, :, :, :, i]]} for i in range(c.shape[-1])] + + elif self.cond_stage_key == 'coordinates_bbox': + assert 'original_image_size' in self.split_input_params, 'BoudingBoxRescaling is missing original_image_size' + + # assuming padding of unfold is always 0 and its dilation is always 1 + n_patches_per_row = int((w - ks[0]) / stride[0] + 1) + full_img_h, full_img_w = self.split_input_params['original_image_size'] + # as we are operating on latents, we need the factor from the original image size to the + # spatial latent size to properly rescale the crops for regenerating the bbox annotations + num_downs = self.first_stage_model.encoder.num_resolutions - 1 + rescale_latent = 2 ** (num_downs) + + # get top left postions of patches as conforming for the bbbox tokenizer, therefore we + # need to rescale the tl patch coordinates to be in between (0,1) + tl_patch_coordinates = [(rescale_latent * stride[0] * (patch_nr % n_patches_per_row) / full_img_w, + rescale_latent * stride[1] * (patch_nr // n_patches_per_row) / full_img_h) + for patch_nr in range(z.shape[-1])] + + # patch_limits are tl_coord, width and height coordinates as (x_tl, y_tl, h, w) + patch_limits = [(x_tl, y_tl, + rescale_latent * ks[0] / full_img_w, + rescale_latent * ks[1] / full_img_h) for x_tl, y_tl in tl_patch_coordinates] + # patch_values = [(np.arange(x_tl,min(x_tl+ks, 1.)),np.arange(y_tl,min(y_tl+ks, 1.))) for x_tl, y_tl in tl_patch_coordinates] + + # tokenize crop coordinates for the bounding boxes of the respective patches + patch_limits_tknzd = [torch.LongTensor(self.bbox_tokenizer._crop_encoder(bbox))[None].to(self.device) + for bbox in patch_limits] # list of length l with tensors of shape (1, 2) + print(patch_limits_tknzd[0].shape) + # cut tknzd crop position from conditioning + assert isinstance(cond, dict), 'cond must be dict to be fed into model' + cut_cond = cond['c_crossattn'][0][..., :-2].to(self.device) + print(cut_cond.shape) + + adapted_cond = torch.stack([torch.cat([cut_cond, p], dim=1) for p in patch_limits_tknzd]) + adapted_cond = rearrange(adapted_cond, 'l b n -> (l b) n') + print(adapted_cond.shape) + adapted_cond = self.get_learned_conditioning(adapted_cond) + print(adapted_cond.shape) + adapted_cond = rearrange(adapted_cond, '(l b) n d -> l b n d', l=z.shape[-1]) + print(adapted_cond.shape) + + cond_list = [{'c_crossattn': [e]} for e in adapted_cond] + + else: + cond_list = [cond for i in range(z.shape[-1])] # Todo make this more efficient + + # apply model by loop over crops + output_list = [self.model(z_list[i], t, **cond_list[i]) for i in range(z.shape[-1])] + assert not isinstance(output_list[0], + tuple) # todo cant deal with multiple model outputs check this never happens + + o = torch.stack(output_list, axis=-1) + o = o * weighting + # Reverse reshape to img shape + o = o.view((o.shape[0], -1, o.shape[-1])) # (bn, nc * ks[0] * ks[1], L) + # stitch crops together + x_recon = fold(o) / normalization + + else: + x_recon = self.model(x_noisy, t, **cond) + + if isinstance(x_recon, tuple) and not return_ids: + return x_recon[0] + else: + return x_recon + + def _predict_eps_from_xstart(self, x_t, t, pred_xstart): + return (extract_into_tensor(self.sqrt_recip_alphas_cumprod, t, x_t.shape) * x_t - pred_xstart) / \ + extract_into_tensor(self.sqrt_recipm1_alphas_cumprod, t, x_t.shape) + + def _prior_bpd(self, x_start): + """ + Get the prior KL term for the variational lower-bound, measured in + bits-per-dim. + This term can't be optimized, as it only depends on the encoder. + :param x_start: the [N x C x ...] tensor of inputs. + :return: a batch of [N] KL values (in bits), one per batch element. + """ + batch_size = x_start.shape[0] + t = torch.tensor([self.num_timesteps - 1] * batch_size, device=x_start.device) + qt_mean, _, qt_log_variance = self.q_mean_variance(x_start, t) + kl_prior = normal_kl(mean1=qt_mean, logvar1=qt_log_variance, mean2=0.0, logvar2=0.0) + return mean_flat(kl_prior) / np.log(2.0) + + def p_losses(self, x_start, cond, t, noise=None): + noise = default(noise, lambda: torch.randn_like(x_start)) + x_noisy = self.q_sample(x_start=x_start, t=t, noise=noise) + model_output = self.apply_model(x_noisy, t, cond) + + loss_dict = {} + prefix = 'train' if self.training else 'val' + + if self.parameterization == "x0": + target = x_start + elif self.parameterization == "eps": + target = noise + else: + raise NotImplementedError() + + loss_simple = self.get_loss(model_output, target, mean=False).mean([1, 2, 3]) + loss_dict.update({f'{prefix}/loss_simple': loss_simple.mean()}) + + logvar_t = self.logvar[t].to(self.device) + loss = loss_simple / torch.exp(logvar_t) + logvar_t + # loss = loss_simple / torch.exp(self.logvar) + self.logvar + if self.learn_logvar: + loss_dict.update({f'{prefix}/loss_gamma': loss.mean()}) + loss_dict.update({'logvar': self.logvar.data.mean()}) + + loss = self.l_simple_weight * loss.mean() + + loss_vlb = self.get_loss(model_output, target, mean=False).mean(dim=(1, 2, 3)) + loss_vlb = (self.lvlb_weights[t] * loss_vlb).mean() + loss_dict.update({f'{prefix}/loss_vlb': loss_vlb}) + loss += (self.original_elbo_weight * loss_vlb) + loss_dict.update({f'{prefix}/loss': loss}) + + return loss, loss_dict + + def p_mean_variance(self, x, c, t, clip_denoised: bool, return_codebook_ids=False, quantize_denoised=False, + return_x0=False, score_corrector=None, corrector_kwargs=None): + t_in = t + model_out = self.apply_model(x, t_in, c, return_ids=return_codebook_ids) + + if score_corrector is not None: + assert self.parameterization == "eps" + model_out = score_corrector.modify_score(self, model_out, x, t, c, **corrector_kwargs) + + if return_codebook_ids: + model_out, logits = model_out + + if self.parameterization == "eps": + x_recon = self.predict_start_from_noise(x, t=t, noise=model_out) + elif self.parameterization == "x0": + x_recon = model_out + else: + raise NotImplementedError() + + if clip_denoised: + x_recon.clamp_(-1., 1.) + if quantize_denoised: + x_recon, _, [_, _, indices] = self.first_stage_model.quantize(x_recon) + model_mean, posterior_variance, posterior_log_variance = self.q_posterior(x_start=x_recon, x_t=x, t=t) + if return_codebook_ids: + return model_mean, posterior_variance, posterior_log_variance, logits + elif return_x0: + return model_mean, posterior_variance, posterior_log_variance, x_recon + else: + return model_mean, posterior_variance, posterior_log_variance + + @torch.no_grad() + def p_sample(self, x, c, t, clip_denoised=False, repeat_noise=False, + return_codebook_ids=False, quantize_denoised=False, return_x0=False, + temperature=1., noise_dropout=0., score_corrector=None, corrector_kwargs=None): + b, *_, device = *x.shape, x.device + outputs = self.p_mean_variance(x=x, c=c, t=t, clip_denoised=clip_denoised, + return_codebook_ids=return_codebook_ids, + quantize_denoised=quantize_denoised, + return_x0=return_x0, + score_corrector=score_corrector, corrector_kwargs=corrector_kwargs) + if return_codebook_ids: + raise DeprecationWarning("Support dropped.") + model_mean, _, model_log_variance, logits = outputs + elif return_x0: + model_mean, _, model_log_variance, x0 = outputs + else: + model_mean, _, model_log_variance = outputs + + noise = noise_like(x.shape, device, repeat_noise) * temperature + if noise_dropout > 0.: + noise = torch.nn.functional.dropout(noise, p=noise_dropout) + # no noise when t == 0 + nonzero_mask = (1 - (t == 0).float()).reshape(b, *((1,) * (len(x.shape) - 1))) + + if return_codebook_ids: + return model_mean + nonzero_mask * (0.5 * model_log_variance).exp() * noise, logits.argmax(dim=1) + if return_x0: + return model_mean + nonzero_mask * (0.5 * model_log_variance).exp() * noise, x0 + else: + return model_mean + nonzero_mask * (0.5 * model_log_variance).exp() * noise + + @torch.no_grad() + def progressive_denoising(self, cond, shape, verbose=True, callback=None, quantize_denoised=False, + img_callback=None, mask=None, x0=None, temperature=1., noise_dropout=0., + score_corrector=None, corrector_kwargs=None, batch_size=None, x_T=None, start_T=None, + log_every_t=None): + if not log_every_t: + log_every_t = self.log_every_t + timesteps = self.num_timesteps + if batch_size is not None: + b = batch_size if batch_size is not None else shape[0] + shape = [batch_size] + list(shape) + else: + b = batch_size = shape[0] + if x_T is None: + img = torch.randn(shape, device=self.device) + else: + img = x_T + intermediates = [] + if cond is not None: + if isinstance(cond, dict): + cond = {key: cond[key][:batch_size] if not isinstance(cond[key], list) else + list(map(lambda x: x[:batch_size], cond[key])) for key in cond} + else: + cond = [c[:batch_size] for c in cond] if isinstance(cond, list) else cond[:batch_size] + + if start_T is not None: + timesteps = min(timesteps, start_T) + iterator = tqdm(reversed(range(0, timesteps)), desc='Progressive Generation', + total=timesteps) if verbose else reversed( + range(0, timesteps)) + if type(temperature) == float: + temperature = [temperature] * timesteps + + for i in iterator: + ts = torch.full((b,), i, device=self.device, dtype=torch.long) + if self.shorten_cond_schedule: + assert self.model.conditioning_key != 'hybrid' + tc = self.cond_ids[ts].to(cond.device) + cond = self.q_sample(x_start=cond, t=tc, noise=torch.randn_like(cond)) + + img, x0_partial = self.p_sample(img, cond, ts, + clip_denoised=self.clip_denoised, + quantize_denoised=quantize_denoised, return_x0=True, + temperature=temperature[i], noise_dropout=noise_dropout, + score_corrector=score_corrector, corrector_kwargs=corrector_kwargs) + if mask is not None: + assert x0 is not None + img_orig = self.q_sample(x0, ts) + img = img_orig * mask + (1. - mask) * img + + if i % log_every_t == 0 or i == timesteps - 1: + intermediates.append(x0_partial) + if callback: callback(i) + if img_callback: img_callback(img, i) + return img, intermediates + + @torch.no_grad() + def p_sample_loop(self, cond, shape, return_intermediates=False, + x_T=None, verbose=True, callback=None, timesteps=None, quantize_denoised=False, + mask=None, x0=None, img_callback=None, start_T=None, + log_every_t=None): + + if not log_every_t: + log_every_t = self.log_every_t + device = self.betas.device + b = shape[0] + if x_T is None: + img = torch.randn(shape, device=device) + else: + img = x_T + + intermediates = [img] + if timesteps is None: + timesteps = self.num_timesteps + + if start_T is not None: + timesteps = min(timesteps, start_T) + iterator = tqdm(reversed(range(0, timesteps)), desc='Sampling t', total=timesteps) if verbose else reversed( + range(0, timesteps)) + + if mask is not None: + assert x0 is not None + assert x0.shape[2:3] == mask.shape[2:3] # spatial size has to match + + for i in iterator: + ts = torch.full((b,), i, device=device, dtype=torch.long) + if self.shorten_cond_schedule: + assert self.model.conditioning_key != 'hybrid' + tc = self.cond_ids[ts].to(cond.device) + cond = self.q_sample(x_start=cond, t=tc, noise=torch.randn_like(cond)) + + img = self.p_sample(img, cond, ts, + clip_denoised=self.clip_denoised, + quantize_denoised=quantize_denoised) + if mask is not None: + img_orig = self.q_sample(x0, ts) + img = img_orig * mask + (1. - mask) * img + + if i % log_every_t == 0 or i == timesteps - 1: + intermediates.append(img) + if callback: callback(i) + if img_callback: img_callback(img, i) + + if return_intermediates: + return img, intermediates + return img + + @torch.no_grad() + def sample(self, cond, batch_size=16, return_intermediates=False, x_T=None, + verbose=True, timesteps=None, quantize_denoised=False, + mask=None, x0=None, shape=None,**kwargs): + if shape is None: + shape = (batch_size, self.channels, self.image_size, self.image_size) + if cond is not None: + if isinstance(cond, dict): + cond = {key: cond[key][:batch_size] if not isinstance(cond[key], list) else + list(map(lambda x: x[:batch_size], cond[key])) for key in cond} + else: + cond = [c[:batch_size] for c in cond] if isinstance(cond, list) else cond[:batch_size] + return self.p_sample_loop(cond, + shape, + return_intermediates=return_intermediates, x_T=x_T, + verbose=verbose, timesteps=timesteps, quantize_denoised=quantize_denoised, + mask=mask, x0=x0) + + @torch.no_grad() + def sample_log(self,cond,batch_size,ddim, ddim_steps,**kwargs): + + if ddim: + ddim_sampler = DDIMSampler(self) + shape = (self.channels, self.image_size, self.image_size) + samples, intermediates =ddim_sampler.sample(ddim_steps,batch_size, + shape,cond,verbose=False,**kwargs) + + else: + samples, intermediates = self.sample(cond=cond, batch_size=batch_size, + return_intermediates=True,**kwargs) + + return samples, intermediates + + + @torch.no_grad() + def log_images(self, batch, N=8, n_row=4, sample=True, ddim_steps=200, ddim_eta=1., return_keys=None, + quantize_denoised=True, inpaint=True, plot_denoise_rows=False, plot_progressive_rows=True, + plot_diffusion_rows=True, **kwargs): + + use_ddim = ddim_steps is not None + + log = dict() + z, c, x, xrec, xc = self.get_input(batch, self.first_stage_key, + return_first_stage_outputs=True, + force_c_encode=True, + return_original_cond=True, + bs=N) + N = min(x.shape[0], N) + n_row = min(x.shape[0], n_row) + log["inputs"] = x + log["reconstruction"] = xrec + if self.model.conditioning_key is not None: + if hasattr(self.cond_stage_model, "decode"): + xc = self.cond_stage_model.decode(c) + log["conditioning"] = xc + elif self.cond_stage_key in ["caption"]: + xc = log_txt_as_img((x.shape[2], x.shape[3]), batch["caption"]) + log["conditioning"] = xc + elif self.cond_stage_key == 'class_label': + xc = log_txt_as_img((x.shape[2], x.shape[3]), batch["human_label"]) + log['conditioning'] = xc + elif isimage(xc): + log["conditioning"] = xc + if ismap(xc): + log["original_conditioning"] = self.to_rgb(xc) + + if plot_diffusion_rows: + # get diffusion row + diffusion_row = list() + z_start = z[:n_row] + for t in range(self.num_timesteps): + if t % self.log_every_t == 0 or t == self.num_timesteps - 1: + t = repeat(torch.tensor([t]), '1 -> b', b=n_row) + t = t.to(self.device).long() + noise = torch.randn_like(z_start) + z_noisy = self.q_sample(x_start=z_start, t=t, noise=noise) + diffusion_row.append(self.decode_first_stage(z_noisy)) + + diffusion_row = torch.stack(diffusion_row) # n_log_step, n_row, C, H, W + diffusion_grid = rearrange(diffusion_row, 'n b c h w -> b n c h w') + diffusion_grid = rearrange(diffusion_grid, 'b n c h w -> (b n) c h w') + diffusion_grid = make_grid(diffusion_grid, nrow=diffusion_row.shape[0]) + log["diffusion_row"] = diffusion_grid + + if sample: + # get denoise row + with self.ema_scope("Plotting"): + samples, z_denoise_row = self.sample_log(cond=c,batch_size=N,ddim=use_ddim, + ddim_steps=ddim_steps,eta=ddim_eta) + # samples, z_denoise_row = self.sample(cond=c, batch_size=N, return_intermediates=True) + x_samples = self.decode_first_stage(samples) + log["samples"] = x_samples + if plot_denoise_rows: + denoise_grid = self._get_denoise_row_from_list(z_denoise_row) + log["denoise_row"] = denoise_grid + + if quantize_denoised and not isinstance(self.first_stage_model, AutoencoderKL) and not isinstance( + self.first_stage_model, IdentityFirstStage): + # also display when quantizing x0 while sampling + with self.ema_scope("Plotting Quantized Denoised"): + samples, z_denoise_row = self.sample_log(cond=c,batch_size=N,ddim=use_ddim, + ddim_steps=ddim_steps,eta=ddim_eta, + quantize_denoised=True) + # samples, z_denoise_row = self.sample(cond=c, batch_size=N, return_intermediates=True, + # quantize_denoised=True) + x_samples = self.decode_first_stage(samples.to(self.device)) + log["samples_x0_quantized"] = x_samples + + if inpaint: + # make a simple center square + b, h, w = z.shape[0], z.shape[2], z.shape[3] + mask = torch.ones(N, h, w).to(self.device) + # zeros will be filled in + mask[:, h // 4:3 * h // 4, w // 4:3 * w // 4] = 0. + mask = mask[:, None, ...] + with self.ema_scope("Plotting Inpaint"): + + samples, _ = self.sample_log(cond=c,batch_size=N,ddim=use_ddim, eta=ddim_eta, + ddim_steps=ddim_steps, x0=z[:N], mask=mask) + x_samples = self.decode_first_stage(samples.to(self.device)) + log["samples_inpainting"] = x_samples + log["mask"] = mask + + # outpaint + with self.ema_scope("Plotting Outpaint"): + samples, _ = self.sample_log(cond=c, batch_size=N, ddim=use_ddim,eta=ddim_eta, + ddim_steps=ddim_steps, x0=z[:N], mask=mask) + x_samples = self.decode_first_stage(samples.to(self.device)) + log["samples_outpainting"] = x_samples + + if plot_progressive_rows: + with self.ema_scope("Plotting Progressives"): + img, progressives = self.progressive_denoising(c, + shape=(self.channels, self.image_size, self.image_size), + batch_size=N) + prog_row = self._get_denoise_row_from_list(progressives, desc="Progressive Generation") + log["progressive_row"] = prog_row + + if return_keys: + if np.intersect1d(list(log.keys()), return_keys).shape[0] == 0: + return log + else: + return {key: log[key] for key in return_keys} + return log + + def configure_optimizers(self): + lr = self.learning_rate + params = list(self.model.parameters()) + if self.cond_stage_trainable: + print(f"{self.__class__.__name__}: Also optimizing conditioner params!") + params = params + list(self.cond_stage_model.parameters()) + if self.learn_logvar: + print('Diffusion model optimizing logvar') + params.append(self.logvar) + opt = torch.optim.AdamW(params, lr=lr) + if self.use_scheduler: + assert 'target' in self.scheduler_config + scheduler = instantiate_from_config(self.scheduler_config) + + print("Setting up LambdaLR scheduler...") + scheduler = [ + { + 'scheduler': LambdaLR(opt, lr_lambda=scheduler.schedule), + 'interval': 'step', + 'frequency': 1 + }] + return [opt], scheduler + return opt + + @torch.no_grad() + def to_rgb(self, x): + x = x.float() + if not hasattr(self, "colorize"): + self.colorize = torch.randn(3, x.shape[1], 1, 1).to(x) + x = nn.functional.conv2d(x, weight=self.colorize) + x = 2. * (x - x.min()) / (x.max() - x.min()) - 1. + return x + + +class DiffusionWrapper(pl.LightningModule): + def __init__(self, diff_model_config, conditioning_key): + super().__init__() + self.diffusion_model = instantiate_from_config(diff_model_config) + self.conditioning_key = conditioning_key # 'crossattn' for txt2image, concat for inpainting + assert self.conditioning_key in [None, 'concat', 'crossattn', 'hybrid', 'adm'] + + def forward(self, x, t, c_concat: list = None, c_crossattn: list = None): + """param x: tensor with shape:[B,C,mel_len,T]""" + if self.conditioning_key is None: + out = self.diffusion_model(x, t) + elif self.conditioning_key == 'concat': + xc = torch.cat([x] + c_concat, dim=1)# channel dim,x shape (b,3,64,64) c_concat shape(b,4,64,64) + out = self.diffusion_model(xc, t) + elif self.conditioning_key == 'crossattn': + cc = torch.cat(c_crossattn, 1)# [b,seq_len,dim] + out = self.diffusion_model(x, t, context=cc) + elif self.conditioning_key == 'hybrid':# not implemented in the LatentDiffusion + xc = torch.cat([x] + c_concat, dim=1) + cc = torch.cat(c_crossattn, 1) + out = self.diffusion_model(xc, t, context=cc) + elif self.conditioning_key == 'adm': + cc = c_crossattn[0] + out = self.diffusion_model(x, t, y=cc) + else: + raise NotImplementedError() + + return out + + +class Layout2ImgDiffusion(LatentDiffusion): + # TODO: move all layout-specific hacks to this class + def __init__(self, cond_stage_key, *args, **kwargs): + assert cond_stage_key == 'coordinates_bbox', 'Layout2ImgDiffusion only for cond_stage_key="coordinates_bbox"' + super().__init__(cond_stage_key=cond_stage_key, *args, **kwargs) + + def log_images(self, batch, N=8, *args, **kwargs): + logs = super().log_images(batch=batch, N=N, *args, **kwargs) + + key = 'train' if self.training else 'validation' + dset = self.trainer.datamodule.datasets[key] + mapper = dset.conditional_builders[self.cond_stage_key] + + bbox_imgs = [] + map_fn = lambda catno: dset.get_textual_label(dset.get_category_id(catno)) + for tknzd_bbox in batch[self.cond_stage_key][:N]: + bboximg = mapper.plot(tknzd_bbox.detach().cpu(), map_fn, (256, 256)) + bbox_imgs.append(bboximg) + + cond_img = torch.stack(bbox_imgs, dim=0) + logs['bbox_image'] = cond_img + return logs diff --git a/ldm/models/diffusion/ddpm_audio.py b/ldm/models/diffusion/ddpm_audio.py new file mode 100644 index 0000000000000000000000000000000000000000..aaac6df39ec06c2d52b2f0cabf967ab447f9b04a --- /dev/null +++ b/ldm/models/diffusion/ddpm_audio.py @@ -0,0 +1,1262 @@ +""" +wild mixture of +https://github.com/lucidrains/denoising-diffusion-pytorch/blob/7706bdfc6f527f58d33f84b7b522e61e6e3164b3/denoising_diffusion_pytorch/denoising_diffusion_pytorch.py +https://github.com/openai/improved-diffusion/blob/e94489283bb876ac1477d5dd7709bbbd2d9902ce/improved_diffusion/gaussian_diffusion.py +https://github.com/CompVis/taming-transformers +-- merci +""" +import os +import torch +import torch.nn as nn +import numpy as np +import pytorch_lightning as pl +from torch.optim.lr_scheduler import LambdaLR +from einops import rearrange, repeat +from contextlib import contextmanager +from functools import partial +from tqdm import tqdm +from torchvision.utils import make_grid +from pytorch_lightning.utilities.distributed import rank_zero_only + +from ldm.util import log_txt_as_img, exists, default, ismap, isimage, mean_flat, count_params, instantiate_from_config +from ldm.modules.ema import LitEma +from ldm.modules.distributions.distributions import normal_kl, DiagonalGaussianDistribution +from ldm.models.autoencoder import VQModelInterface, IdentityFirstStage, AutoencoderKL +from ldm.modules.diffusionmodules.util import make_beta_schedule, extract_into_tensor, noise_like +from ldm.models.diffusion.ddim import DDIMSampler +from ldm.models.diffusion.ddpm import DDPM, disabled_train +from omegaconf import ListConfig + +__conditioning_keys__ = {'concat': 'c_concat', + 'crossattn': 'c_crossattn', + 'adm': 'y'} + + +class LatentDiffusion_audio(DDPM): + """main class""" + def __init__(self, + first_stage_config, + cond_stage_config, + num_timesteps_cond=None, + mel_dim=80, + mel_length=848, + cond_stage_key="image", + cond_stage_trainable=False, + concat_mode=True, + cond_stage_forward=None, + conditioning_key=None, + scale_factor=1.0, + scale_by_std=False, + *args, **kwargs): + self.num_timesteps_cond = default(num_timesteps_cond, 1) + self.scale_by_std = scale_by_std + assert self.num_timesteps_cond <= kwargs['timesteps'] + # for backwards compatibility after implementation of DiffusionWrapper + if conditioning_key is None: + conditioning_key = 'concat' if concat_mode else 'crossattn' + if cond_stage_config == '__is_unconditional__': + conditioning_key = None + ckpt_path = kwargs.pop("ckpt_path", None) + ignore_keys = kwargs.pop("ignore_keys", []) + super().__init__(conditioning_key=conditioning_key, *args, **kwargs) + self.concat_mode = concat_mode + self.mel_dim = mel_dim + self.mel_length = mel_length + self.cond_stage_trainable = cond_stage_trainable + self.cond_stage_key = cond_stage_key + try: + self.num_downs = len(first_stage_config.params.ddconfig.ch_mult) - 1 + except: + self.num_downs = 0 + if not scale_by_std: + self.scale_factor = scale_factor + else: + self.register_buffer('scale_factor', torch.tensor(scale_factor)) + self.instantiate_first_stage(first_stage_config) + self.instantiate_cond_stage(cond_stage_config) + self.cond_stage_forward = cond_stage_forward + self.clip_denoised = False + self.bbox_tokenizer = None + + self.restarted_from_ckpt = False + if ckpt_path is not None: + self.init_from_ckpt(ckpt_path, ignore_keys) + self.restarted_from_ckpt = True + + def make_cond_schedule(self, ): + self.cond_ids = torch.full(size=(self.num_timesteps,), fill_value=self.num_timesteps - 1, dtype=torch.long) + ids = torch.round(torch.linspace(0, self.num_timesteps - 1, self.num_timesteps_cond)).long() + self.cond_ids[:self.num_timesteps_cond] = ids + + @rank_zero_only + @torch.no_grad() + def on_train_batch_start(self, batch, batch_idx, dataloader_idx): + # only for very first batch + if self.scale_by_std and self.current_epoch == 0 and self.global_step == 0 and batch_idx == 0 and not self.restarted_from_ckpt: + assert self.scale_factor == 1., 'rather not use custom rescaling and std-rescaling simultaneously' + # set rescale weight to 1./std of encodings + print("### USING STD-RESCALING ###") + x = super().get_input(batch, self.first_stage_key) + x = x.to(self.device) + encoder_posterior = self.encode_first_stage(x) + z = self.get_first_stage_encoding(encoder_posterior).detach() + del self.scale_factor + self.register_buffer('scale_factor', 1. / z.flatten().std()) + print(f"setting self.scale_factor to {self.scale_factor}") + print("### USING STD-RESCALING ###") + + def register_schedule(self, + given_betas=None, beta_schedule="linear", timesteps=1000, + linear_start=1e-4, linear_end=2e-2, cosine_s=8e-3): + super().register_schedule(given_betas, beta_schedule, timesteps, linear_start, linear_end, cosine_s) + + self.shorten_cond_schedule = self.num_timesteps_cond > 1 + if self.shorten_cond_schedule: + self.make_cond_schedule() + + def instantiate_first_stage(self, config): + model = instantiate_from_config(config) + self.first_stage_model = model.eval() + self.first_stage_model.train = disabled_train + for param in self.first_stage_model.parameters(): + param.requires_grad = False + + def instantiate_cond_stage(self, config): + if not self.cond_stage_trainable: + if config == "__is_first_stage__": + print("Using first stage also as cond stage.") + self.cond_stage_model = self.first_stage_model + elif config == "__is_unconditional__": + print(f"Training {self.__class__.__name__} as an unconditional model.") + self.cond_stage_model = None + # self.be_unconditional = True + else: + model = instantiate_from_config(config) + self.cond_stage_model = model.eval() + self.cond_stage_model.train = disabled_train + for param in self.cond_stage_model.parameters(): + param.requires_grad = False + else: + assert config != '__is_first_stage__' + assert config != '__is_unconditional__' + model = instantiate_from_config(config) + self.cond_stage_model = model + + def _get_denoise_row_from_list(self, samples, desc='', force_no_decoder_quantization=False): + denoise_row = [] + for zd in tqdm(samples, desc=desc): + denoise_row.append(self.decode_first_stage(zd.to(self.device), + force_not_quantize=force_no_decoder_quantization)) + n_imgs_per_row = len(denoise_row) + denoise_row = torch.stack(denoise_row) # n_log_step, n_row, C, H, W + denoise_grid = rearrange(denoise_row, 'n b c h w -> b n c h w') + denoise_grid = rearrange(denoise_grid, 'b n c h w -> (b n) c h w') + denoise_grid = make_grid(denoise_grid, nrow=n_imgs_per_row) + return denoise_grid + + def get_first_stage_encoding(self, encoder_posterior): + if isinstance(encoder_posterior, DiagonalGaussianDistribution): + z = encoder_posterior.sample() + elif isinstance(encoder_posterior, torch.Tensor): + z = encoder_posterior + else: + raise NotImplementedError(f"encoder_posterior of type '{type(encoder_posterior)}' not yet implemented") + return self.scale_factor * z + + def get_learned_conditioning(self, c): + if self.cond_stage_forward is None: + if hasattr(self.cond_stage_model, 'encode') and callable(self.cond_stage_model.encode): + c = self.cond_stage_model.encode(c) + if isinstance(c, DiagonalGaussianDistribution): + c = c.mode() + else: + c = self.cond_stage_model(c) + else: + assert hasattr(self.cond_stage_model, self.cond_stage_forward) + c = getattr(self.cond_stage_model, self.cond_stage_forward)(c) + return c + + + @torch.no_grad() + def get_unconditional_conditioning(self, batch_size, null_label=None): + if null_label is not None: + xc = null_label + if isinstance(xc, ListConfig): + xc = list(xc) + if isinstance(xc, dict) or isinstance(xc, list): + c = self.get_learned_conditioning(xc) + else: + if hasattr(xc, "to"): + xc = xc.to(self.device) + c = self.get_learned_conditioning(xc) + else: + if self.cond_stage_key in ["class_label", "cls"]: + xc = self.cond_stage_model.get_unconditional_conditioning(batch_size, device=self.device) + return self.get_learned_conditioning(xc) + else: + raise NotImplementedError("todo") + if isinstance(c, list): # in case the encoder gives us a list + for i in range(len(c)): + c[i] = repeat(c[i], '1 ... -> b ...', b=batch_size).to(self.device) + else: + c = repeat(c, '1 ... -> b ...', b=batch_size).to(self.device) + return c + + def meshgrid(self, h, w): + y = torch.arange(0, h).view(h, 1, 1).repeat(1, w, 1) + x = torch.arange(0, w).view(1, w, 1).repeat(h, 1, 1) + + arr = torch.cat([y, x], dim=-1) + return arr + + def delta_border(self, h, w): + """ + :param h: height + :param w: width + :return: normalized distance to image border, + wtith min distance = 0 at border and max dist = 0.5 at image center + """ + lower_right_corner = torch.tensor([h - 1, w - 1]).view(1, 1, 2) + arr = self.meshgrid(h, w) / lower_right_corner + dist_left_up = torch.min(arr, dim=-1, keepdims=True)[0] + dist_right_down = torch.min(1 - arr, dim=-1, keepdims=True)[0] + edge_dist = torch.min(torch.cat([dist_left_up, dist_right_down], dim=-1), dim=-1)[0] + return edge_dist + + def get_weighting(self, h, w, Ly, Lx, device): + weighting = self.delta_border(h, w) + weighting = torch.clip(weighting, self.split_input_params["clip_min_weight"], + self.split_input_params["clip_max_weight"], ) + weighting = weighting.view(1, h * w, 1).repeat(1, 1, Ly * Lx).to(device) + + if self.split_input_params["tie_braker"]: + L_weighting = self.delta_border(Ly, Lx) + L_weighting = torch.clip(L_weighting, + self.split_input_params["clip_min_tie_weight"], + self.split_input_params["clip_max_tie_weight"]) + + L_weighting = L_weighting.view(1, 1, Ly * Lx).to(device) + weighting = weighting * L_weighting + return weighting + + def get_fold_unfold(self, x, kernel_size, stride, uf=1, df=1): # todo load once not every time, shorten code + """ + :param x: img of size (bs, c, h, w) + :return: n img crops of size (n, bs, c, kernel_size[0], kernel_size[1]) + """ + bs, nc, h, w = x.shape + + # number of crops in image + Ly = (h - kernel_size[0]) // stride[0] + 1 + Lx = (w - kernel_size[1]) // stride[1] + 1 + + if uf == 1 and df == 1: + fold_params = dict(kernel_size=kernel_size, dilation=1, padding=0, stride=stride) + unfold = torch.nn.Unfold(**fold_params) + + fold = torch.nn.Fold(output_size=x.shape[2:], **fold_params) + + weighting = self.get_weighting(kernel_size[0], kernel_size[1], Ly, Lx, x.device).to(x.dtype) + normalization = fold(weighting).view(1, 1, h, w) # normalizes the overlap + weighting = weighting.view((1, 1, kernel_size[0], kernel_size[1], Ly * Lx)) + + elif uf > 1 and df == 1: + fold_params = dict(kernel_size=kernel_size, dilation=1, padding=0, stride=stride) + unfold = torch.nn.Unfold(**fold_params) + + fold_params2 = dict(kernel_size=(kernel_size[0] * uf, kernel_size[0] * uf), + dilation=1, padding=0, + stride=(stride[0] * uf, stride[1] * uf)) + fold = torch.nn.Fold(output_size=(x.shape[2] * uf, x.shape[3] * uf), **fold_params2) + + weighting = self.get_weighting(kernel_size[0] * uf, kernel_size[1] * uf, Ly, Lx, x.device).to(x.dtype) + normalization = fold(weighting).view(1, 1, h * uf, w * uf) # normalizes the overlap + weighting = weighting.view((1, 1, kernel_size[0] * uf, kernel_size[1] * uf, Ly * Lx)) + + elif df > 1 and uf == 1: + fold_params = dict(kernel_size=kernel_size, dilation=1, padding=0, stride=stride) + unfold = torch.nn.Unfold(**fold_params) + + fold_params2 = dict(kernel_size=(kernel_size[0] // df, kernel_size[0] // df), + dilation=1, padding=0, + stride=(stride[0] // df, stride[1] // df)) + fold = torch.nn.Fold(output_size=(x.shape[2] // df, x.shape[3] // df), **fold_params2) + + weighting = self.get_weighting(kernel_size[0] // df, kernel_size[1] // df, Ly, Lx, x.device).to(x.dtype) + normalization = fold(weighting).view(1, 1, h // df, w // df) # normalizes the overlap + weighting = weighting.view((1, 1, kernel_size[0] // df, kernel_size[1] // df, Ly * Lx)) + + else: + raise NotImplementedError + + return fold, unfold, normalization, weighting + + @torch.no_grad() + def get_input(self, batch, k, return_first_stage_outputs=False, force_c_encode=False, + cond_key=None, return_original_cond=False, bs=None): + x = super().get_input(batch, k) + if bs is not None: + x = x[:bs] + x = x.to(self.device) + encoder_posterior = self.encode_first_stage(x) + z = self.get_first_stage_encoding(encoder_posterior).detach() + + if self.model.conditioning_key is not None: + if cond_key is None: + cond_key = self.cond_stage_key + if cond_key != self.first_stage_key: + if cond_key in ['caption', 'coordinates_bbox']: + xc = batch[cond_key] + elif cond_key == 'class_label': + xc = batch + else: + xc = super().get_input(batch, cond_key).to(self.device) + else: + xc = x + if not self.cond_stage_trainable or force_c_encode: + if isinstance(xc, dict) or isinstance(xc, list): + # import pudb; pudb.set_trace() + c = self.get_learned_conditioning(xc) + else: + c = self.get_learned_conditioning(xc.to(self.device)) + else: + c = xc + if bs is not None: + c = c[:bs] + # Testing # + if cond_key == 'masked_image': + mask = super().get_input(batch, "mask") + cc = torch.nn.functional.interpolate(mask, size=c.shape[-2:]) # [B, 1, 10, 106] + c = torch.cat((c, cc), dim=1) # [B, 5, 10, 106] + # Testing # + if self.use_positional_encodings: + pos_x, pos_y = self.compute_latent_shifts(batch) + ckey = __conditioning_keys__[self.model.conditioning_key] + c = {ckey: c, 'pos_x': pos_x, 'pos_y': pos_y} + + else: + c = None + xc = None + if self.use_positional_encodings: + pos_x, pos_y = self.compute_latent_shifts(batch) + c = {'pos_x': pos_x, 'pos_y': pos_y} + out = [z, c] + if return_first_stage_outputs: + xrec = self.decode_first_stage(z) + out.extend([x, xrec]) + if return_original_cond: + out.append(xc) + return out + + @torch.no_grad() + def decode_first_stage(self, z, predict_cids=False, force_not_quantize=False): + if predict_cids: + if z.dim() == 4: + z = torch.argmax(z.exp(), dim=1).long() + z = self.first_stage_model.quantize.get_codebook_entry(z, shape=None) + z = rearrange(z, 'b h w c -> b c h w').contiguous() + + z = 1. / self.scale_factor * z + + if hasattr(self, "split_input_params"): + if self.split_input_params["patch_distributed_vq"]: + ks = self.split_input_params["ks"] # eg. (128, 128) + stride = self.split_input_params["stride"] # eg. (64, 64) + uf = self.split_input_params["vqf"] + bs, nc, h, w = z.shape + if ks[0] > h or ks[1] > w: + ks = (min(ks[0], h), min(ks[1], w)) + print("reducing Kernel") + + if stride[0] > h or stride[1] > w: + stride = (min(stride[0], h), min(stride[1], w)) + print("reducing stride") + + fold, unfold, normalization, weighting = self.get_fold_unfold(z, ks, stride, uf=uf) + + z = unfold(z) # (bn, nc * prod(**ks), L) + # 1. Reshape to img shape + z = z.view((z.shape[0], -1, ks[0], ks[1], z.shape[-1])) # (bn, nc, ks[0], ks[1], L ) + + # 2. apply model loop over last dim + if isinstance(self.first_stage_model, VQModelInterface): + output_list = [self.first_stage_model.decode(z[:, :, :, :, i], + force_not_quantize=predict_cids or force_not_quantize) + for i in range(z.shape[-1])] + else: + + output_list = [self.first_stage_model.decode(z[:, :, :, :, i]) + for i in range(z.shape[-1])] + + o = torch.stack(output_list, axis=-1) # # (bn, nc, ks[0], ks[1], L) + o = o * weighting + # Reverse 1. reshape to img shape + o = o.view((o.shape[0], -1, o.shape[-1])) # (bn, nc * ks[0] * ks[1], L) + # stitch crops together + decoded = fold(o) + decoded = decoded / normalization # norm is shape (1, 1, h, w) + return decoded + else: + if isinstance(self.first_stage_model, VQModelInterface): + return self.first_stage_model.decode(z, force_not_quantize=predict_cids or force_not_quantize) + else: + return self.first_stage_model.decode(z) + + else: + if isinstance(self.first_stage_model, VQModelInterface): + return self.first_stage_model.decode(z, force_not_quantize=predict_cids or force_not_quantize) + else: + return self.first_stage_model.decode(z) + + # same as above but without decorator + def differentiable_decode_first_stage(self, z, predict_cids=False, force_not_quantize=False): + if predict_cids: + if z.dim() == 4: + z = torch.argmax(z.exp(), dim=1).long() + z = self.first_stage_model.quantize.get_codebook_entry(z, shape=None) + z = rearrange(z, 'b h w c -> b c h w').contiguous() + + z = 1. / self.scale_factor * z + + if hasattr(self, "split_input_params"): + if self.split_input_params["patch_distributed_vq"]: + ks = self.split_input_params["ks"] # eg. (128, 128) + stride = self.split_input_params["stride"] # eg. (64, 64) + uf = self.split_input_params["vqf"] + bs, nc, h, w = z.shape + if ks[0] > h or ks[1] > w: + ks = (min(ks[0], h), min(ks[1], w)) + print("reducing Kernel") + + if stride[0] > h or stride[1] > w: + stride = (min(stride[0], h), min(stride[1], w)) + print("reducing stride") + + fold, unfold, normalization, weighting = self.get_fold_unfold(z, ks, stride, uf=uf) + + z = unfold(z) # (bn, nc * prod(**ks), L) + # 1. Reshape to img shape + z = z.view((z.shape[0], -1, ks[0], ks[1], z.shape[-1])) # (bn, nc, ks[0], ks[1], L ) + + # 2. apply model loop over last dim + if isinstance(self.first_stage_model, VQModelInterface): + output_list = [self.first_stage_model.decode(z[:, :, :, :, i], + force_not_quantize=predict_cids or force_not_quantize) + for i in range(z.shape[-1])] + else: + + output_list = [self.first_stage_model.decode(z[:, :, :, :, i]) + for i in range(z.shape[-1])] + + o = torch.stack(output_list, axis=-1) # # (bn, nc, ks[0], ks[1], L) + o = o * weighting + # Reverse 1. reshape to img shape + o = o.view((o.shape[0], -1, o.shape[-1])) # (bn, nc * ks[0] * ks[1], L) + # stitch crops together + decoded = fold(o) + decoded = decoded / normalization # norm is shape (1, 1, h, w) + return decoded + else: + if isinstance(self.first_stage_model, VQModelInterface): + return self.first_stage_model.decode(z, force_not_quantize=predict_cids or force_not_quantize) + else: + return self.first_stage_model.decode(z) + + else: + if isinstance(self.first_stage_model, VQModelInterface): + return self.first_stage_model.decode(z, force_not_quantize=predict_cids or force_not_quantize) + else: + return self.first_stage_model.decode(z) + + @torch.no_grad() + def encode_first_stage(self, x): + if hasattr(self, "split_input_params"): + if self.split_input_params["patch_distributed_vq"]: + ks = self.split_input_params["ks"] # eg. (128, 128) + stride = self.split_input_params["stride"] # eg. (64, 64) + df = self.split_input_params["vqf"] + self.split_input_params['original_image_size'] = x.shape[-2:] + bs, nc, h, w = x.shape + if ks[0] > h or ks[1] > w: + ks = (min(ks[0], h), min(ks[1], w)) + print("reducing Kernel") + + if stride[0] > h or stride[1] > w: + stride = (min(stride[0], h), min(stride[1], w)) + print("reducing stride") + + fold, unfold, normalization, weighting = self.get_fold_unfold(x, ks, stride, df=df) + z = unfold(x) # (bn, nc * prod(**ks), L) + # Reshape to img shape + z = z.view((z.shape[0], -1, ks[0], ks[1], z.shape[-1])) # (bn, nc, ks[0], ks[1], L ) + + output_list = [self.first_stage_model.encode(z[:, :, :, :, i]) + for i in range(z.shape[-1])] + + o = torch.stack(output_list, axis=-1) + o = o * weighting + + # Reverse reshape to img shape + o = o.view((o.shape[0], -1, o.shape[-1])) # (bn, nc * ks[0] * ks[1], L) + # stitch crops together + decoded = fold(o) + decoded = decoded / normalization + return decoded + + else: + return self.first_stage_model.encode(x) + else: + return self.first_stage_model.encode(x) + + def shared_step(self, batch, **kwargs): + x, c = self.get_input(batch, self.first_stage_key) + loss = self(x, c) + return loss + + def test_step(self,batch,batch_idx): + cond = batch[self.cond_stage_key] * self.test_repeat + cond = self.get_learned_conditioning(cond) # c: string -> [B, T, Context_dim] + batch_size = len(cond) + enc_emb = self.sample(cond,batch_size,timesteps=self.test_numsteps)# shape = [batch_size,self.channels,self.mel_dim,self.mel_length] + xrec = self.decode_first_stage(enc_emb) + reconstructions = (xrec + 1)/2 # to mel scale + test_ckpt_path = os.path.basename(self.trainer.tested_ckpt_path) + savedir = os.path.join(self.trainer.log_dir,f'output_imgs_{test_ckpt_path}','fake_class') + if not os.path.exists(savedir): + os.makedirs(savedir) + + file_names = batch['f_name'] + nfiles = len(file_names) + reconstructions = reconstructions.cpu().numpy().squeeze(1) # squuze channel dim + for k in range(reconstructions.shape[0]): + b,repeat = k % nfiles, k // nfiles + vname_num_split_index = file_names[b].rfind('_')# file_names[b]:video_name+'_'+num + v_n,num = file_names[b][:vname_num_split_index],file_names[b][vname_num_split_index+1:] + save_img_path = os.path.join(savedir,f'{v_n}_sample_{num}_{repeat}.npy')# the num_th caption, the repeat_th repitition + np.save(save_img_path,reconstructions[b]) + + return None + + def forward(self, x, c, *args, **kwargs): + t = torch.randint(0, self.num_timesteps, (x.shape[0],), device=self.device).long() + if self.model.conditioning_key is not None: + assert c is not None + if self.cond_stage_trainable: + c = self.get_learned_conditioning(c) # c: string -> [B, T, Context_dim] + if self.shorten_cond_schedule: # TODO: drop this option + tc = self.cond_ids[t].to(self.device) + c = self.q_sample(x_start=c, t=tc, noise=torch.randn_like(c.float())) + return self.p_losses(x, c, t, *args, **kwargs) + + def _rescale_annotations(self, bboxes, crop_coordinates): # TODO: move to dataset + def rescale_bbox(bbox): + x0 = clamp((bbox[0] - crop_coordinates[0]) / crop_coordinates[2]) + y0 = clamp((bbox[1] - crop_coordinates[1]) / crop_coordinates[3]) + w = min(bbox[2] / crop_coordinates[2], 1 - x0) + h = min(bbox[3] / crop_coordinates[3], 1 - y0) + return x0, y0, w, h + + return [rescale_bbox(b) for b in bboxes] + + def apply_model(self, x_noisy, t, cond, return_ids=False): + + if isinstance(cond, dict): + # hybrid case, cond is exptected to be a dict + pass + else: + if not isinstance(cond, list): + cond = [cond] + key = 'c_concat' if self.model.conditioning_key == 'concat' else 'c_crossattn' + cond = {key: cond} + + if hasattr(self, "split_input_params"): + assert len(cond) == 1 # todo can only deal with one conditioning atm + assert not return_ids + ks = self.split_input_params["ks"] # eg. (128, 128) + stride = self.split_input_params["stride"] # eg. (64, 64) + + h, w = x_noisy.shape[-2:] + + fold, unfold, normalization, weighting = self.get_fold_unfold(x_noisy, ks, stride) + + z = unfold(x_noisy) # (bn, nc * prod(**ks), L) + # Reshape to img shape + z = z.view((z.shape[0], -1, ks[0], ks[1], z.shape[-1])) # (bn, nc, ks[0], ks[1], L ) + z_list = [z[:, :, :, :, i] for i in range(z.shape[-1])] + + if self.cond_stage_key in ["image", "LR_image", "segmentation", + 'bbox_img'] and self.model.conditioning_key: # todo check for completeness + c_key = next(iter(cond.keys())) # get key + c = next(iter(cond.values())) # get value + assert (len(c) == 1) # todo extend to list with more than one elem + c = c[0] # get element + + c = unfold(c) + c = c.view((c.shape[0], -1, ks[0], ks[1], c.shape[-1])) # (bn, nc, ks[0], ks[1], L ) + + cond_list = [{c_key: [c[:, :, :, :, i]]} for i in range(c.shape[-1])] + + elif self.cond_stage_key == 'coordinates_bbox': + assert 'original_image_size' in self.split_input_params, 'BoudingBoxRescaling is missing original_image_size' + + # assuming padding of unfold is always 0 and its dilation is always 1 + n_patches_per_row = int((w - ks[0]) / stride[0] + 1) + full_img_h, full_img_w = self.split_input_params['original_image_size'] + # as we are operating on latents, we need the factor from the original image size to the + # spatial latent size to properly rescale the crops for regenerating the bbox annotations + num_downs = self.first_stage_model.encoder.num_resolutions - 1 + rescale_latent = 2 ** (num_downs) + + # get top left postions of patches as conforming for the bbbox tokenizer, therefore we + # need to rescale the tl patch coordinates to be in between (0,1) + tl_patch_coordinates = [(rescale_latent * stride[0] * (patch_nr % n_patches_per_row) / full_img_w, + rescale_latent * stride[1] * (patch_nr // n_patches_per_row) / full_img_h) + for patch_nr in range(z.shape[-1])] + + # patch_limits are tl_coord, width and height coordinates as (x_tl, y_tl, h, w) + patch_limits = [(x_tl, y_tl, + rescale_latent * ks[0] / full_img_w, + rescale_latent * ks[1] / full_img_h) for x_tl, y_tl in tl_patch_coordinates] + # patch_values = [(np.arange(x_tl,min(x_tl+ks, 1.)),np.arange(y_tl,min(y_tl+ks, 1.))) for x_tl, y_tl in tl_patch_coordinates] + + # tokenize crop coordinates for the bounding boxes of the respective patches + patch_limits_tknzd = [torch.LongTensor(self.bbox_tokenizer._crop_encoder(bbox))[None].to(self.device) + for bbox in patch_limits] # list of length l with tensors of shape (1, 2) + print(patch_limits_tknzd[0].shape) + # cut tknzd crop position from conditioning + assert isinstance(cond, dict), 'cond must be dict to be fed into model' + cut_cond = cond['c_crossattn'][0][..., :-2].to(self.device) + print(cut_cond.shape) + + adapted_cond = torch.stack([torch.cat([cut_cond, p], dim=1) for p in patch_limits_tknzd]) + adapted_cond = rearrange(adapted_cond, 'l b n -> (l b) n') + print(adapted_cond.shape) + adapted_cond = self.get_learned_conditioning(adapted_cond) + print(adapted_cond.shape) + adapted_cond = rearrange(adapted_cond, '(l b) n d -> l b n d', l=z.shape[-1]) + print(adapted_cond.shape) + + cond_list = [{'c_crossattn': [e]} for e in adapted_cond] + + else: + cond_list = [cond for i in range(z.shape[-1])] # Todo make this more efficient + + # apply model by loop over crops + output_list = [self.model(z_list[i], t, **cond_list[i]) for i in range(z.shape[-1])] + assert not isinstance(output_list[0], + tuple) # todo cant deal with multiple model outputs check this never happens + + o = torch.stack(output_list, axis=-1) + o = o * weighting + # Reverse reshape to img shape + o = o.view((o.shape[0], -1, o.shape[-1])) # (bn, nc * ks[0] * ks[1], L) + # stitch crops together + x_recon = fold(o) / normalization + + else: + x_recon = self.model(x_noisy, t, **cond) + + if isinstance(x_recon, tuple) and not return_ids: + return x_recon[0] + else: + return x_recon + + def _predict_eps_from_xstart(self, x_t, t, pred_xstart): + return (extract_into_tensor(self.sqrt_recip_alphas_cumprod, t, x_t.shape) * x_t - pred_xstart) / \ + extract_into_tensor(self.sqrt_recipm1_alphas_cumprod, t, x_t.shape) + + def _prior_bpd(self, x_start): + """ + Get the prior KL term for the variational lower-bound, measured in + bits-per-dim. + This term can't be optimized, as it only depends on the encoder. + :param x_start: the [N x C x ...] tensor of inputs. + :return: a batch of [N] KL values (in bits), one per batch element. + """ + batch_size = x_start.shape[0] + t = torch.tensor([self.num_timesteps - 1] * batch_size, device=x_start.device) + qt_mean, _, qt_log_variance = self.q_mean_variance(x_start, t) + kl_prior = normal_kl(mean1=qt_mean, logvar1=qt_log_variance, mean2=0.0, logvar2=0.0) + return mean_flat(kl_prior) / np.log(2.0) + + def p_losses(self, x_start, cond, t, noise=None): + noise = default(noise, lambda: torch.randn_like(x_start)) + x_noisy = self.q_sample(x_start=x_start, t=t, noise=noise) + model_output = self.apply_model(x_noisy, t, cond) + + loss_dict = {} + prefix = 'train' if self.training else 'val' + + if self.parameterization == "x0": + target = x_start + elif self.parameterization == "eps": + target = noise + else: + raise NotImplementedError() + + loss_simple = self.get_loss(model_output, target, mean=False).mean([1, 2, 3]) + loss_dict.update({f'{prefix}/loss_simple': loss_simple.mean()}) + + logvar_t = self.logvar[t].to(self.device) + loss = loss_simple / torch.exp(logvar_t) + logvar_t + # loss = loss_simple / torch.exp(self.logvar) + self.logvar + if self.learn_logvar: + loss_dict.update({f'{prefix}/loss_gamma': loss.mean()}) + loss_dict.update({'logvar': self.logvar.data.mean()}) + + loss = self.l_simple_weight * loss.mean() + + loss_vlb = self.get_loss(model_output, target, mean=False).mean(dim=(1, 2, 3)) + loss_vlb = (self.lvlb_weights[t] * loss_vlb).mean() + loss_dict.update({f'{prefix}/loss_vlb': loss_vlb}) + loss += (self.original_elbo_weight * loss_vlb) + loss_dict.update({f'{prefix}/loss': loss}) + + return loss, loss_dict + + def p_mean_variance(self, x, c, t, clip_denoised: bool, return_codebook_ids=False, quantize_denoised=False, + return_x0=False, score_corrector=None, corrector_kwargs=None): + t_in = t + model_out = self.apply_model(x, t_in, c, return_ids=return_codebook_ids) + + if score_corrector is not None: + assert self.parameterization == "eps" + model_out = score_corrector.modify_score(self, model_out, x, t, c, **corrector_kwargs) + + if return_codebook_ids: + model_out, logits = model_out + + if self.parameterization == "eps": + x_recon = self.predict_start_from_noise(x, t=t, noise=model_out) + elif self.parameterization == "x0": + x_recon = model_out + else: + raise NotImplementedError() + + if clip_denoised: + x_recon.clamp_(-1., 1.) + if quantize_denoised: + x_recon, _, [_, _, indices] = self.first_stage_model.quantize(x_recon) + model_mean, posterior_variance, posterior_log_variance = self.q_posterior(x_start=x_recon, x_t=x, t=t) + if return_codebook_ids: + return model_mean, posterior_variance, posterior_log_variance, logits + elif return_x0: + return model_mean, posterior_variance, posterior_log_variance, x_recon + else: + return model_mean, posterior_variance, posterior_log_variance + + @torch.no_grad() + def p_sample(self, x, c, t, clip_denoised=False, repeat_noise=False, + return_codebook_ids=False, quantize_denoised=False, return_x0=False, + temperature=1., noise_dropout=0., score_corrector=None, corrector_kwargs=None): + b, *_, device = *x.shape, x.device + outputs = self.p_mean_variance(x=x, c=c, t=t, clip_denoised=clip_denoised, + return_codebook_ids=return_codebook_ids, + quantize_denoised=quantize_denoised, + return_x0=return_x0, + score_corrector=score_corrector, corrector_kwargs=corrector_kwargs) + if return_codebook_ids: + raise DeprecationWarning("Support dropped.") + model_mean, _, model_log_variance, logits = outputs + elif return_x0: + model_mean, _, model_log_variance, x0 = outputs + else: + model_mean, _, model_log_variance = outputs + + noise = noise_like(x.shape, device, repeat_noise) * temperature + if noise_dropout > 0.: + noise = torch.nn.functional.dropout(noise, p=noise_dropout) + # no noise when t == 0 + nonzero_mask = (1 - (t == 0).float()).reshape(b, *((1,) * (len(x.shape) - 1))) + + if return_codebook_ids: + return model_mean + nonzero_mask * (0.5 * model_log_variance).exp() * noise, logits.argmax(dim=1) + if return_x0: + return model_mean + nonzero_mask * (0.5 * model_log_variance).exp() * noise, x0 + else: + return model_mean + nonzero_mask * (0.5 * model_log_variance).exp() * noise + + @torch.no_grad() + def progressive_denoising(self, cond, shape, verbose=True, callback=None, quantize_denoised=False, + img_callback=None, mask=None, x0=None, temperature=1., noise_dropout=0., + score_corrector=None, corrector_kwargs=None, batch_size=None, x_T=None, start_T=None, + log_every_t=None): + if not log_every_t: + log_every_t = self.log_every_t + timesteps = self.num_timesteps + if batch_size is not None: + b = batch_size if batch_size is not None else shape[0] + shape = [batch_size] + list(shape) + else: + b = batch_size = shape[0] + if x_T is None: + img = torch.randn(shape, device=self.device) + else: + img = x_T + intermediates = [] + if cond is not None: + if isinstance(cond, dict): + cond = {key: cond[key][:batch_size] if not isinstance(cond[key], list) else + list(map(lambda x: x[:batch_size], cond[key])) for key in cond} + else: + cond = [c[:batch_size] for c in cond] if isinstance(cond, list) else cond[:batch_size] + + if start_T is not None: + timesteps = min(timesteps, start_T) + iterator = tqdm(reversed(range(0, timesteps)), desc='Progressive Generation', + total=timesteps) if verbose else reversed( + range(0, timesteps)) + if type(temperature) == float: + temperature = [temperature] * timesteps + + for i in iterator: + ts = torch.full((b,), i, device=self.device, dtype=torch.long) + if self.shorten_cond_schedule: + assert self.model.conditioning_key != 'hybrid' + tc = self.cond_ids[ts].to(cond.device) + cond = self.q_sample(x_start=cond, t=tc, noise=torch.randn_like(cond)) + + img, x0_partial = self.p_sample(img, cond, ts, + clip_denoised=self.clip_denoised, + quantize_denoised=quantize_denoised, return_x0=True, + temperature=temperature[i], noise_dropout=noise_dropout, + score_corrector=score_corrector, corrector_kwargs=corrector_kwargs) + if mask is not None: + assert x0 is not None + img_orig = self.q_sample(x0, ts) + img = img_orig * mask + (1. - mask) * img + + if i % log_every_t == 0 or i == timesteps - 1: + intermediates.append(x0_partial) + if callback: callback(i) + if img_callback: img_callback(img, i) + return img, intermediates + + @torch.no_grad() + def p_sample_loop(self, cond, shape, return_intermediates=False, + x_T=None, verbose=True, callback=None, timesteps=None, quantize_denoised=False, + mask=None, x0=None, img_callback=None, start_T=None, + log_every_t=None): + + if not log_every_t: + log_every_t = self.log_every_t + device = self.betas.device + b = shape[0] + if x_T is None: + img = torch.randn(shape, device=device) + else: + img = x_T + + intermediates = [img] + if timesteps is None: + timesteps = self.num_timesteps + + if start_T is not None: + timesteps = min(timesteps, start_T) + iterator = tqdm(reversed(range(0, timesteps)), desc='Sampling t', total=timesteps) if verbose else reversed( + range(0, timesteps)) + + if mask is not None: + assert x0 is not None + assert x0.shape[2:3] == mask.shape[2:3] # spatial size has to match + + for i in iterator: + ts = torch.full((b,), i, device=device, dtype=torch.long) + if self.shorten_cond_schedule: + assert self.model.conditioning_key != 'hybrid' + tc = self.cond_ids[ts].to(cond.device) + cond = self.q_sample(x_start=cond, t=tc, noise=torch.randn_like(cond)) + + img = self.p_sample(img, cond, ts, + clip_denoised=self.clip_denoised, + quantize_denoised=quantize_denoised) + if mask is not None: + img_orig = self.q_sample(x0, ts) + img = img_orig * mask + (1. - mask) * img + + if i % log_every_t == 0 or i == timesteps - 1: + intermediates.append(img) + if callback: callback(i) + if img_callback: img_callback(img, i) + + if return_intermediates: + return img, intermediates + return img + + @torch.no_grad() + def sample(self, cond, batch_size=16, return_intermediates=False, x_T=None, + verbose=True, timesteps=None, quantize_denoised=False, + mask=None, x0=None, shape=None,**kwargs): + if shape is None: + shape = (batch_size, self.channels, self.mel_dim, self.mel_length) + if cond is not None: + if isinstance(cond, dict): + cond = {key: cond[key][:batch_size] if not isinstance(cond[key], list) else + list(map(lambda x: x[:batch_size], cond[key])) for key in cond} + else: + cond = [c[:batch_size] for c in cond] if isinstance(cond, list) else cond[:batch_size] + return self.p_sample_loop(cond, + shape, + return_intermediates=return_intermediates, x_T=x_T, + verbose=verbose, timesteps=timesteps, quantize_denoised=quantize_denoised, + mask=mask, x0=x0) + + @torch.no_grad() + def sample_log(self,cond,batch_size,ddim, ddim_steps,**kwargs): + + if ddim: + ddim_sampler = DDIMSampler(self) + shape = (self.channels, self.mel_dim, self.mel_length) + samples, intermediates =ddim_sampler.sample(ddim_steps,batch_size, + shape,cond,verbose=False,**kwargs) + + else: + samples, intermediates = self.sample(cond=cond, batch_size=batch_size, + return_intermediates=True,**kwargs) + + return samples, intermediates + + + @torch.no_grad() + def log_images(self, batch, N=8, n_row=4, sample=True, ddim_steps=200, ddim_eta=1., return_keys=None, + quantize_denoised=True, inpaint=True, plot_denoise_rows=False, plot_progressive_rows=True, + plot_diffusion_rows=True, **kwargs): + + use_ddim = ddim_steps is not None + + log = dict() + z, c, x, xrec, xc = self.get_input(batch, self.first_stage_key, + return_first_stage_outputs=True, + force_c_encode=True, + return_original_cond=True, + bs=N) + N = min(x.shape[0], N) + n_row = min(x.shape[0], n_row) + log["inputs"] = x + log["reconstruction"] = xrec + if self.model.conditioning_key is not None: + if hasattr(self.cond_stage_model, "decode") and self.cond_stage_key != "masked_image": + xc = self.cond_stage_model.decode(c) + log["conditioning"] = xc + elif self.cond_stage_key == "masked_image": + log["mask"] = c[:, -1, :, :][:, None, :, :] + xc = self.cond_stage_model.decode(c[:, :self.cond_stage_model.embed_dim, :, :]) + log["conditioning"] = xc + elif self.cond_stage_key in ["caption"]: + xc = log_txt_as_img((256, 256), batch["caption"]) + log["conditioning"] = xc + elif self.cond_stage_key == 'class_label': + xc = log_txt_as_img((x.shape[2], x.shape[3]), batch["human_label"]) + log['conditioning'] = xc + elif isimage(xc): + log["conditioning"] = xc + if ismap(xc): + log["original_conditioning"] = self.to_rgb(xc) + + if plot_diffusion_rows: + # get diffusion row + diffusion_row = list() + z_start = z[:n_row] + for t in range(self.num_timesteps): + if t % self.log_every_t == 0 or t == self.num_timesteps - 1: + t = repeat(torch.tensor([t]), '1 -> b', b=n_row) + t = t.to(self.device).long() + noise = torch.randn_like(z_start) + z_noisy = self.q_sample(x_start=z_start, t=t, noise=noise) + diffusion_row.append(self.decode_first_stage(z_noisy)) + + diffusion_row = torch.stack(diffusion_row) # n_log_step, n_row, C, H, W + diffusion_grid = rearrange(diffusion_row, 'n b c h w -> b n c h w') + diffusion_grid = rearrange(diffusion_grid, 'b n c h w -> (b n) c h w') + diffusion_grid = make_grid(diffusion_grid, nrow=diffusion_row.shape[0]) + log["diffusion_row"] = diffusion_grid + + if sample: + # get denoise row + with self.ema_scope("Plotting"): + samples, z_denoise_row = self.sample_log(cond=c,batch_size=N,ddim=use_ddim, + ddim_steps=ddim_steps,eta=ddim_eta) + # samples, z_denoise_row = self.sample(cond=c, batch_size=N, return_intermediates=True) + x_samples = self.decode_first_stage(samples) + log["samples"] = x_samples + if plot_denoise_rows: + denoise_grid = self._get_denoise_row_from_list(z_denoise_row) + log["denoise_row"] = denoise_grid + + if quantize_denoised and not isinstance(self.first_stage_model, AutoencoderKL) and not isinstance( + self.first_stage_model, IdentityFirstStage): + # also display when quantizing x0 while sampling + with self.ema_scope("Plotting Quantized Denoised"): + samples, z_denoise_row = self.sample_log(cond=c,batch_size=N,ddim=use_ddim, + ddim_steps=ddim_steps,eta=ddim_eta, + quantize_denoised=True) + # samples, z_denoise_row = self.sample(cond=c, batch_size=N, return_intermediates=True, + # quantize_denoised=True) + x_samples = self.decode_first_stage(samples.to(self.device)) + log["samples_x0_quantized"] = x_samples + + if inpaint: + # make a simple center square + b, h, w = z.shape[0], z.shape[2], z.shape[3] + mask = torch.ones(N, h, w).to(self.device) + # zeros will be filled in + mask[:, h // 4:3 * h // 4, w // 4:3 * w // 4] = 0. + mask = mask[:, None, ...] + with self.ema_scope("Plotting Inpaint"): + + samples, _ = self.sample_log(cond=c,batch_size=N,ddim=use_ddim, eta=ddim_eta, + ddim_steps=ddim_steps, x0=z[:N], mask=mask) + x_samples = self.decode_first_stage(samples.to(self.device)) + log["samples_inpainting"] = x_samples + log["mask_inpainting"] = mask + + # outpaint + mask = 1 - mask + with self.ema_scope("Plotting Outpaint"): + samples, _ = self.sample_log(cond=c, batch_size=N, ddim=use_ddim,eta=ddim_eta, + ddim_steps=ddim_steps, x0=z[:N], mask=mask) + x_samples = self.decode_first_stage(samples.to(self.device)) + log["samples_outpainting"] = x_samples + log["mask_outpainting"] = mask + + if plot_progressive_rows: + with self.ema_scope("Plotting Progressives"): + img, progressives = self.progressive_denoising(c, + shape=(self.channels, self.mel_dim, self.mel_length), + batch_size=N) + prog_row = self._get_denoise_row_from_list(progressives, desc="Progressive Generation") + log["progressive_row"] = prog_row + + if return_keys: + if np.intersect1d(list(log.keys()), return_keys).shape[0] == 0: + return log + else: + return {key: log[key] for key in return_keys} + return log + + def configure_optimizers(self): + lr = self.learning_rate + params = list(self.model.parameters()) + if self.cond_stage_trainable: + print(f"{self.__class__.__name__}: Also optimizing conditioner params!") + params = params + list(self.cond_stage_model.parameters()) + if self.learn_logvar: + print('Diffusion model optimizing logvar') + params.append(self.logvar) + opt = torch.optim.AdamW(params, lr=lr) + if self.use_scheduler: + assert 'target' in self.scheduler_config + scheduler = instantiate_from_config(self.scheduler_config) + + print("Setting up LambdaLR scheduler...") + scheduler = [ + { + 'scheduler': LambdaLR(opt, lr_lambda=scheduler.schedule), + 'interval': 'step', + 'frequency': 1 + }] + return [opt], scheduler + return opt + + @torch.no_grad() + def to_rgb(self, x): + x = x.float() + if not hasattr(self, "colorize"): + self.colorize = torch.randn(3, x.shape[1], 1, 1).to(x) + x = nn.functional.conv2d(x, weight=self.colorize) + x = 2. * (x - x.min()) / (x.max() - x.min()) - 1. + return x + + +class LatentFinetuneDiffusion(LatentDiffusion_audio): + """ + Basis for different finetunas, such as inpainting or depth2image + To disable finetuning mode, set finetune_keys to None + """ + + def __init__(self, + concat_keys: tuple, + finetune_keys=("model.diffusion_model.input_blocks.0.0.weight", + "model_ema.diffusion_modelinput_blocks00weight" + ), + keep_finetune_dims=4, + # if model was trained without concat mode before and we would like to keep these channels + c_concat_log_start=None, # to log reconstruction of c_concat codes + c_concat_log_end=None, + *args, **kwargs + ): + ckpt_path = kwargs.pop("ckpt_path", None) + ignore_keys = kwargs.pop("ignore_keys", list()) + super().__init__(*args, **kwargs) + self.finetune_keys = finetune_keys + self.concat_keys = concat_keys + self.keep_dims = keep_finetune_dims + self.c_concat_log_start = c_concat_log_start + self.c_concat_log_end = c_concat_log_end + + if exists(self.finetune_keys): assert exists(ckpt_path), 'can only finetune from a given checkpoint' + if exists(ckpt_path): + self.init_from_ckpt(ckpt_path, ignore_keys) + + def init_from_ckpt(self, path, ignore_keys=list(), only_model=False): + sd = torch.load(path, map_location="cpu") + if "state_dict" in list(sd.keys()): + sd = sd["state_dict"] + keys = list(sd.keys()) + + for k in keys: + for ik in ignore_keys: + if k.startswith(ik): + print("Deleting key {} from state_dict.".format(k)) + del sd[k] + + # make it explicit, finetune by including extra input channels + if exists(self.finetune_keys) and k in self.finetune_keys: + new_entry = None + for name, param in self.named_parameters(): + if name in self.finetune_keys: + print( + f"modifying key '{name}' and keeping its original {self.keep_dims} (channels) dimensions only") + new_entry = torch.zeros_like(param) # zero init + assert exists(new_entry), 'did not find matching parameter to modify' + new_entry[:, :self.keep_dims, ...] = sd[k] + sd[k] = new_entry + + missing, unexpected = self.load_state_dict(sd, strict=False) if not only_model else self.model.load_state_dict(sd, strict=False) + print(f"Restored from {path} with {len(missing)} missing and {len(unexpected)} unexpected keys") + if len(missing) > 0: + print(f"Missing Keys: {missing}") + if len(unexpected) > 0: + print(f"Unexpected Keys: {unexpected}") + + @torch.no_grad() + def log_images(self, batch, N=8, n_row=4, sample=True, ddim_steps=200, ddim_eta=1., return_keys=None, + quantize_denoised=True, inpaint=True, plot_denoise_rows=False, plot_progressive_rows=True, + plot_diffusion_rows=True, unconditional_guidance_scale=1., unconditional_guidance_label=None, + use_ema_scope=True, + **kwargs): + use_ddim = ddim_steps is not None + + log = dict() + z, c, x, xrec, xc = self.get_input(batch, self.first_stage_key, bs=N, return_first_stage_outputs=True) + c_cat, c = c["c_concat"][0], c["c_crossattn"][0] + N = min(x.shape[0], N) + n_row = min(x.shape[0], n_row) + log["inputs"] = x + log["reconstruction"] = xrec + if self.model.conditioning_key is not None: + if hasattr(self.cond_stage_model, "decode"): + xc = self.cond_stage_model.decode(c) + log["conditioning"] = xc + elif self.cond_stage_key in ["caption"]: + xc = log_txt_as_img((x.shape[2], x.shape[3]), batch["caption"]) + log["conditioning"] = xc + elif self.cond_stage_key == 'class_label': + xc = log_txt_as_img((x.shape[2], x.shape[3]), batch["human_label"]) + log['conditioning'] = xc + elif isimage(xc): + log["conditioning"] = xc + if ismap(xc): + log["original_conditioning"] = self.to_rgb(xc) + + if not (self.c_concat_log_start is None and self.c_concat_log_end is None): + log["c_concat_decoded"] = self.decode_first_stage(c_cat[:, self.c_concat_log_start:self.c_concat_log_end]) + + if plot_diffusion_rows: + # get diffusion row + diffusion_row = list() + z_start = z[:n_row] + for t in range(self.num_timesteps): + if t % self.log_every_t == 0 or t == self.num_timesteps - 1: + t = repeat(torch.tensor([t]), '1 -> b', b=n_row) + t = t.to(self.device).long() + noise = torch.randn_like(z_start) + z_noisy = self.q_sample(x_start=z_start, t=t, noise=noise) + diffusion_row.append(self.decode_first_stage(z_noisy)) + + diffusion_row = torch.stack(diffusion_row) # n_log_step, n_row, C, H, W + diffusion_grid = rearrange(diffusion_row, 'n b c h w -> b n c h w') + diffusion_grid = rearrange(diffusion_grid, 'b n c h w -> (b n) c h w') + diffusion_grid = make_grid(diffusion_grid, nrow=diffusion_row.shape[0]) + log["diffusion_row"] = diffusion_grid + + if sample: + # get denoise row + with self.ema_scope("Sampling"): + samples, z_denoise_row = self.sample_log(cond={"c_concat": [c_cat], "c_crossattn": [c]}, + batch_size=N, ddim=use_ddim, + ddim_steps=ddim_steps, eta=ddim_eta) + # samples, z_denoise_row = self.sample(cond=c, batch_size=N, return_intermediates=True) + x_samples = self.decode_first_stage(samples) + log["samples"] = x_samples + if plot_denoise_rows: + denoise_grid = self._get_denoise_row_from_list(z_denoise_row) + log["denoise_row"] = denoise_grid + + if unconditional_guidance_scale > 1.0: + uc_cross = self.get_unconditional_conditioning(N, unconditional_guidance_label) + uc_cat = c_cat + uc_full = {"c_concat": [uc_cat], "c_crossattn": [uc_cross]} + with self.ema_scope("Sampling with classifier-free guidance"): + samples_cfg, _ = self.sample_log(cond={"c_concat": [c_cat], "c_crossattn": [c]}, + batch_size=N, ddim=use_ddim, + ddim_steps=ddim_steps, eta=ddim_eta, + unconditional_guidance_scale=unconditional_guidance_scale, + unconditional_conditioning=uc_full, + ) + x_samples_cfg = self.decode_first_stage(samples_cfg) + log[f"samples_cfg_scale_{unconditional_guidance_scale:.2f}"] = x_samples_cfg + + return log + + +class LatentInpaintDiffusion(LatentFinetuneDiffusion): + """ + can either run as pure inpainting model (only concat mode) or with mixed conditionings, + e.g. mask as concat and text via cross-attn. + To disable finetuning mode, set finetune_keys to None + """ + + def __init__(self, + concat_keys=("mask", "masked_image"), + masked_image_key="masked_image", + *args, **kwargs + ): + super().__init__(concat_keys, *args, **kwargs) + self.masked_image_key = masked_image_key + assert self.masked_image_key in concat_keys + + @torch.no_grad() + def get_input(self, batch, k, cond_key=None, bs=None, return_first_stage_outputs=False): + # note: restricted to non-trainable encoders currently + assert not self.cond_stage_trainable, 'trainable cond stages not yet supported for inpainting' + z, c, x, xrec, xc = super().get_input(batch, self.first_stage_key, return_first_stage_outputs=True, + force_c_encode=True, return_original_cond=True, bs=bs) + + assert exists(self.concat_keys) + c_cat = list() + for ck in self.concat_keys: + if len(batch[ck].shape) == 3: + batch[ck] = batch[ck][..., None] + cc = rearrange(batch[ck], 'b h w c -> b c h w').to(memory_format=torch.contiguous_format).float() + if bs is not None: + cc = cc[:bs] + cc = cc.to(self.device) + bchw = z.shape + if ck != self.masked_image_key: + cc = torch.nn.functional.interpolate(cc, size=bchw[-2:]) + else: + cc = self.get_first_stage_encoding(self.encode_first_stage(cc)) + c_cat.append(cc) + c_cat = torch.cat(c_cat, dim=1) + all_conds = {"c_concat": [c_cat], "c_crossattn": [c]} + if return_first_stage_outputs: + return z, all_conds, x, xrec, xc + return z, all_conds + + @torch.no_grad() + def log_images(self, *args, **kwargs): + log = super(LatentInpaintDiffusion, self).log_images(*args, **kwargs) + log["masked_image"] = rearrange(args[0]["masked_image"], + 'b h w c -> b c h w').to(memory_format=torch.contiguous_format).float() + return log diff --git a/ldm/models/diffusion/ddpm_audio_inpaint.py b/ldm/models/diffusion/ddpm_audio_inpaint.py new file mode 100644 index 0000000000000000000000000000000000000000..1541a74cd3082d8b44ba7a7988aeb65c2dd84a24 --- /dev/null +++ b/ldm/models/diffusion/ddpm_audio_inpaint.py @@ -0,0 +1,1081 @@ +""" +wild mixture of +https://github.com/lucidrains/denoising-diffusion-pytorch/blob/7706bdfc6f527f58d33f84b7b522e61e6e3164b3/denoising_diffusion_pytorch/denoising_diffusion_pytorch.py +https://github.com/openai/improved-diffusion/blob/e94489283bb876ac1477d5dd7709bbbd2d9902ce/improved_diffusion/gaussian_diffusion.py +https://github.com/CompVis/taming-transformers +-- merci +""" +import os +import torch +import torch.nn as nn +import numpy as np +import pytorch_lightning as pl +from torch.optim.lr_scheduler import LambdaLR +from einops import rearrange, repeat +from contextlib import contextmanager +from functools import partial +from tqdm import tqdm +from torchvision.utils import make_grid +from pytorch_lightning.utilities.distributed import rank_zero_only + +from ldm.util import log_txt_as_img, exists, default, ismap, isimage, mean_flat, count_params, instantiate_from_config +from ldm.modules.ema import LitEma +from ldm.modules.distributions.distributions import normal_kl, DiagonalGaussianDistribution +from ldm.models.autoencoder import VQModelInterface, IdentityFirstStage, AutoencoderKL +from ldm.modules.diffusionmodules.util import make_beta_schedule, extract_into_tensor, noise_like +from ldm.models.diffusion.ddim import DDIMSampler +from ldm.models.diffusion.ddpm import DDPM, disabled_train + +__conditioning_keys__ = {'concat': 'c_concat', + 'crossattn': 'c_crossattn', + 'adm': 'y'} + +# add mel_dim and mel_length params to ensure correct shape +class LatentDiffusion_audioinpaint(DDPM): + """main class""" + def __init__(self, + first_stage_config, + cond_stage_config, + num_timesteps_cond=None, + mel_dim=80, + mel_length=848, + cond_stage_key="image", + cond_stage_trainable=False, + concat_mode=True, + cond_stage_forward=None, + conditioning_key=None, + scale_factor=1.0, + scale_by_std=False, + test_repeat=1, + test_numsteps = None, + *args, **kwargs): + self.num_timesteps_cond = default(num_timesteps_cond, 1) + self.scale_by_std = scale_by_std + assert self.num_timesteps_cond <= kwargs['timesteps'] + # for backwards compatibility after implementation of DiffusionWrapper + if conditioning_key is None: + conditioning_key = 'concat' if concat_mode else 'crossattn' + if cond_stage_config == '__is_unconditional__': + conditioning_key = None + ckpt_path = kwargs.pop("ckpt_path", None) + ignore_keys = kwargs.pop("ignore_keys", []) + super().__init__(conditioning_key=conditioning_key, *args, **kwargs) + self.test_repeat = test_repeat + if test_numsteps == None: + self.test_numsteps = self.num_timesteps + self.concat_mode = concat_mode + self.mel_dim = mel_dim + self.mel_length = mel_length + self.cond_stage_trainable = cond_stage_trainable + self.cond_stage_key = cond_stage_key + try: + self.num_downs = len(first_stage_config.params.ddconfig.ch_mult) - 1 + except: + self.num_downs = 0 + if not scale_by_std: + self.scale_factor = scale_factor + else: + self.register_buffer('scale_factor', torch.tensor(scale_factor)) + self.instantiate_first_stage(first_stage_config) + self.instantiate_cond_stage(cond_stage_config) + self.cond_stage_forward = cond_stage_forward + self.clip_denoised = False + self.bbox_tokenizer = None + + self.restarted_from_ckpt = False + if ckpt_path is not None: + self.init_from_ckpt(ckpt_path, ignore_keys) + self.restarted_from_ckpt = True + + def make_cond_schedule(self, ): + self.cond_ids = torch.full(size=(self.num_timesteps,), fill_value=self.num_timesteps - 1, dtype=torch.long) + ids = torch.round(torch.linspace(0, self.num_timesteps - 1, self.num_timesteps_cond)).long() + self.cond_ids[:self.num_timesteps_cond] = ids + + @rank_zero_only + @torch.no_grad() + def on_train_batch_start(self, batch, batch_idx, dataloader_idx): + # only for very first batch + if self.scale_by_std and self.current_epoch == 0 and self.global_step == 0 and batch_idx == 0 and not self.restarted_from_ckpt: + assert self.scale_factor == 1., 'rather not use custom rescaling and std-rescaling simultaneously' + # set rescale weight to 1./std of encodings + print("### USING STD-RESCALING ###") + x = super().get_input(batch, self.first_stage_key) + x = x.to(self.device) + encoder_posterior = self.encode_first_stage(x) + z = self.get_first_stage_encoding(encoder_posterior).detach() + del self.scale_factor + self.register_buffer('scale_factor', 1. / z.flatten().std()) + print(f"setting self.scale_factor to {self.scale_factor}") + print("### USING STD-RESCALING ###") + + def register_schedule(self, + given_betas=None, beta_schedule="linear", timesteps=1000, + linear_start=1e-4, linear_end=2e-2, cosine_s=8e-3): + super().register_schedule(given_betas, beta_schedule, timesteps, linear_start, linear_end, cosine_s) + + self.shorten_cond_schedule = self.num_timesteps_cond > 1 + if self.shorten_cond_schedule: + self.make_cond_schedule() + + def instantiate_first_stage(self, config): + model = instantiate_from_config(config) + self.first_stage_model = model.eval() + self.first_stage_model.train = disabled_train + for param in self.first_stage_model.parameters(): + param.requires_grad = False + + def instantiate_cond_stage(self, config): + if not self.cond_stage_trainable: + if config == "__is_first_stage__":# for no_text inpainting task + print("Using first stage also as cond stage.") + self.cond_stage_model = self.first_stage_model + elif config == "__is_unconditional__":# for unconditional image generation such as human face、ImageNet + print(f"Training {self.__class__.__name__} as an unconditional model.") + self.cond_stage_model = None + # self.be_unconditional = True + else: + model = instantiate_from_config(config) + self.cond_stage_model = model.eval() + self.cond_stage_model.train = disabled_train + for param in self.cond_stage_model.parameters(): + param.requires_grad = False + else: + assert config != '__is_first_stage__' + assert config != '__is_unconditional__' + model = instantiate_from_config(config) + self.cond_stage_model = model + + def _get_denoise_row_from_list(self, samples, desc='', force_no_decoder_quantization=False): + denoise_row = [] + for zd in tqdm(samples, desc=desc): + denoise_row.append(self.decode_first_stage(zd.to(self.device), + force_not_quantize=force_no_decoder_quantization)) + n_imgs_per_row = len(denoise_row) + denoise_row = torch.stack(denoise_row) # n_log_step, n_row, C, H, W + denoise_grid = rearrange(denoise_row, 'n b c h w -> b n c h w') + denoise_grid = rearrange(denoise_grid, 'b n c h w -> (b n) c h w') + denoise_grid = make_grid(denoise_grid, nrow=n_imgs_per_row) + return denoise_grid + + def get_first_stage_encoding(self, encoder_posterior):# encode_emb from autoencoder + if isinstance(encoder_posterior, DiagonalGaussianDistribution): + z = encoder_posterior.sample() + elif isinstance(encoder_posterior, torch.Tensor): + z = encoder_posterior + else: + raise NotImplementedError(f"encoder_posterior of type '{type(encoder_posterior)}' not yet implemented") + return self.scale_factor * z + + def get_learned_conditioning(self, c): + if self.cond_stage_forward is None: + if hasattr(self.cond_stage_model, 'encode') and callable(self.cond_stage_model.encode): + c = self.cond_stage_model.encode(c) + if isinstance(c, DiagonalGaussianDistribution): + c = c.mode() + else: + c = self.cond_stage_model(c) + else: + assert hasattr(self.cond_stage_model, self.cond_stage_forward) + c = getattr(self.cond_stage_model, self.cond_stage_forward)(c) + return c + + def meshgrid(self, h, w): + y = torch.arange(0, h).view(h, 1, 1).repeat(1, w, 1) + x = torch.arange(0, w).view(1, w, 1).repeat(h, 1, 1) + + arr = torch.cat([y, x], dim=-1) + return arr + + def delta_border(self, h, w): + """ + :param h: height + :param w: width + :return: normalized distance to image border, + wtith min distance = 0 at border and max dist = 0.5 at image center + """ + lower_right_corner = torch.tensor([h - 1, w - 1]).view(1, 1, 2) + arr = self.meshgrid(h, w) / lower_right_corner + dist_left_up = torch.min(arr, dim=-1, keepdims=True)[0] + dist_right_down = torch.min(1 - arr, dim=-1, keepdims=True)[0] + edge_dist = torch.min(torch.cat([dist_left_up, dist_right_down], dim=-1), dim=-1)[0] + return edge_dist + + def get_weighting(self, h, w, Ly, Lx, device): + weighting = self.delta_border(h, w) + weighting = torch.clip(weighting, self.split_input_params["clip_min_weight"], + self.split_input_params["clip_max_weight"], ) + weighting = weighting.view(1, h * w, 1).repeat(1, 1, Ly * Lx).to(device) + + if self.split_input_params["tie_braker"]: + L_weighting = self.delta_border(Ly, Lx) + L_weighting = torch.clip(L_weighting, + self.split_input_params["clip_min_tie_weight"], + self.split_input_params["clip_max_tie_weight"]) + + L_weighting = L_weighting.view(1, 1, Ly * Lx).to(device) + weighting = weighting * L_weighting + return weighting + + def get_fold_unfold(self, x, kernel_size, stride, uf=1, df=1): # todo load once not every time, shorten code + """ + :param x: img of size (bs, c, h, w) + :return: n img crops of size (n, bs, c, kernel_size[0], kernel_size[1]) + """ + bs, nc, h, w = x.shape + + # number of crops in image + Ly = (h - kernel_size[0]) // stride[0] + 1 + Lx = (w - kernel_size[1]) // stride[1] + 1 + + if uf == 1 and df == 1: + fold_params = dict(kernel_size=kernel_size, dilation=1, padding=0, stride=stride) + unfold = torch.nn.Unfold(**fold_params) + + fold = torch.nn.Fold(output_size=x.shape[2:], **fold_params) + + weighting = self.get_weighting(kernel_size[0], kernel_size[1], Ly, Lx, x.device).to(x.dtype) + normalization = fold(weighting).view(1, 1, h, w) # normalizes the overlap + weighting = weighting.view((1, 1, kernel_size[0], kernel_size[1], Ly * Lx)) + + elif uf > 1 and df == 1: + fold_params = dict(kernel_size=kernel_size, dilation=1, padding=0, stride=stride) + unfold = torch.nn.Unfold(**fold_params) + + fold_params2 = dict(kernel_size=(kernel_size[0] * uf, kernel_size[0] * uf), + dilation=1, padding=0, + stride=(stride[0] * uf, stride[1] * uf)) + fold = torch.nn.Fold(output_size=(x.shape[2] * uf, x.shape[3] * uf), **fold_params2) + + weighting = self.get_weighting(kernel_size[0] * uf, kernel_size[1] * uf, Ly, Lx, x.device).to(x.dtype) + normalization = fold(weighting).view(1, 1, h * uf, w * uf) # normalizes the overlap + weighting = weighting.view((1, 1, kernel_size[0] * uf, kernel_size[1] * uf, Ly * Lx)) + + elif df > 1 and uf == 1: + fold_params = dict(kernel_size=kernel_size, dilation=1, padding=0, stride=stride) + unfold = torch.nn.Unfold(**fold_params) + + fold_params2 = dict(kernel_size=(kernel_size[0] // df, kernel_size[0] // df), + dilation=1, padding=0, + stride=(stride[0] // df, stride[1] // df)) + fold = torch.nn.Fold(output_size=(x.shape[2] // df, x.shape[3] // df), **fold_params2) + + weighting = self.get_weighting(kernel_size[0] // df, kernel_size[1] // df, Ly, Lx, x.device).to(x.dtype) + normalization = fold(weighting).view(1, 1, h // df, w // df) # normalizes the overlap + weighting = weighting.view((1, 1, kernel_size[0] // df, kernel_size[1] // df, Ly * Lx)) + + else: + raise NotImplementedError + + return fold, unfold, normalization, weighting + + @torch.no_grad() + def get_input(self, batch, k, return_first_stage_outputs=False, force_c_encode=False, + cond_key=None, return_original_cond=False, bs=None): + x = super().get_input(batch, k) + if bs is not None: + x = x[:bs] + x = x.to(self.device) + encoder_posterior = self.encode_first_stage(x) + z = self.get_first_stage_encoding(encoder_posterior).detach() + + if self.model.conditioning_key is not None:# 'crossattn' for txt2image, 'hybird' for txt_inpaint + if cond_key is None: + cond_key = self.cond_stage_key # 'caption' for txt_inpaint + if self.model.conditioning_key == 'hybrid': + xc = {} + assert cond_key == 'caption' # only txt_inpaint is implemented now + assert 'masked_image' in batch.keys() + assert 'mask' in batch.keys() + masked_image = super().get_input(batch,'masked_image') + mask = super().get_input(batch,'mask') + if bs is not None: + masked_image,mask = masked_image[:bs],mask[:bs] + masked_image,mask = masked_image.to(self.device),mask.to(self.device) + masked_image = self.get_first_stage_encoding(self.encode_first_stage(masked_image)).detach() + resized_mask = torch.nn.functional.interpolate(mask,size=masked_image.shape[-2:]) + xc['c_concat'] = torch.cat((masked_image,resized_mask),dim = 1) + xc[cond_key] = batch[cond_key] + else: + if cond_key != self.first_stage_key: + if cond_key in ['caption', 'coordinates_bbox']: + xc = batch[cond_key] + elif cond_key == 'class_label': + xc = batch + else: + xc = super().get_input(batch, cond_key).to(self.device) + else:# cond_key == 'image' + xc = x + if not self.cond_stage_trainable or force_c_encode:# cond_stage_trainable is true for txt2img，force_c_encoder = True，when called in log_images + if isinstance(xc, list): + # import pudb; pudb.set_trace() + c = self.get_learned_conditioning(xc)# 因为log_images内接下来要调用sample_log,所以需要预先得到处理好的c + if isinstance(xc, dict): + c = {} + c['c_concat'] = xc['c_concat'] + c['c_crossattn'] = self.get_learned_conditioning(xc[cond_key]) + else: + c = self.get_learned_conditioning(xc.to(self.device)) + else: + c = xc + if bs is not None: + if isinstance(c,dict): + for k in c.keys(): + c[k] = c[k][:bs] + else: + c = c[:bs] + + if self.use_positional_encodings: + pos_x, pos_y = self.compute_latent_shifts(batch) + ckey = __conditioning_keys__[self.model.conditioning_key] + c = {ckey: c, 'pos_x': pos_x, 'pos_y': pos_y} + + else: + c = None + xc = None + if self.use_positional_encodings: + pos_x, pos_y = self.compute_latent_shifts(batch) + c = {'pos_x': pos_x, 'pos_y': pos_y} + out = [z, c] + if return_first_stage_outputs: + xrec = self.decode_first_stage(z) + out.extend([x, xrec]) + if return_original_cond: + out.append(xc) + return out + + @torch.no_grad() + def decode_first_stage(self, z, predict_cids=False, force_not_quantize=False): + if predict_cids: + if z.dim() == 4: + z = torch.argmax(z.exp(), dim=1).long() + z = self.first_stage_model.quantize.get_codebook_entry(z, shape=None) + z = rearrange(z, 'b h w c -> b c h w').contiguous() + + z = 1. / self.scale_factor * z + + if hasattr(self, "split_input_params"): + if self.split_input_params["patch_distributed_vq"]: + ks = self.split_input_params["ks"] # eg. (128, 128) + stride = self.split_input_params["stride"] # eg. (64, 64) + uf = self.split_input_params["vqf"] + bs, nc, h, w = z.shape + if ks[0] > h or ks[1] > w: + ks = (min(ks[0], h), min(ks[1], w)) + print("reducing Kernel") + + if stride[0] > h or stride[1] > w: + stride = (min(stride[0], h), min(stride[1], w)) + print("reducing stride") + + fold, unfold, normalization, weighting = self.get_fold_unfold(z, ks, stride, uf=uf) + + z = unfold(z) # (bn, nc * prod(**ks), L) + # 1. Reshape to img shape + z = z.view((z.shape[0], -1, ks[0], ks[1], z.shape[-1])) # (bn, nc, ks[0], ks[1], L ) + + # 2. apply model loop over last dim + if isinstance(self.first_stage_model, VQModelInterface): + output_list = [self.first_stage_model.decode(z[:, :, :, :, i], + force_not_quantize=predict_cids or force_not_quantize) + for i in range(z.shape[-1])] + else: + + output_list = [self.first_stage_model.decode(z[:, :, :, :, i]) + for i in range(z.shape[-1])] + + o = torch.stack(output_list, axis=-1) # # (bn, nc, ks[0], ks[1], L) + o = o * weighting + # Reverse 1. reshape to img shape + o = o.view((o.shape[0], -1, o.shape[-1])) # (bn, nc * ks[0] * ks[1], L) + # stitch crops together + decoded = fold(o) + decoded = decoded / normalization # norm is shape (1, 1, h, w) + return decoded + else: + if isinstance(self.first_stage_model, VQModelInterface): + return self.first_stage_model.decode(z, force_not_quantize=predict_cids or force_not_quantize) + else: + return self.first_stage_model.decode(z) + + else: + if isinstance(self.first_stage_model, VQModelInterface): + return self.first_stage_model.decode(z, force_not_quantize=predict_cids or force_not_quantize) + else: + return self.first_stage_model.decode(z) + + # same as above but without decorator + def differentiable_decode_first_stage(self, z, predict_cids=False, force_not_quantize=False): + if predict_cids: + if z.dim() == 4: + z = torch.argmax(z.exp(), dim=1).long() + z = self.first_stage_model.quantize.get_codebook_entry(z, shape=None) + z = rearrange(z, 'b h w c -> b c h w').contiguous() + + z = 1. / self.scale_factor * z + + if hasattr(self, "split_input_params"): + if self.split_input_params["patch_distributed_vq"]: + ks = self.split_input_params["ks"] # eg. (128, 128) + stride = self.split_input_params["stride"] # eg. (64, 64) + uf = self.split_input_params["vqf"] + bs, nc, h, w = z.shape + if ks[0] > h or ks[1] > w: + ks = (min(ks[0], h), min(ks[1], w)) + print("reducing Kernel") + + if stride[0] > h or stride[1] > w: + stride = (min(stride[0], h), min(stride[1], w)) + print("reducing stride") + + fold, unfold, normalization, weighting = self.get_fold_unfold(z, ks, stride, uf=uf) + + z = unfold(z) # (bn, nc * prod(**ks), L) + # 1. Reshape to img shape + z = z.view((z.shape[0], -1, ks[0], ks[1], z.shape[-1])) # (bn, nc, ks[0], ks[1], L ) + + # 2. apply model loop over last dim + if isinstance(self.first_stage_model, VQModelInterface): + output_list = [self.first_stage_model.decode(z[:, :, :, :, i], + force_not_quantize=predict_cids or force_not_quantize) + for i in range(z.shape[-1])] + else: + + output_list = [self.first_stage_model.decode(z[:, :, :, :, i]) + for i in range(z.shape[-1])] + + o = torch.stack(output_list, axis=-1) # # (bn, nc, ks[0], ks[1], L) + o = o * weighting + # Reverse 1. reshape to img shape + o = o.view((o.shape[0], -1, o.shape[-1])) # (bn, nc * ks[0] * ks[1], L) + # stitch crops together + decoded = fold(o) + decoded = decoded / normalization # norm is shape (1, 1, h, w) + return decoded + else: + if isinstance(self.first_stage_model, VQModelInterface): + return self.first_stage_model.decode(z, force_not_quantize=predict_cids or force_not_quantize) + else: + return self.first_stage_model.decode(z) + + else: + if isinstance(self.first_stage_model, VQModelInterface): + return self.first_stage_model.decode(z, force_not_quantize=predict_cids or force_not_quantize) + else: + return self.first_stage_model.decode(z) + + @torch.no_grad() + def encode_first_stage(self, x): + if hasattr(self, "split_input_params"): + if self.split_input_params["patch_distributed_vq"]: + ks = self.split_input_params["ks"] # eg. (128, 128) + stride = self.split_input_params["stride"] # eg. (64, 64) + df = self.split_input_params["vqf"] + self.split_input_params['original_image_size'] = x.shape[-2:] + bs, nc, h, w = x.shape + if ks[0] > h or ks[1] > w: + ks = (min(ks[0], h), min(ks[1], w)) + print("reducing Kernel") + + if stride[0] > h or stride[1] > w: + stride = (min(stride[0], h), min(stride[1], w)) + print("reducing stride") + + fold, unfold, normalization, weighting = self.get_fold_unfold(x, ks, stride, df=df) + z = unfold(x) # (bn, nc * prod(**ks), L) + # Reshape to img shape + z = z.view((z.shape[0], -1, ks[0], ks[1], z.shape[-1])) # (bn, nc, ks[0], ks[1], L ) + + output_list = [self.first_stage_model.encode(z[:, :, :, :, i]) + for i in range(z.shape[-1])] + + o = torch.stack(output_list, axis=-1) + o = o * weighting + + # Reverse reshape to img shape + o = o.view((o.shape[0], -1, o.shape[-1])) # (bn, nc * ks[0] * ks[1], L) + # stitch crops together + decoded = fold(o) + decoded = decoded / normalization + return decoded + + else: + return self.first_stage_model.encode(x) + else: + return self.first_stage_model.encode(x) + + def shared_step(self, batch, **kwargs): + x, c = self.get_input(batch, self.first_stage_key)# get latent and condition + loss = self(x, c) + return loss + + def test_step(self,batch,batch_idx): + # TODO make self.test_repeat work + cond = {} + cond[self.cond_stage_key] = batch[self.cond_stage_key] + cond[self.cond_stage_key] = self.get_learned_conditioning(cond[self.cond_stage_key]) # c: string -> [B, T, Context_dim] + cond['c_crossattn'] = cond.pop(self.cond_stage_key) + masked_image = super().get_input(batch,'masked_image') + mask = super().get_input(batch,'mask') + masked_image,mask = masked_image.to(self.device),mask.to(self.device) + masked_image = self.get_first_stage_encoding(self.encode_first_stage(masked_image)).detach() + resized_mask = torch.nn.functional.interpolate(mask,size=masked_image.shape[-2:]) + cond['c_concat'] = torch.cat((masked_image,resized_mask),dim = 1) + batch_size = len(batch[self.cond_stage_key]) + # shape = [batch_size,self.channels,self.mel_dim,self.mel_length] + enc_emb = self.sample(cond,batch_size,timesteps=self.test_numsteps) + xrec = self.decode_first_stage(enc_emb) + reconstructions = (xrec + 1)/2 # to mel scale + test_ckpt_path = os.path.basename(self.trainer.tested_ckpt_path) + savedir = os.path.join(self.trainer.log_dir,f'output_imgs_{test_ckpt_path}','fake_class') + if not os.path.exists(savedir): + os.makedirs(savedir) + + file_names = batch['f_name'] + nfiles = len(file_names) + reconstructions = reconstructions.cpu().numpy().squeeze(1) # squuze channel dim + for k in range(reconstructions.shape[0]): + b,repeat = k % nfiles, k // nfiles + vname_num_split_index = file_names[b].rfind('_')# file_names[b]:video_name+'_'+num + v_n,num = file_names[b][:vname_num_split_index],file_names[b][vname_num_split_index+1:] + save_img_path = os.path.join(savedir,f'{v_n}_sample_{num}_{repeat}.npy')# the num_th caption, the repeat_th repitition + np.save(save_img_path,reconstructions[b]) + + return None + + def forward(self, x, c, *args, **kwargs): + t = torch.randint(0, self.num_timesteps, (x.shape[0],), device=self.device).long() + if self.model.conditioning_key is not None: + assert c is not None + if self.cond_stage_trainable: + if isinstance(c,dict): + c[self.cond_stage_key] = self.get_learned_conditioning(c[self.cond_stage_key]) + c['c_crossattn'] = c.pop(self.cond_stage_key) + else: + c = self.get_learned_conditioning(c) # c: string -> [B, T, Context_dim] + if self.shorten_cond_schedule: # TODO: drop this option + tc = self.cond_ids[t].to(self.device) + c = self.q_sample(x_start=c, t=tc, noise=torch.randn_like(c.float())) + return self.p_losses(x, c, t, *args, **kwargs) + + def _rescale_annotations(self, bboxes, crop_coordinates): # TODO: move to dataset + def rescale_bbox(bbox): + x0 = torch.clamp((bbox[0] - crop_coordinates[0]) / crop_coordinates[2]) + y0 = torch.clamp((bbox[1] - crop_coordinates[1]) / crop_coordinates[3]) + w = min(bbox[2] / crop_coordinates[2], 1 - x0) + h = min(bbox[3] / crop_coordinates[3], 1 - y0) + return x0, y0, w, h + + return [rescale_bbox(b) for b in bboxes] + + def apply_model(self, x_noisy, t, cond, return_ids=False): + # make values to list to enable concat operation in + if isinstance(cond, dict): + # hybrid case, cond is exptected to be a dict. (txt2inpaint) + cond_tmp = {}# use cond_tmp to avoid inplace edit + for k,v in cond.items(): + if not isinstance(v, list): + cond_tmp[k] = [cond[k]] + else: + cond_tmp[k] = cond[k] + cond = cond_tmp + else: + if not isinstance(cond, list): + cond = [cond] + key = 'c_concat' if self.model.conditioning_key == 'concat' else 'c_crossattn' + cond = {key: cond} + + if hasattr(self, "split_input_params"): + assert len(cond) == 1 # todo can only deal with one conditioning atm + assert not return_ids + ks = self.split_input_params["ks"] # eg. (128, 128) + stride = self.split_input_params["stride"] # eg. (64, 64) + + h, w = x_noisy.shape[-2:] + + fold, unfold, normalization, weighting = self.get_fold_unfold(x_noisy, ks, stride) + + z = unfold(x_noisy) # (bn, nc * prod(**ks), L) + # Reshape to img shape + z = z.view((z.shape[0], -1, ks[0], ks[1], z.shape[-1])) # (bn, nc, ks[0], ks[1], L ) + z_list = [z[:, :, :, :, i] for i in range(z.shape[-1])] + + if self.cond_stage_key in ["image", "LR_image", "segmentation", + 'bbox_img'] and self.model.conditioning_key: # todo check for completeness + c_key = next(iter(cond.keys())) # get key + c = next(iter(cond.values())) # get value + assert (len(c) == 1) # todo extend to list with more than one elem + c = c[0] # get element + + c = unfold(c) + c = c.view((c.shape[0], -1, ks[0], ks[1], c.shape[-1])) # (bn, nc, ks[0], ks[1], L ) + + cond_list = [{c_key: [c[:, :, :, :, i]]} for i in range(c.shape[-1])] + + elif self.cond_stage_key == 'coordinates_bbox': + assert 'original_image_size' in self.split_input_params, 'BoudingBoxRescaling is missing original_image_size' + + # assuming padding of unfold is always 0 and its dilation is always 1 + n_patches_per_row = int((w - ks[0]) / stride[0] + 1) + full_img_h, full_img_w = self.split_input_params['original_image_size'] + # as we are operating on latents, we need the factor from the original image size to the + # spatial latent size to properly rescale the crops for regenerating the bbox annotations + num_downs = self.first_stage_model.encoder.num_resolutions - 1 + rescale_latent = 2 ** (num_downs) + + # get top left postions of patches as conforming for the bbbox tokenizer, therefore we + # need to rescale the tl patch coordinates to be in between (0,1) + tl_patch_coordinates = [(rescale_latent * stride[0] * (patch_nr % n_patches_per_row) / full_img_w, + rescale_latent * stride[1] * (patch_nr // n_patches_per_row) / full_img_h) + for patch_nr in range(z.shape[-1])] + + # patch_limits are tl_coord, width and height coordinates as (x_tl, y_tl, h, w) + patch_limits = [(x_tl, y_tl, + rescale_latent * ks[0] / full_img_w, + rescale_latent * ks[1] / full_img_h) for x_tl, y_tl in tl_patch_coordinates] + # patch_values = [(np.arange(x_tl,min(x_tl+ks, 1.)),np.arange(y_tl,min(y_tl+ks, 1.))) for x_tl, y_tl in tl_patch_coordinates] + + # tokenize crop coordinates for the bounding boxes of the respective patches + patch_limits_tknzd = [torch.LongTensor(self.bbox_tokenizer._crop_encoder(bbox))[None].to(self.device) + for bbox in patch_limits] # list of length l with tensors of shape (1, 2) + print(patch_limits_tknzd[0].shape) + # cut tknzd crop position from conditioning + assert isinstance(cond, dict), 'cond must be dict to be fed into model' + cut_cond = cond['c_crossattn'][0][..., :-2].to(self.device) + print(cut_cond.shape) + + adapted_cond = torch.stack([torch.cat([cut_cond, p], dim=1) for p in patch_limits_tknzd]) + adapted_cond = rearrange(adapted_cond, 'l b n -> (l b) n') + print(adapted_cond.shape) + adapted_cond = self.get_learned_conditioning(adapted_cond) + print(adapted_cond.shape) + adapted_cond = rearrange(adapted_cond, '(l b) n d -> l b n d', l=z.shape[-1]) + print(adapted_cond.shape) + + cond_list = [{'c_crossattn': [e]} for e in adapted_cond] + + else: + cond_list = [cond for i in range(z.shape[-1])] # Todo make this more efficient + + # apply model by loop over crops + output_list = [self.model(z_list[i], t, **cond_list[i]) for i in range(z.shape[-1])] + assert not isinstance(output_list[0], + tuple) # todo cant deal with multiple model outputs check this never happens + + o = torch.stack(output_list, axis=-1) + o = o * weighting + # Reverse reshape to img shape + o = o.view((o.shape[0], -1, o.shape[-1])) # (bn, nc * ks[0] * ks[1], L) + # stitch crops together + x_recon = fold(o) / normalization + + else: + # x_noisy is tensor with shape [b,c,mel_len,T] + # if condition is caption ,cond['c_crossattn'] is a list, each item shape is [1, 77, 1280] + x_recon = self.model(x_noisy, t, **cond)# tensor with shape [b,c,mel_len,T] + + if isinstance(x_recon, tuple) and not return_ids: + return x_recon[0] + else: + return x_recon + + def _predict_eps_from_xstart(self, x_t, t, pred_xstart): + return (extract_into_tensor(self.sqrt_recip_alphas_cumprod, t, x_t.shape) * x_t - pred_xstart) / \ + extract_into_tensor(self.sqrt_recipm1_alphas_cumprod, t, x_t.shape) + + def _prior_bpd(self, x_start): + """ + Get the prior KL term for the variational lower-bound, measured in + bits-per-dim. + This term can't be optimized, as it only depends on the encoder. + :param x_start: the [N x C x ...] tensor of inputs. + :return: a batch of [N] KL values (in bits), one per batch element. + """ + batch_size = x_start.shape[0] + t = torch.tensor([self.num_timesteps - 1] * batch_size, device=x_start.device) + qt_mean, _, qt_log_variance = self.q_mean_variance(x_start, t) + kl_prior = normal_kl(mean1=qt_mean, logvar1=qt_log_variance, mean2=0.0, logvar2=0.0) + return mean_flat(kl_prior) / np.log(2.0) + + def p_losses(self, x_start, cond, t, noise=None): + noise = default(noise, lambda: torch.randn_like(x_start)) + x_noisy = self.q_sample(x_start=x_start, t=t, noise=noise) + model_output = self.apply_model(x_noisy, t, cond) + + loss_dict = {} + prefix = 'train' if self.training else 'val' + + if self.parameterization == "x0": + target = x_start + elif self.parameterization == "eps": + target = noise + else: + raise NotImplementedError() + + loss_simple = self.get_loss(model_output, target, mean=False).mean([1, 2, 3]) + loss_dict.update({f'{prefix}/loss_simple': loss_simple.mean()}) + + logvar_t = self.logvar[t].to(self.device) + loss = loss_simple / torch.exp(logvar_t) + logvar_t + # loss = loss_simple / torch.exp(self.logvar) + self.logvar + if self.learn_logvar: + loss_dict.update({f'{prefix}/loss_gamma': loss.mean()}) + loss_dict.update({'logvar': self.logvar.data.mean()}) + + loss = self.l_simple_weight * loss.mean() + + loss_vlb = self.get_loss(model_output, target, mean=False).mean(dim=(1, 2, 3)) + loss_vlb = (self.lvlb_weights[t] * loss_vlb).mean() + loss_dict.update({f'{prefix}/loss_vlb': loss_vlb}) + loss += (self.original_elbo_weight * loss_vlb) + loss_dict.update({f'{prefix}/loss': loss}) + + return loss, loss_dict + + def p_mean_variance(self, x, c, t, clip_denoised: bool, return_codebook_ids=False, quantize_denoised=False, + return_x0=False, score_corrector=None, corrector_kwargs=None): + t_in = t + model_out = self.apply_model(x, t_in, c, return_ids=return_codebook_ids) + + if score_corrector is not None: + assert self.parameterization == "eps" + model_out = score_corrector.modify_score(self, model_out, x, t, c, **corrector_kwargs) + + if return_codebook_ids: + model_out, logits = model_out + + if self.parameterization == "eps": + x_recon = self.predict_start_from_noise(x, t=t, noise=model_out) + elif self.parameterization == "x0": + x_recon = model_out + else: + raise NotImplementedError() + + if clip_denoised: + x_recon.clamp_(-1., 1.) + if quantize_denoised: + x_recon, _, [_, _, indices] = self.first_stage_model.quantize(x_recon) + model_mean, posterior_variance, posterior_log_variance = self.q_posterior(x_start=x_recon, x_t=x, t=t) + if return_codebook_ids: + return model_mean, posterior_variance, posterior_log_variance, logits + elif return_x0: + return model_mean, posterior_variance, posterior_log_variance, x_recon + else: + return model_mean, posterior_variance, posterior_log_variance + + @torch.no_grad() + def p_sample(self, x, c, t, clip_denoised=False, repeat_noise=False, + return_codebook_ids=False, quantize_denoised=False, return_x0=False, + temperature=1., noise_dropout=0., score_corrector=None, corrector_kwargs=None): + b, *_, device = *x.shape, x.device + outputs = self.p_mean_variance(x=x, c=c, t=t, clip_denoised=clip_denoised, + return_codebook_ids=return_codebook_ids, + quantize_denoised=quantize_denoised, + return_x0=return_x0, + score_corrector=score_corrector, corrector_kwargs=corrector_kwargs) + if return_codebook_ids: + raise DeprecationWarning("Support dropped.") + model_mean, _, model_log_variance, logits = outputs + elif return_x0: + model_mean, _, model_log_variance, x0 = outputs + else: + model_mean, _, model_log_variance = outputs + + noise = noise_like(x.shape, device, repeat_noise) * temperature + if noise_dropout > 0.: + noise = torch.nn.functional.dropout(noise, p=noise_dropout) + # no noise when t == 0 + nonzero_mask = (1 - (t == 0).float()).reshape(b, *((1,) * (len(x.shape) - 1))) + + if return_codebook_ids: + return model_mean + nonzero_mask * (0.5 * model_log_variance).exp() * noise, logits.argmax(dim=1) + if return_x0: + return model_mean + nonzero_mask * (0.5 * model_log_variance).exp() * noise, x0 + else: + return model_mean + nonzero_mask * (0.5 * model_log_variance).exp() * noise + + @torch.no_grad() + def progressive_denoising(self, cond, shape, verbose=True, callback=None, quantize_denoised=False, + img_callback=None, mask=None, x0=None, temperature=1., noise_dropout=0., + score_corrector=None, corrector_kwargs=None, batch_size=None, x_T=None, start_T=None, + log_every_t=None): + if not log_every_t: + log_every_t = self.log_every_t + timesteps = self.num_timesteps + if batch_size is not None: + b = batch_size if batch_size is not None else shape[0] + shape = [batch_size] + list(shape) + else: + b = batch_size = shape[0] + if x_T is None: + img = torch.randn(shape, device=self.device) + else: + img = x_T + intermediates = [] + if cond is not None: + if isinstance(cond, dict): + cond = {key: cond[key][:batch_size] if not isinstance(cond[key], list) else + list(map(lambda x: x[:batch_size], cond[key])) for key in cond} + else: + cond = [c[:batch_size] for c in cond] if isinstance(cond, list) else cond[:batch_size] + + if start_T is not None: + timesteps = min(timesteps, start_T) + iterator = tqdm(reversed(range(0, timesteps)), desc='Progressive Generation', + total=timesteps) if verbose else reversed( + range(0, timesteps)) + if type(temperature) == float: + temperature = [temperature] * timesteps + + for i in iterator: + ts = torch.full((b,), i, device=self.device, dtype=torch.long) + if self.shorten_cond_schedule: + assert self.model.conditioning_key != 'hybrid' + tc = self.cond_ids[ts].to(cond.device) + cond = self.q_sample(x_start=cond, t=tc, noise=torch.randn_like(cond)) + + img, x0_partial = self.p_sample(img, cond, ts, + clip_denoised=self.clip_denoised, + quantize_denoised=quantize_denoised, return_x0=True, + temperature=temperature[i], noise_dropout=noise_dropout, + score_corrector=score_corrector, corrector_kwargs=corrector_kwargs) + if mask is not None: + assert x0 is not None + img_orig = self.q_sample(x0, ts) + img = img_orig * mask + (1. - mask) * img + + if i % log_every_t == 0 or i == timesteps - 1: + intermediates.append(x0_partial) + if callback: callback(i) + if img_callback: img_callback(img, i) + return img, intermediates + + @torch.no_grad() + def p_sample_loop(self, cond, shape, return_intermediates=False, + x_T=None, verbose=True, callback=None, timesteps=None, quantize_denoised=False, + mask=None, x0=None, img_callback=None, start_T=None, + log_every_t=None): + + if not log_every_t: + log_every_t = self.log_every_t + device = self.betas.device + b = shape[0] + if x_T is None: + img = torch.randn(shape, device=device) + else: + img = x_T + + intermediates = [img] + if timesteps is None: + timesteps = self.num_timesteps + + if start_T is not None: + timesteps = min(timesteps, start_T) + iterator = tqdm(reversed(range(0, timesteps)), desc='Sampling t', total=timesteps) if verbose else reversed( + range(0, timesteps)) + + if mask is not None: + assert x0 is not None + assert x0.shape[2:3] == mask.shape[2:3] # spatial size has to match + + for i in iterator: + ts = torch.full((b,), i, device=device, dtype=torch.long) + if self.shorten_cond_schedule: + assert self.model.conditioning_key != 'hybrid' + tc = self.cond_ids[ts].to(cond.device) + cond = self.q_sample(x_start=cond, t=tc, noise=torch.randn_like(cond)) + + img = self.p_sample(img, cond, ts, + clip_denoised=self.clip_denoised, + quantize_denoised=quantize_denoised) + if mask is not None: + img_orig = self.q_sample(x0, ts) + img = img_orig * mask + (1. - mask) * img + + if i % log_every_t == 0 or i == timesteps - 1: + intermediates.append(img) + if callback: callback(i) + if img_callback: img_callback(img, i) + + if return_intermediates: + return img, intermediates + return img + + @torch.no_grad() + def sample(self, cond, batch_size=16, return_intermediates=False, x_T=None, + verbose=True, timesteps=None, quantize_denoised=False, + mask=None, x0=None, shape=None,**kwargs): + if shape is None: + shape = (batch_size, self.channels, self.mel_dim, self.mel_length) + if cond is not None: + if isinstance(cond, dict): + cond = {key: cond[key][:batch_size] if not isinstance(cond[key], list) else + list(map(lambda x: x[:batch_size], cond[key])) for key in cond} + else: + cond = [c[:batch_size] for c in cond] if isinstance(cond, list) else cond[:batch_size] + return self.p_sample_loop(cond, + shape, + return_intermediates=return_intermediates, x_T=x_T, + verbose=verbose, timesteps=timesteps, quantize_denoised=quantize_denoised, + mask=mask, x0=x0) + + @torch.no_grad() + def sample_log(self,cond,batch_size,ddim, ddim_steps,**kwargs): + if ddim: + ddim_sampler = DDIMSampler(self) + shape = (self.channels, self.mel_dim, self.mel_length) + samples, intermediates =ddim_sampler.sample(ddim_steps,batch_size, + shape,cond,verbose=False,**kwargs) + + else: + samples, intermediates = self.sample(cond=cond, batch_size=batch_size, + return_intermediates=True,**kwargs) + + return samples, intermediates + + @torch.no_grad() + def log_images(self, batch, N=8, n_row=4, sample=True, ddim_steps=200, ddim_eta=1., return_keys=None, + quantize_denoised=True, inpaint=True, plot_denoise_rows=False, plot_progressive_rows=True, + plot_diffusion_rows=True, **kwargs): + + use_ddim = ddim_steps is not None + + log = dict() + z, c, x, xrec, xc = self.get_input(batch, self.first_stage_key, + return_first_stage_outputs=True, + force_c_encode=True, + return_original_cond=True, + bs=N) + + N = min(x.shape[0], N) + n_row = min(x.shape[0], n_row) + log["inputs"] = x # 原始输入图像 + log["reconstruction"] = xrec # 重建得到的图像 + if self.model.conditioning_key is not None: + if hasattr(self.cond_stage_model, "decode"):# when cond_stage is first_stage. (bert embedder doesnot have decode) + xc = self.cond_stage_model.decode(c)# decoded masked image + log["conditioning"] = xc # 重建后的图像 + elif self.cond_stage_key in ["caption"]: + xc = log_txt_as_img((x.shape[2], x.shape[3]), batch["caption"]) + log["conditioning"] = xc # 含有文本的图像 + if self.model.conditioning_key == 'hybrid': + log["decoded_maskedimg"] = self.first_stage_model.decode(c['c_concat'][:,:self.first_stage_model.embed_dim])# c_concat is the concat result of masked_img latent and resized mask. get latent here to decode + elif self.cond_stage_key == 'class_label': + xc = log_txt_as_img((x.shape[2], x.shape[3]), batch["human_label"]) + log['conditioning'] = xc # 文本为类标签的图像 + elif isimage(xc): + log["conditioning"] = xc + if ismap(xc): + log["original_conditioning"] = self.to_rgb(xc) + + if plot_diffusion_rows:# diffusion每一步的图像 + # get diffusion row + diffusion_row = list() + z_start = z[:n_row] + for t in range(self.num_timesteps): + if t % self.log_every_t == 0 or t == self.num_timesteps - 1: + t = repeat(torch.tensor([t]), '1 -> b', b=n_row) + t = t.to(self.device).long() + noise = torch.randn_like(z_start) + z_noisy = self.q_sample(x_start=z_start, t=t, noise=noise) + diffusion_row.append(self.decode_first_stage(z_noisy)) + + diffusion_row = torch.stack(diffusion_row) # n_log_step, n_row, C, H, W + diffusion_grid = rearrange(diffusion_row, 'n b c h w -> b n c h w') + diffusion_grid = rearrange(diffusion_grid, 'b n c h w -> (b n) c h w') + diffusion_grid = make_grid(diffusion_grid, nrow=diffusion_row.shape[0]) + log["diffusion_row"] = diffusion_grid + + if sample:# + # get denoise row + with self.ema_scope("Plotting"): + samples, z_denoise_row = self.sample_log(cond=c,batch_size=N,ddim=use_ddim, + ddim_steps=ddim_steps,eta=ddim_eta) + # samples, z_denoise_row = self.sample(cond=c, batch_size=N, return_intermediates=True) + x_samples = self.decode_first_stage(samples) + log["samples"] = x_samples + if plot_denoise_rows: + denoise_grid = self._get_denoise_row_from_list(z_denoise_row) + log["denoise_row"] = denoise_grid + + if quantize_denoised and not isinstance(self.first_stage_model, AutoencoderKL) and not isinstance( + self.first_stage_model, IdentityFirstStage): + # also display when quantizing x0 while sampling + with self.ema_scope("Plotting Quantized Denoised"): + samples, z_denoise_row = self.sample_log(cond=c,batch_size=N,ddim=use_ddim, + ddim_steps=ddim_steps,eta=ddim_eta, + quantize_denoised=True) + # samples, z_denoise_row = self.sample(cond=c, batch_size=N, return_intermediates=True, + # quantize_denoised=True) + x_samples = self.decode_first_stage(samples.to(self.device)) + log["samples_x0_quantized"] = x_samples + + if inpaint: + # make a simple center square + b, h, w = z.shape[0], z.shape[2], z.shape[3] + mask = torch.ones(N, h, w).to(self.device) + # zeros will be filled in + mask[:, h // 4:3 * h // 4, w // 4:3 * w // 4] = 0. + mask = mask[:, None, ...]# N,1,H,W + with self.ema_scope("Plotting Inpaint"): + samples, _ = self.sample_log(cond=c,batch_size=N,ddim=use_ddim, eta=ddim_eta, + ddim_steps=ddim_steps, x0=z[:N], mask=mask) + x_samples = self.decode_first_stage(samples.to(self.device)) + log["samples_inpainting"] = x_samples + log["mask"] = mask + + # outpaint + with self.ema_scope("Plotting Outpaint"): + samples, _ = self.sample_log(cond=c, batch_size=N, ddim=use_ddim,eta=ddim_eta, + ddim_steps=ddim_steps, x0=z[:N], mask=mask) + x_samples = self.decode_first_stage(samples.to(self.device)) + log["samples_outpainting"] = x_samples + + if plot_progressive_rows: + with self.ema_scope("Plotting Progressives"): + img, progressives = self.progressive_denoising(c, + shape=(self.channels, self.mel_dim, self.mel_length), + batch_size=N) + prog_row = self._get_denoise_row_from_list(progressives, desc="Progressive Generation") + log["progressive_row"] = prog_row + + if return_keys: + if np.intersect1d(list(log.keys()), return_keys).shape[0] == 0: + return log + else: + return {key: log[key] for key in return_keys} + return log + + def configure_optimizers(self): + lr = self.learning_rate + params = list(self.model.parameters()) + if self.cond_stage_trainable: + print(f"{self.__class__.__name__}: Also optimizing conditioner params!") + params = params + list(self.cond_stage_model.parameters()) + if self.learn_logvar: + print('Diffusion model optimizing logvar') + params.append(self.logvar) + opt = torch.optim.AdamW(params, lr=lr) + if self.use_scheduler: + assert 'target' in self.scheduler_config + scheduler = instantiate_from_config(self.scheduler_config) + + print("Setting up LambdaLR scheduler...") + scheduler = [ + { + 'scheduler': LambdaLR(opt, lr_lambda=scheduler.schedule), + 'interval': 'step', + 'frequency': 1 + }] + return [opt], scheduler + return opt + + @torch.no_grad() + def to_rgb(self, x): + x = x.float() + if not hasattr(self, "colorize"): + self.colorize = torch.randn(3, x.shape[1], 1, 1).to(x) + x = nn.functional.conv2d(x, weight=self.colorize) + x = 2. * (x - x.min()) / (x.max() - x.min()) - 1. + return x + diff --git a/ldm/models/diffusion/plms.py b/ldm/models/diffusion/plms.py new file mode 100644 index 0000000000000000000000000000000000000000..78eeb1003aa45d27bdbfc6b4a1d7ccbff57cd2e3 --- /dev/null +++ b/ldm/models/diffusion/plms.py @@ -0,0 +1,236 @@ +"""SAMPLING ONLY.""" + +import torch +import numpy as np +from tqdm import tqdm +from functools import partial + +from ldm.modules.diffusionmodules.util import make_ddim_sampling_parameters, make_ddim_timesteps, noise_like + + +class PLMSSampler(object): + def __init__(self, model, schedule="linear", **kwargs): + super().__init__() + self.model = model + self.ddpm_num_timesteps = model.num_timesteps + self.schedule = schedule + + def register_buffer(self, name, attr): + if type(attr) == torch.Tensor: + if attr.device != torch.device("cuda"): + attr = attr.to(torch.device("cuda")) + setattr(self, name, attr) + + def make_schedule(self, ddim_num_steps, ddim_discretize="uniform", ddim_eta=0., verbose=True): + if ddim_eta != 0: + raise ValueError('ddim_eta must be 0 for PLMS') + self.ddim_timesteps = make_ddim_timesteps(ddim_discr_method=ddim_discretize, num_ddim_timesteps=ddim_num_steps, + num_ddpm_timesteps=self.ddpm_num_timesteps,verbose=verbose) + alphas_cumprod = self.model.alphas_cumprod + assert alphas_cumprod.shape[0] == self.ddpm_num_timesteps, 'alphas have to be defined for each timestep' + to_torch = lambda x: x.clone().detach().to(torch.float32).to(self.model.device) + + self.register_buffer('betas', to_torch(self.model.betas)) + self.register_buffer('alphas_cumprod', to_torch(alphas_cumprod)) + self.register_buffer('alphas_cumprod_prev', to_torch(self.model.alphas_cumprod_prev)) + + # calculations for diffusion q(x_t | x_{t-1}) and others + self.register_buffer('sqrt_alphas_cumprod', to_torch(np.sqrt(alphas_cumprod.cpu()))) + self.register_buffer('sqrt_one_minus_alphas_cumprod', to_torch(np.sqrt(1. - alphas_cumprod.cpu()))) + self.register_buffer('log_one_minus_alphas_cumprod', to_torch(np.log(1. - alphas_cumprod.cpu()))) + self.register_buffer('sqrt_recip_alphas_cumprod', to_torch(np.sqrt(1. / alphas_cumprod.cpu()))) + self.register_buffer('sqrt_recipm1_alphas_cumprod', to_torch(np.sqrt(1. / alphas_cumprod.cpu() - 1))) + + # ddim sampling parameters + ddim_sigmas, ddim_alphas, ddim_alphas_prev = make_ddim_sampling_parameters(alphacums=alphas_cumprod.cpu(), + ddim_timesteps=self.ddim_timesteps, + eta=ddim_eta,verbose=verbose) + self.register_buffer('ddim_sigmas', ddim_sigmas) + self.register_buffer('ddim_alphas', ddim_alphas) + self.register_buffer('ddim_alphas_prev', ddim_alphas_prev) + self.register_buffer('ddim_sqrt_one_minus_alphas', np.sqrt(1. - ddim_alphas)) + sigmas_for_original_sampling_steps = ddim_eta * torch.sqrt( + (1 - self.alphas_cumprod_prev) / (1 - self.alphas_cumprod) * ( + 1 - self.alphas_cumprod / self.alphas_cumprod_prev)) + self.register_buffer('ddim_sigmas_for_original_num_steps', sigmas_for_original_sampling_steps) + + @torch.no_grad() + def sample(self, + S, + batch_size, + shape, + conditioning=None, + callback=None, + normals_sequence=None, + img_callback=None, + quantize_x0=False, + eta=0., + mask=None, + x0=None, + temperature=1., + noise_dropout=0., + score_corrector=None, + corrector_kwargs=None, + verbose=True, + x_T=None, + log_every_t=100, + unconditional_guidance_scale=1., + unconditional_conditioning=None, + # this has to come in the same format as the conditioning, # e.g. as encoded tokens, ... + **kwargs + ): + if conditioning is not None: + if isinstance(conditioning, dict): + cbs = conditioning[list(conditioning.keys())[0]].shape[0] + if cbs != batch_size: + print(f"Warning: Got {cbs} conditionings but batch-size is {batch_size}") + else: + if conditioning.shape[0] != batch_size: + print(f"Warning: Got {conditioning.shape[0]} conditionings but batch-size is {batch_size}") + + self.make_schedule(ddim_num_steps=S, ddim_eta=eta, verbose=verbose) + # sampling + C, H, W = shape + size = (batch_size, C, H, W) + print(f'Data shape for PLMS sampling is {size}') + + samples, intermediates = self.plms_sampling(conditioning, size, + callback=callback, + img_callback=img_callback, + quantize_denoised=quantize_x0, + mask=mask, x0=x0, + ddim_use_original_steps=False, + noise_dropout=noise_dropout, + temperature=temperature, + score_corrector=score_corrector, + corrector_kwargs=corrector_kwargs, + x_T=x_T, + log_every_t=log_every_t, + unconditional_guidance_scale=unconditional_guidance_scale, + unconditional_conditioning=unconditional_conditioning, + ) + return samples, intermediates + + @torch.no_grad() + def plms_sampling(self, cond, shape, + x_T=None, ddim_use_original_steps=False, + callback=None, timesteps=None, quantize_denoised=False, + mask=None, x0=None, img_callback=None, log_every_t=100, + temperature=1., noise_dropout=0., score_corrector=None, corrector_kwargs=None, + unconditional_guidance_scale=1., unconditional_conditioning=None,): + device = self.model.betas.device + b = shape[0] + if x_T is None: + img = torch.randn(shape, device=device) + else: + img = x_T + + if timesteps is None: + timesteps = self.ddpm_num_timesteps if ddim_use_original_steps else self.ddim_timesteps + elif timesteps is not None and not ddim_use_original_steps: + subset_end = int(min(timesteps / self.ddim_timesteps.shape[0], 1) * self.ddim_timesteps.shape[0]) - 1 + timesteps = self.ddim_timesteps[:subset_end] + + intermediates = {'x_inter': [img], 'pred_x0': [img]} + time_range = list(reversed(range(0,timesteps))) if ddim_use_original_steps else np.flip(timesteps) + total_steps = timesteps if ddim_use_original_steps else timesteps.shape[0] + print(f"Running PLMS Sampling with {total_steps} timesteps") + + iterator = tqdm(time_range, desc='PLMS Sampler', total=total_steps) + old_eps = [] + + for i, step in enumerate(iterator): + index = total_steps - i - 1 + ts = torch.full((b,), step, device=device, dtype=torch.long) + ts_next = torch.full((b,), time_range[min(i + 1, len(time_range) - 1)], device=device, dtype=torch.long) + + if mask is not None: + assert x0 is not None + img_orig = self.model.q_sample(x0, ts) # TODO: deterministic forward pass? + img = img_orig * mask + (1. - mask) * img + + outs = self.p_sample_plms(img, cond, ts, index=index, use_original_steps=ddim_use_original_steps, + quantize_denoised=quantize_denoised, temperature=temperature, + noise_dropout=noise_dropout, score_corrector=score_corrector, + corrector_kwargs=corrector_kwargs, + unconditional_guidance_scale=unconditional_guidance_scale, + unconditional_conditioning=unconditional_conditioning, + old_eps=old_eps, t_next=ts_next) + img, pred_x0, e_t = outs + old_eps.append(e_t) + if len(old_eps) >= 4: + old_eps.pop(0) + if callback: callback(i) + if img_callback: img_callback(pred_x0, i) + + if index % log_every_t == 0 or index == total_steps - 1: + intermediates['x_inter'].append(img) + intermediates['pred_x0'].append(pred_x0) + + return img, intermediates + + @torch.no_grad() + def p_sample_plms(self, x, c, t, index, repeat_noise=False, use_original_steps=False, quantize_denoised=False, + temperature=1., noise_dropout=0., score_corrector=None, corrector_kwargs=None, + unconditional_guidance_scale=1., unconditional_conditioning=None, old_eps=None, t_next=None): + b, *_, device = *x.shape, x.device + + def get_model_output(x, t): + if unconditional_conditioning is None or unconditional_guidance_scale == 1.: + e_t = self.model.apply_model(x, t, c) + else: + x_in = torch.cat([x] * 2) + t_in = torch.cat([t] * 2) + c_in = torch.cat([unconditional_conditioning, c]) + e_t_uncond, e_t = self.model.apply_model(x_in, t_in, c_in).chunk(2) + e_t = e_t_uncond + unconditional_guidance_scale * (e_t - e_t_uncond) + + if score_corrector is not None: + assert self.model.parameterization == "eps" + e_t = score_corrector.modify_score(self.model, e_t, x, t, c, **corrector_kwargs) + + return e_t + + alphas = self.model.alphas_cumprod if use_original_steps else self.ddim_alphas + alphas_prev = self.model.alphas_cumprod_prev if use_original_steps else self.ddim_alphas_prev + sqrt_one_minus_alphas = self.model.sqrt_one_minus_alphas_cumprod if use_original_steps else self.ddim_sqrt_one_minus_alphas + sigmas = self.model.ddim_sigmas_for_original_num_steps if use_original_steps else self.ddim_sigmas + + def get_x_prev_and_pred_x0(e_t, index): + # select parameters corresponding to the currently considered timestep + a_t = torch.full((b, 1, 1, 1), alphas[index], device=device) + a_prev = torch.full((b, 1, 1, 1), alphas_prev[index], device=device) + sigma_t = torch.full((b, 1, 1, 1), sigmas[index], device=device) + sqrt_one_minus_at = torch.full((b, 1, 1, 1), sqrt_one_minus_alphas[index],device=device) + + # current prediction for x_0 + pred_x0 = (x - sqrt_one_minus_at * e_t) / a_t.sqrt() + if quantize_denoised: + pred_x0, _, *_ = self.model.first_stage_model.quantize(pred_x0) + # direction pointing to x_t + dir_xt = (1. - a_prev - sigma_t**2).sqrt() * e_t + noise = sigma_t * noise_like(x.shape, device, repeat_noise) * temperature + if noise_dropout > 0.: + noise = torch.nn.functional.dropout(noise, p=noise_dropout) + x_prev = a_prev.sqrt() * pred_x0 + dir_xt + noise + return x_prev, pred_x0 + + e_t = get_model_output(x, t) + if len(old_eps) == 0: + # Pseudo Improved Euler (2nd order) + x_prev, pred_x0 = get_x_prev_and_pred_x0(e_t, index) + e_t_next = get_model_output(x_prev, t_next) + e_t_prime = (e_t + e_t_next) / 2 + elif len(old_eps) == 1: + # 2nd order Pseudo Linear Multistep (Adams-Bashforth) + e_t_prime = (3 * e_t - old_eps[-1]) / 2 + elif len(old_eps) == 2: + # 3nd order Pseudo Linear Multistep (Adams-Bashforth) + e_t_prime = (23 * e_t - 16 * old_eps[-1] + 5 * old_eps[-2]) / 12 + elif len(old_eps) >= 3: + # 4nd order Pseudo Linear Multistep (Adams-Bashforth) + e_t_prime = (55 * e_t - 59 * old_eps[-1] + 37 * old_eps[-2] - 9 * old_eps[-3]) / 24 + + x_prev, pred_x0 = get_x_prev_and_pred_x0(e_t_prime, index) + + return x_prev, pred_x0, e_t diff --git a/ldm/modules/__pycache__/attention.cpython-38.pyc b/ldm/modules/__pycache__/attention.cpython-38.pyc new file mode 100644 index 0000000000000000000000000000000000000000..76235ab54bf21a77c46cbbfbe7146e10231321b1 Binary files /dev/null and b/ldm/modules/__pycache__/attention.cpython-38.pyc differ diff --git a/ldm/modules/__pycache__/attention.cpython-39.pyc b/ldm/modules/__pycache__/attention.cpython-39.pyc new file mode 100644 index 0000000000000000000000000000000000000000..9a9e0efc40c739a086173ffb04b79446717a8f9c Binary files /dev/null and b/ldm/modules/__pycache__/attention.cpython-39.pyc differ diff --git a/ldm/modules/__pycache__/ema.cpython-38.pyc b/ldm/modules/__pycache__/ema.cpython-38.pyc new file mode 100644 index 0000000000000000000000000000000000000000..2ae677c17f622416a6c5b11615093c0760b99fa7 Binary files /dev/null and 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torch import nn, einsum +from einops import rearrange, repeat + +from ldm.modules.diffusionmodules.util import checkpoint + + +def exists(val): + return val is not None + + +def uniq(arr): + return{el: True for el in arr}.keys() + + +def default(val, d): + if exists(val): + return val + return d() if isfunction(d) else d + + +def max_neg_value(t): + return -torch.finfo(t.dtype).max + + +def init_(tensor): + dim = tensor.shape[-1] + std = 1 / math.sqrt(dim) + tensor.uniform_(-std, std) + return tensor + + +# feedforward +class GEGLU(nn.Module): + def __init__(self, dim_in, dim_out): + super().__init__() + self.proj = nn.Linear(dim_in, dim_out * 2) + + def forward(self, x): + x, gate = self.proj(x).chunk(2, dim=-1) + return x * F.gelu(gate) + + +class FeedForward(nn.Module): + def __init__(self, dim, dim_out=None, mult=4, glu=False, dropout=0.): + super().__init__() + inner_dim = int(dim * mult) + dim_out = default(dim_out, dim) + project_in = nn.Sequential( + nn.Linear(dim, inner_dim), + nn.GELU() + ) if not glu else GEGLU(dim, inner_dim) + + self.net = nn.Sequential( + project_in, + nn.Dropout(dropout), + nn.Linear(inner_dim, dim_out) + ) + + def forward(self, x): + return self.net(x) + + +def zero_module(module): + """ + Zero out the parameters of a module and return it. + """ + for p in module.parameters(): + p.detach().zero_() + return module + + +def Normalize(in_channels): + return torch.nn.GroupNorm(num_groups=32, num_channels=in_channels, eps=1e-6, affine=True) + + +class LinearAttention(nn.Module): + def __init__(self, dim, heads=4, dim_head=32): + super().__init__() + self.heads = heads + hidden_dim = dim_head * heads + self.to_qkv = nn.Conv2d(dim, hidden_dim * 3, 1, bias = False) + self.to_out = nn.Conv2d(hidden_dim, dim, 1) + + def forward(self, x): + b, c, h, w = x.shape + qkv = self.to_qkv(x) + q, k, v = rearrange(qkv, 'b (qkv heads c) h w -> qkv b heads c (h w)', heads = self.heads, qkv=3) + k = k.softmax(dim=-1) + context = torch.einsum('bhdn,bhen->bhde', k, v) + out = torch.einsum('bhde,bhdn->bhen', context, q) + out = rearrange(out, 'b heads c (h w) -> b (heads c) h w', heads=self.heads, h=h, w=w) + return self.to_out(out) + + +class SpatialSelfAttention(nn.Module): + def __init__(self, in_channels): + super().__init__() + self.in_channels = in_channels + + self.norm = Normalize(in_channels) + self.q = torch.nn.Conv2d(in_channels, + in_channels, + kernel_size=1, + stride=1, + padding=0) + self.k = torch.nn.Conv2d(in_channels, + in_channels, + kernel_size=1, + stride=1, + padding=0) + self.v = torch.nn.Conv2d(in_channels, + in_channels, + kernel_size=1, + stride=1, + padding=0) + self.proj_out = torch.nn.Conv2d(in_channels, + in_channels, + kernel_size=1, + stride=1, + padding=0) + + def forward(self, x): + h_ = x + h_ = self.norm(h_) + q = self.q(h_) + k = self.k(h_) + v = self.v(h_) + + # compute attention + b,c,h,w = q.shape + q = rearrange(q, 'b c h w -> b (h w) c') + k = rearrange(k, 'b c h w -> b c (h w)') + w_ = torch.einsum('bij,bjk->bik', q, k) + + w_ = w_ * (int(c)**(-0.5)) + w_ = torch.nn.functional.softmax(w_, dim=2) + + # attend to values + v = rearrange(v, 'b c h w -> b c (h w)') + w_ = rearrange(w_, 'b i j -> b j i') + h_ = torch.einsum('bij,bjk->bik', v, w_) + h_ = rearrange(h_, 'b c (h w) -> b c h w', h=h) + h_ = self.proj_out(h_) + + return x+h_ + + +class CrossAttention(nn.Module): + def __init__(self, query_dim, context_dim=None, heads=8, dim_head=64, dropout=0.):# 如果设置了context_dim就不是自注意力了 + super().__init__() + inner_dim = dim_head * heads # inner_dim == SpatialTransformer.model_channels + context_dim = default(context_dim, query_dim) + + self.scale = dim_head ** -0.5 + self.heads = heads + + self.to_q = nn.Linear(query_dim, inner_dim, bias=False) + self.to_k = nn.Linear(context_dim, inner_dim, bias=False) + self.to_v = nn.Linear(context_dim, inner_dim, bias=False) + + self.to_out = nn.Sequential( + nn.Linear(inner_dim, query_dim), + nn.Dropout(dropout) + ) + + def forward(self, x, context=None, mask=None):# x:(b,h*w,c), context:(b,seq_len,context_dim) + h = self.heads + + q = self.to_q(x)# q:(b,h*w,inner_dim) + context = default(context, x) + k = self.to_k(context)# (b,seq_len,inner_dim) + v = self.to_v(context)# (b,seq_len,inner_dim) + + q, k, v = map(lambda t: rearrange(t, 'b n (h d) -> (b h) n d', h=h), (q, k, v))# n is seq_len for k and v + + sim = einsum('b i d, b j d -> b i j', q, k) * self.scale # (b*head,h*w,seq_len) + + if exists(mask):# false + mask = rearrange(mask, 'b ... -> b (...)') + max_neg_value = -torch.finfo(sim.dtype).max + mask = repeat(mask, 'b j -> (b h) () j', h=h) + sim.masked_fill_(~mask, max_neg_value) + + # attention, what we cannot get enough of + attn = sim.softmax(dim=-1) + + out = einsum('b i j, b j d -> b i d', attn, v)# (b*head,h*w,inner_dim/head) + out = rearrange(out, '(b h) n d -> b n (h d)', h=h)# (b,h*w,inner_dim) + return self.to_out(out) + + +class BasicTransformerBlock(nn.Module): + def __init__(self, dim, n_heads, d_head, dropout=0., context_dim=None, gated_ff=True, checkpoint=True): + super().__init__() + self.attn1 = CrossAttention(query_dim=dim, heads=n_heads, dim_head=d_head, dropout=dropout) # is a self-attention + self.ff = FeedForward(dim, dropout=dropout, glu=gated_ff) + self.attn2 = CrossAttention(query_dim=dim, context_dim=context_dim, + heads=n_heads, dim_head=d_head, dropout=dropout) # is self-attn if context is none + self.norm1 = nn.LayerNorm(dim) + self.norm2 = nn.LayerNorm(dim) + self.norm3 = nn.LayerNorm(dim) + self.checkpoint = checkpoint + + def forward(self, x, context=None): + return checkpoint(self._forward, (x, context), self.parameters(), self.checkpoint) + + def _forward(self, x, context=None): + x = self.attn1(self.norm1(x)) + x + x = self.attn2(self.norm2(x), context=context) + x + x = self.ff(self.norm3(x)) + x + return x + + +class SpatialTransformer(nn.Module): + """ + Transformer block for image-like data. + First, project the input (aka embedding) + and reshape to b, t, d. + Then apply standard transformer action. + Finally, reshape to image + """ + def __init__(self, in_channels, n_heads, d_head, + depth=1, dropout=0., context_dim=None): + super().__init__() + self.in_channels = in_channels + inner_dim = n_heads * d_head + self.norm = Normalize(in_channels) + + self.proj_in = nn.Conv2d(in_channels, + inner_dim, + kernel_size=1, + stride=1, + padding=0) + + self.transformer_blocks = nn.ModuleList( + [BasicTransformerBlock(inner_dim, n_heads, d_head, dropout=dropout, context_dim=context_dim) + for d in range(depth)] + ) + + self.proj_out = zero_module(nn.Conv2d(inner_dim, + in_channels, + kernel_size=1, + stride=1, + padding=0)) + + def forward(self, x, context=None): + # note: if no context is given, cross-attention defaults to self-attention + b, c, h, w = x.shape # such as [2,320,10,106] + x_in = x + x = self.norm(x)# group norm + x = self.proj_in(x)# no shape change + x = rearrange(x, 'b c h w -> b (h w) c') + for block in self.transformer_blocks: + x = block(x, context=context)# context shape [b,seq_len=77,context_dim] + x = rearrange(x, 'b (h w) c -> b c h w', h=h, w=w) + x = self.proj_out(x) + return x + x_in \ No newline at end of file diff --git a/ldm/modules/diffusionmodules/__init__.py b/ldm/modules/diffusionmodules/__init__.py new file mode 100644 index 0000000000000000000000000000000000000000..e69de29bb2d1d6434b8b29ae775ad8c2e48c5391 diff --git a/ldm/modules/diffusionmodules/__pycache__/__init__.cpython-38.pyc b/ldm/modules/diffusionmodules/__pycache__/__init__.cpython-38.pyc new file mode 100644 index 0000000000000000000000000000000000000000..9fdc2f12026fbc4aa56c470a0a4d0dcf0686f466 Binary files /dev/null and b/ldm/modules/diffusionmodules/__pycache__/__init__.cpython-38.pyc differ diff --git a/ldm/modules/diffusionmodules/__pycache__/__init__.cpython-39.pyc b/ldm/modules/diffusionmodules/__pycache__/__init__.cpython-39.pyc new file mode 100644 index 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+1,368 @@ +from abc import abstractmethod +from functools import partial +import math +from typing import Iterable + +import numpy as np +import torch as th +import torch.nn as nn +import torch.nn.functional as F + +from ldm.modules.diffusionmodules.util import ( + checkpoint, + conv_nd, + linear, + avg_pool_nd, + zero_module, + normalization, + timestep_embedding, +) +from ldm.modules.attention import SpatialTransformer +from ldm.modules.diffusionmodules.openaimodel import convert_module_to_f16, convert_module_to_f32, AttentionPool2d, \ + TimestepBlock, TimestepEmbedSequential, Upsample, TransposedUpsample, Downsample, ResBlock, AttentionBlock, count_flops_attn, \ + QKVAttentionLegacy, QKVAttention + + +class UNetModel(nn.Module): + """ + The full UNet model with attention and timestep embedding. + :param in_channels: channels in the input Tensor. + :param model_channels: base channel count for the model. + :param out_channels: channels in the output Tensor. + :param num_res_blocks: number of residual blocks per downsample. + :param attention_resolutions: a collection of downsample rates at which + attention will take place. May be a set, list, or tuple. + For example, if this contains 4, then at 4x downsampling, attention + will be used. + :param dropout: the dropout probability. + :param channel_mult: channel multiplier for each level of the UNet. + :param conv_resample: if True, use learned convolutions for upsampling and + downsampling. + :param dims: determines if the signal is 1D, 2D, or 3D. + :param num_classes: if specified (as an int), then this model will be + class-conditional with `num_classes` classes. + :param use_checkpoint: use gradient checkpointing to reduce memory usage. + :param num_heads: the number of attention heads in each attention layer. + :param num_heads_channels: if specified, ignore num_heads and instead use + a fixed channel width per attention head. + :param num_heads_upsample: works with num_heads to set a different number + of heads for upsampling. Deprecated. + :param use_scale_shift_norm: use a FiLM-like conditioning mechanism. + :param resblock_updown: use residual blocks for up/downsampling. + :param use_new_attention_order: use a different attention pattern for potentially + increased efficiency. + """ + + def __init__( + self, + image_size, + in_channels, + model_channels, + out_channels, + num_res_blocks, + attention_resolutions, + dropout=0, + channel_mult=(1, 2, 4, 8), + conv_resample=True, + dims=2, + num_classes=None, + use_checkpoint=False, + use_fp16=False, + num_heads=-1, + num_head_channels=-1, + num_heads_upsample=-1, + use_scale_shift_norm=False, + resblock_updown=False, + use_new_attention_order=False, + use_spatial_transformer=False, # custom transformer support + transformer_depth=1, # custom transformer support + context_dim=None, # custom transformer support + n_embed=None, # custom support for prediction of discrete ids into codebook of first stage vq model + legacy=True, + use_context_project=False, # custom text to audio support + use_context_attn=True # custom text to audio support + ): + super().__init__() + if use_spatial_transformer: + assert context_dim is not None, 'Fool!! You forgot to include the dimension of your cross-attention conditioning...' + + if context_dim is not None and not use_context_project: + assert use_spatial_transformer, 'Fool!! You forgot to use the spatial transformer for your cross-attention conditioning...' + from omegaconf.listconfig import ListConfig + if type(context_dim) == ListConfig: + context_dim = list(context_dim) + + if num_heads_upsample == -1: + num_heads_upsample = num_heads + + if num_heads == -1: + assert num_head_channels != -1, 'Either num_heads or num_head_channels has to be set' + + if num_head_channels == -1: + assert num_heads != -1, 'Either num_heads or num_head_channels has to be set' + + self.image_size = image_size + self.in_channels = in_channels + self.model_channels = model_channels + self.out_channels = out_channels + self.num_res_blocks = num_res_blocks + self.attention_resolutions = attention_resolutions + self.dropout = dropout + self.channel_mult = channel_mult + self.conv_resample = conv_resample + self.num_classes = num_classes + self.use_checkpoint = use_checkpoint + self.dtype = th.float16 if use_fp16 else th.float32 + self.num_heads = num_heads + self.num_head_channels = num_head_channels + self.num_heads_upsample = num_heads_upsample + self.predict_codebook_ids = n_embed is not None + + time_embed_dim = model_channels * 4 + self.time_embed = nn.Sequential( + linear(model_channels, time_embed_dim), + nn.SiLU(), + linear(time_embed_dim, time_embed_dim), + ) + + if self.num_classes is not None: + self.label_emb = nn.Embedding(num_classes, time_embed_dim) + + self.input_blocks = nn.ModuleList( + [ + TimestepEmbedSequential( + conv_nd(dims, in_channels, model_channels, 3, padding=1) + ) + ] + ) + self._feature_size = model_channels + input_block_chans = [model_channels] + ch = model_channels + ds = 1 + for level, mult in enumerate(channel_mult): + for _ in range(num_res_blocks): + layers = [ + ResBlock( + ch, + time_embed_dim, + dropout, + out_channels=mult * model_channels, + dims=dims, + use_checkpoint=use_checkpoint, + use_scale_shift_norm=use_scale_shift_norm, + ) + ] + ch = mult * model_channels + if ds in attention_resolutions: + if num_head_channels == -1: + dim_head = ch // num_heads + else: + num_heads = ch // num_head_channels + dim_head = num_head_channels + if legacy: + #num_heads = 1 + dim_head = ch // num_heads if use_spatial_transformer else num_head_channels + layers.append( + AttentionBlock( + ch, + use_checkpoint=use_checkpoint, + num_heads=num_heads, + num_head_channels=dim_head, + use_new_attention_order=use_new_attention_order, + ) if not use_spatial_transformer else SpatialTransformer( + ch, num_heads, dim_head, depth=transformer_depth, context_dim=context_dim + ) + ) + self.input_blocks.append(TimestepEmbedSequential(*layers)) + self._feature_size += ch + input_block_chans.append(ch) + if level != len(channel_mult) - 1: + out_ch = ch + self.input_blocks.append( + TimestepEmbedSequential( + ResBlock( + ch, + time_embed_dim, + dropout, + out_channels=out_ch, + dims=dims, + use_checkpoint=use_checkpoint, + use_scale_shift_norm=use_scale_shift_norm, + down=True, + ) + if resblock_updown + else Downsample( + ch, conv_resample, dims=dims, out_channels=out_ch + ) + ) + ) + ch = out_ch + input_block_chans.append(ch) + ds *= 2 + self._feature_size += ch + + if num_head_channels == -1: + dim_head = ch // num_heads + else: + num_heads = ch // num_head_channels + dim_head = num_head_channels + if legacy: + #num_heads = 1 + dim_head = ch // num_heads if use_spatial_transformer else num_head_channels + self.middle_block = TimestepEmbedSequential( + ResBlock( + ch, + time_embed_dim, + dropout, + dims=dims, + use_checkpoint=use_checkpoint, + use_scale_shift_norm=use_scale_shift_norm, + ), + AttentionBlock( + ch, + use_checkpoint=use_checkpoint, + num_heads=num_heads, + num_head_channels=dim_head, + use_new_attention_order=use_new_attention_order, + ) if not use_spatial_transformer else SpatialTransformer( + ch, num_heads, dim_head, depth=transformer_depth, context_dim=context_dim + ), + ResBlock( + ch, + time_embed_dim, + dropout, + dims=dims, + use_checkpoint=use_checkpoint, + use_scale_shift_norm=use_scale_shift_norm, + ), + ) + self._feature_size += ch + + self.output_blocks = nn.ModuleList([]) + for level, mult in list(enumerate(channel_mult))[::-1]: + for i in range(num_res_blocks + 1): + ich = input_block_chans.pop() + layers = [ + ResBlock( + ch + ich, + time_embed_dim, + dropout, + out_channels=model_channels * mult, + dims=dims, + use_checkpoint=use_checkpoint, + use_scale_shift_norm=use_scale_shift_norm, + ) + ] + ch = model_channels * mult + if ds in attention_resolutions: + if num_head_channels == -1: + dim_head = ch // num_heads + else: + num_heads = ch // num_head_channels + dim_head = num_head_channels + if legacy: + #num_heads = 1 + dim_head = ch // num_heads if use_spatial_transformer else num_head_channels + layers.append( + AttentionBlock( + ch, + use_checkpoint=use_checkpoint, + num_heads=num_heads_upsample, + num_head_channels=dim_head, + use_new_attention_order=use_new_attention_order, + ) if not use_spatial_transformer else SpatialTransformer( + ch, num_heads, dim_head, depth=transformer_depth, context_dim=context_dim + ) + ) + if level and i == num_res_blocks: + out_ch = ch + layers.append( + ResBlock( + ch, + time_embed_dim, + dropout, + out_channels=out_ch, + dims=dims, + use_checkpoint=use_checkpoint, + use_scale_shift_norm=use_scale_shift_norm, + up=True, + ) + if resblock_updown + else Upsample(ch, conv_resample, dims=dims, out_channels=out_ch) + ) + ds //= 2 + self.output_blocks.append(TimestepEmbedSequential(*layers)) + self._feature_size += ch + + self.out = nn.Sequential( + normalization(ch), + nn.SiLU(), + zero_module(conv_nd(dims, model_channels, out_channels, 3, padding=1)), + ) + if self.predict_codebook_ids: + self.id_predictor = nn.Sequential( + normalization(ch), + conv_nd(dims, model_channels, n_embed, 1), + #nn.LogSoftmax(dim=1) # change to cross_entropy and produce non-normalized logits + ) + + self.use_context_project = use_context_project + if use_context_project: + self.context_project = linear(context_dim, time_embed_dim) + self.use_context_attn = use_context_attn + + + def convert_to_fp16(self): + """ + Convert the torso of the model to float16. + """ + self.input_blocks.apply(convert_module_to_f16) + self.middle_block.apply(convert_module_to_f16) + self.output_blocks.apply(convert_module_to_f16) + + def convert_to_fp32(self): + """ + Convert the torso of the model to float32. + """ + self.input_blocks.apply(convert_module_to_f32) + self.middle_block.apply(convert_module_to_f32) + self.output_blocks.apply(convert_module_to_f32) + + def forward(self, x, timesteps=None, context=None, y=None,**kwargs): + """ + Apply the model to an input batch. + :param x: an [N x C x ...] Tensor of inputs. + :param timesteps: a 1-D batch of timesteps. + :param context: conditioning plugged in via crossattn + :param y: an [N] Tensor of labels, if class-conditional. + :return: an [N x C x ...] Tensor of outputs. + """ + assert (y is not None) == ( + self.num_classes is not None + ), "must specify y if and only if the model is class-conditional" + hs = [] + t_emb = timestep_embedding(timesteps, self.model_channels, repeat_only=False) + emb = self.time_embed(t_emb) + + if self.num_classes is not None: + assert y.shape == (x.shape[0],) + emb = emb + self.label_emb(y) + + # For text-to-audio using global CLIP + if self.use_context_project: + context = self.context_project(context) + emb = emb + context.squeeze(1) + + h = x.type(self.dtype) + for module in self.input_blocks: + h = module(h, emb, context if self.use_context_attn else None) + hs.append(h) + h = self.middle_block(h, emb, context if self.use_context_attn else None) + for module in self.output_blocks: + h = th.cat([h, hs.pop()], dim=1) + h = module(h, emb, context if self.use_context_attn else None) + h = h.type(x.dtype) + if self.predict_codebook_ids: + return self.id_predictor(h) + else: + return self.out(h) diff --git a/ldm/modules/diffusionmodules/model.py b/ldm/modules/diffusionmodules/model.py new file mode 100644 index 0000000000000000000000000000000000000000..2746d74c16cd9a7a418487599399cdea8dc1bbac --- /dev/null +++ b/ldm/modules/diffusionmodules/model.py @@ -0,0 +1,835 @@ +# pytorch_diffusion + derived encoder decoder +import math +import torch +import torch.nn as nn +import numpy as np +from einops import rearrange + +from ldm.util import instantiate_from_config +from ldm.modules.attention import LinearAttention + + +def get_timestep_embedding(timesteps, embedding_dim): + """ + This matches the implementation in Denoising Diffusion Probabilistic Models: + From Fairseq. + Build sinusoidal embeddings. + This matches the implementation in tensor2tensor, but differs slightly + from the description in Section 3.5 of "Attention Is All You Need". + """ + assert len(timesteps.shape) == 1 + + half_dim = embedding_dim // 2 + emb = math.log(10000) / (half_dim - 1) + emb = torch.exp(torch.arange(half_dim, dtype=torch.float32) * -emb) + emb = emb.to(device=timesteps.device) + emb = timesteps.float()[:, None] * emb[None, :] + emb = torch.cat([torch.sin(emb), torch.cos(emb)], dim=1) + if embedding_dim % 2 == 1: # zero pad + emb = torch.nn.functional.pad(emb, (0,1,0,0)) + return emb + + +def nonlinearity(x): + # swish + return x*torch.sigmoid(x) + + +def Normalize(in_channels, num_groups=32): + return torch.nn.GroupNorm(num_groups=num_groups, num_channels=in_channels, eps=1e-6, affine=True) + + +class Upsample(nn.Module): + def __init__(self, in_channels, with_conv): + super().__init__() + self.with_conv = with_conv + if self.with_conv: + self.conv = torch.nn.Conv2d(in_channels, + in_channels, + kernel_size=3, + stride=1, + padding=1) + + def forward(self, x): + x = torch.nn.functional.interpolate(x, scale_factor=2.0, mode="nearest") + if self.with_conv: + x = self.conv(x) + return x + + +class Downsample(nn.Module): + def __init__(self, in_channels, with_conv): + super().__init__() + self.with_conv = with_conv + if self.with_conv: + # no asymmetric padding in torch conv, must do it ourselves + self.conv = torch.nn.Conv2d(in_channels, + in_channels, + kernel_size=3, + stride=2, + padding=0) + + def forward(self, x): + if self.with_conv: + pad = (0,1,0,1) + x = torch.nn.functional.pad(x, pad, mode="constant", value=0) + x = self.conv(x) + else: + x = torch.nn.functional.avg_pool2d(x, kernel_size=2, stride=2) + return x + + +class ResnetBlock(nn.Module): + def __init__(self, *, in_channels, out_channels=None, conv_shortcut=False, + dropout, temb_channels=512): + super().__init__() + self.in_channels = in_channels + out_channels = in_channels if out_channels is None else out_channels + self.out_channels = out_channels + self.use_conv_shortcut = conv_shortcut + + self.norm1 = Normalize(in_channels) + self.conv1 = torch.nn.Conv2d(in_channels, + out_channels, + kernel_size=3, + stride=1, + padding=1) + if temb_channels > 0: + self.temb_proj = torch.nn.Linear(temb_channels, + out_channels) + self.norm2 = Normalize(out_channels) + self.dropout = torch.nn.Dropout(dropout) + self.conv2 = torch.nn.Conv2d(out_channels, + out_channels, + kernel_size=3, + stride=1, + padding=1) + if self.in_channels != self.out_channels: + if self.use_conv_shortcut: + self.conv_shortcut = torch.nn.Conv2d(in_channels, + out_channels, + kernel_size=3, + stride=1, + padding=1) + else: + self.nin_shortcut = torch.nn.Conv2d(in_channels, + out_channels, + kernel_size=1, + stride=1, + padding=0) + + def forward(self, x, temb): + h = x + h = self.norm1(h) + h = nonlinearity(h) + h = self.conv1(h) + + if temb is not None: + h = h + self.temb_proj(nonlinearity(temb))[:,:,None,None] + + h = self.norm2(h) + h = nonlinearity(h) + h = self.dropout(h) + h = self.conv2(h) + + if self.in_channels != self.out_channels: + if self.use_conv_shortcut: + x = self.conv_shortcut(x) + else: + x = self.nin_shortcut(x) + + return x+h + + +class LinAttnBlock(LinearAttention): + """to match AttnBlock usage""" + def __init__(self, in_channels): + super().__init__(dim=in_channels, heads=1, dim_head=in_channels) + + +class AttnBlock(nn.Module): + def __init__(self, in_channels): + super().__init__() + self.in_channels = in_channels + + self.norm = Normalize(in_channels) + self.q = torch.nn.Conv2d(in_channels, + in_channels, + kernel_size=1, + stride=1, + padding=0) + self.k = torch.nn.Conv2d(in_channels, + in_channels, + kernel_size=1, + stride=1, + padding=0) + self.v = torch.nn.Conv2d(in_channels, + in_channels, + kernel_size=1, + stride=1, + padding=0) + self.proj_out = torch.nn.Conv2d(in_channels, + in_channels, + kernel_size=1, + stride=1, + padding=0) + + + def forward(self, x): + h_ = x + h_ = self.norm(h_) + q = self.q(h_) + k = self.k(h_) + v = self.v(h_) + + # compute attention + b,c,h,w = q.shape + q = q.reshape(b,c,h*w) + q = q.permute(0,2,1) # b,hw,c + k = k.reshape(b,c,h*w) # b,c,hw + w_ = torch.bmm(q,k) # b,hw,hw w[b,i,j]=sum_c q[b,i,c]k[b,c,j] + w_ = w_ * (int(c)**(-0.5)) + w_ = torch.nn.functional.softmax(w_, dim=2) + + # attend to values + v = v.reshape(b,c,h*w) + w_ = w_.permute(0,2,1) # b,hw,hw (first hw of k, second of q) + h_ = torch.bmm(v,w_) # b, c,hw (hw of q) h_[b,c,j] = sum_i v[b,c,i] w_[b,i,j] + h_ = h_.reshape(b,c,h,w) + + h_ = self.proj_out(h_) + + return x+h_ + + +def make_attn(in_channels, attn_type="vanilla"): + assert attn_type in ["vanilla", "linear", "none"], f'attn_type {attn_type} unknown' + print(f"making attention of type '{attn_type}' with {in_channels} in_channels") + if attn_type == "vanilla": + return AttnBlock(in_channels) + elif attn_type == "none": + return nn.Identity(in_channels) + else: + return LinAttnBlock(in_channels) + + +class Model(nn.Module): + def __init__(self, *, ch, out_ch, ch_mult=(1,2,4,8), num_res_blocks, + attn_resolutions, dropout=0.0, resamp_with_conv=True, in_channels, + resolution, use_timestep=True, use_linear_attn=False, attn_type="vanilla"): + super().__init__() + if use_linear_attn: attn_type = "linear" + self.ch = ch + self.temb_ch = self.ch*4 + self.num_resolutions = len(ch_mult) + self.num_res_blocks = num_res_blocks + self.resolution = resolution + self.in_channels = in_channels + + self.use_timestep = use_timestep + if self.use_timestep: + # timestep embedding + self.temb = nn.Module() + self.temb.dense = nn.ModuleList([ + torch.nn.Linear(self.ch, + self.temb_ch), + torch.nn.Linear(self.temb_ch, + self.temb_ch), + ]) + + # downsampling + self.conv_in = torch.nn.Conv2d(in_channels, + self.ch, + kernel_size=3, + stride=1, + padding=1) + + curr_res = resolution + in_ch_mult = (1,)+tuple(ch_mult) + self.down = nn.ModuleList() + for i_level in range(self.num_resolutions): + block = nn.ModuleList() + attn = nn.ModuleList() + block_in = ch*in_ch_mult[i_level] + block_out = ch*ch_mult[i_level] + for i_block in range(self.num_res_blocks): + block.append(ResnetBlock(in_channels=block_in, + out_channels=block_out, + temb_channels=self.temb_ch, + dropout=dropout)) + block_in = block_out + if curr_res in attn_resolutions: + attn.append(make_attn(block_in, attn_type=attn_type)) + down = nn.Module() + down.block = block + down.attn = attn + if i_level != self.num_resolutions-1: + down.downsample = Downsample(block_in, resamp_with_conv) + curr_res = curr_res // 2 + self.down.append(down) + + # middle + self.mid = nn.Module() + self.mid.block_1 = ResnetBlock(in_channels=block_in, + out_channels=block_in, + temb_channels=self.temb_ch, + dropout=dropout) + self.mid.attn_1 = make_attn(block_in, attn_type=attn_type) + self.mid.block_2 = ResnetBlock(in_channels=block_in, + out_channels=block_in, + temb_channels=self.temb_ch, + dropout=dropout) + + # upsampling + self.up = nn.ModuleList() + for i_level in reversed(range(self.num_resolutions)): + block = nn.ModuleList() + attn = nn.ModuleList() + block_out = ch*ch_mult[i_level] + skip_in = ch*ch_mult[i_level] + for i_block in range(self.num_res_blocks+1): + if i_block == self.num_res_blocks: + skip_in = ch*in_ch_mult[i_level] + block.append(ResnetBlock(in_channels=block_in+skip_in, + out_channels=block_out, + temb_channels=self.temb_ch, + dropout=dropout)) + block_in = block_out + if curr_res in attn_resolutions: + attn.append(make_attn(block_in, attn_type=attn_type)) + up = nn.Module() + up.block = block + up.attn = attn + if i_level != 0: + up.upsample = Upsample(block_in, resamp_with_conv) + curr_res = curr_res * 2 + self.up.insert(0, up) # prepend to get consistent order + + # end + self.norm_out = Normalize(block_in) + self.conv_out = torch.nn.Conv2d(block_in, + out_ch, + kernel_size=3, + stride=1, + padding=1) + + def forward(self, x, t=None, context=None): + #assert x.shape[2] == x.shape[3] == self.resolution + if context is not None: + # assume aligned context, cat along channel axis + x = torch.cat((x, context), dim=1) + if self.use_timestep: + # timestep embedding + assert t is not None + temb = get_timestep_embedding(t, self.ch) + temb = self.temb.dense[0](temb) + temb = nonlinearity(temb) + temb = self.temb.dense[1](temb) + else: + temb = None + + # downsampling + hs = [self.conv_in(x)] + for i_level in range(self.num_resolutions): + for i_block in range(self.num_res_blocks): + h = self.down[i_level].block[i_block](hs[-1], temb) + if len(self.down[i_level].attn) > 0: + h = self.down[i_level].attn[i_block](h) + hs.append(h) + if i_level != self.num_resolutions-1: + hs.append(self.down[i_level].downsample(hs[-1])) + + # middle + h = hs[-1] + h = self.mid.block_1(h, temb) + h = self.mid.attn_1(h) + h = self.mid.block_2(h, temb) + + # upsampling + for i_level in reversed(range(self.num_resolutions)): + for i_block in range(self.num_res_blocks+1): + h = self.up[i_level].block[i_block]( + torch.cat([h, hs.pop()], dim=1), temb) + if len(self.up[i_level].attn) > 0: + h = self.up[i_level].attn[i_block](h) + if i_level != 0: + h = self.up[i_level].upsample(h) + + # end + h = self.norm_out(h) + h = nonlinearity(h) + h = self.conv_out(h) + return h + + def get_last_layer(self): + return self.conv_out.weight + + +class Encoder(nn.Module): + def __init__(self, *, ch, out_ch, ch_mult=(1,2,4,8), num_res_blocks, + attn_resolutions, dropout=0.0, resamp_with_conv=True, in_channels, + resolution, z_channels, double_z=True, use_linear_attn=False, attn_type="vanilla", + **ignore_kwargs): + super().__init__() + if use_linear_attn: attn_type = "linear" + self.ch = ch + self.temb_ch = 0 + self.num_resolutions = len(ch_mult) + self.num_res_blocks = num_res_blocks + self.resolution = resolution + self.in_channels = in_channels + + # downsampling + self.conv_in = torch.nn.Conv2d(in_channels, + self.ch, + kernel_size=3, + stride=1, + padding=1) + + curr_res = resolution + in_ch_mult = (1,)+tuple(ch_mult) + self.in_ch_mult = in_ch_mult + self.down = nn.ModuleList() + for i_level in range(self.num_resolutions): + block = nn.ModuleList() + attn = nn.ModuleList() + block_in = ch*in_ch_mult[i_level] + block_out = ch*ch_mult[i_level] + for i_block in range(self.num_res_blocks): + block.append(ResnetBlock(in_channels=block_in, + out_channels=block_out, + temb_channels=self.temb_ch, + dropout=dropout)) + block_in = block_out + if curr_res in attn_resolutions: + attn.append(make_attn(block_in, attn_type=attn_type))# vanilla attention + down = nn.Module() + down.block = block + down.attn = attn + if i_level != self.num_resolutions-1: + down.downsample = Downsample(block_in, resamp_with_conv) + curr_res = curr_res // 2 + self.down.append(down) + + # middle + self.mid = nn.Module() + self.mid.block_1 = ResnetBlock(in_channels=block_in, + out_channels=block_in, + temb_channels=self.temb_ch, + dropout=dropout) + self.mid.attn_1 = make_attn(block_in, attn_type=attn_type) + self.mid.block_2 = ResnetBlock(in_channels=block_in, + out_channels=block_in, + temb_channels=self.temb_ch, + dropout=dropout) + + # end + self.norm_out = Normalize(block_in)# GroupNorm + self.conv_out = torch.nn.Conv2d(block_in, + 2*z_channels if double_z else z_channels, + kernel_size=3, + stride=1, + padding=1) + + def forward(self, x): + # timestep embedding + temb = None + + # downsampling + hs = [self.conv_in(x)] + for i_level in range(self.num_resolutions): + for i_block in range(self.num_res_blocks): + h = self.down[i_level].block[i_block](hs[-1], temb) + if len(self.down[i_level].attn) > 0: + h = self.down[i_level].attn[i_block](h) + hs.append(h) + if i_level != self.num_resolutions-1: + hs.append(self.down[i_level].downsample(hs[-1])) + + # middle + h = hs[-1] + h = self.mid.block_1(h, temb) + h = self.mid.attn_1(h) + h = self.mid.block_2(h, temb) + + # end + h = self.norm_out(h) + h = nonlinearity(h) + h = self.conv_out(h) + return h + + +class Decoder(nn.Module): + def __init__(self, *, ch, out_ch, ch_mult=(1,2,4,8), num_res_blocks, + attn_resolutions, dropout=0.0, resamp_with_conv=True, in_channels, + resolution, z_channels, give_pre_end=False, tanh_out=False, use_linear_attn=False, + attn_type="vanilla", **ignorekwargs): + super().__init__() + if use_linear_attn: attn_type = "linear" + self.ch = ch + self.temb_ch = 0 + self.num_resolutions = len(ch_mult) + self.num_res_blocks = num_res_blocks + self.resolution = resolution + self.in_channels = in_channels + self.give_pre_end = give_pre_end + self.tanh_out = tanh_out + + # compute in_ch_mult, block_in and curr_res at lowest res + in_ch_mult = (1,)+tuple(ch_mult) + block_in = ch*ch_mult[self.num_resolutions-1] + curr_res = resolution // 2**(self.num_resolutions-1) + self.z_shape = (1,z_channels,curr_res,curr_res) + print("Working with z of shape {} = {} dimensions.".format( + self.z_shape, np.prod(self.z_shape))) + + # z to block_in + self.conv_in = torch.nn.Conv2d(z_channels, + block_in, + kernel_size=3, + stride=1, + padding=1) + + # middle + self.mid = nn.Module() + self.mid.block_1 = ResnetBlock(in_channels=block_in, + out_channels=block_in, + temb_channels=self.temb_ch, + dropout=dropout) + self.mid.attn_1 = make_attn(block_in, attn_type=attn_type) + self.mid.block_2 = ResnetBlock(in_channels=block_in, + out_channels=block_in, + temb_channels=self.temb_ch, + dropout=dropout) + + # upsampling + self.up = nn.ModuleList() + for i_level in reversed(range(self.num_resolutions)): + block = nn.ModuleList() + attn = nn.ModuleList() + block_out = ch*ch_mult[i_level] + for i_block in range(self.num_res_blocks+1): + block.append(ResnetBlock(in_channels=block_in, + out_channels=block_out, + temb_channels=self.temb_ch, + dropout=dropout)) + block_in = block_out + if curr_res in attn_resolutions: + attn.append(make_attn(block_in, attn_type=attn_type)) + up = nn.Module() + up.block = block + up.attn = attn + if i_level != 0: + up.upsample = Upsample(block_in, resamp_with_conv) + curr_res = curr_res * 2 + self.up.insert(0, up) # prepend to get consistent order + + # end + self.norm_out = Normalize(block_in) + self.conv_out = torch.nn.Conv2d(block_in, + out_ch, + kernel_size=3, + stride=1, + padding=1) + + def forward(self, z): + #assert z.shape[1:] == self.z_shape[1:] + self.last_z_shape = z.shape + + # timestep embedding + temb = None + + # z to block_in + h = self.conv_in(z) + + # middle + h = self.mid.block_1(h, temb) + h = self.mid.attn_1(h) + h = self.mid.block_2(h, temb) + + # upsampling + for i_level in reversed(range(self.num_resolutions)): + for i_block in range(self.num_res_blocks+1): + h = self.up[i_level].block[i_block](h, temb) + if len(self.up[i_level].attn) > 0: + h = self.up[i_level].attn[i_block](h) + if i_level != 0: + h = self.up[i_level].upsample(h) + + # end + if self.give_pre_end: + return h + + h = self.norm_out(h) + h = nonlinearity(h) + h = self.conv_out(h) + if self.tanh_out: + h = torch.tanh(h) + return h + + +class SimpleDecoder(nn.Module): + def __init__(self, in_channels, out_channels, *args, **kwargs): + super().__init__() + self.model = nn.ModuleList([nn.Conv2d(in_channels, in_channels, 1), + ResnetBlock(in_channels=in_channels, + out_channels=2 * in_channels, + temb_channels=0, dropout=0.0), + ResnetBlock(in_channels=2 * in_channels, + out_channels=4 * in_channels, + temb_channels=0, dropout=0.0), + ResnetBlock(in_channels=4 * in_channels, + out_channels=2 * in_channels, + temb_channels=0, dropout=0.0), + nn.Conv2d(2*in_channels, in_channels, 1), + Upsample(in_channels, with_conv=True)]) + # end + self.norm_out = Normalize(in_channels) + self.conv_out = torch.nn.Conv2d(in_channels, + out_channels, + kernel_size=3, + stride=1, + padding=1) + + def forward(self, x): + for i, layer in enumerate(self.model): + if i in [1,2,3]: + x = layer(x, None) + else: + x = layer(x) + + h = self.norm_out(x) + h = nonlinearity(h) + x = self.conv_out(h) + return x + + +class UpsampleDecoder(nn.Module): + def __init__(self, in_channels, out_channels, ch, num_res_blocks, resolution, + ch_mult=(2,2), dropout=0.0): + super().__init__() + # upsampling + self.temb_ch = 0 + self.num_resolutions = len(ch_mult) + self.num_res_blocks = num_res_blocks + block_in = in_channels + curr_res = resolution // 2 ** (self.num_resolutions - 1) + self.res_blocks = nn.ModuleList() + self.upsample_blocks = nn.ModuleList() + for i_level in range(self.num_resolutions): + res_block = [] + block_out = ch * ch_mult[i_level] + for i_block in range(self.num_res_blocks + 1): + res_block.append(ResnetBlock(in_channels=block_in, + out_channels=block_out, + temb_channels=self.temb_ch, + dropout=dropout)) + block_in = block_out + self.res_blocks.append(nn.ModuleList(res_block)) + if i_level != self.num_resolutions - 1: + self.upsample_blocks.append(Upsample(block_in, True)) + curr_res = curr_res * 2 + + # end + self.norm_out = Normalize(block_in) + self.conv_out = torch.nn.Conv2d(block_in, + out_channels, + kernel_size=3, + stride=1, + padding=1) + + def forward(self, x): + # upsampling + h = x + for k, i_level in enumerate(range(self.num_resolutions)): + for i_block in range(self.num_res_blocks + 1): + h = self.res_blocks[i_level][i_block](h, None) + if i_level != self.num_resolutions - 1: + h = self.upsample_blocks[k](h) + h = self.norm_out(h) + h = nonlinearity(h) + h = self.conv_out(h) + return h + + +class LatentRescaler(nn.Module): + def __init__(self, factor, in_channels, mid_channels, out_channels, depth=2): + super().__init__() + # residual block, interpolate, residual block + self.factor = factor + self.conv_in = nn.Conv2d(in_channels, + mid_channels, + kernel_size=3, + stride=1, + padding=1) + self.res_block1 = nn.ModuleList([ResnetBlock(in_channels=mid_channels, + out_channels=mid_channels, + temb_channels=0, + dropout=0.0) for _ in range(depth)]) + self.attn = AttnBlock(mid_channels) + self.res_block2 = nn.ModuleList([ResnetBlock(in_channels=mid_channels, + out_channels=mid_channels, + temb_channels=0, + dropout=0.0) for _ in range(depth)]) + + self.conv_out = nn.Conv2d(mid_channels, + out_channels, + kernel_size=1, + ) + + def forward(self, x): + x = self.conv_in(x) + for block in self.res_block1: + x = block(x, None) + x = torch.nn.functional.interpolate(x, size=(int(round(x.shape[2]*self.factor)), int(round(x.shape[3]*self.factor)))) + x = self.attn(x) + for block in self.res_block2: + x = block(x, None) + x = self.conv_out(x) + return x + + +class MergedRescaleEncoder(nn.Module): + def __init__(self, in_channels, ch, resolution, out_ch, num_res_blocks, + attn_resolutions, dropout=0.0, resamp_with_conv=True, + ch_mult=(1,2,4,8), rescale_factor=1.0, rescale_module_depth=1): + super().__init__() + intermediate_chn = ch * ch_mult[-1] + self.encoder = Encoder(in_channels=in_channels, num_res_blocks=num_res_blocks, ch=ch, ch_mult=ch_mult, + z_channels=intermediate_chn, double_z=False, resolution=resolution, + attn_resolutions=attn_resolutions, dropout=dropout, resamp_with_conv=resamp_with_conv, + out_ch=None) + self.rescaler = LatentRescaler(factor=rescale_factor, in_channels=intermediate_chn, + mid_channels=intermediate_chn, out_channels=out_ch, depth=rescale_module_depth) + + def forward(self, x): + x = self.encoder(x) + x = self.rescaler(x) + return x + + +class MergedRescaleDecoder(nn.Module): + def __init__(self, z_channels, out_ch, resolution, num_res_blocks, attn_resolutions, ch, ch_mult=(1,2,4,8), + dropout=0.0, resamp_with_conv=True, rescale_factor=1.0, rescale_module_depth=1): + super().__init__() + tmp_chn = z_channels*ch_mult[-1] + self.decoder = Decoder(out_ch=out_ch, z_channels=tmp_chn, attn_resolutions=attn_resolutions, dropout=dropout, + resamp_with_conv=resamp_with_conv, in_channels=None, num_res_blocks=num_res_blocks, + ch_mult=ch_mult, resolution=resolution, ch=ch) + self.rescaler = LatentRescaler(factor=rescale_factor, in_channels=z_channels, mid_channels=tmp_chn, + out_channels=tmp_chn, depth=rescale_module_depth) + + def forward(self, x): + x = self.rescaler(x) + x = self.decoder(x) + return x + + +class Upsampler(nn.Module): + def __init__(self, in_size, out_size, in_channels, out_channels, ch_mult=2): + super().__init__() + assert out_size >= in_size + num_blocks = int(np.log2(out_size//in_size))+1 + factor_up = 1.+ (out_size % in_size) + print(f"Building {self.__class__.__name__} with in_size: {in_size} --> out_size {out_size} and factor {factor_up}") + self.rescaler = LatentRescaler(factor=factor_up, in_channels=in_channels, mid_channels=2*in_channels, + out_channels=in_channels) + self.decoder = Decoder(out_ch=out_channels, resolution=out_size, z_channels=in_channels, num_res_blocks=2, + attn_resolutions=[], in_channels=None, ch=in_channels, + ch_mult=[ch_mult for _ in range(num_blocks)]) + + def forward(self, x): + x = self.rescaler(x) + x = self.decoder(x) + return x + + +class Resize(nn.Module): + def __init__(self, in_channels=None, learned=False, mode="bilinear"): + super().__init__() + self.with_conv = learned + self.mode = mode + if self.with_conv: + print(f"Note: {self.__class__.__name} uses learned downsampling and will ignore the fixed {mode} mode") + raise NotImplementedError() + assert in_channels is not None + # no asymmetric padding in torch conv, must do it ourselves + self.conv = torch.nn.Conv2d(in_channels, + in_channels, + kernel_size=4, + stride=2, + padding=1) + + def forward(self, x, scale_factor=1.0): + if scale_factor==1.0: + return x + else: + x = torch.nn.functional.interpolate(x, mode=self.mode, align_corners=False, scale_factor=scale_factor) + return x + +class FirstStagePostProcessor(nn.Module): + + def __init__(self, ch_mult:list, in_channels, + pretrained_model:nn.Module=None, + reshape=False, + n_channels=None, + dropout=0., + pretrained_config=None): + super().__init__() + if pretrained_config is None: + assert pretrained_model is not None, 'Either "pretrained_model" or "pretrained_config" must not be None' + self.pretrained_model = pretrained_model + else: + assert pretrained_config is not None, 'Either "pretrained_model" or "pretrained_config" must not be None' + self.instantiate_pretrained(pretrained_config) + + self.do_reshape = reshape + + if n_channels is None: + n_channels = self.pretrained_model.encoder.ch + + self.proj_norm = Normalize(in_channels,num_groups=in_channels//2) + self.proj = nn.Conv2d(in_channels,n_channels,kernel_size=3, + stride=1,padding=1) + + blocks = [] + downs = [] + ch_in = n_channels + for m in ch_mult: + blocks.append(ResnetBlock(in_channels=ch_in,out_channels=m*n_channels,dropout=dropout)) + ch_in = m * n_channels + downs.append(Downsample(ch_in, with_conv=False)) + + self.model = nn.ModuleList(blocks) + self.downsampler = nn.ModuleList(downs) + + + def instantiate_pretrained(self, config): + model = instantiate_from_config(config) + self.pretrained_model = model.eval() + # self.pretrained_model.train = False + for param in self.pretrained_model.parameters(): + param.requires_grad = False + + + @torch.no_grad() + def encode_with_pretrained(self,x): + c = self.pretrained_model.encode(x) + if isinstance(c, DiagonalGaussianDistribution): + c = c.mode() + return c + + def forward(self,x): + z_fs = self.encode_with_pretrained(x) + z = self.proj_norm(z_fs) + z = self.proj(z) + z = nonlinearity(z) + + for submodel, downmodel in zip(self.model,self.downsampler): + z = submodel(z,temb=None) + z = downmodel(z) + + if self.do_reshape: + z = rearrange(z,'b c h w -> b (h w) c') + return z + diff --git a/ldm/modules/diffusionmodules/openaimodel.py b/ldm/modules/diffusionmodules/openaimodel.py new file mode 100644 index 0000000000000000000000000000000000000000..0a274d84dfe6ef3e02848861f5b7a7c7e242ca98 --- /dev/null +++ b/ldm/modules/diffusionmodules/openaimodel.py @@ -0,0 +1,963 @@ +from abc import abstractmethod +from functools import partial +import math +from typing import Iterable + +import numpy as np +import torch as th +import torch.nn as nn +import torch.nn.functional as F + +from ldm.modules.diffusionmodules.util import ( + checkpoint, + conv_nd, + linear, + avg_pool_nd, + zero_module, + normalization, + timestep_embedding, +) +from ldm.modules.attention import SpatialTransformer + + +# dummy replace +def convert_module_to_f16(x): + pass + +def convert_module_to_f32(x): + pass + + +## go +class AttentionPool2d(nn.Module): + """ + Adapted from CLIP: https://github.com/openai/CLIP/blob/main/clip/model.py + """ + + def __init__( + self, + spacial_dim: int, + embed_dim: int, + num_heads_channels: int, + output_dim: int = None, + ): + super().__init__() + self.positional_embedding = nn.Parameter(th.randn(embed_dim, spacial_dim ** 2 + 1) / embed_dim ** 0.5) + self.qkv_proj = conv_nd(1, embed_dim, 3 * embed_dim, 1) + self.c_proj = conv_nd(1, embed_dim, output_dim or embed_dim, 1) + self.num_heads = embed_dim // num_heads_channels + self.attention = QKVAttention(self.num_heads) + + def forward(self, x): + b, c, *_spatial = x.shape + x = x.reshape(b, c, -1) # NC(HW) + x = th.cat([x.mean(dim=-1, keepdim=True), x], dim=-1) # NC(HW+1) + x = x + self.positional_embedding[None, :, :].to(x.dtype) # NC(HW+1) + x = self.qkv_proj(x) + x = self.attention(x) + x = self.c_proj(x) + return x[:, :, 0] + + +class TimestepBlock(nn.Module): + """ + Any module where forward() takes timestep embeddings as a second argument. + """ + + @abstractmethod + def forward(self, x, emb): + """ + Apply the module to `x` given `emb` timestep embeddings. + """ + + +class TimestepEmbedSequential(nn.Sequential, TimestepBlock): + """ + A sequential module that passes timestep embeddings to the children that + support it as an extra input. + """ + + def forward(self, x, emb, context=None): + for layer in self: + if isinstance(layer, TimestepBlock): + x = layer(x, emb) + elif isinstance(layer, SpatialTransformer): + x = layer(x, context) + else: + x = layer(x) + return x + + +class Upsample(nn.Module): + """ + An upsampling layer with an optional convolution. + :param channels: channels in the inputs and outputs. + :param use_conv: a bool determining if a convolution is applied. + :param dims: determines if the signal is 1D, 2D, or 3D. If 3D, then + upsampling occurs in the inner-two dimensions. + """ + + def __init__(self, channels, use_conv, dims=2, out_channels=None, padding=1): + super().__init__() + self.channels = channels + self.out_channels = out_channels or channels + self.use_conv = use_conv + self.dims = dims + if use_conv: + self.conv = conv_nd(dims, self.channels, self.out_channels, 3, padding=padding) + + def forward(self, x): + assert x.shape[1] == self.channels + if self.dims == 3: + x = F.interpolate( + x, (x.shape[2], x.shape[3] * 2, x.shape[4] * 2), mode="nearest" + ) + else: + x = F.interpolate(x, scale_factor=2, mode="nearest") + if self.use_conv: + x = self.conv(x) + return x + +class TransposedUpsample(nn.Module): + 'Learned 2x upsampling without padding' + def __init__(self, channels, out_channels=None, ks=5): + super().__init__() + self.channels = channels + self.out_channels = out_channels or channels + + self.up = nn.ConvTranspose2d(self.channels,self.out_channels,kernel_size=ks,stride=2) + + def forward(self,x): + return self.up(x) + + +class Downsample(nn.Module): + """ + A downsampling layer with an optional convolution. + :param channels: channels in the inputs and outputs. + :param use_conv: a bool determining if a convolution is applied. + :param dims: determines if the signal is 1D, 2D, or 3D. If 3D, then + downsampling occurs in the inner-two dimensions. + """ + + def __init__(self, channels, use_conv, dims=2, out_channels=None,padding=1): + super().__init__() + self.channels = channels + self.out_channels = out_channels or channels + self.use_conv = use_conv + self.dims = dims + stride = 2 if dims != 3 else (1, 2, 2) + if use_conv: + self.op = conv_nd( + dims, self.channels, self.out_channels, 3, stride=stride, padding=padding + ) + else: + assert self.channels == self.out_channels + self.op = avg_pool_nd(dims, kernel_size=stride, stride=stride) + + def forward(self, x): + assert x.shape[1] == self.channels + return self.op(x) + + +class ResBlock(TimestepBlock): + """ + A residual block that can optionally change the number of channels. + :param channels: the number of input channels. + :param emb_channels: the number of timestep embedding channels. + :param dropout: the rate of dropout. + :param out_channels: if specified, the number of out channels. + :param use_conv: if True and out_channels is specified, use a spatial + convolution instead of a smaller 1x1 convolution to change the + channels in the skip connection. + :param dims: determines if the signal is 1D, 2D, or 3D. + :param use_checkpoint: if True, use gradient checkpointing on this module. + :param up: if True, use this block for upsampling. + :param down: if True, use this block for downsampling. + """ + + def __init__( + self, + channels, + emb_channels, + dropout, + out_channels=None, + use_conv=False, + use_scale_shift_norm=False, + dims=2, + use_checkpoint=False, + up=False, + down=False, + ): + super().__init__() + self.channels = channels + self.emb_channels = emb_channels + self.dropout = dropout + self.out_channels = out_channels or channels + self.use_conv = use_conv + self.use_checkpoint = use_checkpoint + self.use_scale_shift_norm = use_scale_shift_norm + + self.in_layers = nn.Sequential( + normalization(channels), + nn.SiLU(), + conv_nd(dims, channels, self.out_channels, 3, padding=1), + ) + + self.updown = up or down + + if up: + self.h_upd = Upsample(channels, False, dims) + self.x_upd = Upsample(channels, False, dims) + elif down: + self.h_upd = Downsample(channels, False, dims) + self.x_upd = Downsample(channels, False, dims) + else: + self.h_upd = self.x_upd = nn.Identity() + + self.emb_layers = nn.Sequential( + nn.SiLU(), + linear( + emb_channels, + 2 * self.out_channels if use_scale_shift_norm else self.out_channels, + ), + ) + self.out_layers = nn.Sequential( + normalization(self.out_channels), + nn.SiLU(), + nn.Dropout(p=dropout), + zero_module( + conv_nd(dims, self.out_channels, self.out_channels, 3, padding=1) + ), + ) + + if self.out_channels == channels: + self.skip_connection = nn.Identity() + elif use_conv: + self.skip_connection = conv_nd( + dims, channels, self.out_channels, 3, padding=1 + ) + else: + self.skip_connection = conv_nd(dims, channels, self.out_channels, 1) + + def forward(self, x, emb): + """ + Apply the block to a Tensor, conditioned on a timestep embedding. + :param x: an [N x C x ...] Tensor of features. + :param emb: an [N x emb_channels] Tensor of timestep embeddings. + :return: an [N x C x ...] Tensor of outputs. + """ + return checkpoint( + self._forward, (x, emb), self.parameters(), self.use_checkpoint + ) + + + def _forward(self, x, emb): + if self.updown: + in_rest, in_conv = self.in_layers[:-1], self.in_layers[-1] + h = in_rest(x) + h = self.h_upd(h) + x = self.x_upd(x) + h = in_conv(h) + else: + h = self.in_layers(x) + emb_out = self.emb_layers(emb).type(h.dtype) + while len(emb_out.shape) < len(h.shape): + emb_out = emb_out[..., None] + if self.use_scale_shift_norm: + out_norm, out_rest = self.out_layers[0], self.out_layers[1:] + scale, shift = th.chunk(emb_out, 2, dim=1) + h = out_norm(h) * (1 + scale) + shift + h = out_rest(h) + else: + h = h + emb_out + h = self.out_layers(h) + return self.skip_connection(x) + h + + +class AttentionBlock(nn.Module): + """ + An attention block that allows spatial positions to attend to each other. + Originally ported from here, but adapted to the N-d case. + https://github.com/hojonathanho/diffusion/blob/1e0dceb3b3495bbe19116a5e1b3596cd0706c543/diffusion_tf/models/unet.py#L66. + """ + + def __init__( + self, + channels, + num_heads=1, + num_head_channels=-1, + use_checkpoint=False, + use_new_attention_order=False, + ): + super().__init__() + self.channels = channels + if num_head_channels == -1: + self.num_heads = num_heads + else: + assert ( + channels % num_head_channels == 0 + ), f"q,k,v channels {channels} is not divisible by num_head_channels {num_head_channels}" + self.num_heads = channels // num_head_channels + self.use_checkpoint = use_checkpoint + self.norm = normalization(channels) + self.qkv = conv_nd(1, channels, channels * 3, 1) + if use_new_attention_order: + # split qkv before split heads + self.attention = QKVAttention(self.num_heads) + else: + # split heads before split qkv + self.attention = QKVAttentionLegacy(self.num_heads) + + self.proj_out = zero_module(conv_nd(1, channels, channels, 1)) + + def forward(self, x): + return checkpoint(self._forward, (x,), self.parameters(), True) # TODO: check checkpoint usage, is True # TODO: fix the .half call!!! + #return pt_checkpoint(self._forward, x) # pytorch + + def _forward(self, x): + b, c, *spatial = x.shape + x = x.reshape(b, c, -1) + qkv = self.qkv(self.norm(x)) + h = self.attention(qkv) + h = self.proj_out(h) + return (x + h).reshape(b, c, *spatial) + + +def count_flops_attn(model, _x, y): + """ + A counter for the `thop` package to count the operations in an + attention operation. + Meant to be used like: + macs, params = thop.profile( + model, + inputs=(inputs, timestamps), + custom_ops={QKVAttention: QKVAttention.count_flops}, + ) + """ + b, c, *spatial = y[0].shape + num_spatial = int(np.prod(spatial)) + # We perform two matmuls with the same number of ops. + # The first computes the weight matrix, the second computes + # the combination of the value vectors. + matmul_ops = 2 * b * (num_spatial ** 2) * c + model.total_ops += th.DoubleTensor([matmul_ops]) + + +class QKVAttentionLegacy(nn.Module): + """ + A module which performs QKV attention. Matches legacy QKVAttention + input/ouput heads shaping + """ + + def __init__(self, n_heads): + super().__init__() + self.n_heads = n_heads + + def forward(self, qkv): + """ + Apply QKV attention. + :param qkv: an [N x (H * 3 * C) x T] tensor of Qs, Ks, and Vs. + :return: an [N x (H * C) x T] tensor after attention. + """ + bs, width, length = qkv.shape + assert width % (3 * self.n_heads) == 0 + ch = width // (3 * self.n_heads) + q, k, v = qkv.reshape(bs * self.n_heads, ch * 3, length).split(ch, dim=1) + scale = 1 / math.sqrt(math.sqrt(ch)) + weight = th.einsum( + "bct,bcs->bts", q * scale, k * scale + ) # More stable with f16 than dividing afterwards + weight = th.softmax(weight.float(), dim=-1).type(weight.dtype) + a = th.einsum("bts,bcs->bct", weight, v) + return a.reshape(bs, -1, length) + + @staticmethod + def count_flops(model, _x, y): + return count_flops_attn(model, _x, y) + + +class QKVAttention(nn.Module): + """ + A module which performs QKV attention and splits in a different order. + """ + + def __init__(self, n_heads): + super().__init__() + self.n_heads = n_heads + + def forward(self, qkv): + """ + Apply QKV attention. + :param qkv: an [N x (3 * H * C) x T] tensor of Qs, Ks, and Vs. + :return: an [N x (H * C) x T] tensor after attention. + """ + bs, width, length = qkv.shape + assert width % (3 * self.n_heads) == 0 + ch = width // (3 * self.n_heads) + q, k, v = qkv.chunk(3, dim=1) + scale = 1 / math.sqrt(math.sqrt(ch)) + weight = th.einsum( + "bct,bcs->bts", + (q * scale).view(bs * self.n_heads, ch, length), + (k * scale).view(bs * self.n_heads, ch, length), + ) # More stable with f16 than dividing afterwards + weight = th.softmax(weight.float(), dim=-1).type(weight.dtype) + a = th.einsum("bts,bcs->bct", weight, v.reshape(bs * self.n_heads, ch, length)) + return a.reshape(bs, -1, length) + + @staticmethod + def count_flops(model, _x, y): + return count_flops_attn(model, _x, y) + + +class UNetModel(nn.Module): + """ + The full UNet model with attention and timestep embedding. + :param in_channels: channels in the input Tensor. + :param model_channels: base channel count for the model. + :param out_channels: channels in the output Tensor. + :param num_res_blocks: number of residual blocks per downsample. + :param attention_resolutions: a collection of downsample rates at which + attention will take place. May be a set, list, or tuple. + For example, if this contains 4, then at 4x downsampling, attention + will be used. + :param dropout: the dropout probability. + :param channel_mult: channel multiplier for each level of the UNet. + :param conv_resample: if True, use learned convolutions for upsampling and + downsampling. + :param dims: determines if the signal is 1D, 2D, or 3D. + :param num_classes: if specified (as an int), then this model will be + class-conditional with `num_classes` classes. + :param use_checkpoint: use gradient checkpointing to reduce memory usage. + :param num_heads: the number of attention heads in each attention layer. + :param num_heads_channels: if specified, ignore num_heads and instead use + a fixed channel width per attention head. + :param num_heads_upsample: works with num_heads to set a different number + of heads for upsampling. Deprecated. + :param use_scale_shift_norm: use a FiLM-like conditioning mechanism. + :param resblock_updown: use residual blocks for up/downsampling. + :param use_new_attention_order: use a different attention pattern for potentially + increased efficiency. + """ + + def __init__( + self, + image_size, + in_channels, + model_channels, + out_channels, + num_res_blocks, + attention_resolutions, + dropout=0, + channel_mult=(1, 2, 4, 8), + conv_resample=True, + dims=2, + num_classes=None, + use_checkpoint=False, + use_fp16=False, + num_heads=-1, + num_head_channels=-1, + num_heads_upsample=-1, + use_scale_shift_norm=False, + resblock_updown=False, + use_new_attention_order=False, + use_spatial_transformer=False, # custom transformer support + transformer_depth=1, # custom transformer support + context_dim=None, # custom transformer support + n_embed=None, # custom support for prediction of discrete ids into codebook of first stage vq model + legacy=True, + ): + super().__init__() + if use_spatial_transformer: + assert context_dim is not None, 'Fool!! You forgot to include the dimension of your cross-attention conditioning...' + + if context_dim is not None: + assert use_spatial_transformer, 'Fool!! You forgot to use the spatial transformer for your cross-attention conditioning...' + from omegaconf.listconfig import ListConfig + if type(context_dim) == ListConfig: + context_dim = list(context_dim) + + if num_heads_upsample == -1: + num_heads_upsample = num_heads + + if num_heads == -1: + assert num_head_channels != -1, 'Either num_heads or num_head_channels has to be set' + + if num_head_channels == -1: + assert num_heads != -1, 'Either num_heads or num_head_channels has to be set' + + self.image_size = image_size + self.in_channels = in_channels + self.model_channels = model_channels + self.out_channels = out_channels + self.num_res_blocks = num_res_blocks + self.attention_resolutions = attention_resolutions + self.dropout = dropout + self.channel_mult = channel_mult + self.conv_resample = conv_resample + self.num_classes = num_classes + self.use_checkpoint = use_checkpoint + self.dtype = th.float16 if use_fp16 else th.float32 + self.num_heads = num_heads + self.num_head_channels = num_head_channels + self.num_heads_upsample = num_heads_upsample + self.predict_codebook_ids = n_embed is not None + + time_embed_dim = model_channels * 4 + self.time_embed = nn.Sequential( + linear(model_channels, time_embed_dim), + nn.SiLU(), + linear(time_embed_dim, time_embed_dim), + ) + + if self.num_classes is not None: + self.label_emb = nn.Embedding(num_classes, time_embed_dim) + + self.input_blocks = nn.ModuleList( + [ + TimestepEmbedSequential( + conv_nd(dims, in_channels, model_channels, 3, padding=1)# conv2d for txt2img/audio + ) + ] + ) + self._feature_size = model_channels + input_block_chans = [model_channels] + ch = model_channels + ds = 1 + # downsample blocks + for level, mult in enumerate(channel_mult): + for _ in range(num_res_blocks): + layers = [ + ResBlock( + ch, + time_embed_dim, + dropout, + out_channels=mult * model_channels, + dims=dims, + use_checkpoint=use_checkpoint, + use_scale_shift_norm=use_scale_shift_norm, + ) + ] + ch = mult * model_channels + if ds in attention_resolutions: + if num_head_channels == -1: + dim_head = ch // num_heads + else: + num_heads = ch // num_head_channels + dim_head = num_head_channels + if legacy: + #num_heads = 1 + dim_head = ch // num_heads if use_spatial_transformer else num_head_channels + layers.append( + AttentionBlock( + ch, + use_checkpoint=use_checkpoint, + num_heads=num_heads, + num_head_channels=dim_head, + use_new_attention_order=use_new_attention_order, + ) if not use_spatial_transformer else SpatialTransformer(# transformer_depth is 1 + ch, num_heads, dim_head, depth=transformer_depth, context_dim=context_dim + ) + ) + self.input_blocks.append(TimestepEmbedSequential(*layers)) + self._feature_size += ch + input_block_chans.append(ch) + if level != len(channel_mult) - 1: + out_ch = ch + self.input_blocks.append( + TimestepEmbedSequential( + ResBlock( + ch, + time_embed_dim, + dropout, + out_channels=out_ch, + dims=dims, + use_checkpoint=use_checkpoint, + use_scale_shift_norm=use_scale_shift_norm, + down=True, + ) + if resblock_updown + else Downsample( + ch, conv_resample, dims=dims, out_channels=out_ch + ) + ) + ) + ch = out_ch + input_block_chans.append(ch) + ds *= 2 + self._feature_size += ch + + if num_head_channels == -1: + dim_head = ch // num_heads + else: + num_heads = ch // num_head_channels + dim_head = num_head_channels + if legacy: + #num_heads = 1 + dim_head = ch // num_heads if use_spatial_transformer else num_head_channels + self.middle_block = TimestepEmbedSequential( + ResBlock( + ch, + time_embed_dim, + dropout, + dims=dims, + use_checkpoint=use_checkpoint, + use_scale_shift_norm=use_scale_shift_norm, + ), + AttentionBlock( + ch, + use_checkpoint=use_checkpoint, + num_heads=num_heads, + num_head_channels=dim_head, + use_new_attention_order=use_new_attention_order, + ) if not use_spatial_transformer else SpatialTransformer( + ch, num_heads, dim_head, depth=transformer_depth, context_dim=context_dim + ), + ResBlock( + ch, + time_embed_dim, + dropout, + dims=dims, + use_checkpoint=use_checkpoint, + use_scale_shift_norm=use_scale_shift_norm, + ), + ) + self._feature_size += ch + # upsample blocks + self.output_blocks = nn.ModuleList([]) + for level, mult in list(enumerate(channel_mult))[::-1]: + for i in range(num_res_blocks + 1): + ich = input_block_chans.pop() + layers = [ + ResBlock( + ch + ich, + time_embed_dim, + dropout, + out_channels=model_channels * mult, + dims=dims, + use_checkpoint=use_checkpoint, + use_scale_shift_norm=use_scale_shift_norm, + ) + ] + ch = model_channels * mult + if ds in attention_resolutions: + if num_head_channels == -1: + dim_head = ch // num_heads + else: + num_heads = ch // num_head_channels + dim_head = num_head_channels + if legacy: + #num_heads = 1 + dim_head = ch // num_heads if use_spatial_transformer else num_head_channels + layers.append( + AttentionBlock( + ch, + use_checkpoint=use_checkpoint, + num_heads=num_heads_upsample, + num_head_channels=dim_head, + use_new_attention_order=use_new_attention_order, + ) if not use_spatial_transformer else SpatialTransformer( + ch, num_heads, dim_head, depth=transformer_depth, context_dim=context_dim + ) + ) + if level and i == num_res_blocks: + out_ch = ch + layers.append( + ResBlock( + ch, + time_embed_dim, + dropout, + out_channels=out_ch, + dims=dims, + use_checkpoint=use_checkpoint, + use_scale_shift_norm=use_scale_shift_norm, + up=True, + ) + if resblock_updown + else Upsample(ch, conv_resample, dims=dims, out_channels=out_ch) + ) + ds //= 2 + self.output_blocks.append(TimestepEmbedSequential(*layers)) + self._feature_size += ch + + self.out = nn.Sequential( + normalization(ch), + nn.SiLU(), + zero_module(conv_nd(dims, model_channels, out_channels, 3, padding=1)), + ) + if self.predict_codebook_ids: + self.id_predictor = nn.Sequential( + normalization(ch), + conv_nd(dims, model_channels, n_embed, 1), + #nn.LogSoftmax(dim=1) # change to cross_entropy and produce non-normalized logits + ) + + def convert_to_fp16(self): + """ + Convert the torso of the model to float16. + """ + self.input_blocks.apply(convert_module_to_f16) + self.middle_block.apply(convert_module_to_f16) + self.output_blocks.apply(convert_module_to_f16) + + def convert_to_fp32(self): + """ + Convert the torso of the model to float32. + """ + self.input_blocks.apply(convert_module_to_f32) + self.middle_block.apply(convert_module_to_f32) + self.output_blocks.apply(convert_module_to_f32) + + def forward(self, x, timesteps=None, context=None, y=None,**kwargs): + """ + Apply the model to an input batch. + :param x: an [N x C x ...] Tensor of inputs. + :param timesteps: a 1-D batch of timesteps,shape [N] + :param context: conditioning plugged in via crossattn. for txt2img shape is [N,77,context_dim] + :param y: an [N] Tensor of labels, if class-conditional. + :return: an [N x C x ...] Tensor of outputs. + """ + # print(f"in unet {x.shape}") + assert (y is not None) == ( + self.num_classes is not None + ), "must specify y if and only if the model is class-conditional" + hs = [] + t_emb = timestep_embedding(timesteps, self.model_channels, repeat_only=False)# shape [N,self.model_channels] + emb = self.time_embed(t_emb)# shape [N,context_dim] + + if self.num_classes is not None:# only for class label + assert y.shape == (x.shape[0],) + emb = emb + self.label_emb(y) + + h = x.type(self.dtype)# [N,C,10,106] + for module in self.input_blocks: + h = module(h, emb, context)# 0:[N,self.model_channels,10,106],1:[N,self.model_channels,10,106],2:[N,self.model_channels,10,106] 3:[N,self.model_channels,5,53] 4:[N,self.model_channels,5,53] 5:[N,self.model_channels*2,5,53] + hs.append(h) + h = self.middle_block(h, emb, context)# no shape change + for module in self.output_blocks: + h = th.cat([h, hs.pop()], dim=1)# 在这里c维度乘2或+self.model_channels，其余维度不变 + h = module(h, emb, context)# 在这里c维度/2回到之前维度，h,w不变或*2 + h = h.type(x.dtype)# 至此h维度和输入x维度回到相同状态 + if self.predict_codebook_ids: + return self.id_predictor(h) + else: + return self.out(h) + + +class EncoderUNetModel(nn.Module): + """ + The half UNet model with attention and timestep embedding. + For usage, see UNet. + """ + + def __init__( + self, + image_size, + in_channels, + model_channels, + out_channels, + num_res_blocks, + attention_resolutions, + dropout=0, + channel_mult=(1, 2, 4, 8), + conv_resample=True, + dims=2, + use_checkpoint=False, + use_fp16=False, + num_heads=1, + num_head_channels=-1, + num_heads_upsample=-1, + use_scale_shift_norm=False, + resblock_updown=False, + use_new_attention_order=False, + pool="adaptive", + *args, + **kwargs + ): + super().__init__() + + if num_heads_upsample == -1: + num_heads_upsample = num_heads + + self.in_channels = in_channels + self.model_channels = model_channels + self.out_channels = out_channels + self.num_res_blocks = num_res_blocks + self.attention_resolutions = attention_resolutions + self.dropout = dropout + self.channel_mult = channel_mult + self.conv_resample = conv_resample + self.use_checkpoint = use_checkpoint + self.dtype = th.float16 if use_fp16 else th.float32 + self.num_heads = num_heads + self.num_head_channels = num_head_channels + self.num_heads_upsample = num_heads_upsample + + time_embed_dim = model_channels * 4 + self.time_embed = nn.Sequential( + linear(model_channels, time_embed_dim), + nn.SiLU(), + linear(time_embed_dim, time_embed_dim), + ) + + self.input_blocks = nn.ModuleList( + [ + TimestepEmbedSequential( + conv_nd(dims, in_channels, model_channels, 3, padding=1) + ) + ] + ) + self._feature_size = model_channels + input_block_chans = [model_channels] + ch = model_channels + ds = 1 + for level, mult in enumerate(channel_mult): + for _ in range(num_res_blocks): + layers = [ + ResBlock( + ch, + time_embed_dim, + dropout, + out_channels=mult * model_channels, + dims=dims, + use_checkpoint=use_checkpoint, + use_scale_shift_norm=use_scale_shift_norm, + ) + ] + ch = mult * model_channels + if ds in attention_resolutions: + layers.append( + AttentionBlock( + ch, + use_checkpoint=use_checkpoint, + num_heads=num_heads, + num_head_channels=num_head_channels, + use_new_attention_order=use_new_attention_order, + ) + ) + self.input_blocks.append(TimestepEmbedSequential(*layers)) + self._feature_size += ch + input_block_chans.append(ch) + if level != len(channel_mult) - 1: + out_ch = ch + self.input_blocks.append( + TimestepEmbedSequential( + ResBlock( + ch, + time_embed_dim, + dropout, + out_channels=out_ch, + dims=dims, + use_checkpoint=use_checkpoint, + use_scale_shift_norm=use_scale_shift_norm, + down=True, + ) + if resblock_updown + else Downsample( + ch, conv_resample, dims=dims, out_channels=out_ch + ) + ) + ) + ch = out_ch + input_block_chans.append(ch) + ds *= 2 + self._feature_size += ch + + self.middle_block = TimestepEmbedSequential( + ResBlock( + ch, + time_embed_dim, + dropout, + dims=dims, + use_checkpoint=use_checkpoint, + use_scale_shift_norm=use_scale_shift_norm, + ), + AttentionBlock( + ch, + use_checkpoint=use_checkpoint, + num_heads=num_heads, + num_head_channels=num_head_channels, + use_new_attention_order=use_new_attention_order, + ), + ResBlock( + ch, + time_embed_dim, + dropout, + dims=dims, + use_checkpoint=use_checkpoint, + use_scale_shift_norm=use_scale_shift_norm, + ), + ) + self._feature_size += ch + self.pool = pool + if pool == "adaptive": + self.out = nn.Sequential( + normalization(ch), + nn.SiLU(), + nn.AdaptiveAvgPool2d((1, 1)), + zero_module(conv_nd(dims, ch, out_channels, 1)), + nn.Flatten(), + ) + elif pool == "attention": + assert num_head_channels != -1 + self.out = nn.Sequential( + normalization(ch), + nn.SiLU(), + AttentionPool2d( + (image_size // ds), ch, num_head_channels, out_channels + ), + ) + elif pool == "spatial": + self.out = nn.Sequential( + nn.Linear(self._feature_size, 2048), + nn.ReLU(), + nn.Linear(2048, self.out_channels), + ) + elif pool == "spatial_v2": + self.out = nn.Sequential( + nn.Linear(self._feature_size, 2048), + normalization(2048), + nn.SiLU(), + nn.Linear(2048, self.out_channels), + ) + else: + raise NotImplementedError(f"Unexpected {pool} pooling") + + def convert_to_fp16(self): + """ + Convert the torso of the model to float16. + """ + self.input_blocks.apply(convert_module_to_f16) + self.middle_block.apply(convert_module_to_f16) + + def convert_to_fp32(self): + """ + Convert the torso of the model to float32. + """ + self.input_blocks.apply(convert_module_to_f32) + self.middle_block.apply(convert_module_to_f32) + + def forward(self, x, timesteps): + """ + Apply the model to an input batch. + :param x: an [N x C x ...] Tensor of inputs. + :param timesteps: a 1-D batch of timesteps. + :return: an [N x K] Tensor of outputs. + """ + emb = self.time_embed(timestep_embedding(timesteps, self.model_channels)) + + results = [] + h = x.type(self.dtype) + for module in self.input_blocks: + h = module(h, emb) + if self.pool.startswith("spatial"): + results.append(h.type(x.dtype).mean(dim=(2, 3))) + h = self.middle_block(h, emb) + if self.pool.startswith("spatial"): + results.append(h.type(x.dtype).mean(dim=(2, 3))) + h = th.cat(results, axis=-1) + return self.out(h) + else: + h = h.type(x.dtype) + return self.out(h) + diff --git a/ldm/modules/diffusionmodules/util.py b/ldm/modules/diffusionmodules/util.py new file mode 100644 index 0000000000000000000000000000000000000000..a952e6c40308c33edd422da0ce6a60f47e73661b --- /dev/null +++ b/ldm/modules/diffusionmodules/util.py @@ -0,0 +1,267 @@ +# adopted from +# https://github.com/openai/improved-diffusion/blob/main/improved_diffusion/gaussian_diffusion.py +# and +# https://github.com/lucidrains/denoising-diffusion-pytorch/blob/7706bdfc6f527f58d33f84b7b522e61e6e3164b3/denoising_diffusion_pytorch/denoising_diffusion_pytorch.py +# and +# https://github.com/openai/guided-diffusion/blob/0ba878e517b276c45d1195eb29f6f5f72659a05b/guided_diffusion/nn.py +# +# thanks! + + +import os +import math +import torch +import torch.nn as nn +import numpy as np +from einops import repeat + +from ldm.util import instantiate_from_config + + +def make_beta_schedule(schedule, n_timestep, linear_start=1e-4, linear_end=2e-2, cosine_s=8e-3): + if schedule == "linear": + betas = ( + torch.linspace(linear_start ** 0.5, linear_end ** 0.5, n_timestep, dtype=torch.float64) ** 2 + ) + + elif schedule == "cosine": + timesteps = ( + torch.arange(n_timestep + 1, dtype=torch.float64) / n_timestep + cosine_s + ) + alphas = timesteps / (1 + cosine_s) * np.pi / 2 + alphas = torch.cos(alphas).pow(2) + alphas = alphas / alphas[0] + betas = 1 - alphas[1:] / alphas[:-1] + betas = np.clip(betas, a_min=0, a_max=0.999) + + elif schedule == "sqrt_linear": + betas = torch.linspace(linear_start, linear_end, n_timestep, dtype=torch.float64) + elif schedule == "sqrt": + betas = torch.linspace(linear_start, linear_end, n_timestep, dtype=torch.float64) ** 0.5 + else: + raise ValueError(f"schedule '{schedule}' unknown.") + return betas.numpy() + + +def make_ddim_timesteps(ddim_discr_method, num_ddim_timesteps, num_ddpm_timesteps, verbose=True): + if ddim_discr_method == 'uniform': + c = num_ddpm_timesteps // num_ddim_timesteps + ddim_timesteps = np.asarray(list(range(0, num_ddpm_timesteps, c))) + elif ddim_discr_method == 'quad': + ddim_timesteps = ((np.linspace(0, np.sqrt(num_ddpm_timesteps * .8), num_ddim_timesteps)) ** 2).astype(int) + else: + raise NotImplementedError(f'There is no ddim discretization method called "{ddim_discr_method}"') + + # assert ddim_timesteps.shape[0] == num_ddim_timesteps + # add one to get the final alpha values right (the ones from first scale to data during sampling) + steps_out = ddim_timesteps + 1 + if verbose: + print(f'Selected timesteps for ddim sampler: {steps_out}') + return steps_out + + +def make_ddim_sampling_parameters(alphacums, ddim_timesteps, eta, verbose=True): + # select alphas for computing the variance schedule + alphas = alphacums[ddim_timesteps] + alphas_prev = np.asarray([alphacums[0]] + alphacums[ddim_timesteps[:-1]].tolist()) + + # according the the formula provided in https://arxiv.org/abs/2010.02502 + sigmas = eta * np.sqrt((1 - alphas_prev) / (1 - alphas) * (1 - alphas / alphas_prev)) + if verbose: + print(f'Selected alphas for ddim sampler: a_t: {alphas}; a_(t-1): {alphas_prev}') + print(f'For the chosen value of eta, which is {eta}, ' + f'this results in the following sigma_t schedule for ddim sampler {sigmas}') + return sigmas, alphas, alphas_prev + + +def betas_for_alpha_bar(num_diffusion_timesteps, alpha_bar, max_beta=0.999): + """ + Create a beta schedule that discretizes the given alpha_t_bar function, + which defines the cumulative product of (1-beta) over time from t = [0,1]. + :param num_diffusion_timesteps: the number of betas to produce. + :param alpha_bar: a lambda that takes an argument t from 0 to 1 and + produces the cumulative product of (1-beta) up to that + part of the diffusion process. + :param max_beta: the maximum beta to use; use values lower than 1 to + prevent singularities. + """ + betas = [] + for i in range(num_diffusion_timesteps): + t1 = i / num_diffusion_timesteps + t2 = (i + 1) / num_diffusion_timesteps + betas.append(min(1 - alpha_bar(t2) / alpha_bar(t1), max_beta)) + return np.array(betas) + + +def extract_into_tensor(a, t, x_shape): + b, *_ = t.shape + out = a.gather(-1, t) + return out.reshape(b, *((1,) * (len(x_shape) - 1))) + + +def checkpoint(func, inputs, params, flag): + """ + Evaluate a function without caching intermediate activations, allowing for + reduced memory at the expense of extra compute in the backward pass. + :param func: the function to evaluate. + :param inputs: the argument sequence to pass to `func`. + :param params: a sequence of parameters `func` depends on but does not + explicitly take as arguments. + :param flag: if False, disable gradient checkpointing. + """ + if flag: + args = tuple(inputs) + tuple(params) + return CheckpointFunction.apply(func, len(inputs), *args) + else: + return func(*inputs) + + +class CheckpointFunction(torch.autograd.Function): + @staticmethod + def forward(ctx, run_function, length, *args): + ctx.run_function = run_function + ctx.input_tensors = list(args[:length]) + ctx.input_params = list(args[length:]) + + with torch.no_grad(): + output_tensors = ctx.run_function(*ctx.input_tensors) + return output_tensors + + @staticmethod + def backward(ctx, *output_grads): + ctx.input_tensors = [x.detach().requires_grad_(True) for x in ctx.input_tensors] + with torch.enable_grad(): + # Fixes a bug where the first op in run_function modifies the + # Tensor storage in place, which is not allowed for detach()'d + # Tensors. + shallow_copies = [x.view_as(x) for x in ctx.input_tensors] + output_tensors = ctx.run_function(*shallow_copies) + input_grads = torch.autograd.grad( + output_tensors, + ctx.input_tensors + ctx.input_params, + output_grads, + allow_unused=True, + ) + del ctx.input_tensors + del ctx.input_params + del output_tensors + return (None, None) + input_grads + + +def timestep_embedding(timesteps, dim, max_period=10000, repeat_only=False): + """ + Create sinusoidal timestep embeddings. + :param timesteps: a 1-D Tensor of N indices, one per batch element. + These may be fractional. + :param dim: the dimension of the output. + :param max_period: controls the minimum frequency of the embeddings. + :return: an [N x dim] Tensor of positional embeddings. + """ + if not repeat_only: + half = dim // 2 + freqs = torch.exp( + -math.log(max_period) * torch.arange(start=0, end=half, dtype=torch.float32) / half + ).to(device=timesteps.device) + args = timesteps[:, None].float() * freqs[None] + embedding = torch.cat([torch.cos(args), torch.sin(args)], dim=-1) + if dim % 2: + embedding = torch.cat([embedding, torch.zeros_like(embedding[:, :1])], dim=-1) + else: + embedding = repeat(timesteps, 'b -> b d', d=dim) + return embedding + + +def zero_module(module): + """ + Zero out the parameters of a module and return it. + """ + for p in module.parameters(): + p.detach().zero_() + return module + + +def scale_module(module, scale): + """ + Scale the parameters of a module and return it. + """ + for p in module.parameters(): + p.detach().mul_(scale) + return module + + +def mean_flat(tensor): + """ + Take the mean over all non-batch dimensions. + """ + return tensor.mean(dim=list(range(1, len(tensor.shape)))) + + +def normalization(channels): + """ + Make a standard normalization layer. + :param channels: number of input channels. + :return: an nn.Module for normalization. + """ + return GroupNorm32(32, channels) + + +# PyTorch 1.7 has SiLU, but we support PyTorch 1.5. +class SiLU(nn.Module): + def forward(self, x): + return x * torch.sigmoid(x) + + +class GroupNorm32(nn.GroupNorm): + def forward(self, x): + return super().forward(x.float()).type(x.dtype) + +def conv_nd(dims, *args, **kwargs): + """ + Create a 1D, 2D, or 3D convolution module. + """ + if dims == 1: + return nn.Conv1d(*args, **kwargs) + elif dims == 2: + return nn.Conv2d(*args, **kwargs) + elif dims == 3: + return nn.Conv3d(*args, **kwargs) + raise ValueError(f"unsupported dimensions: {dims}") + + +def linear(*args, **kwargs): + """ + Create a linear module. + """ + return nn.Linear(*args, **kwargs) + + +def avg_pool_nd(dims, *args, **kwargs): + """ + Create a 1D, 2D, or 3D average pooling module. + """ + if dims == 1: + return nn.AvgPool1d(*args, **kwargs) + elif dims == 2: + return nn.AvgPool2d(*args, **kwargs) + elif dims == 3: + return nn.AvgPool3d(*args, **kwargs) + raise ValueError(f"unsupported dimensions: {dims}") + + +class HybridConditioner(nn.Module): + + def __init__(self, c_concat_config, c_crossattn_config): + super().__init__() + self.concat_conditioner = instantiate_from_config(c_concat_config) + self.crossattn_conditioner = instantiate_from_config(c_crossattn_config) + + def forward(self, c_concat, c_crossattn): + c_concat = self.concat_conditioner(c_concat) + c_crossattn = self.crossattn_conditioner(c_crossattn) + return {'c_concat': [c_concat], 'c_crossattn': [c_crossattn]} + + +def noise_like(shape, device, repeat=False): + repeat_noise = lambda: torch.randn((1, *shape[1:]), device=device).repeat(shape[0], *((1,) * (len(shape) - 1))) + noise = lambda: torch.randn(shape, device=device) + return repeat_noise() if repeat else noise() \ No newline at end of file diff --git a/ldm/modules/discriminator/__pycache__/model.cpython-38.pyc b/ldm/modules/discriminator/__pycache__/model.cpython-38.pyc new file mode 100644 index 0000000000000000000000000000000000000000..cc359ece2d861243c84819eb8f557d8724d112f3 Binary files /dev/null and b/ldm/modules/discriminator/__pycache__/model.cpython-38.pyc differ diff --git a/ldm/modules/discriminator/__pycache__/model.cpython-39.pyc b/ldm/modules/discriminator/__pycache__/model.cpython-39.pyc new file mode 100644 index 0000000000000000000000000000000000000000..adbf67f6171fd90995fc61d14fb30beb4971a947 Binary files /dev/null and b/ldm/modules/discriminator/__pycache__/model.cpython-39.pyc differ diff --git a/ldm/modules/discriminator/__pycache__/multi_window_disc.cpython-38.pyc b/ldm/modules/discriminator/__pycache__/multi_window_disc.cpython-38.pyc new file mode 100644 index 0000000000000000000000000000000000000000..e0528148df879a9f2bba97b82438c352f0cfc733 Binary files /dev/null and b/ldm/modules/discriminator/__pycache__/multi_window_disc.cpython-38.pyc differ diff --git a/ldm/modules/discriminator/model.py b/ldm/modules/discriminator/model.py new file mode 100644 index 0000000000000000000000000000000000000000..5263368a5e74d9d07840399469ca12a54e7fecbc --- /dev/null +++ b/ldm/modules/discriminator/model.py @@ -0,0 +1,295 @@ +import functools +import torch.nn as nn + + +class ActNorm(nn.Module): + def __init__(self, num_features, logdet=False, affine=True, + allow_reverse_init=False): + assert affine + super().__init__() + self.logdet = logdet + self.loc = nn.Parameter(torch.zeros(1, num_features, 1, 1)) + self.scale = nn.Parameter(torch.ones(1, num_features, 1, 1)) + self.allow_reverse_init = allow_reverse_init + + self.register_buffer('initialized', torch.tensor(0, dtype=torch.uint8)) + + def initialize(self, input): + with torch.no_grad(): + flatten = input.permute(1, 0, 2, 3).contiguous().view(input.shape[1], -1) + mean = ( + flatten.mean(1) + .unsqueeze(1) + .unsqueeze(2) + .unsqueeze(3) + .permute(1, 0, 2, 3) + ) + std = ( + flatten.std(1) + .unsqueeze(1) + .unsqueeze(2) + .unsqueeze(3) + .permute(1, 0, 2, 3) + ) + + self.loc.data.copy_(-mean) + self.scale.data.copy_(1 / (std + 1e-6)) + + def forward(self, input, reverse=False): + if reverse: + return self.reverse(input) + if len(input.shape) == 2: + input = input[:, :, None, None] + squeeze = True + else: + squeeze = False + + _, _, height, width = input.shape + + if self.training and self.initialized.item() == 0: + self.initialize(input) + self.initialized.fill_(1) + + h = self.scale * (input + self.loc) + + if squeeze: + h = h.squeeze(-1).squeeze(-1) + + if self.logdet: + log_abs = torch.log(torch.abs(self.scale)) + logdet = height * width * torch.sum(log_abs) + logdet = logdet * torch.ones(input.shape[0]).to(input) + return h, logdet + + return h + + def reverse(self, output): + if self.training and self.initialized.item() == 0: + if not self.allow_reverse_init: + raise RuntimeError( + "Initializing ActNorm in reverse direction is " + "disabled by default. Use allow_reverse_init=True to enable." + ) + else: + self.initialize(output) + self.initialized.fill_(1) + + if len(output.shape) == 2: + output = output[:, :, None, None] + squeeze = True + else: + squeeze = False + + h = output / self.scale - self.loc + + if squeeze: + h = h.squeeze(-1).squeeze(-1) + return h + +def weights_init(m): + classname = m.__class__.__name__ + if classname.find('Conv') != -1: + nn.init.normal_(m.weight.data, 0.0, 0.02) + elif classname.find('BatchNorm') != -1: + nn.init.normal_(m.weight.data, 1.0, 0.02) + nn.init.constant_(m.bias.data, 0) + + +class NLayerDiscriminator(nn.Module): + """Defines a PatchGAN discriminator as in Pix2Pix + --> see https://github.com/junyanz/pytorch-CycleGAN-and-pix2pix/blob/master/models/networks.py + """ + def __init__(self, input_nc=3, ndf=64, n_layers=3, use_actnorm=False): + """Construct a PatchGAN discriminator + Parameters: + input_nc (int) -- the number of channels in input images + ndf (int) -- the number of filters in the last conv layer + n_layers (int) -- the number of conv layers in the discriminator + norm_layer -- normalization layer + """ + super(NLayerDiscriminator, self).__init__() + if not use_actnorm: + norm_layer = nn.BatchNorm2d + else: + norm_layer = ActNorm + if type(norm_layer) == functools.partial: # no need to use bias as BatchNorm2d has affine parameters + use_bias = norm_layer.func != nn.BatchNorm2d + else: + use_bias = norm_layer != nn.BatchNorm2d + + kw = 4 + padw = 1 + sequence = [nn.Conv2d(input_nc, ndf, kernel_size=kw, stride=2, padding=padw), nn.LeakyReLU(0.2, True)] + nf_mult = 1 + nf_mult_prev = 1 + for n in range(1, n_layers): # gradually increase the number of filters + nf_mult_prev = nf_mult + nf_mult = min(2 ** n, 8) + sequence += [ + nn.Conv2d(ndf * nf_mult_prev, ndf * nf_mult, kernel_size=kw, stride=2, padding=padw, bias=use_bias), + norm_layer(ndf * nf_mult), + nn.LeakyReLU(0.2, True) + ] + + nf_mult_prev = nf_mult + nf_mult = min(2 ** n_layers, 8) + sequence += [ + nn.Conv2d(ndf * nf_mult_prev, ndf * nf_mult, kernel_size=kw, stride=1, padding=padw, bias=use_bias), + norm_layer(ndf * nf_mult), + nn.LeakyReLU(0.2, True) + ] + # output 1 channel prediction map + sequence += [nn.Conv2d(ndf * nf_mult, 1, kernel_size=kw, stride=1, padding=padw)] + self.main = nn.Sequential(*sequence) + + def forward(self, input): + """Standard forward.""" + return self.main(input) + +class NLayerDiscriminator1dFeats(NLayerDiscriminator): + """Defines a PatchGAN discriminator as in Pix2Pix + --> see https://github.com/junyanz/pytorch-CycleGAN-and-pix2pix/blob/master/models/networks.py + """ + def __init__(self, input_nc=3, ndf=64, n_layers=3, use_actnorm=False): + """Construct a PatchGAN discriminator + Parameters: + input_nc (int) -- the number of channels in input feats + ndf (int) -- the number of filters in the last conv layer + n_layers (int) -- the number of conv layers in the discriminator + norm_layer -- normalization layer + """ + super().__init__(input_nc=input_nc, ndf=64, n_layers=n_layers, use_actnorm=use_actnorm) + + if not use_actnorm: + norm_layer = nn.BatchNorm1d + else: + norm_layer = ActNorm + if type(norm_layer) == functools.partial: # no need to use bias as BatchNorm has affine parameters + use_bias = norm_layer.func != nn.BatchNorm1d + else: + use_bias = norm_layer != nn.BatchNorm1d + + kw = 4 + padw = 1 + sequence = [nn.Conv1d(input_nc, input_nc//2, kernel_size=kw, stride=2, padding=padw), nn.LeakyReLU(0.2, True)] + nf_mult = input_nc//2 + nf_mult_prev = 1 + for n in range(1, n_layers): # gradually decrease the number of filters + nf_mult_prev = nf_mult + nf_mult = max(nf_mult_prev // (2 ** n), 8) + sequence += [ + nn.Conv1d(nf_mult_prev, nf_mult, kernel_size=kw, stride=2, padding=padw, bias=use_bias), + norm_layer(nf_mult), + nn.LeakyReLU(0.2, True) + ] + + nf_mult_prev = nf_mult + nf_mult = max(nf_mult_prev // (2 ** n), 8) + sequence += [ + nn.Conv1d(nf_mult_prev, nf_mult, kernel_size=kw, stride=1, padding=padw, bias=use_bias), + norm_layer(nf_mult), + nn.LeakyReLU(0.2, True) + ] + nf_mult_prev = nf_mult + nf_mult = max(nf_mult_prev // (2 ** n), 8) + sequence += [ + nn.Conv1d(nf_mult_prev, nf_mult, kernel_size=kw, stride=1, padding=padw, bias=use_bias), + norm_layer(nf_mult), + nn.LeakyReLU(0.2, True) + ] + # output 1 channel prediction map + sequence += [nn.Conv1d(nf_mult, 1, kernel_size=kw, stride=1, padding=padw)] + self.main = nn.Sequential(*sequence) + + +class NLayerDiscriminator1dSpecs(NLayerDiscriminator): + """Defines a PatchGAN discriminator as in Pix2Pix + --> see https://github.com/junyanz/pytorch-CycleGAN-and-pix2pix/blob/master/models/networks.py + """ + def __init__(self, input_nc=80, ndf=64, n_layers=3, use_actnorm=False): + """Construct a PatchGAN discriminator + Parameters: + input_nc (int) -- the number of channels in input specs + ndf (int) -- the number of filters in the last conv layer + n_layers (int) -- the number of conv layers in the discriminator + norm_layer -- normalization layer + """ + super().__init__(input_nc=input_nc, ndf=64, n_layers=n_layers, use_actnorm=use_actnorm) + + if not use_actnorm: + norm_layer = nn.BatchNorm1d + else: + norm_layer = ActNorm + if type(norm_layer) == functools.partial: # no need to use bias as BatchNorm has affine parameters + use_bias = norm_layer.func != nn.BatchNorm1d + else: + use_bias = norm_layer != nn.BatchNorm1d + + kw = 4 + padw = 1 + sequence = [nn.Conv1d(input_nc, ndf, kernel_size=kw, stride=2, padding=padw), nn.LeakyReLU(0.2, True)] + nf_mult = 1 + nf_mult_prev = 1 + for n in range(1, n_layers): # gradually decrease the number of filters + nf_mult_prev = nf_mult + nf_mult = min(2 ** n, 8) + sequence += [ + nn.Conv1d(ndf * nf_mult_prev, ndf * nf_mult, kernel_size=kw, stride=2, padding=padw, bias=use_bias), + norm_layer(ndf * nf_mult), + nn.LeakyReLU(0.2, True) + ] + + nf_mult_prev = nf_mult + nf_mult = min(2 ** n_layers, 8) + sequence += [ + nn.Conv1d(ndf * nf_mult_prev, ndf * nf_mult, kernel_size=kw, stride=1, padding=padw, bias=use_bias), + norm_layer(ndf * nf_mult), + nn.LeakyReLU(0.2, True) + ] + # output 1 channel prediction map + sequence += [nn.Conv1d(ndf * nf_mult, 1, kernel_size=kw, stride=1, padding=padw)] + self.main = nn.Sequential(*sequence) + + def forward(self, input): + """Standard forward.""" + # (B, C, L) + input = input.squeeze(1) + input = self.main(input) + return input + + +if __name__ == '__main__': + import torch + + ## FEATURES + disc_in_channels = 2048 + disc_num_layers = 2 + use_actnorm = False + disc_ndf = 64 + discriminator = NLayerDiscriminator1dFeats(input_nc=disc_in_channels, n_layers=disc_num_layers, + use_actnorm=use_actnorm, ndf=disc_ndf).apply(weights_init) + inputs = torch.rand((6, 2048, 212)) + outputs = discriminator(inputs) + print(outputs.shape) + + ## AUDIO + disc_in_channels = 1 + disc_num_layers = 3 + use_actnorm = False + disc_ndf = 64 + discriminator = NLayerDiscriminator(input_nc=disc_in_channels, n_layers=disc_num_layers, + use_actnorm=use_actnorm, ndf=disc_ndf).apply(weights_init) + inputs = torch.rand((6, 1, 80, 848)) + outputs = discriminator(inputs) + print(outputs.shape) + + ## IMAGE + disc_in_channels = 3 + disc_num_layers = 3 + use_actnorm = False + disc_ndf = 64 + discriminator = NLayerDiscriminator(input_nc=disc_in_channels, n_layers=disc_num_layers, + use_actnorm=use_actnorm, ndf=disc_ndf).apply(weights_init) + inputs = torch.rand((6, 3, 256, 256)) + outputs = discriminator(inputs) + print(outputs.shape) diff --git a/ldm/modules/discriminator/multi_window_disc.py b/ldm/modules/discriminator/multi_window_disc.py new file mode 100644 index 0000000000000000000000000000000000000000..1aef6493c90c7cf5206ff92f7fe8831a0821664f --- /dev/null +++ b/ldm/modules/discriminator/multi_window_disc.py @@ -0,0 +1,196 @@ +import numpy as np +import torch +import torch.nn as nn + + +class Discriminator2DFactory(nn.Module): + def __init__(self, time_length, freq_length=80, kernel=(3, 3), c_in=1, hidden_size=128, + norm_type='bn', reduction='sum'): + super(Discriminator2DFactory, self).__init__() + padding = (kernel[0] // 2, kernel[1] // 2) + + def discriminator_block(in_filters, out_filters, first=False): + """ + Input: (B, in, 2H, 2W) + Output:(B, out, H, W) + """ + conv = nn.Conv2d(in_filters, out_filters, kernel, (2, 2), padding) + if norm_type == 'sn': + conv = nn.utils.spectral_norm(conv) + block = [ + conv, # padding = kernel//2 + nn.LeakyReLU(0.2, inplace=True), + nn.Dropout2d(0.25) + ] + if norm_type == 'bn' and not first: + block.append(nn.BatchNorm2d(out_filters, 0.8)) + if norm_type == 'in' and not first: + block.append(nn.InstanceNorm2d(out_filters, affine=True)) + block = nn.Sequential(*block) + return block + + self.model = nn.ModuleList([ + discriminator_block(c_in, hidden_size, first=True), + discriminator_block(hidden_size, hidden_size), + discriminator_block(hidden_size, hidden_size), + ]) + + self.reduction = reduction + ds_size = (time_length // 2 ** 3, (freq_length + 7) // 2 ** 3) + if reduction != 'none': + # The height and width of downsampled image + self.adv_layer = nn.Linear(hidden_size * ds_size[0] * ds_size[1], 1) + else: + self.adv_layer = nn.Linear(hidden_size * ds_size[1], 1) + + def forward(self, x): + """ + + :param x: [B, C, T, n_bins] + :return: validity: [B, 1], h: List of hiddens + """ + h = [] + for l in self.model: + x = l(x) + h.append(x) + if self.reduction != 'none': + x = x.view(x.shape[0], -1) + validity = self.adv_layer(x) # [B, 1] + else: + B, _, T_, _ = x.shape + x = x.transpose(1, 2).reshape(B, T_, -1) + validity = self.adv_layer(x)[:, :, 0] # [B, T] + return validity, h + + +class MultiWindowDiscriminator(nn.Module): + def __init__(self, time_lengths, cond_size=0, freq_length=80, kernel=(3, 3), + c_in=1, hidden_size=128, norm_type='bn', reduction='sum'): + super(MultiWindowDiscriminator, self).__init__() + self.win_lengths = time_lengths + self.reduction = reduction + + self.conv_layers = nn.ModuleList() + if cond_size > 0: + self.cond_proj_layers = nn.ModuleList() + self.mel_proj_layers = nn.ModuleList() + for time_length in time_lengths: + conv_layer = [ + Discriminator2DFactory( + time_length, freq_length, kernel, c_in=c_in, hidden_size=hidden_size, + norm_type=norm_type, reduction=reduction) + ] + self.conv_layers += conv_layer + if cond_size > 0: + self.cond_proj_layers.append(nn.Linear(cond_size, freq_length)) + self.mel_proj_layers.append(nn.Linear(freq_length, freq_length)) + + def forward(self, x, x_len, cond=None, start_frames_wins=None): + ''' + Args: + x (tensor): input mel, (B, c_in, T, n_bins). + x_length (tensor): len of per mel. (B,). + + Returns: + tensor : (B). + ''' + validity = [] + if start_frames_wins is None: + start_frames_wins = [None] * len(self.conv_layers) + h = [] + for i, start_frames in zip(range(len(self.conv_layers)), start_frames_wins): + x_clip, c_clip, start_frames = self.clip( + x, cond, x_len, self.win_lengths[i], start_frames) # (B, win_length, C) + start_frames_wins[i] = start_frames + if x_clip is None: + continue + if cond is not None: + x_clip = self.mel_proj_layers[i](x_clip) # (B, 1, win_length, C) + c_clip = self.cond_proj_layers[i](c_clip)[:, None] # (B, 1, win_length, C) + x_clip = x_clip + c_clip + x_clip, h_ = self.conv_layers[i](x_clip) + h += h_ + validity.append(x_clip) + if len(validity) != len(self.conv_layers): + return None, start_frames_wins, h + if self.reduction == 'sum': + validity = sum(validity) # [B] + elif self.reduction == 'stack': + validity = torch.stack(validity, -1) # [B, W_L] + elif self.reduction == 'none': + validity = torch.cat(validity, -1) # [B, W_sum] + return validity, start_frames_wins, h + + def clip(self, x, cond, x_len, win_length, start_frames=None): + '''Ramdom clip x to win_length. + Args: + x (tensor) : (B, c_in, T, n_bins). + cond (tensor) : (B, T, H). + x_len (tensor) : (B,). + win_length (int): target clip length + + Returns: + (tensor) : (B, c_in, win_length, n_bins). + + ''' + T_start = 0 + T_end = x_len.max() - win_length + if T_end < 0: + return None, None, start_frames + T_end = T_end.item() + if start_frames is None: + start_frame = np.random.randint(low=T_start, high=T_end + 1) + start_frames = [start_frame] * x.size(0) + else: + start_frame = start_frames[0] + x_batch = x[:, :, start_frame: start_frame + win_length] + c_batch = cond[:, start_frame: start_frame + win_length] if cond is not None else None + return x_batch, c_batch, start_frames + + +class Discriminator(nn.Module): + def __init__(self, time_lengths=[32, 64, 128], freq_length=80, cond_size=0, kernel=(3, 3), c_in=1, + hidden_size=128, norm_type='bn', reduction='sum', uncond_disc=True): + super(Discriminator, self).__init__() + self.time_lengths = time_lengths + self.cond_size = cond_size + self.reduction = reduction + self.uncond_disc = uncond_disc + if uncond_disc: + self.discriminator = MultiWindowDiscriminator( + freq_length=freq_length, + time_lengths=time_lengths, + kernel=kernel, + c_in=c_in, hidden_size=hidden_size, norm_type=norm_type, + reduction=reduction + ) + if cond_size > 0: + self.cond_disc = MultiWindowDiscriminator( + freq_length=freq_length, + time_lengths=time_lengths, + cond_size=cond_size, + kernel=kernel, + c_in=c_in, hidden_size=hidden_size, norm_type=norm_type, + reduction=reduction + ) + + def forward(self, x, cond=None, start_frames_wins=None): + """ + + :param x: [B, T, 80] + :param cond: [B, T, cond_size] + :param return_y_only: + :return: + """ + if len(x.shape) == 3: + x = x[:, None, :, :] + x_len = x.sum([1, -1]).ne(0).int().sum([-1]) + ret = {'y_c': None, 'y': None} + if self.uncond_disc: + ret['y'], start_frames_wins, ret['h'] = self.discriminator( + x, x_len, start_frames_wins=start_frames_wins) + if self.cond_size > 0 and cond is not None: + ret['y_c'], start_frames_wins, ret['h_c'] = self.cond_disc( + x, x_len, cond, start_frames_wins=start_frames_wins) + ret['start_frames_wins'] = start_frames_wins + return ret \ No newline at end of file diff --git a/ldm/modules/distributions/__init__.py b/ldm/modules/distributions/__init__.py new file mode 100644 index 0000000000000000000000000000000000000000..e69de29bb2d1d6434b8b29ae775ad8c2e48c5391 diff --git a/ldm/modules/distributions/__pycache__/__init__.cpython-38.pyc b/ldm/modules/distributions/__pycache__/__init__.cpython-38.pyc new file mode 100644 index 0000000000000000000000000000000000000000..be75ad64e873a3be2d7ab5baec2d05a879b68c2c Binary files /dev/null and b/ldm/modules/distributions/__pycache__/__init__.cpython-38.pyc differ diff --git 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b/ldm/modules/distributions/__pycache__/distributions.cpython-39.pyc differ diff --git a/ldm/modules/distributions/distributions.py b/ldm/modules/distributions/distributions.py new file mode 100644 index 0000000000000000000000000000000000000000..f2b8ef901130efc171aa69742ca0244d94d3f2e9 --- /dev/null +++ b/ldm/modules/distributions/distributions.py @@ -0,0 +1,92 @@ +import torch +import numpy as np + + +class AbstractDistribution: + def sample(self): + raise NotImplementedError() + + def mode(self): + raise NotImplementedError() + + +class DiracDistribution(AbstractDistribution): + def __init__(self, value): + self.value = value + + def sample(self): + return self.value + + def mode(self): + return self.value + + +class DiagonalGaussianDistribution(object): + def __init__(self, parameters, deterministic=False): + self.parameters = parameters + self.mean, self.logvar = torch.chunk(parameters, 2, dim=1) + self.logvar = torch.clamp(self.logvar, -30.0, 20.0) + self.deterministic = deterministic + self.std = torch.exp(0.5 * self.logvar) + self.var = torch.exp(self.logvar) + if self.deterministic: + self.var = self.std = torch.zeros_like(self.mean).to(device=self.parameters.device) + + def sample(self): + x = self.mean + self.std * torch.randn(self.mean.shape).to(device=self.parameters.device) + return x + + def kl(self, other=None): + if self.deterministic: + return torch.Tensor([0.]) + else: + if other is None: + return 0.5 * torch.sum(torch.pow(self.mean, 2) + + self.var - 1.0 - self.logvar, + dim=[1, 2, 3]) + else: + return 0.5 * torch.sum( + torch.pow(self.mean - other.mean, 2) / other.var + + self.var / other.var - 1.0 - self.logvar + other.logvar, + dim=[1, 2, 3]) + + def nll(self, sample, dims=[1,2,3]): + if self.deterministic: + return torch.Tensor([0.]) + logtwopi = np.log(2.0 * np.pi) + return 0.5 * torch.sum( + logtwopi + self.logvar + torch.pow(sample - self.mean, 2) / self.var, + dim=dims) + + def mode(self): + return self.mean + + +def normal_kl(mean1, logvar1, mean2, logvar2): + """ + source: https://github.com/openai/guided-diffusion/blob/27c20a8fab9cb472df5d6bdd6c8d11c8f430b924/guided_diffusion/losses.py#L12 + Compute the KL divergence between two gaussians. + Shapes are automatically broadcasted, so batches can be compared to + scalars, among other use cases. + """ + tensor = None + for obj in (mean1, logvar1, mean2, logvar2): + if isinstance(obj, torch.Tensor): + tensor = obj + break + assert tensor is not None, "at least one argument must be a Tensor" + + # Force variances to be Tensors. Broadcasting helps convert scalars to + # Tensors, but it does not work for torch.exp(). + logvar1, logvar2 = [ + x if isinstance(x, torch.Tensor) else torch.tensor(x).to(tensor) + for x in (logvar1, logvar2) + ] + + return 0.5 * ( + -1.0 + + logvar2 + - logvar1 + + torch.exp(logvar1 - logvar2) + + ((mean1 - mean2) ** 2) * torch.exp(-logvar2) + ) diff --git a/ldm/modules/ema.py b/ldm/modules/ema.py new file mode 100644 index 0000000000000000000000000000000000000000..c8c75af43565f6e140287644aaaefa97dd6e67c5 --- /dev/null +++ b/ldm/modules/ema.py @@ -0,0 +1,76 @@ +import torch +from torch import nn + + +class LitEma(nn.Module): + def __init__(self, model, decay=0.9999, use_num_upates=True): + super().__init__() + if decay < 0.0 or decay > 1.0: + raise ValueError('Decay must be between 0 and 1') + + self.m_name2s_name = {} + self.register_buffer('decay', torch.tensor(decay, dtype=torch.float32)) + self.register_buffer('num_updates', torch.tensor(0,dtype=torch.int) if use_num_upates + else torch.tensor(-1,dtype=torch.int)) + + for name, p in model.named_parameters(): + if p.requires_grad: + #remove as '.'-character is not allowed in buffers + s_name = name.replace('.','') + self.m_name2s_name.update({name:s_name}) + self.register_buffer(s_name,p.clone().detach().data) + + self.collected_params = [] + + def forward(self,model): + decay = self.decay + + if self.num_updates >= 0: + self.num_updates += 1 + decay = min(self.decay,(1 + self.num_updates) / (10 + self.num_updates)) + + one_minus_decay = 1.0 - decay + + with torch.no_grad(): + m_param = dict(model.named_parameters()) + shadow_params = dict(self.named_buffers()) + + for key in m_param: + if m_param[key].requires_grad: + sname = self.m_name2s_name[key] + shadow_params[sname] = shadow_params[sname].type_as(m_param[key]) + shadow_params[sname].sub_(one_minus_decay * (shadow_params[sname] - m_param[key])) + else: + assert not key in self.m_name2s_name + + def copy_to(self, model): + m_param = dict(model.named_parameters()) + shadow_params = dict(self.named_buffers()) + for key in m_param: + if m_param[key].requires_grad: + m_param[key].data.copy_(shadow_params[self.m_name2s_name[key]].data) + else: + assert not key in self.m_name2s_name + + def store(self, parameters): + """ + Save the current parameters for restoring later. + Args: + parameters: Iterable of `torch.nn.Parameter`; the parameters to be + temporarily stored. + """ + self.collected_params = [param.clone() for param in parameters] + + def restore(self, parameters): + """ + Restore the parameters stored with the `store` method. + Useful to validate the model with EMA parameters without affecting the + original optimization process. Store the parameters before the + `copy_to` method. After validation (or model saving), use this to + restore the former parameters. + Args: + parameters: Iterable of `torch.nn.Parameter`; the parameters to be + updated with the stored parameters. + """ + for c_param, param in zip(self.collected_params, parameters): + param.data.copy_(c_param.data) diff --git a/ldm/modules/encoders/CLAP/CLAPWrapper.py b/ldm/modules/encoders/CLAP/CLAPWrapper.py new file mode 100644 index 0000000000000000000000000000000000000000..b26af847dcfdd314d10aa2c795362deac1e1fac7 --- /dev/null +++ b/ldm/modules/encoders/CLAP/CLAPWrapper.py @@ -0,0 +1,257 @@ +import random +import torchaudio +from torch._six import string_classes +import collections +import re +import torch.nn.functional as F +import numpy as np +from transformers import AutoTokenizer +from ldm.modules.encoders.CLAP.utils import read_config_as_args +from ldm.modules.encoders.CLAP.clap import CLAP +import math +import torchaudio.transforms as T +import os +import torch +from importlib_resources import files + + +class CLAPWrapper(): + """ + A class for interfacing CLAP model. + """ + + def __init__(self, model_fp, device): + self.np_str_obj_array_pattern = re.compile(r'[SaUO]') + self.file_path = os.path.realpath(__file__) + self.default_collate_err_msg_format = ( + "default_collate: batch must contain tensors, numpy arrays, numbers, " + "dicts or lists; found {}") + self.config_as_str = files('ldm').joinpath('modules/encoders/CLAP/config.yml').read_text() + self.model_fp = model_fp + self.device = device + self.clap, self.tokenizer, self.args = self.load_clap() + + def load_clap(self): + r"""Load CLAP model with args from config file""" + + args = read_config_as_args(self.config_as_str, is_config_str=True) + + if 'bert' in args.text_model: + self.token_keys = ['input_ids', 'token_type_ids', 'attention_mask'] + else: + self.token_keys = ['input_ids', 'attention_mask'] + + clap = CLAP( + audioenc_name=args.audioenc_name, + sample_rate=args.sampling_rate, + window_size=args.window_size, + hop_size=args.hop_size, + mel_bins=args.mel_bins, + fmin=args.fmin, + fmax=args.fmax, + classes_num=args.num_classes, + out_emb=args.out_emb, + text_model=args.text_model, + transformer_embed_dim=args.transformer_embed_dim, + d_proj=args.d_proj + ) + + # Load pretrained weights for model + model_state_dict = torch.load(self.model_fp, map_location=torch.device('cpu'))['model'] + clap.load_state_dict(model_state_dict) + + clap.eval() # set clap in eval mode + tokenizer = AutoTokenizer.from_pretrained(args.text_model) + + clap = clap.to(self.device) + tokenizer = tokenizer.to(self.device) + + return clap, tokenizer, args + + def default_collate(self, batch): + r"""Puts each data field into a tensor with outer dimension batch size""" + elem = batch[0] + elem_type = type(elem) + if isinstance(elem, torch.Tensor): + out = None + if torch.utils.data.get_worker_info() is not None: + # If we're in a background process, concatenate directly into a + # shared memory tensor to avoid an extra copy + numel = sum([x.numel() for x in batch]) + storage = elem.storage()._new_shared(numel) + out = elem.new(storage) + return torch.stack(batch, 0, out=out) + elif elem_type.__module__ == 'numpy' and elem_type.__name__ != 'str_' \ + and elem_type.__name__ != 'string_': + if elem_type.__name__ == 'ndarray' or elem_type.__name__ == 'memmap': + # array of string classes and object + if self.np_str_obj_array_pattern.search(elem.dtype.str) is not None: + raise TypeError( + self.default_collate_err_msg_format.format(elem.dtype)) + + return self.default_collate([torch.as_tensor(b) for b in batch]) + elif elem.shape == (): # scalars + return torch.as_tensor(batch) + elif isinstance(elem, float): + return torch.tensor(batch, dtype=torch.float64) + elif isinstance(elem, int): + return torch.tensor(batch) + elif isinstance(elem, string_classes): + return batch + elif isinstance(elem, collections.abc.Mapping): + return {key: self.default_collate([d[key] for d in batch]) for key in elem} + elif isinstance(elem, tuple) and hasattr(elem, '_fields'): # namedtuple + return elem_type(*(self.default_collate(samples) for samples in zip(*batch))) + elif isinstance(elem, collections.abc.Sequence): + # check to make sure that the elements in batch have consistent size + it = iter(batch) + elem_size = len(next(it)) + if not all(len(elem) == elem_size for elem in it): + raise RuntimeError( + 'each element in list of batch should be of equal size') + transposed = zip(*batch) + return [self.default_collate(samples) for samples in transposed] + + raise TypeError(self.default_collate_err_msg_format.format(elem_type)) + + def load_audio_into_tensor(self, audio_path, audio_duration, resample=False): + r"""Loads audio file and returns raw audio.""" + # Randomly sample a segment of audio_duration from the clip or pad to match duration + audio_time_series, sample_rate = torchaudio.load(audio_path) + resample_rate = self.args.sampling_rate + if resample: + resampler = T.Resample(sample_rate, resample_rate) + audio_time_series = resampler(audio_time_series) + audio_time_series = audio_time_series.reshape(-1) + + # audio_time_series is shorter than predefined audio duration, + # so audio_time_series is extended + if audio_duration*sample_rate >= audio_time_series.shape[0]: + repeat_factor = int(np.ceil((audio_duration*sample_rate) / + audio_time_series.shape[0])) + # Repeat audio_time_series by repeat_factor to match audio_duration + audio_time_series = audio_time_series.repeat(repeat_factor) + # remove excess part of audio_time_series + audio_time_series = audio_time_series[0:audio_duration*sample_rate] + else: + # audio_time_series is longer than predefined audio duration, + # so audio_time_series is trimmed + start_index = random.randrange( + audio_time_series.shape[0] - audio_duration*sample_rate) + audio_time_series = audio_time_series[start_index:start_index + + audio_duration*sample_rate] + return torch.FloatTensor(audio_time_series) + + def preprocess_audio(self, audio_files, resample): + r"""Load list of audio files and return raw audio""" + audio_tensors = [] + for audio_file in audio_files: + audio_tensor = self.load_audio_into_tensor( + audio_file, self.args.duration, resample) + audio_tensor = audio_tensor.reshape(1, -1).to(self.device) + audio_tensors.append(audio_tensor) + return self.default_collate(audio_tensors) + + def preprocess_text(self, text_queries, text_len=100): + r"""Load list of class labels and return tokenized text""" + device = next(self.clap.parameters()).device + tokenized_texts = [] + for ttext in text_queries: + tok = self.tokenizer.encode_plus( + text=ttext, add_special_tokens=True, max_length=text_len, pad_to_max_length=True, return_tensors="pt") + for key in self.token_keys: + tok[key] = tok[key].reshape(-1).to(device) + tokenized_texts.append(tok) + return self.default_collate(tokenized_texts) + + def get_text_embeddings(self, class_labels): + r"""Load list of class labels and return text embeddings""" + preprocessed_text = self.preprocess_text(class_labels) + text_embeddings = self._get_text_embeddings(preprocessed_text) + text_embeddings = text_embeddings/torch.norm(text_embeddings, dim=-1, keepdim=True) + return text_embeddings + + def get_audio_embeddings(self, audio_files, resample): + r"""Load list of audio files and return a audio embeddings""" + preprocessed_audio = self.preprocess_audio(audio_files, resample) + audio_embeddings = self._get_audio_embeddings(preprocessed_audio) + audio_embeddings = audio_embeddings/torch.norm(audio_embeddings, dim=-1, keepdim=True) + return audio_embeddings + + def _get_text_embeddings(self, preprocessed_text): + r"""Load preprocessed text and return text embeddings""" + with torch.no_grad(): + text_embeddings = self.clap.caption_encoder(preprocessed_text) + text_embeddings = text_embeddings/torch.norm(text_embeddings, dim=-1, keepdim=True) + return text_embeddings + + def _get_audio_embeddings(self, preprocessed_audio): + r"""Load preprocessed audio and return a audio embeddings""" + with torch.no_grad(): + preprocessed_audio = preprocessed_audio.reshape( + preprocessed_audio.shape[0], preprocessed_audio.shape[2]) + #Append [0] the audio emebdding, [1] has output class probabilities + audio_embeddings = self.clap.audio_encoder(preprocessed_audio)[0] + audio_embeddings = audio_embeddings/torch.norm(audio_embeddings, dim=-1, keepdim=True) + return audio_embeddings + + def compute_similarity(self, audio_embeddings, text_embeddings): + r"""Compute similarity between text and audio embeddings""" + logit_scale = self.clap.logit_scale.exp() + similarity = logit_scale*text_embeddings @ audio_embeddings.T + return similarity.T + + def _generic_batch_inference(self, func, *args): + r"""Process audio and/or text per batch""" + input_tmp = args[0] + batch_size = args[-1] + # args[0] has audio_files, args[1] has class_labels + inputs = [args[0], args[1]] if len(args) == 3 else [args[0]] + args0_len = len(args[0]) + # compute text_embeddings once for all the audio_files batches + if len(inputs) == 2: + text_embeddings = self.get_text_embeddings(args[1]) + inputs = [args[0], args[1], text_embeddings] + dataset_idx = 0 + for _ in range(math.ceil(args0_len/batch_size)): + next_batch_idx = dataset_idx + batch_size + # batch size is bigger than available audio/text items + if next_batch_idx >= args0_len: + inputs[0] = input_tmp[dataset_idx:] + return func(*tuple(inputs)) + else: + inputs[0] = input_tmp[dataset_idx:next_batch_idx] + yield func(*tuple(inputs)) + dataset_idx = next_batch_idx + + def get_audio_embeddings_per_batch(self, audio_files, batch_size): + r"""Load preprocessed audio and return a audio embeddings per batch""" + return self._generic_batch_inference(self.get_audio_embeddings, audio_files, batch_size) + + def get_text_embeddings_per_batch(self, class_labels, batch_size): + r"""Load preprocessed text and return text embeddings per batch""" + return self._generic_batch_inference(self.get_text_embeddings, class_labels, batch_size) + + def classify_audio_files_per_batch(self, audio_files, class_labels, batch_size): + r"""Compute classification probabilities for each audio recording in a batch and each class label""" + return self._generic_batch_inference(self.classify_audio_files, audio_files, class_labels, batch_size) + +if __name__ == '__main__': + + # Load and initialize CLAP + weights_path = "/home1/huangrongjie/Project/Diffusion/LatentDiffusion/CLAP/CLAP_weights_2022.pth" + clap_model = CLAPWrapper(weights_path, use_cuda=False) + + y = ["A woman talks nearby as water pours", "Multiple clanging and clanking sounds"] + x = ['/home2/huangjiawei/data/audiocaps/train/Yr1nicOVtvkQ.wav', '/home2/huangjiawei/data/audiocaps/train/YUDGBjjwyaqE.wav'] + + # Computing text embeddings + text_embeddings = clap_model.get_text_embeddings(y) + + import ipdb + ipdb.set_trace() + + # Computing audio embeddings + audio_embeddings = clap_model.get_audio_embeddings(x, resample=True) + similarity = clap_model.compute_similarity(audio_embeddings, text_embeddings) + diff --git a/ldm/modules/encoders/CLAP/__init__.py b/ldm/modules/encoders/CLAP/__init__.py new file mode 100644 index 0000000000000000000000000000000000000000..aadad97ebc9ec23fdebab974a99e343de90f8afd --- /dev/null +++ b/ldm/modules/encoders/CLAP/__init__.py @@ -0,0 +1,3 @@ +from . import clap +from . import audio +from . import utils \ No newline at end of file diff --git a/ldm/modules/encoders/CLAP/__pycache__/__init__.cpython-38.pyc b/ldm/modules/encoders/CLAP/__pycache__/__init__.cpython-38.pyc new file mode 100644 index 0000000000000000000000000000000000000000..c2a0254817095ace7fd09e73a47ba9d3f909ef04 Binary files /dev/null and b/ldm/modules/encoders/CLAP/__pycache__/__init__.cpython-38.pyc differ diff --git a/ldm/modules/encoders/CLAP/__pycache__/__init__.cpython-39.pyc b/ldm/modules/encoders/CLAP/__pycache__/__init__.cpython-39.pyc new file mode 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b/ldm/modules/encoders/CLAP/__pycache__/utils.cpython-39.pyc new file mode 100644 index 0000000000000000000000000000000000000000..f2eb67972dafe19b163ba48152edeae3427aaecc Binary files /dev/null and b/ldm/modules/encoders/CLAP/__pycache__/utils.cpython-39.pyc differ diff --git a/ldm/modules/encoders/CLAP/audio.py b/ldm/modules/encoders/CLAP/audio.py new file mode 100644 index 0000000000000000000000000000000000000000..0980d729dd3b579fee0380d0b9d7055e6843ba12 --- /dev/null +++ b/ldm/modules/encoders/CLAP/audio.py @@ -0,0 +1,179 @@ +import torch +import torch.nn as nn +import torch.nn.functional as F +from torchlibrosa.stft import Spectrogram, LogmelFilterBank + +def get_audio_encoder(name: str): + if name == "Cnn14": + return Cnn14 + else: + raise Exception('The audio encoder name {} is incorrect or not supported'.format(name)) + + +class ConvBlock(nn.Module): + def __init__(self, in_channels, out_channels): + + super(ConvBlock, self).__init__() + + self.conv1 = nn.Conv2d(in_channels=in_channels, + out_channels=out_channels, + kernel_size=(3, 3), stride=(1, 1), + padding=(1, 1), bias=False) + + self.conv2 = nn.Conv2d(in_channels=out_channels, + out_channels=out_channels, + kernel_size=(3, 3), stride=(1, 1), + padding=(1, 1), bias=False) + + self.bn1 = nn.BatchNorm2d(out_channels) + self.bn2 = nn.BatchNorm2d(out_channels) + + + def forward(self, input, pool_size=(2, 2), pool_type='avg'): + + x = input + x = F.relu_(self.bn1(self.conv1(x))) + x = F.relu_(self.bn2(self.conv2(x))) + if pool_type == 'max': + x = F.max_pool2d(x, kernel_size=pool_size) + elif pool_type == 'avg': + x = F.avg_pool2d(x, kernel_size=pool_size) + elif pool_type == 'avg+max': + x1 = F.avg_pool2d(x, kernel_size=pool_size) + x2 = F.max_pool2d(x, kernel_size=pool_size) + x = x1 + x2 + else: + raise Exception('Incorrect argument!') + + return x + + +class ConvBlock5x5(nn.Module): + def __init__(self, in_channels, out_channels): + + super(ConvBlock5x5, self).__init__() + + self.conv1 = nn.Conv2d(in_channels=in_channels, + out_channels=out_channels, + kernel_size=(5, 5), stride=(1, 1), + padding=(2, 2), bias=False) + + self.bn1 = nn.BatchNorm2d(out_channels) + + + def forward(self, input, pool_size=(2, 2), pool_type='avg'): + + x = input + x = F.relu_(self.bn1(self.conv1(x))) + if pool_type == 'max': + x = F.max_pool2d(x, kernel_size=pool_size) + elif pool_type == 'avg': + x = F.avg_pool2d(x, kernel_size=pool_size) + elif pool_type == 'avg+max': + x1 = F.avg_pool2d(x, kernel_size=pool_size) + x2 = F.max_pool2d(x, kernel_size=pool_size) + x = x1 + x2 + else: + raise Exception('Incorrect argument!') + + return x + + +class AttBlock(nn.Module): + def __init__(self, n_in, n_out, activation='linear', temperature=1.): + super(AttBlock, self).__init__() + + self.activation = activation + self.temperature = temperature + self.att = nn.Conv1d(in_channels=n_in, out_channels=n_out, kernel_size=1, stride=1, padding=0, bias=True) + self.cla = nn.Conv1d(in_channels=n_in, out_channels=n_out, kernel_size=1, stride=1, padding=0, bias=True) + + self.bn_att = nn.BatchNorm1d(n_out) + + def forward(self, x): + # x: (n_samples, n_in, n_time) + norm_att = torch.softmax(torch.clamp(self.att(x), -10, 10), dim=-1) + cla = self.nonlinear_transform(self.cla(x)) + x = torch.sum(norm_att * cla, dim=2) + return x, norm_att, cla + + def nonlinear_transform(self, x): + if self.activation == 'linear': + return x + elif self.activation == 'sigmoid': + return torch.sigmoid(x) + + +class Cnn14(nn.Module): + def __init__(self, sample_rate, window_size, hop_size, mel_bins, fmin, + fmax, classes_num, out_emb): + + super(Cnn14, self).__init__() + + window = 'hann' + center = True + pad_mode = 'reflect' + ref = 1.0 + amin = 1e-10 + top_db = None + + # Spectrogram extractor + self.spectrogram_extractor = Spectrogram(n_fft=window_size, hop_length=hop_size, + win_length=window_size, window=window, center=center, pad_mode=pad_mode, + freeze_parameters=True) + + # Logmel feature extractor + self.logmel_extractor = LogmelFilterBank(sr=sample_rate, n_fft=window_size, + n_mels=mel_bins, fmin=fmin, fmax=fmax, ref=ref, amin=amin, top_db=top_db, + freeze_parameters=True) + + self.bn0 = nn.BatchNorm2d(64) + + self.conv_block1 = ConvBlock(in_channels=1, out_channels=64) + self.conv_block2 = ConvBlock(in_channels=64, out_channels=128) + self.conv_block3 = ConvBlock(in_channels=128, out_channels=256) + self.conv_block4 = ConvBlock(in_channels=256, out_channels=512) + self.conv_block5 = ConvBlock(in_channels=512, out_channels=1024) + self.conv_block6 = ConvBlock(in_channels=1024, out_channels=2048) + + # out_emb is 2048 for best Cnn14 + self.fc1 = nn.Linear(2048, out_emb, bias=True) + self.fc_audioset = nn.Linear(out_emb, classes_num, bias=True) + + def forward(self, input, mixup_lambda=None): + """ + Input: (batch_size, data_length) + """ + + x = self.spectrogram_extractor(input) # (batch_size, 1, time_steps, freq_bins) + x = self.logmel_extractor(x) # (batch_size, 1, time_steps, mel_bins) + + x = x.transpose(1, 3) + x = self.bn0(x) + x = x.transpose(1, 3) + + x = self.conv_block1(x, pool_size=(2, 2), pool_type='avg') + x = F.dropout(x, p=0.2, training=self.training) + x = self.conv_block2(x, pool_size=(2, 2), pool_type='avg') + x = F.dropout(x, p=0.2, training=self.training) + x = self.conv_block3(x, pool_size=(2, 2), pool_type='avg') + x = F.dropout(x, p=0.2, training=self.training) + x = self.conv_block4(x, pool_size=(2, 2), pool_type='avg') + x = F.dropout(x, p=0.2, training=self.training) + x = self.conv_block5(x, pool_size=(2, 2), pool_type='avg') + x = F.dropout(x, p=0.2, training=self.training) + x = self.conv_block6(x, pool_size=(1, 1), pool_type='avg') + x = F.dropout(x, p=0.2, training=self.training) + x = torch.mean(x, dim=3) + + (x1, _) = torch.max(x, dim=2) + x2 = torch.mean(x, dim=2) + x = x1 + x2 + x = F.dropout(x, p=0.5, training=self.training) + x = F.relu_(self.fc1(x)) + embedding = F.dropout(x, p=0.5, training=self.training) + clipwise_output = torch.sigmoid(self.fc_audioset(x)) + + output_dict = {'clipwise_output': clipwise_output, 'embedding': embedding} + + return output_dict \ No newline at end of file diff --git a/ldm/modules/encoders/CLAP/clap.py b/ldm/modules/encoders/CLAP/clap.py new file mode 100644 index 0000000000000000000000000000000000000000..3141e47ec7b7df2e3cb81d11582b4738a5d23c1a --- /dev/null +++ b/ldm/modules/encoders/CLAP/clap.py @@ -0,0 +1,89 @@ +import numpy as np +import torch +import torch.nn.functional as F +from torch import nn +from transformers import AutoModel +from .audio import get_audio_encoder + +class Projection(nn.Module): + def __init__(self, d_in: int, d_out: int, p: float=0.5) -> None: + super().__init__() + self.linear1 = nn.Linear(d_in, d_out, bias=False) + self.linear2 = nn.Linear(d_out, d_out, bias=False) + self.layer_norm = nn.LayerNorm(d_out) + self.drop = nn.Dropout(p) + + def forward(self, x: torch.Tensor) -> torch.Tensor: + embed1 = self.linear1(x) + embed2 = self.drop(self.linear2(F.gelu(embed1))) + embeds = self.layer_norm(embed1 + embed2) + return embeds + +class AudioEncoder(nn.Module): + def __init__(self, audioenc_name:str, d_in: int, d_out: int, sample_rate: int, window_size: int, + hop_size: int, mel_bins: int, fmin: int, fmax: int, classes_num: int) -> None: + super().__init__() + + audio_encoder = get_audio_encoder(audioenc_name) + + self.base = audio_encoder( + sample_rate, window_size, + hop_size, mel_bins, fmin, fmax, + classes_num, d_in) + + self.projection = Projection(d_in, d_out) + + def forward(self, x): + out_dict = self.base(x) + audio_features, audio_classification_output = out_dict['embedding'], out_dict['clipwise_output'] + projected_vec = self.projection(audio_features) + return projected_vec, audio_classification_output + +class TextEncoder(nn.Module): + def __init__(self, d_out: int, text_model: str, transformer_embed_dim: int) -> None: + super().__init__() + self.base = AutoModel.from_pretrained(text_model) + self.projection = Projection(transformer_embed_dim, d_out) + + def forward(self, x): + out = self.base(**x)[0] + out = out[:, 0, :] # get CLS token output + projected_vec = self.projection(out) + return projected_vec + +class CLAP(nn.Module): + def __init__(self, + # audio + audioenc_name: str, + sample_rate: int, + window_size: int, + hop_size: int, + mel_bins: int, + fmin: int, + fmax: int, + classes_num: int, + out_emb: int, + # text + text_model: str, + transformer_embed_dim: int, + # common + d_proj: int, + ): + super().__init__() + + + self.audio_encoder = AudioEncoder( + audioenc_name, out_emb, d_proj, + sample_rate, window_size, hop_size, mel_bins, fmin, fmax, classes_num) + + self.caption_encoder = TextEncoder( + d_proj, text_model, transformer_embed_dim + ) + + self.logit_scale = nn.Parameter(torch.ones([]) * np.log(1 / 0.07)) + + def forward(self, audio, text): + audio_embed, _ = self.audio_encoder(audio) + caption_embed = self.caption_encoder(text) + + return caption_embed, audio_embed, self.logit_scale.exp() \ No newline at end of file diff --git a/ldm/modules/encoders/CLAP/config.yml b/ldm/modules/encoders/CLAP/config.yml new file mode 100644 index 0000000000000000000000000000000000000000..1b01939eb3348d63e79395363d09be947d31d97f --- /dev/null +++ b/ldm/modules/encoders/CLAP/config.yml @@ -0,0 +1,26 @@ +# TEXT ENCODER CONFIG +text_model: 'bert-base-uncased' +text_len: 100 +transformer_embed_dim: 768 +freeze_text_encoder_weights: True + +# AUDIO ENCODER CONFIG +audioenc_name: 'Cnn14' +out_emb: 2048 +sampling_rate: 44100 +duration: 5 +fmin: 50 +fmax: 14000 +n_fft: 1028 +hop_size: 320 +mel_bins: 64 +window_size: 1024 + +# PROJECTION SPACE CONFIG +d_proj: 1024 +temperature: 0.003 + +# TRAINING AND EVALUATION CONFIG +num_classes: 527 +batch_size: 1024 +demo: False diff --git a/ldm/modules/encoders/CLAP/utils.py b/ldm/modules/encoders/CLAP/utils.py new file mode 100644 index 0000000000000000000000000000000000000000..f95931fb1c422cbd8349b88e1effb9323f170b2b --- /dev/null +++ b/ldm/modules/encoders/CLAP/utils.py @@ -0,0 +1,26 @@ +import argparse +import yaml +import sys + +def read_config_as_args(config_path,args=None,is_config_str=False): + return_dict = {} + + if config_path is not None: + if is_config_str: + yml_config = yaml.load(config_path, Loader=yaml.FullLoader) + else: + with open(config_path, "r") as f: + yml_config = yaml.load(f, Loader=yaml.FullLoader) + + if args != None: + for k, v in yml_config.items(): + if k in args.__dict__: + args.__dict__[k] = v + else: + sys.stderr.write("Ignored unknown parameter {} in yaml.\n".format(k)) + else: + for k, v in yml_config.items(): + return_dict[k] = v + + args = args if args != None else return_dict + return argparse.Namespace(**args) diff --git a/ldm/modules/encoders/__init__.py b/ldm/modules/encoders/__init__.py new file mode 100644 index 0000000000000000000000000000000000000000..e69de29bb2d1d6434b8b29ae775ad8c2e48c5391 diff --git a/ldm/modules/encoders/__pycache__/__init__.cpython-39.pyc b/ldm/modules/encoders/__pycache__/__init__.cpython-39.pyc new file mode 100644 index 0000000000000000000000000000000000000000..ae937456dce3b6db50e713b48d292f79eba1a372 Binary files /dev/null and b/ldm/modules/encoders/__pycache__/__init__.cpython-39.pyc differ diff --git a/ldm/modules/encoders/__pycache__/modules.cpython-39.pyc b/ldm/modules/encoders/__pycache__/modules.cpython-39.pyc new file mode 100644 index 0000000000000000000000000000000000000000..6094da1f35c634ee102ab45313fa176162044289 Binary files /dev/null and b/ldm/modules/encoders/__pycache__/modules.cpython-39.pyc differ diff --git a/ldm/modules/encoders/modules.py b/ldm/modules/encoders/modules.py new file mode 100644 index 0000000000000000000000000000000000000000..f6769e9d154301a88e6dc94e463dafd14835d8ba --- /dev/null +++ b/ldm/modules/encoders/modules.py @@ -0,0 +1,314 @@ +import torch +import torch.nn as nn +from functools import partial + +from ldm.modules.x_transformer import Encoder, TransformerWrapper # TODO: can we directly rely on lucidrains code and simply add this as a reuirement? --> test +from torch.utils.checkpoint import checkpoint +from transformers import T5Tokenizer, T5EncoderModel, CLIPTokenizer, CLIPTextModel, AutoTokenizer +from importlib_resources import files +from ldm.modules.encoders.CLAP.utils import read_config_as_args +from ldm.modules.encoders.CLAP.clap import TextEncoder +from ldm.util import default, count_params + + +class AbstractEncoder(nn.Module): + def __init__(self): + super().__init__() + + def encode(self, *args, **kwargs): + raise NotImplementedError + + +class ClassEmbedder(nn.Module): + def __init__(self, embed_dim, n_classes=1000, key='class'): + super().__init__() + self.key = key + self.embedding = nn.Embedding(n_classes, embed_dim) + + def forward(self, batch, key=None): + if key is None: + key = self.key + # this is for use in crossattn + c = batch[key][:, None]# (bsz,1) + c = self.embedding(c) + return c + + +class TransformerEmbedder(AbstractEncoder): + """Some transformer encoder layers""" + def __init__(self, n_embed, n_layer, vocab_size, max_seq_len=77, device="cuda"): + super().__init__() + self.device = device + self.transformer = TransformerWrapper(num_tokens=vocab_size, max_seq_len=max_seq_len, + attn_layers=Encoder(dim=n_embed, depth=n_layer)) + + def forward(self, tokens): + tokens = tokens.to(self.device) # meh + z = self.transformer(tokens, return_embeddings=True) + return z + + def encode(self, x): + return self(x) + + +class BERTTokenizer(AbstractEncoder): + """ Uses a pretrained BERT tokenizer by huggingface. Vocab size: 30522 (?)""" + def __init__(self, device="cuda", vq_interface=True, max_length=77): + super().__init__() + from transformers import BertTokenizerFast # TODO: add to reuquirements + self.tokenizer = BertTokenizerFast.from_pretrained("bert-base-uncased") + self.device = device + self.vq_interface = vq_interface + self.max_length = max_length + + def forward(self, text): + batch_encoding = self.tokenizer(text, truncation=True, max_length=self.max_length, return_length=True, + return_overflowing_tokens=False, padding="max_length", return_tensors="pt") + tokens = batch_encoding["input_ids"].to(self.device) + return tokens + + @torch.no_grad() + def encode(self, text): + tokens = self(text) + if not self.vq_interface: + return tokens + return None, None, [None, None, tokens] + + def decode(self, text): + return text + + +class BERTEmbedder(AbstractEncoder):# 这里不是用的pretrained bert,是用的transformers的BertTokenizer加自定义的TransformerWrapper + """Uses the BERT tokenizr model and add some transformer encoder layers""" + def __init__(self, n_embed, n_layer, vocab_size=30522, max_seq_len=77, + device="cuda",use_tokenizer=True, embedding_dropout=0.0): + super().__init__() + self.use_tknz_fn = use_tokenizer + if self.use_tknz_fn: + self.tknz_fn = BERTTokenizer(vq_interface=False, max_length=max_seq_len) + self.device = device + self.transformer = TransformerWrapper(num_tokens=vocab_size, max_seq_len=max_seq_len, + attn_layers=Encoder(dim=n_embed, depth=n_layer), + emb_dropout=embedding_dropout) + + def forward(self, text): + if self.use_tknz_fn: + tokens = self.tknz_fn(text)#.to(self.device) + else: + tokens = text + z = self.transformer(tokens, return_embeddings=True) + return z + + def encode(self, text): + # output of length 77 + return self(text) + + +class SpatialRescaler(nn.Module): + def __init__(self, + n_stages=1, + method='bilinear', + multiplier=0.5, + in_channels=3, + out_channels=None, + bias=False): + super().__init__() + self.n_stages = n_stages + assert self.n_stages >= 0 + assert method in ['nearest','linear','bilinear','trilinear','bicubic','area'] + self.multiplier = multiplier + self.interpolator = partial(torch.nn.functional.interpolate, mode=method) + self.remap_output = out_channels is not None + if self.remap_output: + print(f'Spatial Rescaler mapping from {in_channels} to {out_channels} channels after resizing.') + self.channel_mapper = nn.Conv2d(in_channels,out_channels,1,bias=bias) + + def forward(self,x): + for stage in range(self.n_stages): + x = self.interpolator(x, scale_factor=self.multiplier) + + + if self.remap_output: + x = self.channel_mapper(x) + return x + + def encode(self, x): + return self(x) + +def disabled_train(self, mode=True): + """Overwrite model.train with this function to make sure train/eval mode + does not change anymore.""" + return self + +class FrozenT5Embedder(AbstractEncoder): + """Uses the T5 transformer encoder for text""" + def __init__(self, version="google/t5-v1_1-large", device="cuda", max_length=77, freeze=True): # others are google/t5-v1_1-xl and google/t5-v1_1-xxl + super().__init__() + self.tokenizer = T5Tokenizer.from_pretrained(version) + self.transformer = T5EncoderModel.from_pretrained(version) + self.device = device + self.max_length = max_length # TODO: typical value? + if freeze: + self.freeze() + + def freeze(self): + self.transformer = self.transformer.eval() + #self.train = disabled_train + for param in self.parameters(): + param.requires_grad = False + + def forward(self, text): + batch_encoding = self.tokenizer(text, truncation=True, max_length=self.max_length, return_length=True, + return_overflowing_tokens=False, padding="max_length", return_tensors="pt") + tokens = batch_encoding["input_ids"].to(self.device) + outputs = self.transformer(input_ids=tokens) + + z = outputs.last_hidden_state + return z + + def encode(self, text): + return self(text) + + +class FrozenCLAPEmbedder(AbstractEncoder): + """Uses the CLAP transformer encoder for text (from huggingface)""" + def __init__(self, weights_path, freeze=True, device="cuda", max_length=77): # clip-vit-base-patch32 + super().__init__() + + model_state_dict = torch.load(weights_path, map_location=torch.device('cpu'))['model'] + match_params = dict() + for key in list(model_state_dict.keys()): + if 'caption_encoder' in key: + match_params[key.replace('caption_encoder.', '')] = model_state_dict[key] + + config_as_str = files('ldm').joinpath('modules/encoders/CLAP/config.yml').read_text() + args = read_config_as_args(config_as_str, is_config_str=True) + + # To device + self.tokenizer = AutoTokenizer.from_pretrained(args.text_model) # args.text_model + self.caption_encoder = TextEncoder( + args.d_proj, args.text_model, args.transformer_embed_dim + ) + + self.max_length = max_length + self.device = device + if freeze: self.freeze() + + print(f"{self.caption_encoder.__class__.__name__} comes with {count_params(self.caption_encoder) * 1.e-6:.2f} M params.") + + def freeze(self): + self.caption_encoder.base = self.caption_encoder.base.eval() + for param in self.caption_encoder.base.parameters(): + param.requires_grad = False + + + def encode(self, text): + batch_encoding = self.tokenizer(text, truncation=True, max_length=self.max_length, return_length=True, + return_overflowing_tokens=False, padding="max_length", return_tensors="pt") + tokens = batch_encoding["input_ids"].to(self.device) + + outputs = self.caption_encoder.base(input_ids=tokens) + z = self.caption_encoder.projection(outputs.last_hidden_state) + return z + +class FrozenCLAPEmbedderNoLoad(AbstractEncoder): + def __init__(self, config, freeze=True, device="cpu", max_length=77): + super().__init__() + args = config + + # To device + self.tokenizer = AutoTokenizer.from_pretrained(args.text_model) # args.text_model + self.caption_encoder = TextEncoder( + args.d_proj, args.text_model, args.transformer_embed_dim + ) + + self.max_length = max_length + self.device = device + if freeze: self.freeze() + + print(f"{self.caption_encoder.__class__.__name__} comes with {count_params(self.caption_encoder) * 1.e-6:.2f} M params.") + + def freeze(self): + self.caption_encoder.base = self.caption_encoder.base.eval() + for param in self.caption_encoder.base.parameters(): + param.requires_grad = False + + + def encode(self, text): + batch_encoding = self.tokenizer(text, truncation=True, max_length=self.max_length, return_length=True, + return_overflowing_tokens=False, padding="max_length", return_tensors="pt") + tokens = batch_encoding["input_ids"].to(self.device) + + outputs = self.caption_encoder.base(input_ids=tokens) + z = self.caption_encoder.projection(outputs.last_hidden_state) + return z + + +class NewFrozenCLAPEmbedder(AbstractEncoder): + """Uses the CLAP transformer encoder for text (from huggingface)""" + def __init__(self, weights_path, freeze=True, device="cuda", max_length=77): # clip-vit-base-patch32 + super().__init__() + # To device + from transformers import RobertaTokenizer + from ldm.modules.encoders.open_clap import create_model + + + model, model_cfg = create_model( + 'HTSAT-tiny', + 'roberta', + weights_path, + enable_fusion=True, + fusion_type='aff_2d' + ) + + del model.audio_branch, model.audio_transform, model.audio_projection + self.tokenizer = RobertaTokenizer.from_pretrained('roberta-base') + self.model = model + + self.max_length = max_length + self.device = device + if freeze: self.freeze() + + param_num = sum(p.numel() for p in model.parameters() if p.requires_grad) + print(f'{self.model.__class__.__name__} comes with: {param_num / 1e+6:.3f} M params.') + + def freeze(self): + self.model = self.model.eval() + for param in self.model.parameters(): + param.requires_grad = False + + def encode(self, text): + batch_encoding = self.tokenizer(text, truncation=True, max_length=self.max_length, return_length=True, + return_overflowing_tokens=False, padding="max_length", return_tensors="pt") + outputs = self.model.text_branch(input_ids=batch_encoding["input_ids"].to(self.device), attention_mask=batch_encoding["attention_mask"].to(self.device)) + z = self.model.text_projection(outputs.last_hidden_state) + return z + +class FrozenFLANEmbedder(AbstractEncoder): + """Uses the T5 transformer encoder for text""" + def __init__(self, version="google/flan-t5-large", device="cuda", max_length=77, freeze=True): # others are google/t5-v1_1-xl and google/t5-v1_1-xxl + super().__init__() + self.tokenizer = T5Tokenizer.from_pretrained(version) + self.transformer = T5EncoderModel.from_pretrained(version) + self.device = device + self.max_length = max_length # TODO: typical value? + if freeze: + self.freeze() + + def freeze(self): + self.transformer = self.transformer.eval() + #self.train = disabled_train + for param in self.parameters(): + param.requires_grad = False + + def forward(self, text): + batch_encoding = self.tokenizer(text, truncation=True, max_length=self.max_length, return_length=True, + return_overflowing_tokens=False, padding="max_length", return_tensors="pt") + tokens = batch_encoding["input_ids"].to(self.device) + outputs = self.transformer(input_ids=tokens) + + z = outputs.last_hidden_state + return z + + def encode(self, text): + return self(text) \ No newline at end of file diff --git a/ldm/modules/encoders/open_clap/__init__.py b/ldm/modules/encoders/open_clap/__init__.py new file mode 100644 index 0000000000000000000000000000000000000000..96ccf3e709b62e0548572ea424bb03a1a67a4b2e --- /dev/null +++ b/ldm/modules/encoders/open_clap/__init__.py @@ -0,0 +1,8 @@ +from .factory import list_models, create_model, create_model_and_transforms, add_model_config +from .loss import ClipLoss, gather_features, LPLoss, lp_gather_features, LPMetrics +from .model import CLAP, CLAPTextCfg, CLAPVisionCfg, CLAPAudioCfp, convert_weights_to_fp16, trace_model +from .openai import load_openai_model, list_openai_models +from .pretrained import list_pretrained, list_pretrained_tag_models, list_pretrained_model_tags,\ + get_pretrained_url, download_pretrained +from .tokenizer import SimpleTokenizer, tokenize +from .transform import image_transform diff --git a/ldm/modules/encoders/open_clap/__pycache__/__init__.cpython-38.pyc b/ldm/modules/encoders/open_clap/__pycache__/__init__.cpython-38.pyc new file mode 100644 index 0000000000000000000000000000000000000000..4776aa91aff4802271028f05fa85090e9f98c149 Binary files /dev/null and b/ldm/modules/encoders/open_clap/__pycache__/__init__.cpython-38.pyc differ diff --git a/ldm/modules/encoders/open_clap/__pycache__/factory.cpython-38.pyc b/ldm/modules/encoders/open_clap/__pycache__/factory.cpython-38.pyc new file mode 100644 index 0000000000000000000000000000000000000000..c3d03a10b6c109f74fe5efc85959d57c4b6dfc2a Binary files /dev/null and b/ldm/modules/encoders/open_clap/__pycache__/factory.cpython-38.pyc differ diff --git a/ldm/modules/encoders/open_clap/__pycache__/feature_fusion.cpython-38.pyc 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new file mode 100644 index 0000000000000000000000000000000000000000..005e72dec67e4b1c05063dbd4d024166344fd2c4 --- /dev/null +++ b/ldm/modules/encoders/open_clap/bert.py @@ -0,0 +1,32 @@ +from transformers import BertTokenizer, BertModel +tokenizer = BertTokenizer.from_pretrained('bert-base-uncased') +model = BertModel.from_pretrained("bert-base-uncased") +text = "Replace me by any text you'd like." + +def bert_embeddings(text): + # text = "Replace me by any text you'd like." + encoded_input = tokenizer(text, return_tensors='pt') + output = model(**encoded_input) + return output + +from transformers import RobertaTokenizer, RobertaModel + +tokenizer = RobertaTokenizer.from_pretrained('roberta-base') +model = RobertaModel.from_pretrained('roberta-base') +text = "Replace me by any text you'd like." +def Roberta_embeddings(text): + # text = "Replace me by any text you'd like." + encoded_input = tokenizer(text, return_tensors='pt') + output = model(**encoded_input) + return output + +from transformers import BartTokenizer, BartModel + +tokenizer = BartTokenizer.from_pretrained('facebook/bart-base') +model = BartModel.from_pretrained('facebook/bart-base') +text = "Replace me by any text you'd like." +def bart_embeddings(text): + # text = "Replace me by any text you'd like." + encoded_input = tokenizer(text, return_tensors='pt') + output = model(**encoded_input) + return output \ No newline at end of file diff --git a/ldm/modules/encoders/open_clap/bpe_simple_vocab_16e6.txt.gz b/ldm/modules/encoders/open_clap/bpe_simple_vocab_16e6.txt.gz new file mode 100644 index 0000000000000000000000000000000000000000..36a15856e00a06a9fbed8cdd34d2393fea4a3113 --- /dev/null +++ b/ldm/modules/encoders/open_clap/bpe_simple_vocab_16e6.txt.gz @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:924691ac288e54409236115652ad4aa250f48203de50a9e4722a6ecd48d6804a +size 1356917 diff --git a/ldm/modules/encoders/open_clap/factory.py b/ldm/modules/encoders/open_clap/factory.py new file mode 100644 index 0000000000000000000000000000000000000000..3c3b28658adb03462b9c4b5405548d4e0d1edc5e --- /dev/null +++ b/ldm/modules/encoders/open_clap/factory.py @@ -0,0 +1,257 @@ +import json +import logging +import os +import pathlib +import re +from copy import deepcopy +from pathlib import Path + +import torch + +from .model import CLAP, convert_weights_to_fp16 +from .openai import load_openai_model +from .pretrained import get_pretrained_url, download_pretrained +from .transform import image_transform + +_MODEL_CONFIG_PATHS = [Path(__file__).parent / f"model_configs/"] +_MODEL_CONFIGS = {} # directory (model_name: config) of model architecture configs + + +def _natural_key(string_): + return [int(s) if s.isdigit() else s for s in re.split(r"(\d+)", string_.lower())] + + +def _rescan_model_configs(): + global _MODEL_CONFIGS + + config_ext = (".json",) + config_files = [] + for config_path in _MODEL_CONFIG_PATHS: + if config_path.is_file() and config_path.suffix in config_ext: + config_files.append(config_path) + elif config_path.is_dir(): + for ext in config_ext: + config_files.extend(config_path.glob(f"*{ext}")) + + for cf in config_files: + with open(cf, "r") as f: + model_cfg = json.load(f) + if all(a in model_cfg for a in ("embed_dim", "audio_cfg", "text_cfg")): + _MODEL_CONFIGS[cf.stem] = model_cfg + + _MODEL_CONFIGS = { + k: v + for k, v in sorted(_MODEL_CONFIGS.items(), key=lambda x: _natural_key(x[0])) + } + + +_rescan_model_configs() # initial populate of model config registry + + +def load_state_dict(checkpoint_path: str, map_location="cpu", skip_params=True): + checkpoint = torch.load(checkpoint_path, map_location=map_location) + if isinstance(checkpoint, dict) and "state_dict" in checkpoint: + state_dict = checkpoint["state_dict"] + else: + state_dict = checkpoint + if skip_params: + if next(iter(state_dict.items()))[0].startswith("module"): + state_dict = {k[7:]: v for k, v in state_dict.items()} + # for k in state_dict: + # if k.startswith('transformer'): + # v = state_dict.pop(k) + # state_dict['text_branch.' + k[12:]] = v + return state_dict + + +def create_model( + amodel_name: str, + tmodel_name: str, + pretrained: str = "", + precision: str = "fp32", + device: torch.device = torch.device("cpu"), + jit: bool = False, + force_quick_gelu: bool = False, + openai_model_cache_dir: str = os.path.expanduser("~/.cache/clip"), + skip_params=True, + pretrained_audio: str = "", + pretrained_text: str = "", + enable_fusion: bool = False, + fusion_type: str = 'None' + # pretrained_image: bool = False, +): + amodel_name = amodel_name.replace( + "/", "-" + ) # for callers using old naming with / in ViT names + pretrained_orig = pretrained + pretrained = pretrained.lower() + if pretrained == "openai": + if amodel_name in _MODEL_CONFIGS: + logging.info(f"Loading {amodel_name} model config.") + model_cfg = deepcopy(_MODEL_CONFIGS[amodel_name]) + else: + logging.error( + f"Model config for {amodel_name} not found; available models {list_models()}." + ) + raise RuntimeError(f"Model config for {amodel_name} not found.") + + logging.info(f"Loading pretrained ViT-B-16 text encoder from OpenAI.") + # Hard Code in model name + model_cfg["text_cfg"]["model_type"] = tmodel_name + model = load_openai_model( + "ViT-B-16", + model_cfg, + device=device, + jit=jit, + cache_dir=openai_model_cache_dir, + enable_fusion=enable_fusion, + fusion_type=fusion_type + ) + # See https://discuss.pytorch.org/t/valueerror-attemting-to-unscale-fp16-gradients/81372 + if precision == "amp" or precision == "fp32": + model = model.float() + else: + if amodel_name in _MODEL_CONFIGS: + logging.info(f"Loading {amodel_name} model config.") + model_cfg = deepcopy(_MODEL_CONFIGS[amodel_name]) + else: + logging.error( + f"Model config for {amodel_name} not found; available models {list_models()}." + ) + raise RuntimeError(f"Model config for {amodel_name} not found.") + + if force_quick_gelu: + # override for use of QuickGELU on non-OpenAI transformer models + model_cfg["quick_gelu"] = True + + # if pretrained_image: + # if 'timm_amodel_name' in model_cfg.get('vision_cfg', {}): + # # pretrained weight loading for timm models set via vision_cfg + # model_cfg['vision_cfg']['timm_model_pretrained'] = True + # else: + # assert False, 'pretrained image towers currently only supported for timm models' + model_cfg["text_cfg"]["model_type"] = tmodel_name + model_cfg["enable_fusion"] = enable_fusion + model_cfg["fusion_type"] = fusion_type + model = CLAP(**model_cfg) + + if pretrained: + checkpoint_path = "" + url = get_pretrained_url(amodel_name, pretrained) + if url: + checkpoint_path = download_pretrained(url, root=openai_model_cache_dir) + elif os.path.exists(pretrained_orig): + checkpoint_path = pretrained_orig + if checkpoint_path: + logging.info(f"Loading pretrained {amodel_name}-{tmodel_name} weights ({pretrained}).") + ckpt = load_state_dict(checkpoint_path, skip_params=True) + model.load_state_dict(ckpt) + param_names = [n for n, p in model.named_parameters()] + for n in param_names: + print(n, "\t", "Loaded" if n in ckpt else "Unloaded") + else: + logging.warning( + f"Pretrained weights ({pretrained}) not found for model {amodel_name}." + ) + raise RuntimeError( + f"Pretrained weights ({pretrained}) not found for model {amodel_name}." + ) + + if pretrained_audio: + if amodel_name.startswith('PANN'): + if 'Cnn14_mAP' in pretrained_audio: # official checkpoint + audio_ckpt = torch.load(pretrained_audio, map_location='cpu') + audio_ckpt = audio_ckpt['model'] + keys = list(audio_ckpt.keys()) + for key in keys: + if 'spectrogram_extractor' not in key and 'logmel_extractor' not in key: + v = audio_ckpt.pop(key) + audio_ckpt['audio_branch.' + key] = v + elif os.path.basename(pretrained_audio).startswith('PANN'): # checkpoint trained via HTSAT codebase + audio_ckpt = torch.load(pretrained_audio, map_location='cpu') + audio_ckpt = audio_ckpt['state_dict'] + keys = list(audio_ckpt.keys()) + for key in keys: + if key.startswith('sed_model'): + v = audio_ckpt.pop(key) + audio_ckpt['audio_branch.' + key[10:]] = v + elif os.path.basename(pretrained_audio).startswith('finetuned'): # checkpoint trained via linear probe codebase + audio_ckpt = torch.load(pretrained_audio, map_location='cpu') + else: + raise ValueError('Unknown audio checkpoint') + elif amodel_name.startswith('HTSAT'): + if 'HTSAT_AudioSet_Saved' in pretrained_audio: # official checkpoint + audio_ckpt = torch.load(pretrained_audio, map_location='cpu') + audio_ckpt = audio_ckpt['state_dict'] + keys = list(audio_ckpt.keys()) + for key in keys: + if key.startswith('sed_model') and ('spectrogram_extractor' not in key + and 'logmel_extractor' not in key): + v = audio_ckpt.pop(key) + audio_ckpt['audio_branch.' + key[10:]] = v + elif os.path.basename(pretrained_audio).startswith('HTSAT'): # checkpoint trained via HTSAT codebase + audio_ckpt = torch.load(pretrained_audio, map_location='cpu') + audio_ckpt = audio_ckpt['state_dict'] + keys = list(audio_ckpt.keys()) + for key in keys: + if key.startswith('sed_model'): + v = audio_ckpt.pop(key) + audio_ckpt['audio_branch.' + key[10:]] = v + elif os.path.basename(pretrained_audio).startswith('finetuned'): # checkpoint trained via linear probe codebase + audio_ckpt = torch.load(pretrained_audio, map_location='cpu') + else: + raise ValueError('Unknown audio checkpoint') + else: + raise f'this audio encoder pretrained checkpoint is not support' + + model.load_state_dict(audio_ckpt, strict=False) + logging.info(f"Loading pretrained {amodel_name} weights ({pretrained_audio}).") + param_names = [n for n, p in model.named_parameters()] + for n in param_names: + print(n, "\t", "Loaded" if n in audio_ckpt else "Unloaded") + + model.to(device=device) + if precision == "fp16": + assert device.type != "cpu" + convert_weights_to_fp16(model) + + if jit: + model = torch.jit.script(model) + + return model, model_cfg + + +def create_model_and_transforms( + model_name: str, + pretrained: str = "", + precision: str = "fp32", + device: torch.device = torch.device("cpu"), + jit: bool = False, + force_quick_gelu: bool = False, + # pretrained_image: bool = False, +): + model = create_model( + model_name, + pretrained, + precision, + device, + jit, + force_quick_gelu=force_quick_gelu, + # pretrained_image=pretrained_image + ) + preprocess_train = image_transform(model.visual.image_size, is_train=True) + preprocess_val = image_transform(model.visual.image_size, is_train=False) + return model, preprocess_train, preprocess_val + + +def list_models(): + """enumerate available model architectures based on config files""" + return list(_MODEL_CONFIGS.keys()) + + +def add_model_config(path): + """add model config path or file and update registry""" + if not isinstance(path, Path): + path = Path(path) + _MODEL_CONFIG_PATHS.append(path) + _rescan_model_configs() diff --git a/ldm/modules/encoders/open_clap/feature_fusion.py b/ldm/modules/encoders/open_clap/feature_fusion.py new file mode 100644 index 0000000000000000000000000000000000000000..c2419516b76931f0aa801d78e1b5f04a92a909e6 --- /dev/null +++ b/ldm/modules/encoders/open_clap/feature_fusion.py @@ -0,0 +1,193 @@ +''' +Feature Fusion for Varible-Length Data Processing +AFF/iAFF is referred and modified from https://github.com/YimianDai/open-aff/blob/master/aff_pytorch/aff_net/fusion.py +According to the paper: Yimian Dai et al, Attentional Feature Fusion, IEEE Winter Conference on Applications of Computer Vision, WACV 2021 +''' + +import torch +import torch.nn as nn + + +class DAF(nn.Module): + ''' + 直接相加 DirectAddFuse + ''' + + def __init__(self): + super(DAF, self).__init__() + + def forward(self, x, residual): + return x + residual + + +class iAFF(nn.Module): + ''' + 多特征融合 iAFF + ''' + + def __init__(self, channels=64, r=4, type='2D'): + super(iAFF, self).__init__() + inter_channels = int(channels // r) + + if type == '1D': + # 本地注意力 + self.local_att = nn.Sequential( + nn.Conv1d(channels, inter_channels, kernel_size=1, stride=1, padding=0), + nn.BatchNorm1d(inter_channels), + nn.ReLU(inplace=True), + nn.Conv1d(inter_channels, channels, kernel_size=1, stride=1, padding=0), + nn.BatchNorm1d(channels), + ) + + # 全局注意力 + self.global_att = nn.Sequential( + nn.AdaptiveAvgPool1d(1), + nn.Conv1d(channels, inter_channels, kernel_size=1, stride=1, padding=0), + nn.BatchNorm1d(inter_channels), + nn.ReLU(inplace=True), + nn.Conv1d(inter_channels, channels, kernel_size=1, stride=1, padding=0), + nn.BatchNorm1d(channels), + ) + + # 第二次本地注意力 + self.local_att2 = nn.Sequential( + nn.Conv1d(channels, inter_channels, kernel_size=1, stride=1, padding=0), + nn.BatchNorm1d(inter_channels), + nn.ReLU(inplace=True), + nn.Conv1d(inter_channels, channels, kernel_size=1, stride=1, padding=0), + nn.BatchNorm1d(channels), + ) + # 第二次全局注意力 + self.global_att2 = nn.Sequential( + nn.AdaptiveAvgPool1d(1), + nn.Conv1d(channels, inter_channels, kernel_size=1, stride=1, padding=0), + nn.BatchNorm1d(inter_channels), + nn.ReLU(inplace=True), + nn.Conv1d(inter_channels, channels, kernel_size=1, stride=1, padding=0), + nn.BatchNorm1d(channels), + ) + elif type == '2D': + # 本地注意力 + self.local_att = nn.Sequential( + nn.Conv2d(channels, inter_channels, kernel_size=1, stride=1, padding=0), + nn.BatchNorm2d(inter_channels), + nn.ReLU(inplace=True), + nn.Conv2d(inter_channels, channels, kernel_size=1, stride=1, padding=0), + nn.BatchNorm2d(channels), + ) + + # 全局注意力 + self.global_att = nn.Sequential( + nn.AdaptiveAvgPool2d(1), + nn.Conv2d(channels, inter_channels, kernel_size=1, stride=1, padding=0), + nn.BatchNorm2d(inter_channels), + nn.ReLU(inplace=True), + nn.Conv2d(inter_channels, channels, kernel_size=1, stride=1, padding=0), + nn.BatchNorm2d(channels), + ) + + # 第二次本地注意力 + self.local_att2 = nn.Sequential( + nn.Conv2d(channels, inter_channels, kernel_size=1, stride=1, padding=0), + nn.BatchNorm2d(inter_channels), + nn.ReLU(inplace=True), + nn.Conv2d(inter_channels, channels, kernel_size=1, stride=1, padding=0), + nn.BatchNorm2d(channels), + ) + # 第二次全局注意力 + self.global_att2 = nn.Sequential( + nn.AdaptiveAvgPool2d(1), + nn.Conv2d(channels, inter_channels, kernel_size=1, stride=1, padding=0), + nn.BatchNorm2d(inter_channels), + nn.ReLU(inplace=True), + nn.Conv2d(inter_channels, channels, kernel_size=1, stride=1, padding=0), + nn.BatchNorm2d(channels), + ) + else: + raise f'the type is not supported' + + self.sigmoid = nn.Sigmoid() + + def forward(self, x, residual): + flag = False + xa = x + residual + if xa.size(0) == 1: + xa = torch.cat([xa,xa],dim=0) + flag = True + xl = self.local_att(xa) + xg = self.global_att(xa) + xlg = xl + xg + wei = self.sigmoid(xlg) + xi = x * wei + residual * (1 - wei) + + xl2 = self.local_att2(xi) + xg2 = self.global_att(xi) + xlg2 = xl2 + xg2 + wei2 = self.sigmoid(xlg2) + xo = x * wei2 + residual * (1 - wei2) + if flag: + xo = xo[0].unsqueeze(0) + return xo + + +class AFF(nn.Module): + ''' + 多特征融合 AFF + ''' + + def __init__(self, channels=64, r=4, type='2D'): + super(AFF, self).__init__() + inter_channels = int(channels // r) + + if type == '1D': + self.local_att = nn.Sequential( + nn.Conv1d(channels, inter_channels, kernel_size=1, stride=1, padding=0), + nn.BatchNorm1d(inter_channels), + nn.ReLU(inplace=True), + nn.Conv1d(inter_channels, channels, kernel_size=1, stride=1, padding=0), + nn.BatchNorm1d(channels), + ) + self.global_att = nn.Sequential( + nn.AdaptiveAvgPool1d(1), + nn.Conv1d(channels, inter_channels, kernel_size=1, stride=1, padding=0), + nn.BatchNorm1d(inter_channels), + nn.ReLU(inplace=True), + nn.Conv1d(inter_channels, channels, kernel_size=1, stride=1, padding=0), + nn.BatchNorm1d(channels), + ) + elif type == '2D': + self.local_att = nn.Sequential( + nn.Conv2d(channels, inter_channels, kernel_size=1, stride=1, padding=0), + nn.BatchNorm2d(inter_channels), + nn.ReLU(inplace=True), + nn.Conv2d(inter_channels, channels, kernel_size=1, stride=1, padding=0), + nn.BatchNorm2d(channels), + ) + self.global_att = nn.Sequential( + nn.AdaptiveAvgPool2d(1), + nn.Conv2d(channels, inter_channels, kernel_size=1, stride=1, padding=0), + nn.BatchNorm2d(inter_channels), + nn.ReLU(inplace=True), + nn.Conv2d(inter_channels, channels, kernel_size=1, stride=1, padding=0), + nn.BatchNorm2d(channels), + ) + else: + raise f'the type is not supported.' + + self.sigmoid = nn.Sigmoid() + + def forward(self, x, residual): + flag = False + xa = x + residual + if xa.size(0) == 1: + xa = torch.cat([xa,xa],dim=0) + flag = True + xl = self.local_att(xa) + xg = self.global_att(xa) + xlg = xl + xg + wei = self.sigmoid(xlg) + xo = 2 * x * wei + 2 * residual * (1 - wei) + if flag: + xo = xo[0].unsqueeze(0) + return xo + diff --git a/ldm/modules/encoders/open_clap/htsat.py b/ldm/modules/encoders/open_clap/htsat.py new file mode 100644 index 0000000000000000000000000000000000000000..db96116286d307a73943886f947450215e061ba2 --- /dev/null +++ b/ldm/modules/encoders/open_clap/htsat.py @@ -0,0 +1,1022 @@ +# Ke Chen +# knutchen@ucsd.edu +# HTS-AT: A HIERARCHICAL TOKEN-SEMANTIC AUDIO TRANSFORMER FOR SOUND CLASSIFICATION AND DETECTION +# Some layers designed on the model +# below codes are based and referred from https://github.com/microsoft/Swin-Transformer +# Swin Transformer for Computer Vision: https://arxiv.org/pdf/2103.14030.pdf + +import torch +import torch.nn as nn +import torch.nn.functional as F +from itertools import repeat +import collections.abc +import math +import warnings + +from torch.nn.init import _calculate_fan_in_and_fan_out +import torch.utils.checkpoint as checkpoint + +import random + +from torchlibrosa.stft import Spectrogram, LogmelFilterBank +from torchlibrosa.augmentation import SpecAugmentation + +from itertools import repeat +from .utils import do_mixup, interpolate + +from .feature_fusion import iAFF, AFF, DAF + +# from PyTorch internals +def _ntuple(n): + def parse(x): + if isinstance(x, collections.abc.Iterable): + return x + return tuple(repeat(x, n)) + return parse + +to_1tuple = _ntuple(1) +to_2tuple = _ntuple(2) +to_3tuple = _ntuple(3) +to_4tuple = _ntuple(4) +to_ntuple = _ntuple + +def drop_path(x, drop_prob: float = 0., training: bool = False): + """Drop paths (Stochastic Depth) per sample (when applied in main path of residual blocks). + This is the same as the DropConnect impl I created for EfficientNet, etc networks, however, + the original name is misleading as 'Drop Connect' is a different form of dropout in a separate paper... + See discussion: https://github.com/tensorflow/tpu/issues/494#issuecomment-532968956 ... I've opted for + changing the layer and argument names to 'drop path' rather than mix DropConnect as a layer name and use + 'survival rate' as the argument. + """ + if drop_prob == 0. or not training: + return x + keep_prob = 1 - drop_prob + shape = (x.shape[0],) + (1,) * (x.ndim - 1) # work with diff dim tensors, not just 2D ConvNets + random_tensor = keep_prob + torch.rand(shape, dtype=x.dtype, device=x.device) + random_tensor.floor_() # binarize + output = x.div(keep_prob) * random_tensor + return output + + +class DropPath(nn.Module): + """Drop paths (Stochastic Depth) per sample (when applied in main path of residual blocks). + """ + def __init__(self, drop_prob=None): + super(DropPath, self).__init__() + self.drop_prob = drop_prob + + def forward(self, x): + return drop_path(x, self.drop_prob, self.training) + +class PatchEmbed(nn.Module): + """ 2D Image to Patch Embedding + """ + def __init__(self, img_size=224, patch_size=16, in_chans=3, embed_dim=768, norm_layer=None, flatten=True, patch_stride = 16, + enable_fusion=False, fusion_type='None'): + super().__init__() + img_size = to_2tuple(img_size) + patch_size = to_2tuple(patch_size) + patch_stride = to_2tuple(patch_stride) + self.img_size = img_size + self.patch_size = patch_size + self.patch_stride = patch_stride + self.grid_size = (img_size[0] // patch_stride[0], img_size[1] // patch_stride[1]) + self.num_patches = self.grid_size[0] * self.grid_size[1] + self.flatten = flatten + self.in_chans = in_chans + self.embed_dim = embed_dim + + self.enable_fusion = enable_fusion + self.fusion_type = fusion_type + + padding = ((patch_size[0] - patch_stride[0]) // 2, (patch_size[1] - patch_stride[1]) // 2) + + if (self.enable_fusion) and (self.fusion_type == 'channel_map'): + self.proj = nn.Conv2d(in_chans*4, embed_dim, kernel_size=patch_size, stride=patch_stride, padding=padding) + else: + self.proj = nn.Conv2d(in_chans, embed_dim, kernel_size=patch_size, stride=patch_stride, padding=padding) + self.norm = norm_layer(embed_dim) if norm_layer else nn.Identity() + + if (self.enable_fusion) and (self.fusion_type in ['daf_2d','aff_2d','iaff_2d']): + self.mel_conv2d = nn.Conv2d(in_chans, embed_dim, kernel_size=(patch_size[0], patch_size[1]*3), stride=(patch_stride[0], patch_stride[1] * 3), padding=padding) + if self.fusion_type == 'daf_2d': + self.fusion_model = DAF() + elif self.fusion_type == 'aff_2d': + self.fusion_model = AFF(channels=embed_dim, type='2D') + elif self.fusion_type == 'iaff_2d': + self.fusion_model = iAFF(channels=embed_dim, type='2D') + def forward(self, x, longer_idx = None): + if (self.enable_fusion) and (self.fusion_type in ['daf_2d','aff_2d','iaff_2d']): + global_x = x[:,0:1,:,:] + + + # global processing + B, C, H, W = global_x.shape + assert H == self.img_size[0] and W == self.img_size[1], \ + f"Input image size ({H}*{W}) doesn't match model ({self.img_size[0]}*{self.img_size[1]})." + global_x = self.proj(global_x) + TW = global_x.size(-1) + if len(longer_idx) > 0: + # local processing + local_x = x[longer_idx,1:,:,:].contiguous() + B, C, H, W = local_x.shape + local_x = local_x.view(B*C,1,H,W) + local_x = self.mel_conv2d(local_x) + local_x = local_x.view(B,C,local_x.size(1),local_x.size(2),local_x.size(3)) + local_x = local_x.permute((0,2,3,1,4)).contiguous().flatten(3) + TB,TC,TH,_ = local_x.size() + if local_x.size(-1) < TW: + local_x = torch.cat([local_x, torch.zeros((TB,TC,TH,TW-local_x.size(-1)), device=global_x.device)], dim=-1) + else: + local_x = local_x[:,:,:,:TW] + + global_x[longer_idx] = self.fusion_model(global_x[longer_idx],local_x) + x = global_x + else: + B, C, H, W = x.shape + assert H == self.img_size[0] and W == self.img_size[1], \ + f"Input image size ({H}*{W}) doesn't match model ({self.img_size[0]}*{self.img_size[1]})." + x = self.proj(x) + + if self.flatten: + x = x.flatten(2).transpose(1, 2) # BCHW -> BNC + x = self.norm(x) + return x + +class Mlp(nn.Module): + """ MLP as used in Vision Transformer, MLP-Mixer and related networks + """ + def __init__(self, in_features, hidden_features=None, out_features=None, act_layer=nn.GELU, drop=0.): + super().__init__() + out_features = out_features or in_features + hidden_features = hidden_features or in_features + self.fc1 = nn.Linear(in_features, hidden_features) + self.act = act_layer() + self.fc2 = nn.Linear(hidden_features, out_features) + self.drop = nn.Dropout(drop) + + def forward(self, x): + x = self.fc1(x) + x = self.act(x) + x = self.drop(x) + x = self.fc2(x) + x = self.drop(x) + return x + +def _no_grad_trunc_normal_(tensor, mean, std, a, b): + # Cut & paste from PyTorch official master until it's in a few official releases - RW + # Method based on https://people.sc.fsu.edu/~jburkardt/presentations/truncated_normal.pdf + def norm_cdf(x): + # Computes standard normal cumulative distribution function + return (1. + math.erf(x / math.sqrt(2.))) / 2. + + if (mean < a - 2 * std) or (mean > b + 2 * std): + warnings.warn("mean is more than 2 std from [a, b] in nn.init.trunc_normal_. " + "The distribution of values may be incorrect.", + stacklevel=2) + + with torch.no_grad(): + # Values are generated by using a truncated uniform distribution and + # then using the inverse CDF for the normal distribution. + # Get upper and lower cdf values + l = norm_cdf((a - mean) / std) + u = norm_cdf((b - mean) / std) + + # Uniformly fill tensor with values from [l, u], then translate to + # [2l-1, 2u-1]. + tensor.uniform_(2 * l - 1, 2 * u - 1) + + # Use inverse cdf transform for normal distribution to get truncated + # standard normal + tensor.erfinv_() + + # Transform to proper mean, std + tensor.mul_(std * math.sqrt(2.)) + tensor.add_(mean) + + # Clamp to ensure it's in the proper range + tensor.clamp_(min=a, max=b) + return tensor + + +def trunc_normal_(tensor, mean=0., std=1., a=-2., b=2.): + # type: (Tensor, float, float, float, float) -> Tensor + r"""Fills the input Tensor with values drawn from a truncated + normal distribution. The values are effectively drawn from the + normal distribution :math:`\mathcal{N}(\text{mean}, \text{std}^2)` + with values outside :math:`[a, b]` redrawn until they are within + the bounds. The method used for generating the random values works + best when :math:`a \leq \text{mean} \leq b`. + Args: + tensor: an n-dimensional `torch.Tensor` + mean: the mean of the normal distribution + std: the standard deviation of the normal distribution + a: the minimum cutoff value + b: the maximum cutoff value + Examples: + >>> w = torch.empty(3, 5) + >>> nn.init.trunc_normal_(w) + """ + return _no_grad_trunc_normal_(tensor, mean, std, a, b) + + +def variance_scaling_(tensor, scale=1.0, mode='fan_in', distribution='normal'): + fan_in, fan_out = _calculate_fan_in_and_fan_out(tensor) + if mode == 'fan_in': + denom = fan_in + elif mode == 'fan_out': + denom = fan_out + elif mode == 'fan_avg': + denom = (fan_in + fan_out) / 2 + + variance = scale / denom + + if distribution == "truncated_normal": + # constant is stddev of standard normal truncated to (-2, 2) + trunc_normal_(tensor, std=math.sqrt(variance) / .87962566103423978) + elif distribution == "normal": + tensor.normal_(std=math.sqrt(variance)) + elif distribution == "uniform": + bound = math.sqrt(3 * variance) + tensor.uniform_(-bound, bound) + else: + raise ValueError(f"invalid distribution {distribution}") + + +def lecun_normal_(tensor): + variance_scaling_(tensor, mode='fan_in', distribution='truncated_normal') + +def window_partition(x, window_size): + """ + Args: + x: (B, H, W, C) + window_size (int): window size + Returns: + windows: (num_windows*B, window_size, window_size, C) + """ + B, H, W, C = x.shape + x = x.view(B, H // window_size, window_size, W // window_size, window_size, C) + windows = x.permute(0, 1, 3, 2, 4, 5).contiguous().view(-1, window_size, window_size, C) + return windows + + +def window_reverse(windows, window_size, H, W): + """ + Args: + windows: (num_windows*B, window_size, window_size, C) + window_size (int): Window size + H (int): Height of image + W (int): Width of image + Returns: + x: (B, H, W, C) + """ + B = int(windows.shape[0] / (H * W / window_size / window_size)) + x = windows.view(B, H // window_size, W // window_size, window_size, window_size, -1) + x = x.permute(0, 1, 3, 2, 4, 5).contiguous().view(B, H, W, -1) + return x + + +class WindowAttention(nn.Module): + r""" Window based multi-head self attention (W-MSA) module with relative position bias. + It supports both of shifted and non-shifted window. + Args: + dim (int): Number of input channels. + window_size (tuple[int]): The height and width of the window. + num_heads (int): Number of attention heads. + qkv_bias (bool, optional): If True, add a learnable bias to query, key, value. Default: True + qk_scale (float | None, optional): Override default qk scale of head_dim ** -0.5 if set + attn_drop (float, optional): Dropout ratio of attention weight. Default: 0.0 + proj_drop (float, optional): Dropout ratio of output. Default: 0.0 + """ + + def __init__(self, dim, window_size, num_heads, qkv_bias=True, qk_scale=None, attn_drop=0., proj_drop=0.): + + super().__init__() + self.dim = dim + self.window_size = window_size # Wh, Ww + self.num_heads = num_heads + head_dim = dim // num_heads + self.scale = qk_scale or head_dim ** -0.5 + + # define a parameter table of relative position bias + self.relative_position_bias_table = nn.Parameter( + torch.zeros((2 * window_size[0] - 1) * (2 * window_size[1] - 1), num_heads)) # 2*Wh-1 * 2*Ww-1, nH + + # get pair-wise relative position index for each token inside the window + coords_h = torch.arange(self.window_size[0]) + coords_w = torch.arange(self.window_size[1]) + coords = torch.stack(torch.meshgrid([coords_h, coords_w])) # 2, Wh, Ww + coords_flatten = torch.flatten(coords, 1) # 2, Wh*Ww + relative_coords = coords_flatten[:, :, None] - coords_flatten[:, None, :] # 2, Wh*Ww, Wh*Ww + relative_coords = relative_coords.permute(1, 2, 0).contiguous() # Wh*Ww, Wh*Ww, 2 + relative_coords[:, :, 0] += self.window_size[0] - 1 # shift to start from 0 + relative_coords[:, :, 1] += self.window_size[1] - 1 + relative_coords[:, :, 0] *= 2 * self.window_size[1] - 1 + relative_position_index = relative_coords.sum(-1) # Wh*Ww, Wh*Ww + self.register_buffer("relative_position_index", relative_position_index) + + self.qkv = nn.Linear(dim, dim * 3, bias=qkv_bias) + self.attn_drop = nn.Dropout(attn_drop) + self.proj = nn.Linear(dim, dim) + self.proj_drop = nn.Dropout(proj_drop) + + trunc_normal_(self.relative_position_bias_table, std=.02) + self.softmax = nn.Softmax(dim=-1) + + def forward(self, x, mask=None): + """ + Args: + x: input features with shape of (num_windows*B, N, C) + mask: (0/-inf) mask with shape of (num_windows, Wh*Ww, Wh*Ww) or None + """ + B_, N, C = x.shape + qkv = self.qkv(x).reshape(B_, N, 3, self.num_heads, C // self.num_heads).permute(2, 0, 3, 1, 4) + q, k, v = qkv[0], qkv[1], qkv[2] # make torchscript happy (cannot use tensor as tuple) + + q = q * self.scale + attn = (q @ k.transpose(-2, -1)) + + relative_position_bias = self.relative_position_bias_table[self.relative_position_index.view(-1)].view( + self.window_size[0] * self.window_size[1], self.window_size[0] * self.window_size[1], -1) # Wh*Ww,Wh*Ww,nH + relative_position_bias = relative_position_bias.permute(2, 0, 1).contiguous() # nH, Wh*Ww, Wh*Ww + attn = attn + relative_position_bias.unsqueeze(0) + + if mask is not None: + nW = mask.shape[0] + attn = attn.view(B_ // nW, nW, self.num_heads, N, N) + mask.unsqueeze(1).unsqueeze(0) + attn = attn.view(-1, self.num_heads, N, N) + attn = self.softmax(attn) + else: + attn = self.softmax(attn) + + attn = self.attn_drop(attn) + + x = (attn @ v).transpose(1, 2).reshape(B_, N, C) + x = self.proj(x) + x = self.proj_drop(x) + return x, attn + + def extra_repr(self): + return f'dim={self.dim}, window_size={self.window_size}, num_heads={self.num_heads}' + + +# We use the model based on Swintransformer Block, therefore we can use the swin-transformer pretrained model +class SwinTransformerBlock(nn.Module): + r""" Swin Transformer Block. + Args: + dim (int): Number of input channels. + input_resolution (tuple[int]): Input resulotion. + num_heads (int): Number of attention heads. + window_size (int): Window size. + shift_size (int): Shift size for SW-MSA. + mlp_ratio (float): Ratio of mlp hidden dim to embedding dim. + qkv_bias (bool, optional): If True, add a learnable bias to query, key, value. Default: True + qk_scale (float | None, optional): Override default qk scale of head_dim ** -0.5 if set. + drop (float, optional): Dropout rate. Default: 0.0 + attn_drop (float, optional): Attention dropout rate. Default: 0.0 + drop_path (float, optional): Stochastic depth rate. Default: 0.0 + act_layer (nn.Module, optional): Activation layer. Default: nn.GELU + norm_layer (nn.Module, optional): Normalization layer. Default: nn.LayerNorm + """ + + def __init__(self, dim, input_resolution, num_heads, window_size=7, shift_size=0, + mlp_ratio=4., qkv_bias=True, qk_scale=None, drop=0., attn_drop=0., drop_path=0., + act_layer=nn.GELU, norm_layer=nn.LayerNorm, norm_before_mlp='ln'): + super().__init__() + self.dim = dim + self.input_resolution = input_resolution + self.num_heads = num_heads + self.window_size = window_size + self.shift_size = shift_size + self.mlp_ratio = mlp_ratio + self.norm_before_mlp = norm_before_mlp + if min(self.input_resolution) <= self.window_size: + # if window size is larger than input resolution, we don't partition windows + self.shift_size = 0 + self.window_size = min(self.input_resolution) + assert 0 <= self.shift_size < self.window_size, "shift_size must in 0-window_size" + + self.norm1 = norm_layer(dim) + self.attn = WindowAttention( + dim, window_size=to_2tuple(self.window_size), num_heads=num_heads, + qkv_bias=qkv_bias, qk_scale=qk_scale, attn_drop=attn_drop, proj_drop=drop) + + self.drop_path = DropPath(drop_path) if drop_path > 0. else nn.Identity() + if self.norm_before_mlp == 'ln': + self.norm2 = nn.LayerNorm(dim) + elif self.norm_before_mlp == 'bn': + self.norm2 = lambda x: nn.BatchNorm1d(dim)(x.transpose(1, 2)).transpose(1, 2) + else: + raise NotImplementedError + mlp_hidden_dim = int(dim * mlp_ratio) + self.mlp = Mlp(in_features=dim, hidden_features=mlp_hidden_dim, act_layer=act_layer, drop=drop) + + if self.shift_size > 0: + # calculate attention mask for SW-MSA + H, W = self.input_resolution + img_mask = torch.zeros((1, H, W, 1)) # 1 H W 1 + h_slices = (slice(0, -self.window_size), + slice(-self.window_size, -self.shift_size), + slice(-self.shift_size, None)) + w_slices = (slice(0, -self.window_size), + slice(-self.window_size, -self.shift_size), + slice(-self.shift_size, None)) + cnt = 0 + for h in h_slices: + for w in w_slices: + img_mask[:, h, w, :] = cnt + cnt += 1 + + mask_windows = window_partition(img_mask, self.window_size) # nW, window_size, window_size, 1 + mask_windows = mask_windows.view(-1, self.window_size * self.window_size) + attn_mask = mask_windows.unsqueeze(1) - mask_windows.unsqueeze(2) + attn_mask = attn_mask.masked_fill(attn_mask != 0, float(-100.0)).masked_fill(attn_mask == 0, float(0.0)) + else: + attn_mask = None + + self.register_buffer("attn_mask", attn_mask) + + def forward(self, x): + # pdb.set_trace() + H, W = self.input_resolution + # print("H: ", H) + # print("W: ", W) + # pdb.set_trace() + B, L, C = x.shape + # assert L == H * W, "input feature has wrong size" + + shortcut = x + x = self.norm1(x) + x = x.view(B, H, W, C) + + # cyclic shift + if self.shift_size > 0: + shifted_x = torch.roll(x, shifts=(-self.shift_size, -self.shift_size), dims=(1, 2)) + else: + shifted_x = x + + # partition windows + x_windows = window_partition(shifted_x, self.window_size) # nW*B, window_size, window_size, C + x_windows = x_windows.view(-1, self.window_size * self.window_size, C) # nW*B, window_size*window_size, C + + # W-MSA/SW-MSA + attn_windows, attn = self.attn(x_windows, mask=self.attn_mask) # nW*B, window_size*window_size, C + + # merge windows + attn_windows = attn_windows.view(-1, self.window_size, self.window_size, C) + shifted_x = window_reverse(attn_windows, self.window_size, H, W) # B H' W' C + + # reverse cyclic shift + if self.shift_size > 0: + x = torch.roll(shifted_x, shifts=(self.shift_size, self.shift_size), dims=(1, 2)) + else: + x = shifted_x + x = x.view(B, H * W, C) + + # FFN + x = shortcut + self.drop_path(x) + x = x + self.drop_path(self.mlp(self.norm2(x))) + + return x, attn + + def extra_repr(self): + return f"dim={self.dim}, input_resolution={self.input_resolution}, num_heads={self.num_heads}, " \ + f"window_size={self.window_size}, shift_size={self.shift_size}, mlp_ratio={self.mlp_ratio}" + + + +class PatchMerging(nn.Module): + r""" Patch Merging Layer. + Args: + input_resolution (tuple[int]): Resolution of input feature. + dim (int): Number of input channels. + norm_layer (nn.Module, optional): Normalization layer. Default: nn.LayerNorm + """ + + def __init__(self, input_resolution, dim, norm_layer=nn.LayerNorm): + super().__init__() + self.input_resolution = input_resolution + self.dim = dim + self.reduction = nn.Linear(4 * dim, 2 * dim, bias=False) + self.norm = norm_layer(4 * dim) + + def forward(self, x): + """ + x: B, H*W, C + """ + H, W = self.input_resolution + B, L, C = x.shape + assert L == H * W, "input feature has wrong size" + assert H % 2 == 0 and W % 2 == 0, f"x size ({H}*{W}) are not even." + + x = x.view(B, H, W, C) + + x0 = x[:, 0::2, 0::2, :] # B H/2 W/2 C + x1 = x[:, 1::2, 0::2, :] # B H/2 W/2 C + x2 = x[:, 0::2, 1::2, :] # B H/2 W/2 C + x3 = x[:, 1::2, 1::2, :] # B H/2 W/2 C + x = torch.cat([x0, x1, x2, x3], -1) # B H/2 W/2 4*C + x = x.view(B, -1, 4 * C) # B H/2*W/2 4*C + + x = self.norm(x) + x = self.reduction(x) + + return x + + def extra_repr(self): + return f"input_resolution={self.input_resolution}, dim={self.dim}" + + +class BasicLayer(nn.Module): + """ A basic Swin Transformer layer for one stage. + Args: + dim (int): Number of input channels. + input_resolution (tuple[int]): Input resolution. + depth (int): Number of blocks. + num_heads (int): Number of attention heads. + window_size (int): Local window size. + mlp_ratio (float): Ratio of mlp hidden dim to embedding dim. + qkv_bias (bool, optional): If True, add a learnable bias to query, key, value. Default: True + qk_scale (float | None, optional): Override default qk scale of head_dim ** -0.5 if set. + drop (float, optional): Dropout rate. Default: 0.0 + attn_drop (float, optional): Attention dropout rate. Default: 0.0 + drop_path (float | tuple[float], optional): Stochastic depth rate. Default: 0.0 + norm_layer (nn.Module, optional): Normalization layer. Default: nn.LayerNorm + downsample (nn.Module | None, optional): Downsample layer at the end of the layer. Default: None + use_checkpoint (bool): Whether to use checkpointing to save memory. Default: False. + """ + + def __init__(self, dim, input_resolution, depth, num_heads, window_size, + mlp_ratio=4., qkv_bias=True, qk_scale=None, drop=0., attn_drop=0., + drop_path=0., norm_layer=nn.LayerNorm, downsample=None, use_checkpoint=False, + norm_before_mlp='ln'): + + super().__init__() + self.dim = dim + self.input_resolution = input_resolution + self.depth = depth + self.use_checkpoint = use_checkpoint + + # build blocks + self.blocks = nn.ModuleList([ + SwinTransformerBlock(dim=dim, input_resolution=input_resolution, + num_heads=num_heads, window_size=window_size, + shift_size=0 if (i % 2 == 0) else window_size // 2, + mlp_ratio=mlp_ratio, + qkv_bias=qkv_bias, qk_scale=qk_scale, + drop=drop, attn_drop=attn_drop, + drop_path=drop_path[i] if isinstance(drop_path, list) else drop_path, + norm_layer=norm_layer, norm_before_mlp=norm_before_mlp) + for i in range(depth)]) + + # patch merging layer + if downsample is not None: + self.downsample = downsample(input_resolution, dim=dim, norm_layer=norm_layer) + else: + self.downsample = None + + def forward(self, x): + attns = [] + for blk in self.blocks: + if self.use_checkpoint: + x = checkpoint.checkpoint(blk, x) + else: + x, attn = blk(x) + if not self.training: + attns.append(attn.unsqueeze(0)) + if self.downsample is not None: + x = self.downsample(x) + if not self.training: + attn = torch.cat(attns, dim = 0) + attn = torch.mean(attn, dim = 0) + return x, attn + + def extra_repr(self): + return f"dim={self.dim}, input_resolution={self.input_resolution}, depth={self.depth}" + + +# The Core of HTSAT +class HTSAT_Swin_Transformer(nn.Module): + r"""HTSAT based on the Swin Transformer + Args: + spec_size (int | tuple(int)): Input Spectrogram size. Default 256 + patch_size (int | tuple(int)): Patch size. Default: 4 + path_stride (iot | tuple(int)): Patch Stride for Frequency and Time Axis. Default: 4 + in_chans (int): Number of input image channels. Default: 1 (mono) + num_classes (int): Number of classes for classification head. Default: 527 + embed_dim (int): Patch embedding dimension. Default: 96 + depths (tuple(int)): Depth of each HTSAT-Swin Transformer layer. + num_heads (tuple(int)): Number of attention heads in different layers. + window_size (int): Window size. Default: 8 + mlp_ratio (float): Ratio of mlp hidden dim to embedding dim. Default: 4 + qkv_bias (bool): If True, add a learnable bias to query, key, value. Default: True + qk_scale (float): Override default qk scale of head_dim ** -0.5 if set. Default: None + drop_rate (float): Dropout rate. Default: 0 + attn_drop_rate (float): Attention dropout rate. Default: 0 + drop_path_rate (float): Stochastic depth rate. Default: 0.1 + norm_layer (nn.Module): Normalization layer. Default: nn.LayerNorm. + ape (bool): If True, add absolute position embedding to the patch embedding. Default: False + patch_norm (bool): If True, add normalization after patch embedding. Default: True + use_checkpoint (bool): Whether to use checkpointing to save memory. Default: False + config (module): The configuration Module from config.py + """ + + def __init__(self, spec_size=256, patch_size=4, patch_stride=(4,4), + in_chans=1, num_classes=527, + embed_dim=96, depths=[2, 2, 6, 2], num_heads=[4, 8, 16, 32], + window_size=8, mlp_ratio=4., qkv_bias=True, qk_scale=None, + drop_rate=0., attn_drop_rate=0., drop_path_rate=0.1, + norm_layer=nn.LayerNorm, + ape=False, patch_norm=True, + use_checkpoint=False, norm_before_mlp='ln', config = None, + enable_fusion = False, fusion_type = 'None', **kwargs): + super(HTSAT_Swin_Transformer, self).__init__() + + self.config = config + self.spec_size = spec_size + self.patch_stride = patch_stride + self.patch_size = patch_size + self.window_size = window_size + self.embed_dim = embed_dim + self.depths = depths + self.ape = ape + self.in_chans = in_chans + self.num_classes = num_classes + self.num_heads = num_heads + self.num_layers = len(self.depths) + self.num_features = int(self.embed_dim * 2 ** (self.num_layers - 1)) + + self.drop_rate = drop_rate + self.attn_drop_rate = attn_drop_rate + self.drop_path_rate = drop_path_rate + + self.qkv_bias = qkv_bias + self.qk_scale = None + + self.patch_norm = patch_norm + self.norm_layer = norm_layer if self.patch_norm else None + self.norm_before_mlp = norm_before_mlp + self.mlp_ratio = mlp_ratio + + self.use_checkpoint = use_checkpoint + + self.enable_fusion = enable_fusion + self.fusion_type = fusion_type + + # process mel-spec ; used only once + self.freq_ratio = self.spec_size // self.config.mel_bins + window = 'hann' + center = True + pad_mode = 'reflect' + ref = 1.0 + amin = 1e-10 + top_db = None + self.interpolate_ratio = 32 # Downsampled ratio + # Spectrogram extractor + self.spectrogram_extractor = Spectrogram(n_fft=config.window_size, hop_length=config.hop_size, + win_length=config.window_size, window=window, center=center, pad_mode=pad_mode, + freeze_parameters=True) + # Logmel feature extractor + self.logmel_extractor = LogmelFilterBank(sr=config.sample_rate, n_fft=config.window_size, + n_mels=config.mel_bins, fmin=config.fmin, fmax=config.fmax, ref=ref, amin=amin, top_db=top_db, + freeze_parameters=True) + # Spec augmenter + self.spec_augmenter = SpecAugmentation(time_drop_width=64, time_stripes_num=2, + freq_drop_width=8, freq_stripes_num=2) # 2 2 + self.bn0 = nn.BatchNorm2d(self.config.mel_bins) + + + # split spctrogram into non-overlapping patches + self.patch_embed = PatchEmbed( + img_size=self.spec_size, patch_size=self.patch_size, in_chans=self.in_chans, + embed_dim=self.embed_dim, norm_layer=self.norm_layer, patch_stride = patch_stride, + enable_fusion=self.enable_fusion, fusion_type=self.fusion_type + ) + + num_patches = self.patch_embed.num_patches + patches_resolution = self.patch_embed.grid_size + self.patches_resolution = patches_resolution + + # absolute position embedding + if self.ape: + self.absolute_pos_embed = nn.Parameter(torch.zeros(1, num_patches, self.embed_dim)) + trunc_normal_(self.absolute_pos_embed, std=.02) + + self.pos_drop = nn.Dropout(p=self.drop_rate) + + # stochastic depth + dpr = [x.item() for x in torch.linspace(0, self.drop_path_rate, sum(self.depths))] # stochastic depth decay rule + + # build layers + self.layers = nn.ModuleList() + for i_layer in range(self.num_layers): + layer = BasicLayer(dim=int(self.embed_dim * 2 ** i_layer), + input_resolution=(patches_resolution[0] // (2 ** i_layer), + patches_resolution[1] // (2 ** i_layer)), + depth=self.depths[i_layer], + num_heads=self.num_heads[i_layer], + window_size=self.window_size, + mlp_ratio=self.mlp_ratio, + qkv_bias=self.qkv_bias, qk_scale=self.qk_scale, + drop=self.drop_rate, attn_drop=self.attn_drop_rate, + drop_path=dpr[sum(self.depths[:i_layer]):sum(self.depths[:i_layer + 1])], + norm_layer=self.norm_layer, + downsample=PatchMerging if (i_layer < self.num_layers - 1) else None, + use_checkpoint=use_checkpoint, + norm_before_mlp=self.norm_before_mlp) + self.layers.append(layer) + + self.norm = self.norm_layer(self.num_features) + self.avgpool = nn.AdaptiveAvgPool1d(1) + self.maxpool = nn.AdaptiveMaxPool1d(1) + + SF = self.spec_size // (2 ** (len(self.depths) - 1)) // self.patch_stride[0] // self.freq_ratio + self.tscam_conv = nn.Conv2d( + in_channels = self.num_features, + out_channels = self.num_classes, + kernel_size = (SF,3), + padding = (0,1) + ) + self.head = nn.Linear(num_classes, num_classes) + + if (self.enable_fusion) and (self.fusion_type in ['daf_1d','aff_1d','iaff_1d']): + self.mel_conv1d = nn.Sequential( + nn.Conv1d(64, 64, kernel_size=5, stride=3, padding=2), + nn.BatchNorm1d(64) + ) + if self.fusion_type == 'daf_1d': + self.fusion_model = DAF() + elif self.fusion_type == 'aff_1d': + self.fusion_model = AFF(channels=64, type='1D') + elif self.fusion_type == 'iaff_1d': + self.fusion_model = iAFF(channels=64, type='1D') + + self.apply(self._init_weights) + + def _init_weights(self, m): + if isinstance(m, nn.Linear): + trunc_normal_(m.weight, std=.02) + if isinstance(m, nn.Linear) and m.bias is not None: + nn.init.constant_(m.bias, 0) + elif isinstance(m, nn.LayerNorm): + nn.init.constant_(m.bias, 0) + nn.init.constant_(m.weight, 1.0) + + @torch.jit.ignore + def no_weight_decay(self): + return {'absolute_pos_embed'} + + @torch.jit.ignore + def no_weight_decay_keywords(self): + return {'relative_position_bias_table'} + + + def forward_features(self, x, longer_idx = None): + # A deprecated optimization for using a hierarchical output from different blocks + + frames_num = x.shape[2] + x = self.patch_embed(x, longer_idx = longer_idx) + if self.ape: + x = x + self.absolute_pos_embed + x = self.pos_drop(x) + for i, layer in enumerate(self.layers): + x, attn = layer(x) + # for x + x = self.norm(x) + B, N, C = x.shape + SF = frames_num // (2 ** (len(self.depths) - 1)) // self.patch_stride[0] + ST = frames_num // (2 ** (len(self.depths) - 1)) // self.patch_stride[1] + x = x.permute(0,2,1).contiguous().reshape(B, C, SF, ST) + B, C, F, T = x.shape + # group 2D CNN + c_freq_bin = F // self.freq_ratio + x = x.reshape(B, C, F // c_freq_bin, c_freq_bin, T) + x = x.permute(0,1,3,2,4).contiguous().reshape(B, C, c_freq_bin, -1) + # get latent_output + fine_grained_latent_output = torch.mean(x, dim = 2) + fine_grained_latent_output = interpolate(fine_grained_latent_output.permute(0,2,1).contiguous(), 8 * self.patch_stride[1]) + + latent_output = self.avgpool(torch.flatten(x,2)) + latent_output = torch.flatten(latent_output, 1) + + # display the attention map, if needed + + x = self.tscam_conv(x) + x = torch.flatten(x, 2) # B, C, T + + fpx = interpolate(torch.sigmoid(x).permute(0,2,1).contiguous(), 8 * self.patch_stride[1]) + + x = self.avgpool(x) + x = torch.flatten(x, 1) + + output_dict = { + 'framewise_output': fpx, # already sigmoided + 'clipwise_output': torch.sigmoid(x), + 'fine_grained_embedding': fine_grained_latent_output, + 'embedding': latent_output + } + + return output_dict + + def crop_wav(self, x, crop_size, spe_pos = None): + time_steps = x.shape[2] + tx = torch.zeros(x.shape[0], x.shape[1], crop_size, x.shape[3]).to(x.device) + for i in range(len(x)): + if spe_pos is None: + crop_pos = random.randint(0, time_steps - crop_size - 1) + else: + crop_pos = spe_pos + tx[i][0] = x[i, 0, crop_pos:crop_pos + crop_size,:] + return tx + + # Reshape the wavform to a img size, if you want to use the pretrained swin transformer model + def reshape_wav2img(self, x): + B, C, T, F = x.shape + target_T = int(self.spec_size * self.freq_ratio) + target_F = self.spec_size // self.freq_ratio + assert T <= target_T and F <= target_F, "the wav size should less than or equal to the swin input size" + # to avoid bicubic zero error + if T < target_T: + x = nn.functional.interpolate(x, (target_T, x.shape[3]), mode="bicubic", align_corners=True) + if F < target_F: + x = nn.functional.interpolate(x, (x.shape[2], target_F), mode="bicubic", align_corners=True) + x = x.permute(0,1,3,2).contiguous() + x = x.reshape(x.shape[0], x.shape[1], x.shape[2], self.freq_ratio, x.shape[3] // self.freq_ratio) + # print(x.shape) + x = x.permute(0,1,3,2,4).contiguous() + x = x.reshape(x.shape[0], x.shape[1], x.shape[2] * x.shape[3], x.shape[4]) + return x + + # Repeat the wavform to a img size, if you want to use the pretrained swin transformer model + def repeat_wat2img(self, x, cur_pos): + B, C, T, F = x.shape + target_T = int(self.spec_size * self.freq_ratio) + target_F = self.spec_size // self.freq_ratio + assert T <= target_T and F <= target_F, "the wav size should less than or equal to the swin input size" + # to avoid bicubic zero error + if T < target_T: + x = nn.functional.interpolate(x, (target_T, x.shape[3]), mode="bicubic", align_corners=True) + if F < target_F: + x = nn.functional.interpolate(x, (x.shape[2], target_F), mode="bicubic", align_corners=True) + x = x.permute(0,1,3,2).contiguous() # B C F T + x = x[:,:,:,cur_pos:cur_pos + self.spec_size] + x = x.repeat(repeats = (1,1,4,1)) + return x + + def forward(self, x: torch.Tensor, mixup_lambda = None, infer_mode = False, device=None):# out_feat_keys: List[str] = None): + + if self.enable_fusion and x["longer"].sum() == 0: + # if no audio is longer than 10s, then randomly select one audio to be longer + x["longer"][torch.randint(0, x["longer"].shape[0], (1,))] = True + + if not self.enable_fusion: + x = x["waveform"].to(device=device, non_blocking=True) + x = self.spectrogram_extractor(x) # (batch_size, 1, time_steps, freq_bins) + x = self.logmel_extractor(x) # (batch_size, 1, time_steps, mel_bins) + x = x.transpose(1, 3) + x = self.bn0(x) + x = x.transpose(1, 3) + if self.training: + x = self.spec_augmenter(x) + + if self.training and mixup_lambda is not None: + x = do_mixup(x, mixup_lambda) + + x = self.reshape_wav2img(x) + output_dict = self.forward_features(x) + else: + longer_list = x["longer"].to(device=device, non_blocking=True) + x = x["mel_fusion"].to(device=device, non_blocking=True) + x = x.transpose(1, 3) + x = self.bn0(x) + x = x.transpose(1, 3) + longer_list_idx = torch.where(longer_list)[0] + if self.fusion_type in ['daf_1d','aff_1d','iaff_1d']: + new_x = x[:,0:1,:,:].clone().contiguous() + if len(longer_list_idx) > 0: + # local processing + fusion_x_local = x[longer_list_idx,1:,:,:].clone().contiguous() + FB,FC,FT,FF = fusion_x_local.size() + fusion_x_local = fusion_x_local.view(FB * FC, FT, FF) + fusion_x_local = torch.permute(fusion_x_local, (0,2,1)).contiguous() + fusion_x_local = self.mel_conv1d(fusion_x_local) + fusion_x_local = fusion_x_local.view(FB,FC,FF,fusion_x_local.size(-1)) + fusion_x_local = torch.permute(fusion_x_local, (0,2,1,3)).contiguous().flatten(2) + if fusion_x_local.size(-1) < FT: + fusion_x_local = torch.cat([fusion_x_local, torch.zeros((FB,FF,FT- fusion_x_local.size(-1)), device=device)], dim=-1) + else: + fusion_x_local = fusion_x_local[:,:,:FT] + # 1D fusion + new_x = new_x.squeeze(1).permute((0,2,1)).contiguous() + new_x[longer_list_idx] = self.fusion_model(new_x[longer_list_idx], fusion_x_local) + x = new_x.permute((0,2,1)).contiguous()[:,None,:,:] + else: + x = new_x + + elif self.fusion_type in ['daf_2d','aff_2d','iaff_2d','channel_map']: + x = x # no change + + if self.training: + x = self.spec_augmenter(x) + if self.training and mixup_lambda is not None: + x = do_mixup(x, mixup_lambda) + + x = self.reshape_wav2img(x) + output_dict = self.forward_features(x, longer_idx = longer_list_idx) + + # if infer_mode: + # # in infer mode. we need to handle different length audio input + # frame_num = x.shape[2] + # target_T = int(self.spec_size * self.freq_ratio) + # repeat_ratio = math.floor(target_T / frame_num) + # x = x.repeat(repeats=(1,1,repeat_ratio,1)) + # x = self.reshape_wav2img(x) + # output_dict = self.forward_features(x) + # else: + # if x.shape[2] > self.freq_ratio * self.spec_size: + # if self.training: + # x = self.crop_wav(x, crop_size=self.freq_ratio * self.spec_size) + # x = self.reshape_wav2img(x) + # output_dict = self.forward_features(x) + # else: + # # Change: Hard code here + # overlap_size = (x.shape[2] - 1) // 4 + # output_dicts = [] + # crop_size = (x.shape[2] - 1) // 2 + # for cur_pos in range(0, x.shape[2] - crop_size - 1, overlap_size): + # tx = self.crop_wav(x, crop_size = crop_size, spe_pos = cur_pos) + # tx = self.reshape_wav2img(tx) + # output_dicts.append(self.forward_features(tx)) + # clipwise_output = torch.zeros_like(output_dicts[0]["clipwise_output"]).float().to(x.device) + # framewise_output = torch.zeros_like(output_dicts[0]["framewise_output"]).float().to(x.device) + # for d in output_dicts: + # clipwise_output += d["clipwise_output"] + # framewise_output += d["framewise_output"] + # clipwise_output = clipwise_output / len(output_dicts) + # framewise_output = framewise_output / len(output_dicts) + # output_dict = { + # 'framewise_output': framewise_output, + # 'clipwise_output': clipwise_output + # } + # else: # this part is typically used, and most easy one + # x = self.reshape_wav2img(x) + # output_dict = self.forward_features(x) + # x = self.head(x) + + # We process the data in the dataloader part, in that here we only consider the input_T < fixed_T + + + + return output_dict + +def create_htsat_model(audio_cfg, enable_fusion=False, fusion_type='None'): + try: + + assert audio_cfg.model_name in ["tiny", "base", "large"], "model name for HTS-AT is wrong!" + if audio_cfg.model_name == "tiny": + model = HTSAT_Swin_Transformer( + spec_size=256, + patch_size=4, + patch_stride=(4,4), + num_classes=audio_cfg.class_num, + embed_dim=96, + depths=[2,2,6,2], + num_heads=[4,8,16,32], + window_size=8, + config = audio_cfg, + enable_fusion = enable_fusion, + fusion_type = fusion_type + ) + elif audio_cfg.model_name == "base": + model = HTSAT_Swin_Transformer( + spec_size=256, + patch_size=4, + patch_stride=(4,4), + num_classes=audio_cfg.class_num, + embed_dim=128, + depths=[2,2,12,2], + num_heads=[4,8,16,32], + window_size=8, + config = audio_cfg, + enable_fusion = enable_fusion, + fusion_type = fusion_type + ) + elif audio_cfg.model_name == "large": + model = HTSAT_Swin_Transformer( + spec_size=256, + patch_size=4, + patch_stride=(4,4), + num_classes=audio_cfg.class_num, + embed_dim=256, + depths=[2,2,12,2], + num_heads=[4,8,16,32], + window_size=8, + config = audio_cfg, + enable_fusion = enable_fusion, + fusion_type = fusion_type + ) + + return model + except: + raise RuntimeError(f'Import Model for {audio_cfg.model_name} not found, or the audio cfg parameters are not enough.') + \ No newline at end of file diff --git a/ldm/modules/encoders/open_clap/linear_probe.py b/ldm/modules/encoders/open_clap/linear_probe.py new file mode 100644 index 0000000000000000000000000000000000000000..bb2841dd4e28201db8b5bd4a215e1b8b9a60d25a --- /dev/null +++ b/ldm/modules/encoders/open_clap/linear_probe.py @@ -0,0 +1,63 @@ +import numpy as np +import torch.nn.functional as F +from torch import nn +from .model import MLPLayers + + +class LinearProbe(nn.Module): + def __init__(self, model, mlp, freeze, in_ch, out_ch, act=None): + """ + Args: + model: nn.Module + mlp: bool, if True, then use the MLP layer as the linear probe module + freeze: bool, if Ture, then freeze all the CLAP model's layers when training the linear probe + in_ch: int, the output channel from CLAP model + out_ch: int, the output channel from linear probe (class_num) + act: torch.nn.functional, the activation function before the loss function + """ + super().__init__() + in_ch = 512 + self.clap_model = model + self.clap_model.text_branch = None # to save memory + self.freeze = freeze + if mlp: + self.lp_layer = MLPLayers(units=[in_ch, in_ch * 2, out_ch]) + else: + self.lp_layer = nn.Linear(in_ch, out_ch) + + if self.freeze: + for param in self.clap_model.parameters(): + param.requires_grad = False + + if act == 'None': + self.act = None + elif act == 'relu': + self.act = nn.ReLU() + elif act == 'elu': + self.act = nn.ELU() + elif act == 'prelu': + self.act = nn.PReLU(num_parameters=in_ch) + elif act == 'softmax': + self.act = nn.Softmax(dim=-1) + elif act == 'sigmoid': + self.act = nn.Sigmoid() + + def forward(self, x, mix_lambda=None, device=None): + """ + Args: + x: waveform, torch.tensor [batch, t_samples] / batch of mel_spec and longer list + mix_lambda: torch.tensor [batch], the mixup lambda + Returns: + class_prob: torch.tensor [batch, class_num] + + """ + # batchnorm cancel grandient + if self.freeze: + self.clap_model.eval() + + x = self.clap_model.audio_projection( + self.clap_model.audio_branch(x, mixup_lambda=mix_lambda, device=device)["embedding"]) + out = self.lp_layer(x) + if self.act is not None: + out = self.act(out) + return out diff --git a/ldm/modules/encoders/open_clap/loss.py b/ldm/modules/encoders/open_clap/loss.py new file mode 100644 index 0000000000000000000000000000000000000000..53bbedd959813b072b146c16c14cd96df6cada14 --- /dev/null +++ b/ldm/modules/encoders/open_clap/loss.py @@ -0,0 +1,307 @@ +from multiprocessing.sharedctypes import Value +import torch +import torch.distributed.nn +from torch import distributed as dist, nn as nn +from torch.nn import functional as F +import numpy as np +from sklearn.metrics import average_precision_score, roc_auc_score, accuracy_score + +try: + import horovod.torch as hvd +except ImportError: + hvd = None + + +def gather_features( + audio_features, + text_features, + audio_features_mlp=None, + text_features_mlp=None, + local_loss=False, + gather_with_grad=False, + rank=0, + world_size=1, + use_horovod=False, + mlp_loss=False +): + if use_horovod: + assert hvd is not None, 'Please install horovod' + if gather_with_grad: + all_audio_features = hvd.allgather(audio_features) + all_text_features = hvd.allgather(text_features) + if mlp_loss: + all_audio_features_mlp = hvd.allgather(audio_features_mlp) + all_text_features_mlp = hvd.allgather(text_features_mlp) + else: + with torch.no_grad(): + all_audio_features = hvd.allgather(audio_features) + all_text_features = hvd.allgather(text_features) + if mlp_loss: + all_audio_features_mlp = hvd.allgather(audio_features_mlp) + all_text_features_mlp = hvd.allgather(text_features_mlp) + if not local_loss: + # ensure grads for local rank when all_* features don't have a gradient + gathered_audio_features = list(all_audio_features.chunk(world_size, dim=0)) + gathered_text_features = list(all_text_features.chunk(world_size, dim=0)) + gathered_audio_features[rank] = audio_features + gathered_text_features[rank] = text_features + all_audio_features = torch.cat(gathered_audio_features, dim=0) + all_text_features = torch.cat(gathered_text_features, dim=0) + if mlp_loss: + gathered_audio_features_mlp = list(all_audio_features_mlp.chunk(world_size, dim=0)) + gathered_text_features_mlp = list(all_text_features_mlp.chunk(world_size, dim=0)) + gathered_audio_features_mlp[rank] = audio_features_mlp + gathered_text_features_mlp[rank] = text_features_mlp + all_audio_features_mlp = torch.cat(gathered_audio_features_mlp, dim=0) + all_text_features_mlp = torch.cat(gathered_text_features_mlp, dim=0) + else: + # We gather tensors from all gpus + if gather_with_grad: + all_audio_features = torch.cat(torch.distributed.nn.all_gather(audio_features), dim=0) + all_text_features = torch.cat(torch.distributed.nn.all_gather(text_features), dim=0) + if mlp_loss: + all_audio_features_mlp = torch.cat(torch.distributed.nn.all_gather(audio_features_mlp), dim=0) + all_text_features_mlp = torch.cat(torch.distributed.nn.all_gather(text_features_mlp), dim=0) + else: + gathered_audio_features = [torch.zeros_like(audio_features) for _ in range(world_size)] + gathered_text_features = [torch.zeros_like(text_features) for _ in range(world_size)] + dist.all_gather(gathered_audio_features, audio_features) + dist.all_gather(gathered_text_features, text_features) + if mlp_loss: + gathered_audio_features_mlp = [torch.zeros_like(audio_features_mlp) for _ in range(world_size)] + gathered_text_features_mlp = [torch.zeros_like(text_features_mlp) for _ in range(world_size)] + dist.all_gather(gathered_audio_features_mlp, audio_features_mlp) + dist.all_gather(gathered_text_features_mlp, text_features_mlp) + if not local_loss: + # ensure grads for local rank when all_* features don't have a gradient + gathered_audio_features[rank] = audio_features + gathered_text_features[rank] = text_features + if mlp_loss: + gathered_audio_features_mlp[rank] = audio_features_mlp + gathered_text_features_mlp[rank] = text_features_mlp + + all_audio_features = torch.cat(gathered_audio_features, dim=0) + all_text_features = torch.cat(gathered_text_features, dim=0) + if mlp_loss: + all_audio_features_mlp = torch.cat(gathered_audio_features_mlp, dim=0) + all_text_features_mlp = torch.cat(gathered_text_features_mlp, dim=0) + if mlp_loss: + return all_audio_features, all_text_features, all_audio_features_mlp, all_text_features_mlp + else: + return all_audio_features, all_text_features + +class ClipLoss(nn.Module): + + def __init__( + self, + local_loss=False, + gather_with_grad=False, + cache_labels=False, + rank=0, + world_size=1, + use_horovod=False, + mlp_loss=False, + weight_loss_kappa=0, + ): + super().__init__() + self.local_loss = local_loss + self.gather_with_grad = gather_with_grad + self.cache_labels = cache_labels + self.rank = rank + self.world_size = world_size + self.use_horovod = use_horovod + self.mlp_loss = mlp_loss + self.weighted_loss = bool(weight_loss_kappa!=0) + self.weight_loss_kappa = weight_loss_kappa + # cache state + self.prev_num_logits = 0 + self.labels = {} + + def forward(self, audio_features, text_features, logit_scale_a, logit_scale_t=None, audio_features_mlp=None, text_features_mlp=None): + device = audio_features.device + if self.mlp_loss: + if self.world_size > 1: + all_audio_features, all_text_features, all_audio_features_mlp, all_text_features_mlp = gather_features( + audio_features=audio_features,text_features=text_features, + audio_features_mlp=audio_features_mlp,text_features_mlp=text_features_mlp, + local_loss=self.local_loss,gather_with_grad=self.gather_with_grad, + rank=self.rank,world_size=self.world_size,use_horovod=self.use_horovod, + mlp_loss=self.mlp_loss + ) + if self.local_loss: + a_logits_per_audio = logit_scale_a * audio_features @ all_text_features_mlp.T + a_logits_per_text = logit_scale_a * text_features_mlp @ all_audio_features.T + t_logits_per_audio = logit_scale_t * audio_features_mlp @ all_text_features.T + t_logits_per_text = logit_scale_t * text_features @ all_audio_features_mlp.T + else: + a_logits_per_audio = logit_scale_a * all_audio_features @ all_text_features_mlp.T + a_logits_per_text = a_logits_per_audio.T + t_logits_per_audio = logit_scale_t * all_audio_features_mlp @ all_text_features.T + t_logits_per_text = t_logits_per_audio.T + else: + a_logits_per_audio = logit_scale_a * audio_features @ text_features_mlp.T + a_logits_per_text = logit_scale_a * text_features_mlp @ audio_features.T + t_logits_per_audio = logit_scale_t * audio_features_mlp @ text_features.T + t_logits_per_text = logit_scale_t * text_features @ audio_features_mlp.T + + # calculated ground-truth and cache if enabled + num_logits = a_logits_per_audio.shape[0] + if self.prev_num_logits != num_logits or device not in self.labels: + labels = torch.arange(num_logits, device=device, dtype=torch.long) + if self.world_size > 1 and self.local_loss: + labels = labels + num_logits * self.rank + if self.cache_labels: + self.labels[device] = labels + self.prev_num_logits = num_logits + else: + labels = self.labels[device] + + if not self.weighted_loss: + total_loss = ( + F.cross_entropy(a_logits_per_audio, labels) + + F.cross_entropy(a_logits_per_text, labels) + + F.cross_entropy(t_logits_per_audio, labels) + + F.cross_entropy(t_logits_per_text, labels) + ) / 4 + else: + audio_weight = (audio_features@audio_features.T).detach() + audio_weight = (torch.exp(torch.sum(audio_weight, axis=1)/(self.weight_loss_kappa*len(audio_weight)))).detach() + text_weight = (text_features@text_features.T).detach() + text_weight = (torch.exp(torch.sum(text_weight, axis=1)/(self.weight_loss_kappa*len(text_features)))).detach() + total_loss = ( + F.cross_entropy(a_logits_per_audio, labels, weight=audio_weight) + + F.cross_entropy(a_logits_per_text, labels, weight=audio_weight) + + F.cross_entropy(t_logits_per_audio, labels, weight=text_weight) + + F.cross_entropy(t_logits_per_text, labels, weight=text_weight) + ) / 4 + else: + if self.world_size > 1: + all_audio_features, all_text_features = gather_features( + audio_features=audio_features,text_features=text_features, + local_loss=self.local_loss,gather_with_grad=self.gather_with_grad, + rank=self.rank,world_size=self.world_size,use_horovod=self.use_horovod, + mlp_loss=self.mlp_loss + ) + + if self.local_loss: + logits_per_audio = logit_scale_a * audio_features @ all_text_features.T + logits_per_text = logit_scale_a * text_features @ all_audio_features.T + else: + logits_per_audio = logit_scale_a * all_audio_features @ all_text_features.T + logits_per_text = logits_per_audio.T + else: + logits_per_audio = logit_scale_a * audio_features @ text_features.T + logits_per_text = logit_scale_a * text_features @ audio_features.T + + # calculated ground-truth and cache if enabled + num_logits = logits_per_audio.shape[0] + if self.prev_num_logits != num_logits or device not in self.labels: + labels = torch.arange(num_logits, device=device, dtype=torch.long) + if self.world_size > 1 and self.local_loss: + labels = labels + num_logits * self.rank + if self.cache_labels: + self.labels[device] = labels + self.prev_num_logits = num_logits + else: + labels = self.labels[device] + if not self.weighted_loss: + total_loss = ( + F.cross_entropy(logits_per_audio, labels) + + F.cross_entropy(logits_per_text, labels) + ) / 2 + else: + audio_weight = (all_audio_features@all_audio_features.T).detach() + audio_weight = (torch.exp(torch.sum(audio_weight, axis=1)/(self.weight_loss_kappa*len(all_audio_features)))).detach() + text_weight = (all_text_features@all_text_features.T).detach() + text_weight = (torch.exp(torch.sum(text_weight, axis=1)/(self.weight_loss_kappa*len(all_text_features)))).detach() + total_loss = ( + F.cross_entropy(logits_per_audio, labels, weight=text_weight) + + F.cross_entropy(logits_per_text, labels, weight=audio_weight) + ) / 2 + return total_loss + +def lp_gather_features( + pred, + target, + world_size=1, + use_horovod=False +): + if use_horovod: + assert hvd is not None, 'Please install horovod' + with torch.no_grad(): + all_preds = hvd.allgather(pred) + all_targets = hvd.allgath(target) + else: + gathered_preds = [torch.zeros_like(pred) for _ in range(world_size)] + gathered_targets = [torch.zeros_like(target) for _ in range(world_size)] + + dist.all_gather(gathered_preds, pred) + dist.all_gather(gathered_targets, target) + all_preds = torch.cat(gathered_preds, dim=0) + all_targets = torch.cat(gathered_targets, dim=0) + + return all_preds, all_targets + + +def get_map(pred, target): + pred = torch.sigmoid(pred).numpy() + target = target.numpy() + return np.mean(average_precision_score(target, pred, average=None)) + +def get_acc(pred, target): + pred = torch.argmax(pred,1).numpy() + target = torch.argmax(target,1).numpy() + return accuracy_score(target, pred) + +def get_mauc(pred, target): + pred = torch.sigmoid(pred).numpy() + target = target.numpy() + return np.mean(roc_auc_score(target, pred, average=None)) + + +class LPMetrics(object): + def __init__(self, metric_names = ['map','acc','mauc']): + self.metrics = [] + for name in metric_names: + self.metrics.append(self.get_metric(name)) + self.metric_names = metric_names + + def get_metric(self,name): + if name == 'map': + return get_map + elif name == 'acc': + return get_acc + elif name == 'mauc': + return get_mauc + else: + raise ValueError(f'the metric should be at least one of [map, acc, mauc]') + + def evaluate_mertics(self, pred, target): + metric_dict = {} + for i in range(len(self.metric_names)): + metric_dict[self.metric_names[i]] = self.metrics[i](pred, target) + return metric_dict + + +def calc_celoss(pred, target): + target = torch.argmax(target, 1).long() + return nn.CrossEntropyLoss()(pred, target) + + +class LPLoss(nn.Module): + + def __init__(self, loss_name): + super().__init__() + if loss_name == 'bce': + self.loss_func = nn.BCEWithLogitsLoss() + elif loss_name == 'ce': + self.loss_func = calc_celoss + elif loss_name == 'mse': + self.loss_func = nn.MSELoss() + else: + raise ValueError(f'the loss func should be at least one of [bce, ce, mse]') + + def forward(self, pred, target): + loss = self.loss_func(pred, target) + return loss + \ No newline at end of file diff --git a/ldm/modules/encoders/open_clap/model.py b/ldm/modules/encoders/open_clap/model.py new file mode 100644 index 0000000000000000000000000000000000000000..58d52b9825d085aca220972e30ca3fabc8257c34 --- /dev/null +++ b/ldm/modules/encoders/open_clap/model.py @@ -0,0 +1,913 @@ +""" CLAP Model + +Adapted from CLIP: https://github.com/openai/CLIP. Originally MIT License, Copyright (c) 2021 OpenAI. +Adapted to the Audio Task. +""" + +from collections import OrderedDict +from dataclasses import dataclass +from email.mime import audio +from typing import Tuple, Union, Callable, Optional + +import numpy as np +import torch +import torch.nn.functional as F +from torch import nn + +from .timm_model import TimmModel +import logging +from .utils import freeze_batch_norm_2d + +from .pann_model import create_pann_model +from .htsat import create_htsat_model +from transformers import BertModel, RobertaModel, BartModel +from transformers.tokenization_utils_base import BatchEncoding + + +class MLPLayers(nn.Module): + def __init__(self, units=[512, 512, 512], nonlin=nn.ReLU(), dropout=0.1): + super(MLPLayers, self).__init__() + self.nonlin = nonlin + self.dropout = dropout + + sequence = [] + for u0, u1 in zip(units[:-1], units[1:]): + sequence.append(nn.Linear(u0, u1)) + sequence.append(self.nonlin) + sequence.append(nn.Dropout(self.dropout)) + sequence = sequence[:-2] + + self.sequential = nn.Sequential(*sequence) + + def forward(self, X): + X = self.sequential(X) + return X + + +class Bottleneck(nn.Module): + expansion = 4 + + def __init__(self, inplanes, planes, stride=1): + super().__init__() + + # all conv layers have stride 1. an avgpool is performed after the second convolution when stride > 1 + self.conv1 = nn.Conv2d(inplanes, planes, 1, bias=False) + self.bn1 = nn.BatchNorm2d(planes) + + self.conv2 = nn.Conv2d(planes, planes, 3, padding=1, bias=False) + self.bn2 = nn.BatchNorm2d(planes) + + self.avgpool = nn.AvgPool2d(stride) if stride > 1 else nn.Identity() + + self.conv3 = nn.Conv2d(planes, planes * self.expansion, 1, bias=False) + self.bn3 = nn.BatchNorm2d(planes * self.expansion) + + self.relu = nn.ReLU(inplace=True) + self.downsample = None + self.stride = stride + + if stride > 1 or inplanes != planes * Bottleneck.expansion: + # downsampling layer is prepended with an avgpool, and the subsequent convolution has stride 1 + self.downsample = nn.Sequential( + OrderedDict( + [ + ("-1", nn.AvgPool2d(stride)), + ( + "0", + nn.Conv2d( + inplanes, + planes * self.expansion, + 1, + stride=1, + bias=False, + ), + ), + ("1", nn.BatchNorm2d(planes * self.expansion)), + ] + ) + ) + + def forward(self, x: torch.Tensor): + identity = x + + out = self.relu(self.bn1(self.conv1(x))) + out = self.relu(self.bn2(self.conv2(out))) + out = self.avgpool(out) + out = self.bn3(self.conv3(out)) + + if self.downsample is not None: + identity = self.downsample(x) + + out += identity + out = self.relu(out) + return out + + +class AttentionPool2d(nn.Module): + def __init__( + self, spacial_dim: int, embed_dim: int, num_heads: int, output_dim: int = None + ): + super().__init__() + self.positional_embedding = nn.Parameter( + torch.randn(spacial_dim**2 + 1, embed_dim) / embed_dim**0.5 + ) + self.k_proj = nn.Linear(embed_dim, embed_dim) + self.q_proj = nn.Linear(embed_dim, embed_dim) + self.v_proj = nn.Linear(embed_dim, embed_dim) + self.c_proj = nn.Linear(embed_dim, output_dim or embed_dim) + self.num_heads = num_heads + + def forward(self, x): + x = x.reshape(x.shape[0], x.shape[1], x.shape[2] * x.shape[3]).permute( + 2, 0, 1 + ) # NCHW -> (HW)NC + x = torch.cat([x.mean(dim=0, keepdim=True), x], dim=0) # (HW+1)NC + x = x + self.positional_embedding[:, None, :].to(x.dtype) # (HW+1)NC + x, _ = F.multi_head_attention_forward( + query=x, + key=x, + value=x, + embed_dim_to_check=x.shape[-1], + num_heads=self.num_heads, + q_proj_weight=self.q_proj.weight, + k_proj_weight=self.k_proj.weight, + v_proj_weight=self.v_proj.weight, + in_proj_weight=None, + in_proj_bias=torch.cat( + [self.q_proj.bias, self.k_proj.bias, self.v_proj.bias] + ), + bias_k=None, + bias_v=None, + add_zero_attn=False, + dropout_p=0, + out_proj_weight=self.c_proj.weight, + out_proj_bias=self.c_proj.bias, + use_separate_proj_weight=True, + training=self.training, + need_weights=False, + ) + + return x[0] + + +class ModifiedResNet(nn.Module): + """ + A ResNet class that is similar to torchvision's but contains the following changes: + - There are now 3 "stem" convolutions as opposed to 1, with an average pool instead of a max pool. + - Performs anti-aliasing strided convolutions, where an avgpool is prepended to convolutions with stride > 1 + - The final pooling layer is a QKV attention instead of an average pool + """ + + def __init__(self, layers, output_dim, heads, image_size=224, width=64): + super().__init__() + self.output_dim = output_dim + self.image_size = image_size + + # the 3-layer stem + self.conv1 = nn.Conv2d( + 3, width // 2, kernel_size=3, stride=2, padding=1, bias=False + ) + self.bn1 = nn.BatchNorm2d(width // 2) + self.conv2 = nn.Conv2d( + width // 2, width // 2, kernel_size=3, padding=1, bias=False + ) + self.bn2 = nn.BatchNorm2d(width // 2) + self.conv3 = nn.Conv2d(width // 2, width, kernel_size=3, padding=1, bias=False) + self.bn3 = nn.BatchNorm2d(width) + self.avgpool = nn.AvgPool2d(2) + self.relu = nn.ReLU(inplace=True) + + # residual layers + self._inplanes = width # this is a *mutable* variable used during construction + self.layer1 = self._make_layer(width, layers[0]) + self.layer2 = self._make_layer(width * 2, layers[1], stride=2) + self.layer3 = self._make_layer(width * 4, layers[2], stride=2) + self.layer4 = self._make_layer(width * 8, layers[3], stride=2) + + embed_dim = width * 32 # the ResNet feature dimension + self.attnpool = AttentionPool2d(image_size // 32, embed_dim, heads, output_dim) + + self.init_parameters() + + def _make_layer(self, planes, blocks, stride=1): + layers = [Bottleneck(self._inplanes, planes, stride)] + + self._inplanes = planes * Bottleneck.expansion + for _ in range(1, blocks): + layers.append(Bottleneck(self._inplanes, planes)) + + return nn.Sequential(*layers) + + def init_parameters(self): + if self.attnpool is not None: + std = self.attnpool.c_proj.in_features**-0.5 + nn.init.normal_(self.attnpool.q_proj.weight, std=std) + nn.init.normal_(self.attnpool.k_proj.weight, std=std) + nn.init.normal_(self.attnpool.v_proj.weight, std=std) + nn.init.normal_(self.attnpool.c_proj.weight, std=std) + + for resnet_block in [self.layer1, self.layer2, self.layer3, self.layer4]: + for name, param in resnet_block.named_parameters(): + if name.endswith("bn3.weight"): + nn.init.zeros_(param) + + def lock(self, unlocked_groups=0, freeze_bn_stats=False): + assert ( + unlocked_groups == 0 + ), "partial locking not currently supported for this model" + for param in self.parameters(): + param.requires_grad = False + if freeze_bn_stats: + freeze_batch_norm_2d(self) + + def stem(self, x): + for conv, bn in [ + (self.conv1, self.bn1), + (self.conv2, self.bn2), + (self.conv3, self.bn3), + ]: + x = self.relu(bn(conv(x))) + x = self.avgpool(x) + return x + + def forward(self, x): + x = self.stem(x) + x = self.layer1(x) + x = self.layer2(x) + x = self.layer3(x) + x = self.layer4(x) + x = self.attnpool(x) + + return x + + +class LayerNorm(nn.LayerNorm): + """Subclass torch's LayerNorm to handle fp16.""" + + def forward(self, x: torch.Tensor): + orig_type = x.dtype + x = F.layer_norm(x, self.normalized_shape, self.weight, self.bias, self.eps) + return x.to(orig_type) + + +class QuickGELU(nn.Module): + # NOTE This is slower than nn.GELU or nn.SiLU and uses more GPU memory + def forward(self, x: torch.Tensor): + return x * torch.sigmoid(1.702 * x) + + +class ResidualAttentionBlock(nn.Module): + def __init__(self, d_model: int, n_head: int, act_layer: Callable = nn.GELU): + super().__init__() + + self.attn = nn.MultiheadAttention(d_model, n_head) + self.ln_1 = LayerNorm(d_model) + self.mlp = nn.Sequential( + OrderedDict( + [ + ("c_fc", nn.Linear(d_model, d_model * 4)), + ("gelu", act_layer()), + ("c_proj", nn.Linear(d_model * 4, d_model)), + ] + ) + ) + self.ln_2 = LayerNorm(d_model) + + def attention(self, x: torch.Tensor, attn_mask: Optional[torch.Tensor] = None): + return self.attn(x, x, x, need_weights=False, attn_mask=attn_mask)[0] + + def forward(self, x: torch.Tensor, attn_mask: Optional[torch.Tensor] = None): + x = x + self.attention(self.ln_1(x), attn_mask=attn_mask) + x = x + self.mlp(self.ln_2(x)) + return x + + +class Transformer(nn.Module): + def __init__( + self, width: int, layers: int, heads: int, act_layer: Callable = nn.GELU + ): + super().__init__() + self.width = width + self.layers = layers + self.resblocks = nn.ModuleList( + [ + ResidualAttentionBlock(width, heads, act_layer=act_layer) + for _ in range(layers) + ] + ) + + def forward(self, x: torch.Tensor, attn_mask: Optional[torch.Tensor] = None): + for r in self.resblocks: + x = r(x, attn_mask=attn_mask) + return x + + +class VisualTransformer(nn.Module): + def __init__( + self, + image_size: int, + patch_size: int, + width: int, + layers: int, + heads: int, + output_dim: int, + act_layer: Callable = nn.GELU, + ): + super().__init__() + self.image_size = image_size + self.output_dim = output_dim + self.conv1 = nn.Conv2d( + in_channels=3, + out_channels=width, + kernel_size=patch_size, + stride=patch_size, + bias=False, + ) + + scale = width**-0.5 + self.class_embedding = nn.Parameter(scale * torch.randn(width)) + self.positional_embedding = nn.Parameter( + scale * torch.randn((image_size // patch_size) ** 2 + 1, width) + ) + self.ln_pre = LayerNorm(width) + + self.text_branch = Transformer(width, layers, heads, act_layer=act_layer) + + self.ln_post = LayerNorm(width) + self.proj = nn.Parameter(scale * torch.randn(width, output_dim)) + + def lock(self, unlocked_groups=0, freeze_bn_stats=False): + assert ( + unlocked_groups == 0 + ), "partial locking not currently supported for this model" + for param in self.parameters(): + param.requires_grad = False + + def forward(self, x: torch.Tensor): + x = self.conv1(x) # shape = [*, width, grid, grid] + x = x.reshape(x.shape[0], x.shape[1], -1) # shape = [*, width, grid ** 2] + x = x.permute(0, 2, 1) # shape = [*, grid ** 2, width] + x = torch.cat( + [ + self.class_embedding.to(x.dtype) + + torch.zeros( + x.shape[0], 1, x.shape[-1], dtype=x.dtype, device=x.device + ), + x, + ], + dim=1, + ) # shape = [*, grid ** 2 + 1, width] + x = x + self.positional_embedding.to(x.dtype) + x = self.ln_pre(x) + + x = x.permute(1, 0, 2) # NLD -> LND + x = self.text_branch(x) + x = x.permute(1, 0, 2) # LND -> NLD + + x = self.ln_post(x[:, 0, :]) + + if self.proj is not None: + x = x @ self.proj + + return x + + +@dataclass +class CLAPVisionCfg: + layers: Union[Tuple[int, int, int, int], int] = 12 + width: int = 768 + patch_size: int = 16 + image_size: Union[Tuple[int, int], int] = 224 + timm_model_name: str = ( + None # a valid model name overrides layers, width, patch_size + ) + timm_model_pretrained: bool = ( + False # use (imagenet) pretrained weights for named model + ) + timm_pool: str = ( + "avg" # feature pooling for timm model ('abs_attn', 'rot_attn', 'avg', '') + ) + timm_proj: str = ( + "linear" # linear projection for timm model output ('linear', 'mlp', '') + ) + + +# Audio Config Class +@dataclass +class CLAPAudioCfp: + model_type: str = "PANN" + model_name: str = "Cnn14" + sample_rate: int = 48000 + # Param + audio_length: int = 1024 + window_size: int = 1024 + hop_size: int = 1024 + fmin: int = 50 + fmax: int = 14000 + class_num: int = 527 + mel_bins: int = 64 + clip_samples: int = 480000 + + +@dataclass +class CLAPTextCfg: + context_length: int + vocab_size: int + width: int + heads: int + layers: int + model_type: str + + +class CLAP(nn.Module): + def __init__( + self, + embed_dim: int, + audio_cfg: CLAPAudioCfp, + text_cfg: CLAPTextCfg, + quick_gelu: bool = False, + enable_fusion: bool = False, + fusion_type: str = 'None', + joint_embed_shape: int = 512, + mlp_act: str = 'relu', + ): + super().__init__() + if isinstance(audio_cfg, dict): + audio_cfg = CLAPAudioCfp(**audio_cfg) + if isinstance(text_cfg, dict): + text_cfg = CLAPTextCfg(**text_cfg) + + self.audio_cfg = audio_cfg + self.text_cfg = text_cfg + self.enable_fusion = enable_fusion + self.fusion_type = fusion_type + self.joint_embed_shape = joint_embed_shape + self.mlp_act = mlp_act + + + self.context_length = text_cfg.context_length + + # OpenAI models are pretrained w/ QuickGELU but native nn.GELU is both faster and more + # memory efficient in recent PyTorch releases (>= 1.10). + # NOTE: timm models always use native GELU regardless of quick_gelu flag. + act_layer = QuickGELU if quick_gelu else nn.GELU + + if mlp_act == 'relu': + mlp_act_layer = nn.ReLU() + elif mlp_act == 'gelu': + mlp_act_layer = nn.GELU() + else: + raise NotImplementedError + + # audio branch + # audio branch parameters + if audio_cfg.model_type == "PANN": + self.audio_branch = create_pann_model(audio_cfg, enable_fusion, fusion_type) + elif audio_cfg.model_type == "HTSAT": + self.audio_branch = create_htsat_model(audio_cfg, enable_fusion, fusion_type) + else: + logging.error(f"Model config for {audio_cfg.model_type} not found") + raise RuntimeError(f"Model config for {audio_cfg.model_type} not found.") + + + # text branch + # text branch parameters + if text_cfg.model_type == "transformer": + self.text_branch = Transformer( + width=text_cfg.width, + layers=text_cfg.layers, + heads=text_cfg.heads, + act_layer=act_layer, + ) + self.vocab_size = text_cfg.vocab_size + self.token_embedding = nn.Embedding(text_cfg.vocab_size, text_cfg.width) + self.positional_embedding = nn.Parameter( + torch.empty(self.context_length, text_cfg.width) + ) + self.ln_final = LayerNorm(text_cfg.width) + self.text_transform = MLPLayers(units=[self.joint_embed_shape, + self.joint_embed_shape, + self.joint_embed_shape], dropout=0.1) + self.text_projection = nn.Sequential( + nn.Linear(text_cfg.width, self.joint_embed_shape), + mlp_act_layer, + nn.Linear(self.joint_embed_shape, self.joint_embed_shape) + ) + elif text_cfg.model_type == "bert": + self.text_branch = BertModel.from_pretrained("bert-base-uncased") + self.text_transform = MLPLayers(units=[self.joint_embed_shape, + self.joint_embed_shape, + self.joint_embed_shape], dropout=0.1) + self.text_projection = nn.Sequential( + nn.Linear(768, self.joint_embed_shape), + mlp_act_layer, + nn.Linear(self.joint_embed_shape, self.joint_embed_shape) + ) + elif text_cfg.model_type == "roberta": + self.text_branch = RobertaModel.from_pretrained('roberta-base') + self.text_transform = MLPLayers(units=[self.joint_embed_shape, + self.joint_embed_shape, + self.joint_embed_shape], dropout=0.1) + self.text_projection = nn.Sequential( + nn.Linear(768, self.joint_embed_shape), + mlp_act_layer, + nn.Linear(self.joint_embed_shape, self.joint_embed_shape) + ) + elif text_cfg.model_type == "bart": + self.text_branch = BartModel.from_pretrained('facebook/bart-base') + self.text_transform = MLPLayers(units=[self.joint_embed_shape, + self.joint_embed_shape, + self.joint_embed_shape], dropout=0.1) + self.text_projection = nn.Sequential( + nn.Linear(768, self.joint_embed_shape), + mlp_act_layer, + nn.Linear(self.joint_embed_shape, self.joint_embed_shape) + ) + else: + logging.error(f"Model config for {text_cfg.model_type} not found") + raise RuntimeError(f"Model config for {text_cfg.model_type} not found.") + self.text_branch_type = text_cfg.model_type + # text branch parameters + + # audio branch parameters + self.audio_transform = MLPLayers(units=[self.joint_embed_shape, + self.joint_embed_shape, + self.joint_embed_shape], dropout=0.1) + + # below here is text branch parameters + + # ============================================================================================================ + self.audio_projection = nn.Sequential( + nn.Linear(embed_dim, self.joint_embed_shape), + mlp_act_layer, + nn.Linear(self.joint_embed_shape, self.joint_embed_shape) + ) + + self.logit_scale_a = nn.Parameter(torch.ones([]) * np.log(1 / 0.07)) + self.logit_scale_t = nn.Parameter(torch.ones([]) * np.log(1 / 0.07)) + self.register_buffer("attn_mask", self.build_attention_mask(), persistent=False) + + self.init_text_branch_parameters() + + def init_text_branch_parameters(self): + if self.text_branch_type == "transformer": + nn.init.normal_(self.token_embedding.weight, std=0.02) + nn.init.normal_(self.positional_embedding, std=0.01) + proj_std = (self.text_branch.width**-0.5) * ( + (2 * self.text_branch.layers) ** -0.5 + ) + attn_std = self.text_branch.width**-0.5 + fc_std = (2 * self.text_branch.width) ** -0.5 + for block in self.text_branch.resblocks: + nn.init.normal_(block.attn.in_proj_weight, std=attn_std) + nn.init.normal_(block.attn.out_proj.weight, std=proj_std) + nn.init.normal_(block.mlp.c_fc.weight, std=fc_std) + nn.init.normal_(block.mlp.c_proj.weight, std=proj_std) + if self.text_branch_type == "bert" or self.text_branch_type == "roberta": + width = self.text_branch.embeddings.word_embeddings.weight.shape[-1] + elif self.text_branch_type == "bart": + width = self.text_branch.shared.weight.shape[-1] + else: + width = self.text_branch.width + nn.init.constant_(self.logit_scale_a, np.log(1 / 0.07)) + nn.init.constant_(self.logit_scale_t, np.log(1 / 0.07)) + + # deprecated + # if hasattr(self.visual, 'init_parameters'): + # self.visual.init_parameters() + + # if self.text_projection is not None: + # nn.init.normal_(self.text_projection, std=width**-0.5) + + def build_attention_mask(self): + # lazily create causal attention mask, with full attention between the vision tokens + # pytorch uses additive attention mask; fill with -inf + mask = torch.empty(self.context_length, self.context_length) + mask.fill_(float("-inf")) + mask.triu_(1) # zero out the lower diagonal + return mask + + def encode_audio(self, audio, device): + return self.audio_branch(audio, mixup_lambda=None, device=device) # mix lambda needs to add + + # def list_of_dict_of_tensor2dict_of_tensor(self, x, device): + # tmp = {} + # for k in x[0].keys(): + # tmp[k] = [] + # for i in range(len(x)): + # tmp[k].append(x[i][k][:77]) + # for k in x[0].keys(): + # tmp[k] = torch.tensor(tmp[k]).to(device=device, non_blocking=True) + # return tmp + + def encode_text(self, text, device): + if self.text_branch_type == "transformer": + text = text.to(device=device, non_blocking=True) + x = self.token_embedding(text) # [batch_size, n_ctx, d_model] + + x = x + self.positional_embedding + x = x.permute(1, 0, 2) # NLD -> LND + x = self.text_branch(x, attn_mask=self.attn_mask) + x = x.permute(1, 0, 2) # LND -> NLD + x = self.ln_final(x) + + # x.shape = [batch_size, n_ctx, transformer.width] + # take features from the eot embedding (eot_token is the highest number in each sequence) + x = self.text_projection(x[torch.arange(x.shape[0]), text.argmax(dim=-1)]) + elif self.text_branch_type == "bert": + # text = self.list_of_dict_of_tensor2dict_of_tensor(text, device) + # text = BatchEncoding(text) + x = self.text_branch( + input_ids=text["input_ids"].to(device=device, non_blocking=True), + attention_mask=text["attention_mask"].to( + device=device, non_blocking=True + ), + token_type_ids=text["token_type_ids"].to( + device=device, non_blocking=True + ), + )["pooler_output"] + x = self.text_projection(x) + elif self.text_branch_type == "roberta": + x = self.text_branch( + input_ids=text["input_ids"].to(device=device, non_blocking=True), + attention_mask=text["attention_mask"].to( + device=device, non_blocking=True + ), + )["pooler_output"] + + x = self.text_projection(x) + elif self.text_branch_type == "bart": + x = torch.mean(self.text_branch( + input_ids=text["input_ids"].to(device=device, non_blocking=True), + attention_mask=text["attention_mask"].to( + device=device, non_blocking=True + ), + )["encoder_last_hidden_state"],axis=1) + x = self.text_projection(x) + else: + logging.error(f"Model type {self.text_branch_type} not found") + raise RuntimeError(f"Model type {self.text_branch_type} not found.") + return x + + def forward(self, audio, text, device=None): + """Forward audio and text into the CLAP + + Parameters + ---------- + audio: torch.Tensor (batch_size, audio_length) + the time-domain audio input / the batch of mel_spec and longer list. + text: torch.Tensor () // need to add + the text token input + """ + if device is None: + if audio is not None: + device = audio.device + elif text is not None: + device = text.device + if audio is None and text is None: + # a hack to get the logit scale + return self.logit_scale_a.exp(), self.logit_scale_t.exp() + elif audio is None: + return self.encode_text(text, device=device) + elif text is None: + return self.audio_projection(self.encode_audio(audio, device=device)["embedding"]) + audio_features = self.audio_projection(self.encode_audio(audio, device=device)["embedding"]) + audio_features = F.normalize(audio_features, dim=-1) + + text_features = self.encode_text( + text, device=device + ) + # print("text_features", text_features) + # print("text_features.shape", text_features.shape) + # print("text_features.type", type(text_features)) + text_features = F.normalize(text_features, dim=-1) + + audio_features_mlp = self.audio_transform(audio_features) + text_features_mlp = self.text_transform(text_features) + # Four outputs: audio features (basic & MLP), text features (basic & MLP) + return ( + audio_features, + text_features, + audio_features_mlp, + text_features_mlp, + self.logit_scale_a.exp(), + self.logit_scale_t.exp(), + ) + + def get_logit_scale(self): + return self.logit_scale_a.exp(), self.logit_scale_t.exp() + + def get_textual_embedding(self, data): + + device = next(self.parameters()).device + for k in data: + data[k] = data[k].to(device) + + # if self.text_branch_type == "roberta": + text_embeds = self.text_branch( + input_ids=data["input_ids"].to(device=device, non_blocking=True), + attention_mask=data["attention_mask"].to(device=device, non_blocking=True), + )["last_hidden_state"] + + text_embeds = self.text_projection(text_embeds) + + text_embeds = F.normalize(text_embeds, dim=-1) + + return text_embeds + + def get_text_embedding(self, data): + """Get the text embedding from the model + + Parameters + ---------- + data: torch.Tensor + a tensor of text embedding + + Returns + ---------- + text_embed: torch.Tensor + a tensor of text_embeds (N, D) + + """ + device = next(self.parameters()).device + for k in data: + data[k] = data[k].to(device) + text_embeds = self.encode_text(data, device=device) + text_embeds = F.normalize(text_embeds, dim=-1) + + return text_embeds + + def get_audio_embedding(self, data): + """Get the audio embedding from the model + + Parameters + ---------- + data: a list of dict + the audio input dict list from 'get_audio_feature' method + + Returns + ---------- + audio_embed: torch.Tensor + a tensor of audio_embeds (N, D) + + """ + device = next(self.parameters()).device + input_dict = {} + keys = data[0].keys() + for k in keys: + input_dict[k] = torch.cat([d[k].unsqueeze(0) for d in data], dim=0).to(device) + + audio_embeds = self.audio_projection(self.encode_audio(input_dict, device=device)["embedding"]) + audio_embeds = F.normalize(audio_embeds, dim=-1) + + return audio_embeds + + + + def audio_infer(self, audio, hopsize=None, device=None): + """Forward one audio and produce the audio embedding + + Parameters + ---------- + audio: (audio_length) + the time-domain audio input, notice that it must be only one input + hopsize: int + the overlap hopsize as the sliding window + + Returns + ---------- + output_dict: { + key: [n, (embedding_shape)] if "HTS-AT" + or + key: [(embedding_shape)] if "PANN" + } + the list of key values of the audio branch + + """ + + assert not self.training, "the inference mode must be run at eval stage" + output_dict = {} + # PANN + if self.audio_cfg.model_type == "PANN": + audio_input = audio.unsqueeze(dim=0) + output_dict[key] = self.encode_audio(audio_input, device=device)[key].squeeze(dim=0) + elif self.audio_cfg.model_type == "HTSAT": + # repeat + audio_len = len(audio) + k = self.audio_cfg.clip_samples // audio_len + if k > 1: + audio = audio.repeat(k) + audio_len = len(audio) + + if hopsize is None: + hopsize = min(hopsize, audio_len) + + if audio_len > self.audio_cfg.clip_samples: + audio_input = [ + audio[pos : pos + self.audio_cfg.clip_samples].clone() + for pos in range( + 0, audio_len - self.audio_cfg.clip_samples, hopsize + ) + ] + audio_input.append(audio[-self.audio_cfg.clip_samples :].clone()) + audio_input = torch.stack(audio_input) + output_dict[key] = self.encode_audio(audio_input, device=device)[key] + else: + audio_input = audio.unsqueeze(dim=0) + output_dict[key] = self.encode_audio(audio_input, device=device)[key].squeeze(dim=0) + + return output_dict + + +def convert_weights_to_fp16(model: nn.Module): + """Convert applicable model parameters to fp16""" + + def _convert_weights_to_fp16(l): + if isinstance(l, (nn.Conv1d, nn.Conv2d, nn.Linear)): + l.weight.data = l.weight.data.half() + if l.bias is not None: + l.bias.data = l.bias.data.half() + + if isinstance(l, nn.MultiheadAttention): + for attr in [ + *[f"{s}_proj_weight" for s in ["in", "q", "k", "v"]], + "in_proj_bias", + "bias_k", + "bias_v", + ]: + tensor = getattr(l, attr) + if tensor is not None: + tensor.data = tensor.data.half() + + for name in ["text_projection", "proj"]: + if hasattr(l, name): + attr = getattr(l, name) + if attr is not None: + attr.data = attr.data.half() + + model.apply(_convert_weights_to_fp16) + + +# Ignore the state dict of the vision part +def build_model_from_openai_state_dict(state_dict: dict, model_cfg, enable_fusion: bool = False, fusion_type: str = 'None'): + + embed_dim = model_cfg["embed_dim"] + audio_cfg = model_cfg["audio_cfg"] + text_cfg = model_cfg["text_cfg"] + context_length = state_dict["positional_embedding"].shape[0] + vocab_size = state_dict["token_embedding.weight"].shape[0] + transformer_width = state_dict["ln_final.weight"].shape[0] + transformer_heads = transformer_width // 64 + transformer_layers = len( + set( + k.split(".")[2] + for k in state_dict + if k.startswith(f"transformer.resblocks") + ) + ) + + audio_cfg = CLAPAudioCfp(**audio_cfg) + text_cfg = CLAPTextCfg(**text_cfg) + + model = CLAP( + embed_dim, + audio_cfg=audio_cfg, + text_cfg=text_cfg, + quick_gelu=True, # OpenAI models were trained with QuickGELU + enable_fusion=enable_fusion, + fusion_type=fusion_type + ) + state_dict["logit_scale_a"] = state_dict["logit_scale"] + state_dict["logit_scale_t"] = state_dict["logit_scale"] + pop_keys = list(state_dict.keys())[::] + # pop the visual branch saved weights + for key in pop_keys: + if key.startswith("visual."): + state_dict.pop(key, None) + + for key in ["logit_scale", "input_resolution", "context_length", "vocab_size"]: + state_dict.pop(key, None) + + # not use fp16 + # convert_weights_to_fp16(model) + model.load_state_dict(state_dict, strict=False) + return model.eval() + + +def trace_model(model, batch_size=256, device=torch.device("cpu")): + model.eval() + audio_length = model.audio_cfg.audio_length + example_audio = torch.ones((batch_size, audio_length), device=device) + example_text = torch.zeros( + (batch_size, model.context_length), dtype=torch.int, device=device + ) + model = torch.jit.trace_module( + model, + inputs=dict( + forward=(example_audio, example_text), + encode_text=(example_text,), + encode_image=(example_audio,), + ), + ) + model.audio_cfg.audio_length = audio_length # Question: what does this do? + return model diff --git a/ldm/modules/encoders/open_clap/model_configs/HTSAT-base.json b/ldm/modules/encoders/open_clap/model_configs/HTSAT-base.json new file mode 100644 index 0000000000000000000000000000000000000000..6cef625a89daf4431f1c9f72e10bc9640eef2ba8 --- /dev/null +++ b/ldm/modules/encoders/open_clap/model_configs/HTSAT-base.json @@ -0,0 +1,23 @@ +{ + "embed_dim": 1024, + "audio_cfg": { + "audio_length": 1024, + "clip_samples": 480000, + "mel_bins": 64, + "sample_rate": 48000, + "window_size": 1024, + "hop_size": 480, + "fmin": 50, + "fmax": 14000, + "class_num": 527, + "model_type": "HTSAT", + "model_name": "base" + }, + "text_cfg": { + "context_length": 77, + "vocab_size": 49408, + "width": 512, + "heads": 8, + "layers": 12 + } +} \ No newline at end of file diff --git a/ldm/modules/encoders/open_clap/model_configs/HTSAT-large.json b/ldm/modules/encoders/open_clap/model_configs/HTSAT-large.json new file mode 100644 index 0000000000000000000000000000000000000000..699cdb1b16855582606551e4196b24aba2ffd871 --- /dev/null +++ b/ldm/modules/encoders/open_clap/model_configs/HTSAT-large.json @@ -0,0 +1,23 @@ +{ + "embed_dim": 2048, + "audio_cfg": { + "audio_length": 1024, + "clip_samples": 480000, + "mel_bins": 64, + "sample_rate": 48000, + "window_size": 1024, + "hop_size": 480, + "fmin": 50, + "fmax": 14000, + "class_num": 527, + "model_type": "HTSAT", + "model_name": "large" + }, + "text_cfg": { + "context_length": 77, + "vocab_size": 49408, + "width": 512, + "heads": 8, + "layers": 12 + } +} \ No newline at end of file diff --git a/ldm/modules/encoders/open_clap/model_configs/HTSAT-tiny-win-1536.json b/ldm/modules/encoders/open_clap/model_configs/HTSAT-tiny-win-1536.json new file mode 100644 index 0000000000000000000000000000000000000000..73e42990fe8361a0df502e7f93d29f19f58c9ecb --- /dev/null +++ b/ldm/modules/encoders/open_clap/model_configs/HTSAT-tiny-win-1536.json @@ -0,0 +1,23 @@ +{ + "embed_dim": 768, + "audio_cfg": { + "audio_length": 1024, + "clip_samples": 480000, + "mel_bins": 64, + "sample_rate": 48000, + "window_size": 1536, + "hop_size": 480, + "fmin": 50, + "fmax": 14000, + "class_num": 527, + "model_type": "HTSAT", + "model_name": "tiny" + }, + "text_cfg": { + "context_length": 77, + "vocab_size": 49408, + "width": 512, + "heads": 8, + "layers": 12 + } +} \ No newline at end of file diff --git a/ldm/modules/encoders/open_clap/model_configs/HTSAT-tiny.json b/ldm/modules/encoders/open_clap/model_configs/HTSAT-tiny.json new file mode 100644 index 0000000000000000000000000000000000000000..a6e7821163d9afa81c27345a1e472475b92af169 --- /dev/null +++ b/ldm/modules/encoders/open_clap/model_configs/HTSAT-tiny.json @@ -0,0 +1,23 @@ +{ + "embed_dim": 768, + "audio_cfg": { + "audio_length": 1024, + "clip_samples": 480000, + "mel_bins": 64, + "sample_rate": 48000, + "window_size": 1024, + "hop_size": 480, + "fmin": 50, + "fmax": 14000, + "class_num": 527, + "model_type": "HTSAT", + "model_name": "tiny" + }, + "text_cfg": { + "context_length": 77, + "vocab_size": 49408, + "width": 512, + "heads": 8, + "layers": 12 + } +} \ No newline at end of file diff --git a/ldm/modules/encoders/open_clap/model_configs/PANN-10.json b/ldm/modules/encoders/open_clap/model_configs/PANN-10.json new file mode 100644 index 0000000000000000000000000000000000000000..954ddf62921aed7dde9c37ffffec98a2e96a4ee7 --- /dev/null +++ b/ldm/modules/encoders/open_clap/model_configs/PANN-10.json @@ -0,0 +1,23 @@ +{ + "embed_dim": 1024, + "audio_cfg": { + "audio_length": 1024, + "clip_samples": 480000, + "mel_bins": 64, + "sample_rate": 48000, + "window_size": 1024, + "hop_size": 480, + "fmin": 50, + "fmax": 14000, + "class_num": 527, + "model_type": "PANN", + "model_name": "Cnn10" + }, + "text_cfg": { + "context_length": 77, + "vocab_size": 49408, + "width": 512, + "heads": 8, + "layers": 12 + } +} \ No newline at end of file diff --git a/ldm/modules/encoders/open_clap/model_configs/PANN-14-fmax-18k.json b/ldm/modules/encoders/open_clap/model_configs/PANN-14-fmax-18k.json new file mode 100644 index 0000000000000000000000000000000000000000..b7989bc0cd95d0d39049b7524eba508b3e386439 --- /dev/null +++ b/ldm/modules/encoders/open_clap/model_configs/PANN-14-fmax-18k.json @@ -0,0 +1,23 @@ +{ + "embed_dim": 2048, + "audio_cfg": { + "audio_length": 1024, + "clip_samples": 480000, + "mel_bins": 64, + "sample_rate": 48000, + "window_size": 1024, + "hop_size": 480, + "fmin": 50, + "fmax": 18000, + "class_num": 527, + "model_type": "PANN", + "model_name": "Cnn14" + }, + "text_cfg": { + "context_length": 77, + "vocab_size": 49408, + "width": 512, + "heads": 8, + "layers": 12 + } +} \ No newline at end of file diff --git a/ldm/modules/encoders/open_clap/model_configs/PANN-14-fmax-8k-20s.json b/ldm/modules/encoders/open_clap/model_configs/PANN-14-fmax-8k-20s.json new file mode 100644 index 0000000000000000000000000000000000000000..56bdb56bedc304ffa52d8bf5988cea2c1d82d14e --- /dev/null +++ b/ldm/modules/encoders/open_clap/model_configs/PANN-14-fmax-8k-20s.json @@ -0,0 +1,23 @@ +{ + "embed_dim": 2048, + "audio_cfg": { + "audio_length": 1024, + "clip_samples": 960000, + "mel_bins": 64, + "sample_rate": 48000, + "window_size": 1024, + "hop_size": 360, + "fmin": 50, + "fmax": 8000, + "class_num": 527, + "model_type": "PANN", + "model_name": "Cnn14" + }, + "text_cfg": { + "context_length": 77, + "vocab_size": 49408, + "width": 512, + "heads": 8, + "layers": 12 + } +} \ No newline at end of file diff --git a/ldm/modules/encoders/open_clap/model_configs/PANN-14-tiny-transformer.json b/ldm/modules/encoders/open_clap/model_configs/PANN-14-tiny-transformer.json new file mode 100644 index 0000000000000000000000000000000000000000..5756e3bebc97cc985f512cb081930fee4e49bec1 --- /dev/null +++ b/ldm/modules/encoders/open_clap/model_configs/PANN-14-tiny-transformer.json @@ -0,0 +1,23 @@ +{ + "embed_dim": 2048, + "audio_cfg": { + "audio_length": 1024, + "clip_samples": 480000, + "mel_bins": 64, + "sample_rate": 48000, + "window_size": 1024, + "hop_size": 480, + "fmin": 50, + "fmax": 14000, + "class_num": 527, + "model_type": "PANN", + "model_name": "Cnn14" + }, + "text_cfg": { + "context_length": 77, + "vocab_size": 49408, + "width": 512, + "heads": 8, + "layers": 4 + } +} \ No newline at end of file diff --git a/ldm/modules/encoders/open_clap/model_configs/PANN-14-win-1536.json b/ldm/modules/encoders/open_clap/model_configs/PANN-14-win-1536.json new file mode 100644 index 0000000000000000000000000000000000000000..5a9e7e208b661619d5e26625e849da1adda8a475 --- /dev/null +++ b/ldm/modules/encoders/open_clap/model_configs/PANN-14-win-1536.json @@ -0,0 +1,23 @@ +{ + "embed_dim": 2048, + "audio_cfg": { + "audio_length": 1024, + "clip_samples": 480000, + "mel_bins": 64, + "sample_rate": 48000, + "window_size": 1536, + "hop_size": 480, + "fmin": 50, + "fmax": 14000, + "class_num": 527, + "model_type": "PANN", + "model_name": "Cnn14" + }, + "text_cfg": { + "context_length": 77, + "vocab_size": 49408, + "width": 512, + "heads": 8, + "layers": 12 + } +} \ No newline at end of file diff --git a/ldm/modules/encoders/open_clap/model_configs/PANN-14.json b/ldm/modules/encoders/open_clap/model_configs/PANN-14.json new file mode 100644 index 0000000000000000000000000000000000000000..39a5134cde1d8c50f4758377c952ef22f07bab41 --- /dev/null +++ b/ldm/modules/encoders/open_clap/model_configs/PANN-14.json @@ -0,0 +1,23 @@ +{ + "embed_dim": 2048, + "audio_cfg": { + "audio_length": 1024, + "clip_samples": 480000, + "mel_bins": 64, + "sample_rate": 48000, + "window_size": 1024, + "hop_size": 480, + "fmin": 50, + "fmax": 14000, + "class_num": 527, + "model_type": "PANN", + "model_name": "Cnn14" + }, + "text_cfg": { + "context_length": 77, + "vocab_size": 49408, + "width": 512, + "heads": 8, + "layers": 12 + } +} \ No newline at end of file diff --git a/ldm/modules/encoders/open_clap/model_configs/PANN-6.json b/ldm/modules/encoders/open_clap/model_configs/PANN-6.json new file mode 100644 index 0000000000000000000000000000000000000000..21ebc344326de260c386ba77e0ad63cf9b04febf --- /dev/null +++ b/ldm/modules/encoders/open_clap/model_configs/PANN-6.json @@ -0,0 +1,23 @@ +{ + "embed_dim": 512, + "audio_cfg": { + "audio_length": 1024, + "clip_samples": 480000, + "mel_bins": 64, + "sample_rate": 48000, + "window_size": 1024, + "hop_size": 480, + "fmin": 50, + "fmax": 14000, + "class_num": 527, + "model_type": "PANN", + "model_name": "Cnn6" + }, + "text_cfg": { + "context_length": 77, + "vocab_size": 49408, + "width": 512, + "heads": 8, + "layers": 12 + } +} \ No newline at end of file diff --git a/ldm/modules/encoders/open_clap/model_configs/RN101-quickgelu.json b/ldm/modules/encoders/open_clap/model_configs/RN101-quickgelu.json new file mode 100644 index 0000000000000000000000000000000000000000..d0db2c161d13138788c4609d373b023b8454d624 --- /dev/null +++ b/ldm/modules/encoders/open_clap/model_configs/RN101-quickgelu.json @@ -0,0 +1,22 @@ +{ + "embed_dim": 512, + "quick_gelu": true, + "vision_cfg": { + "image_size": 224, + "layers": [ + 3, + 4, + 23, + 3 + ], + "width": 64, + "patch_size": null + }, + "text_cfg": { + "context_length": 77, + "vocab_size": 49408, + "width": 512, + "heads": 8, + "layers": 12 + } +} \ No newline at end of file diff --git a/ldm/modules/encoders/open_clap/model_configs/RN101.json b/ldm/modules/encoders/open_clap/model_configs/RN101.json new file mode 100644 index 0000000000000000000000000000000000000000..b88b4d3acbaa701c614ab0ea65fc88fcfe289c32 --- /dev/null +++ b/ldm/modules/encoders/open_clap/model_configs/RN101.json @@ -0,0 +1,21 @@ +{ + "embed_dim": 512, + "vision_cfg": { + "image_size": 224, + "layers": [ + 3, + 4, + 23, + 3 + ], + "width": 64, + "patch_size": null + }, + "text_cfg": { + "context_length": 77, + "vocab_size": 49408, + "width": 512, + "heads": 8, + "layers": 12 + } +} \ No newline at end of file diff --git a/ldm/modules/encoders/open_clap/model_configs/RN50-quickgelu.json b/ldm/modules/encoders/open_clap/model_configs/RN50-quickgelu.json new file mode 100644 index 0000000000000000000000000000000000000000..8c2f91260cdeb043434dc1e893cce81d4ce7f0d1 --- /dev/null +++ b/ldm/modules/encoders/open_clap/model_configs/RN50-quickgelu.json @@ -0,0 +1,22 @@ +{ + "embed_dim": 1024, + "quick_gelu": true, + "vision_cfg": { + "image_size": 224, + "layers": [ + 3, + 4, + 6, + 3 + ], + "width": 64, + "patch_size": null + }, + "text_cfg": { + "context_length": 77, + "vocab_size": 49408, + "width": 512, + "heads": 8, + "layers": 12 + } +} diff --git a/ldm/modules/encoders/open_clap/model_configs/RN50.json b/ldm/modules/encoders/open_clap/model_configs/RN50.json new file mode 100644 index 0000000000000000000000000000000000000000..33aa884d54fee0076c33676831e49d5e1ffcb8f2 --- /dev/null +++ b/ldm/modules/encoders/open_clap/model_configs/RN50.json @@ -0,0 +1,21 @@ +{ + "embed_dim": 1024, + "vision_cfg": { + "image_size": 224, + "layers": [ + 3, + 4, + 6, + 3 + ], + "width": 64, + "patch_size": null + }, + "text_cfg": { + "context_length": 77, + "vocab_size": 49408, + "width": 512, + "heads": 8, + "layers": 12 + } +} \ No newline at end of file diff --git a/ldm/modules/encoders/open_clap/model_configs/RN50x16.json b/ldm/modules/encoders/open_clap/model_configs/RN50x16.json new file mode 100644 index 0000000000000000000000000000000000000000..3161e1a2c9a839161e652a4d729c2cdc971161db --- /dev/null +++ b/ldm/modules/encoders/open_clap/model_configs/RN50x16.json @@ -0,0 +1,21 @@ +{ + "embed_dim": 768, + "vision_cfg": { + "image_size": 384, + "layers": [ + 6, + 8, + 18, + 8 + ], + "width": 96, + "patch_size": null + }, + "text_cfg": { + "context_length": 77, + "vocab_size": 49408, + "width": 768, + "heads": 12, + "layers": 12 + } +} \ No newline at end of file diff --git a/ldm/modules/encoders/open_clap/model_configs/RN50x4.json b/ldm/modules/encoders/open_clap/model_configs/RN50x4.json new file mode 100644 index 0000000000000000000000000000000000000000..e155237f8ce1026aaaeecc80751eabe6f329f0bb --- /dev/null +++ b/ldm/modules/encoders/open_clap/model_configs/RN50x4.json @@ -0,0 +1,21 @@ +{ + "embed_dim": 640, + "vision_cfg": { + "image_size": 288, + "layers": [ + 4, + 6, + 10, + 6 + ], + "width": 80, + "patch_size": null + }, + "text_cfg": { + "context_length": 77, + "vocab_size": 49408, + "width": 640, + "heads": 10, + "layers": 12 + } +} \ No newline at end of file diff --git a/ldm/modules/encoders/open_clap/model_configs/ViT-B-16.json b/ldm/modules/encoders/open_clap/model_configs/ViT-B-16.json new file mode 100644 index 0000000000000000000000000000000000000000..395eea77ec3907c0611531aba63459b193e67b9c --- /dev/null +++ b/ldm/modules/encoders/open_clap/model_configs/ViT-B-16.json @@ -0,0 +1,16 @@ +{ + "embed_dim": 512, + "vision_cfg": { + "image_size": 224, + "layers": 12, + "width": 768, + "patch_size": 16 + }, + "text_cfg": { + "context_length": 77, + "vocab_size": 49408, + "width": 512, + "heads": 8, + "layers": 12 + } +} \ No newline at end of file diff --git a/ldm/modules/encoders/open_clap/model_configs/ViT-B-32-quickgelu.json b/ldm/modules/encoders/open_clap/model_configs/ViT-B-32-quickgelu.json new file mode 100644 index 0000000000000000000000000000000000000000..ce6bd923593293ed50dfcfb28b73ca7403bcf3c5 --- /dev/null +++ b/ldm/modules/encoders/open_clap/model_configs/ViT-B-32-quickgelu.json @@ -0,0 +1,17 @@ +{ + "embed_dim": 512, + "quick_gelu": true, + "vision_cfg": { + "image_size": 224, + "layers": 12, + "width": 768, + "patch_size": 32 + }, + "text_cfg": { + "context_length": 77, + "vocab_size": 49408, + "width": 512, + "heads": 8, + "layers": 12 + } +} \ No newline at end of file diff --git a/ldm/modules/encoders/open_clap/model_configs/ViT-B-32.json b/ldm/modules/encoders/open_clap/model_configs/ViT-B-32.json new file mode 100644 index 0000000000000000000000000000000000000000..07c8e28eb06fa1813ba932fe4eec668262d1c47f --- /dev/null +++ b/ldm/modules/encoders/open_clap/model_configs/ViT-B-32.json @@ -0,0 +1,16 @@ +{ + "embed_dim": 512, + "vision_cfg": { + "image_size": 224, + "layers": 12, + "width": 768, + "patch_size": 32 + }, + "text_cfg": { + "context_length": 77, + "vocab_size": 49408, + "width": 512, + "heads": 8, + "layers": 12 + } +} \ No newline at end of file diff --git a/ldm/modules/encoders/open_clap/model_configs/ViT-L-14.json b/ldm/modules/encoders/open_clap/model_configs/ViT-L-14.json new file mode 100644 index 0000000000000000000000000000000000000000..d4a4bbb1dd4ed4edb317d3ace4f3ad13b211c241 --- /dev/null +++ b/ldm/modules/encoders/open_clap/model_configs/ViT-L-14.json @@ -0,0 +1,16 @@ +{ + "embed_dim": 768, + "vision_cfg": { + "image_size": 224, + "layers": 24, + "width": 1024, + "patch_size": 14 + }, + "text_cfg": { + "context_length": 77, + "vocab_size": 49408, + "width": 768, + "heads": 12, + "layers": 12 + } +} \ No newline at end of file diff --git a/ldm/modules/encoders/open_clap/openai.py b/ldm/modules/encoders/open_clap/openai.py new file mode 100644 index 0000000000000000000000000000000000000000..9911b6e135e51970177fcac067c12192b0b57c1c --- /dev/null +++ b/ldm/modules/encoders/open_clap/openai.py @@ -0,0 +1,129 @@ +""" OpenAI pretrained model functions + +Adapted from https://github.com/openai/CLIP. Originally MIT License, Copyright (c) 2021 OpenAI. +""" + +import os +import warnings +from typing import Union, List + +import torch + +from .model import build_model_from_openai_state_dict +from .pretrained import get_pretrained_url, list_pretrained_tag_models, download_pretrained + +__all__ = ["list_openai_models", "load_openai_model"] + + +def list_openai_models() -> List[str]: + """Returns the names of available CLIP models""" + return list_pretrained_tag_models('openai') + + +def load_openai_model( + name: str, + model_cfg, + device: Union[str, torch.device] = "cuda" if torch.cuda.is_available() else "cpu", + jit=True, + cache_dir=os.path.expanduser("~/.cache/clip"), + enable_fusion: bool = False, + fusion_type: str = 'None' +): + """Load a CLIP model, preserve its text pretrained part, and set in the CLAP model + + Parameters + ---------- + name : str + A model name listed by `clip.available_models()`, or the path to a model checkpoint containing the state_dict + device : Union[str, torch.device] + The device to put the loaded model + jit : bool + Whether to load the optimized JIT model (default) or more hackable non-JIT model. + + Returns + ------- + model : torch.nn.Module + The CLAP model + preprocess : Callable[[PIL.Image], torch.Tensor] + A torchvision transform that converts a PIL image into a tensor that the returned model can take as its input + """ + if get_pretrained_url(name, 'openai'): + model_path = download_pretrained(get_pretrained_url(name, 'openai'), root=cache_dir) + elif os.path.isfile(name): + model_path = name + else: + raise RuntimeError(f"Model {name} not found; available models = {list_openai_models()}") + + try: + # loading JIT archive + model = torch.jit.load(model_path, map_location=device if jit else "cpu").eval() + state_dict = None + except RuntimeError: + # loading saved state dict + if jit: + warnings.warn(f"File {model_path} is not a JIT archive. Loading as a state dict instead") + jit = False + state_dict = torch.load(model_path, map_location="cpu") + + if not jit: + try: + model = build_model_from_openai_state_dict(state_dict or model.state_dict(), model_cfg, enable_fusion, fusion_type).to(device) + except KeyError: + sd = {k[7:]: v for k, v in state_dict["state_dict"].items()} + model = build_model_from_openai_state_dict(sd, model_cfg, enable_fusion, fusion_type).to(device) + + if str(device) == "cpu": + model.float() + return model + + # patch the device names + device_holder = torch.jit.trace(lambda: torch.ones([]).to(torch.device(device)), example_inputs=[]) + device_node = [n for n in device_holder.graph.findAllNodes("prim::Constant") if "Device" in repr(n)][-1] + + def patch_device(module): + try: + graphs = [module.graph] if hasattr(module, "graph") else [] + except RuntimeError: + graphs = [] + + if hasattr(module, "forward1"): + graphs.append(module.forward1.graph) + + for graph in graphs: + for node in graph.findAllNodes("prim::Constant"): + if "value" in node.attributeNames() and str(node["value"]).startswith("cuda"): + node.copyAttributes(device_node) + + model.apply(patch_device) + patch_device(model.encode_audio) + patch_device(model.encode_text) + + # patch dtype to float32 on CPU + if str(device) == "cpu": + float_holder = torch.jit.trace(lambda: torch.ones([]).float(), example_inputs=[]) + float_input = list(float_holder.graph.findNode("aten::to").inputs())[1] + float_node = float_input.node() + + def patch_float(module): + try: + graphs = [module.graph] if hasattr(module, "graph") else [] + except RuntimeError: + graphs = [] + + if hasattr(module, "forward1"): + graphs.append(module.forward1.graph) + + for graph in graphs: + for node in graph.findAllNodes("aten::to"): + inputs = list(node.inputs()) + for i in [1, 2]: # dtype can be the second or third argument to aten::to() + if inputs[i].node()["value"] == 5: + inputs[i].node().copyAttributes(float_node) + + model.apply(patch_float) + patch_float(model.encode_audio) + patch_float(model.encode_text) + model.float() + + model.audio_branch.audio_length = model.audio_cfg.audio_length + return model diff --git a/ldm/modules/encoders/open_clap/pann_model.py b/ldm/modules/encoders/open_clap/pann_model.py new file mode 100644 index 0000000000000000000000000000000000000000..109db5f418a0bad32cae2452742589ff52a19b85 --- /dev/null +++ b/ldm/modules/encoders/open_clap/pann_model.py @@ -0,0 +1,543 @@ +# PANNs: Large-Scale Pretrained Audio Neural Networks for Audio Pattern Recognition +# Reference from https://github.com/qiuqiangkong/audioset_tagging_cnn +# Some layers are re-designed for CLAP +import os +os.environ['NUMBA_CACHE_DIR'] = '/tmp/' + +import torch +import torch.nn as nn +import torch.nn.functional as F +from torchlibrosa.stft import Spectrogram, LogmelFilterBank +from torchlibrosa.augmentation import SpecAugmentation + +from .utils import do_mixup, interpolate, pad_framewise_output +from .feature_fusion import iAFF, AFF, DAF + + +def init_layer(layer): + """Initialize a Linear or Convolutional layer. """ + nn.init.xavier_uniform_(layer.weight) + + if hasattr(layer, 'bias'): + if layer.bias is not None: + layer.bias.data.fill_(0.) + + +def init_bn(bn): + """Initialize a Batchnorm layer. """ + bn.bias.data.fill_(0.) + bn.weight.data.fill_(1.) + + +class ConvBlock(nn.Module): + def __init__(self, in_channels, out_channels): + + super(ConvBlock, self).__init__() + + self.conv1 = nn.Conv2d(in_channels=in_channels, + out_channels=out_channels, + kernel_size=(3, 3), stride=(1, 1), + padding=(1, 1), bias=False) + + self.conv2 = nn.Conv2d(in_channels=out_channels, + out_channels=out_channels, + kernel_size=(3, 3), stride=(1, 1), + padding=(1, 1), bias=False) + + self.bn1 = nn.BatchNorm2d(out_channels) + self.bn2 = nn.BatchNorm2d(out_channels) + + self.init_weight() + + def init_weight(self): + init_layer(self.conv1) + init_layer(self.conv2) + init_bn(self.bn1) + init_bn(self.bn2) + + + def forward(self, input, pool_size=(2, 2), pool_type='avg'): + + x = input + x = F.relu_(self.bn1(self.conv1(x))) + x = F.relu_(self.bn2(self.conv2(x))) + if pool_type == 'max': + x = F.max_pool2d(x, kernel_size=pool_size) + elif pool_type == 'avg': + x = F.avg_pool2d(x, kernel_size=pool_size) + elif pool_type == 'avg+max': + x1 = F.avg_pool2d(x, kernel_size=pool_size) + x2 = F.max_pool2d(x, kernel_size=pool_size) + x = x1 + x2 + else: + raise Exception('Incorrect argument!') + + return x + + +class ConvBlock5x5(nn.Module): + def __init__(self, in_channels, out_channels): + + super(ConvBlock5x5, self).__init__() + + self.conv1 = nn.Conv2d(in_channels=in_channels, + out_channels=out_channels, + kernel_size=(5, 5), stride=(1, 1), + padding=(2, 2), bias=False) + + self.bn1 = nn.BatchNorm2d(out_channels) + + self.init_weight() + + def init_weight(self): + init_layer(self.conv1) + init_bn(self.bn1) + + + def forward(self, input, pool_size=(2, 2), pool_type='avg'): + + x = input + x = F.relu_(self.bn1(self.conv1(x))) + if pool_type == 'max': + x = F.max_pool2d(x, kernel_size=pool_size) + elif pool_type == 'avg': + x = F.avg_pool2d(x, kernel_size=pool_size) + elif pool_type == 'avg+max': + x1 = F.avg_pool2d(x, kernel_size=pool_size) + x2 = F.max_pool2d(x, kernel_size=pool_size) + x = x1 + x2 + else: + raise Exception('Incorrect argument!') + + return x + + +class AttBlock(nn.Module): + def __init__(self, n_in, n_out, activation='linear', temperature=1.): + super(AttBlock, self).__init__() + + self.activation = activation + self.temperature = temperature + self.att = nn.Conv1d(in_channels=n_in, out_channels=n_out, kernel_size=1, stride=1, padding=0, bias=True) + self.cla = nn.Conv1d(in_channels=n_in, out_channels=n_out, kernel_size=1, stride=1, padding=0, bias=True) + + self.bn_att = nn.BatchNorm1d(n_out) + self.init_weights() + + def init_weights(self): + init_layer(self.att) + init_layer(self.cla) + init_bn(self.bn_att) + + def forward(self, x): + # x: (n_samples, n_in, n_time) + norm_att = torch.softmax(torch.clamp(self.att(x), -10, 10), dim=-1) + cla = self.nonlinear_transform(self.cla(x)) + x = torch.sum(norm_att * cla, dim=2) + return x, norm_att, cla + + def nonlinear_transform(self, x): + if self.activation == 'linear': + return x + elif self.activation == 'sigmoid': + return torch.sigmoid(x) + + +class Cnn14(nn.Module): + def __init__(self, sample_rate, window_size, hop_size, mel_bins, fmin, + fmax, classes_num, enable_fusion=False, fusion_type='None'): + + super(Cnn14, self).__init__() + + window = 'hann' + center = True + pad_mode = 'reflect' + ref = 1.0 + amin = 1e-10 + top_db = None + + self.enable_fusion = enable_fusion + self.fusion_type = fusion_type + + # Spectrogram extractor + self.spectrogram_extractor = Spectrogram(n_fft=window_size, hop_length=hop_size, + win_length=window_size, window=window, center=center, pad_mode=pad_mode, + freeze_parameters=True) + + # Logmel feature extractor + self.logmel_extractor = LogmelFilterBank(sr=sample_rate, n_fft=window_size, + n_mels=mel_bins, fmin=fmin, fmax=fmax, ref=ref, amin=amin, top_db=top_db, + freeze_parameters=True) + + # Spec augmenter + self.spec_augmenter = SpecAugmentation(time_drop_width=64, time_stripes_num=2, + freq_drop_width=8, freq_stripes_num=2) + + self.bn0 = nn.BatchNorm2d(64) + + if (self.enable_fusion) and (self.fusion_type == 'channel_map'): + self.conv_block1 = ConvBlock(in_channels=4, out_channels=64) + else: + self.conv_block1 = ConvBlock(in_channels=1, out_channels=64) + self.conv_block2 = ConvBlock(in_channels=64, out_channels=128) + self.conv_block3 = ConvBlock(in_channels=128, out_channels=256) + self.conv_block4 = ConvBlock(in_channels=256, out_channels=512) + self.conv_block5 = ConvBlock(in_channels=512, out_channels=1024) + self.conv_block6 = ConvBlock(in_channels=1024, out_channels=2048) + + self.fc1 = nn.Linear(2048, 2048, bias=True) + self.fc_audioset = nn.Linear(2048, classes_num, bias=True) + + if (self.enable_fusion) and (self.fusion_type in ['daf_1d','aff_1d','iaff_1d']): + self.mel_conv1d = nn.Sequential( + nn.Conv1d(64, 64, kernel_size=5, stride=3, padding=2), + nn.BatchNorm1d(64) # No Relu + ) + if self.fusion_type == 'daf_1d': + self.fusion_model = DAF() + elif self.fusion_type == 'aff_1d': + self.fusion_model = AFF(channels=64, type='1D') + elif self.fusion_type == 'iaff_1d': + self.fusion_model = iAFF(channels=64, type='1D') + + if (self.enable_fusion) and (self.fusion_type in ['daf_2d','aff_2d','iaff_2d']): + self.mel_conv2d = nn.Sequential( + nn.Conv2d(1, 64, kernel_size=(5,5), stride=(6, 2), padding=(2,2)), + nn.BatchNorm2d(64), + nn.ReLU(inplace=True) + ) + + if self.fusion_type == 'daf_2d': + self.fusion_model = DAF() + elif self.fusion_type == 'aff_2d': + self.fusion_model = AFF(channels=64, type='2D') + elif self.fusion_type == 'iaff_2d': + self.fusion_model = iAFF(channels=64, type='2D') + self.init_weight() + + def init_weight(self): + init_bn(self.bn0) + init_layer(self.fc1) + init_layer(self.fc_audioset) + + def forward(self, input, mixup_lambda=None, device=None): + """ + Input: (batch_size, data_length)""" + + if self.enable_fusion and input["longer"].sum() == 0: + # if no audio is longer than 10s, then randomly select one audio to be longer + input["longer"][torch.randint(0, input["longer"].shape[0], (1,))] = True + + if not self.enable_fusion: + x = self.spectrogram_extractor(input['waveform'].to(device=device, non_blocking=True)) # (batch_size, 1, time_steps, freq_bins) + x = self.logmel_extractor(x) # (batch_size, 1, time_steps, mel_bins) + + x = x.transpose(1, 3) + x = self.bn0(x) + x = x.transpose(1, 3) + else: + longer_list = input["longer"].to(device=device, non_blocking=True) + x = input["mel_fusion"].to(device=device, non_blocking=True) + longer_list_idx = torch.where(longer_list)[0] + x = x.transpose(1, 3) + x = self.bn0(x) + x = x.transpose(1, 3) + if self.fusion_type in ['daf_1d','aff_1d','iaff_1d']: + new_x = x[:,0:1,:,:].clone().contiguous() + # local processing + if len(longer_list_idx) > 0: + fusion_x_local = x[longer_list_idx,1:,:,:].clone().contiguous() + FB,FC,FT,FF = fusion_x_local.size() + fusion_x_local = fusion_x_local.view(FB * FC, FT, FF) + fusion_x_local = torch.permute(fusion_x_local, (0,2,1)).contiguous() + fusion_x_local = self.mel_conv1d(fusion_x_local) + fusion_x_local = fusion_x_local.view(FB,FC,FF,fusion_x_local.size(-1)) + fusion_x_local = torch.permute(fusion_x_local, (0,2,1,3)).contiguous().flatten(2) + if fusion_x_local.size(-1) < FT: + fusion_x_local = torch.cat([fusion_x_local, torch.zeros((FB,FF,FT- fusion_x_local.size(-1)), device=device)], dim=-1) + else: + fusion_x_local = fusion_x_local[:,:,:FT] + # 1D fusion + new_x = new_x.squeeze(1).permute((0,2,1)).contiguous() + new_x[longer_list_idx] = self.fusion_model(new_x[longer_list_idx], fusion_x_local) + x = new_x.permute((0,2,1)).contiguous()[:,None,:,:] + else: + x = new_x + elif self.fusion_type in ['daf_2d','aff_2d','iaff_2d','channel_map']: + x = x # no change + + if self.training: + x = self.spec_augmenter(x) + # Mixup on spectrogram + if self.training and mixup_lambda is not None: + x = do_mixup(x, mixup_lambda) + if (self.enable_fusion) and (self.fusion_type in ['daf_2d','aff_2d','iaff_2d']): + global_x = x[:,0:1,:,:] + + # global processing + B, C, H, W = global_x.shape + global_x = self.conv_block1(global_x, pool_size=(2, 2), pool_type='avg') + if len(longer_list_idx) > 0: + local_x = x[longer_list_idx,1:,:,:].contiguous() + TH = global_x.size(-2) + # local processing + B, C, H, W = local_x.shape + local_x = local_x.view(B*C,1,H,W) + local_x = self.mel_conv2d(local_x) + local_x = local_x.view(B,C,local_x.size(1),local_x.size(2),local_x.size(3)) + local_x = local_x.permute((0,2,1,3,4)).contiguous().flatten(2,3) + TB,TC,_,TW = local_x.size() + if local_x.size(-2) < TH: + local_x = torch.cat([local_x, torch.zeros((TB,TC,TH-local_x.size(-2),TW), device=global_x.device)], dim=-2) + else: + local_x = local_x[:,:,:TH,:] + + global_x[longer_list_idx] = self.fusion_model(global_x[longer_list_idx],local_x) + x = global_x + else: + x = self.conv_block1(x, pool_size=(2, 2), pool_type='avg') + + x = F.dropout(x, p=0.2, training=self.training) + x = self.conv_block2(x, pool_size=(2, 2), pool_type='avg') + x = F.dropout(x, p=0.2, training=self.training) + x = self.conv_block3(x, pool_size=(2, 2), pool_type='avg') + x = F.dropout(x, p=0.2, training=self.training) + x = self.conv_block4(x, pool_size=(2, 2), pool_type='avg') + x = F.dropout(x, p=0.2, training=self.training) + x = self.conv_block5(x, pool_size=(2, 2), pool_type='avg') + x = F.dropout(x, p=0.2, training=self.training) + x = self.conv_block6(x, pool_size=(1, 1), pool_type='avg') + x = F.dropout(x, p=0.2, training=self.training) + x = torch.mean(x, dim=3) + + latent_x1 = F.max_pool1d(x, kernel_size=3, stride=1, padding=1) + latent_x2 = F.avg_pool1d(x, kernel_size=3, stride=1, padding=1) + latent_x = latent_x1 + latent_x2 + latent_x = latent_x.transpose(1, 2) + latent_x = F.relu_(self.fc1(latent_x)) + latent_output = interpolate(latent_x, 32) + + + (x1, _) = torch.max(x, dim=2) + x2 = torch.mean(x, dim=2) + x = x1 + x2 + x = F.dropout(x, p=0.5, training=self.training) + x = F.relu_(self.fc1(x)) + embedding = F.dropout(x, p=0.5, training=self.training) + clipwise_output = torch.sigmoid(self.fc_audioset(x)) + + output_dict = {'clipwise_output': clipwise_output, 'embedding': embedding, 'fine_grained_embedding': latent_output} + return output_dict + + +class Cnn6(nn.Module): + def __init__(self, sample_rate, window_size, hop_size, mel_bins, fmin, + fmax, classes_num, enable_fusion=False, fusion_type='None'): + + super(Cnn6, self).__init__() + + window = 'hann' + center = True + pad_mode = 'reflect' + ref = 1.0 + amin = 1e-10 + top_db = None + + self.enable_fusion = enable_fusion + self.fusion_type = fusion_type + + # Spectrogram extractor + self.spectrogram_extractor = Spectrogram(n_fft=window_size, hop_length=hop_size, + win_length=window_size, window=window, center=center, pad_mode=pad_mode, + freeze_parameters=True) + + # Logmel feature extractor + self.logmel_extractor = LogmelFilterBank(sr=sample_rate, n_fft=window_size, + n_mels=mel_bins, fmin=fmin, fmax=fmax, ref=ref, amin=amin, top_db=top_db, + freeze_parameters=True) + + # Spec augmenter + self.spec_augmenter = SpecAugmentation(time_drop_width=64, time_stripes_num=2, + freq_drop_width=8, freq_stripes_num=2) + + self.bn0 = nn.BatchNorm2d(64) + + self.conv_block1 = ConvBlock5x5(in_channels=1, out_channels=64) + self.conv_block2 = ConvBlock5x5(in_channels=64, out_channels=128) + self.conv_block3 = ConvBlock5x5(in_channels=128, out_channels=256) + self.conv_block4 = ConvBlock5x5(in_channels=256, out_channels=512) + + self.fc1 = nn.Linear(512, 512, bias=True) + self.fc_audioset = nn.Linear(512, classes_num, bias=True) + + self.init_weight() + + def init_weight(self): + init_bn(self.bn0) + init_layer(self.fc1) + init_layer(self.fc_audioset) + + def forward(self, input, mixup_lambda=None, device=None): + """ + Input: (batch_size, data_length)""" + + x = self.spectrogram_extractor(input) # (batch_size, 1, time_steps, freq_bins) + x = self.logmel_extractor(x) # (batch_size, 1, time_steps, mel_bins) + + x = x.transpose(1, 3) + x = self.bn0(x) + x = x.transpose(1, 3) + + if self.training: + x = self.spec_augmenter(x) + + # Mixup on spectrogram + if self.training and mixup_lambda is not None: + x = do_mixup(x, mixup_lambda) + + x = self.conv_block1(x, pool_size=(2, 2), pool_type='avg') + x = F.dropout(x, p=0.2, training=self.training) + x = self.conv_block2(x, pool_size=(2, 2), pool_type='avg') + x = F.dropout(x, p=0.2, training=self.training) + x = self.conv_block3(x, pool_size=(2, 2), pool_type='avg') + x = F.dropout(x, p=0.2, training=self.training) + x = self.conv_block4(x, pool_size=(2, 2), pool_type='avg') + x = F.dropout(x, p=0.2, training=self.training) + x = torch.mean(x, dim=3) + + latent_x1 = F.max_pool1d(x, kernel_size=3, stride=1, padding=1) + latent_x2 = F.avg_pool1d(x, kernel_size=3, stride=1, padding=1) + latent_x = latent_x1 + latent_x2 + latent_x = latent_x.transpose(1, 2) + latent_x = F.relu_(self.fc1(latent_x)) + latent_output = interpolate(latent_x, 16) + + (x1, _) = torch.max(x, dim=2) + x2 = torch.mean(x, dim=2) + x = x1 + x2 + x = F.dropout(x, p=0.5, training=self.training) + x = F.relu_(self.fc1(x)) + embedding = F.dropout(x, p=0.5, training=self.training) + clipwise_output = torch.sigmoid(self.fc_audioset(x)) + + output_dict = {'clipwise_output': clipwise_output, 'embedding': embedding, 'fine_grained_embedding': latent_output} + + return output_dict + + +class Cnn10(nn.Module): + def __init__(self, sample_rate, window_size, hop_size, mel_bins, fmin, + fmax, classes_num, enable_fusion=False, fusion_type='None'): + + super(Cnn10, self).__init__() + + window = 'hann' + center = True + pad_mode = 'reflect' + ref = 1.0 + amin = 1e-10 + top_db = None + + self.enable_fusion = enable_fusion + self.fusion_type = fusion_type + + # Spectrogram extractor + self.spectrogram_extractor = Spectrogram(n_fft=window_size, hop_length=hop_size, + win_length=window_size, window=window, center=center, pad_mode=pad_mode, + freeze_parameters=True) + + # Logmel feature extractor + self.logmel_extractor = LogmelFilterBank(sr=sample_rate, n_fft=window_size, + n_mels=mel_bins, fmin=fmin, fmax=fmax, ref=ref, amin=amin, top_db=top_db, + freeze_parameters=True) + + # Spec augmenter + self.spec_augmenter = SpecAugmentation(time_drop_width=64, time_stripes_num=2, + freq_drop_width=8, freq_stripes_num=2) + + self.bn0 = nn.BatchNorm2d(64) + + self.conv_block1 = ConvBlock(in_channels=1, out_channels=64) + self.conv_block2 = ConvBlock(in_channels=64, out_channels=128) + self.conv_block3 = ConvBlock(in_channels=128, out_channels=256) + self.conv_block4 = ConvBlock(in_channels=256, out_channels=512) + self.conv_block5 = ConvBlock(in_channels=512, out_channels=1024) + + self.fc1 = nn.Linear(1024, 1024, bias=True) + self.fc_audioset = nn.Linear(1024, classes_num, bias=True) + + self.init_weight() + + def init_weight(self): + init_bn(self.bn0) + init_layer(self.fc1) + init_layer(self.fc_audioset) + + def forward(self, input, mixup_lambda=None, device=None): + """ + Input: (batch_size, data_length)""" + + x = self.spectrogram_extractor(input) # (batch_size, 1, time_steps, freq_bins) + x = self.logmel_extractor(x) # (batch_size, 1, time_steps, mel_bins) + + x = x.transpose(1, 3) + x = self.bn0(x) + x = x.transpose(1, 3) + + if self.training: + x = self.spec_augmenter(x) + + # Mixup on spectrogram + if self.training and mixup_lambda is not None: + x = do_mixup(x, mixup_lambda) + + x = self.conv_block1(x, pool_size=(2, 2), pool_type='avg') + x = F.dropout(x, p=0.2, training=self.training) + x = self.conv_block2(x, pool_size=(2, 2), pool_type='avg') + x = F.dropout(x, p=0.2, training=self.training) + x = self.conv_block3(x, pool_size=(2, 2), pool_type='avg') + x = F.dropout(x, p=0.2, training=self.training) + x = self.conv_block4(x, pool_size=(2, 2), pool_type='avg') + x = F.dropout(x, p=0.2, training=self.training) + x = self.conv_block5(x, pool_size=(2, 2), pool_type='avg') + x = F.dropout(x, p=0.2, training=self.training) + x = torch.mean(x, dim=3) + + latent_x1 = F.max_pool1d(x, kernel_size=3, stride=1, padding=1) + latent_x2 = F.avg_pool1d(x, kernel_size=3, stride=1, padding=1) + latent_x = latent_x1 + latent_x2 + latent_x = latent_x.transpose(1, 2) + latent_x = F.relu_(self.fc1(latent_x)) + latent_output = interpolate(latent_x, 32) + + (x1, _) = torch.max(x, dim=2) + x2 = torch.mean(x, dim=2) + x = x1 + x2 + x = F.dropout(x, p=0.5, training=self.training) + x = F.relu_(self.fc1(x)) + embedding = F.dropout(x, p=0.5, training=self.training) + clipwise_output = torch.sigmoid(self.fc_audioset(x)) + + output_dict = {'clipwise_output': clipwise_output, 'embedding': embedding, 'fine_grained_embedding': latent_output} + + return output_dict + + +def create_pann_model(audio_cfg, enable_fusion=False, fusion_type='None'): + try: + ModelProto = eval(audio_cfg.model_name) + model = ModelProto( + sample_rate = audio_cfg.sample_rate, + window_size = audio_cfg.window_size, + hop_size =audio_cfg.hop_size, + mel_bins = audio_cfg.mel_bins, + fmin = audio_cfg.fmin, + fmax = audio_cfg.fmax, + classes_num = audio_cfg.class_num, + enable_fusion = enable_fusion, + fusion_type = fusion_type + ) + return model + except: + raise RuntimeError(f'Import Model for {audio_cfg.model_name} not found, or the audio cfg parameters are not enough.') + diff --git a/ldm/modules/encoders/open_clap/pretrained.py b/ldm/modules/encoders/open_clap/pretrained.py new file mode 100644 index 0000000000000000000000000000000000000000..723619a9fd511cf8619def49c4631ec701891b93 --- /dev/null +++ b/ldm/modules/encoders/open_clap/pretrained.py @@ -0,0 +1,147 @@ +import hashlib +import os +import urllib +import warnings + +from tqdm import tqdm + +_RN50 = dict( + openai="https://openaipublic.azureedge.net/clip/models/afeb0e10f9e5a86da6080e35cf09123aca3b358a0c3e3b6c78a7b63bc04b6762/RN50.pt", + yfcc15m="https://github.com/mlfoundations/open_clip/releases/download/v0.2-weights/rn50-quickgelu-yfcc15m-455df137.pt", + cc12m="https://github.com/mlfoundations/open_clip/releases/download/v0.2-weights/rn50-quickgelu-cc12m-f000538c.pt" +) + +_RN50_quickgelu = dict( + openai="https://openaipublic.azureedge.net/clip/models/afeb0e10f9e5a86da6080e35cf09123aca3b358a0c3e3b6c78a7b63bc04b6762/RN50.pt", + yfcc15m="https://github.com/mlfoundations/open_clip/releases/download/v0.2-weights/rn50-quickgelu-yfcc15m-455df137.pt", + cc12m="https://github.com/mlfoundations/open_clip/releases/download/v0.2-weights/rn50-quickgelu-cc12m-f000538c.pt" +) + +_RN101 = dict( + openai="https://openaipublic.azureedge.net/clip/models/8fa8567bab74a42d41c5915025a8e4538c3bdbe8804a470a72f30b0d94fab599/RN101.pt", + yfcc15m="https://github.com/mlfoundations/open_clip/releases/download/v0.2-weights/rn101-quickgelu-yfcc15m-3e04b30e.pt" +) + +_RN101_quickgelu = dict( + openai="https://openaipublic.azureedge.net/clip/models/8fa8567bab74a42d41c5915025a8e4538c3bdbe8804a470a72f30b0d94fab599/RN101.pt", + yfcc15m="https://github.com/mlfoundations/open_clip/releases/download/v0.2-weights/rn101-quickgelu-yfcc15m-3e04b30e.pt" +) + +_RN50x4 = dict( + openai="https://openaipublic.azureedge.net/clip/models/7e526bd135e493cef0776de27d5f42653e6b4c8bf9e0f653bb11773263205fdd/RN50x4.pt", +) + +_RN50x16 = dict( + openai="https://openaipublic.azureedge.net/clip/models/52378b407f34354e150460fe41077663dd5b39c54cd0bfd2b27167a4a06ec9aa/RN50x16.pt", +) + +_RN50x64 = dict( + openai="https://openaipublic.azureedge.net/clip/models/be1cfb55d75a9666199fb2206c106743da0f6468c9d327f3e0d0a543a9919d9c/RN50x64.pt", +) + +_VITB32 = dict( + openai="https://openaipublic.azureedge.net/clip/models/40d365715913c9da98579312b702a82c18be219cc2a73407c4526f58eba950af/ViT-B-32.pt", + laion400m_e31="https://github.com/mlfoundations/open_clip/releases/download/v0.2-weights/vit_b_32-quickgelu-laion400m_e31-d867053b.pt", + laion400m_e32="https://github.com/mlfoundations/open_clip/releases/download/v0.2-weights/vit_b_32-quickgelu-laion400m_e32-46683a32.pt", + laion400m_avg="https://github.com/mlfoundations/open_clip/releases/download/v0.2-weights/vit_b_32-quickgelu-laion400m_avg-8a00ab3c.pt", +) + +_VITB32_quickgelu = dict( + openai="https://openaipublic.azureedge.net/clip/models/40d365715913c9da98579312b702a82c18be219cc2a73407c4526f58eba950af/ViT-B-32.pt", + laion400m_e31="https://github.com/mlfoundations/open_clip/releases/download/v0.2-weights/vit_b_32-quickgelu-laion400m_e31-d867053b.pt", + laion400m_e32="https://github.com/mlfoundations/open_clip/releases/download/v0.2-weights/vit_b_32-quickgelu-laion400m_e32-46683a32.pt", + laion400m_avg="https://github.com/mlfoundations/open_clip/releases/download/v0.2-weights/vit_b_32-quickgelu-laion400m_avg-8a00ab3c.pt", +) + +_VITB16 = dict( + openai="https://openaipublic.azureedge.net/clip/models/5806e77cd80f8b59890b7e101eabd078d9fb84e6937f9e85e4ecb61988df416f/ViT-B-16.pt", +) + +_VITL14 = dict( + openai="https://openaipublic.azureedge.net/clip/models/b8cca3fd41ae0c99ba7e8951adf17d267cdb84cd88be6f7c2e0eca1737a03836/ViT-L-14.pt", +) + +_PRETRAINED = { + "RN50": _RN50, + "RN50-quickgelu": _RN50_quickgelu, + "RN101": _RN101, + "RN101-quickgelu": _RN101_quickgelu, + "RN50x4": _RN50x4, + "RN50x16": _RN50x16, + "ViT-B-32": _VITB32, + "ViT-B-32-quickgelu": _VITB32_quickgelu, + "ViT-B-16": _VITB16, + "ViT-L-14": _VITL14, +} + + +def list_pretrained(as_str: bool = False): + """ returns list of pretrained models + Returns a tuple (model_name, pretrain_tag) by default or 'name:tag' if as_str == True + """ + return [':'.join([k, t]) if as_str else (k, t) for k in _PRETRAINED.keys() for t in _PRETRAINED[k].keys()] + + +def list_pretrained_tag_models(tag: str): + """ return all models having the specified pretrain tag """ + models = [] + for k in _PRETRAINED.keys(): + if tag in _PRETRAINED[k]: + models.append(k) + return models + + +def list_pretrained_model_tags(model: str): + """ return all pretrain tags for the specified model architecture """ + tags = [] + if model in _PRETRAINED: + tags.extend(_PRETRAINED[model].keys()) + return tags + + +def get_pretrained_url(model: str, tag: str): + if model not in _PRETRAINED: + return '' + model_pretrained = _PRETRAINED[model] + if tag not in model_pretrained: + return '' + return model_pretrained[tag] + + +def download_pretrained(url: str, root: str = os.path.expanduser("~/.cache/clip")): + os.makedirs(root, exist_ok=True) + filename = os.path.basename(url) + + if 'openaipublic' in url: + expected_sha256 = url.split("/")[-2] + else: + expected_sha256 = '' + + download_target = os.path.join(root, filename) + + if os.path.exists(download_target) and not os.path.isfile(download_target): + raise RuntimeError(f"{download_target} exists and is not a regular file") + + if os.path.isfile(download_target): + if expected_sha256: + if hashlib.sha256(open(download_target, "rb").read()).hexdigest() == expected_sha256: + return download_target + else: + warnings.warn(f"{download_target} exists, but the SHA256 checksum does not match; re-downloading the file") + else: + return download_target + + with urllib.request.urlopen(url) as source, open(download_target, "wb") as output: + with tqdm(total=int(source.info().get("Content-Length")), ncols=80, unit='iB', unit_scale=True) as loop: + while True: + buffer = source.read(8192) + if not buffer: + break + + output.write(buffer) + loop.update(len(buffer)) + + if expected_sha256 and hashlib.sha256(open(download_target, "rb").read()).hexdigest() != expected_sha256: + raise RuntimeError(f"Model has been downloaded but the SHA256 checksum does not not match") + + return download_target diff --git a/ldm/modules/encoders/open_clap/timm_model.py b/ldm/modules/encoders/open_clap/timm_model.py new file mode 100644 index 0000000000000000000000000000000000000000..071dd148c772f398e87ecbfc836dcfa4a3ae01af --- /dev/null +++ b/ldm/modules/encoders/open_clap/timm_model.py @@ -0,0 +1,106 @@ +""" timm model adapter + +Wraps timm (https://github.com/rwightman/pytorch-image-models) models for use as a vision tower in CLIP model. +""" +from collections import OrderedDict + +import torch.nn as nn + +try: + import timm + from timm.models.layers import Mlp, to_2tuple + from timm.models.layers.attention_pool2d import RotAttentionPool2d + from timm.models.layers.attention_pool2d import AttentionPool2d as AbsAttentionPool2d +except ImportError as e: + timm = None + +from .utils import freeze_batch_norm_2d + + +class TimmModel(nn.Module): + """ timm model adapter + # FIXME this adapter is a work in progress, may change in ways that break weight compat + """ + + def __init__( + self, + model_name, + embed_dim, + image_size=224, + pool='avg', + proj='linear', + drop=0., + pretrained=False): + super().__init__() + if timm is None: + raise RuntimeError("Please `pip install timm` to use timm models.") + + self.image_size = to_2tuple(image_size) + self.trunk = timm.create_model(model_name, pretrained=pretrained) + feat_size = self.trunk.default_cfg.get('pool_size', None) + feature_ndim = 1 if not feat_size else 2 + if pool in ('abs_attn', 'rot_attn'): + assert feature_ndim == 2 + # if attn pooling used, remove both classifier and default pool + self.trunk.reset_classifier(0, global_pool='') + else: + # reset global pool if pool config set, otherwise leave as network default + reset_kwargs = dict(global_pool=pool) if pool else {} + self.trunk.reset_classifier(0, **reset_kwargs) + prev_chs = self.trunk.num_features + + head_layers = OrderedDict() + if pool == 'abs_attn': + head_layers['pool'] = AbsAttentionPool2d(prev_chs, feat_size=feat_size, out_features=embed_dim) + prev_chs = embed_dim + elif pool == 'rot_attn': + head_layers['pool'] = RotAttentionPool2d(prev_chs, out_features=embed_dim) + prev_chs = embed_dim + else: + assert proj, 'projection layer needed if non-attention pooling is used.' + + # NOTE attention pool ends with a projection layer, so proj should usually be set to '' if such pooling is used + if proj == 'linear': + head_layers['drop'] = nn.Dropout(drop) + head_layers['proj'] = nn.Linear(prev_chs, embed_dim) + elif proj == 'mlp': + head_layers['mlp'] = Mlp(prev_chs, 2 * embed_dim, embed_dim, drop=drop) + + self.head = nn.Sequential(head_layers) + + def lock(self, unlocked_groups=0, freeze_bn_stats=False): + """ lock modules + Args: + unlocked_groups (int): leave last n layer groups unlocked (default: 0) + """ + if not unlocked_groups: + # lock full model + for param in self.trunk.parameters(): + param.requires_grad = False + if freeze_bn_stats: + freeze_batch_norm_2d(self.trunk) + else: + # NOTE: partial freeze requires latest timm (master) branch and is subject to change + try: + # FIXME import here until API stable and in an official release + from timm.models.helpers import group_parameters, group_modules + except ImportError: + raise RuntimeError( + 'Please install latest timm `pip install git+https://github.com/rwightman/pytorch-image-models`') + matcher = self.trunk.group_matcher() + gparams = group_parameters(self.trunk, matcher) + max_layer_id = max(gparams.keys()) + max_layer_id = max_layer_id - unlocked_groups + for group_idx in range(max_layer_id + 1): + group = gparams[group_idx] + for param in group: + self.trunk.get_parameter(param).requires_grad = False + if freeze_bn_stats: + gmodules = group_modules(self.trunk, matcher, reverse=True) + gmodules = {k for k, v in gmodules.items() if v <= max_layer_id} + freeze_batch_norm_2d(self.trunk, gmodules) + + def forward(self, x): + x = self.trunk(x) + x = self.head(x) + return x diff --git a/ldm/modules/encoders/open_clap/tokenizer.py b/ldm/modules/encoders/open_clap/tokenizer.py new file mode 100644 index 0000000000000000000000000000000000000000..5b4a238b987ce66f2932b11451d916e40816b8a3 --- /dev/null +++ b/ldm/modules/encoders/open_clap/tokenizer.py @@ -0,0 +1,180 @@ +""" CLIP tokenizer + +Copied from https://github.com/openai/CLIP. Originally MIT License, Copyright (c) 2021 OpenAI. +""" +import gzip +import html +import os +from functools import lru_cache +from typing import Union, List + +import ftfy +import regex as re +import torch + + +@lru_cache() +def default_bpe(): + return os.path.join(os.path.dirname(os.path.abspath(__file__)), "bpe_simple_vocab_16e6.txt.gz") + + +@lru_cache() +def bytes_to_unicode(): + """ + Returns list of utf-8 byte and a corresponding list of unicode strings. + The reversible bpe codes work on unicode strings. + This means you need a large # of unicode characters in your vocab if you want to avoid UNKs. + When you're at something like a 10B token dataset you end up needing around 5K for decent coverage. + This is a signficant percentage of your normal, say, 32K bpe vocab. + To avoid that, we want lookup tables between utf-8 bytes and unicode strings. + And avoids mapping to whitespace/control characters the bpe code barfs on. + """ + bs = list(range(ord("!"), ord("~")+1))+list(range(ord("¡"), ord("¬")+1))+list(range(ord("®"), ord("ÿ")+1)) + cs = bs[:] + n = 0 + for b in range(2**8): + if b not in bs: + bs.append(b) + cs.append(2**8+n) + n += 1 + cs = [chr(n) for n in cs] + return dict(zip(bs, cs)) + + +def get_pairs(word): + """Return set of symbol pairs in a word. + Word is represented as tuple of symbols (symbols being variable-length strings). + """ + pairs = set() + prev_char = word[0] + for char in word[1:]: + pairs.add((prev_char, char)) + prev_char = char + return pairs + + +def basic_clean(text): + text = ftfy.fix_text(text) + text = html.unescape(html.unescape(text)) + return text.strip() + + +def whitespace_clean(text): + text = re.sub(r'\s+', ' ', text) + text = text.strip() + return text + + +class SimpleTokenizer(object): + def __init__(self, bpe_path: str = default_bpe(), special_tokens=None): + self.byte_encoder = bytes_to_unicode() + self.byte_decoder = {v: k for k, v in self.byte_encoder.items()} + merges = gzip.open(bpe_path).read().decode("utf-8").split('\n') + merges = merges[1:49152-256-2+1] + merges = [tuple(merge.split()) for merge in merges] + vocab = list(bytes_to_unicode().values()) + vocab = vocab + [v+'' for v in vocab] + for merge in merges: + vocab.append(''.join(merge)) + if not special_tokens: + special_tokens = ['', ''] + else: + special_tokens = ['', ''] + special_tokens + vocab.extend(special_tokens) + self.encoder = dict(zip(vocab, range(len(vocab)))) + self.decoder = {v: k for k, v in self.encoder.items()} + self.bpe_ranks = dict(zip(merges, range(len(merges)))) + self.cache = {t:t for t in special_tokens} + special = "|".join(special_tokens) + self.pat = re.compile(special + r"""|'s|'t|'re|'ve|'m|'ll|'d|[\p{L}]+|[\p{N}]|[^\s\p{L}\p{N}]+""", re.IGNORECASE) + + self.vocab_size = len(self.encoder) + self.all_special_ids = [self.encoder[t] for t in special_tokens] + + def bpe(self, token): + if token in self.cache: + return self.cache[token] + word = tuple(token[:-1]) + ( token[-1] + '',) + pairs = get_pairs(word) + + if not pairs: + return token+'' + + while True: + bigram = min(pairs, key = lambda pair: self.bpe_ranks.get(pair, float('inf'))) + if bigram not in self.bpe_ranks: + break + first, second = bigram + new_word = [] + i = 0 + while i < len(word): + try: + j = word.index(first, i) + new_word.extend(word[i:j]) + i = j + except: + new_word.extend(word[i:]) + break + + if word[i] == first and i < len(word)-1 and word[i+1] == second: + new_word.append(first+second) + i += 2 + else: + new_word.append(word[i]) + i += 1 + new_word = tuple(new_word) + word = new_word + if len(word) == 1: + break + else: + pairs = get_pairs(word) + word = ' '.join(word) + self.cache[token] = word + return word + + def encode(self, text): + bpe_tokens = [] + text = whitespace_clean(basic_clean(text)).lower() + for token in re.findall(self.pat, text): + token = ''.join(self.byte_encoder[b] for b in token.encode('utf-8')) + bpe_tokens.extend(self.encoder[bpe_token] for bpe_token in self.bpe(token).split(' ')) + return bpe_tokens + + def decode(self, tokens): + text = ''.join([self.decoder[token] for token in tokens]) + text = bytearray([self.byte_decoder[c] for c in text]).decode('utf-8', errors="replace").replace('', ' ') + return text + + +_tokenizer = SimpleTokenizer() + + +def tokenize(texts: Union[str, List[str]], context_length: int = 77) -> torch.LongTensor: + """ + Returns the tokenized representation of given input string(s) + + Parameters + ---------- + texts : Union[str, List[str]] + An input string or a list of input strings to tokenize + context_length : int + The context length to use; all CLIP models use 77 as the context length + + Returns + ------- + A two-dimensional tensor containing the resulting tokens, shape = [number of input strings, context_length] + """ + if isinstance(texts, str): + texts = [texts] + + sot_token = _tokenizer.encoder[""] + eot_token = _tokenizer.encoder[""] + all_tokens = [[sot_token] + _tokenizer.encode(text) + [eot_token] for text in texts] + result = torch.zeros(len(all_tokens), context_length, dtype=torch.long) + + for i, tokens in enumerate(all_tokens): + if len(tokens) > context_length: + tokens = tokens[:context_length] # Truncate + result[i, :len(tokens)] = torch.tensor(tokens) + + return result diff --git a/ldm/modules/encoders/open_clap/transform.py b/ldm/modules/encoders/open_clap/transform.py new file mode 100644 index 0000000000000000000000000000000000000000..7014c926f153a351d2256c869c67c02d57b30913 --- /dev/null +++ b/ldm/modules/encoders/open_clap/transform.py @@ -0,0 +1,30 @@ +from torchvision.transforms import Normalize, Compose, RandomResizedCrop, InterpolationMode, ToTensor, Resize, \ + CenterCrop + + +def _convert_to_rgb(image): + return image.convert('RGB') + + +def image_transform( + image_size: int, + is_train: bool, + mean=(0.48145466, 0.4578275, 0.40821073), + std=(0.26862954, 0.26130258, 0.27577711) +): + normalize = Normalize(mean=mean, std=std) + if is_train: + return Compose([ + RandomResizedCrop(image_size, scale=(0.9, 1.0), interpolation=InterpolationMode.BICUBIC), + _convert_to_rgb, + ToTensor(), + normalize, + ]) + else: + return Compose([ + Resize(image_size, interpolation=InterpolationMode.BICUBIC), + CenterCrop(image_size), + _convert_to_rgb, + ToTensor(), + normalize, + ]) diff --git a/ldm/modules/encoders/open_clap/utils.py b/ldm/modules/encoders/open_clap/utils.py new file mode 100644 index 0000000000000000000000000000000000000000..ad9801c0ac819473f738e2c1fbbdf711006ea440 --- /dev/null +++ b/ldm/modules/encoders/open_clap/utils.py @@ -0,0 +1,369 @@ +import numpy as np +import torch +from torch import nn as nn +from torchvision.ops.misc import FrozenBatchNorm2d +import logging +import h5py +from tqdm import tqdm +import random +import json +import os +import pathlib + +# TODO: (yusong) this not a good place to store those information and does not scale. Need to be fixed later. +dataset_split = { + "audiocaps": ["train", "valid", "test"], + "audioset": ["balanced_train", "unbalanced_train", "eval"], + "BBCSoundEffects": ["train", "test"], + "Clotho": ["train", "test", "valid"], + "free_to_use_sounds": ["train", "test"], + "paramount_motion": ["train", "test"], + "sonniss_game_effects": ["train", "test"], + "wesoundeffects": ["train", "test"], + "MACS": ["train", "test"], + "freesound": ["train", "test"], + "FSD50K": ["train", "test", "valid"], + "fsd50k_class_label": ["train", "test", "valid"], + "esc50": ["train", "test"], + "audiostock": ["train", "test"], + "freesound_no_overlap_noesc50": ["train", "test"], + "epidemic_sound_effects": ["train", "test"], + "VGGSound": ["train", "test"], + "urbansound8k_class_label": ["train", "test"], + "audioset_t5": ["balanced_train", "unbalanced_train", "eval"], + "epidemic_sound_effects_t5": ["train", "test"], + "WavText5K": ["train", "test"], + "esc50_no_overlap": ["train", "test"], + "usd8k_no_overlap": ["train", "test"], + "fsd50k_200_class_label": ["train", "test", "valid"] +} + + +def freeze_batch_norm_2d(module, module_match={}, name=""): + """ + Converts all `BatchNorm2d` and `SyncBatchNorm` layers of provided module into `FrozenBatchNorm2d`. If `module` is + itself an instance of either `BatchNorm2d` or `SyncBatchNorm`, it is converted into `FrozenBatchNorm2d` and + returned. Otherwise, the module is walked recursively and submodules are converted in place. + + Args: + module (torch.nn.Module): Any PyTorch module. + module_match (dict): Dictionary of full module names to freeze (all if empty) + name (str): Full module name (prefix) + + Returns: + torch.nn.Module: Resulting module + + Inspired by https://github.com/pytorch/pytorch/blob/a5895f85be0f10212791145bfedc0261d364f103/torch/nn/modules/batchnorm.py#L762 + """ + res = module + is_match = True + if module_match: + is_match = name in module_match + if is_match and isinstance( + module, (nn.modules.batchnorm.BatchNorm2d, nn.modules.batchnorm.SyncBatchNorm) + ): + res = FrozenBatchNorm2d(module.num_features) + res.num_features = module.num_features + res.affine = module.affine + if module.affine: + res.weight.data = module.weight.data.clone().detach() + res.bias.data = module.bias.data.clone().detach() + res.running_mean.data = module.running_mean.data + res.running_var.data = module.running_var.data + res.eps = module.eps + else: + for child_name, child in module.named_children(): + full_child_name = ".".join([name, child_name]) if name else child_name + new_child = freeze_batch_norm_2d(child, module_match, full_child_name) + if new_child is not child: + res.add_module(child_name, new_child) + return res + + +def exist(dataset_name, dataset_type): + """ + Check if dataset exists + """ + if dataset_type in dataset_split[dataset_name]: + return True + else: + return False + + +def get_tar_path_from_dataset_name( + dataset_names, + dataset_types, + islocal, + dataset_path, + proportion=1, + full_dataset=None +): + """ + Get tar path from dataset name and type + """ + output = [] + for n in dataset_names: + if full_dataset is not None and n in full_dataset: + current_dataset_types = dataset_split[n] + else: + current_dataset_types = dataset_types + for s in current_dataset_types: + tmp = [] + if islocal: + sizefilepath_ = f"{dataset_path}/{n}/{s}/sizes.json" + if not os.path.exists(sizefilepath_): + sizefilepath_ = f"./json_files/{n}/{s}/sizes.json" + else: + sizefilepath_ = f"./json_files/{n}/{s}/sizes.json" + if not os.path.exists(sizefilepath_): + continue + sizes = json.load(open(sizefilepath_, "r")) + for k in sizes.keys(): + if islocal: + tmp.append(f"{dataset_path}/{n}/{s}/{k}") + else: + tmp.append( + f"pipe:aws s3 --cli-connect-timeout 0 cp s3://s-laion-audio/webdataset_tar/{n}/{s}/{k} -" + ) + if proportion != 1: + tmp = random.sample(tmp, int(proportion * len(tmp))) + output.append(tmp) + return sum(output, []) + + +def get_tar_path_from_txts(txt_path, islocal, proportion=1): + """ + Get tar path from txt path + """ + if isinstance(txt_path, (list, tuple)): + return sum( + [ + get_tar_path_from_txts( + txt_path[i], islocal=islocal, proportion=proportion + ) + for i in range(len(txt_path)) + ], + [], + ) + if isinstance(txt_path, str): + with open(txt_path) as f: + lines = f.readlines() + if islocal: + lines = [ + lines[i] + .split("\n")[0] + .replace("pipe:aws s3 cp s3://s-laion-audio/", "/mnt/audio_clip/") + for i in range(len(lines)) + ] + else: + lines = [ + lines[i].split("\n")[0].replace(".tar", ".tar -") + for i in range(len(lines)) + ] + if proportion != 1: + print("Sampling tars with proportion of {}".format(proportion)) + lines = random.sample(lines, int(proportion * len(lines))) + return lines + + +def get_mix_lambda(mixup_alpha, batch_size): + mixup_lambdas = [ + np.random.beta(mixup_alpha, mixup_alpha, 1)[0] for _ in range(batch_size) + ] + return np.array(mixup_lambdas).astype(np.float32) + + +def do_mixup(x, mixup_lambda): + """ + Args: + x: (batch_size , ...) + mixup_lambda: (batch_size,) + Returns: + out: (batch_size, ...) + """ + out = ( + x.transpose(0, -1) * mixup_lambda + + torch.flip(x, dims=[0]).transpose(0, -1) * (1 - mixup_lambda) + ).transpose(0, -1) + return out + + +def interpolate(x, ratio): + """Interpolate data in time domain. This is used to compensate the + resolution reduction in downsampling of a CNN. + + Args: + x: (batch_size, time_steps, classes_num) + ratio: int, ratio to interpolate + Returns: + upsampled: (batch_size, time_steps * ratio, classes_num) + """ + (batch_size, time_steps, classes_num) = x.shape + upsampled = x[:, :, None, :].repeat(1, 1, ratio, 1) + upsampled = upsampled.reshape(batch_size, time_steps * ratio, classes_num) + return upsampled + + +def pad_framewise_output(framewise_output, frames_num): + """Pad framewise_output to the same length as input frames. The pad value + is the same as the value of the last frame. + Args: + framewise_output: (batch_size, frames_num, classes_num) + frames_num: int, number of frames to pad + Outputs: + output: (batch_size, frames_num, classes_num) + """ + pad = framewise_output[:, -1:, :].repeat( + 1, frames_num - framewise_output.shape[1], 1 + ) + """tensor for padding""" + + output = torch.cat((framewise_output, pad), dim=1) + """(batch_size, frames_num, classes_num)""" + + +def process_ipc(index_path, classes_num, filename): + # load data + logging.info("Load Data...............") + ipc = [[] for _ in range(classes_num)] + with h5py.File(index_path, "r") as f: + for i in tqdm(range(len(f["target"]))): + t_class = np.where(f["target"][i])[0] + for t in t_class: + ipc[t].append(i) + print(ipc) + np.save(filename, ipc) + logging.info("Load Data Succeed...............") + + +def save_to_dict(s, o_={}): + sp = s.split(": ") + o_.update({sp[0]: float(sp[1])}) + return o_ + + +def get_data_from_log(txt_path): + """ + Output dictionary from out.txt log file + """ + with open(txt_path) as f: + lines = f.readlines() + val_data = {} + train_data = {} + train_losses = [] + train_losses_epoch = [] + for i in range(len(lines)): + if "| INFO |" in lines[i]: + if "Eval Epoch" in lines[i]: + if "val_loss" in lines[i]: + # float(regex.sub("", lines[310].split(" ")[-1]).replace(" ", "")) + line = lines[i].split("Eval Epoch: ")[-1] + num_epoch = int(line.split(" ")[0].split(" ")[0]) + d = { + line.split(" ")[0] + .split(" ")[1] + .replace(":", ""): float(line.split(" ")[0].split(" ")[-1]) + } + for i in range(1, len(line.split(" "))): + d = save_to_dict(line.split(" ")[i], d) + val_data[num_epoch] = d + elif "Train Epoch" in lines[i]: + num_epoch = int(lines[i].split("Train Epoch: ")[1][0]) + loss = float(lines[i].split("Loss: ")[-1].split(" (")[0]) + train_losses.append(loss) + train_losses_epoch.append(num_epoch) + for i in range(len(train_losses)): + train_data[i] = { + "num_epoch": train_losses_epoch[i], + "train_loss": train_losses[i], + } + return train_data, val_data + + +def save_p(obj, filename): + import pickle + + try: + from deepdiff import DeepDiff + except: + os.system("pip install deepdiff") + from deepdiff import DeepDiff + with open(filename, "wb") as file: + pickle.dump(obj, file, protocol=pickle.HIGHEST_PROTOCOL) # highest protocol + with open(filename, "rb") as file: + z = pickle.load(file) + assert ( + DeepDiff(obj, z, ignore_string_case=True) == {} + ), "there is something wrong with the saving process" + return + + +def load_p(filename): + import pickle + + with open(filename, "rb") as file: + z = pickle.load(file) + return z + + +def save_json(data, name="data.json"): + import json + with open(name, 'w') as fp: + json.dump(data, fp) + return + + +def load_json(name): + import json + with open(name, 'r') as fp: + data = json.load(fp) + return data + + +from multiprocessing import Process, Manager +from multiprocessing import Process, Value, Array +from ctypes import c_wchar + + +def load_class_label(path): + # https://stackoverflow.com/questions/48004243/how-to-share-large-read-only-dictionary-list-across-processes-in-multiprocessing + # https://stackoverflow.com/questions/45693949/storing-strings-in-a-multiprocessing-sharedctypes-array + out = None + if path is not None: + if pathlib.Path(path).suffix in [".pkl", ".pickle"]: + out = load_p(path) + elif pathlib.Path(path).suffix in [".json", ".txt"]: + out = load_json(path) + elif pathlib.Path(path).suffix in [".npy", ".npz"]: + out = np.load(path) + elif pathlib.Path(path).suffix in [".csv"]: + import pandas as pd + out = pd.read_csv(path) + return out + # if out is None: + # return None + # else: + # key = Array(c_wchar, '\n'.join(list(out.keys())), lock=False) + # val = Array('i', out.values(), lock=False) + # return (key, val) + + +from torch import optim + + +def get_optimizer(params, lr, betas, eps, momentum, optimizer_name): + if optimizer_name.lower() == "adamw": + optimizer = optim.AdamW( + params, lr=lr, betas=betas, eps=eps + ) + elif optimizer_name.lower() == "sgd": + optimizer = optim.SGD( + params, lr=lr, momentum=momentum + ) + elif optimizer_name.lower() == "adam": + optimizer = optim.Adam( + params, lr=lr, betas=betas, eps=eps + ) + else: + raise ValueError("optimizer name is not correct") + return optimizer diff --git a/ldm/modules/encoders/open_clap/version.py b/ldm/modules/encoders/open_clap/version.py new file mode 100644 index 0000000000000000000000000000000000000000..fc79d63d5430b972ac6ec1c4bfea9af80922da4d --- /dev/null +++ b/ldm/modules/encoders/open_clap/version.py @@ -0,0 +1 @@ +__version__ = '0.2.1' diff --git a/ldm/modules/image_degradation/__init__.py b/ldm/modules/image_degradation/__init__.py new file mode 100644 index 0000000000000000000000000000000000000000..7836cada81f90ded99c58d5942eea4c3477f58fc --- /dev/null +++ b/ldm/modules/image_degradation/__init__.py @@ -0,0 +1,2 @@ +from ldm.modules.image_degradation.bsrgan import degradation_bsrgan_variant as degradation_fn_bsr +from ldm.modules.image_degradation.bsrgan_light import degradation_bsrgan_variant as degradation_fn_bsr_light diff --git a/ldm/modules/image_degradation/bsrgan.py b/ldm/modules/image_degradation/bsrgan.py new file mode 100644 index 0000000000000000000000000000000000000000..32ef56169978e550090261cddbcf5eb611a6173b --- /dev/null +++ b/ldm/modules/image_degradation/bsrgan.py @@ -0,0 +1,730 @@ +# -*- coding: utf-8 -*- +""" +# -------------------------------------------- +# Super-Resolution +# -------------------------------------------- +# +# Kai Zhang (cskaizhang@gmail.com) +# https://github.com/cszn +# From 2019/03--2021/08 +# -------------------------------------------- +""" + +import numpy as np +import cv2 +import torch + +from functools import partial +import random +from scipy import ndimage +import scipy +import scipy.stats as ss +from scipy.interpolate import interp2d +from scipy.linalg import orth +import albumentations + +import ldm.modules.image_degradation.utils_image as util + + +def modcrop_np(img, sf): + ''' + Args: + img: numpy image, WxH or WxHxC + sf: scale factor + Return: + cropped image + ''' + w, h = img.shape[:2] + im = np.copy(img) + return im[:w - w % sf, :h - h % sf, ...] + + +""" +# -------------------------------------------- +# anisotropic Gaussian kernels +# -------------------------------------------- +""" + + +def analytic_kernel(k): + """Calculate the X4 kernel from the X2 kernel (for proof see appendix in paper)""" + k_size = k.shape[0] + # Calculate the big kernels size + big_k = np.zeros((3 * k_size - 2, 3 * k_size - 2)) + # Loop over the small kernel to fill the big one + for r in range(k_size): + for c in range(k_size): + big_k[2 * r:2 * r + k_size, 2 * c:2 * c + k_size] += k[r, c] * k + # Crop the edges of the big kernel to ignore very small values and increase run time of SR + crop = k_size // 2 + cropped_big_k = big_k[crop:-crop, crop:-crop] + # Normalize to 1 + return cropped_big_k / cropped_big_k.sum() + + +def anisotropic_Gaussian(ksize=15, theta=np.pi, l1=6, l2=6): + """ generate an anisotropic Gaussian kernel + Args: + ksize : e.g., 15, kernel size + theta : [0, pi], rotation angle range + l1 : [0.1,50], scaling of eigenvalues + l2 : [0.1,l1], scaling of eigenvalues + If l1 = l2, will get an isotropic Gaussian kernel. + Returns: + k : kernel + """ + + v = np.dot(np.array([[np.cos(theta), -np.sin(theta)], [np.sin(theta), np.cos(theta)]]), np.array([1., 0.])) + V = np.array([[v[0], v[1]], [v[1], -v[0]]]) + D = np.array([[l1, 0], [0, l2]]) + Sigma = np.dot(np.dot(V, D), np.linalg.inv(V)) + k = gm_blur_kernel(mean=[0, 0], cov=Sigma, size=ksize) + + return k + + +def gm_blur_kernel(mean, cov, size=15): + center = size / 2.0 + 0.5 + k = np.zeros([size, size]) + for y in range(size): + for x in range(size): + cy = y - center + 1 + cx = x - center + 1 + k[y, x] = ss.multivariate_normal.pdf([cx, cy], mean=mean, cov=cov) + + k = k / np.sum(k) + return k + + +def shift_pixel(x, sf, upper_left=True): + """shift pixel for super-resolution with different scale factors + Args: + x: WxHxC or WxH + sf: scale factor + upper_left: shift direction + """ + h, w = x.shape[:2] + shift = (sf - 1) * 0.5 + xv, yv = np.arange(0, w, 1.0), np.arange(0, h, 1.0) + if upper_left: + x1 = xv + shift + y1 = yv + shift + else: + x1 = xv - shift + y1 = yv - shift + + x1 = np.clip(x1, 0, w - 1) + y1 = np.clip(y1, 0, h - 1) + + if x.ndim == 2: + x = interp2d(xv, yv, x)(x1, y1) + if x.ndim == 3: + for i in range(x.shape[-1]): + x[:, :, i] = interp2d(xv, yv, x[:, :, i])(x1, y1) + + return x + + +def blur(x, k): + ''' + x: image, NxcxHxW + k: kernel, Nx1xhxw + ''' + n, c = x.shape[:2] + p1, p2 = (k.shape[-2] - 1) // 2, (k.shape[-1] - 1) // 2 + x = torch.nn.functional.pad(x, pad=(p1, p2, p1, p2), mode='replicate') + k = k.repeat(1, c, 1, 1) + k = k.view(-1, 1, k.shape[2], k.shape[3]) + x = x.view(1, -1, x.shape[2], x.shape[3]) + x = torch.nn.functional.conv2d(x, k, bias=None, stride=1, padding=0, groups=n * c) + x = x.view(n, c, x.shape[2], x.shape[3]) + + return x + + +def gen_kernel(k_size=np.array([15, 15]), scale_factor=np.array([4, 4]), min_var=0.6, max_var=10., noise_level=0): + """" + # modified version of https://github.com/assafshocher/BlindSR_dataset_generator + # Kai Zhang + # min_var = 0.175 * sf # variance of the gaussian kernel will be sampled between min_var and max_var + # max_var = 2.5 * sf + """ + # Set random eigen-vals (lambdas) and angle (theta) for COV matrix + lambda_1 = min_var + np.random.rand() * (max_var - min_var) + lambda_2 = min_var + np.random.rand() * (max_var - min_var) + theta = np.random.rand() * np.pi # random theta + noise = -noise_level + np.random.rand(*k_size) * noise_level * 2 + + # Set COV matrix using Lambdas and Theta + LAMBDA = np.diag([lambda_1, lambda_2]) + Q = np.array([[np.cos(theta), -np.sin(theta)], + [np.sin(theta), np.cos(theta)]]) + SIGMA = Q @ LAMBDA @ Q.T + INV_SIGMA = np.linalg.inv(SIGMA)[None, None, :, :] + + # Set expectation position (shifting kernel for aligned image) + MU = k_size // 2 - 0.5 * (scale_factor - 1) # - 0.5 * (scale_factor - k_size % 2) + MU = MU[None, None, :, None] + + # Create meshgrid for Gaussian + [X, Y] = np.meshgrid(range(k_size[0]), range(k_size[1])) + Z = np.stack([X, Y], 2)[:, :, :, None] + + # Calcualte Gaussian for every pixel of the kernel + ZZ = Z - MU + ZZ_t = ZZ.transpose(0, 1, 3, 2) + raw_kernel = np.exp(-0.5 * np.squeeze(ZZ_t @ INV_SIGMA @ ZZ)) * (1 + noise) + + # shift the kernel so it will be centered + # raw_kernel_centered = kernel_shift(raw_kernel, scale_factor) + + # Normalize the kernel and return + # kernel = raw_kernel_centered / np.sum(raw_kernel_centered) + kernel = raw_kernel / np.sum(raw_kernel) + return kernel + + +def fspecial_gaussian(hsize, sigma): + hsize = [hsize, hsize] + siz = [(hsize[0] - 1.0) / 2.0, (hsize[1] - 1.0) / 2.0] + std = sigma + [x, y] = np.meshgrid(np.arange(-siz[1], siz[1] + 1), np.arange(-siz[0], siz[0] + 1)) + arg = -(x * x + y * y) / (2 * std * std) + h = np.exp(arg) + h[h < scipy.finfo(float).eps * h.max()] = 0 + sumh = h.sum() + if sumh != 0: + h = h / sumh + return h + + +def fspecial_laplacian(alpha): + alpha = max([0, min([alpha, 1])]) + h1 = alpha / (alpha + 1) + h2 = (1 - alpha) / (alpha + 1) + h = [[h1, h2, h1], [h2, -4 / (alpha + 1), h2], [h1, h2, h1]] + h = np.array(h) + return h + + +def fspecial(filter_type, *args, **kwargs): + ''' + python code from: + https://github.com/ronaldosena/imagens-medicas-2/blob/40171a6c259edec7827a6693a93955de2bd39e76/Aulas/aula_2_-_uniform_filter/matlab_fspecial.py + ''' + if filter_type == 'gaussian': + return fspecial_gaussian(*args, **kwargs) + if filter_type == 'laplacian': + return fspecial_laplacian(*args, **kwargs) + + +""" +# -------------------------------------------- +# degradation models +# -------------------------------------------- +""" + + +def bicubic_degradation(x, sf=3): + ''' + Args: + x: HxWxC image, [0, 1] + sf: down-scale factor + Return: + bicubicly downsampled LR image + ''' + x = util.imresize_np(x, scale=1 / sf) + return x + + +def srmd_degradation(x, k, sf=3): + ''' blur + bicubic downsampling + Args: + x: HxWxC image, [0, 1] + k: hxw, double + sf: down-scale factor + Return: + downsampled LR image + Reference: + @inproceedings{zhang2018learning, + title={Learning a single convolutional super-resolution network for multiple degradations}, + author={Zhang, Kai and Zuo, Wangmeng and Zhang, Lei}, + booktitle={IEEE Conference on Computer Vision and Pattern Recognition}, + pages={3262--3271}, + year={2018} + } + ''' + x = ndimage.filters.convolve(x, np.expand_dims(k, axis=2), mode='wrap') # 'nearest' | 'mirror' + x = bicubic_degradation(x, sf=sf) + return x + + +def dpsr_degradation(x, k, sf=3): + ''' bicubic downsampling + blur + Args: + x: HxWxC image, [0, 1] + k: hxw, double + sf: down-scale factor + Return: + downsampled LR image + Reference: + @inproceedings{zhang2019deep, + title={Deep Plug-and-Play Super-Resolution for Arbitrary Blur Kernels}, + author={Zhang, Kai and Zuo, Wangmeng and Zhang, Lei}, + booktitle={IEEE Conference on Computer Vision and Pattern Recognition}, + pages={1671--1681}, + year={2019} + } + ''' + x = bicubic_degradation(x, sf=sf) + x = ndimage.filters.convolve(x, np.expand_dims(k, axis=2), mode='wrap') + return x + + +def classical_degradation(x, k, sf=3): + ''' blur + downsampling + Args: + x: HxWxC image, [0, 1]/[0, 255] + k: hxw, double + sf: down-scale factor + Return: + downsampled LR image + ''' + x = ndimage.filters.convolve(x, np.expand_dims(k, axis=2), mode='wrap') + # x = filters.correlate(x, np.expand_dims(np.flip(k), axis=2)) + st = 0 + return x[st::sf, st::sf, ...] + + +def add_sharpening(img, weight=0.5, radius=50, threshold=10): + """USM sharpening. borrowed from real-ESRGAN + Input image: I; Blurry image: B. + 1. K = I + weight * (I - B) + 2. Mask = 1 if abs(I - B) > threshold, else: 0 + 3. Blur mask: + 4. Out = Mask * K + (1 - Mask) * I + Args: + img (Numpy array): Input image, HWC, BGR; float32, [0, 1]. + weight (float): Sharp weight. Default: 1. + radius (float): Kernel size of Gaussian blur. Default: 50. + threshold (int): + """ + if radius % 2 == 0: + radius += 1 + blur = cv2.GaussianBlur(img, (radius, radius), 0) + residual = img - blur + mask = np.abs(residual) * 255 > threshold + mask = mask.astype('float32') + soft_mask = cv2.GaussianBlur(mask, (radius, radius), 0) + + K = img + weight * residual + K = np.clip(K, 0, 1) + return soft_mask * K + (1 - soft_mask) * img + + +def add_blur(img, sf=4): + wd2 = 4.0 + sf + wd = 2.0 + 0.2 * sf + if random.random() < 0.5: + l1 = wd2 * random.random() + l2 = wd2 * random.random() + k = anisotropic_Gaussian(ksize=2 * random.randint(2, 11) + 3, theta=random.random() * np.pi, l1=l1, l2=l2) + else: + k = fspecial('gaussian', 2 * random.randint(2, 11) + 3, wd * random.random()) + img = ndimage.filters.convolve(img, np.expand_dims(k, axis=2), mode='mirror') + + return img + + +def add_resize(img, sf=4): + rnum = np.random.rand() + if rnum > 0.8: # up + sf1 = random.uniform(1, 2) + elif rnum < 0.7: # down + sf1 = random.uniform(0.5 / sf, 1) + else: + sf1 = 1.0 + img = cv2.resize(img, (int(sf1 * img.shape[1]), int(sf1 * img.shape[0])), interpolation=random.choice([1, 2, 3])) + img = np.clip(img, 0.0, 1.0) + + return img + + +# def add_Gaussian_noise(img, noise_level1=2, noise_level2=25): +# noise_level = random.randint(noise_level1, noise_level2) +# rnum = np.random.rand() +# if rnum > 0.6: # add color Gaussian noise +# img += np.random.normal(0, noise_level / 255.0, img.shape).astype(np.float32) +# elif rnum < 0.4: # add grayscale Gaussian noise +# img += np.random.normal(0, noise_level / 255.0, (*img.shape[:2], 1)).astype(np.float32) +# else: # add noise +# L = noise_level2 / 255. +# D = np.diag(np.random.rand(3)) +# U = orth(np.random.rand(3, 3)) +# conv = np.dot(np.dot(np.transpose(U), D), U) +# img += np.random.multivariate_normal([0, 0, 0], np.abs(L ** 2 * conv), img.shape[:2]).astype(np.float32) +# img = np.clip(img, 0.0, 1.0) +# return img + +def add_Gaussian_noise(img, noise_level1=2, noise_level2=25): + noise_level = random.randint(noise_level1, noise_level2) + rnum = np.random.rand() + if rnum > 0.6: # add color Gaussian noise + img = img + np.random.normal(0, noise_level / 255.0, img.shape).astype(np.float32) + elif rnum < 0.4: # add grayscale Gaussian noise + img = img + np.random.normal(0, noise_level / 255.0, (*img.shape[:2], 1)).astype(np.float32) + else: # add noise + L = noise_level2 / 255. + D = np.diag(np.random.rand(3)) + U = orth(np.random.rand(3, 3)) + conv = np.dot(np.dot(np.transpose(U), D), U) + img = img + np.random.multivariate_normal([0, 0, 0], np.abs(L ** 2 * conv), img.shape[:2]).astype(np.float32) + img = np.clip(img, 0.0, 1.0) + return img + + +def add_speckle_noise(img, noise_level1=2, noise_level2=25): + noise_level = random.randint(noise_level1, noise_level2) + img = np.clip(img, 0.0, 1.0) + rnum = random.random() + if rnum > 0.6: + img += img * np.random.normal(0, noise_level / 255.0, img.shape).astype(np.float32) + elif rnum < 0.4: + img += img * np.random.normal(0, noise_level / 255.0, (*img.shape[:2], 1)).astype(np.float32) + else: + L = noise_level2 / 255. + D = np.diag(np.random.rand(3)) + U = orth(np.random.rand(3, 3)) + conv = np.dot(np.dot(np.transpose(U), D), U) + img += img * np.random.multivariate_normal([0, 0, 0], np.abs(L ** 2 * conv), img.shape[:2]).astype(np.float32) + img = np.clip(img, 0.0, 1.0) + return img + + +def add_Poisson_noise(img): + img = np.clip((img * 255.0).round(), 0, 255) / 255. + vals = 10 ** (2 * random.random() + 2.0) # [2, 4] + if random.random() < 0.5: + img = np.random.poisson(img * vals).astype(np.float32) / vals + else: + img_gray = np.dot(img[..., :3], [0.299, 0.587, 0.114]) + img_gray = np.clip((img_gray * 255.0).round(), 0, 255) / 255. + noise_gray = np.random.poisson(img_gray * vals).astype(np.float32) / vals - img_gray + img += noise_gray[:, :, np.newaxis] + img = np.clip(img, 0.0, 1.0) + return img + + +def add_JPEG_noise(img): + quality_factor = random.randint(30, 95) + img = cv2.cvtColor(util.single2uint(img), cv2.COLOR_RGB2BGR) + result, encimg = cv2.imencode('.jpg', img, [int(cv2.IMWRITE_JPEG_QUALITY), quality_factor]) + img = cv2.imdecode(encimg, 1) + img = cv2.cvtColor(util.uint2single(img), cv2.COLOR_BGR2RGB) + return img + + +def random_crop(lq, hq, sf=4, lq_patchsize=64): + h, w = lq.shape[:2] + rnd_h = random.randint(0, h - lq_patchsize) + rnd_w = random.randint(0, w - lq_patchsize) + lq = lq[rnd_h:rnd_h + lq_patchsize, rnd_w:rnd_w + lq_patchsize, :] + + rnd_h_H, rnd_w_H = int(rnd_h * sf), int(rnd_w * sf) + hq = hq[rnd_h_H:rnd_h_H + lq_patchsize * sf, rnd_w_H:rnd_w_H + lq_patchsize * sf, :] + return lq, hq + + +def degradation_bsrgan(img, sf=4, lq_patchsize=72, isp_model=None): + """ + This is the degradation model of BSRGAN from the paper + "Designing a Practical Degradation Model for Deep Blind Image Super-Resolution" + ---------- + img: HXWXC, [0, 1], its size should be large than (lq_patchsizexsf)x(lq_patchsizexsf) + sf: scale factor + isp_model: camera ISP model + Returns + ------- + img: low-quality patch, size: lq_patchsizeXlq_patchsizeXC, range: [0, 1] + hq: corresponding high-quality patch, size: (lq_patchsizexsf)X(lq_patchsizexsf)XC, range: [0, 1] + """ + isp_prob, jpeg_prob, scale2_prob = 0.25, 0.9, 0.25 + sf_ori = sf + + h1, w1 = img.shape[:2] + img = img.copy()[:w1 - w1 % sf, :h1 - h1 % sf, ...] # mod crop + h, w = img.shape[:2] + + if h < lq_patchsize * sf or w < lq_patchsize * sf: + raise ValueError(f'img size ({h1}X{w1}) is too small!') + + hq = img.copy() + + if sf == 4 and random.random() < scale2_prob: # downsample1 + if np.random.rand() < 0.5: + img = cv2.resize(img, (int(1 / 2 * img.shape[1]), int(1 / 2 * img.shape[0])), + interpolation=random.choice([1, 2, 3])) + else: + img = util.imresize_np(img, 1 / 2, True) + img = np.clip(img, 0.0, 1.0) + sf = 2 + + shuffle_order = random.sample(range(7), 7) + idx1, idx2 = shuffle_order.index(2), shuffle_order.index(3) + if idx1 > idx2: # keep downsample3 last + shuffle_order[idx1], shuffle_order[idx2] = shuffle_order[idx2], shuffle_order[idx1] + + for i in shuffle_order: + + if i == 0: + img = add_blur(img, sf=sf) + + elif i == 1: + img = add_blur(img, sf=sf) + + elif i == 2: + a, b = img.shape[1], img.shape[0] + # downsample2 + if random.random() < 0.75: + sf1 = random.uniform(1, 2 * sf) + img = cv2.resize(img, (int(1 / sf1 * img.shape[1]), int(1 / sf1 * img.shape[0])), + interpolation=random.choice([1, 2, 3])) + else: + k = fspecial('gaussian', 25, random.uniform(0.1, 0.6 * sf)) + k_shifted = shift_pixel(k, sf) + k_shifted = k_shifted / k_shifted.sum() # blur with shifted kernel + img = ndimage.filters.convolve(img, np.expand_dims(k_shifted, axis=2), mode='mirror') + img = img[0::sf, 0::sf, ...] # nearest downsampling + img = np.clip(img, 0.0, 1.0) + + elif i == 3: + # downsample3 + img = cv2.resize(img, (int(1 / sf * a), int(1 / sf * b)), interpolation=random.choice([1, 2, 3])) + img = np.clip(img, 0.0, 1.0) + + elif i == 4: + # add Gaussian noise + img = add_Gaussian_noise(img, noise_level1=2, noise_level2=25) + + elif i == 5: + # add JPEG noise + if random.random() < jpeg_prob: + img = add_JPEG_noise(img) + + elif i == 6: + # add processed camera sensor noise + if random.random() < isp_prob and isp_model is not None: + with torch.no_grad(): + img, hq = isp_model.forward(img.copy(), hq) + + # add final JPEG compression noise + img = add_JPEG_noise(img) + + # random crop + img, hq = random_crop(img, hq, sf_ori, lq_patchsize) + + return img, hq + + +# todo no isp_model? +def degradation_bsrgan_variant(image, sf=4, isp_model=None): + """ + This is the degradation model of BSRGAN from the paper + "Designing a Practical Degradation Model for Deep Blind Image Super-Resolution" + ---------- + sf: scale factor + isp_model: camera ISP model + Returns + ------- + img: low-quality patch, size: lq_patchsizeXlq_patchsizeXC, range: [0, 1] + hq: corresponding high-quality patch, size: (lq_patchsizexsf)X(lq_patchsizexsf)XC, range: [0, 1] + """ + image = util.uint2single(image) + isp_prob, jpeg_prob, scale2_prob = 0.25, 0.9, 0.25 + sf_ori = sf + + h1, w1 = image.shape[:2] + image = image.copy()[:w1 - w1 % sf, :h1 - h1 % sf, ...] # mod crop + h, w = image.shape[:2] + + hq = image.copy() + + if sf == 4 and random.random() < scale2_prob: # downsample1 + if np.random.rand() < 0.5: + image = cv2.resize(image, (int(1 / 2 * image.shape[1]), int(1 / 2 * image.shape[0])), + interpolation=random.choice([1, 2, 3])) + else: + image = util.imresize_np(image, 1 / 2, True) + image = np.clip(image, 0.0, 1.0) + sf = 2 + + shuffle_order = random.sample(range(7), 7) + idx1, idx2 = shuffle_order.index(2), shuffle_order.index(3) + if idx1 > idx2: # keep downsample3 last + shuffle_order[idx1], shuffle_order[idx2] = shuffle_order[idx2], shuffle_order[idx1] + + for i in shuffle_order: + + if i == 0: + image = add_blur(image, sf=sf) + + elif i == 1: + image = add_blur(image, sf=sf) + + elif i == 2: + a, b = image.shape[1], image.shape[0] + # downsample2 + if random.random() < 0.75: + sf1 = random.uniform(1, 2 * sf) + image = cv2.resize(image, (int(1 / sf1 * image.shape[1]), int(1 / sf1 * image.shape[0])), + interpolation=random.choice([1, 2, 3])) + else: + k = fspecial('gaussian', 25, random.uniform(0.1, 0.6 * sf)) + k_shifted = shift_pixel(k, sf) + k_shifted = k_shifted / k_shifted.sum() # blur with shifted kernel + image = ndimage.filters.convolve(image, np.expand_dims(k_shifted, axis=2), mode='mirror') + image = image[0::sf, 0::sf, ...] # nearest downsampling + image = np.clip(image, 0.0, 1.0) + + elif i == 3: + # downsample3 + image = cv2.resize(image, (int(1 / sf * a), int(1 / sf * b)), interpolation=random.choice([1, 2, 3])) + image = np.clip(image, 0.0, 1.0) + + elif i == 4: + # add Gaussian noise + image = add_Gaussian_noise(image, noise_level1=2, noise_level2=25) + + elif i == 5: + # add JPEG noise + if random.random() < jpeg_prob: + image = add_JPEG_noise(image) + + # elif i == 6: + # # add processed camera sensor noise + # if random.random() < isp_prob and isp_model is not None: + # with torch.no_grad(): + # img, hq = isp_model.forward(img.copy(), hq) + + # add final JPEG compression noise + image = add_JPEG_noise(image) + image = util.single2uint(image) + example = {"image":image} + return example + + +# TODO incase there is a pickle error one needs to replace a += x with a = a + x in add_speckle_noise etc... +def degradation_bsrgan_plus(img, sf=4, shuffle_prob=0.5, use_sharp=True, lq_patchsize=64, isp_model=None): + """ + This is an extended degradation model by combining + the degradation models of BSRGAN and Real-ESRGAN + ---------- + img: HXWXC, [0, 1], its size should be large than (lq_patchsizexsf)x(lq_patchsizexsf) + sf: scale factor + use_shuffle: the degradation shuffle + use_sharp: sharpening the img + Returns + ------- + img: low-quality patch, size: lq_patchsizeXlq_patchsizeXC, range: [0, 1] + hq: corresponding high-quality patch, size: (lq_patchsizexsf)X(lq_patchsizexsf)XC, range: [0, 1] + """ + + h1, w1 = img.shape[:2] + img = img.copy()[:w1 - w1 % sf, :h1 - h1 % sf, ...] # mod crop + h, w = img.shape[:2] + + if h < lq_patchsize * sf or w < lq_patchsize * sf: + raise ValueError(f'img size ({h1}X{w1}) is too small!') + + if use_sharp: + img = add_sharpening(img) + hq = img.copy() + + if random.random() < shuffle_prob: + shuffle_order = random.sample(range(13), 13) + else: + shuffle_order = list(range(13)) + # local shuffle for noise, JPEG is always the last one + shuffle_order[2:6] = random.sample(shuffle_order[2:6], len(range(2, 6))) + shuffle_order[9:13] = random.sample(shuffle_order[9:13], len(range(9, 13))) + + poisson_prob, speckle_prob, isp_prob = 0.1, 0.1, 0.1 + + for i in shuffle_order: + if i == 0: + img = add_blur(img, sf=sf) + elif i == 1: + img = add_resize(img, sf=sf) + elif i == 2: + img = add_Gaussian_noise(img, noise_level1=2, noise_level2=25) + elif i == 3: + if random.random() < poisson_prob: + img = add_Poisson_noise(img) + elif i == 4: + if random.random() < speckle_prob: + img = add_speckle_noise(img) + elif i == 5: + if random.random() < isp_prob and isp_model is not None: + with torch.no_grad(): + img, hq = isp_model.forward(img.copy(), hq) + elif i == 6: + img = add_JPEG_noise(img) + elif i == 7: + img = add_blur(img, sf=sf) + elif i == 8: + img = add_resize(img, sf=sf) + elif i == 9: + img = add_Gaussian_noise(img, noise_level1=2, noise_level2=25) + elif i == 10: + if random.random() < poisson_prob: + img = add_Poisson_noise(img) + elif i == 11: + if random.random() < speckle_prob: + img = add_speckle_noise(img) + elif i == 12: + if random.random() < isp_prob and isp_model is not None: + with torch.no_grad(): + img, hq = isp_model.forward(img.copy(), hq) + else: + print('check the shuffle!') + + # resize to desired size + img = cv2.resize(img, (int(1 / sf * hq.shape[1]), int(1 / sf * hq.shape[0])), + interpolation=random.choice([1, 2, 3])) + + # add final JPEG compression noise + img = add_JPEG_noise(img) + + # random crop + img, hq = random_crop(img, hq, sf, lq_patchsize) + + return img, hq + + +if __name__ == '__main__': + print("hey") + img = util.imread_uint('utils/test.png', 3) + print(img) + img = util.uint2single(img) + print(img) + img = img[:448, :448] + h = img.shape[0] // 4 + print("resizing to", h) + sf = 4 + deg_fn = partial(degradation_bsrgan_variant, sf=sf) + for i in range(20): + print(i) + img_lq = deg_fn(img) + print(img_lq) + img_lq_bicubic = albumentations.SmallestMaxSize(max_size=h, interpolation=cv2.INTER_CUBIC)(image=img)["image"] + print(img_lq.shape) + print("bicubic", img_lq_bicubic.shape) + print(img_hq.shape) + lq_nearest = cv2.resize(util.single2uint(img_lq), (int(sf * img_lq.shape[1]), int(sf * img_lq.shape[0])), + interpolation=0) + lq_bicubic_nearest = cv2.resize(util.single2uint(img_lq_bicubic), (int(sf * img_lq.shape[1]), int(sf * img_lq.shape[0])), + interpolation=0) + img_concat = np.concatenate([lq_bicubic_nearest, lq_nearest, util.single2uint(img_hq)], axis=1) + util.imsave(img_concat, str(i) + '.png') + + diff --git a/ldm/modules/image_degradation/bsrgan_light.py b/ldm/modules/image_degradation/bsrgan_light.py new file mode 100644 index 0000000000000000000000000000000000000000..9e1f823996bf559e9b015ea9aa2b3cd38dd13af1 --- /dev/null +++ b/ldm/modules/image_degradation/bsrgan_light.py @@ -0,0 +1,650 @@ +# -*- coding: utf-8 -*- +import numpy as np +import cv2 +import torch + +from functools import partial +import random +from scipy import ndimage +import scipy +import scipy.stats as ss +from scipy.interpolate import interp2d +from scipy.linalg import orth +import albumentations + +import ldm.modules.image_degradation.utils_image as util + +""" +# -------------------------------------------- +# Super-Resolution +# -------------------------------------------- +# +# Kai Zhang (cskaizhang@gmail.com) +# https://github.com/cszn +# From 2019/03--2021/08 +# -------------------------------------------- +""" + + +def modcrop_np(img, sf): + ''' + Args: + img: numpy image, WxH or WxHxC + sf: scale factor + Return: + cropped image + ''' + w, h = img.shape[:2] + im = np.copy(img) + return im[:w - w % sf, :h - h % sf, ...] + + +""" +# -------------------------------------------- +# anisotropic Gaussian kernels +# -------------------------------------------- +""" + + +def analytic_kernel(k): + """Calculate the X4 kernel from the X2 kernel (for proof see appendix in paper)""" + k_size = k.shape[0] + # Calculate the big kernels size + big_k = np.zeros((3 * k_size - 2, 3 * k_size - 2)) + # Loop over the small kernel to fill the big one + for r in range(k_size): + for c in range(k_size): + big_k[2 * r:2 * r + k_size, 2 * c:2 * c + k_size] += k[r, c] * k + # Crop the edges of the big kernel to ignore very small values and increase run time of SR + crop = k_size // 2 + cropped_big_k = big_k[crop:-crop, crop:-crop] + # Normalize to 1 + return cropped_big_k / cropped_big_k.sum() + + +def anisotropic_Gaussian(ksize=15, theta=np.pi, l1=6, l2=6): + """ generate an anisotropic Gaussian kernel + Args: + ksize : e.g., 15, kernel size + theta : [0, pi], rotation angle range + l1 : [0.1,50], scaling of eigenvalues + l2 : [0.1,l1], scaling of eigenvalues + If l1 = l2, will get an isotropic Gaussian kernel. + Returns: + k : kernel + """ + + v = np.dot(np.array([[np.cos(theta), -np.sin(theta)], [np.sin(theta), np.cos(theta)]]), np.array([1., 0.])) + V = np.array([[v[0], v[1]], [v[1], -v[0]]]) + D = np.array([[l1, 0], [0, l2]]) + Sigma = np.dot(np.dot(V, D), np.linalg.inv(V)) + k = gm_blur_kernel(mean=[0, 0], cov=Sigma, size=ksize) + + return k + + +def gm_blur_kernel(mean, cov, size=15): + center = size / 2.0 + 0.5 + k = np.zeros([size, size]) + for y in range(size): + for x in range(size): + cy = y - center + 1 + cx = x - center + 1 + k[y, x] = ss.multivariate_normal.pdf([cx, cy], mean=mean, cov=cov) + + k = k / np.sum(k) + return k + + +def shift_pixel(x, sf, upper_left=True): + """shift pixel for super-resolution with different scale factors + Args: + x: WxHxC or WxH + sf: scale factor + upper_left: shift direction + """ + h, w = x.shape[:2] + shift = (sf - 1) * 0.5 + xv, yv = np.arange(0, w, 1.0), np.arange(0, h, 1.0) + if upper_left: + x1 = xv + shift + y1 = yv + shift + else: + x1 = xv - shift + y1 = yv - shift + + x1 = np.clip(x1, 0, w - 1) + y1 = np.clip(y1, 0, h - 1) + + if x.ndim == 2: + x = interp2d(xv, yv, x)(x1, y1) + if x.ndim == 3: + for i in range(x.shape[-1]): + x[:, :, i] = interp2d(xv, yv, x[:, :, i])(x1, y1) + + return x + + +def blur(x, k): + ''' + x: image, NxcxHxW + k: kernel, Nx1xhxw + ''' + n, c = x.shape[:2] + p1, p2 = (k.shape[-2] - 1) // 2, (k.shape[-1] - 1) // 2 + x = torch.nn.functional.pad(x, pad=(p1, p2, p1, p2), mode='replicate') + k = k.repeat(1, c, 1, 1) + k = k.view(-1, 1, k.shape[2], k.shape[3]) + x = x.view(1, -1, x.shape[2], x.shape[3]) + x = torch.nn.functional.conv2d(x, k, bias=None, stride=1, padding=0, groups=n * c) + x = x.view(n, c, x.shape[2], x.shape[3]) + + return x + + +def gen_kernel(k_size=np.array([15, 15]), scale_factor=np.array([4, 4]), min_var=0.6, max_var=10., noise_level=0): + """" + # modified version of https://github.com/assafshocher/BlindSR_dataset_generator + # Kai Zhang + # min_var = 0.175 * sf # variance of the gaussian kernel will be sampled between min_var and max_var + # max_var = 2.5 * sf + """ + # Set random eigen-vals (lambdas) and angle (theta) for COV matrix + lambda_1 = min_var + np.random.rand() * (max_var - min_var) + lambda_2 = min_var + np.random.rand() * (max_var - min_var) + theta = np.random.rand() * np.pi # random theta + noise = -noise_level + np.random.rand(*k_size) * noise_level * 2 + + # Set COV matrix using Lambdas and Theta + LAMBDA = np.diag([lambda_1, lambda_2]) + Q = np.array([[np.cos(theta), -np.sin(theta)], + [np.sin(theta), np.cos(theta)]]) + SIGMA = Q @ LAMBDA @ Q.T + INV_SIGMA = np.linalg.inv(SIGMA)[None, None, :, :] + + # Set expectation position (shifting kernel for aligned image) + MU = k_size // 2 - 0.5 * (scale_factor - 1) # - 0.5 * (scale_factor - k_size % 2) + MU = MU[None, None, :, None] + + # Create meshgrid for Gaussian + [X, Y] = np.meshgrid(range(k_size[0]), range(k_size[1])) + Z = np.stack([X, Y], 2)[:, :, :, None] + + # Calcualte Gaussian for every pixel of the kernel + ZZ = Z - MU + ZZ_t = ZZ.transpose(0, 1, 3, 2) + raw_kernel = np.exp(-0.5 * np.squeeze(ZZ_t @ INV_SIGMA @ ZZ)) * (1 + noise) + + # shift the kernel so it will be centered + # raw_kernel_centered = kernel_shift(raw_kernel, scale_factor) + + # Normalize the kernel and return + # kernel = raw_kernel_centered / np.sum(raw_kernel_centered) + kernel = raw_kernel / np.sum(raw_kernel) + return kernel + + +def fspecial_gaussian(hsize, sigma): + hsize = [hsize, hsize] + siz = [(hsize[0] - 1.0) / 2.0, (hsize[1] - 1.0) / 2.0] + std = sigma + [x, y] = np.meshgrid(np.arange(-siz[1], siz[1] + 1), np.arange(-siz[0], siz[0] + 1)) + arg = -(x * x + y * y) / (2 * std * std) + h = np.exp(arg) + h[h < scipy.finfo(float).eps * h.max()] = 0 + sumh = h.sum() + if sumh != 0: + h = h / sumh + return h + + +def fspecial_laplacian(alpha): + alpha = max([0, min([alpha, 1])]) + h1 = alpha / (alpha + 1) + h2 = (1 - alpha) / (alpha + 1) + h = [[h1, h2, h1], [h2, -4 / (alpha + 1), h2], [h1, h2, h1]] + h = np.array(h) + return h + + +def fspecial(filter_type, *args, **kwargs): + ''' + python code from: + https://github.com/ronaldosena/imagens-medicas-2/blob/40171a6c259edec7827a6693a93955de2bd39e76/Aulas/aula_2_-_uniform_filter/matlab_fspecial.py + ''' + if filter_type == 'gaussian': + return fspecial_gaussian(*args, **kwargs) + if filter_type == 'laplacian': + return fspecial_laplacian(*args, **kwargs) + + +""" +# -------------------------------------------- +# degradation models +# -------------------------------------------- +""" + + +def bicubic_degradation(x, sf=3): + ''' + Args: + x: HxWxC image, [0, 1] + sf: down-scale factor + Return: + bicubicly downsampled LR image + ''' + x = util.imresize_np(x, scale=1 / sf) + return x + + +def srmd_degradation(x, k, sf=3): + ''' blur + bicubic downsampling + Args: + x: HxWxC image, [0, 1] + k: hxw, double + sf: down-scale factor + Return: + downsampled LR image + Reference: + @inproceedings{zhang2018learning, + title={Learning a single convolutional super-resolution network for multiple degradations}, + author={Zhang, Kai and Zuo, Wangmeng and Zhang, Lei}, + booktitle={IEEE Conference on Computer Vision and Pattern Recognition}, + pages={3262--3271}, + year={2018} + } + ''' + x = ndimage.filters.convolve(x, np.expand_dims(k, axis=2), mode='wrap') # 'nearest' | 'mirror' + x = bicubic_degradation(x, sf=sf) + return x + + +def dpsr_degradation(x, k, sf=3): + ''' bicubic downsampling + blur + Args: + x: HxWxC image, [0, 1] + k: hxw, double + sf: down-scale factor + Return: + downsampled LR image + Reference: + @inproceedings{zhang2019deep, + title={Deep Plug-and-Play Super-Resolution for Arbitrary Blur Kernels}, + author={Zhang, Kai and Zuo, Wangmeng and Zhang, Lei}, + booktitle={IEEE Conference on Computer Vision and Pattern Recognition}, + pages={1671--1681}, + year={2019} + } + ''' + x = bicubic_degradation(x, sf=sf) + x = ndimage.filters.convolve(x, np.expand_dims(k, axis=2), mode='wrap') + return x + + +def classical_degradation(x, k, sf=3): + ''' blur + downsampling + Args: + x: HxWxC image, [0, 1]/[0, 255] + k: hxw, double + sf: down-scale factor + Return: + downsampled LR image + ''' + x = ndimage.filters.convolve(x, np.expand_dims(k, axis=2), mode='wrap') + # x = filters.correlate(x, np.expand_dims(np.flip(k), axis=2)) + st = 0 + return x[st::sf, st::sf, ...] + + +def add_sharpening(img, weight=0.5, radius=50, threshold=10): + """USM sharpening. borrowed from real-ESRGAN + Input image: I; Blurry image: B. + 1. K = I + weight * (I - B) + 2. Mask = 1 if abs(I - B) > threshold, else: 0 + 3. Blur mask: + 4. Out = Mask * K + (1 - Mask) * I + Args: + img (Numpy array): Input image, HWC, BGR; float32, [0, 1]. + weight (float): Sharp weight. Default: 1. + radius (float): Kernel size of Gaussian blur. Default: 50. + threshold (int): + """ + if radius % 2 == 0: + radius += 1 + blur = cv2.GaussianBlur(img, (radius, radius), 0) + residual = img - blur + mask = np.abs(residual) * 255 > threshold + mask = mask.astype('float32') + soft_mask = cv2.GaussianBlur(mask, (radius, radius), 0) + + K = img + weight * residual + K = np.clip(K, 0, 1) + return soft_mask * K + (1 - soft_mask) * img + + +def add_blur(img, sf=4): + wd2 = 4.0 + sf + wd = 2.0 + 0.2 * sf + + wd2 = wd2/4 + wd = wd/4 + + if random.random() < 0.5: + l1 = wd2 * random.random() + l2 = wd2 * random.random() + k = anisotropic_Gaussian(ksize=random.randint(2, 11) + 3, theta=random.random() * np.pi, l1=l1, l2=l2) + else: + k = fspecial('gaussian', random.randint(2, 4) + 3, wd * random.random()) + img = ndimage.filters.convolve(img, np.expand_dims(k, axis=2), mode='mirror') + + return img + + +def add_resize(img, sf=4): + rnum = np.random.rand() + if rnum > 0.8: # up + sf1 = random.uniform(1, 2) + elif rnum < 0.7: # down + sf1 = random.uniform(0.5 / sf, 1) + else: + sf1 = 1.0 + img = cv2.resize(img, (int(sf1 * img.shape[1]), int(sf1 * img.shape[0])), interpolation=random.choice([1, 2, 3])) + img = np.clip(img, 0.0, 1.0) + + return img + + +# def add_Gaussian_noise(img, noise_level1=2, noise_level2=25): +# noise_level = random.randint(noise_level1, noise_level2) +# rnum = np.random.rand() +# if rnum > 0.6: # add color Gaussian noise +# img += np.random.normal(0, noise_level / 255.0, img.shape).astype(np.float32) +# elif rnum < 0.4: # add grayscale Gaussian noise +# img += np.random.normal(0, noise_level / 255.0, (*img.shape[:2], 1)).astype(np.float32) +# else: # add noise +# L = noise_level2 / 255. +# D = np.diag(np.random.rand(3)) +# U = orth(np.random.rand(3, 3)) +# conv = np.dot(np.dot(np.transpose(U), D), U) +# img += np.random.multivariate_normal([0, 0, 0], np.abs(L ** 2 * conv), img.shape[:2]).astype(np.float32) +# img = np.clip(img, 0.0, 1.0) +# return img + +def add_Gaussian_noise(img, noise_level1=2, noise_level2=25): + noise_level = random.randint(noise_level1, noise_level2) + rnum = np.random.rand() + if rnum > 0.6: # add color Gaussian noise + img = img + np.random.normal(0, noise_level / 255.0, img.shape).astype(np.float32) + elif rnum < 0.4: # add grayscale Gaussian noise + img = img + np.random.normal(0, noise_level / 255.0, (*img.shape[:2], 1)).astype(np.float32) + else: # add noise + L = noise_level2 / 255. + D = np.diag(np.random.rand(3)) + U = orth(np.random.rand(3, 3)) + conv = np.dot(np.dot(np.transpose(U), D), U) + img = img + np.random.multivariate_normal([0, 0, 0], np.abs(L ** 2 * conv), img.shape[:2]).astype(np.float32) + img = np.clip(img, 0.0, 1.0) + return img + + +def add_speckle_noise(img, noise_level1=2, noise_level2=25): + noise_level = random.randint(noise_level1, noise_level2) + img = np.clip(img, 0.0, 1.0) + rnum = random.random() + if rnum > 0.6: + img += img * np.random.normal(0, noise_level / 255.0, img.shape).astype(np.float32) + elif rnum < 0.4: + img += img * np.random.normal(0, noise_level / 255.0, (*img.shape[:2], 1)).astype(np.float32) + else: + L = noise_level2 / 255. + D = np.diag(np.random.rand(3)) + U = orth(np.random.rand(3, 3)) + conv = np.dot(np.dot(np.transpose(U), D), U) + img += img * np.random.multivariate_normal([0, 0, 0], np.abs(L ** 2 * conv), img.shape[:2]).astype(np.float32) + img = np.clip(img, 0.0, 1.0) + return img + + +def add_Poisson_noise(img): + img = np.clip((img * 255.0).round(), 0, 255) / 255. + vals = 10 ** (2 * random.random() + 2.0) # [2, 4] + if random.random() < 0.5: + img = np.random.poisson(img * vals).astype(np.float32) / vals + else: + img_gray = np.dot(img[..., :3], [0.299, 0.587, 0.114]) + img_gray = np.clip((img_gray * 255.0).round(), 0, 255) / 255. + noise_gray = np.random.poisson(img_gray * vals).astype(np.float32) / vals - img_gray + img += noise_gray[:, :, np.newaxis] + img = np.clip(img, 0.0, 1.0) + return img + + +def add_JPEG_noise(img): + quality_factor = random.randint(80, 95) + img = cv2.cvtColor(util.single2uint(img), cv2.COLOR_RGB2BGR) + result, encimg = cv2.imencode('.jpg', img, [int(cv2.IMWRITE_JPEG_QUALITY), quality_factor]) + img = cv2.imdecode(encimg, 1) + img = cv2.cvtColor(util.uint2single(img), cv2.COLOR_BGR2RGB) + return img + + +def random_crop(lq, hq, sf=4, lq_patchsize=64): + h, w = lq.shape[:2] + rnd_h = random.randint(0, h - lq_patchsize) + rnd_w = random.randint(0, w - lq_patchsize) + lq = lq[rnd_h:rnd_h + lq_patchsize, rnd_w:rnd_w + lq_patchsize, :] + + rnd_h_H, rnd_w_H = int(rnd_h * sf), int(rnd_w * sf) + hq = hq[rnd_h_H:rnd_h_H + lq_patchsize * sf, rnd_w_H:rnd_w_H + lq_patchsize * sf, :] + return lq, hq + + +def degradation_bsrgan(img, sf=4, lq_patchsize=72, isp_model=None): + """ + This is the degradation model of BSRGAN from the paper + "Designing a Practical Degradation Model for Deep Blind Image Super-Resolution" + ---------- + img: HXWXC, [0, 1], its size should be large than (lq_patchsizexsf)x(lq_patchsizexsf) + sf: scale factor + isp_model: camera ISP model + Returns + ------- + img: low-quality patch, size: lq_patchsizeXlq_patchsizeXC, range: [0, 1] + hq: corresponding high-quality patch, size: (lq_patchsizexsf)X(lq_patchsizexsf)XC, range: [0, 1] + """ + isp_prob, jpeg_prob, scale2_prob = 0.25, 0.9, 0.25 + sf_ori = sf + + h1, w1 = img.shape[:2] + img = img.copy()[:w1 - w1 % sf, :h1 - h1 % sf, ...] # mod crop + h, w = img.shape[:2] + + if h < lq_patchsize * sf or w < lq_patchsize * sf: + raise ValueError(f'img size ({h1}X{w1}) is too small!') + + hq = img.copy() + + if sf == 4 and random.random() < scale2_prob: # downsample1 + if np.random.rand() < 0.5: + img = cv2.resize(img, (int(1 / 2 * img.shape[1]), int(1 / 2 * img.shape[0])), + interpolation=random.choice([1, 2, 3])) + else: + img = util.imresize_np(img, 1 / 2, True) + img = np.clip(img, 0.0, 1.0) + sf = 2 + + shuffle_order = random.sample(range(7), 7) + idx1, idx2 = shuffle_order.index(2), shuffle_order.index(3) + if idx1 > idx2: # keep downsample3 last + shuffle_order[idx1], shuffle_order[idx2] = shuffle_order[idx2], shuffle_order[idx1] + + for i in shuffle_order: + + if i == 0: + img = add_blur(img, sf=sf) + + elif i == 1: + img = add_blur(img, sf=sf) + + elif i == 2: + a, b = img.shape[1], img.shape[0] + # downsample2 + if random.random() < 0.75: + sf1 = random.uniform(1, 2 * sf) + img = cv2.resize(img, (int(1 / sf1 * img.shape[1]), int(1 / sf1 * img.shape[0])), + interpolation=random.choice([1, 2, 3])) + else: + k = fspecial('gaussian', 25, random.uniform(0.1, 0.6 * sf)) + k_shifted = shift_pixel(k, sf) + k_shifted = k_shifted / k_shifted.sum() # blur with shifted kernel + img = ndimage.filters.convolve(img, np.expand_dims(k_shifted, axis=2), mode='mirror') + img = img[0::sf, 0::sf, ...] # nearest downsampling + img = np.clip(img, 0.0, 1.0) + + elif i == 3: + # downsample3 + img = cv2.resize(img, (int(1 / sf * a), int(1 / sf * b)), interpolation=random.choice([1, 2, 3])) + img = np.clip(img, 0.0, 1.0) + + elif i == 4: + # add Gaussian noise + img = add_Gaussian_noise(img, noise_level1=2, noise_level2=8) + + elif i == 5: + # add JPEG noise + if random.random() < jpeg_prob: + img = add_JPEG_noise(img) + + elif i == 6: + # add processed camera sensor noise + if random.random() < isp_prob and isp_model is not None: + with torch.no_grad(): + img, hq = isp_model.forward(img.copy(), hq) + + # add final JPEG compression noise + img = add_JPEG_noise(img) + + # random crop + img, hq = random_crop(img, hq, sf_ori, lq_patchsize) + + return img, hq + + +# todo no isp_model? +def degradation_bsrgan_variant(image, sf=4, isp_model=None): + """ + This is the degradation model of BSRGAN from the paper + "Designing a Practical Degradation Model for Deep Blind Image Super-Resolution" + ---------- + sf: scale factor + isp_model: camera ISP model + Returns + ------- + img: low-quality patch, size: lq_patchsizeXlq_patchsizeXC, range: [0, 1] + hq: corresponding high-quality patch, size: (lq_patchsizexsf)X(lq_patchsizexsf)XC, range: [0, 1] + """ + image = util.uint2single(image) + isp_prob, jpeg_prob, scale2_prob = 0.25, 0.9, 0.25 + sf_ori = sf + + h1, w1 = image.shape[:2] + image = image.copy()[:w1 - w1 % sf, :h1 - h1 % sf, ...] # mod crop + h, w = image.shape[:2] + + hq = image.copy() + + if sf == 4 and random.random() < scale2_prob: # downsample1 + if np.random.rand() < 0.5: + image = cv2.resize(image, (int(1 / 2 * image.shape[1]), int(1 / 2 * image.shape[0])), + interpolation=random.choice([1, 2, 3])) + else: + image = util.imresize_np(image, 1 / 2, True) + image = np.clip(image, 0.0, 1.0) + sf = 2 + + shuffle_order = random.sample(range(7), 7) + idx1, idx2 = shuffle_order.index(2), shuffle_order.index(3) + if idx1 > idx2: # keep downsample3 last + shuffle_order[idx1], shuffle_order[idx2] = shuffle_order[idx2], shuffle_order[idx1] + + for i in shuffle_order: + + if i == 0: + image = add_blur(image, sf=sf) + + # elif i == 1: + # image = add_blur(image, sf=sf) + + if i == 0: + pass + + elif i == 2: + a, b = image.shape[1], image.shape[0] + # downsample2 + if random.random() < 0.8: + sf1 = random.uniform(1, 2 * sf) + image = cv2.resize(image, (int(1 / sf1 * image.shape[1]), int(1 / sf1 * image.shape[0])), + interpolation=random.choice([1, 2, 3])) + else: + k = fspecial('gaussian', 25, random.uniform(0.1, 0.6 * sf)) + k_shifted = shift_pixel(k, sf) + k_shifted = k_shifted / k_shifted.sum() # blur with shifted kernel + image = ndimage.filters.convolve(image, np.expand_dims(k_shifted, axis=2), mode='mirror') + image = image[0::sf, 0::sf, ...] # nearest downsampling + + image = np.clip(image, 0.0, 1.0) + + elif i == 3: + # downsample3 + image = cv2.resize(image, (int(1 / sf * a), int(1 / sf * b)), interpolation=random.choice([1, 2, 3])) + image = np.clip(image, 0.0, 1.0) + + elif i == 4: + # add Gaussian noise + image = add_Gaussian_noise(image, noise_level1=1, noise_level2=2) + + elif i == 5: + # add JPEG noise + if random.random() < jpeg_prob: + image = add_JPEG_noise(image) + # + # elif i == 6: + # # add processed camera sensor noise + # if random.random() < isp_prob and isp_model is not None: + # with torch.no_grad(): + # img, hq = isp_model.forward(img.copy(), hq) + + # add final JPEG compression noise + image = add_JPEG_noise(image) + image = util.single2uint(image) + example = {"image": image} + return example + + + + +if __name__ == '__main__': + print("hey") + img = util.imread_uint('utils/test.png', 3) + img = img[:448, :448] + h = img.shape[0] // 4 + print("resizing to", h) + sf = 4 + deg_fn = partial(degradation_bsrgan_variant, sf=sf) + for i in range(20): + print(i) + img_hq = img + img_lq = deg_fn(img)["image"] + img_hq, img_lq = util.uint2single(img_hq), util.uint2single(img_lq) + print(img_lq) + img_lq_bicubic = albumentations.SmallestMaxSize(max_size=h, interpolation=cv2.INTER_CUBIC)(image=img_hq)["image"] + print(img_lq.shape) + print("bicubic", img_lq_bicubic.shape) + print(img_hq.shape) + lq_nearest = cv2.resize(util.single2uint(img_lq), (int(sf * img_lq.shape[1]), int(sf * img_lq.shape[0])), + interpolation=0) + lq_bicubic_nearest = cv2.resize(util.single2uint(img_lq_bicubic), + (int(sf * img_lq.shape[1]), int(sf * img_lq.shape[0])), + interpolation=0) + img_concat = np.concatenate([lq_bicubic_nearest, lq_nearest, util.single2uint(img_hq)], axis=1) + util.imsave(img_concat, str(i) + '.png') diff --git a/ldm/modules/image_degradation/utils/test.png b/ldm/modules/image_degradation/utils/test.png new file mode 100644 index 0000000000000000000000000000000000000000..4249b43de0f22707758d13c240268a401642f6e6 Binary files /dev/null and b/ldm/modules/image_degradation/utils/test.png differ diff --git a/ldm/modules/image_degradation/utils_image.py b/ldm/modules/image_degradation/utils_image.py new file mode 100644 index 0000000000000000000000000000000000000000..0175f155ad900ae33c3c46ed87f49b352e3faf98 --- /dev/null +++ b/ldm/modules/image_degradation/utils_image.py @@ -0,0 +1,916 @@ +import os +import math +import random +import numpy as np +import torch +import cv2 +from torchvision.utils import make_grid +from datetime import datetime +#import matplotlib.pyplot as plt # TODO: check with Dominik, also bsrgan.py vs bsrgan_light.py + + +os.environ["KMP_DUPLICATE_LIB_OK"]="TRUE" + + +''' +# -------------------------------------------- +# Kai Zhang (github: https://github.com/cszn) +# 03/Mar/2019 +# -------------------------------------------- +# https://github.com/twhui/SRGAN-pyTorch +# https://github.com/xinntao/BasicSR +# -------------------------------------------- +''' + + +IMG_EXTENSIONS = ['.jpg', '.JPG', '.jpeg', '.JPEG', '.png', '.PNG', '.ppm', '.PPM', '.bmp', '.BMP', '.tif'] + + +def is_image_file(filename): + return any(filename.endswith(extension) for extension in IMG_EXTENSIONS) + + +def get_timestamp(): + return datetime.now().strftime('%y%m%d-%H%M%S') + + +def imshow(x, title=None, cbar=False, figsize=None): + plt.figure(figsize=figsize) + plt.imshow(np.squeeze(x), interpolation='nearest', cmap='gray') + if title: + plt.title(title) + if cbar: + plt.colorbar() + plt.show() + + +def surf(Z, cmap='rainbow', figsize=None): + plt.figure(figsize=figsize) + ax3 = plt.axes(projection='3d') + + w, h = Z.shape[:2] + xx = np.arange(0,w,1) + yy = np.arange(0,h,1) + X, Y = np.meshgrid(xx, yy) + ax3.plot_surface(X,Y,Z,cmap=cmap) + #ax3.contour(X,Y,Z, zdim='z',offset=-2，cmap=cmap) + plt.show() + + +''' +# -------------------------------------------- +# get image pathes +# -------------------------------------------- +''' + + +def get_image_paths(dataroot): + paths = None # return None if dataroot is None + if dataroot is not None: + paths = sorted(_get_paths_from_images(dataroot)) + return paths + + +def _get_paths_from_images(path): + assert os.path.isdir(path), '{:s} is not a valid directory'.format(path) + images = [] + for dirpath, _, fnames in sorted(os.walk(path)): + for fname in sorted(fnames): + if is_image_file(fname): + img_path = os.path.join(dirpath, fname) + images.append(img_path) + assert images, '{:s} has no valid image file'.format(path) + return images + + +''' +# -------------------------------------------- +# split large images into small images +# -------------------------------------------- +''' + + +def patches_from_image(img, p_size=512, p_overlap=64, p_max=800): + w, h = img.shape[:2] + patches = [] + if w > p_max and h > p_max: + w1 = list(np.arange(0, w-p_size, p_size-p_overlap, dtype=np.int)) + h1 = list(np.arange(0, h-p_size, p_size-p_overlap, dtype=np.int)) + w1.append(w-p_size) + h1.append(h-p_size) +# print(w1) +# print(h1) + for i in w1: + for j in h1: + patches.append(img[i:i+p_size, j:j+p_size,:]) + else: + patches.append(img) + + return patches + + +def imssave(imgs, img_path): + """ + imgs: list, N images of size WxHxC + """ + img_name, ext = os.path.splitext(os.path.basename(img_path)) + + for i, img in enumerate(imgs): + if img.ndim == 3: + img = img[:, :, [2, 1, 0]] + new_path = os.path.join(os.path.dirname(img_path), img_name+str('_s{:04d}'.format(i))+'.png') + cv2.imwrite(new_path, img) + + +def split_imageset(original_dataroot, taget_dataroot, n_channels=3, p_size=800, p_overlap=96, p_max=1000): + """ + split the large images from original_dataroot into small overlapped images with size (p_size)x(p_size), + and save them into taget_dataroot; only the images with larger size than (p_max)x(p_max) + will be splitted. + Args: + original_dataroot: + taget_dataroot: + p_size: size of small images + p_overlap: patch size in training is a good choice + p_max: images with smaller size than (p_max)x(p_max) keep unchanged. + """ + paths = get_image_paths(original_dataroot) + for img_path in paths: + # img_name, ext = os.path.splitext(os.path.basename(img_path)) + img = imread_uint(img_path, n_channels=n_channels) + patches = patches_from_image(img, p_size, p_overlap, p_max) + imssave(patches, os.path.join(taget_dataroot,os.path.basename(img_path))) + #if original_dataroot == taget_dataroot: + #del img_path + +''' +# -------------------------------------------- +# makedir +# -------------------------------------------- +''' + + +def mkdir(path): + if not os.path.exists(path): + os.makedirs(path) + + +def mkdirs(paths): + if isinstance(paths, str): + mkdir(paths) + else: + for path in paths: + mkdir(path) + + +def mkdir_and_rename(path): + if os.path.exists(path): + new_name = path + '_archived_' + get_timestamp() + print('Path already exists. Rename it to [{:s}]'.format(new_name)) + os.rename(path, new_name) + os.makedirs(path) + + +''' +# -------------------------------------------- +# read image from path +# opencv is fast, but read BGR numpy image +# -------------------------------------------- +''' + + +# -------------------------------------------- +# get uint8 image of size HxWxn_channles (RGB) +# -------------------------------------------- +def imread_uint(path, n_channels=3): + # input: path + # output: HxWx3(RGB or GGG), or HxWx1 (G) + if n_channels == 1: + img = cv2.imread(path, 0) # cv2.IMREAD_GRAYSCALE + img = np.expand_dims(img, axis=2) # HxWx1 + elif n_channels == 3: + img = cv2.imread(path, cv2.IMREAD_UNCHANGED) # BGR or G + if img.ndim == 2: + img = cv2.cvtColor(img, cv2.COLOR_GRAY2RGB) # GGG + else: + img = cv2.cvtColor(img, cv2.COLOR_BGR2RGB) # RGB + return img + + +# -------------------------------------------- +# matlab's imwrite +# -------------------------------------------- +def imsave(img, img_path): + img = np.squeeze(img) + if img.ndim == 3: + img = img[:, :, [2, 1, 0]] + cv2.imwrite(img_path, img) + +def imwrite(img, img_path): + img = np.squeeze(img) + if img.ndim == 3: + img = img[:, :, [2, 1, 0]] + cv2.imwrite(img_path, img) + + + +# -------------------------------------------- +# get single image of size HxWxn_channles (BGR) +# -------------------------------------------- +def read_img(path): + # read image by cv2 + # return: Numpy float32, HWC, BGR, [0,1] + img = cv2.imread(path, cv2.IMREAD_UNCHANGED) # cv2.IMREAD_GRAYSCALE + img = img.astype(np.float32) / 255. + if img.ndim == 2: + img = np.expand_dims(img, axis=2) + # some images have 4 channels + if img.shape[2] > 3: + img = img[:, :, :3] + return img + + +''' +# -------------------------------------------- +# image format conversion +# -------------------------------------------- +# numpy(single) <---> numpy(unit) +# numpy(single) <---> tensor +# numpy(unit) <---> tensor +# -------------------------------------------- +''' + + +# -------------------------------------------- +# numpy(single) [0, 1] <---> numpy(unit) +# -------------------------------------------- + + +def uint2single(img): + + return np.float32(img/255.) + + +def single2uint(img): + + return np.uint8((img.clip(0, 1)*255.).round()) + + +def uint162single(img): + + return np.float32(img/65535.) + + +def single2uint16(img): + + return np.uint16((img.clip(0, 1)*65535.).round()) + + +# -------------------------------------------- +# numpy(unit) (HxWxC or HxW) <---> tensor +# -------------------------------------------- + + +# convert uint to 4-dimensional torch tensor +def uint2tensor4(img): + if img.ndim == 2: + img = np.expand_dims(img, axis=2) + return torch.from_numpy(np.ascontiguousarray(img)).permute(2, 0, 1).float().div(255.).unsqueeze(0) + + +# convert uint to 3-dimensional torch tensor +def uint2tensor3(img): + if img.ndim == 2: + img = np.expand_dims(img, axis=2) + return torch.from_numpy(np.ascontiguousarray(img)).permute(2, 0, 1).float().div(255.) + + +# convert 2/3/4-dimensional torch tensor to uint +def tensor2uint(img): + img = img.data.squeeze().float().clamp_(0, 1).cpu().numpy() + if img.ndim == 3: + img = np.transpose(img, (1, 2, 0)) + return np.uint8((img*255.0).round()) + + +# -------------------------------------------- +# numpy(single) (HxWxC) <---> tensor +# -------------------------------------------- + + +# convert single (HxWxC) to 3-dimensional torch tensor +def single2tensor3(img): + return torch.from_numpy(np.ascontiguousarray(img)).permute(2, 0, 1).float() + + +# convert single (HxWxC) to 4-dimensional torch tensor +def single2tensor4(img): + return torch.from_numpy(np.ascontiguousarray(img)).permute(2, 0, 1).float().unsqueeze(0) + + +# convert torch tensor to single +def tensor2single(img): + img = img.data.squeeze().float().cpu().numpy() + if img.ndim == 3: + img = np.transpose(img, (1, 2, 0)) + + return img + +# convert torch tensor to single +def tensor2single3(img): + img = img.data.squeeze().float().cpu().numpy() + if img.ndim == 3: + img = np.transpose(img, (1, 2, 0)) + elif img.ndim == 2: + img = np.expand_dims(img, axis=2) + return img + + +def single2tensor5(img): + return torch.from_numpy(np.ascontiguousarray(img)).permute(2, 0, 1, 3).float().unsqueeze(0) + + +def single32tensor5(img): + return torch.from_numpy(np.ascontiguousarray(img)).float().unsqueeze(0).unsqueeze(0) + + +def single42tensor4(img): + return torch.from_numpy(np.ascontiguousarray(img)).permute(2, 0, 1, 3).float() + + +# from skimage.io import imread, imsave +def tensor2img(tensor, out_type=np.uint8, min_max=(0, 1)): + ''' + Converts a torch Tensor into an image Numpy array of BGR channel order + Input: 4D(B,(3/1),H,W), 3D(C,H,W), or 2D(H,W), any range, RGB channel order + Output: 3D(H,W,C) or 2D(H,W), [0,255], np.uint8 (default) + ''' + tensor = tensor.squeeze().float().cpu().clamp_(*min_max) # squeeze first, then clamp + tensor = (tensor - min_max[0]) / (min_max[1] - min_max[0]) # to range [0,1] + n_dim = tensor.dim() + if n_dim == 4: + n_img = len(tensor) + img_np = make_grid(tensor, nrow=int(math.sqrt(n_img)), normalize=False).numpy() + img_np = np.transpose(img_np[[2, 1, 0], :, :], (1, 2, 0)) # HWC, BGR + elif n_dim == 3: + img_np = tensor.numpy() + img_np = np.transpose(img_np[[2, 1, 0], :, :], (1, 2, 0)) # HWC, BGR + elif n_dim == 2: + img_np = tensor.numpy() + else: + raise TypeError( + 'Only support 4D, 3D and 2D tensor. But received with dimension: {:d}'.format(n_dim)) + if out_type == np.uint8: + img_np = (img_np * 255.0).round() + # Important. Unlike matlab, numpy.unit8() WILL NOT round by default. + return img_np.astype(out_type) + + +''' +# -------------------------------------------- +# Augmentation, flipe and/or rotate +# -------------------------------------------- +# The following two are enough. +# (1) augmet_img: numpy image of WxHxC or WxH +# (2) augment_img_tensor4: tensor image 1xCxWxH +# -------------------------------------------- +''' + + +def augment_img(img, mode=0): + '''Kai Zhang (github: https://github.com/cszn) + ''' + if mode == 0: + return img + elif mode == 1: + return np.flipud(np.rot90(img)) + elif mode == 2: + return np.flipud(img) + elif mode == 3: + return np.rot90(img, k=3) + elif mode == 4: + return np.flipud(np.rot90(img, k=2)) + elif mode == 5: + return np.rot90(img) + elif mode == 6: + return np.rot90(img, k=2) + elif mode == 7: + return np.flipud(np.rot90(img, k=3)) + + +def augment_img_tensor4(img, mode=0): + '''Kai Zhang (github: https://github.com/cszn) + ''' + if mode == 0: + return img + elif mode == 1: + return img.rot90(1, [2, 3]).flip([2]) + elif mode == 2: + return img.flip([2]) + elif mode == 3: + return img.rot90(3, [2, 3]) + elif mode == 4: + return img.rot90(2, [2, 3]).flip([2]) + elif mode == 5: + return img.rot90(1, [2, 3]) + elif mode == 6: + return img.rot90(2, [2, 3]) + elif mode == 7: + return img.rot90(3, [2, 3]).flip([2]) + + +def augment_img_tensor(img, mode=0): + '''Kai Zhang (github: https://github.com/cszn) + ''' + img_size = img.size() + img_np = img.data.cpu().numpy() + if len(img_size) == 3: + img_np = np.transpose(img_np, (1, 2, 0)) + elif len(img_size) == 4: + img_np = np.transpose(img_np, (2, 3, 1, 0)) + img_np = augment_img(img_np, mode=mode) + img_tensor = torch.from_numpy(np.ascontiguousarray(img_np)) + if len(img_size) == 3: + img_tensor = img_tensor.permute(2, 0, 1) + elif len(img_size) == 4: + img_tensor = img_tensor.permute(3, 2, 0, 1) + + return img_tensor.type_as(img) + + +def augment_img_np3(img, mode=0): + if mode == 0: + return img + elif mode == 1: + return img.transpose(1, 0, 2) + elif mode == 2: + return img[::-1, :, :] + elif mode == 3: + img = img[::-1, :, :] + img = img.transpose(1, 0, 2) + return img + elif mode == 4: + return img[:, ::-1, :] + elif mode == 5: + img = img[:, ::-1, :] + img = img.transpose(1, 0, 2) + return img + elif mode == 6: + img = img[:, ::-1, :] + img = img[::-1, :, :] + return img + elif mode == 7: + img = img[:, ::-1, :] + img = img[::-1, :, :] + img = img.transpose(1, 0, 2) + return img + + +def augment_imgs(img_list, hflip=True, rot=True): + # horizontal flip OR rotate + hflip = hflip and random.random() < 0.5 + vflip = rot and random.random() < 0.5 + rot90 = rot and random.random() < 0.5 + + def _augment(img): + if hflip: + img = img[:, ::-1, :] + if vflip: + img = img[::-1, :, :] + if rot90: + img = img.transpose(1, 0, 2) + return img + + return [_augment(img) for img in img_list] + + +''' +# -------------------------------------------- +# modcrop and shave +# -------------------------------------------- +''' + + +def modcrop(img_in, scale): + # img_in: Numpy, HWC or HW + img = np.copy(img_in) + if img.ndim == 2: + H, W = img.shape + H_r, W_r = H % scale, W % scale + img = img[:H - H_r, :W - W_r] + elif img.ndim == 3: + H, W, C = img.shape + H_r, W_r = H % scale, W % scale + img = img[:H - H_r, :W - W_r, :] + else: + raise ValueError('Wrong img ndim: [{:d}].'.format(img.ndim)) + return img + + +def shave(img_in, border=0): + # img_in: Numpy, HWC or HW + img = np.copy(img_in) + h, w = img.shape[:2] + img = img[border:h-border, border:w-border] + return img + + +''' +# -------------------------------------------- +# image processing process on numpy image +# channel_convert(in_c, tar_type, img_list): +# rgb2ycbcr(img, only_y=True): +# bgr2ycbcr(img, only_y=True): +# ycbcr2rgb(img): +# -------------------------------------------- +''' + + +def rgb2ycbcr(img, only_y=True): + '''same as matlab rgb2ycbcr + only_y: only return Y channel + Input: + uint8, [0, 255] + float, [0, 1] + ''' + in_img_type = img.dtype + img.astype(np.float32) + if in_img_type != np.uint8: + img *= 255. + # convert + if only_y: + rlt = np.dot(img, [65.481, 128.553, 24.966]) / 255.0 + 16.0 + else: + rlt = np.matmul(img, [[65.481, -37.797, 112.0], [128.553, -74.203, -93.786], + [24.966, 112.0, -18.214]]) / 255.0 + [16, 128, 128] + if in_img_type == np.uint8: + rlt = rlt.round() + else: + rlt /= 255. + return rlt.astype(in_img_type) + + +def ycbcr2rgb(img): + '''same as matlab ycbcr2rgb + Input: + uint8, [0, 255] + float, [0, 1] + ''' + in_img_type = img.dtype + img.astype(np.float32) + if in_img_type != np.uint8: + img *= 255. + # convert + rlt = np.matmul(img, [[0.00456621, 0.00456621, 0.00456621], [0, -0.00153632, 0.00791071], + [0.00625893, -0.00318811, 0]]) * 255.0 + [-222.921, 135.576, -276.836] + if in_img_type == np.uint8: + rlt = rlt.round() + else: + rlt /= 255. + return rlt.astype(in_img_type) + + +def bgr2ycbcr(img, only_y=True): + '''bgr version of rgb2ycbcr + only_y: only return Y channel + Input: + uint8, [0, 255] + float, [0, 1] + ''' + in_img_type = img.dtype + img.astype(np.float32) + if in_img_type != np.uint8: + img *= 255. + # convert + if only_y: + rlt = np.dot(img, [24.966, 128.553, 65.481]) / 255.0 + 16.0 + else: + rlt = np.matmul(img, [[24.966, 112.0, -18.214], [128.553, -74.203, -93.786], + [65.481, -37.797, 112.0]]) / 255.0 + [16, 128, 128] + if in_img_type == np.uint8: + rlt = rlt.round() + else: + rlt /= 255. + return rlt.astype(in_img_type) + + +def channel_convert(in_c, tar_type, img_list): + # conversion among BGR, gray and y + if in_c == 3 and tar_type == 'gray': # BGR to gray + gray_list = [cv2.cvtColor(img, cv2.COLOR_BGR2GRAY) for img in img_list] + return [np.expand_dims(img, axis=2) for img in gray_list] + elif in_c == 3 and tar_type == 'y': # BGR to y + y_list = [bgr2ycbcr(img, only_y=True) for img in img_list] + return [np.expand_dims(img, axis=2) for img in y_list] + elif in_c == 1 and tar_type == 'RGB': # gray/y to BGR + return [cv2.cvtColor(img, cv2.COLOR_GRAY2BGR) for img in img_list] + else: + return img_list + + +''' +# -------------------------------------------- +# metric, PSNR and SSIM +# -------------------------------------------- +''' + + +# -------------------------------------------- +# PSNR +# -------------------------------------------- +def calculate_psnr(img1, img2, border=0): + # img1 and img2 have range [0, 255] + #img1 = img1.squeeze() + #img2 = img2.squeeze() + if not img1.shape == img2.shape: + raise ValueError('Input images must have the same dimensions.') + h, w = img1.shape[:2] + img1 = img1[border:h-border, border:w-border] + img2 = img2[border:h-border, border:w-border] + + img1 = img1.astype(np.float64) + img2 = img2.astype(np.float64) + mse = np.mean((img1 - img2)**2) + if mse == 0: + return float('inf') + return 20 * math.log10(255.0 / math.sqrt(mse)) + + +# -------------------------------------------- +# SSIM +# -------------------------------------------- +def calculate_ssim(img1, img2, border=0): + '''calculate SSIM + the same outputs as MATLAB's + img1, img2: [0, 255] + ''' + #img1 = img1.squeeze() + #img2 = img2.squeeze() + if not img1.shape == img2.shape: + raise ValueError('Input images must have the same dimensions.') + h, w = img1.shape[:2] + img1 = img1[border:h-border, border:w-border] + img2 = img2[border:h-border, border:w-border] + + if img1.ndim == 2: + return ssim(img1, img2) + elif img1.ndim == 3: + if img1.shape[2] == 3: + ssims = [] + for i in range(3): + ssims.append(ssim(img1[:,:,i], img2[:,:,i])) + return np.array(ssims).mean() + elif img1.shape[2] == 1: + return ssim(np.squeeze(img1), np.squeeze(img2)) + else: + raise ValueError('Wrong input image dimensions.') + + +def ssim(img1, img2): + C1 = (0.01 * 255)**2 + C2 = (0.03 * 255)**2 + + img1 = img1.astype(np.float64) + img2 = img2.astype(np.float64) + kernel = cv2.getGaussianKernel(11, 1.5) + window = np.outer(kernel, kernel.transpose()) + + mu1 = cv2.filter2D(img1, -1, window)[5:-5, 5:-5] # valid + mu2 = cv2.filter2D(img2, -1, window)[5:-5, 5:-5] + mu1_sq = mu1**2 + mu2_sq = mu2**2 + mu1_mu2 = mu1 * mu2 + sigma1_sq = cv2.filter2D(img1**2, -1, window)[5:-5, 5:-5] - mu1_sq + sigma2_sq = cv2.filter2D(img2**2, -1, window)[5:-5, 5:-5] - mu2_sq + sigma12 = cv2.filter2D(img1 * img2, -1, window)[5:-5, 5:-5] - mu1_mu2 + + ssim_map = ((2 * mu1_mu2 + C1) * (2 * sigma12 + C2)) / ((mu1_sq + mu2_sq + C1) * + (sigma1_sq + sigma2_sq + C2)) + return ssim_map.mean() + + +''' +# -------------------------------------------- +# matlab's bicubic imresize (numpy and torch) [0, 1] +# -------------------------------------------- +''' + + +# matlab 'imresize' function, now only support 'bicubic' +def cubic(x): + absx = torch.abs(x) + absx2 = absx**2 + absx3 = absx**3 + return (1.5*absx3 - 2.5*absx2 + 1) * ((absx <= 1).type_as(absx)) + \ + (-0.5*absx3 + 2.5*absx2 - 4*absx + 2) * (((absx > 1)*(absx <= 2)).type_as(absx)) + + +def calculate_weights_indices(in_length, out_length, scale, kernel, kernel_width, antialiasing): + if (scale < 1) and (antialiasing): + # Use a modified kernel to simultaneously interpolate and antialias- larger kernel width + kernel_width = kernel_width / scale + + # Output-space coordinates + x = torch.linspace(1, out_length, out_length) + + # Input-space coordinates. Calculate the inverse mapping such that 0.5 + # in output space maps to 0.5 in input space, and 0.5+scale in output + # space maps to 1.5 in input space. + u = x / scale + 0.5 * (1 - 1 / scale) + + # What is the left-most pixel that can be involved in the computation? + left = torch.floor(u - kernel_width / 2) + + # What is the maximum number of pixels that can be involved in the + # computation? Note: it's OK to use an extra pixel here; if the + # corresponding weights are all zero, it will be eliminated at the end + # of this function. + P = math.ceil(kernel_width) + 2 + + # The indices of the input pixels involved in computing the k-th output + # pixel are in row k of the indices matrix. + indices = left.view(out_length, 1).expand(out_length, P) + torch.linspace(0, P - 1, P).view( + 1, P).expand(out_length, P) + + # The weights used to compute the k-th output pixel are in row k of the + # weights matrix. + distance_to_center = u.view(out_length, 1).expand(out_length, P) - indices + # apply cubic kernel + if (scale < 1) and (antialiasing): + weights = scale * cubic(distance_to_center * scale) + else: + weights = cubic(distance_to_center) + # Normalize the weights matrix so that each row sums to 1. + weights_sum = torch.sum(weights, 1).view(out_length, 1) + weights = weights / weights_sum.expand(out_length, P) + + # If a column in weights is all zero, get rid of it. only consider the first and last column. + weights_zero_tmp = torch.sum((weights == 0), 0) + if not math.isclose(weights_zero_tmp[0], 0, rel_tol=1e-6): + indices = indices.narrow(1, 1, P - 2) + weights = weights.narrow(1, 1, P - 2) + if not math.isclose(weights_zero_tmp[-1], 0, rel_tol=1e-6): + indices = indices.narrow(1, 0, P - 2) + weights = weights.narrow(1, 0, P - 2) + weights = weights.contiguous() + indices = indices.contiguous() + sym_len_s = -indices.min() + 1 + sym_len_e = indices.max() - in_length + indices = indices + sym_len_s - 1 + return weights, indices, int(sym_len_s), int(sym_len_e) + + +# -------------------------------------------- +# imresize for tensor image [0, 1] +# -------------------------------------------- +def imresize(img, scale, antialiasing=True): + # Now the scale should be the same for H and W + # input: img: pytorch tensor, CHW or HW [0,1] + # output: CHW or HW [0,1] w/o round + need_squeeze = True if img.dim() == 2 else False + if need_squeeze: + img.unsqueeze_(0) + in_C, in_H, in_W = img.size() + out_C, out_H, out_W = in_C, math.ceil(in_H * scale), math.ceil(in_W * scale) + kernel_width = 4 + kernel = 'cubic' + + # Return the desired dimension order for performing the resize. The + # strategy is to perform the resize first along the dimension with the + # smallest scale factor. + # Now we do not support this. + + # get weights and indices + weights_H, indices_H, sym_len_Hs, sym_len_He = calculate_weights_indices( + in_H, out_H, scale, kernel, kernel_width, antialiasing) + weights_W, indices_W, sym_len_Ws, sym_len_We = calculate_weights_indices( + in_W, out_W, scale, kernel, kernel_width, antialiasing) + # process H dimension + # symmetric copying + img_aug = torch.FloatTensor(in_C, in_H + sym_len_Hs + sym_len_He, in_W) + img_aug.narrow(1, sym_len_Hs, in_H).copy_(img) + + sym_patch = img[:, :sym_len_Hs, :] + inv_idx = torch.arange(sym_patch.size(1) - 1, -1, -1).long() + sym_patch_inv = sym_patch.index_select(1, inv_idx) + img_aug.narrow(1, 0, sym_len_Hs).copy_(sym_patch_inv) + + sym_patch = img[:, -sym_len_He:, :] + inv_idx = torch.arange(sym_patch.size(1) - 1, -1, -1).long() + sym_patch_inv = sym_patch.index_select(1, inv_idx) + img_aug.narrow(1, sym_len_Hs + in_H, sym_len_He).copy_(sym_patch_inv) + + out_1 = torch.FloatTensor(in_C, out_H, in_W) + kernel_width = weights_H.size(1) + for i in range(out_H): + idx = int(indices_H[i][0]) + for j in range(out_C): + out_1[j, i, :] = img_aug[j, idx:idx + kernel_width, :].transpose(0, 1).mv(weights_H[i]) + + # process W dimension + # symmetric copying + out_1_aug = torch.FloatTensor(in_C, out_H, in_W + sym_len_Ws + sym_len_We) + out_1_aug.narrow(2, sym_len_Ws, in_W).copy_(out_1) + + sym_patch = out_1[:, :, :sym_len_Ws] + inv_idx = torch.arange(sym_patch.size(2) - 1, -1, -1).long() + sym_patch_inv = sym_patch.index_select(2, inv_idx) + out_1_aug.narrow(2, 0, sym_len_Ws).copy_(sym_patch_inv) + + sym_patch = out_1[:, :, -sym_len_We:] + inv_idx = torch.arange(sym_patch.size(2) - 1, -1, -1).long() + sym_patch_inv = sym_patch.index_select(2, inv_idx) + out_1_aug.narrow(2, sym_len_Ws + in_W, sym_len_We).copy_(sym_patch_inv) + + out_2 = torch.FloatTensor(in_C, out_H, out_W) + kernel_width = weights_W.size(1) + for i in range(out_W): + idx = int(indices_W[i][0]) + for j in range(out_C): + out_2[j, :, i] = out_1_aug[j, :, idx:idx + kernel_width].mv(weights_W[i]) + if need_squeeze: + out_2.squeeze_() + return out_2 + + +# -------------------------------------------- +# imresize for numpy image [0, 1] +# -------------------------------------------- +def imresize_np(img, scale, antialiasing=True): + # Now the scale should be the same for H and W + # input: img: Numpy, HWC or HW [0,1] + # output: HWC or HW [0,1] w/o round + img = torch.from_numpy(img) + need_squeeze = True if img.dim() == 2 else False + if need_squeeze: + img.unsqueeze_(2) + + in_H, in_W, in_C = img.size() + out_C, out_H, out_W = in_C, math.ceil(in_H * scale), math.ceil(in_W * scale) + kernel_width = 4 + kernel = 'cubic' + + # Return the desired dimension order for performing the resize. The + # strategy is to perform the resize first along the dimension with the + # smallest scale factor. + # Now we do not support this. + + # get weights and indices + weights_H, indices_H, sym_len_Hs, sym_len_He = calculate_weights_indices( + in_H, out_H, scale, kernel, kernel_width, antialiasing) + weights_W, indices_W, sym_len_Ws, sym_len_We = calculate_weights_indices( + in_W, out_W, scale, kernel, kernel_width, antialiasing) + # process H dimension + # symmetric copying + img_aug = torch.FloatTensor(in_H + sym_len_Hs + sym_len_He, in_W, in_C) + img_aug.narrow(0, sym_len_Hs, in_H).copy_(img) + + sym_patch = img[:sym_len_Hs, :, :] + inv_idx = torch.arange(sym_patch.size(0) - 1, -1, -1).long() + sym_patch_inv = sym_patch.index_select(0, inv_idx) + img_aug.narrow(0, 0, sym_len_Hs).copy_(sym_patch_inv) + + sym_patch = img[-sym_len_He:, :, :] + inv_idx = torch.arange(sym_patch.size(0) - 1, -1, -1).long() + sym_patch_inv = sym_patch.index_select(0, inv_idx) + img_aug.narrow(0, sym_len_Hs + in_H, sym_len_He).copy_(sym_patch_inv) + + out_1 = torch.FloatTensor(out_H, in_W, in_C) + kernel_width = weights_H.size(1) + for i in range(out_H): + idx = int(indices_H[i][0]) + for j in range(out_C): + out_1[i, :, j] = img_aug[idx:idx + kernel_width, :, j].transpose(0, 1).mv(weights_H[i]) + + # process W dimension + # symmetric copying + out_1_aug = torch.FloatTensor(out_H, in_W + sym_len_Ws + sym_len_We, in_C) + out_1_aug.narrow(1, sym_len_Ws, in_W).copy_(out_1) + + sym_patch = out_1[:, :sym_len_Ws, :] + inv_idx = torch.arange(sym_patch.size(1) - 1, -1, -1).long() + sym_patch_inv = sym_patch.index_select(1, inv_idx) + out_1_aug.narrow(1, 0, sym_len_Ws).copy_(sym_patch_inv) + + sym_patch = out_1[:, -sym_len_We:, :] + inv_idx = torch.arange(sym_patch.size(1) - 1, -1, -1).long() + sym_patch_inv = sym_patch.index_select(1, inv_idx) + out_1_aug.narrow(1, sym_len_Ws + in_W, sym_len_We).copy_(sym_patch_inv) + + out_2 = torch.FloatTensor(out_H, out_W, in_C) + kernel_width = weights_W.size(1) + for i in range(out_W): + idx = int(indices_W[i][0]) + for j in range(out_C): + out_2[:, i, j] = out_1_aug[:, idx:idx + kernel_width, j].mv(weights_W[i]) + if need_squeeze: + out_2.squeeze_() + + return out_2.numpy() + + +if __name__ == '__main__': + print('---') +# img = imread_uint('test.bmp', 3) +# img = uint2single(img) +# img_bicubic = imresize_np(img, 1/4) \ No newline at end of file diff --git a/ldm/modules/losses_audio/__init__.py b/ldm/modules/losses_audio/__init__.py new file mode 100644 index 0000000000000000000000000000000000000000..64ef1664cf934381870a61c96ba9aba498b715d5 --- /dev/null +++ b/ldm/modules/losses_audio/__init__.py @@ -0,0 +1,7 @@ +from ldm.modules.losses_audio.vqperceptual import DummyLoss + +# relative imports pain +import os +import sys +path = os.path.join(os.path.dirname(os.path.realpath(__file__)), 'vggishish') +sys.path.append(path) diff --git a/ldm/modules/losses_audio/__pycache__/__init__.cpython-38.pyc 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0000000000000000000000000000000000000000..13e279764f4c7bc1fd8f4234e1de12cc0bcf6dfa Binary files /dev/null and b/ldm/modules/losses_audio/__pycache__/vqperceptual.cpython-39.pyc differ diff --git a/ldm/modules/losses_audio/contperceptual.py b/ldm/modules/losses_audio/contperceptual.py new file mode 100644 index 0000000000000000000000000000000000000000..3e3018da79c5c24d85af1687f6f0875530dcc7c6 --- /dev/null +++ b/ldm/modules/losses_audio/contperceptual.py @@ -0,0 +1,123 @@ +import torch +import torch.nn as nn +import torch.nn.functional as F +import sys + +sys.path.insert(0, '.') # nopep8 +from ldm.modules.losses_audio.vqperceptual import * + + +class LPAPSWithDiscriminator(nn.Module): + def __init__(self, disc_start, logvar_init=0.0, kl_weight=1.0, pixelloss_weight=1.0, + disc_num_layers=3, disc_in_channels=3, disc_factor=1.0, disc_weight=1.0, + perceptual_weight=1.0, use_actnorm=False, disc_conditional=False, + disc_loss="hinge"): + + super().__init__() + assert disc_loss in ["hinge", "vanilla"] + self.kl_weight = kl_weight + self.pixel_weight = pixelloss_weight + self.perceptual_loss = LPAPS().eval()# LPIPS用于日常图像，而LPAPS用于梅尔谱图 + self.perceptual_weight = perceptual_weight + # output log variance + self.logvar = nn.Parameter(torch.ones(size=()) * logvar_init) + + self.discriminator = NLayerDiscriminator(input_nc=disc_in_channels, + n_layers=disc_num_layers, + use_actnorm=use_actnorm, + ).apply(weights_init) + self.discriminator_iter_start = disc_start + if disc_loss == "hinge": + self.disc_loss = hinge_d_loss + elif disc_loss == "vanilla": + self.disc_loss = vanilla_d_loss + else: + raise ValueError(f"Unknown GAN loss '{disc_loss}'.") + print(f"LPAPSWithDiscriminator running with {disc_loss} loss.") + self.disc_factor = disc_factor + self.discriminator_weight = disc_weight + self.disc_conditional = disc_conditional + + + def calculate_adaptive_weight(self, nll_loss, g_loss, last_layer=None): + if last_layer is not None: + nll_grads = torch.autograd.grad(nll_loss, last_layer, retain_graph=True)[0] + g_grads = torch.autograd.grad(g_loss, last_layer, retain_graph=True)[0] + else: + nll_grads = torch.autograd.grad(nll_loss, self.last_layer[0], retain_graph=True)[0] + g_grads = torch.autograd.grad(g_loss, self.last_layer[0], retain_graph=True)[0] + + d_weight = torch.norm(nll_grads) / (torch.norm(g_grads) + 1e-4) + d_weight = torch.clamp(d_weight, 0.0, 1e4).detach() + d_weight = d_weight * self.discriminator_weight + return d_weight + + def forward(self, inputs, reconstructions, posteriors, optimizer_idx, + global_step, last_layer=None, cond=None, split="train", weights=None): + rec_loss = torch.abs(inputs.contiguous() - reconstructions.contiguous()) + if self.perceptual_weight > 0: + p_loss = self.perceptual_loss(inputs.contiguous(), reconstructions.contiguous()) + # print(f"p_loss {p_loss}") + rec_loss = rec_loss + self.perceptual_weight * p_loss + else: + p_loss = torch.tensor([0.0]) + + nll_loss = rec_loss / torch.exp(self.logvar) + self.logvar + weighted_nll_loss = nll_loss + if weights is not None: + weighted_nll_loss = weights*nll_loss + weighted_nll_loss = torch.sum(weighted_nll_loss) / weighted_nll_loss.shape[0] + nll_loss = torch.sum(nll_loss) / nll_loss.shape[0] + kl_loss = posteriors.kl() + kl_loss = torch.sum(kl_loss) / kl_loss.shape[0] + + # now the GAN part + if optimizer_idx == 0: + # generator update + if cond is None: + assert not self.disc_conditional + logits_fake = self.discriminator(reconstructions.contiguous()) + else: + assert self.disc_conditional + logits_fake = self.discriminator(torch.cat((reconstructions.contiguous(), cond), dim=1)) + g_loss = -torch.mean(logits_fake) + + try: + d_weight = self.calculate_adaptive_weight(nll_loss, g_loss, last_layer=last_layer) + except RuntimeError: + assert not self.training + d_weight = torch.tensor(0.0) + + disc_factor = adopt_weight(self.disc_factor, global_step, threshold=self.discriminator_iter_start) + loss = weighted_nll_loss + self.kl_weight * kl_loss + d_weight * disc_factor * g_loss + + log = {"{}/total_loss".format(split): loss.clone().detach().mean(), + "{}/logvar".format(split): self.logvar.detach(), + "{}/kl_loss".format(split): kl_loss.detach().mean(), + "{}/nll_loss".format(split): nll_loss.detach().mean(), + "{}/rec_loss".format(split): rec_loss.detach().mean(), + "{}/d_weight".format(split): d_weight.detach(), + "{}/disc_factor".format(split): torch.tensor(disc_factor), + "{}/g_loss".format(split): g_loss.detach().mean(), + } + return loss, log + + if optimizer_idx == 1: + # second pass for discriminator update + if cond is None: + logits_real = self.discriminator(inputs.contiguous().detach()) + logits_fake = self.discriminator(reconstructions.contiguous().detach()) + else: + logits_real = self.discriminator(torch.cat((inputs.contiguous().detach(), cond), dim=1)) + logits_fake = self.discriminator(torch.cat((reconstructions.contiguous().detach(), cond), dim=1)) + + disc_factor = adopt_weight(self.disc_factor, global_step, threshold=self.discriminator_iter_start) + d_loss = disc_factor * self.disc_loss(logits_real, logits_fake) + + log = {"{}/disc_loss".format(split): d_loss.clone().detach().mean(), + "{}/logits_real".format(split): logits_real.detach().mean(), + "{}/logits_fake".format(split): logits_fake.detach().mean() + } + return d_loss, log + + diff --git a/ldm/modules/losses_audio/contperceptual_dis.py b/ldm/modules/losses_audio/contperceptual_dis.py new file mode 100644 index 0000000000000000000000000000000000000000..be47add559612aaf8c667afd554d88e23fd8fd56 --- /dev/null +++ b/ldm/modules/losses_audio/contperceptual_dis.py @@ -0,0 +1,137 @@ +import torch +import torch.nn as nn +import torch.nn.functional as F +import sys + +sys.path.insert(0, '.') # nopep8 +from ldm.modules.losses_audio.vqperceptual import * +from ldm.modules.discriminator.multi_window_disc import Discriminator + +class LPAPSWithDiscriminator(nn.Module):# 相比于contperceptual.py添加了MultiWindowDiscriminator + def __init__(self, disc_start, logvar_init=0.0, kl_weight=1.0, pixelloss_weight=1.0, + disc_num_layers=3, disc_in_channels=3, disc_factor=1.0, disc_weight=1.0, + perceptual_weight=1.0, use_actnorm=False, disc_conditional=False, + disc_loss="hinge"): + + super().__init__() + assert disc_loss in ["hinge", "vanilla"] + self.kl_weight = kl_weight + self.pixel_weight = pixelloss_weight + self.perceptual_loss = LPAPS().eval() + self.perceptual_weight = perceptual_weight + # output log variance + self.logvar = nn.Parameter(torch.ones(size=()) * logvar_init) + + self.discriminator = NLayerDiscriminator(input_nc=disc_in_channels, + n_layers=disc_num_layers, + use_actnorm=use_actnorm, + ).apply(weights_init) + self.discriminator_iter_start = disc_start + if disc_loss == "hinge": + self.disc_loss = hinge_d_loss + elif disc_loss == "vanilla": + self.disc_loss = vanilla_d_loss + else: + raise ValueError(f"Unknown GAN loss '{disc_loss}'.") + print(f"LPAPSWithDiscriminator running with {disc_loss} loss.") + self.disc_factor = disc_factor + self.discriminator_weight = disc_weight + self.disc_conditional = disc_conditional + + disc_win_num = 3 + mel_disc_hidden_size = 128 + self.discriminator_multi = Discriminator(time_lengths=[32, 64, 128][:disc_win_num], + freq_length=80, hidden_size=mel_disc_hidden_size, kernel=(3, 3), + cond_size=0, norm_type="in", reduction="stack") + + def calculate_adaptive_weight(self, nll_loss, g_loss, last_layer=None): + if last_layer is not None: + nll_grads = torch.autograd.grad(nll_loss, last_layer, retain_graph=True)[0] + g_grads = torch.autograd.grad(g_loss, last_layer, retain_graph=True)[0] + else: + nll_grads = torch.autograd.grad(nll_loss, self.last_layer[0], retain_graph=True)[0] + g_grads = torch.autograd.grad(g_loss, self.last_layer[0], retain_graph=True)[0] + + d_weight = torch.norm(nll_grads) / (torch.norm(g_grads) + 1e-4) + d_weight = torch.clamp(d_weight, 0.0, 1e4).detach() + d_weight = d_weight * self.discriminator_weight + return d_weight + + def forward(self, inputs, reconstructions, posteriors, optimizer_idx, + global_step, last_layer=None, cond=None, split="train", weights=None): + rec_loss = torch.abs(inputs.contiguous() - reconstructions.contiguous()) + if self.perceptual_weight > 0: + p_loss = self.perceptual_loss(inputs.contiguous(), reconstructions.contiguous()) + rec_loss = rec_loss + self.perceptual_weight * p_loss + else: + p_loss = torch.tensor([0.0]) + + nll_loss = rec_loss / torch.exp(self.logvar) + self.logvar + weighted_nll_loss = nll_loss + if weights is not None: + weighted_nll_loss = weights*nll_loss + weighted_nll_loss = torch.sum(weighted_nll_loss) / weighted_nll_loss.shape[0] + nll_loss = torch.sum(nll_loss) / nll_loss.shape[0] + kl_loss = posteriors.kl() + kl_loss = torch.sum(kl_loss) / kl_loss.shape[0] + + # now the GAN part + if optimizer_idx == 0: + # generator update + if cond is None: + assert not self.disc_conditional + logits_fake = self.discriminator(reconstructions.contiguous()) + else: + assert self.disc_conditional + logits_fake = self.discriminator(torch.cat((reconstructions.contiguous(), cond), dim=1)) + + logits_fake_multi = self.discriminator_multi(reconstructions.contiguous().squeeze(1).transpose(1, 2)) + + g_loss = -torch.mean(logits_fake) + g_loss_multi = -torch.mean(logits_fake_multi['y']) + + try: + d_weight = self.calculate_adaptive_weight(nll_loss, g_loss, last_layer=last_layer) + d_weight_multi = self.calculate_adaptive_weight(nll_loss, g_loss_multi, last_layer=last_layer) + except RuntimeError: + assert not self.training + d_weight = d_weight_multi = torch.tensor(0.0) + + disc_factor = adopt_weight(self.disc_factor, global_step, threshold=self.discriminator_iter_start) + loss = weighted_nll_loss + self.kl_weight * kl_loss + d_weight * disc_factor * g_loss + d_weight_multi * disc_factor * g_loss_multi + + log = {"{}/total_loss".format(split): loss.clone().detach().mean(), + "{}/logvar".format(split): self.logvar.detach(), + "{}/kl_loss".format(split): kl_loss.detach().mean(), + "{}/nll_loss".format(split): nll_loss.detach().mean(), + "{}/rec_loss".format(split): rec_loss.detach().mean(), + "{}/d_weight".format(split): d_weight.detach(), + "{}/disc_factor".format(split): torch.tensor(disc_factor), + "{}/g_loss".format(split): g_loss.detach().mean(), + "{}/g_loss_multi".format(split): g_loss_multi.detach().mean(), + } + return loss, log + + if optimizer_idx == 1: + # second pass for discriminator update + if cond is None: + logits_real = self.discriminator(inputs.contiguous().detach()) + logits_fake = self.discriminator(reconstructions.contiguous().detach()) + else: + logits_real = self.discriminator(torch.cat((inputs.contiguous().detach(), cond), dim=1)) + logits_fake = self.discriminator(torch.cat((reconstructions.contiguous().detach(), cond), dim=1)) + + logits_real_multi = self.discriminator_multi(inputs.contiguous().detach().squeeze(1).transpose(1, 2)) + logits_fake_multi = self.discriminator_multi(reconstructions.contiguous().detach().squeeze(1).transpose(1, 2)) + + disc_factor = adopt_weight(self.disc_factor, global_step, threshold=self.discriminator_iter_start) + d_loss = disc_factor * self.disc_loss(logits_real, logits_fake) + d_loss_multi = disc_factor * self.disc_loss(logits_real_multi['y'], logits_fake_multi['y']) + + log = {"{}/disc_loss".format(split): d_loss.clone().detach().mean(), + "{}/disc_loss_multi".format(split): d_loss_multi.clone().detach().mean(), + "{}/logits_real".format(split): logits_real.detach().mean(), + "{}/logits_fake".format(split): logits_fake.detach().mean() + } + return d_loss+d_loss_multi, log + diff --git a/ldm/modules/losses_audio/lpaps.py b/ldm/modules/losses_audio/lpaps.py new file mode 100644 index 0000000000000000000000000000000000000000..8ddf64a08f7efb2a6039cee7b6ba0fdece4dcb5e --- /dev/null +++ b/ldm/modules/losses_audio/lpaps.py @@ -0,0 +1,152 @@ +""" + Based on https://github.com/CompVis/taming-transformers/blob/52720829/taming/modules/losses/lpips.py + Adapted for spectrograms by Vladimir Iashin (v-iashin) +""" +from collections import namedtuple + +import numpy as np +import torch +import torch.nn as nn + +import sys +sys.path.insert(0, '.') # nopep8 +from ldm.modules.losses_audio.vggishish.model import VGGishish +from ldm.util import get_ckpt_path + + +class LPAPS(nn.Module): + # Learned perceptual metric + def __init__(self, use_dropout=True): + super().__init__() + self.scaling_layer = ScalingLayer() + self.chns = [64, 128, 256, 512, 512] # vggish16 features + self.net = vggishish16(pretrained=True, requires_grad=False) + self.lin0 = NetLinLayer(self.chns[0], use_dropout=use_dropout) + self.lin1 = NetLinLayer(self.chns[1], use_dropout=use_dropout) + self.lin2 = NetLinLayer(self.chns[2], use_dropout=use_dropout) + self.lin3 = NetLinLayer(self.chns[3], use_dropout=use_dropout) + self.lin4 = NetLinLayer(self.chns[4], use_dropout=use_dropout) + self.load_from_pretrained() + for param in self.parameters(): + param.requires_grad = False + + def load_from_pretrained(self, name="vggishish_lpaps"): + ckpt = get_ckpt_path(name, "ldm/modules/autoencoder/lpaps") + self.load_state_dict(torch.load(ckpt, map_location=torch.device("cpu")), strict=False) + print("loaded pretrained LPAPS loss from {}".format(ckpt)) + + @classmethod + def from_pretrained(cls, name="vggishish_lpaps"): + if name != "vggishish_lpaps": + raise NotImplementedError + model = cls() + ckpt = get_ckpt_path(name) + model.load_state_dict(torch.load(ckpt, map_location=torch.device("cpu")), strict=False) + return model + + def forward(self, input, target): + in0_input, in1_input = (self.scaling_layer(input), self.scaling_layer(target)) + outs0, outs1 = self.net(in0_input), self.net(in1_input) + feats0, feats1, diffs = {}, {}, {} + lins = [self.lin0, self.lin1, self.lin2, self.lin3, self.lin4] + for kk in range(len(self.chns)): + feats0[kk], feats1[kk] = normalize_tensor(outs0[kk]), normalize_tensor(outs1[kk]) + diffs[kk] = (feats0[kk] - feats1[kk]) ** 2 + + res = [spatial_average(lins[kk].model(diffs[kk]), keepdim=True) for kk in range(len(self.chns))] + val = res[0] + for l in range(1, len(self.chns)): + val += res[l] + return val + +class ScalingLayer(nn.Module): + def __init__(self): + super(ScalingLayer, self).__init__() + # we are gonna use get_ckpt_path to donwload the stats as well + stat_path = get_ckpt_path('vggishish_mean_std_melspec_10s_22050hz', 'ldm/modules/autoencoder/lpaps') + # if for images we normalize on the channel dim, in spectrogram we will norm on frequency dimension + means, stds = np.loadtxt(stat_path, dtype=np.float32).T + # the normalization in means and stds are given for [0, 1], but specvqgan expects [-1, 1]: + means = 2 * means - 1 + stds = 2 * stds + # input is expected to be (B, 1, F, T) + self.register_buffer('shift', torch.from_numpy(means)[None, None, :, None]) + self.register_buffer('scale', torch.from_numpy(stds)[None, None, :, None]) + + def forward(self, inp): + return (inp - self.shift) / self.scale + + +class NetLinLayer(nn.Module): + """ A single linear layer which does a 1x1 conv """ + def __init__(self, chn_in, chn_out=1, use_dropout=False): + super(NetLinLayer, self).__init__() + layers = [nn.Dropout(), ] if (use_dropout) else [] + layers += [nn.Conv2d(chn_in, chn_out, 1, stride=1, padding=0, bias=False), ] + self.model = nn.Sequential(*layers) + +class vggishish16(torch.nn.Module): + def __init__(self, requires_grad=False, pretrained=True): + super().__init__() + vgg_pretrained_features = self.vggishish16(pretrained=pretrained).features + self.slice1 = torch.nn.Sequential() + self.slice2 = torch.nn.Sequential() + self.slice3 = torch.nn.Sequential() + self.slice4 = torch.nn.Sequential() + self.slice5 = torch.nn.Sequential() + self.N_slices = 5 + for x in range(4): + self.slice1.add_module(str(x), vgg_pretrained_features[x]) + for x in range(4, 9): + self.slice2.add_module(str(x), vgg_pretrained_features[x]) + for x in range(9, 16): + self.slice3.add_module(str(x), vgg_pretrained_features[x]) + for x in range(16, 23): + self.slice4.add_module(str(x), vgg_pretrained_features[x]) + for x in range(23, 30): + self.slice5.add_module(str(x), vgg_pretrained_features[x]) + if not requires_grad: + for param in self.parameters(): + param.requires_grad = False + + def forward(self, X): + h = self.slice1(X) + h_relu1_2 = h + h = self.slice2(h) + h_relu2_2 = h + h = self.slice3(h) + h_relu3_3 = h + h = self.slice4(h) + h_relu4_3 = h + h = self.slice5(h) + h_relu5_3 = h + vgg_outputs = namedtuple("VggOutputs", ['relu1_2', 'relu2_2', 'relu3_3', 'relu4_3', 'relu5_3']) + out = vgg_outputs(h_relu1_2, h_relu2_2, h_relu3_3, h_relu4_3, h_relu5_3) + return out + + def vggishish16(self, pretrained: bool = True) -> VGGishish: + # loading vggishish pretrained on vggsound + num_classes_vggsound = 309 + conv_layers = [64, 64, 'MP', 128, 128, 'MP', 256, 256, 256, 'MP', 512, 512, 512, 'MP', 512, 512, 512] + model = VGGishish(conv_layers, use_bn=False, num_classes=num_classes_vggsound) + if pretrained: + ckpt_path = get_ckpt_path('vggishish_lpaps', "ldm/modules/autoencoder/lpaps") + ckpt = torch.load(ckpt_path, map_location=torch.device("cpu")) + model.load_state_dict(ckpt, strict=False) + return model + +def normalize_tensor(x, eps=1e-10): + norm_factor = torch.sqrt(torch.sum(x**2, dim=1, keepdim=True)) + return x / (norm_factor+eps) + +def spatial_average(x, keepdim=True): + return x.mean([2, 3], keepdim=keepdim) + + +if __name__ == '__main__': + inputs = torch.rand((16, 1, 80, 848)) + reconstructions = torch.rand((16, 1, 80, 848)) + lpips = LPAPS().eval() + loss_p = lpips(inputs.contiguous(), reconstructions.contiguous()) + # (16, 1, 1, 1) + print(loss_p.shape) diff --git a/ldm/modules/losses_audio/vggishish/__pycache__/model.cpython-38.pyc b/ldm/modules/losses_audio/vggishish/__pycache__/model.cpython-38.pyc new file mode 100644 index 0000000000000000000000000000000000000000..7c6218051e1a2d981087f40a7ce6fe676de2eb51 Binary files /dev/null and b/ldm/modules/losses_audio/vggishish/__pycache__/model.cpython-38.pyc differ diff --git a/ldm/modules/losses_audio/vggishish/__pycache__/model.cpython-39.pyc b/ldm/modules/losses_audio/vggishish/__pycache__/model.cpython-39.pyc new file mode 100644 index 0000000000000000000000000000000000000000..bd9e19c0cc0f7c1f2dfaef8194a2d454b1d0ccfa Binary files /dev/null and b/ldm/modules/losses_audio/vggishish/__pycache__/model.cpython-39.pyc differ diff --git a/ldm/modules/losses_audio/vggishish/configs/melception.yaml b/ldm/modules/losses_audio/vggishish/configs/melception.yaml new file mode 100644 index 0000000000000000000000000000000000000000..4c0316968a3e779804223d33e25f4574bea75392 --- /dev/null +++ b/ldm/modules/losses_audio/vggishish/configs/melception.yaml @@ -0,0 +1,24 @@ +seed: 1337 +log_code_state: True +# patterns to ignore when backing up the code folder +patterns_to_ignore: ['logs', '.git', '__pycache__', 'data', 'checkpoints', '*.pt'] + +# data: +mels_path: '/home/nvme/data/vggsound/features/melspec_10s_22050hz/' +spec_shape: [80, 860] +cropped_size: [80, 848] +random_crop: False + +# train: +device: 'cuda:0' +batch_size: 8 +num_workers: 0 +optimizer: adam +betas: [0.9, 0.999] +momentum: 0.9 +learning_rate: 3e-4 +weight_decay: 0 +num_epochs: 100 +patience: 3 +logdir: './logs' +cls_weights_in_loss: False diff --git a/ldm/modules/losses_audio/vggishish/configs/vggish.yaml b/ldm/modules/losses_audio/vggishish/configs/vggish.yaml new file mode 100644 index 0000000000000000000000000000000000000000..f97359658fe257f995037e17b66244879a630498 --- /dev/null +++ b/ldm/modules/losses_audio/vggishish/configs/vggish.yaml @@ -0,0 +1,34 @@ +seed: 1337 +log_code_state: True +# patterns to ignore when backing up the code folder +patterns_to_ignore: ['logs', '.git', '__pycache__'] + +# data: +mels_path: '/home/nvme/data/vggsound/features/melspec_10s_22050hz/' +spec_shape: [80, 860] +cropped_size: [80, 848] +random_crop: False + +# model: +# original vgg family except for MP is missing at the end +# 'vggish': [64, 'MP', 128, 'MP', 256, 256, 'MP', 512, 512] +# 'vgg11': [64, 'MP', 128, 'MP', 256, 256, 'MP', 512, 512, 'MP', 512, 512], +# 'vgg13': [64, 64, 'MP', 128, 128, 'MP', 256, 256, 'MP', 512, 512, 'MP', 512, 512], +# 'vgg16': [64, 64, 'MP', 128, 128, 'MP', 256, 256, 256, 'MP', 512, 512, 512, 'MP', 512, 512, 512], +# 'vgg19': [64, 64, 'MP', 128, 128, 'MP', 256, 256, 256, 256, 'MP', 512, 512, 512, 512, 'MP', 512, 512, 512, 512], +conv_layers: [64, 64, 'MP', 128, 128, 'MP', 256, 256, 256, 'MP', 512, 512, 512, 'MP', 512, 512, 512] +use_bn: False + +# train: +device: 'cuda:0' +batch_size: 32 +num_workers: 0 +optimizer: adam +betas: [0.9, 0.999] +momentum: 0.9 +learning_rate: 3e-4 +weight_decay: 0.0001 +num_epochs: 100 +patience: 3 +logdir: './logs' +cls_weights_in_loss: False diff --git a/ldm/modules/losses_audio/vggishish/data/train_means_stds_melspec_10s_22050hz.txt b/ldm/modules/losses_audio/vggishish/data/train_means_stds_melspec_10s_22050hz.txt new file mode 100644 index 0000000000000000000000000000000000000000..5338cad02b7e23f683e28daaabdb8aecb02071ba --- /dev/null +++ b/ldm/modules/losses_audio/vggishish/data/train_means_stds_melspec_10s_22050hz.txt @@ -0,0 +1,80 @@ +0.51234712 0.08187601 +0.52630101 0.08393201 +0.52002938 0.08533191 +0.51831866 0.08651366 +0.52457265 0.08795700 +0.51129235 0.08924046 +0.51403755 0.09011565 +0.51189406 0.09138965 +0.50142221 0.09215379 +0.50632402 0.09251092 +0.49724399 0.09356590 +0.49062130 0.09333057 +0.49971113 0.09446411 +0.48442903 0.09400197 +0.48598301 0.09477853 +0.48681630 0.09487848 +0.47436119 0.09424447 +0.48031359 0.09417475 +0.47422810 0.09498061 +0.46369397 0.09362415 +0.47413055 0.09453825 +0.46062686 0.09481226 +0.45677793 0.09359464 +0.46135474 0.09437913 +0.45246800 0.09384014 +0.45232766 0.09438247 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trumpet,train +0iUagfwNjeM,6,baby crying,train +0iVM2GY3R_c,30,ambulance siren,train +0iVNCDS9-10,70,playing harp,train +0iWWVbtR-Rs,30,car engine starting,train +0iX0QmnbrK4,85,playing trombone,test +0iXsvAUguaQ,33,pumping water,train +0iY8WQZ81Lg,310,female singing,train +0i_BtUBN2K8,30,playing drum kit,train +0i_lGB3GshY,220,"playing violin, fiddle",train +0ics7pTH64c,30,"rowboat, canoe, kayak rowing",train +0idAn0WAn8U,9,"motorboat, speedboat acceleration",train +0ifCdqIOTjo,163,playing trumpet,train +0ifCdqIOTjo,228,playing trumpet,train +0iftG6TSBgg,20,turkey gobbling,train +0ig9g_-jXNY,53,playing clarinet,train +0igJQMIXtMw,30,"male speech, man speaking",train +0ih7brz--Pw,26,sheep bleating,train +0ihU0MXB2uQ,30,"female speech, woman speaking",train +0iiWcNW53Zs,140,skidding,train +0iiwZ2_9Vi8,90,people coughing,test +0ikwrhc18qI,30,female singing,train +0ioRPfQzAW0,30,playing acoustic guitar,train +0ipb6JCmWqY,0,people babbling,train +0iqGid7b7nY,30,"engine accelerating, 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accordion,train +0jJ92N6nrdY,0,playing clarinet,train +0jJ92N6nrdY,13,playing clarinet,train +0jJg8srMMCk,284,tractor digging,train +0jM2IQ0f58I,30,female singing,train +0jPN5nGZ1zQ,30,playing acoustic guitar,train +0jPvRXOqtm0,30,playing electric guitar,train +0jQPB-ubZwI,233,playing bass drum,train +0jQPB-ubZwI,244,playing bass drum,train +0jU7Aon2p4k,13,dog growling,train +0jU7Aon2p4k,24,dog growling,train +0jVvlyJKzQc,10,typing on computer keyboard,train +0jWAWkL8Pfw,11,turkey gobbling,train +0jXvXdkhmOk,30,"playing violin, fiddle",train +0jZc78A0Y3I,19,coyote howling,train +0jZtLuEdjrk,110,"female speech, woman speaking",train +0jai7WnP5qE,30,playing electric guitar,train +0jc73rjot9g,440,basketball bounce,train +0jdMumJ6Qfk,133,slot machine,train +0jeIShNthBM,268,vacuum cleaner cleaning floors,train +0jeKMqksU-0,5,dog howling,train +0jeLcHNUkDk,30,"female speech, woman speaking",train +0jfI9LI9kUM,30,car passing by,train +0jg63Vvpgwk,130,playing french horn,train 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double bass,train +2hNBsHzJKdA,30,"male speech, man speaking",train +2hNEs5VCQR8,106,playing trumpet,test +2hNwQIohyEM,30,car passing by,train +2hOWK-O3exw,114,tornado roaring,train +2hOWK-O3exw,181,tornado roaring,train +2hQDzWMrmK4,30,horse clip-clop,train +2hQmSuIt894,30,playing electric guitar,train +2hSZby4O6_g,2,ripping paper,train +2hSZby4O6_g,96,ripping paper,train +2hV3JUQO2OQ,684,people slurping,train +2hV3JUQO2OQ,736,people slurping,train +2hV9-B1lD3c,250,skiing,train +2hVYLo6dcoc,30,"male speech, man speaking",train +2hVtRan2qSU,30,car engine starting,train +2haVZc--rC8,157,playing cornet,train +2haVZc--rC8,258,playing cornet,train +2hb2kFmnUmI,30,male singing,train +2hbxKYdO_cw,20,fireworks banging,train +2hchxeQxiUQ,20,"electric shaver, electric razor shaving",train +2hdK1BEE9VE,96,people marching,train +2hdK1BEE9VE,110,people marching,train +2hdVO28f9Lo,87,door slamming,train +2hdVO28f9Lo,107,door slamming,train +2he57w-Abu4,90,tap dancing,train 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drum kit,train +5o5Nzcaddg4,30,female singing,train +5o5XSPv9N1U,19,cap gun shooting,train +5o5XSPv9N1U,32,cap gun shooting,train +5o6fYtv1T0E,40,people booing,train +5o73GKzkWfU,2,air conditioning noise,train +5o9FtsoodsE,30,playing trombone,train +5oCU7_p8_-k,30,female singing,train +5oCorN0ARBM,0,using sewing machines,train +5oCwlEyxHx8,67,rapping,train +5oDKDSJTwgk,30,singing choir,train +5oGE-59ZVJg,101,playing guiro,train +5oGE-59ZVJg,123,playing guiro,train +5oK_2-mHzqw,0,otter growling,train +5oK_2-mHzqw,12,otter growling,train +5oLW0epzQ2c,2,"motorboat, speedboat acceleration",train +5oMbn1p15Y8,30,playing accordion,train +5oNr8S4lfU8,2,playing double bass,train +5oNr8S4lfU8,432,playing double bass,train +5oNtj6wW6-A,30,skateboarding,train +5oPlF8LSdmg,94,hedge trimmer running,test +5oSD7TmYmiA,460,"race car, auto racing",train +5oVOiR69Pz0,30,wind noise,train +5oY9-wTnsL8,320,machine gun shooting,train +5oY9-wTnsL8,465,machine gun shooting,train +5oZ9xLbuU5o,380,"race car, 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man speaking",train +7shXEFuxHAA,171,playing vibraphone,train +7shXEFuxHAA,539,playing vibraphone,train +7siHuS7Veio,10,toilet flushing,train +7si_4wdoQAI,29,playing squash,train +7sigklQO3J4,7,zebra braying,train +7sitGun2gfk,0,church bell ringing,train +7sjPS_ru0Kw,30,typing on typewriter,test +7skxjiI46vk,64,playing steelpan,train +7skxjiI46vk,339,playing steelpan,train +7sljd98evM0,300,playing saxophone,train +7smSW2dZsYw,42,baby babbling,train +7snwIJmwKMs,20,frog croaking,train +7snwIJmwKMs,34,frog croaking,train +7sp6XEVXoJI,30,chicken crowing,train +7spdv5tLe6c,33,playing timbales,train +7spdv5tLe6c,69,playing timbales,train +7splzPZ1yRA,550,orchestra,train +7spslajA0hc,99,strike lighter,test +7sqHS0trrdg,30,playing banjo,train +7sqKizBX0Zk,20,fireworks banging,train +7ssJOOGuWbg,70,playing hammond organ,train +7stDKwYaPJo,0,"race car, auto racing",train +7swjv2fsVx8,550,playing bagpipes,train +7t-44vYH4hk,130,"race car, auto racing",train +7t2HZLZ1PQs,28,people clapping,test 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with cutlery,train +OvuYi3Ea56E,340,helicopter,train +Ovwr3IroFXo,5,police radio chatter,test +OvwtwT8q5ro,9,cat meowing,train +OvwtwT8q5ro,26,cat meowing,train +Ow1Rncezmm4,30,playing accordion,train +Ow1ZEhmP3qU,116,typing on typewriter,train +Ow3-rYY9QnU,139,hail,train +Ow3-rYY9QnU,150,hail,train +Ow4LFYQPggQ,70,playing bagpipes,train +Ow58ZIBBG-k,2,playing bassoon,train +Ow5DYhaWfiM,10,"railroad car, train wagon",train +Ow5LOFowgCg,170,orchestra,train +Ow5eY9twQZE,87,playing trombone,train +Ow68wFIwpIM,200,pig oinking,train +Ow6Axg4omII,5,playing harmonica,train +Ow6Axg4omII,172,playing harmonica,train +Ow6yTHmge-M,236,skateboarding,train +OwAUGABGrqk,30,people coughing,test +OwDHloaTC6Q,18,lions roaring,train +OwDJ6ZDKHRA,210,playing bass drum,train +OwDJ6ZDKHRA,228,playing bass drum,train +OwEBevISJCA,26,playing synthesizer,train +OwEBevISJCA,148,playing synthesizer,train +OwFtdf9w38U,38,tapping guitar,train +OwFtdf9w38U,59,tapping guitar,train +OwHJuvt02qg,126,playing vibraphone,train +OwHJuvt02qg,136,playing vibraphone,train +OwI31SvnQZg,17,bird squawking,train +OwI31SvnQZg,37,bird squawking,train +OwI9OZU2e68,207,opening or closing car doors,train +OwI9OZU2e68,250,opening or closing car doors,train +OwN9bm8syKc,48,chopping wood,train +OwPGw-P_jkQ,194,people cheering,train +OwRk45aJHJg,13,wind chime,train +OwRk45aJHJg,24,wind chime,train +OwS8f-KCof4,20,people belly laughing,train +OwTJWsAFGbM,180,orchestra,train +OwUXsddhkqA,362,playing bass drum,test +OwV9izKdbi4,30,orchestra,train +OwZCerPpio4,16,francolin calling,train +OwZCerPpio4,40,francolin calling,train +OwZDN1PUu8I,30,horse clip-clop,train +Ow_c5x0VaBI,124,black capped chickadee calling,test +Owait9dg14k,30,"subway, metro, underground",train +OwbF9baVlN4,264,hammering nails,test +Owc8mtMcS0s,120,playing vibraphone,train +Owc8mtMcS0s,150,playing vibraphone,train +Owd6y0r9tUE,3,dog howling,train +Owd6y0r9tUE,15,dog howling,train +Owg_XU9XmRM,30,driving buses,test +OwjrPjoK3_8,0,skiing,train +OwkOp5fVJK4,470,wind noise,test +OwkTp7rchnM,19,chicken crowing,train +Owl4N8tqzAE,30,people running,train +OwneapwDV9Q,150,"child speech, kid speaking",train +Owno_kzv8R4,30,cattle mooing,train +OwoNq892iYg,30,wind rustling leaves,train +Owp_rOHx4HA,75,"playing marimba, xylophone",train +Owqod5PUtmc,7,penguins braying,train +Owqod5PUtmc,29,penguins braying,train +OwtgahZgc3o,22,mouse pattering,train +OwwJ--gcLgs,73,civil defense siren,train +OwwJ--gcLgs,182,civil defense siren,train +Owx8zDoYMvI,1,elephant trumpeting,train +OwxFBUmR7Fo,200,dog barking,train +OwxqZBLA0RA,30,people running,train +Ox-UI9e1fj8,151,baby babbling,train +Ox-UI9e1fj8,175,baby babbling,train +Ox06RFTPG6A,162,playing squash,test +Ox06RFTPG6A,217,playing squash,test +Ox2JexqHWKg,440,playing bass drum,train +Ox2O7TbQ-WE,43,baby babbling,train +Ox2O7TbQ-WE,97,baby babbling,train +Ox32JwD3kxg,10,chainsawing trees,train +Ox48EwVTVZc,0,elephant trumpeting,train +OxCUcM2CCO4,173,singing bowl,train +OxD-VrOay2Y,53,playing tympani,train +OxDbBTIyeUw,500,"railroad car, train wagon",train +OxDlXq2Vncc,17,dog howling,train +OxEqaY0qDsE,170,playing clarinet,train +OxFIJVwKJJ0,39,civil defense siren,train +OxK9sbrtE_U,68,cap gun shooting,train +OxKk0WZey78,70,church bell ringing,train +OxM9cBdvewo,0,people clapping,train +OxPfSWirUtk,16,planing timber,test +OxPnZzn1_L8,859,scuba diving,train +OxPnZzn1_L8,883,scuba diving,train +OxRNAdZIE4c,170,orchestra,train +OxTDlXyo6WI,20,people farting,train +OxW7l-T6L4g,5,cat hissing,train +OxW7l-T6L4g,23,cat hissing,train +OxbHUMFOxnk,30,baby laughter,test +OxcmRG6j88I,10,playing electronic organ,train +Oxgn3WcgExo,10,snake hissing,train +OxhqeRAqtDo,634,cap gun shooting,train +OxiNmZHTGBg,0,"subway, metro, underground",train +OxoOSohmaag,139,airplane,train +OxpPJTGSCTE,2,cricket chirping,train +OxpPJTGSCTE,42,cricket chirping,train +Oxpj4hVJh3E,350,"bird chirping, tweeting",train +Oxq1DfD_C5E,348,airplane,train +Oxq_kAnEcsw,30,"motorboat, speedboat acceleration",train +OxqrPlqeBIc,20,strike lighter,train +Oxu7L3yqdn8,143,playing oboe,train +Oxuqcl2H2Qw,216,playing hammond organ,train +Oy-5zAtAmZE,21,magpie calling,train +Oy0ZU6T_Bo0,130,singing choir,train +Oy2a0_QY00M,3,lions roaring,train +Oy2a0_QY00M,77,lions roaring,train +Oy6dnnjI0o0,52,printer printing,train +Oy7G46NjNKU,280,fire truck siren,train +Oy8NtclRrGI,60,playing hammond organ,train +Oy8NtclRrGI,153,playing hammond organ,train +OyAZpXGZV80,51,playing tympani,train +OyKAIqUSRBA,340,turkey gobbling,train +OyKAIqUSRBA,437,turkey gobbling,train +OyKYOUI4Ttw,342,playing didgeridoo,train +OyLjVys4ba0,23,smoke detector beeping,train +OyLjVys4ba0,41,smoke detector beeping,train +OyPZgNpoq4Q,160,singing choir,train +OyQikgTc1l0,57,playing darts,train +OyRuzjdWGGI,30,church bell ringing,train +OyS0I9soWvA,59,people slurping,train +OyS0I9soWvA,161,people slurping,train +OyUeFDg5jCM,10,airplane,train +OyUeFDg5jCM,65,airplane,train +OyUhodfPRe8,32,smoke detector beeping,train +OyUl4gB7uwI,4,chicken crowing,train +OyWvAkX2lG0,4,arc welding,train +Oy_JlG7C-T8,180,male singing,train +Oy_ZI_Y51nY,60,singing choir,train +OybiXxxkQG8,151,playing theremin,train +Oybw24afAoY,100,typing on computer keyboard,train +Oyf2M0a-1EE,40,playing accordion,train +Oyf9oFcuMWY,100,sailing,train +Oyfht_iJE6Y,7,people shuffling,train +OygDilCU1_s,75,playing cello,train +OygLAdvcXTU,1,goat bleating,train +Oygdxx0HsXg,67,blowtorch igniting,train +OyhFlaEVvsg,0,fireworks banging,train +OyikEA05EhM,7,horse neighing,train +OyikEA05EhM,24,horse neighing,train +OylITjs652I,21,duck quacking,train +OymNWeioDDQ,6,"motorboat, speedboat acceleration",train +OymOmAUGcRQ,320,lawn mowing,train +OymrmlaZJfw,67,playing didgeridoo,train +OyoJ99jDQdo,97,playing didgeridoo,train +OyoJ99jDQdo,147,playing didgeridoo,train +OyoMr0zI3Hk,30,cap gun shooting,train +Oyq1SbbcZpY,30,playing french horn,train +OyqtK9YCrcU,30,playing acoustic guitar,train +Oys9b2H7kdc,184,playing bongo,train +OysV_xmKw7Y,30,people running,train +OyswnFZN_Dw,100,"bee, wasp, etc. buzzing",train +OytRYa6XVb0,55,swimming,train +OytRYa6XVb0,66,swimming,train +OytW_Wbwd7E,190,chainsawing trees,train +OywMoY5h7UY,10,turkey gobbling,train +OyyUDW68Ku4,329,playing table tennis,train +Oz-FAKdI4oM,30,playing harp,train +Oz62zf79G7g,40,helicopter,train +Oz6Wp-SdXag,30,sloshing water,train +Oz9RpapKTFQ,60,"child speech, kid speaking",train +OzCtkXTYng8,30,playing accordion,train +OzFmEvPQJFw,124,missile launch,train +OzGhWCj9K4Y,207,playing harp,train +OzH8vjux7Ck,132,people marching,train +OzH8vjux7Ck,214,people marching,train +OzI2LCaPIXc,30,"playing violin, fiddle",train +OzJcF-dP2uY,360,people crowd,train +OzMAwy8qSEo,30,people sobbing,test +OzMvtTlUwzM,133,playing glockenspiel,test +OzNXUQ5HVMU,22,fox barking,train +OzSlxd6d010,16,playing steelpan,train +OzSlxd6d010,29,playing steelpan,train +OzU99NcrLFU,60,church bell ringing,train +OzYQyy9__vw,30,people sobbing,train +Oz_0Lxd7AHA,175,playing table tennis,train +Oz_KKFTMJSg,152,playing squash,train +OzbwvIb4i_s,10,"pigeon, dove cooing",train +OzkXh76Ew84,89,people booing,train +OzlNC8yTBdQ,92,dog howling,train +OzlNC8yTBdQ,136,dog howling,train +OzlperRLFdg,20,chainsawing trees,train +Ozmif9BbcVQ,17,fire crackling,train +Ozmif9BbcVQ,38,fire crackling,train +OzqcfLSu7FY,10,"race car, auto racing",train +OzsLL5o71U4,436,arc welding,train +Ozt43eCJ6AM,260,ambulance siren,train +OztLVC4KBx8,8,playing squash,train +OzuW6btwFGQ,36,people burping,train +OzvDaM8huEM,273,playing tuning fork,test +OzwmmnFrkIM,30,horse neighing,train +Ozx7yHe0qPE,44,playing zither,train +Ozyg65hy7tA,59,lighting firecrackers,train +P--ofjiqyfY,124,singing bowl,train +P-0T2m_sMK0,0,"vehicle horn, car horn, honking",train +P-6QiY30HcA,30,"rowboat, canoe, kayak rowing",train +P-8YN5SNaRY,44,lathe spinning,train +P-8YN5SNaRY,57,lathe spinning,train +P-AHXNO92VY,15,dog barking,train +P-DxqtUY4W4,2,ripping paper,train +P-DxqtUY4W4,40,ripping paper,train +P-FjCcHJQxA,0,playing lacrosse,train +P-KszOT2TNw,64,barn swallow calling,test +P-Liu32cG0U,300,beat boxing,train +P-LlugNJwNI,0,canary calling,train +P-NCCepeHdI,125,"electric shaver, electric razor shaving",test +P-NyUCG4aLM,501,lip smacking,train +P-PBEtBHvn4,495,rapping,train +P-PI_KK3Su0,30,"playing violin, fiddle",train +P-PTDlSlZpE,210,people crowd,train +P-PogJ1fooo,45,playing tambourine,train +P-Py1i-Z7N4,688,forging swords,train +P-Py1i-Z7N4,822,forging swords,train +P-RNW9OIiig,13,playing steelpan,train +P-RNW9OIiig,38,playing steelpan,train +P-Rdpdcoops,30,"playing violin, fiddle",train +P-T1EydRDJg,73,airplane flyby,train +P-UZCWmbEBw,26,playing glockenspiel,train +P-UdUEoT93U,330,people clapping,train +P-Y42qs7YZw,36,police radio chatter,train +P-_3zRy5Iuw,40,singing choir,train +P-_zS7htxh0,150,opening or closing car electric windows,train +P-aVTL6CYAU,10,car passing by,train +P-aYw9FOPH8,30,playing harpsichord,train +P-cg5axYDZM,57,playing darts,train +P-dXj-WcGP4,0,penguins braying,train +P-dXj-WcGP4,10,penguins braying,train +P-e--E4_1QA,14,playing theremin,train +P-e--E4_1QA,102,playing theremin,train +P-eIuIMUREo,230,playing trumpet,train +P-h317k111o,182,people slurping,test +P-iJ2Avh5hM,30,playing piano,train +P-lWYHYzlno,12,driving snowmobile,train +P-lWYHYzlno,24,driving snowmobile,train +P-nqYgA6x0U,110,playing piano,train +P-spkBL14pc,3,people burping,train +P-td0H7XSOE,36,slot machine,train +P-twuisZlFs,76,missile launch,train +P-w1bgU08t4,86,playing didgeridoo,train +P-yKe9mOK5M,76,ripping paper,train +P-zsFUm_ixM,30,playing banjo,train +P01kLGPNt4M,30,horse neighing,train +P02Zdk3amjk,30,baby crying,test +P03cLOaAOlI,14,playing didgeridoo,train +P04gfMIYGbE,160,sloshing water,train +P05GOLFJRYU,44,frog croaking,train +P05JoC1gDU4,140,playing trombone,train +P06HmPH7zt0,200,eating with cutlery,train +P07nHWET1dw,180,playing saxophone,train +P08031HrefE,20,tractor digging,train +P08rPBL1q-M,200,playing electronic organ,train +P09qD5REYso,61,airplane,train +P09qD5REYso,73,airplane,train +P0CkYiXUsyY,13,pig oinking,train +P0CkYiXUsyY,55,pig oinking,train +P0ITOoLOfVc,50,dog bow-wow,train +P0Jgj2LbMsc,30,church bell ringing,train +P0MQxvXE_WA,94,"ice cream truck, ice cream van",train +P0NbfzAYpXg,2,playing volleyball,train +P0Nvk6Dk6Bw,30,wind noise,train +P0OPGC5tS4I,30,"engine accelerating, revving, vroom",train +P0Ow-qPK9Mc,35,child singing,train +P0Rc9VqEdPI,20,typing on computer keyboard,train +P0UAS0Jf5Yk,60,sailing,train +P0UhVax_hmw,50,playing bass guitar,train +P0XVxdo-Jgo,340,"railroad car, train wagon",train +P0aoguO_tvI,7,people whistling,train +P0cKBX763rg,40,playing banjo,train +P0cZ-o6-jc4,115,"electric shaver, electric razor shaving",train +P0cpKrMQaq4,604,playing bass guitar,train +P0iX1OyXMa0,30,singing choir,test +P0jSgSM2YLk,18,mynah bird singing,train +P0kVxo0NPEk,15,playing banjo,train +P0lFEEX9Ydo,3,black capped chickadee calling,train +P0npYmwQpHc,32,driving snowmobile,train +P0npYmwQpHc,88,driving snowmobile,train +P0oDp1u4z90,110,orchestra,train +P0r7dhoOKt0,3,driving snowmobile,train +P0r7dhoOKt0,23,driving snowmobile,train +P0s8bVYXAvg,100,cap gun shooting,train +P0uoGIuFUrA,290,driving buses,train +P0wLwOznlNE,150,playing flute,train +P0wMuHRbgss,20,people crowd,train +P0yBIweL0qk,25,owl hooting,train +P0yBIweL0qk,68,owl hooting,train +P0z_oDvC_Q4,54,missile launch,train +P13bN4xItZs,30,horse clip-clop,train +P15JhIEHVHg,30,dog barking,test +P18LE-xrfj8,300,playing hammond organ,train +P18VbGGgLcw,0,opening or closing car electric windows,test +P18gh1g6ZIo,8,children shouting,train +P1JceSCi7g0,57,"ice cream truck, ice cream van",train +P1K4r7iWTNo,18,dog barking,train +P1LygmOqewM,157,playing congas,test +P1NXEL4BgpM,7,playing congas,train +P1NXEL4BgpM,38,playing congas,train +P1QYQtMarTM,60,"playing marimba, xylophone",train +P1QoN7IyNiI,30,pumping water,test +P1SPYODXs1c,45,writing on blackboard with chalk,train +P1Sj8IORoHg,30,horse clip-clop,train +P1SwIuCQHyU,10,cap gun shooting,train +P1SwIuCQHyU,59,cap gun shooting,train +P1Wlv1SqBcQ,270,ambulance siren,train +P1Y5fIwe6PQ,160,people screaming,train +P1aVPodZ6Z8,0,ambulance siren,train +P1eMMIK0cTs,11,mynah bird singing,train +P1h5Qd47M9c,0,civil defense siren,train +P1irYSW_Avs,67,dog baying,train +P1irYSW_Avs,166,dog baying,train +P1l7YpIXYkw,409,tractor digging,train +P1o-SRI5WF8,350,playing trombone,train +P1rI1doF494,290,"bird chirping, tweeting",train +P1t9tm8jsAI,30,playing harp,train +P1wWdVltXX4,153,yodelling,train +P1xhDRxd6hQ,3,disc scratching,train +P23mNJiLi0Y,0,playing tennis,train +P23mNJiLi0Y,10,playing tennis,train +P28pGnbewHk,10,"race car, auto racing",train +P2BTNxhVAyA,44,playing timpani,train +P2FJBJOYkW4,30,waterfall burbling,train +P2GQ0Dyyrc4,266,yodelling,train +P2GQ0Dyyrc4,304,yodelling,train +P2GpecJsMqk,30,dog barking,train +P2KJkHonBzo,65,people sneezing,train +P2Qd8bsbW7s,30,"playing violin, fiddle",train +P2SQ_85uduY,0,people hiccup,train +P2YZfAa9y_o,14,opening or closing car doors,train +P2YZfAa9y_o,46,opening or closing car doors,train +P2YkB91Z-b4,8,playing bongo,train +P2YkB91Z-b4,72,playing bongo,train +P2_I5_GTb3w,140,"race car, auto racing",train +P2cC4rFXr_4,0,chainsawing trees,train +P2eiYFnaQQc,86,mynah bird singing,train +P2eiYFnaQQc,154,mynah bird singing,train +P2fW77cs5cE,60,woodpecker pecking tree,train +P2fW77cs5cE,78,woodpecker pecking tree,train +P2gDkcpYnMc,258,playing bagpipes,train +P2gDkcpYnMc,475,playing bagpipes,train +P2hXbLvUWFg,32,basketball bounce,train +P2hvc6v9MsI,21,horse clip-clop,train +P2j1SzMK7BY,27,canary calling,train +P2j1SzMK7BY,80,canary calling,train +P2ldhAT8KHc,280,"subway, metro, underground",test +P2m-uEOYgCI,0,"vehicle horn, car horn, honking",train +P2nwgFceY7g,215,playing lacrosse,train +P2ps6lzulYQ,20,people crowd,train +P2qidaMsfTo,149,slot machine,train +P2tWpPUHw6E,3,owl hooting,train +P2tWpPUHw6E,18,owl hooting,train +P2taxpwuzcw,53,wood thrush calling,train +P2uvDwUV9HM,138,skiing,train +P2uvDwUV9HM,149,skiing,train +P2wbv4C6bBA,210,barn swallow calling,train +P2wbv4C6bBA,353,barn swallow calling,train +P2zdsTYar8Q,147,bowling impact,train +P2zdsTYar8Q,170,bowling impact,train +P3-qLAC-wGY,30,playing cymbal,train +P32LqDKgG3E,154,people eating noodle,train +P32LqDKgG3E,231,people eating noodle,train +P35m_Rn7HbA,30,"motorboat, speedboat acceleration",train +P3ABOqvERSE,0,raining,train +P3CD6PPmazY,30,chicken clucking,train +P3Ch_zAdiPU,30,"race car, auto racing",train +P3HeNZ00Uic,20,playing bagpipes,train +P3KLZBOIJB4,30,playing harmonica,train +P3KLZBOIJB4,388,playing harmonica,train +P3MBQciFeHo,56,playing synthesizer,train +P3MBQciFeHo,79,playing synthesizer,train +P3OLqJxqmsg,30,"engine accelerating, revving, vroom",train +P3QhZuHbAUY,76,disc scratching,test +P3RGkgRORw4,50,"motorboat, speedboat acceleration",train +P3RtC5qUIt8,110,skidding,train +P3RtC5qUIt8,146,skidding,train +P3TTML7h27o,37,missile launch,train +P3WTmZQxdbg,40,orchestra,train +P3YZTglOM44,7,baby crying,train +P3ZF6sM5198,270,basketball bounce,train +P3ZXktbwKJI,146,skateboarding,train +P3ZXktbwKJI,328,skateboarding,train +P3ZsHcaaIz4,75,people shuffling,train +P3ZsHcaaIz4,124,people shuffling,train +P3bzwRoSojM,30,car engine knocking,train +P3f_S-v5cdQ,66,slot machine,train +P3fysMI_2uo,152,playing erhu,train +P3fysMI_2uo,203,playing erhu,train +P3hmig98GDY,334,dog howling,train +P3hmig98GDY,345,dog howling,train +P3lHm_yQ9Ts,1,lighting firecrackers,train +P3lHm_yQ9Ts,30,lighting firecrackers,train +P3leS31LAvk,110,helicopter,train +P3ml1mVRVSw,37,"ice cream truck, ice cream van",train +P3ml1mVRVSw,299,"ice cream truck, ice cream van",train +P3mo9ihwFFk,13,"playing steel guitar, slide guitar",train +P3mo9ihwFFk,70,"playing steel guitar, slide guitar",train +P3rUvGoV1Vo,57,woodpecker pecking tree,train +P3rUvGoV1Vo,72,woodpecker pecking tree,train +P3uhm1unuTI,40,skateboarding,train +P3weDyHnulk,0,people sobbing,train +P3xp8Sh2rao,17,dog growling,train +P3xp8Sh2rao,29,dog growling,train +P4-KeRBfTnI,0,cat hissing,train +P4-KeRBfTnI,31,cat hissing,train +P43XlunR7Ho,8,child singing,train +P43XlunR7Ho,79,child singing,train +P44C0PnC5XA,20,playing zither,train +P44C0PnC5XA,51,playing zither,train +P45RrDHv0_A,53,smoke detector beeping,train +P47dXlN_htA,170,singing choir,test +P47rLtTw5Mw,0,playing flute,test +P484hr4Ed98,30,playing cello,train +P48Um9RcZB4,40,playing synthesizer,train +P4DJZ3p0Sro,24,hammering nails,train +P4DJZ3p0Sro,35,hammering nails,train +P4GT8wykJoA,25,dog growling,train +P4GXI_oMi5A,40,cattle mooing,train +P4GYtGa6bKA,20,frog croaking,train +P4GYtGa6bKA,31,frog croaking,train +P4GgKyVg3vQ,0,train horning,train +P4HrE2PIOQg,320,people crowd,train +P4IYVzQTKZs,30,"motorboat, speedboat acceleration",train +P4KxPBgPCwo,230,playing piano,train +P4Lgf1cNKWE,30,people whispering,train +P4NezcLQtek,70,playing bass guitar,train +P4Nklfew6uo,44,people 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tambourine,train +TsSr6RecQOk,40,people screaming,train +TsTp99UxTHI,290,using sewing machines,train +TsUN5LCJSq4,380,skidding,train +TsUPben7NCw,269,people humming,test +TsUZf3ei7ms,1,dog howling,train +TsWy5u8dCtU,253,firing muskets,test +TsZ4WOu3Ed8,265,typing on typewriter,train +TsZ7X7YnuWI,260,people clapping,train +Ts_8GzI6XVE,70,playing flute,train +TsdDcbn-xGg,124,playing synthesizer,train +TsdDcbn-xGg,159,playing synthesizer,train +Tse5rzNV5dk,84,pheasant crowing,train +TsfFFUMRdvA,13,playing glockenspiel,train +TsfFFUMRdvA,23,playing glockenspiel,train +TshWo2g_oi0,3,coyote howling,train +TshWo2g_oi0,13,coyote howling,train +TsiFbuaAxv8,27,wood thrush calling,train +TsiFbuaAxv8,51,wood thrush calling,train +TsjUIjHu5Jg,127,"fly, housefly buzzing",train +TsjZRGyVjI8,57,people nose blowing,train +TsjykFfMnZA,80,singing choir,train +TslB1VMKXmM,10,toilet flushing,test +TsqbxwQLeEE,30,people farting,test +Tsql5Wv0TiE,93,firing cannon,train +TsrMWciG27E,50,playing saxophone,train 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trombone,train +a4lY2XOOs3E,210,chicken crowing,train +a4q2WyC9lfU,160,wind noise,train +a4slW5RmVEc,120,playing harp,train +a4tIKoN_wpg,196,volcano explosion,train +a4v-AEUHSDM,30,playing piano,train +a4z3zx-FtZU,0,frog croaking,test +a4zojDI3hso,60,playing clarinet,train +a5-VBFT8Kq0,149,cap gun shooting,train +a5-tk3n2cDM,112,people nose blowing,train +a52o2cVH13Y,42,tractor digging,train +a52o2cVH13Y,63,tractor digging,train +a56FTYDtDJU,68,foghorn,train +a56FoohBzTU,0,tractor digging,train +a56we7JBaTE,188,people slurping,train +a57DUeBMeHY,320,"rowboat, canoe, kayak rowing",train +a57mOEAAI3Q,89,crow cawing,train +a57mOEAAI3Q,126,crow cawing,train +a58CSTv4DiY,37,dog growling,train +a59ivTlYoNk,310,people screaming,test +a59j5ZuPRK4,30,raining,train +a5DbJZVJS9E,177,skiing,train +a5GFVMCVjv0,2,mynah bird singing,train +a5GwBjnRl7w,167,"electric shaver, electric razor shaving",train +a5GwBjnRl7w,189,"electric shaver, electric razor shaving",train +a5HTz_H1Phc,255,arc welding,train 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oriole calling,test +a_HzXa0YVIo,170,"rowboat, canoe, kayak rowing",test +a_IEy0PjSzw,0,basketball bounce,train +a_JxbHMx2pM,30,printer printing,train +a_LEqw0beNU,30,people whispering,train +a_R8QxsUSr4,144,blowtorch igniting,train +a_WQFseeFYA,15,cricket chirping,train +a_WQFseeFYA,74,cricket chirping,train +a_Wvjj6Yq1Y,2,crow cawing,train +a_Wvjj6Yq1Y,12,crow cawing,train +a_YY_qkoHz8,510,stream burbling,train +a__69686u5k,12,bird wings flapping,train +a__6TKTOqXE,257,vacuum cleaner cleaning floors,train +a__dMjObbJY,109,slot machine,train +a__wFAetAUE,350,"railroad car, train wagon",train +a_aHTNC78JY,55,people burping,train +a_dHJjMT2BE,1,"bee, wasp, etc. buzzing",train +a_e3HqyG1Lk,30,machine gun shooting,train +a_kgbg3eDAE,66,air horn,test +a_mvCDVzm9Q,0,"electric shaver, electric razor shaving",train +a_nUNftOssw,7,airplane flyby,train +a_q55lP1H30,30,playing hammond organ,train +a_rSPKmxjzI,16,"vehicle horn, car horn, honking",train +a_ruWnMK1MA,350,"bird chirping, tweeting",test +a_s54dmp6Xc,200,female singing,train +a_tTA5W6FAo,47,people cheering,test +a_tTA5W6FAo,176,people cheering,test +a_xNwy2iOVw,6,ice cracking,train +a_xNwy2iOVw,17,ice cracking,train +a_xgmS6wZKo,0,police car (siren),test +a_ygQh90O4A,46,playing didgeridoo,train +aa-UmbdFDMA,30,"bee, wasp, etc. buzzing",train +aa-eCqhz9wg,25,playing timbales,train +aa-eCqhz9wg,107,playing timbales,train +aa09xLmz7sY,23,cat growling,test +aa0Zd7yWzYc,140,playing double bass,train +aa0Zd7yWzYc,274,playing double bass,train +aa1ss4PNNgA,5,frog croaking,train +aa1ss4PNNgA,23,frog croaking,train +aa8M-f7zCEk,100,singing choir,train +aaA_khSU0BE,3,zebra braying,test +aaBj0_exx8w,11,playing volleyball,train +aaCT_bJ5LEY,10,"child speech, kid speaking",train +aaGL5l7WEZc,30,chicken crowing,train +aaGctbzDQ8w,30,police car (siren),train +aaKf6P2nhKg,146,singing choir,train +aaLy5b6Vhx0,0,playing cello,train +aaNETotuNTM,33,hammering nails,train +aaWs--ahZUg,10,playing electronic organ,test 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spinning,train +f39BramwHn8,155,driving snowmobile,train +f3AcdzOld1c,50,stream burbling,train +f3BBysLjR2c,30,singing choir,train +f3BW2XdLoug,30,people screaming,train +f3BcSKsPdmw,8,airplane flyby,train +f3CrVInFzV8,80,wind noise,train +f3ElXm80LcA,0,toilet flushing,train +f3HTG2uw6Bk,453,playing badminton,train +f3Hfqi3HMT4,5,playing tabla,train +f3Hfqi3HMT4,20,playing tabla,train +f3IbyMCo9FI,18,"bird chirping, tweeting",train +f3JufKV0Isw,40,dog barking,train +f3K0GkgpdFc,3,lions roaring,train +f3K0GkgpdFc,13,lions roaring,train +f3LDZSWCkJw,24,people booing,train +f3LDZSWCkJw,56,people booing,train +f3N6ZYQVWYk,10,toilet flushing,train +f3QFdun0Fx8,1,playing castanets,train +f3QFdun0Fx8,130,playing castanets,train +f3RUrGKWPTM,223,popping popcorn,train +f3RUrGKWPTM,234,popping popcorn,train +f3RfZTz-bMg,231,beat boxing,train +f3STEwlDRCI,150,playing bassoon,train +f3SjOePVhcY,10,playing cornet,train +f3SjOePVhcY,122,playing cornet,train +f3Txvl4icig,60,playing banjo,train 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+gAt9uga34JU,92,playing double bass,train +gAvItzCOkMY,8,playing steelpan,train +gAwW-RgVY0Y,429,volcano explosion,train +gAzwbdKn7yM,34,"playing steel guitar, slide guitar",train +gAzwbdKn7yM,96,"playing steel guitar, slide guitar",train +gB1A5kjmS-8,157,people booing,train +gB1ytjgpcW4,190,people screaming,test +gB2yiGeq3wc,230,"playing steel guitar, slide guitar",train +gB3auqvOCjY,0,people nose blowing,train +gB40-dkgnv4,0,train horning,train +gB4Gy5uGDbc,30,people whistling,train +gB6NNcREmYI,0,gibbon howling,train +gB6NNcREmYI,35,gibbon howling,train +gB6lvmxCYLs,48,hail,train +gB7VqiX0Ne4,23,playing volleyball,train +gB7VqiX0Ne4,33,playing volleyball,train +gB9Q8ZVepHw,28,"fly, housefly buzzing",train +gBA1zC5duuI,814,car engine knocking,train +gBA1zC5duuI,824,car engine knocking,train +gBDUlEJghY0,18,snake rattling,train +gBHlKMGENc0,551,people slurping,test +gBIw-oZ6Uts,32,fire truck siren,train +gBIw-oZ6Uts,43,fire truck siren,train +gBOEuwO2X5s,560,playing tabla,train +gBOEuwO2X5s,746,playing tabla,train +gBOntZUHXso,40,toilet flushing,train +gBRTGtmHZW0,154,slot machine,test +gBRVkpc3mFU,200,basketball bounce,train +gBS0CXtNxz4,31,playing bassoon,train +gBS0CXtNxz4,78,playing bassoon,train +gBT66InYFCk,29,tapping guitar,train +gBT66InYFCk,39,tapping guitar,train +gBTRCsSRjco,180,people babbling,train +gBTVT4OzDWA,127,children shouting,train +gBaLysocwAQ,40,"race car, auto racing",train +gBafT_v33j0,81,elk bugling,train +gBajexwsbnk,20,playing electric guitar,train +gBbgK1MNsNQ,1,cap gun shooting,train +gBdKtoFKcoY,533,playing tuning fork,train +gBe-c8f6pmg,130,lawn mowing,train +gBgJPgvYwYY,285,chimpanzee pant-hooting,train +gBgJPgvYwYY,463,chimpanzee pant-hooting,train +gBj-aAmtt7k,50,ocean burbling,train +gBq3ufewetU,405,writing on blackboard with chalk,test +gBq3ufewetU,420,writing on blackboard with chalk,test +gBqgrR6njyo,58,beat boxing,train +gBrH_Typhyk,0,playing ukulele,train +gBrH_Typhyk,130,playing ukulele,train +gBuJmLxuu2E,70,driving motorcycle,train +gBwr-cmTPQg,10,lighting firecrackers,train +gC3cb64cNHc,39,horse clip-clop,train +gC3cb64cNHc,68,horse clip-clop,train +gC3hk2tGIWk,10,splashing water,train +gC7yCsD01yw,10,people babbling,train +gC8aNavOG-k,30,printer printing,train +gCAWIBbcGJA,243,playing washboard,test +gCBhR4DKtgw,137,playing erhu,train +gCCTuOrZVVo,505,golf driving,train +gCCX4MmxwBU,180,playing cymbal,train +gCDE-NYsLvI,13,tractor digging,train +gCHF0oBPZv8,30,dog howling,test +gCHKHIIgjqw,30,"pigeon, dove cooing",train +gCI4upmqTg8,30,people coughing,train +gCK7NDxS27k,359,playing badminton,train +gCKNQ3atskU,2,electric grinder grinding,train +gCLf0M8GwBc,270,playing drum kit,train +gCLlDmbJJrY,111,"heart sounds, heartbeat",train +gCLlDmbJJrY,125,"heart sounds, heartbeat",train +gCLxi0RGm50,30,"playing violin, fiddle",train +gCMq_-yD-sw,39,yodelling,train +gCMq_-yD-sw,54,yodelling,train +gCNbDqm6RrU,130,playing banjo,train +gCOdtHm79t8,39,dog howling,train +gCOdtHm79t8,51,dog howling,train +gCOqV3nUhuw,60,chicken crowing,train +gCQR65p_cq4,328,opening or closing car doors,train +gCQTXVc_PTw,0,lawn mowing,train +gCQppdHwesk,90,playing banjo,train +gCUukPPFqiE,4,playing mandolin,train +gCWegnS7-FY,42,wood thrush calling,train +gCWegnS7-FY,76,wood thrush calling,train +gCXfsIJOYZk,10,machine gun shooting,train +gCYap0uxGjE,10,people coughing,train +gCaAvFqyM94,48,playing erhu,train +gCarMOahTUY,30,goose honking,train +gCc9LmOWyfY,30,duck quacking,train +gCcwf1oDROA,10,machine gun shooting,train +gCeLu9XJ4VI,2,mynah bird singing,train +gCeLu9XJ4VI,43,mynah bird singing,train +gCicuH3P0NA,30,car passing by,train +gCioQBZ3RcI,98,sharpen knife,train +gCioQBZ3RcI,134,sharpen knife,train +gCj883rRFyc,15,gibbon howling,train +gCj883rRFyc,36,gibbon howling,train +gCmHyn3VJOE,102,playing didgeridoo,train +gCmNo9OjgVQ,50,playing acoustic guitar,train +gCnFYqTa5hQ,10,people sobbing,train +gCnOTL_Fzik,210,ambulance siren,test +gCs1kiIAk48,20,driving buses,train 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chirping,train +gDUCEXZJstY,91,warbler chirping,train +gDWEAoafB6o,53,tap dancing,train +gDY9SYlotgg,3,crow cawing,train +gDY9SYlotgg,20,crow cawing,train +gDYYx2PFemo,213,planing timber,train +gD_CxArX6ag,39,yodelling,train +gD_CxArX6ag,85,yodelling,train +gD_TstMYPqE,729,playing french horn,train +gDapZrtS6u8,30,ocean burbling,train +gDbAsjfLLd4,30,car engine knocking,train +gDdegTUpmkQ,210,people screaming,train +gDe8MgOVOyc,170,using sewing machines,train +gDfHzkEq9qU,2,chimpanzee pant-hooting,train +gDfWtMJK920,20,fireworks banging,train +gDfj4eERZ6Q,97,playing mandolin,train +gDfj4eERZ6Q,200,playing mandolin,train +gDgtrwJSxsU,30,driving motorcycle,train +gDhYvCVbhVc,261,playing mandolin,train +gDil6lZPCWw,340,skidding,train +gDkvNaJdWlw,147,lathe spinning,train +gDoHnlSEgcY,27,car passing by,train +gDqW8uAxhgI,100,"heart sounds, heartbeat",train +gDrPz-GTZPk,140,people sniggering,train +gDrtRjDl3w8,10,alarm clock ringing,train +gDsaZgdHs8U,187,chopping wood,train 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+gEqYfPYuy8Q,80,"heart sounds, heartbeat",train +gEqYfPYuy8Q,98,"heart sounds, heartbeat",train +gEripxXVCG8,30,people sniggering,train +gEsdzPXzi7M,130,printer printing,train +gEvCUcZ6w88,30,"female speech, woman speaking",test +gExbIfncEPo,439,scuba diving,train +gEzFAugOlJY,30,cattle mooing,train +gF--wRKoeyU,64,playing mandolin,train +gF--wRKoeyU,128,playing mandolin,train +gF1n6By3_6M,30,dog bow-wow,train +gF5gfblw13c,370,people babbling,test +gF6v83G_AoY,139,elk bugling,train +gF6v83G_AoY,164,elk bugling,train +gF86lgLHXds,17,sloshing water,train +gF9cNURWWk0,338,skiing,train +gFCGMIN1r98,124,people finger snapping,train +gFCGMIN1r98,134,people finger snapping,train +gFCGhjL3vIg,210,chicken clucking,train +gFF3syhBrwc,240,"race car, auto racing",train +gFJjYWXeBn0,30,skidding,test +gFKcMLGNBPY,30,"playing marimba, xylophone",train +gFMsjMRVjYI,13,frog croaking,train +gFOOUbMXsqc,28,chimpanzee pant-hooting,test +gFQRmNXrPoE,40,playing banjo,train +gFQX-V22DNA,3,warbler 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steelpan,train +ga1CtWQDNl4,77,people whispering,train +ga1CtWQDNl4,142,people whispering,train +ga4MO5Uy1fI,20,police car (siren),train +ga6Q5sJLaqU,30,people burping,test +ga85NRvXBik,120,people cheering,train +ga8Z1OLHh1s,12,playing banjo,train +gaBtVS48Ujc,56,playing bassoon,train +gaD2sjpnKUA,0,"engine accelerating, revving, vroom",train +gaFQgJLQHtU,90,car engine starting,test +gaFtxq1hBU4,118,spraying water,train +gaHt8mT4u9U,30,canary calling,test +gaJESUlzB78,30,fire crackling,train +gaKAsgONFLk,20,gibbon howling,train +gaKAsgONFLk,46,gibbon howling,train +gaN61Yfd7_I,280,sliding door,train +gaUFRJAvG4s,30,people sobbing,train +gaWlrce1R1w,48,roller coaster running,train +gaWlrce1R1w,86,roller coaster running,train +gaZ7NCQwmlE,0,playing piano,test +ga_ONeH9MH4,20,"child speech, kid speaking",train +gaaVQc_Y1PE,30,people sniggering,train +gabqew5znZQ,48,elephant trumpeting,train +gabqew5znZQ,206,elephant trumpeting,train +gadAMBP4qzc,0,ambulance siren,train 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car (siren),train +gpa40Y_n6v0,17,civil defense siren,train +gpaX15tTUoc,17,cat growling,train +gpaX15tTUoc,29,cat growling,train +gpbsvGVCADo,54,barn swallow calling,train +gpcQFdOITZg,0,people giggling,train +gpcQFdOITZg,17,people giggling,train +gpe4kNlRcmE,288,bouncing on trampoline,train +gpeTYBH009Y,0,cat caterwauling,train +gpeTYBH009Y,10,cat caterwauling,train +gpgfbakmmfU,0,playing bagpipes,train +gpj1VLZuBeQ,50,people crowd,train +gpnzui_JNhc,10,playing piano,train +gppFnhPec2c,369,people belly laughing,train +gppFnhPec2c,380,people belly laughing,train +gppjlpVwNvA,357,cheetah chirrup,train +gppjlpVwNvA,737,cheetah chirrup,train +gpqLr-ACHJk,37,playing erhu,train +gpqerD-h7hI,176,arc welding,train +gpqerD-h7hI,197,arc welding,train +gpsXKzlirxg,197,yodelling,train +gpsXKzlirxg,267,yodelling,train +gpv2JV8uyOQ,10,dog barking,train +gpvYlHv1Ra4,300,rapping,test +gpy0tsYP8SY,30,people sobbing,train +gpyUJCmt3vE,363,playing trumpet,test +gq0fjAAZJ1s,0,ocean burbling,train 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or closing drawers,train +iczSNXRUaJs,344,playing bassoon,train +iczSNXRUaJs,404,playing bassoon,train +id-GVH4Hcig,540,people clapping,train +id-RLDdMNd0,60,helicopter,train +id-fOnv0ZM4,30,playing accordion,train +id1QK90fbQ8,80,people clapping,train +id5Xv18xiew,120,"heart sounds, heartbeat",train +id5Xv18xiew,160,"heart sounds, heartbeat",train +id5_X_3oTNw,358,playing saxophone,train +id5_X_3oTNw,373,playing saxophone,train +id6eBTnmO6A,38,playing squash,train +id7Kq8CEYdI,0,church bell ringing,train +id7Kq8CEYdI,10,church bell ringing,train +id7schBC4ng,86,black capped chickadee calling,train +idBqPBHeVnM,237,playing banjo,train +idFx2X2L8BU,230,orchestra,train +idJsQ3F071k,140,playing sitar,test +idKNZgediIQ,257,bowling impact,train +idMhqYA4zKA,160,"playing marimba, xylophone",train +idOdL52LXaA,68,people shuffling,train +idPIRRspMp0,121,playing castanets,test +idQtpYxaKAU,70,car engine starting,train +idRWFJDmnSM,83,typing on typewriter,train +idRWFJDmnSM,98,typing on 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trumpet,train +mSry2_MS06w,130,singing choir,train +mSs54Nglr3U,120,playing tabla,train +mSs54Nglr3U,144,playing tabla,train +mSu1h_SGY_4,0,airplane,train +mSw6IeEqUTY,137,warbler chirping,train +mSz0rZGISbg,30,typing on computer keyboard,train +mT0yFesnzCI,200,"female speech, woman speaking",train +mT27OLeYxpc,5,driving buses,train +mT3IKxlLgEc,190,lighting firecrackers,test +mT3mgIGFk-U,63,playing badminton,train +mT49XQjXEvY,0,chicken crowing,train +mT4Vf3z91eo,164,writing on blackboard with chalk,train +mT4Vf3z91eo,392,writing on blackboard with chalk,train +mT4pfrwWaco,0,people farting,test +mT52ciRH9TA,30,car passing by,train +mT6AEdJh7n4,30,"vehicle horn, car horn, honking",train +mT8C3Dw1ccA,39,airplane flyby,train +mT9pDdAoEDk,30,"engine accelerating, revving, vroom",train +mT9y2yX7ZcI,2,civil defense siren,train +mT9y2yX7ZcI,25,civil defense siren,train +mTAmrFpd-zI,120,playing drum kit,train +mTB5jB-kWZM,140,driving buses,train +mTBPT6LmWXQ,0,dog barking,train 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volleyball,train +mUtztENAXKQ,0,playing harmonica,train +mUtztENAXKQ,282,playing harmonica,train +mUxg--2I7q8,2,snake rattling,train +mUxg--2I7q8,13,snake rattling,train +mUyT-IulZ0Y,1,cat caterwauling,train +mUyT-IulZ0Y,65,cat caterwauling,train +mV-pApFX8wY,52,playing tabla,train +mV5N5M1W-58,0,playing tennis,train +mV5N5M1W-58,26,playing tennis,train +mV5V64TWo6E,350,people eating crisps,train +mV5V64TWo6E,430,people eating crisps,train +mV6CDb5HSjI,220,people crowd,train +mV6lkoB3wRw,40,playing vibraphone,train +mV6lkoB3wRw,74,playing vibraphone,train +mV6rvt43rVE,0,playing harmonica,train +mV6rvt43rVE,1,playing harmonica,train +mV7_YzKUVMk,20,bird wings flapping,train +mV8UjOGRhO8,70,playing cymbal,train +mV9NcBMm0aM,885,fire crackling,train +mVABxsfz6UU,1,playing harp,train +mVC9zKS04sM,216,writing on blackboard with chalk,train +mVC9zKS04sM,846,writing on blackboard with chalk,train +mVDyq8Po5XQ,0,basketball bounce,train +mVEI2UME1KI,5,"vehicle horn, car horn, honking",train 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with cutlery,test +reL3Z_L-aiI,17,people eating apple,train +reL3Z_L-aiI,44,people eating apple,train +rePKoQIas5A,4,footsteps on snow,train +reRWw5U1q1E,340,"pigeon, dove cooing",train +reT0H5jgJwM,30,"playing marimba, xylophone",train +reYn1x-pjPE,2,playing table tennis,train +rebe1_T70Yk,20,people whistling,train +recUGAKDR_Y,0,wind rustling leaves,train +redEh50eYgI,20,dog barking,train +redSkbpiRMk,30,chainsawing trees,train +rehnP12cwPw,180,playing drum kit,train +rejNymw01x0,8,fire truck siren,train +rejNymw01x0,39,fire truck siren,train +rel-ED3jLuQ,28,"heart sounds, heartbeat",train +rel-ED3jLuQ,42,"heart sounds, heartbeat",train +remdYhwXGto,20,playing flute,train +remx9Z5rB5Q,100,people hiccup,train +reomA65b50k,30,driving motorcycle,train +res8Qtx-MHE,20,lawn mowing,test +rew84kd5ae0,50,"playing marimba, xylophone",train +reyRKYkQoOU,60,playing harp,train +rf-JaPg1XKo,116,dinosaurs bellowing,train +rf-JaPg1XKo,279,dinosaurs bellowing,train +rf1SWdlbdKo,366,vacuum cleaner 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guitar",test +rfc8EQW5cCk,30,driving buses,train +rff4CsMVHIc,37,"playing steel guitar, slide guitar",train +rff4CsMVHIc,48,"playing steel guitar, slide guitar",train +rffMu_Y9rSY,480,mouse pattering,test +rffkcgs0CS8,12,forging swords,train +rfhn0IkUCRI,23,frog croaking,train +rflXOdLx4t8,70,plastic bottle crushing,test +rfm9037fz6c,40,"railroad car, train wagon",train +rfnyfe1ALGQ,13,bull bellowing,test +rfovRlzg-G0,25,baby laughter,train +rfovRlzg-G0,109,baby laughter,train +rfpK3H5e0S4,2,people belly laughing,train +rfpK3H5e0S4,31,people belly laughing,train +rfqqBv3eriU,160,stream burbling,train +rfs4lznalxk,91,singing choir,train +rfs4lznalxk,261,singing choir,train +rfseWEiUnt0,14,frog croaking,train +rfuLtsr0ZgE,50,skateboarding,train +rfxXEgc9RTQ,28,playing squash,train +rfxXEgc9RTQ,43,playing squash,train +rfyH8ubYgvo,37,playing djembe,train +rg-EtIocbDQ,26,cutting hair with electric trimmers,train +rg-EtIocbDQ,44,cutting hair with electric trimmers,train +rg08MRgL218,21,coyote howling,train +rg08MRgL218,96,coyote howling,train +rg2bj9Ujxsc,80,"bee, wasp, etc. buzzing",train +rg4bhfytF9A,22,playing tympani,test +rg4bhfytF9A,39,playing tympani,test +rg5pXC8AceA,75,playing cornet,train +rg5xxeuPWO4,124,francolin calling,train +rg8nrtY4SQs,160,people sniggering,train +rg9mB3YCXfc,1,magpie calling,train +rg9mB3YCXfc,12,magpie calling,train +rgD9XkJYhu8,23,lathe spinning,train +rgDmvF1fzzs,130,cat purring,train +rgDmvF1fzzs,450,cat purring,train +rgE_z_8ik2c,30,playing electric guitar,train +rgEcbJDfmVg,62,playing zither,train +rgEcbJDfmVg,83,playing zither,train +rgHc4ESdvxo,10,stream burbling,train +rgLTbf7aQXA,30,playing french horn,train +rgLrgrZduC8,4,goose honking,train +rgMlDlJlPq4,10,playing flute,train +rgNYqdIHcs8,30,people whistling,train +rgO1IPDIQoo,162,playing hammond organ,train +rgOIebYfI6Y,35,cat caterwauling,train +rgOIebYfI6Y,139,cat caterwauling,train +rgUNUUP1Ta0,4,goose honking,train +rgUQMMSduMQ,127,scuba diving,train +rgUQMMSduMQ,204,scuba diving,train +rg_wul_gOJs,86,playing vibraphone,train +rg_wul_gOJs,118,playing vibraphone,train +rgcCI4DzzXc,18,shot football,train +rgdMDo5TBic,355,playing squash,train +rgeNP5nGQek,200,dog barking,train +rgfAABfBT7Y,23,"alligators, crocodiles hissing",train +rgfAABfBT7Y,51,"alligators, crocodiles hissing",train +rgfOWw8Rxq4,24,swimming,train +rgfOWw8Rxq4,34,swimming,train +rghkZKnVzaI,113,vacuum cleaner cleaning floors,train +rgji6mjO0zI,190,playing badminton,train +rglibN33aks,12,opening or closing car electric windows,test +rgoYcuInIfo,30,playing cymbal,train +rgpDhy0Nwc0,14,"bird chirping, tweeting",train +rgqJFceVM2I,66,chicken clucking,test +rgqtGsKA_Js,0,"bird chirping, tweeting",train +rgrNIBA0yx4,30,playing electronic organ,train +rgtrTjn74yo,4,dinosaurs bellowing,train +rgtrTjn74yo,38,dinosaurs bellowing,train +rgwNz-tnGBQ,0,toilet flushing,train +rgx-gISdsyg,70,lawn mowing,train +rgznBn8QmNY,10,"subway, metro, underground",train +rh42gVF7geg,23,ripping paper,train +rh42gVF7geg,103,ripping paper,train +rh7OV4m_shI,30,playing accordion,train +rh7nDHH08qg,0,people sobbing,train +rh9-F99SW1k,32,bowling impact,train +rh9-F99SW1k,60,bowling impact,train +rh9KDzNkpSI,772,playing hammond organ,train +rh9SmhazI5k,3,baltimore oriole calling,train +rhAwhUVsWRo,5,goat bleating,test +rhBb41L3PYs,287,tap dancing,train +rhBb41L3PYs,437,tap dancing,train +rhC0pxeuKWc,470,people running,train +rhFKHaA40Y4,3,popping popcorn,train +rhGSqHtz_C4,30,chainsawing trees,train +rhGvg4po4kw,353,planing timber,train +rhGvg4po4kw,376,planing timber,train +rhHezkpxU7o,0,tapping guitar,train +rhHezkpxU7o,45,tapping guitar,train +rhHyb4sL0hY,37,opening or closing drawers,train +rhHyb4sL0hY,81,opening or closing drawers,train +rhK5JBYHrWU,9,bowling impact,train +rhMpRPQlwmY,30,firing cannon,test +rhQwPnEYWI4,30,people sniggering,test +rhRrboWtlH4,30,baby crying,train +rhSH8KnqkBQ,450,car passing by,train +rhT8kzRAEEM,117,playing washboard,test +rhTjyTLYdYk,120,playing clarinet,train +rhUccyfn8po,19,playing bassoon,train +rhW_qFnBst8,230,hair dryer drying,train +rhW_qFnBst8,490,hair dryer drying,train +rhWrIKdn8ic,116,people whispering,test +rh_rFJsOmis,340,singing bowl,test +rhgBTv9bDQk,30,"pigeon, dove cooing",test +rhivN_ULK4c,123,playing ukulele,train +rhivN_ULK4c,178,playing ukulele,train +rhnT1UeL8BI,12,planing timber,test +rho50JrK1I0,310,"bee, wasp, etc. buzzing",train +rho50JrK1I0,400,"bee, wasp, etc. buzzing",train +rhrNqhaH6jc,30,lions growling,train +ri1vy6MjKpo,90,"race car, auto racing",train +ri2DVH0Q-so,40,basketball bounce,train +ri2fX8XU0Tc,444,sharpen knife,train +ri2fX8XU0Tc,462,sharpen knife,train +ri3f1UNKUJU,84,airplane,train +ri3yJEs3HrI,232,playing french horn,train +ri62f10f4x8,134,typing on typewriter,train +ri6F7zosOPg,49,penguins braying,train +ri70pmgQ4b8,40,"race car, auto racing",train +ri7I6QWJreM,20,driving motorcycle,train +ri7S5Qncubg,3,chicken crowing,train +ri7S5Qncubg,65,chicken crowing,train +ri7_zyidR1I,0,bird squawking,train +riA0tk1i86g,290,"railroad car, train wagon",train +riAJa6PUM4E,4,"vehicle horn, car horn, honking",train +riAUfYw25t0,110,wind noise,train +riBduOWWoJk,227,lip smacking,train +riBduOWWoJk,324,lip smacking,train +riHWid1NZas,219,playing bassoon,train +riI4CyB18mU,10,ambulance siren,train +riIE2xqnHyI,80,people sobbing,train +riJD63Xfmio,360,playing didgeridoo,train +riJD63Xfmio,465,playing didgeridoo,train +riJs7DlphEM,4,goose honking,train +riJs7DlphEM,15,goose honking,train +riLxPF82qhA,11,planing timber,test +riMI767O89Q,128,hedge trimmer running,train +riMI767O89Q,143,hedge trimmer running,train +riOBvxQsGbk,30,sliding door,train +riT0l1tLOGA,88,hair dryer drying,train +riT0l1tLOGA,572,hair dryer drying,train +riZWOToUZ18,30,playing cornet,train +riZWOToUZ18,56,playing cornet,train +ricVfprV0Lo,30,playing trombone,train +rie7C5444pM,57,playing double bass,train +riguAWoUU6c,9,playing glockenspiel,train +rihIlsk3KRI,0,skidding,train +riku-enFqus,26,mosquito buzzing,train +rilbNPyjMDI,45,people farting,train +rilbNPyjMDI,168,people farting,train +rilqUvykJzQ,294,lighting firecrackers,train +rin612CdVI4,334,turkey gobbling,train +riokFEd3nZY,10,chainsawing trees,train +rip8xwobksc,314,striking pool,train +rip8xwobksc,396,striking pool,train +riwgcn0LVJQ,496,people eating crisps,train +riznQ-3rjts,60,chicken crowing,train +rj1FsDTd1Eg,40,people babbling,train +rj505jDI4nQ,253,"heart sounds, heartbeat",train +rj505jDI4nQ,319,"heart sounds, heartbeat",train +rj5DZMMg3eQ,192,tornado roaring,test +rj8Qex3xn0M,40,playing trumpet,train +rj936-vl15o,160,driving motorcycle,train +rj9HIP9lpbA,340,helicopter,train +rjCbMgN5dFc,110,"subway, metro, underground",train +rjG5Mcl7Ji0,10,machine gun shooting,train +rjGGEUXj_0I,205,people nose blowing,train +rjGjz5DolWA,30,dog barking,test +rjMg2k1tJss,388,people slurping,train +rjPUfa5T_oQ,381,people slurping,train +rjPUfa5T_oQ,409,people slurping,train +rjPoX4KQ5dw,22,frog croaking,train +rjQLHQktyPg,227,playing hammond organ,train +rjQLHQktyPg,260,playing hammond organ,train +rjR_613gDsw,50,driving motorcycle,train +rjT8KXyEsjg,130,playing flute,train +rjUI97LT0SM,20,duck quacking,train +rjUI97LT0SM,32,duck quacking,train +rjV0GXup6pY,330,people whispering,train +rjVMwYGoSbc,0,playing bongo,train +rjVrsuAwovc,5,civil defense siren,train +rjXgj_5KP-c,179,hair dryer drying,train +rjYARQtW5jg,79,cat meowing,train +rjYARQtW5jg,124,cat meowing,train +rjYPJUBcoN8,33,"donkey, ass braying",test +rjYPJUBcoN8,50,"donkey, ass braying",test +rjYn3wnWpiU,124,parrot talking,train +rjYn3wnWpiU,143,parrot talking,train +rjZNtMmr5jY,170,"railroad car, train wagon",train +rj_TSXwlhGQ,30,"motorboat, speedboat acceleration",train +rjeObu4jfOc,19,"donkey, ass braying",train +rjeObu4jfOc,33,"donkey, ass braying",train +rjednQ1tay8,40,car engine knocking,train +rjfkh7BB_K8,13,firing cannon,train +rjhsLNnKX3k,27,playing hammond organ,train +rjl2sJx8RuQ,0,playing glockenspiel,train +rjl2sJx8RuQ,59,playing glockenspiel,train +rjleL6j-Ny8,174,child singing,train +rjm7BvZELvA,2,people whistling,train +rjm_phhaTqg,12,cat meowing,train +rjnO9Ua8fEY,56,playing snare drum,train +rjnhTp1MvBQ,55,running electric fan,train +rjnhTp1MvBQ,66,running electric fan,train +rjqN_oB89pc,380,playing drum kit,train +rjued58Yqpc,109,air horn,test +rjxeShUI_g8,18,playing harpsichord,train +rjxeShUI_g8,64,playing harpsichord,train +rjzHrx_1xmA,130,playing synthesizer,train +rjzmutRaKDo,49,cricket chirping,train +rjzmutRaKDo,98,cricket chirping,train +rk-HJLn1zLA,11,hail,train +rk-h78dVltc,298,planing timber,train +rk1CY76YNyE,290,playing banjo,train +rk7JDvfIAq8,80,eating with cutlery,train +rk9b_1SDi_A,30,"subway, metro, underground",train +rkCEG_9wYHs,210,"child speech, kid speaking",train +rkCJGVIDJZs,310,people whistling,train +rkEXoDLhz0g,130,helicopter,train +rkI21qU8YV0,2,cheetah chirrup,train +rkI21qU8YV0,16,cheetah chirrup,train +rkLEY5-eveE,30,"pigeon, dove cooing",train +rkMa9cC-zDQ,22,playing guiro,test +rkMgvtv4G2s,80,lawn mowing,train +rkO-YO6NGMM,65,tapping guitar,train +rkO-YO6NGMM,83,tapping guitar,train +rkO7RJMz_fw,43,missile launch,train +rkOSeGg9n08,20,ocean burbling,train +rkP0LyzfYiU,74,fire crackling,train +rkP0LyzfYiU,146,fire crackling,train +rkQr7lgIjpY,179,alarm clock ringing,test +rkS7ve7KCig,0,reversing beeps,train +rkSgAacF3EA,59,wood thrush calling,train +rkVbucSEReo,110,sloshing water,train +rkWmicYDrqM,30,helicopter,train +rk_iOIumShQ,17,car engine knocking,train +rk_iOIumShQ,31,car engine knocking,train +rk_qLtk0m2c,103,child singing,train +rk_tfiTnSDw,105,playing harp,train +rkaM1yOncZ4,0,baby crying,test +rkeazz3aMQs,0,ferret dooking,train +rkghf6x-X_Y,12,skidding,train +rki4wHdj3e4,130,playing banjo,train +rknMqCdmKic,30,goose honking,train +rkolACeUZMg,61,baby laughter,train +rkolACeUZMg,161,baby laughter,train +rkomdEYl9qA,460,"subway, metro, underground",train +rkt-udQn2e0,0,"race car, auto racing",train +rktJa04Bfr4,370,"subway, metro, underground",train +rkw6PdThaxQ,30,police car (siren),train +rkxPaXs2Pn8,24,chicken crowing,train +rky6lT6pkaw,0,scuba diving,train +rkyOp-eEwEw,4,cat hissing,train +rkz3exeN6nE,30,horse neighing,train +rkzCm0aODlE,220,playing trombone,train +rl1PnyPqVG0,9,"bird chirping, tweeting",train +rl1ytzZOHug,179,coyote howling,train +rl1ytzZOHug,199,coyote howling,train +rl5LXtrqAtw,80,toilet flushing,train +rl5zJlrEjRg,0,air horn,train +rl9YHu9ta20,180,chainsawing trees,train +rlCBvkm3fWM,30,"rowboat, canoe, kayak rowing",train +rlCetbDzOfE,61,people marching,test +rlDK_wTtQps,11,sailing,train +rlKVXk7znpQ,38,playing steelpan,train +rlKVXk7znpQ,103,playing steelpan,train +rlKflLrxDeA,60,"subway, metro, underground",train +rlLVaef0_80,0,"playing steel guitar, slide guitar",train +rlNDdIdLpIo,520,playing electronic organ,train +rlPm_5WwWLk,30,playing harp,train +rlTL8b0ik0U,18,tornado roaring,train +rlTL8b0ik0U,28,tornado roaring,train +rlTLzCghEA8,68,skidding,train +rlTwy8rxUFs,450,"race car, auto racing",train +rlW8-TNarIw,120,playing saxophone,train +rlXQ-sxZwfs,30,"playing marimba, xylophone",train +rlaK50nlNRY,959,swimming,train +rlaaSo9EOJs,30,sloshing water,train +rlba2Vs65Cw,8,owl hooting,train +rlba2Vs65Cw,49,owl hooting,train +rlrO8gHIAEY,1,playing snare drum,train +rlrO8gHIAEY,11,playing snare drum,train +rlv5Jk0z5V4,30,"playing marimba, xylophone",train +rlxmevufILI,40,playing harmonica,test +rm06AkLvGmA,41,wood thrush calling,train +rm06AkLvGmA,68,wood thrush calling,train +rm5jhCIEI34,240,orchestra,train +rm6kd2LO8FQ,71,playing washboard,train +rm7yHnusPRI,10,"engine accelerating, revving, vroom",train +rm9Bwksyl_A,30,chicken crowing,train +rmB71qaXQHk,30,"motorboat, speedboat acceleration",train +rmBGiKhRLkE,250,printer printing,train +rmDJpgs3GaU,30,"pigeon, dove cooing",train +rmDOGLbVhTU,0,skateboarding,train +rmI5PeG9tSY,30,"playing violin, fiddle",test +rmK-nmkmQ7U,40,wind rustling leaves,train +rmL21Z7wZuk,20,blowtorch igniting,test +rmPfJgDBeQ8,20,stream burbling,test +rmQ5d7Q-Dy0,149,playing timbales,train +rmQ5d7Q-Dy0,208,playing timbales,train +rmQhPCwioJw,0,dog howling,test +rmSx_mvP60o,2,dog howling,train +rmSx_mvP60o,12,dog howling,train +rmV41ZaJ6PI,30,driving buses,train +rmdf2QeOUJM,48,train whistling,train +rmdf2QeOUJM,142,train whistling,train +rmfebxg-k2Y,40,people crowd,train +rmgSQx8oUt4,6,zebra braying,train +rmgSQx8oUt4,57,zebra braying,train +rmmDnHNevbE,0,playing didgeridoo,train +rmmDnHNevbE,27,playing didgeridoo,train +rmmH7ktQlSo,0,playing bass guitar,train +rmpD3GHh-Gw,240,skateboarding,train +rmpkTEpF38s,210,playing clarinet,train +rmtJy83QmOM,30,fireworks banging,train +rmu5YzaPM2g,0,playing squash,train +rmwhCL-rJ_w,13,goose honking,train +rmxRhByMBMU,30,car engine knocking,test +rmyjT-6mWwk,170,playing flute,test +rmzDww6glq8,200,playing electronic organ,test +rmzYDIM8xsM,28,woodpecker pecking tree,test +rn2MzWR8H_8,240,splashing water,train +rn381TUMxyE,286,arc welding,train +rn381TUMxyE,298,arc welding,train +rn77tAch-WE,448,people shuffling,train +rn9MQhLs1zc,190,playing trombone,train +rnAR_z3mw48,132,people nose blowing,test +rnFbBItICj4,30,wind noise,train +rnFu9UrmEDw,30,playing piano,train +rnHYhyCbcWw,0,playing sitar,train +rnJHuIZuevA,11,barn swallow calling,train +rnJHuIZuevA,39,barn swallow calling,train +rnKrSnPxBGc,317,tap dancing,train +rnQV6Kfeaoc,30,"playing violin, fiddle",train +rnRwoghjuUQ,100,basketball bounce,train +rnSEFgXPzfA,0,"race car, auto racing",train +rnSSF1DaXww,420,cap gun shooting,test +rnTU7MMSAOs,56,tractor digging,train +rnUMYBWOMMQ,34,playing tambourine,train +rnUMYBWOMMQ,121,playing tambourine,train +rnUcQpH4418,490,playing tabla,train +rnUiX7njOV4,0,sailing,train +rnV-AEJIvuY,2,cricket chirping,train +rnV-AEJIvuY,14,cricket chirping,train +rnWPIQBf54c,30,"cattle, bovinae cowbell",test +rn_cDJfkxSo,60,"female speech, woman speaking",train 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+zw7dTh-Lx3o_4000_14000 +zw7dTh-Lx3o_74000_84000 +zwe4CuGYfcE_501000_511000 +zwe4CuGYfcE_94000_104000 +zwm5SCU7hTU_14000_24000 +zwm5SCU7hTU_97000_107000 +zwmDR91EkY8_219000_229000 +zwmDR91EkY8_22000_32000 +zxFx5zG3U94_30000_40000 +zxGlC2IUgy0_20000_30000 +zxId45CV2e4_300000_310000 +zxR0XDlKt_8_0_10000 +zxUbjNFrgWg_131000_141000 +zxlZbpGNJPI_30000_40000 +zyzEV89uLOc_13000_23000 +zz9HCdnTIec_161000_171000 +zzVYJYUPNG8_30000_40000 +zzc_C1D8iqQ_1000_11000 +zzc_C1D8iqQ_28000_38000 +zzmWVOQ2iIE_535000_545000 +zzrK3Q6rDu4_52000_62000 +zzrK3Q6rDu4_8000_18000 +zzsUhaDSqzI_115000_125000 +zzsUhaDSqzI_145000_155000 diff --git a/ldm/modules/losses_audio/vggishish/dataset.py b/ldm/modules/losses_audio/vggishish/dataset.py new file mode 100644 index 0000000000000000000000000000000000000000..c049ef047e209b0488b73ec9ae283bf425b5abe8 --- /dev/null +++ b/ldm/modules/losses_audio/vggishish/dataset.py @@ -0,0 +1,147 @@ +import collections +import csv +import logging +import os +import random +from glob import glob +from pathlib import Path + +import numpy as np +import torch +import torchvision + +logger = logging.getLogger(f'main.{__name__}') + + +class VGGSound(torch.utils.data.Dataset): + + def __init__(self, split, specs_dir, transforms=None, splits_path='./data', meta_path='./data/vggsound.csv'): + super().__init__() + self.split = split + self.specs_dir = specs_dir + self.transforms = transforms + self.splits_path = splits_path + self.meta_path = meta_path + + vggsound_meta = list(csv.reader(open(meta_path), quotechar='"')) + unique_classes = sorted(list(set(row[2] for row in vggsound_meta))) + self.label2target = {label: target for target, label in enumerate(unique_classes)} + self.target2label = {target: label for label, target in self.label2target.items()} + self.video2target = {row[0]: self.label2target[row[2]] for row in vggsound_meta} + + split_clip_ids_path = os.path.join(splits_path, f'vggsound_{split}.txt') + if not os.path.exists(split_clip_ids_path): + self.make_split_files() + clip_ids_with_timestamp = open(split_clip_ids_path).read().splitlines() + clip_paths = [os.path.join(specs_dir, v + '_mel.npy') for v in clip_ids_with_timestamp] + self.dataset = clip_paths + # self.dataset = clip_paths[:10000] # overfit one batch + + # 'zyTX_1BXKDE_16000_26000'[:11] -> 'zyTX_1BXKDE' + vid_classes = [self.video2target[Path(path).stem[:11]] for path in self.dataset] + class2count = collections.Counter(vid_classes) + self.class_counts = torch.tensor([class2count[cls] for cls in range(len(class2count))]) + + # self.sample_weights = [len(self.dataset) / class2count[self.video2target[Path(path).stem[:11]]] for path in self.dataset] + + def __getitem__(self, idx): + item = {} + + spec_path = self.dataset[idx] + # 'zyTX_1BXKDE_16000_26000' -> 'zyTX_1BXKDE' + video_name = Path(spec_path).stem[:11] + + item['input'] = np.load(spec_path) + item['input_path'] = spec_path + + # if self.split in ['train', 'valid']: + item['target'] = self.video2target[video_name] + item['label'] = self.target2label[item['target']] + + if self.transforms is not None: + item = self.transforms(item) + + return item + + def __len__(self): + return len(self.dataset) + + def make_split_files(self): + random.seed(1337) + logger.info(f'The split files do not exist @ {self.splits_path}. Calculating the new ones.') + # The downloaded videos (some went missing on YouTube and no longer available) + available_vid_paths = sorted(glob(os.path.join(self.specs_dir, '*_mel.npy'))) + logger.info(f'The number of clips available after download: {len(available_vid_paths)}') + + # original (full) train and test sets + vggsound_meta = list(csv.reader(open(self.meta_path), quotechar='"')) + train_vids = {row[0] for row in vggsound_meta if row[3] == 'train'} + test_vids = {row[0] for row in vggsound_meta if row[3] == 'test'} + logger.info(f'The number of videos in vggsound train set: {len(train_vids)}') + logger.info(f'The number of videos in vggsound test set: {len(test_vids)}') + + # class counts in test set. We would like to have the same distribution in valid + unique_classes = sorted(list(set(row[2] for row in vggsound_meta))) + label2target = {label: target for target, label in enumerate(unique_classes)} + video2target = {row[0]: label2target[row[2]] for row in vggsound_meta} + test_vid_classes = [video2target[vid] for vid in test_vids] + test_target2count = collections.Counter(test_vid_classes) + + # now given the counts from test set, sample the same count for validation and the rest leave in train + train_vids_wo_valid, valid_vids = set(), set() + for target, label in enumerate(label2target.keys()): + class_train_vids = [vid for vid in train_vids if video2target[vid] == target] + random.shuffle(class_train_vids) + count = test_target2count[target] + valid_vids.update(class_train_vids[:count]) + train_vids_wo_valid.update(class_train_vids[count:]) + + # make file with a list of available test videos (each video should contain timestamps as well) + train_i = valid_i = test_i = 0 + with open(os.path.join(self.splits_path, 'vggsound_train.txt'), 'w') as train_file, \ + open(os.path.join(self.splits_path, 'vggsound_valid.txt'), 'w') as valid_file, \ + open(os.path.join(self.splits_path, 'vggsound_test.txt'), 'w') as test_file: + for path in available_vid_paths: + path = path.replace('_mel.npy', '') + vid_name = Path(path).name + # 'zyTX_1BXKDE_16000_26000'[:11] -> 'zyTX_1BXKDE' + if vid_name[:11] in train_vids_wo_valid: + train_file.write(vid_name + '\n') + train_i += 1 + elif vid_name[:11] in valid_vids: + valid_file.write(vid_name + '\n') + valid_i += 1 + elif vid_name[:11] in test_vids: + test_file.write(vid_name + '\n') + test_i += 1 + else: + raise Exception(f'Clip {vid_name} is neither in train, valid nor test. Strange.') + + logger.info(f'Put {train_i} clips to the train set and saved it to ./data/vggsound_train.txt') + logger.info(f'Put {valid_i} clips to the valid set and saved it to ./data/vggsound_valid.txt') + logger.info(f'Put {test_i} clips to the test set and saved it to ./data/vggsound_test.txt') + + +if __name__ == '__main__': + from transforms import Crop, StandardNormalizeAudio, ToTensor + specs_path = '/home/nvme/data/vggsound/features/melspec_10s_22050hz/' + + transforms = torchvision.transforms.transforms.Compose([ + StandardNormalizeAudio(specs_path), + ToTensor(), + Crop([80, 848]), + ]) + + datasets = { + 'train': VGGSound('train', specs_path, transforms), + 'valid': VGGSound('valid', specs_path, transforms), + 'test': VGGSound('test', specs_path, transforms), + } + + print(datasets['train'][0]) + print(datasets['valid'][0]) + print(datasets['test'][0]) + + print(datasets['train'].class_counts) + print(datasets['valid'].class_counts) + print(datasets['test'].class_counts) diff --git a/ldm/modules/losses_audio/vggishish/logger.py b/ldm/modules/losses_audio/vggishish/logger.py new file mode 100644 index 0000000000000000000000000000000000000000..c9737cc165654ce51bb2204636ce78f34acd0a9e --- /dev/null +++ b/ldm/modules/losses_audio/vggishish/logger.py @@ -0,0 +1,87 @@ +import logging +import os +import time +from shutil import copytree, ignore_patterns + +import torch +from omegaconf import OmegaConf +from torch.utils.tensorboard import SummaryWriter, summary + + +class LoggerWithTBoard(SummaryWriter): + + def __init__(self, cfg): + # current time stamp and experiment log directory + self.start_time = time.strftime('%y-%m-%dT%H-%M-%S', time.localtime()) + self.logdir = os.path.join(cfg.logdir, self.start_time) + # init tboard + super().__init__(self.logdir) + # backup the cfg + OmegaConf.save(cfg, os.path.join(self.log_dir, 'cfg.yaml')) + # backup the code state + if cfg.log_code_state: + dest_dir = os.path.join(self.logdir, 'code') + copytree(os.getcwd(), dest_dir, ignore=ignore_patterns(*cfg.patterns_to_ignore)) + + # init logger which handles printing and logging mostly same things to the log file + self.print_logger = logging.getLogger('main') + self.print_logger.setLevel(logging.INFO) + msgfmt = '[%(levelname)s] %(asctime)s - %(name)s \n %(message)s' + datefmt = '%d %b %Y %H:%M:%S' + formatter = logging.Formatter(msgfmt, datefmt) + # stdout + sh = logging.StreamHandler() + sh.setLevel(logging.DEBUG) + sh.setFormatter(formatter) + self.print_logger.addHandler(sh) + # log file + fh = logging.FileHandler(os.path.join(self.log_dir, 'log.txt')) + fh.setLevel(logging.INFO) + fh.setFormatter(formatter) + self.print_logger.addHandler(fh) + + self.print_logger.info(f'Saving logs and checkpoints @ {self.logdir}') + + def log_param_num(self, model): + param_num = sum(p.numel() for p in model.parameters() if p.requires_grad) + self.print_logger.info(f'The number of parameters: {param_num/1e+6:.3f} mil') + self.add_scalar('num_params', param_num, 0) + return param_num + + def log_iter_loss(self, loss, iter, phase): + self.add_scalar(f'{phase}/loss_iter', loss, iter) + + def log_epoch_loss(self, loss, epoch, phase): + self.add_scalar(f'{phase}/loss', loss, epoch) + self.print_logger.info(f'{phase} ({epoch}): loss {loss:.3f};') + + def log_epoch_metrics(self, metrics_dict, epoch, phase): + for metric, val in metrics_dict.items(): + self.add_scalar(f'{phase}/{metric}', val, epoch) + metrics_dict = {k: round(v, 4) for k, v in metrics_dict.items()} + self.print_logger.info(f'{phase} ({epoch}) metrics: {metrics_dict};') + + def log_test_metrics(self, metrics_dict, hparams_dict, best_epoch): + allowed_types = (int, float, str, bool, torch.Tensor) + hparams_dict = {k: v for k, v in hparams_dict.items() if isinstance(v, allowed_types)} + metrics_dict = {f'test/{k}': round(v, 4) for k, v in metrics_dict.items()} + exp, ssi, sei = summary.hparams(hparams_dict, metrics_dict) + self.file_writer.add_summary(exp) + self.file_writer.add_summary(ssi) + self.file_writer.add_summary(sei) + for k, v in metrics_dict.items(): + self.add_scalar(k, v, best_epoch) + self.print_logger.info(f'test ({best_epoch}) metrics: {metrics_dict};') + + def log_best_model(self, model, loss, epoch, optimizer, metrics_dict): + model_name = model.__class__.__name__ + self.best_model_path = os.path.join(self.logdir, f'{model_name}-{self.start_time}.pt') + checkpoint = { + 'loss': loss, + 'metrics': metrics_dict, + 'epoch': epoch, + 'optimizer': optimizer.state_dict(), + 'model': model.state_dict(), + } + torch.save(checkpoint, self.best_model_path) + self.print_logger.info(f'Saved model in {self.best_model_path}') diff --git a/ldm/modules/losses_audio/vggishish/loss.py b/ldm/modules/losses_audio/vggishish/loss.py new file mode 100644 index 0000000000000000000000000000000000000000..bae76571909eec571aaf075d58e3dea8f6424546 --- /dev/null +++ b/ldm/modules/losses_audio/vggishish/loss.py @@ -0,0 +1,41 @@ +import torch +import torch.nn as nn +import torch.nn.functional as F +import torch.optim as optim + +class WeightedCrossEntropy(nn.CrossEntropyLoss): + + def __init__(self, weights, **pytorch_ce_loss_args) -> None: + super().__init__(reduction='none', **pytorch_ce_loss_args) + self.weights = weights + + def __call__(self, outputs, targets, to_weight=True): + loss = super().__call__(outputs, targets) + if to_weight: + return (loss * self.weights[targets]).sum() / self.weights[targets].sum() + else: + return loss.mean() + + +if __name__ == '__main__': + x = torch.randn(10, 5) + target = torch.randint(0, 5, (10,)) + weights = torch.tensor([1., 2., 3., 4., 5.]) + + # criterion_weighted = nn.CrossEntropyLoss(weight=weights) + # loss_weighted = criterion_weighted(x, target) + + # criterion_weighted_manual = nn.CrossEntropyLoss(reduction='none') + # loss_weighted_manual = criterion_weighted_manual(x, target) + # print(loss_weighted, loss_weighted_manual.mean()) + # loss_weighted_manual = (loss_weighted_manual * weights[target]).sum() / weights[target].sum() + # print(loss_weighted, loss_weighted_manual) + # print(torch.allclose(loss_weighted, loss_weighted_manual)) + + pytorch_weighted = nn.CrossEntropyLoss(weight=weights) + pytorch_unweighted = nn.CrossEntropyLoss() + custom = WeightedCrossEntropy(weights) + + assert torch.allclose(pytorch_weighted(x, target), custom(x, target, to_weight=True)) + assert torch.allclose(pytorch_unweighted(x, target), custom(x, target, to_weight=False)) + print(custom(x, target, to_weight=True), custom(x, target, to_weight=False)) diff --git a/ldm/modules/losses_audio/vggishish/metrics.py b/ldm/modules/losses_audio/vggishish/metrics.py new file mode 100644 index 0000000000000000000000000000000000000000..16905224c665491b9869d7641c1fe17689816a4b --- /dev/null +++ b/ldm/modules/losses_audio/vggishish/metrics.py @@ -0,0 +1,69 @@ +import logging + +import numpy as np +import scipy +import torch +from sklearn.metrics import average_precision_score, roc_auc_score + +logger = logging.getLogger(f'main.{__name__}') + +def metrics(targets, outputs, topk=(1, 5)): + """ + Adapted from https://github.com/hche11/VGGSound/blob/master/utils.py + + Calculate statistics including mAP, AUC, and d-prime. + Args: + output: 2d tensors, (dataset_size, classes_num) - before softmax + target: 1d tensors, (dataset_size, ) + topk: tuple + Returns: + metric_dict: a dict of metrics + """ + metrics_dict = dict() + + num_cls = outputs.shape[-1] + + # accuracy@k + _, preds = torch.topk(outputs, k=max(topk), dim=1) + correct_for_maxtopk = preds == targets.view(-1, 1).expand_as(preds) + for k in topk: + metrics_dict[f'accuracy_{k}'] = float(correct_for_maxtopk[:, :k].sum() / correct_for_maxtopk.shape[0]) + + # avg precision, average roc_auc, and dprime + targets = torch.nn.functional.one_hot(targets, num_classes=num_cls) + + # ids of the predicted classes (same as softmax) + targets_pred = torch.softmax(outputs, dim=1) + + targets = targets.numpy() + targets_pred = targets_pred.numpy() + + # one-vs-rest + avg_p = [average_precision_score(targets[:, c], targets_pred[:, c], average=None) for c in range(num_cls)] + try: + roc_aucs = [roc_auc_score(targets[:, c], targets_pred[:, c], average=None) for c in range(num_cls)] + except ValueError: + logger.warning('Weird... Some classes never occured in targets. Do not trust the metrics.') + roc_aucs = np.array([0.5]) + avg_p = np.array([0]) + + metrics_dict['mAP'] = np.mean(avg_p) + metrics_dict['mROCAUC'] = np.mean(roc_aucs) + # Percent point function (ppf) (inverse of cdf — percentiles). + metrics_dict['dprime'] = scipy.stats.norm().ppf(metrics_dict['mROCAUC']) * np.sqrt(2) + + return metrics_dict + + +if __name__ == '__main__': + targets = torch.tensor([3, 3, 1, 2, 1, 0]) + outputs = torch.tensor([ + [1.2, 1.3, 1.1, 1.5], + [1.3, 1.4, 1.0, 1.1], + [1.5, 1.1, 1.4, 1.3], + [1.0, 1.2, 1.4, 1.5], + [1.2, 1.3, 1.1, 1.1], + [1.2, 1.1, 1.1, 1.1], + ]).float() + metrics_dict = metrics(targets, outputs, topk=(1, 3)) + print(metrics_dict) diff --git a/ldm/modules/losses_audio/vggishish/model.py b/ldm/modules/losses_audio/vggishish/model.py new file mode 100644 index 0000000000000000000000000000000000000000..d5069bad0d9311e6e2c082a63eca165f7a908675 --- /dev/null +++ b/ldm/modules/losses_audio/vggishish/model.py @@ -0,0 +1,77 @@ +import torch +import torch.nn as nn + + +class VGGishish(nn.Module): + + def __init__(self, conv_layers, use_bn, num_classes): + ''' + Mostly from + https://pytorch.org/vision/0.8/_modules/torchvision/models/vgg.html + ''' + super().__init__() + layers = [] + in_channels = 1 + + # a list of channels with 'MP' (maxpool) from config + for v in conv_layers: + if v == 'MP': + layers += [nn.MaxPool2d(kernel_size=2, stride=2)] + else: + conv2d = nn.Conv2d(in_channels, v, kernel_size=3, padding=1, stride=1) + if use_bn: + layers += [conv2d, nn.BatchNorm2d(v), nn.ReLU(inplace=True)] + else: + layers += [conv2d, nn.ReLU(inplace=True)] + in_channels = v + self.features = nn.Sequential(*layers) + + self.avgpool = nn.AdaptiveAvgPool2d((5, 10)) + + self.flatten = nn.Flatten() + self.classifier = nn.Sequential( + nn.Linear(512 * 5 * 10, 4096), + nn.ReLU(True), + nn.Linear(4096, 4096), + nn.ReLU(True), + nn.Linear(4096, num_classes) + ) + + # weight init + self.reset_parameters() + + def forward(self, x): + # adding channel dim for conv2d (B, 1, F, T) <- + x = x.unsqueeze(1) + # backbone (B, 1, 5, 53) <- (B, 1, 80, 860) + x = self.features(x) + # adaptive avg pooling (B, 1, 5, 10) <- (B, 1, 5, 53) – if no MP is used as the end of VGG + x = self.avgpool(x) + # flatten + x = self.flatten(x) + # classify + x = self.classifier(x) + return x + + def reset_parameters(self): + for m in self.modules(): + if isinstance(m, nn.Conv2d): + nn.init.kaiming_normal_(m.weight, mode='fan_out', nonlinearity='relu') + if m.bias is not None: + nn.init.constant_(m.bias, 0) + elif isinstance(m, nn.BatchNorm2d): + nn.init.constant_(m.weight, 1) + nn.init.constant_(m.bias, 0) + elif isinstance(m, nn.Linear): + nn.init.normal_(m.weight, 0, 0.01) + nn.init.constant_(m.bias, 0) + + +if __name__ == '__main__': + num_classes = 309 + inputs = torch.rand(3, 80, 848) + conv_layers = [64, 64, 'MP', 128, 128, 'MP', 256, 256, 256, 'MP', 512, 512, 512, 'MP', 512, 512, 512] + # conv_layers = [64, 'MP', 128, 'MP', 256, 256, 'MP', 512, 512, 'MP'] + model = VGGishish(conv_layers, use_bn=False, num_classes=num_classes) + outputs = model(inputs) + print(outputs.shape) diff --git a/ldm/modules/losses_audio/vggishish/predict.py b/ldm/modules/losses_audio/vggishish/predict.py new file mode 100644 index 0000000000000000000000000000000000000000..e9d13f30153cd43a4a8bcfe2da4b9a53846bf1eb --- /dev/null +++ b/ldm/modules/losses_audio/vggishish/predict.py @@ -0,0 +1,90 @@ +import os +from torch.utils.data import DataLoader +import torchvision +from tqdm import tqdm +from dataset import VGGSound +import torch +import torch.nn as nn +from metrics import metrics +from omegaconf import OmegaConf +from model import VGGishish +from transforms import Crop, StandardNormalizeAudio, ToTensor + + +if __name__ == '__main__': + cfg_cli = OmegaConf.from_cli() + print(cfg_cli.config) + cfg_yml = OmegaConf.load(cfg_cli.config) + # the latter arguments are prioritized + cfg = OmegaConf.merge(cfg_yml, cfg_cli) + OmegaConf.set_readonly(cfg, True) + print(OmegaConf.to_yaml(cfg)) + + # logger = LoggerWithTBoard(cfg) + transforms = [ + StandardNormalizeAudio(cfg.mels_path), + ToTensor(), + ] + if cfg.cropped_size not in [None, 'None', 'none']: + transforms.append(Crop(cfg.cropped_size)) + transforms = torchvision.transforms.transforms.Compose(transforms) + + datasets = { + 'test': VGGSound('test', cfg.mels_path, transforms), + } + + loaders = { + 'test': DataLoader(datasets['test'], batch_size=cfg.batch_size, + num_workers=cfg.num_workers, pin_memory=True) + } + + device = torch.device(cfg.device if torch.cuda.is_available() else 'cpu') + model = VGGishish(cfg.conv_layers, cfg.use_bn, num_classes=len(datasets['test'].target2label)) + model = model.to(device) + + optimizer = torch.optim.Adam(model.parameters(), lr=cfg.learning_rate) + criterion = nn.CrossEntropyLoss() + + # loading the best model + folder_name = os.path.split(cfg.config)[0].split('/')[-1] + print(folder_name) + ckpt = torch.load(f'./logs/{folder_name}/vggishish-{folder_name}.pt', map_location='cpu') + model.load_state_dict(ckpt['model']) + print((f'The model was trained for {ckpt["epoch"]} epochs. Loss: {ckpt["loss"]:.4f}')) + + # Testing the model + model.eval() + running_loss = 0 + preds_from_each_batch = [] + targets_from_each_batch = [] + + for i, batch in enumerate(tqdm(loaders['test'])): + inputs = batch['input'].to(device) + targets = batch['target'].to(device) + + # zero the parameter gradients + optimizer.zero_grad() + + # forward + backward + optimize + with torch.set_grad_enabled(False): + outputs = model(inputs) + loss = criterion(outputs, targets) + + # loss + running_loss += loss.item() + + # for metrics calculation later on + preds_from_each_batch += [outputs.detach().cpu()] + targets_from_each_batch += [targets.cpu()] + + # logging metrics + preds_from_each_batch = torch.cat(preds_from_each_batch) + targets_from_each_batch = torch.cat(targets_from_each_batch) + test_metrics_dict = metrics(targets_from_each_batch, preds_from_each_batch) + test_metrics_dict['avg_loss'] = running_loss / len(loaders['test']) + test_metrics_dict['param_num'] = sum(p.numel() for p in model.parameters() if p.requires_grad) + + # TODO: I have no idea why tboard doesn't keep metrics (hparams) in a tensorboard when + # I run this experiment from cli: `python main.py config=./configs/vggish.yaml` + # while when I run it in vscode debugger the metrics are present in the tboard (weird) + print(test_metrics_dict) diff --git a/ldm/modules/losses_audio/vggishish/train_melception.py b/ldm/modules/losses_audio/vggishish/train_melception.py new file mode 100644 index 0000000000000000000000000000000000000000..8adc5aa6e0e32a66cdbb7b449483a3b23d9b0ef9 --- /dev/null +++ b/ldm/modules/losses_audio/vggishish/train_melception.py @@ -0,0 +1,241 @@ +import random + +import numpy as np +import torch +import torchvision +from omegaconf import OmegaConf +from torch.utils.data.dataloader import DataLoader +from torchvision.models.inception import BasicConv2d, Inception3 +from tqdm import tqdm + +from dataset import VGGSound +from logger import LoggerWithTBoard +from loss import WeightedCrossEntropy +from metrics import metrics +from transforms import Crop, StandardNormalizeAudio, ToTensor + + +# TODO: refactor ./evaluation/feature_extractors/melception.py to handle this class as well. +# So far couldn't do it because of the difference in outputs +class Melception(Inception3): + + def __init__(self, num_classes, **kwargs): + # inception = Melception(num_classes=309) + super().__init__(num_classes=num_classes, **kwargs) + # the same as https://github.com/pytorch/vision/blob/5339e63148/torchvision/models/inception.py#L95 + # but for 1-channel input instead of RGB. + self.Conv2d_1a_3x3 = BasicConv2d(1, 32, kernel_size=3, stride=2) + # also the 'hight' of the mel spec is 80 (vs 299 in RGB) we remove all max pool from Inception + self.maxpool1 = torch.nn.Identity() + self.maxpool2 = torch.nn.Identity() + + def forward(self, x): + x = x.unsqueeze(1) + return super().forward(x) + +def train_inception_scorer(cfg): + logger = LoggerWithTBoard(cfg) + + random.seed(cfg.seed) + np.random.seed(cfg.seed) + torch.manual_seed(cfg.seed) + torch.cuda.manual_seed_all(cfg.seed) + # makes iterations faster (in this case 30%) if your inputs are of a fixed size + # https://discuss.pytorch.org/t/what-does-torch-backends-cudnn-benchmark-do/5936/3 + torch.backends.cudnn.benchmark = True + + meta_path = './data/vggsound.csv' + train_ids_path = './data/vggsound_train.txt' + cache_path = './data/' + splits_path = cache_path + + transforms = [ + StandardNormalizeAudio(cfg.mels_path, train_ids_path, cache_path), + ] + if cfg.cropped_size not in [None, 'None', 'none']: + logger.print_logger.info(f'Using cropping {cfg.cropped_size}') + transforms.append(Crop(cfg.cropped_size)) + transforms.append(ToTensor()) + transforms = torchvision.transforms.transforms.Compose(transforms) + + datasets = { + 'train': VGGSound('train', cfg.mels_path, transforms, splits_path, meta_path), + 'valid': VGGSound('valid', cfg.mels_path, transforms, splits_path, meta_path), + 'test': VGGSound('test', cfg.mels_path, transforms, splits_path, meta_path), + } + + loaders = { + 'train': DataLoader(datasets['train'], batch_size=cfg.batch_size, shuffle=True, drop_last=True, + num_workers=cfg.num_workers, pin_memory=True), + 'valid': DataLoader(datasets['valid'], batch_size=cfg.batch_size, + num_workers=cfg.num_workers, pin_memory=True), + 'test': DataLoader(datasets['test'], batch_size=cfg.batch_size, + num_workers=cfg.num_workers, pin_memory=True), + } + + device = torch.device(cfg.device if torch.cuda.is_available() else 'cpu') + + model = Melception(num_classes=len(datasets['train'].target2label)) + model = model.to(device) + param_num = logger.log_param_num(model) + + if cfg.optimizer == 'adam': + optimizer = torch.optim.Adam( + model.parameters(), lr=cfg.learning_rate, betas=cfg.betas, weight_decay=cfg.weight_decay) + elif cfg.optimizer == 'sgd': + optimizer = torch.optim.SGD( + model.parameters(), lr=cfg.learning_rate, momentum=cfg.momentum, weight_decay=cfg.weight_decay) + else: + raise NotImplementedError + + if cfg.cls_weights_in_loss: + weights = 1 / datasets['train'].class_counts + else: + weights = torch.ones(len(datasets['train'].target2label)) + criterion = WeightedCrossEntropy(weights.to(device)) + + # loop over the train and validation multiple times (typical PT boilerplate) + no_change_epochs = 0 + best_valid_loss = float('inf') + early_stop_triggered = False + + for epoch in range(cfg.num_epochs): + + for phase in ['train', 'valid']: + if phase == 'train': + model.train() + else: + model.eval() + + running_loss = 0 + preds_from_each_batch = [] + targets_from_each_batch = [] + + prog_bar = tqdm(loaders[phase], f'{phase} ({epoch})', ncols=0) + for i, batch in enumerate(prog_bar): + inputs = batch['input'].to(device) + targets = batch['target'].to(device) + + # zero the parameter gradients + optimizer.zero_grad() + + # forward + backward + optimize + with torch.set_grad_enabled(phase == 'train'): + # inception v3 + if phase == 'train': + outputs, aux_outputs = model(inputs) + loss1 = criterion(outputs, targets) + loss2 = criterion(aux_outputs, targets) + loss = loss1 + 0.4*loss2 + loss = criterion(outputs, targets, to_weight=True) + else: + outputs = model(inputs) + loss = criterion(outputs, targets, to_weight=False) + + if phase == 'train': + loss.backward() + optimizer.step() + + # loss + running_loss += loss.item() + + # for metrics calculation later on + preds_from_each_batch += [outputs.detach().cpu()] + targets_from_each_batch += [targets.cpu()] + + # iter logging + if i % 50 == 0: + logger.log_iter_loss(loss.item(), epoch*len(loaders[phase])+i, phase) + # tracks loss in the tqdm progress bar + prog_bar.set_postfix(loss=loss.item()) + + # logging loss + epoch_loss = running_loss / len(loaders[phase]) + logger.log_epoch_loss(epoch_loss, epoch, phase) + + # logging metrics + preds_from_each_batch = torch.cat(preds_from_each_batch) + targets_from_each_batch = torch.cat(targets_from_each_batch) + metrics_dict = metrics(targets_from_each_batch, preds_from_each_batch) + logger.log_epoch_metrics(metrics_dict, epoch, phase) + + # Early stopping + if phase == 'valid': + if epoch_loss < best_valid_loss: + no_change_epochs = 0 + best_valid_loss = epoch_loss + logger.log_best_model(model, epoch_loss, epoch, optimizer, metrics_dict) + else: + no_change_epochs += 1 + logger.print_logger.info( + f'Valid loss hasnt changed for {no_change_epochs} patience: {cfg.patience}' + ) + if no_change_epochs >= cfg.patience: + early_stop_triggered = True + + if early_stop_triggered: + logger.print_logger.info(f'Training is early stopped @ {epoch}') + break + + logger.print_logger.info('Finished Training') + + # loading the best model + ckpt = torch.load(logger.best_model_path) + model.load_state_dict(ckpt['model']) + logger.print_logger.info(f'Loading the best model from {logger.best_model_path}') + logger.print_logger.info((f'The model was trained for {ckpt["epoch"]} epochs. Loss: {ckpt["loss"]:.4f}')) + + # Testing the model + model.eval() + running_loss = 0 + preds_from_each_batch = [] + targets_from_each_batch = [] + + for i, batch in enumerate(loaders['test']): + inputs = batch['input'].to(device) + targets = batch['target'].to(device) + + # zero the parameter gradients + optimizer.zero_grad() + + # forward + backward + optimize + with torch.set_grad_enabled(False): + outputs = model(inputs) + loss = criterion(outputs, targets, to_weight=False) + + # loss + running_loss += loss.item() + + # for metrics calculation later on + preds_from_each_batch += [outputs.detach().cpu()] + targets_from_each_batch += [targets.cpu()] + + # logging metrics + preds_from_each_batch = torch.cat(preds_from_each_batch) + targets_from_each_batch = torch.cat(targets_from_each_batch) + test_metrics_dict = metrics(targets_from_each_batch, preds_from_each_batch) + test_metrics_dict['avg_loss'] = running_loss / len(loaders['test']) + test_metrics_dict['param_num'] = param_num + # TODO: I have no idea why tboard doesn't keep metrics (hparams) when + # I run this experiment from cli: `python train_melception.py config=./configs/vggish.yaml` + # while when I run it in vscode debugger the metrics are logger (wtf) + logger.log_test_metrics(test_metrics_dict, dict(cfg), ckpt['epoch']) + + logger.print_logger.info('Finished the experiment') + + +if __name__ == '__main__': + # input = torch.rand(16, 1, 80, 848) + # output, aux = inception(input) + # print(output.shape, aux.shape) + # Expected input size: (3, 299, 299) in RGB -> (1, 80, 848) in Mel Spec + # train_inception_scorer() + + cfg_cli = OmegaConf.from_cli() + cfg_yml = OmegaConf.load(cfg_cli.config) + # the latter arguments are prioritized + cfg = OmegaConf.merge(cfg_yml, cfg_cli) + OmegaConf.set_readonly(cfg, True) + print(OmegaConf.to_yaml(cfg)) + + train_inception_scorer(cfg) diff --git a/ldm/modules/losses_audio/vggishish/train_vggishish.py b/ldm/modules/losses_audio/vggishish/train_vggishish.py new file mode 100644 index 0000000000000000000000000000000000000000..205668224ec87a9ce571f6428531080231b1c16b --- /dev/null +++ b/ldm/modules/losses_audio/vggishish/train_vggishish.py @@ -0,0 +1,199 @@ +from loss import WeightedCrossEntropy +import random + +import numpy as np +import torch +import torchvision +from omegaconf import OmegaConf +from torch.utils.data.dataloader import DataLoader +from tqdm import tqdm + +from dataset import VGGSound +from transforms import Crop, StandardNormalizeAudio, ToTensor +from logger import LoggerWithTBoard +from metrics import metrics +from model import VGGishish + +if __name__ == "__main__": + cfg_cli = OmegaConf.from_cli() + cfg_yml = OmegaConf.load(cfg_cli.config) + # the latter arguments are prioritized + cfg = OmegaConf.merge(cfg_yml, cfg_cli) + OmegaConf.set_readonly(cfg, True) + print(OmegaConf.to_yaml(cfg)) + + logger = LoggerWithTBoard(cfg) + + random.seed(cfg.seed) + np.random.seed(cfg.seed) + torch.manual_seed(cfg.seed) + torch.cuda.manual_seed_all(cfg.seed) + # makes iterations faster (in this case 30%) if your inputs are of a fixed size + # https://discuss.pytorch.org/t/what-does-torch-backends-cudnn-benchmark-do/5936/3 + torch.backends.cudnn.benchmark = True + + transforms = [ + StandardNormalizeAudio(cfg.mels_path), + ] + if cfg.cropped_size not in [None, 'None', 'none']: + logger.print_logger.info(f'Using cropping {cfg.cropped_size}') + transforms.append(Crop(cfg.cropped_size)) + transforms.append(ToTensor()) + transforms = torchvision.transforms.transforms.Compose(transforms) + + datasets = { + 'train': VGGSound('train', cfg.mels_path, transforms), + 'valid': VGGSound('valid', cfg.mels_path, transforms), + 'test': VGGSound('test', cfg.mels_path, transforms), + } + + loaders = { + 'train': DataLoader(datasets['train'], batch_size=cfg.batch_size, shuffle=True, drop_last=True, + num_workers=cfg.num_workers, pin_memory=True), + 'valid': DataLoader(datasets['valid'], batch_size=cfg.batch_size, + num_workers=cfg.num_workers, pin_memory=True), + 'test': DataLoader(datasets['test'], batch_size=cfg.batch_size, + num_workers=cfg.num_workers, pin_memory=True), + } + + device = torch.device(cfg.device if torch.cuda.is_available() else 'cpu') + + model = VGGishish(cfg.conv_layers, cfg.use_bn, num_classes=len(datasets['train'].target2label)) + model = model.to(device) + param_num = logger.log_param_num(model) + + if cfg.optimizer == 'adam': + optimizer = torch.optim.Adam( + model.parameters(), lr=cfg.learning_rate, betas=cfg.betas, weight_decay=cfg.weight_decay) + elif cfg.optimizer == 'sgd': + optimizer = torch.optim.SGD( + model.parameters(), lr=cfg.learning_rate, momentum=cfg.momentum, weight_decay=cfg.weight_decay) + else: + raise NotImplementedError + + if cfg.cls_weights_in_loss: + weights = 1 / datasets['train'].class_counts + else: + weights = torch.ones(len(datasets['train'].target2label)) + criterion = WeightedCrossEntropy(weights.to(device)) + + # loop over the train and validation multiple times (typical PT boilerplate) + no_change_epochs = 0 + best_valid_loss = float('inf') + early_stop_triggered = False + + for epoch in range(cfg.num_epochs): + + for phase in ['train', 'valid']: + if phase == 'train': + model.train() + else: + model.eval() + + running_loss = 0 + preds_from_each_batch = [] + targets_from_each_batch = [] + + prog_bar = tqdm(loaders[phase], f'{phase} ({epoch})', ncols=0) + for i, batch in enumerate(prog_bar): + inputs = batch['input'].to(device) + targets = batch['target'].to(device) + + # zero the parameter gradients + optimizer.zero_grad() + + # forward + backward + optimize + with torch.set_grad_enabled(phase == 'train'): + outputs = model(inputs) + loss = criterion(outputs, targets, to_weight=phase == 'train') + + if phase == 'train': + loss.backward() + optimizer.step() + + # loss + running_loss += loss.item() + + # for metrics calculation later on + preds_from_each_batch += [outputs.detach().cpu()] + targets_from_each_batch += [targets.cpu()] + + # iter logging + if i % 50 == 0: + logger.log_iter_loss(loss.item(), epoch*len(loaders[phase])+i, phase) + # tracks loss in the tqdm progress bar + prog_bar.set_postfix(loss=loss.item()) + + # logging loss + epoch_loss = running_loss / len(loaders[phase]) + logger.log_epoch_loss(epoch_loss, epoch, phase) + + # logging metrics + preds_from_each_batch = torch.cat(preds_from_each_batch) + targets_from_each_batch = torch.cat(targets_from_each_batch) + metrics_dict = metrics(targets_from_each_batch, preds_from_each_batch) + logger.log_epoch_metrics(metrics_dict, epoch, phase) + + # Early stopping + if phase == 'valid': + if epoch_loss < best_valid_loss: + no_change_epochs = 0 + best_valid_loss = epoch_loss + logger.log_best_model(model, epoch_loss, epoch, optimizer, metrics_dict) + else: + no_change_epochs += 1 + logger.print_logger.info( + f'Valid loss hasnt changed for {no_change_epochs} patience: {cfg.patience}' + ) + if no_change_epochs >= cfg.patience: + early_stop_triggered = True + + if early_stop_triggered: + logger.print_logger.info(f'Training is early stopped @ {epoch}') + break + + logger.print_logger.info('Finished Training') + + # loading the best model + ckpt = torch.load(logger.best_model_path) + model.load_state_dict(ckpt['model']) + logger.print_logger.info(f'Loading the best model from {logger.best_model_path}') + logger.print_logger.info((f'The model was trained for {ckpt["epoch"]} epochs. Loss: {ckpt["loss"]:.4f}')) + + # Testing the model + model.eval() + running_loss = 0 + preds_from_each_batch = [] + targets_from_each_batch = [] + + for i, batch in enumerate(loaders['test']): + inputs = batch['input'].to(device) + targets = batch['target'].to(device) + + # zero the parameter gradients + optimizer.zero_grad() + + # forward + backward + optimize + with torch.set_grad_enabled(False): + outputs = model(inputs) + loss = criterion(outputs, targets, to_weight=False) + + # loss + running_loss += loss.item() + + # for metrics calculation later on + preds_from_each_batch += [outputs.detach().cpu()] + targets_from_each_batch += [targets.cpu()] + + # logging metrics + preds_from_each_batch = torch.cat(preds_from_each_batch) + targets_from_each_batch = torch.cat(targets_from_each_batch) + test_metrics_dict = metrics(targets_from_each_batch, preds_from_each_batch) + test_metrics_dict['avg_loss'] = running_loss / len(loaders['test']) + test_metrics_dict['param_num'] = param_num + # TODO: I have no idea why tboard doesn't keep metrics (hparams) when + # I run this experiment from cli: `python train_vggishish.py config=./configs/vggish.yaml` + # while when I run it in vscode debugger the metrics are logger (wtf) + logger.log_test_metrics(test_metrics_dict, dict(cfg), ckpt['epoch']) + + logger.print_logger.info('Finished the experiment') diff --git a/ldm/modules/losses_audio/vggishish/transforms.py b/ldm/modules/losses_audio/vggishish/transforms.py new file mode 100644 index 0000000000000000000000000000000000000000..bdab7eb6b94ac21e950e2870b89da7bbac1f4a8e --- /dev/null +++ b/ldm/modules/losses_audio/vggishish/transforms.py @@ -0,0 +1,98 @@ +import logging +import os +from pathlib import Path + +import albumentations +import numpy as np +import torch +from tqdm import tqdm + +logger = logging.getLogger(f'main.{__name__}') + + +class StandardNormalizeAudio(object): + ''' + Frequency-wise normalization + ''' + def __init__(self, specs_dir, train_ids_path='./data/vggsound_train.txt', cache_path='./data/'): + self.specs_dir = specs_dir + self.train_ids_path = train_ids_path + # making the stats filename to match the specs dir name + self.cache_path = os.path.join(cache_path, f'train_means_stds_{Path(specs_dir).stem}.txt') + logger.info('Assuming that the input stats are calculated using preprocessed spectrograms (log)') + self.train_stats = self.calculate_or_load_stats() + + def __call__(self, item): + # just to generalizat the input handling. Useful for FID, IS eval and training other staff + if isinstance(item, dict): + if 'input' in item: + input_key = 'input' + elif 'image' in item: + input_key = 'image' + else: + raise NotImplementedError + item[input_key] = (item[input_key] - self.train_stats['means']) / self.train_stats['stds'] + elif isinstance(item, torch.Tensor): + # broadcasts np.ndarray (80, 1) to (1, 80, 1) because item is torch.Tensor (B, 80, T) + item = (item - self.train_stats['means']) / self.train_stats['stds'] + else: + raise NotImplementedError + return item + + def calculate_or_load_stats(self): + try: + # (F, 2) + train_stats = np.loadtxt(self.cache_path) + means, stds = train_stats.T + logger.info('Trying to load train stats for Standard Normalization of inputs') + except OSError: + logger.info('Could not find the precalculated stats for Standard Normalization. Calculating...') + train_vid_ids = open(self.train_ids_path) + specs_paths = [os.path.join(self.specs_dir, f'{i.rstrip()}_mel.npy') for i in train_vid_ids] + means = [None] * len(specs_paths) + stds = [None] * len(specs_paths) + for i, path in enumerate(tqdm(specs_paths)): + spec = np.load(path) + means[i] = spec.mean(axis=1) + stds[i] = spec.std(axis=1) + # (F) <- (num_files, F) + means = np.array(means).mean(axis=0) + stds = np.array(stds).mean(axis=0) + # saving in two columns + np.savetxt(self.cache_path, np.vstack([means, stds]).T, fmt='%0.8f') + means = means.reshape(-1, 1) + stds = stds.reshape(-1, 1) + return {'means': means, 'stds': stds} + +class ToTensor(object): + + def __call__(self, item): + item['input'] = torch.from_numpy(item['input']).float() + # if 'target' in item: + item['target'] = torch.tensor(item['target']) + return item + +class Crop(object): + + def __init__(self, cropped_shape=None, random_crop=False): + self.cropped_shape = cropped_shape + if cropped_shape is not None: + mel_num, spec_len = cropped_shape + if random_crop: + self.cropper = albumentations.RandomCrop + else: + self.cropper = albumentations.CenterCrop + self.preprocessor = albumentations.Compose([self.cropper(mel_num, spec_len)]) + else: + self.preprocessor = lambda **kwargs: kwargs + + def __call__(self, item): + item['input'] = self.preprocessor(image=item['input'])['image'] + return item + + +if __name__ == '__main__': + cropper = Crop([80, 848]) + item = {'input': torch.rand([80, 860])} + outputs = cropper(item) + print(outputs['input'].shape) diff --git a/ldm/modules/losses_audio/vqperceptual.py b/ldm/modules/losses_audio/vqperceptual.py new file mode 100644 index 0000000000000000000000000000000000000000..bfdd9cb6ec12aad67f6c852ddb5e32b6dc28bf68 --- /dev/null +++ b/ldm/modules/losses_audio/vqperceptual.py @@ -0,0 +1,136 @@ +import torch +import torch.nn as nn +import torch.nn.functional as F +import sys +from ldm.util import exists +sys.path.insert(0, '.') # nopep8 +from ldm.modules.discriminator.model import (NLayerDiscriminator, NLayerDiscriminator1dFeats, + NLayerDiscriminator1dSpecs, + weights_init) +from ldm.modules.losses_audio.lpaps import LPAPS +from ldm.modules.losses.vqperceptual import l1, l2, measure_perplexity, hinge_d_loss, vanilla_d_loss, adopt_weight + + + +class DummyLoss(nn.Module): + def __init__(self): + super().__init__() + +class VQLPAPSWithDiscriminator(nn.Module): + def __init__(self, disc_start, codebook_weight=1.0, pixelloss_weight=1.0, + disc_num_layers=3, disc_in_channels=3, disc_factor=1.0, disc_weight=1.0, + perceptual_weight=1.0, use_actnorm=False, disc_conditional=False, + disc_ndf=64, disc_loss="hinge", n_classes=None, pixel_loss="l1"): + super().__init__() + assert disc_loss in ["hinge", "vanilla"] + self.codebook_weight = codebook_weight + self.pixel_weight = pixelloss_weight + self.perceptual_loss = LPAPS().eval() + self.perceptual_weight = perceptual_weight + + if pixel_loss == "l1": + self.pixel_loss = l1 + else: + self.pixel_loss = l2 + + self.discriminator = NLayerDiscriminator(input_nc=disc_in_channels, + n_layers=disc_num_layers, + use_actnorm=use_actnorm, + ndf=disc_ndf + ).apply(weights_init) + self.discriminator_iter_start = disc_start + if disc_loss == "hinge": + self.disc_loss = hinge_d_loss + elif disc_loss == "vanilla": + self.disc_loss = vanilla_d_loss + else: + raise ValueError(f"Unknown GAN loss '{disc_loss}'.") + print(f"VQLPAPSWithDiscriminator running with {disc_loss} loss.") + self.disc_factor = disc_factor + self.discriminator_weight = disc_weight + self.disc_conditional = disc_conditional + self.n_classes = n_classes + + def calculate_adaptive_weight(self, nll_loss, g_loss, last_layer=None): + if last_layer is not None: + nll_grads = torch.autograd.grad(nll_loss, last_layer, retain_graph=True)[0] + g_grads = torch.autograd.grad(g_loss, last_layer, retain_graph=True)[0] + else: + nll_grads = torch.autograd.grad(nll_loss, self.last_layer[0], retain_graph=True)[0] + g_grads = torch.autograd.grad(g_loss, self.last_layer[0], retain_graph=True)[0] + + d_weight = torch.norm(nll_grads) / (torch.norm(g_grads) + 1e-4) + d_weight = torch.clamp(d_weight, 0.0, 1e4).detach() + d_weight = d_weight * self.discriminator_weight + return d_weight + + def forward(self, codebook_loss, inputs, reconstructions, optimizer_idx, + global_step, last_layer=None, cond=None, split="train", predicted_indices=None): + if not exists(codebook_loss): + codebook_loss = torch.tensor([0.]).to(inputs.device) + rec_loss = torch.abs(inputs.contiguous() - reconstructions.contiguous()) + if self.perceptual_weight > 0: + p_loss = self.perceptual_loss(inputs.contiguous(), reconstructions.contiguous()) + rec_loss = rec_loss + self.perceptual_weight * p_loss + else: + p_loss = torch.tensor([0.0]) + + nll_loss = rec_loss + # nll_loss = torch.sum(nll_loss) / nll_loss.shape[0] + nll_loss = torch.mean(nll_loss) + + # now the GAN part + if optimizer_idx == 0: + # generator update + if cond is None: + assert not self.disc_conditional + logits_fake = self.discriminator(reconstructions.contiguous()) + else: + assert self.disc_conditional + logits_fake = self.discriminator(torch.cat((reconstructions.contiguous(), cond), dim=1)) + g_loss = -torch.mean(logits_fake) + + try: + d_weight = self.calculate_adaptive_weight(nll_loss, g_loss, last_layer=last_layer) + except RuntimeError: + assert not self.training + d_weight = torch.tensor(0.0) + + disc_factor = adopt_weight(self.disc_factor, global_step, threshold=self.discriminator_iter_start) + loss = nll_loss + d_weight * disc_factor * g_loss + self.codebook_weight * codebook_loss.mean() + + log = {"{}/total_loss".format(split): loss.clone().detach().mean(), + "{}/quant_loss".format(split): codebook_loss.detach().mean(), + "{}/nll_loss".format(split): nll_loss.detach().mean(), + "{}/rec_loss".format(split): rec_loss.detach().mean(), + "{}/p_loss".format(split): p_loss.detach().mean(), + "{}/d_weight".format(split): d_weight.detach(), + "{}/disc_factor".format(split): torch.tensor(disc_factor), + "{}/g_loss".format(split): g_loss.detach().mean(), + } + # if predicted_indices is not None: + # assert self.n_classes is not None + # with torch.no_grad(): + # perplexity, cluster_usage = measure_perplexity(predicted_indices, self.n_classes) + # log[f"{split}/perplexity"] = perplexity + # log[f"{split}/cluster_usage"] = cluster_usage + return loss, log + + if optimizer_idx == 1: + # second pass for discriminator update + if cond is None: + logits_real = self.discriminator(inputs.contiguous().detach()) + logits_fake = self.discriminator(reconstructions.contiguous().detach()) + else: + logits_real = self.discriminator(torch.cat((inputs.contiguous().detach(), cond), dim=1)) + logits_fake = self.discriminator(torch.cat((reconstructions.contiguous().detach(), cond), dim=1)) + + disc_factor = adopt_weight(self.disc_factor, global_step, threshold=self.discriminator_iter_start) + d_loss = disc_factor * self.disc_loss(logits_real, logits_fake) + + log = {"{}/disc_loss".format(split): d_loss.clone().detach().mean(), + "{}/logits_real".format(split): logits_real.detach().mean(), + "{}/logits_fake".format(split): logits_fake.detach().mean() + } + return d_loss, log + diff --git a/ldm/modules/x_transformer.py b/ldm/modules/x_transformer.py new file mode 100644 index 0000000000000000000000000000000000000000..5fc15bf9cfe0111a910e7de33d04ffdec3877576 --- /dev/null +++ b/ldm/modules/x_transformer.py @@ -0,0 +1,641 @@ +"""shout-out to https://github.com/lucidrains/x-transformers/tree/main/x_transformers""" +import torch +from torch import nn, einsum +import torch.nn.functional as F +from functools import partial +from inspect import isfunction +from collections import namedtuple +from einops import rearrange, repeat, reduce + +# constants + +DEFAULT_DIM_HEAD = 64 + +Intermediates = namedtuple('Intermediates', [ + 'pre_softmax_attn', + 'post_softmax_attn' +]) + +LayerIntermediates = namedtuple('Intermediates', [ + 'hiddens', + 'attn_intermediates' +]) + + +class AbsolutePositionalEmbedding(nn.Module): + def __init__(self, dim, max_seq_len): + super().__init__() + self.emb = nn.Embedding(max_seq_len, dim) + self.init_() + + def init_(self): + nn.init.normal_(self.emb.weight, std=0.02) + + def forward(self, x): + n = torch.arange(x.shape[1], device=x.device) + return self.emb(n)[None, :, :] + + +class FixedPositionalEmbedding(nn.Module): + def __init__(self, dim): + super().__init__() + inv_freq = 1. / (10000 ** (torch.arange(0, dim, 2).float() / dim)) + self.register_buffer('inv_freq', inv_freq) + + def forward(self, x, seq_dim=1, offset=0): + t = torch.arange(x.shape[seq_dim], device=x.device).type_as(self.inv_freq) + offset + sinusoid_inp = torch.einsum('i , j -> i j', t, self.inv_freq) + emb = torch.cat((sinusoid_inp.sin(), sinusoid_inp.cos()), dim=-1) + return emb[None, :, :] + + +# helpers + +def exists(val): + return val is not None + + +def default(val, d): + if exists(val): + return val + return d() if isfunction(d) else d + + +def always(val): + def inner(*args, **kwargs): + return val + return inner + + +def not_equals(val): + def inner(x): + return x != val + return inner + + +def equals(val): + def inner(x): + return x == val + return inner + + +def max_neg_value(tensor): + return -torch.finfo(tensor.dtype).max + + +# keyword argument helpers + +def pick_and_pop(keys, d): + values = list(map(lambda key: d.pop(key), keys)) + return dict(zip(keys, values)) + + +def group_dict_by_key(cond, d): + return_val = [dict(), dict()] + for key in d.keys(): + match = bool(cond(key)) + ind = int(not match) + return_val[ind][key] = d[key] + return (*return_val,) + + +def string_begins_with(prefix, str): + return str.startswith(prefix) + + +def group_by_key_prefix(prefix, d): + return group_dict_by_key(partial(string_begins_with, prefix), d) + + +def groupby_prefix_and_trim(prefix, d): + kwargs_with_prefix, kwargs = group_dict_by_key(partial(string_begins_with, prefix), d) + kwargs_without_prefix = dict(map(lambda x: (x[0][len(prefix):], x[1]), tuple(kwargs_with_prefix.items()))) + return kwargs_without_prefix, kwargs + + +# classes +class Scale(nn.Module): + def __init__(self, value, fn): + super().__init__() + self.value = value + self.fn = fn + + def forward(self, x, **kwargs): + x, *rest = self.fn(x, **kwargs) + return (x * self.value, *rest) + + +class Rezero(nn.Module): + def __init__(self, fn): + super().__init__() + self.fn = fn + self.g = nn.Parameter(torch.zeros(1)) + + def forward(self, x, **kwargs): + x, *rest = self.fn(x, **kwargs) + return (x * self.g, *rest) + + +class ScaleNorm(nn.Module): + def __init__(self, dim, eps=1e-5): + super().__init__() + self.scale = dim ** -0.5 + self.eps = eps + self.g = nn.Parameter(torch.ones(1)) + + def forward(self, x): + norm = torch.norm(x, dim=-1, keepdim=True) * self.scale + return x / norm.clamp(min=self.eps) * self.g + + +class RMSNorm(nn.Module): + def __init__(self, dim, eps=1e-8): + super().__init__() + self.scale = dim ** -0.5 + self.eps = eps + self.g = nn.Parameter(torch.ones(dim)) + + def forward(self, x): + norm = torch.norm(x, dim=-1, keepdim=True) * self.scale + return x / norm.clamp(min=self.eps) * self.g + + +class Residual(nn.Module): + def forward(self, x, residual): + return x + residual + + +class GRUGating(nn.Module): + def __init__(self, dim): + super().__init__() + self.gru = nn.GRUCell(dim, dim) + + def forward(self, x, residual): + gated_output = self.gru( + rearrange(x, 'b n d -> (b n) d'), + rearrange(residual, 'b n d -> (b n) d') + ) + + return gated_output.reshape_as(x) + + +# feedforward + +class GEGLU(nn.Module): + def __init__(self, dim_in, dim_out): + super().__init__() + self.proj = nn.Linear(dim_in, dim_out * 2) + + def forward(self, x): + x, gate = self.proj(x).chunk(2, dim=-1) + return x * F.gelu(gate) + + +class FeedForward(nn.Module): + def __init__(self, dim, dim_out=None, mult=4, glu=False, dropout=0.): + super().__init__() + inner_dim = int(dim * mult) + dim_out = default(dim_out, dim) + project_in = nn.Sequential( + nn.Linear(dim, inner_dim), + nn.GELU() + ) if not glu else GEGLU(dim, inner_dim) + + self.net = nn.Sequential( + project_in, + nn.Dropout(dropout), + nn.Linear(inner_dim, dim_out) + ) + + def forward(self, x): + return self.net(x) + + +# attention. +class Attention(nn.Module): + def __init__( + self, + dim, + dim_head=DEFAULT_DIM_HEAD, + heads=8, + causal=False, + mask=None, + talking_heads=False, + sparse_topk=None, + use_entmax15=False, + num_mem_kv=0, + dropout=0., + on_attn=False + ): + super().__init__() + if use_entmax15: + raise NotImplementedError("Check out entmax activation instead of softmax activation!") + self.scale = dim_head ** -0.5 + self.heads = heads + self.causal = causal + self.mask = mask + + inner_dim = dim_head * heads + + self.to_q = nn.Linear(dim, inner_dim, bias=False) + self.to_k = nn.Linear(dim, inner_dim, bias=False) + self.to_v = nn.Linear(dim, inner_dim, bias=False) + self.dropout = nn.Dropout(dropout) + + # talking heads + self.talking_heads = talking_heads + if talking_heads: + self.pre_softmax_proj = nn.Parameter(torch.randn(heads, heads)) + self.post_softmax_proj = nn.Parameter(torch.randn(heads, heads)) + + # explicit topk sparse attention + self.sparse_topk = sparse_topk + + # entmax + #self.attn_fn = entmax15 if use_entmax15 else F.softmax + self.attn_fn = F.softmax + + # add memory key / values + self.num_mem_kv = num_mem_kv + if num_mem_kv > 0: + self.mem_k = nn.Parameter(torch.randn(heads, num_mem_kv, dim_head)) + self.mem_v = nn.Parameter(torch.randn(heads, num_mem_kv, dim_head)) + + # attention on attention + self.attn_on_attn = on_attn + self.to_out = nn.Sequential(nn.Linear(inner_dim, dim * 2), nn.GLU()) if on_attn else nn.Linear(inner_dim, dim) + + def forward( + self, + x, + context=None, + mask=None, + context_mask=None, + rel_pos=None, + sinusoidal_emb=None, + prev_attn=None, + mem=None + ): + b, n, _, h, talking_heads, device = *x.shape, self.heads, self.talking_heads, x.device + kv_input = default(context, x) + + q_input = x + k_input = kv_input + v_input = kv_input + + if exists(mem): + k_input = torch.cat((mem, k_input), dim=-2) + v_input = torch.cat((mem, v_input), dim=-2) + + if exists(sinusoidal_emb): + # in shortformer, the query would start at a position offset depending on the past cached memory + offset = k_input.shape[-2] - q_input.shape[-2] + q_input = q_input + sinusoidal_emb(q_input, offset=offset) + k_input = k_input + sinusoidal_emb(k_input) + + q = self.to_q(q_input) + k = self.to_k(k_input) + v = self.to_v(v_input) + + q, k, v = map(lambda t: rearrange(t, 'b n (h d) -> b h n d', h=h), (q, k, v)) + + input_mask = None + if any(map(exists, (mask, context_mask))): + q_mask = default(mask, lambda: torch.ones((b, n), device=device).bool()) + k_mask = q_mask if not exists(context) else context_mask + k_mask = default(k_mask, lambda: torch.ones((b, k.shape[-2]), device=device).bool()) + q_mask = rearrange(q_mask, 'b i -> b () i ()') + k_mask = rearrange(k_mask, 'b j -> b () () j') + input_mask = q_mask * k_mask + + if self.num_mem_kv > 0: + mem_k, mem_v = map(lambda t: repeat(t, 'h n d -> b h n d', b=b), (self.mem_k, self.mem_v)) + k = torch.cat((mem_k, k), dim=-2) + v = torch.cat((mem_v, v), dim=-2) + if exists(input_mask): + input_mask = F.pad(input_mask, (self.num_mem_kv, 0), value=True) + + dots = einsum('b h i d, b h j d -> b h i j', q, k) * self.scale + mask_value = max_neg_value(dots) + + if exists(prev_attn): + dots = dots + prev_attn + + pre_softmax_attn = dots + + if talking_heads: + dots = einsum('b h i j, h k -> b k i j', dots, self.pre_softmax_proj).contiguous() + + if exists(rel_pos): + dots = rel_pos(dots) + + if exists(input_mask): + dots.masked_fill_(~input_mask, mask_value) + del input_mask + + if self.causal: + i, j = dots.shape[-2:] + r = torch.arange(i, device=device) + mask = rearrange(r, 'i -> () () i ()') < rearrange(r, 'j -> () () () j') + mask = F.pad(mask, (j - i, 0), value=False) + dots.masked_fill_(mask, mask_value) + del mask + + if exists(self.sparse_topk) and self.sparse_topk < dots.shape[-1]: + top, _ = dots.topk(self.sparse_topk, dim=-1) + vk = top[..., -1].unsqueeze(-1).expand_as(dots) + mask = dots < vk + dots.masked_fill_(mask, mask_value) + del mask + + attn = self.attn_fn(dots, dim=-1) + post_softmax_attn = attn + + attn = self.dropout(attn) + + if talking_heads: + attn = einsum('b h i j, h k -> b k i j', attn, self.post_softmax_proj).contiguous() + + out = einsum('b h i j, b h j d -> b h i d', attn, v) + out = rearrange(out, 'b h n d -> b n (h d)') + + intermediates = Intermediates( + pre_softmax_attn=pre_softmax_attn, + post_softmax_attn=post_softmax_attn + ) + + return self.to_out(out), intermediates + + +class AttentionLayers(nn.Module): + def __init__( + self, + dim, + depth, + heads=8, + causal=False, + cross_attend=False, + only_cross=False, + use_scalenorm=False, + use_rmsnorm=False, + use_rezero=False, + rel_pos_num_buckets=32, + rel_pos_max_distance=128, + position_infused_attn=False, + custom_layers=None, + sandwich_coef=None, + par_ratio=None, + residual_attn=False, + cross_residual_attn=False, + macaron=False, + pre_norm=True, + gate_residual=False, + **kwargs + ): + super().__init__() + ff_kwargs, kwargs = groupby_prefix_and_trim('ff_', kwargs) + attn_kwargs, _ = groupby_prefix_and_trim('attn_', kwargs) + + dim_head = attn_kwargs.get('dim_head', DEFAULT_DIM_HEAD) + + self.dim = dim + self.depth = depth + self.layers = nn.ModuleList([]) + + self.has_pos_emb = position_infused_attn + self.pia_pos_emb = FixedPositionalEmbedding(dim) if position_infused_attn else None + self.rotary_pos_emb = always(None) + + assert rel_pos_num_buckets <= rel_pos_max_distance, 'number of relative position buckets must be less than the relative position max distance' + self.rel_pos = None + + self.pre_norm = pre_norm + + self.residual_attn = residual_attn + self.cross_residual_attn = cross_residual_attn + + norm_class = ScaleNorm if use_scalenorm else nn.LayerNorm + norm_class = RMSNorm if use_rmsnorm else norm_class + norm_fn = partial(norm_class, dim) + + norm_fn = nn.Identity if use_rezero else norm_fn + branch_fn = Rezero if use_rezero else None + + if cross_attend and not only_cross: + default_block = ('a', 'c', 'f') + elif cross_attend and only_cross: + default_block = ('c', 'f') + else: + default_block = ('a', 'f') + + if macaron: + default_block = ('f',) + default_block + + if exists(custom_layers): + layer_types = custom_layers + elif exists(par_ratio): + par_depth = depth * len(default_block) + assert 1 < par_ratio <= par_depth, 'par ratio out of range' + default_block = tuple(filter(not_equals('f'), default_block)) + par_attn = par_depth // par_ratio + depth_cut = par_depth * 2 // 3 # 2 / 3 attention layer cutoff suggested by PAR paper + par_width = (depth_cut + depth_cut // par_attn) // par_attn + assert len(default_block) <= par_width, 'default block is too large for par_ratio' + par_block = default_block + ('f',) * (par_width - len(default_block)) + par_head = par_block * par_attn + layer_types = par_head + ('f',) * (par_depth - len(par_head)) + elif exists(sandwich_coef): + assert sandwich_coef > 0 and sandwich_coef <= depth, 'sandwich coefficient should be less than the depth' + layer_types = ('a',) * sandwich_coef + default_block * (depth - sandwich_coef) + ('f',) * sandwich_coef + else: + layer_types = default_block * depth + + self.layer_types = layer_types + self.num_attn_layers = len(list(filter(equals('a'), layer_types))) + + for layer_type in self.layer_types: + if layer_type == 'a': + layer = Attention(dim, heads=heads, causal=causal, **attn_kwargs) + elif layer_type == 'c': + layer = Attention(dim, heads=heads, **attn_kwargs) + elif layer_type == 'f': + layer = FeedForward(dim, **ff_kwargs) + layer = layer if not macaron else Scale(0.5, layer) + else: + raise Exception(f'invalid layer type {layer_type}') + + if isinstance(layer, Attention) and exists(branch_fn): + layer = branch_fn(layer) + + if gate_residual: + residual_fn = GRUGating(dim) + else: + residual_fn = Residual() + + self.layers.append(nn.ModuleList([ + norm_fn(), + layer, + residual_fn + ])) + + def forward( + self, + x, + context=None, + mask=None, + context_mask=None, + mems=None, + return_hiddens=False + ): + hiddens = [] + intermediates = [] + prev_attn = None + prev_cross_attn = None + + mems = mems.copy() if exists(mems) else [None] * self.num_attn_layers + + for ind, (layer_type, (norm, block, residual_fn)) in enumerate(zip(self.layer_types, self.layers)): + is_last = ind == (len(self.layers) - 1) + + if layer_type == 'a': + hiddens.append(x) + layer_mem = mems.pop(0) + + residual = x + + if self.pre_norm: + x = norm(x) + + if layer_type == 'a': + out, inter = block(x, mask=mask, sinusoidal_emb=self.pia_pos_emb, rel_pos=self.rel_pos, + prev_attn=prev_attn, mem=layer_mem) + elif layer_type == 'c': + out, inter = block(x, context=context, mask=mask, context_mask=context_mask, prev_attn=prev_cross_attn) + elif layer_type == 'f': + out = block(x) + + x = residual_fn(out, residual) + + if layer_type in ('a', 'c'): + intermediates.append(inter) + + if layer_type == 'a' and self.residual_attn: + prev_attn = inter.pre_softmax_attn + elif layer_type == 'c' and self.cross_residual_attn: + prev_cross_attn = inter.pre_softmax_attn + + if not self.pre_norm and not is_last: + x = norm(x) + + if return_hiddens: + intermediates = LayerIntermediates( + hiddens=hiddens, + attn_intermediates=intermediates + ) + + return x, intermediates + + return x + + +class Encoder(AttentionLayers): + def __init__(self, **kwargs): + assert 'causal' not in kwargs, 'cannot set causality on encoder' + super().__init__(causal=False, **kwargs) + + + +class TransformerWrapper(nn.Module): + def __init__( + self, + *, + num_tokens, + max_seq_len, + attn_layers, + emb_dim=None, + max_mem_len=0., + emb_dropout=0., + num_memory_tokens=None, + tie_embedding=False, + use_pos_emb=True + ): + super().__init__() + assert isinstance(attn_layers, AttentionLayers), 'attention layers must be one of Encoder or Decoder' + + dim = attn_layers.dim + emb_dim = default(emb_dim, dim) + + self.max_seq_len = max_seq_len + self.max_mem_len = max_mem_len + self.num_tokens = num_tokens + + self.token_emb = nn.Embedding(num_tokens, emb_dim) + self.pos_emb = AbsolutePositionalEmbedding(emb_dim, max_seq_len) if ( + use_pos_emb and not attn_layers.has_pos_emb) else always(0) + self.emb_dropout = nn.Dropout(emb_dropout) + + self.project_emb = nn.Linear(emb_dim, dim) if emb_dim != dim else nn.Identity() + self.attn_layers = attn_layers + self.norm = nn.LayerNorm(dim) + + self.init_() + + self.to_logits = nn.Linear(dim, num_tokens) if not tie_embedding else lambda t: t @ self.token_emb.weight.t() + + # memory tokens (like [cls]) from Memory Transformers paper + num_memory_tokens = default(num_memory_tokens, 0) + self.num_memory_tokens = num_memory_tokens + if num_memory_tokens > 0: + self.memory_tokens = nn.Parameter(torch.randn(num_memory_tokens, dim)) + + # let funnel encoder know number of memory tokens, if specified + if hasattr(attn_layers, 'num_memory_tokens'): + attn_layers.num_memory_tokens = num_memory_tokens + + def init_(self): + nn.init.normal_(self.token_emb.weight, std=0.02) + + def forward( + self, + x, + return_embeddings=False, + mask=None, + return_mems=False, + return_attn=False, + mems=None, + **kwargs + ): + b, n, device, num_mem = *x.shape, x.device, self.num_memory_tokens + x = self.token_emb(x) + x += self.pos_emb(x) + x = self.emb_dropout(x) + + x = self.project_emb(x) + + if num_mem > 0: + mem = repeat(self.memory_tokens, 'n d -> b n d', b=b) + x = torch.cat((mem, x), dim=1) + + # auto-handle masking after appending memory tokens + if exists(mask): + mask = F.pad(mask, (num_mem, 0), value=True) + + x, intermediates = self.attn_layers(x, mask=mask, mems=mems, return_hiddens=True, **kwargs) + x = self.norm(x) + + mem, x = x[:, :num_mem], x[:, num_mem:] + + out = self.to_logits(x) if not return_embeddings else x + + if return_mems: + hiddens = intermediates.hiddens + new_mems = list(map(lambda pair: torch.cat(pair, dim=-2), zip(mems, hiddens))) if exists(mems) else hiddens + new_mems = list(map(lambda t: t[..., -self.max_mem_len:, :].detach(), new_mems)) + return out, new_mems + + if return_attn: + attn_maps = list(map(lambda t: t.post_softmax_attn, intermediates.attn_intermediates)) + return out, attn_maps + + return out + diff --git a/ldm/util.py b/ldm/util.py new file mode 100644 index 0000000000000000000000000000000000000000..85a90a3af06d6f593d596366a5b85b62b4ddf498 --- /dev/null +++ b/ldm/util.py @@ -0,0 +1,136 @@ +import importlib + +import torch +import numpy as np +from tqdm import tqdm +from inspect import isfunction +from PIL import Image, ImageDraw, ImageFont +import hashlib +import requests +import os + +URL_MAP = { + 'vggishish_lpaps': 'https://a3s.fi/swift/v1/AUTH_a235c0f452d648828f745589cde1219a/specvqgan_public/vggishish16.pt', + 'vggishish_mean_std_melspec_10s_22050hz': 'https://a3s.fi/swift/v1/AUTH_a235c0f452d648828f745589cde1219a/specvqgan_public/train_means_stds_melspec_10s_22050hz.txt', + 'melception': 'https://a3s.fi/swift/v1/AUTH_a235c0f452d648828f745589cde1219a/specvqgan_public/melception-21-05-10T09-28-40.pt', +} + +CKPT_MAP = { + 'vggishish_lpaps': 'vggishish16.pt', + 'vggishish_mean_std_melspec_10s_22050hz': 'train_means_stds_melspec_10s_22050hz.txt', + 'melception': 'melception-21-05-10T09-28-40.pt', +} + +MD5_MAP = { + 'vggishish_lpaps': '197040c524a07ccacf7715d7080a80bd', + 'vggishish_mean_std_melspec_10s_22050hz': 'f449c6fd0e248936c16f6d22492bb625', + 'melception': 'a71a41041e945b457c7d3d814bbcf72d', +} + + +def download(url, local_path, chunk_size=1024): + os.makedirs(os.path.split(local_path)[0], exist_ok=True) + with requests.get(url, stream=True) as r: + total_size = int(r.headers.get("content-length", 0)) + with tqdm(total=total_size, unit="B", unit_scale=True) as pbar: + with open(local_path, "wb") as f: + for data in r.iter_content(chunk_size=chunk_size): + if data: + f.write(data) + pbar.update(chunk_size) + + +def md5_hash(path): + with open(path, "rb") as f: + content = f.read() + return hashlib.md5(content).hexdigest() + + + +def log_txt_as_img(wh, xc, size=10): + # wh a tuple of (width, height) + # xc a list of captions to plot + b = len(xc) + txts = list() + for bi in range(b): + txt = Image.new("RGB", wh, color="white") + draw = ImageDraw.Draw(txt) + font = ImageFont.truetype('data/DejaVuSans.ttf', size=size) + nc = int(40 * (wh[0] / 256)) + lines = "\n".join(xc[bi][start:start + nc] for start in range(0, len(xc[bi]), nc)) + + try: + draw.text((0, 0), lines, fill="black", font=font) + except UnicodeEncodeError: + print("Cant encode string for logging. Skipping.") + + txt = np.array(txt).transpose(2, 0, 1) / 127.5 - 1.0 + txts.append(txt) + txts = np.stack(txts) + txts = torch.tensor(txts) + return txts + + +def ismap(x): + if not isinstance(x, torch.Tensor): + return False + return (len(x.shape) == 4) and (x.shape[1] > 3) + + +def isimage(x): + if not isinstance(x,torch.Tensor): + return False + return (len(x.shape) == 4) and (x.shape[1] == 3 or x.shape[1] == 1) + + +def exists(x): + return x is not None + + +def default(val, d): + if exists(val): + return val + return d() if isfunction(d) else d + + +def mean_flat(tensor): + """ + https://github.com/openai/guided-diffusion/blob/27c20a8fab9cb472df5d6bdd6c8d11c8f430b924/guided_diffusion/nn.py#L86 + Take the mean over all non-batch dimensions. + """ + return tensor.mean(dim=list(range(1, len(tensor.shape)))) + + +def count_params(model, verbose=False): + total_params = sum(p.numel() for p in model.parameters()) + if verbose: + print(f"{model.__class__.__name__} has {total_params*1.e-6:.2f} M params.") + return total_params + + +def instantiate_from_config(config,reload=False): + if not "target" in config: + if config == '__is_first_stage__': + return None + elif config == "__is_unconditional__": + return None + raise KeyError("Expected key `target` to instantiate.") + return get_obj_from_str(config["target"],reload=reload)(**config.get("params", dict())) + + +def get_obj_from_str(string, reload=False): + module, cls = string.rsplit(".", 1) + if reload: + module_imp = importlib.import_module(module) + importlib.reload(module_imp) + return getattr(importlib.import_module(module, package=None), cls) + +def get_ckpt_path(name, root, check=False): + assert name in URL_MAP + path = os.path.join(root, CKPT_MAP[name]) + if not os.path.exists(path) or (check and not md5_hash(path) == MD5_MAP[name]): + print("Downloading {} model from {} to {}".format(name, URL_MAP[name], path)) + download(URL_MAP[name], path) + md5 = md5_hash(path) + assert md5 == MD5_MAP[name], md5 + return path