# Copyright (c) 2021, NVIDIA CORPORATION. All rights reserved. # # NVIDIA CORPORATION and its licensors retain all intellectual property # and proprietary rights in and to this software, related documentation # and any modifications thereto. Any use, reproduction, disclosure or # distribution of this software and related documentation without an express # license agreement from NVIDIA CORPORATION is strictly prohibited. import os import time import copy import json import dill as pickle import psutil import PIL.Image import numpy as np import torch import dnnlib from torch_utils import misc from torch_utils import training_stats from torch_utils.ops import conv2d_gradfix from torch_utils.ops import grid_sample_gradfix from torchvision.utils import save_image import math import legacy from metrics import metric_main import torch.nn.functional as F np.set_printoptions(formatter={'float': '{:0.2f}'.format}) from collections import Counter #---------------------------------------------------------------------------- class SparsestVector: def __init__(self): self.sparsest_vector = None def add(self, vector): """Add a vector, only keeping it if it is sparser than the current stored one.""" if self.sparsest_vector is None: self.sparsest_vector = vector else: current_nonzero = torch.count_nonzero(self.sparsest_vector).item() new_nonzero = torch.count_nonzero(vector).item() # Keep the new vector only if it's sparser (fewer non-zero elements) if new_nonzero < current_nonzero: self.sparsest_vector = vector def check(self): """Returns the sparsest vector currently stored.""" return self.sparsest_vector def setup_snapshot_image_grid(training_set, random_seed=0): rnd = np.random.RandomState(random_seed) gw = int(np.clip(768*2 // training_set.image_shape[2], 7, 32)) gh = int(np.clip(432*2 // training_set.image_shape[1], 4, 32)) # No labels => show random subset of training samples. if not training_set.has_labels: all_indices = list(range(len(training_set))) rnd.shuffle(all_indices) grid_indices = [all_indices[i % len(all_indices)] for i in range(gw * gh)] label_groups = [] else: # Group training samples by label. label_groups = dict() # label => [idx, ...] for idx in range(len(training_set)): label = tuple(training_set.get_details(idx).raw_label.flat[::-1]) if label not in label_groups: label_groups[label] = [] label_groups[label].append(idx) if training_set.image_shape[1] < 256: gw *= 2 gh *= len(label_groups) #gw = min(gw, 16) # Reorder. label_order = sorted(label_groups.keys()) for label in label_order: rnd.shuffle(label_groups[label]) # Organize into grid. grid_indices = [] for y in range(len(label_groups)): label = label_order[y % len(label_order)] indices = label_groups[label] grid_indices += [indices[x % len(indices)] for x in range(gw)] label_groups[label] = [indices[(i + gw) % len(indices)] for i in range(len(indices))] # Load data. images, labels = zip(*[training_set[i] for i in grid_indices]) return (gw, len(label_groups)), np.stack(images), np.stack(labels), len(label_groups) #---------------------------------------------------------------------------- def save_image_grid(img, fname, drange, grid_size): lo, hi = drange img = np.asarray(img, dtype=np.float32) img = (img - lo) * (255 / (hi - lo)) img = np.rint(img).clip(0, 255).astype(np.uint8) gw, gh = grid_size _N, C, H, W = img.shape img = img.reshape(gh, gw, C, H, W) img = img.transpose(0, 3, 1, 4, 2) img = img.reshape(gh * H, gw * W, C) assert C in [1, 3] if C == 1: PIL.Image.fromarray(img[:, :, 0], 'L').save(fname) if C == 3: PIL.Image.fromarray(img, 'RGB').save(fname) class VectorHistoryChecker: def __init__(self, b, d, m): self.b = b self.d = d self.m = m self.history = torch.ones(b, d, m)*1e99 # Initialize history with zeros self.current_index = 0 def update_history(self, new_version): """Update history with the new version of the vector.""" self.history[:, :, self.current_index] = new_version.cpu() self.current_index = (self.current_index + 1) % self.m def check_history(self, input_version): """Check if the input version matches all m history versions for each row.""" consistency = torch.ones(self.b, dtype=torch.bool) # Initialize as True for all rows for i in range(self.m): # Check row-wise equality across the history consistency &= torch.all(self.history[:, :, i] == input_version.cpu(), dim=1) return consistency def get_history(self): """Get the current history.""" return self.history class ColumnHistoryChecker: def __init__(self, b, d, m): self.b = b self.d = d self.m = m self.history = torch.ones(b, d, m)*1e99 # Initialize history with zeros self.current_index = 0 def update_history(self, new_version): """Update history with the new version of the vector.""" self.history[:, :, self.current_index] = new_version.cpu() self.current_index = (self.current_index + 1) % self.m def check_history(self, input_version): """Check if the input version matches all m history versions for each row.""" consistency = torch.ones(self.d, dtype=torch.bool) # Initialize as True for all rows for i in range(self.m): # Check column-wise equality across the history consistency &= torch.all(self.history[:, :, i] == input_version.cpu(), dim=0) return consistency def get_history(self): """Get the current history.""" return self.history #---------------------------------------------------------------------------- def training_loop( run_dir = '.', # Output directory. training_set_kwargs = {}, # Options for training set. data_loader_kwargs = {}, # Options for torch.utils.data.DataLoader. G_kwargs = {}, # Options for generator network. D_kwargs = {}, # Options for discriminator network. G_opt_kwargs = {}, # Options for generator optimizer. D_opt_kwargs = {}, # Options for discriminator optimizer. augment_kwargs = None, # Options for augmentation pipeline. None = disable. loss_kwargs = {}, # Options for loss function. metrics = [], # Metrics to evaluate during training. random_seed = 0, # Global random seed. num_gpus = 1, # Number of GPUs participating in the training. rank = 0, # Rank of the current process in [0, num_gpus[. batch_size = 4, # Total batch size for one training iteration. Can be larger than batch_gpu * num_gpus. batch_gpu = 4, # Number of samples processed at a time by one GPU. ema_kimg = 10, # Half-life of the exponential moving average (EMA) of generator weights. ema_rampup = None, # EMA ramp-up coefficient. G_reg_interval = 4, # How often to perform regularization for G? None = disable lazy regularization. D_reg_interval = 16, # How often to perform regularization for D? None = disable lazy regularization. augment_p = 0, # Initial value of augmentation probability. ada_target = None, # ADA target value. None = fixed p. ada_interval = 4, # How often to perform ADA adjustment? ada_kimg = 500, # ADA adjustment speed, measured in how many kimg it takes for p to increase/decrease by one unit. total_kimg = 25000, # Total length of the training, measured in thousands of real images. kimg_per_tick = 4, # Progress snapshot interval. image_snapshot_ticks = 50, # How often to save image snapshots? None = disable. network_snapshot_ticks = 50, # How often to save network snapshots? None = disable. resume_pkl = None, # Network pickle to resume training from. cudnn_benchmark = True, # Enable torch.backends.cudnn.benchmark? allow_tf32 = False, # Enable torch.backends.cuda.matmul.allow_tf32 and torch.backends.cudnn.allow_tf32? abort_fn = None, # Callback function for determining whether to abort training. Must return consistent results across ranks. progress_fn = None, # Callback function for updating training progress. Called for all ranks. lambda_sparse = None, lambda_entropy = None, lambda_ortho = None, lambda_colvar = None, lambda_rowvar = None, lambda_equal = None, lambda_epsilon = None, lambda_path=None, g_iter=None, temperature=1, ): # Initialize. start_time = time.time() device = torch.device('cuda', rank) np.random.seed(random_seed * num_gpus + rank) torch.manual_seed(random_seed * num_gpus + rank) torch.backends.cudnn.benchmark = cudnn_benchmark # Improves training speed. torch.backends.cuda.matmul.allow_tf32 = allow_tf32 # Allow PyTorch to internally use tf32 for matmul torch.backends.cudnn.allow_tf32 = allow_tf32 # Allow PyTorch to internally use tf32 for convolutions conv2d_gradfix.enabled = True # Improves training speed. grid_sample_gradfix.enabled = True # Avoids errors with the augmentation pipe. # Load training set. if rank == 0: print('Loading training set...') training_set = dnnlib.util.construct_class_by_name(**training_set_kwargs) # subclass of training.dataset.Dataset training_set_sampler = misc.InfiniteSampler(dataset=training_set, rank=rank, num_replicas=num_gpus, seed=random_seed) training_set_iterator = iter(torch.utils.data.DataLoader(dataset=training_set, sampler=training_set_sampler, batch_size=batch_size//num_gpus, **data_loader_kwargs)) if rank == 0: print() print('Num images: ', len(training_set)) print('Image shape:', training_set.image_shape) print('Label shape:', training_set.label_shape) print() # Construct networks. if rank == 0: print('Constructing networks...') common_kwargs = dict(c_dim=training_set.label_dim, img_resolution=training_set.resolution, img_channels=training_set.num_channels) G = dnnlib.util.construct_class_by_name(**G_kwargs, **common_kwargs).train().requires_grad_(False).to(device) # subclass of torch.nn.Module D = dnnlib.util.construct_class_by_name(**D_kwargs, **common_kwargs).train().requires_grad_(False).to(device) # subclass of torch.nn.Module G_ema = copy.deepcopy(G).eval() M_kwargs = dnnlib.EasyDict(class_name='training.networks.ConceptMaskNetwork', c_dim=training_set.label_dim, i_dim=G_kwargs.mapping_kwargs.i_dim) M = dnnlib.util.construct_class_by_name(**M_kwargs).train().requires_grad_(False).to(device) # subclass of torch.nn.Module M_ema = copy.deepcopy(M).eval() # Resume from existing pickle. if (resume_pkl is not None) and (rank == 0): print(f'Resuming from "{resume_pkl}"') with dnnlib.util.open_url(resume_pkl) as f: resume_data = legacy.load_network_pkl(f) for name, module in [('G', G), ('D', D), ('G_ema', G_ema), ('M', M), ('M_ema', M_ema)]: misc.copy_params_and_buffers(resume_data[name], module, require_all=False) # Print network summary tables. if rank == 0: z = torch.empty([batch_gpu, G.z_dim], device=device) c = torch.empty([batch_gpu, G.c_dim], device=device) m = torch.empty([batch_gpu, G_kwargs.mapping_kwargs.i_dim], device=device) img = misc.print_module_summary(G, [z, m]) misc.print_module_summary(D, [img, c]) # Setup augmentation. if rank == 0: print('Setting up augmentation...') augment_pipe = None ada_stats = None if (augment_kwargs is not None) and (augment_p > 0 or ada_target is not None): augment_pipe = dnnlib.util.construct_class_by_name(**augment_kwargs).train().requires_grad_(False).to(device) # subclass of torch.nn.Module augment_pipe.p.copy_(torch.as_tensor(augment_p)) if ada_target is not None: ada_stats = training_stats.Collector(regex='Loss/signs/real') # Distribute across GPUs. if rank == 0: print(f'Distributing across {num_gpus} GPUs...') ddp_modules = dict() for name, module in [('G_mapping', G.mapping), ('G_synthesis', G.synthesis), ('D', D), (None, G_ema), ('augment_pipe', augment_pipe), ('M', M), (None, M_ema) ]: if (num_gpus > 1) and (module is not None) and len(list(module.parameters())) != 0: module.requires_grad_(True) module = torch.nn.parallel.DistributedDataParallel(module, device_ids=[device], broadcast_buffers=False) module.requires_grad_(False) if name is not None: ddp_modules[name] = module # Setup training phases. if rank == 0: print('Setting up training phases...') loss = dnnlib.util.construct_class_by_name(device=device, **ddp_modules, **loss_kwargs) # subclass of training.loss.Loss phases = [] for name, module, opt_kwargs, reg_interval in [('G', G, G_opt_kwargs, G_reg_interval), ('D', D, D_opt_kwargs, D_reg_interval)]: if reg_interval is None: opt = dnnlib.util.construct_class_by_name(params=module.parameters(), **opt_kwargs) # subclass of torch.optim.Optimizer phases += [dnnlib.EasyDict(name=name+'both', module=module, opt=opt, interval=1)] else: # Lazy regularization. mb_ratio = reg_interval / (reg_interval + 1) opt_kwargs = dnnlib.EasyDict(opt_kwargs) opt_kwargs.lr = opt_kwargs.lr * mb_ratio opt_kwargs.betas = [beta ** mb_ratio for beta in opt_kwargs.betas] opt = dnnlib.util.construct_class_by_name(module.parameters(), **opt_kwargs) # subclass of torch.optim.Optimizer phases += [dnnlib.EasyDict(name=name+'main', module=module, opt=opt, interval=1)] if name == 'G' and g_iter>0: phases += ([dnnlib.EasyDict(name=name + 'main', module=module, opt=opt, interval=1)] * g_iter) phases += [dnnlib.EasyDict(name=name+'reg', module=module, opt=opt, interval=reg_interval)] for name, module, opt_kwargs, reg_interval in [('M', M, G_opt_kwargs, G_reg_interval)]: mb_ratio = reg_interval / (reg_interval + 1) opt_kwargs = dnnlib.EasyDict(opt_kwargs) opt_kwargs.lr = opt_kwargs.lr * mb_ratio opt_kwargs.betas = [beta ** mb_ratio for beta in opt_kwargs.betas] #M_opt = dnnlib.util.construct_class_by_name(module.parameters(), **opt_kwargs) # subclass of torch.optim.Optimizer #M_opt = torch.optim.SGD(module.parameters(), lr=0.01, momentum=0.9) print(opt_kwargs.betas, ' >>>>>>>> opt kwargs ssss') M_opt = torch.optim.AdamW(module.parameters(), lr=opt_kwargs.lr, betas=(0.9, 0.999), eps=opt_kwargs.eps, weight_decay=0.01, amsgrad=False) for phase in phases: phase.start_event = None phase.end_event = None if rank == 0: phase.start_event = torch.cuda.Event(enable_timing=True) phase.end_event = torch.cuda.Event(enable_timing=True) # Export sample images. grid_size = None grid_z = None grid_c = None if rank == 0: print('Exporting sample images...') grid_size, images, labels, num_domains = setup_snapshot_image_grid(training_set=training_set) save_image_grid(images, os.path.join(run_dir, 'reals.jpg'), drange=[0,255], grid_size=grid_size) if labels.shape[1] > 0: grid_z = [] for i in range(grid_size[1]//num_domains): random_z = (torch.randn(grid_size[0], G.z_dim, device=device)) for j in range(num_domains): grid_z.append(random_z) grid_z = torch.cat(grid_z, 0).split(batch_gpu) else: grid_z = torch.randn([labels.shape[0], G.z_dim], device=device).split(batch_gpu) grid_c = torch.from_numpy(labels).to(device) grid_c = grid_c.split(batch_gpu) images = torch.cat([G_ema(z=z, c=M_ema(c), noise_mode='const').cpu() for z, c in zip(grid_z, grid_c)]).numpy() save_image_grid(images, os.path.join(run_dir, 'fakes_init.jpg'), drange=[-1,1], grid_size=grid_size) # Initialize logs. if rank == 0: print('Initializing logs...') stats_collector = training_stats.Collector(regex='.*') stats_metrics = dict() stats_jsonl = None stats_tfevents = None if rank == 0: stats_jsonl = open(os.path.join(run_dir, 'stats.jsonl'), 'wt') try: import torch.utils.tensorboard as tensorboard stats_tfevents = tensorboard.SummaryWriter(run_dir) except ImportError as err: print('Skipping tfevents export:', err) # Train. if rank == 0: print(f'Training for {total_kimg} kimg...') print() cur_nimg = 0 cur_tick = 0 tick_start_nimg = cur_nimg tick_start_time = time.time() maintenance_time = tick_start_time - start_time init_temperature = 1.0 min_temperature = 0.5 batch_idx = 0 if progress_fn is not None: progress_fn(0, total_kimg) names = ['Red 0', 'Red 1', 'Green 0', 'Green 1', 'Green 2', 'Green 3', 'Green 4', 'Green 5', 'Green 6', 'Green 7', 'Green 8', 'Green 9', 'Red 2', 'Blue 0', 'Blue 1', 'Blue 2', 'Blue 3', 'Blue 4', 'Blue 5', 'Blue 6', 'Blue 7', 'Blue 8', 'Blue 9', 'Red 3', 'Red 4', 'Red 5', 'Red 6', 'Red 7', 'Red 8', 'Red 9' ] if G.mapping.c_dim == 30: names = [ 'Blue 0', 'Blue 1', 'Blue 2', 'Blue 3', 'Blue 4', 'Blue 5', 'Blue 6', 'Blue 7', 'Blue 8', 'Blue 9', 'Green 0', 'Green 1', 'Green 2', 'Green 3', 'Green 4', 'Green 5', 'Green 6', 'Green 7', 'Green 8', 'Green 9', 'Red 0', 'Red 1', 'Red 2','Red 3', 'Red 4', 'Red 5', 'Red 6', 'Red 7', 'Red 8', 'Red 9' ] elif G.mapping.c_dim == 8: names = [ 'Bald NoSmile Male', 'Bald Smile Male', 'Black NoSmile Female', 'Black NoSmile Male', 'Black Smile Female', 'Black Smile Male', 'Blond NoSmile Female', 'Blond Smile Female' ] #names = ['Green Apple', 'Green Banana', 'Green Pear', 'Red Apple', 'Red Pear', 'Red Strawberry', 'Yellow Banana', 'Yellow Pineapple', 'Yellow StarFruit'] #names = ['Green Apple', 'Green Banana', 'Green Pear', 'Red Apple', 'Red Pear', 'Red Strawberry', 'Yellow Banana', 'Yellow Pineapple', 'Yellow StarFruit'] #names = ['Yellow 1', 'Purple 1', 'Red 1', 'Yellow 2', 'White 1', 'White 2', 'Red 2', 'Purple 2'] version_history_checker = VectorHistoryChecker(G.mapping.c_dim, G.mapping.i_dim, 3) column_history_cheker = ColumnHistoryChecker(G.mapping.c_dim, G.mapping.i_dim, 3) binary_mask_checker = SparsestVector() use_best_binary = 10 while True: ready = False cur_kimg = cur_nimg / 1000.0 should_restart = (cur_tick % 40 ==0) if cur_tick<=5: cur_lambda_rowvar = lambda_rowvar cur_lambda_colvar = 0 cur_lambda_sparse = lambda_sparse cur_entropy_thr = 0.6 cur_lambda_equal = 0 cur_lambda_entropy = lambda_entropy else: cur_lambda_rowvar = 0 cur_lambda_colvar = lambda_colvar cur_lambda_sparse = lambda_sparse cur_entropy_thr = 0.9 cur_lambda_equal = lambda_equal cur_lambda_entropy = lambda_entropy cur_lambda_ortho = lambda_ortho cur_temperature = 1. # Fetch training data. with torch.autograd.profiler.record_function('data_fetch'): phase_real_img, phase_real_c = next(training_set_iterator) phase_real_img = (phase_real_img.to(device).to(torch.float32) / 127.5 - 1).split(batch_gpu) phase_real_c = phase_real_c.to(device).split(batch_gpu) all_gen_z = torch.randn([len(phases) * batch_size, G.z_dim], device=device) all_gen_z = [phase_gen_z.split(batch_gpu) for phase_gen_z in all_gen_z.split(batch_size)] all_gen_c = [training_set.get_label(np.random.randint(len(training_set))) for _ in range(len(phases) * batch_size)] """ all_gen_c = [] for ta in tmp_all_gen_c: all_gen_c.append(F.one_hot(torch.randint(0, 30, (1,)), num_classes=30).float().to(device).squeeze().cpu().numpy()) tmp_all_gen_c = torch.from_numpy(np.stack(tmp_all_gen_c)).to(device) print(all_gen_c.size(), ' >>>>>>>>>>>>>>>>> all genc ', tmp_all_gen_c.size(), ' >>>>>>>>>>>>>>>>> tmp all genc ') """ all_gen_c = torch.from_numpy(np.stack(all_gen_c)).pin_memory().to(device) all_gen_c = [phase_gen_c.split(batch_gpu) for phase_gen_c in all_gen_c.split(batch_size)] loss_dict = {} # Execute training phases. gmain_count = 0 for phase, phase_gen_z, phase_gen_c in zip(phases, all_gen_z, all_gen_c): if batch_idx % phase.interval != 0: continue if phase.name == 'Gmain': gmain_count += 1 only1G = ((cur_tick>use_best_binary) and (gmain_count>1) and (phase.name == 'Gmain')) if only1G: continue # Initialize gradient accumulation. if phase.start_event is not None: phase.start_event.record(torch.cuda.current_stream(device)) phase.opt.zero_grad(set_to_none=True) phase.module.requires_grad_(True) M_opt.zero_grad(set_to_none=True) if phase.name == 'Gmain': M.requires_grad_(True) # Accumulate gradients over multiple rounds. for round_idx, (real_img, real_c, gen_z, gen_c) in enumerate(zip(phase_real_img, phase_real_c, phase_gen_z, phase_gen_c)): sync = (round_idx == batch_size // (batch_gpu * num_gpus) - 1) gain = phase.interval tmp_loss_dict = loss.accumulate_gradients(phase=phase.name, real_img=real_img, real_c=real_c, gen_z=gen_z, gen_c=gen_c, sync=sync, gain=gain, lambda_sparse=cur_lambda_sparse, lambda_entropy=cur_lambda_entropy, lambda_ortho=cur_lambda_ortho, lambda_path=lambda_path, lambda_epsilon=lambda_epsilon, lambda_colvar=cur_lambda_colvar, lambda_rowvar=cur_lambda_rowvar, lambda_equal=cur_lambda_equal, temperature=cur_temperature, entropy_thr=cur_entropy_thr, ) loss_dict.update(tmp_loss_dict) # Update weights. phase.module.requires_grad_(False) M.requires_grad_(False) with torch.autograd.profiler.record_function(phase.name + '_opt'): for param in phase.module.parameters(): if param.grad is not None: misc.nan_to_num(param.grad, nan=0, posinf=1e5, neginf=-1e5, out=param.grad) phase.opt.step() for param in M.parameters(): if param.grad is not None: misc.nan_to_num(param.grad, nan=0, posinf=1e5, neginf=-1e5, out=param.grad) M_opt.step() if phase.end_event is not None: phase.end_event.record(torch.cuda.current_stream(device)) # Update G_ema. with torch.autograd.profiler.record_function('Gema'): ema_nimg = ema_kimg * 1000 if ema_rampup is not None: ema_nimg = min(ema_nimg, cur_nimg * ema_rampup) ema_beta = 0.5 ** (batch_size / max(ema_nimg, 1e-8)) for p_ema, p in zip(G_ema.parameters(), G.parameters()): p_ema.copy_(p.lerp(p_ema, ema_beta)) for b_ema, b in zip(G_ema.buffers(), G.buffers()): b_ema.copy_(b) #ema_beta = 0.9 for p_ema, p in zip(M_ema.parameters(), M.parameters()): p_ema.copy_(p.lerp(p_ema, ema_beta)) for b_ema, b in zip(M_ema.buffers(), M.buffers()): b_ema.copy_(b) # Update state. cur_nimg += batch_size batch_idx += 1 # Execute ADA heuristic. if (ada_stats is not None) and (batch_idx % ada_interval == 0): ada_stats.update() adjust = np.sign(ada_stats['Loss/signs/real'] - ada_target) * (batch_size * ada_interval) / (ada_kimg * 1000) augment_pipe.p.copy_((augment_pipe.p + adjust).max(misc.constant(0, device=device))) # Perform maintenance tasks once per tick. done = (cur_nimg >= total_kimg * 1000) if (not done) and (cur_tick != 0) and (cur_nimg < tick_start_nimg + kimg_per_tick * 1000): continue # Print status line, accumulating the same information in stats_collector. tick_end_time = time.time() fields = [] fields += [f"tick {training_stats.report0('Progress/tick', cur_tick):<5d}"] fields += [f"kimg {training_stats.report0('Progress/kimg', cur_nimg / 1e3):<8.1f}"] fields += [f"time {dnnlib.util.format_time(training_stats.report0('Timing/total_sec', tick_end_time - start_time)):<12s}"] fields += [f"sec/tick {training_stats.report0('Timing/sec_per_tick', tick_end_time - tick_start_time):<7.1f}"] fields += [f"sec/kimg {training_stats.report0('Timing/sec_per_kimg', (tick_end_time - tick_start_time) / (cur_nimg - tick_start_nimg) * 1e3):<7.2f}"] #fields += [f"maintenance {training_stats.report0('Timing/maintenance_sec', maintenance_time):<6.1f}"] #fields += [f"cpumem {training_stats.report0('Resources/cpu_mem_gb', psutil.Process(os.getpid()).memory_info().rss / 2**30):<6.2f}"] #fields += [f"gpumem {training_stats.report0('Resources/peak_gpu_mem_gb', torch.cuda.max_memory_allocated(device) / 2**30):<6.2f}"] fields += [f"sparse {loss_dict['loss_sparse']:.3f}"] fields += [f"entropy {loss_dict['loss_entropy']:.3f}"] fields += [f"path {loss_dict['loss_path']:.3f}"] fields += [f"equal {loss_dict['loss_equal']:.3f}"] fields += [f"rowvar {loss_dict['loss_rowvar']:.3f}"] fields += [f"colvar {loss_dict['loss_colvar']:.3f}"] fields += [f"lambda_sparse {cur_lambda_sparse:.3f}"] fields += [f"lambda_entropy {cur_lambda_entropy:.3f}"] fields += [f"lambda_rowvar {cur_lambda_rowvar:.3f}"] fields += [f"lambda_colvar {cur_lambda_colvar:.3f}"] fields += [f"lambda_path {lambda_path:.3f}"] fields += [f"lambda_equal {lambda_equal:.3f}"] fields += [f"thr {cur_entropy_thr:.3f}"] torch.cuda.reset_peak_memory_stats() #fields += [f"augment {training_stats.report0('Progress/augment', float(augment_pipe.p.cpu()) if augment_pipe is not None else 0):.3f}"] training_stats.report0('Timing/total_hours', (tick_end_time - start_time) / (60 * 60)) training_stats.report0('Timing/total_days', (tick_end_time - start_time) / (24 * 60 * 60)) if rank == 0: print(' '.join(fields)) # Check for abort. if (not done) and (abort_fn is not None) and abort_fn(): done = True if rank == 0: print() print('Aborting...') # Save image snapshot. if (rank == 0) and (image_snapshot_ticks is not None) and (done or cur_tick % image_snapshot_ticks == 0): wss = torch.cat([G_ema.mapping(z,M_ema(c)) for z,c in zip(grid_z, grid_c)]) images = torch.cat([G_ema(z=z, c=M_ema(c), noise_mode='const').cpu() for z, c in zip(grid_z, grid_c)]) def normalize_2nd_moment(x, dim=1, eps=1e-8): return x * (x.square().mean(dim=dim, keepdim=True) + eps).rsqrt() cs = [] for c in grid_c: cs.append(c.argmax(dim=1)) cs = torch.cat(cs, 0).view(G.mapping.c_dim, -1) tmp_imgs = images.reshape(G.mapping.c_dim, -1, images.shape[1], images.shape[2], images.shape[3]) images = images.numpy() wss = wss.reshape(G.mapping.c_dim, -1, wss.shape[1], wss.shape[2]) print(cs.size(), tmp_imgs.shape, wss.shape, ' >>>>>cs size tmp_imgs size <<<<<<<<') save_image_grid(images, os.path.join(run_dir, f'fakes{cur_nimg//1000:06d}.jpg'), drange=[-1,1], grid_size=grid_size) try: print(G_ema.mapping.importance0, G_ema.mapping.importance1) except: pass all_masks = [] with torch.no_grad(): cin = torch.arange(G.mapping.c_dim, device=device) cin = F.one_hot(cin, num_classes=G.mapping.c_dim).float() all_logit = M(cin) all_soft_mask = ((all_logit)) all_hard_mask = (all_soft_mask > 0.5).float() for i in range(G.mapping.c_dim): print('%40s' % names[i], ' ', all_soft_mask[i].cpu().numpy()) for i in range(G.mapping.c_dim): print('%40s' % names[i], ' ', all_hard_mask[i].cpu().numpy().astype(np.uint8)) all_logit = M_ema(cin) all_soft_mask = ((all_logit)) all_hard_mask = (all_soft_mask > 0.5).float() for i in range(G.mapping.c_dim): print('%40s' % names[i], ' ', all_soft_mask[i].cpu().numpy()) for i in range(G.mapping.c_dim): print('%40s' % names[i], ' ', all_hard_mask[i].cpu().numpy().astype(np.uint8)) dscores = [] dhard_masks = all_hard_mask.clone() dsoft_masks = all_soft_mask.clone() for i in range(G.mapping.c_dim): cur_imgs = tmp_imgs[i].to(device) cur_c = F.one_hot(torch.tensor([i]*cur_imgs.size(0), device=device), num_classes=G.mapping.c_dim).float().to(device) d_out = D(cur_imgs, cur_c) d_out = F.softplus(d_out) print('%40s mean: %.2f min: %.2f max: %.2f' % (names[i], d_out.mean().item(), d_out.min().item(), d_out.max().item())) dscores.append(d_out.min().item()) #eval_mask = M(cin, eval=True) #for i in range(G.mapping.c_dim): # print('%10s' % names[i], ' ', eval_mask[i].cpu().numpy().astype(np.uint8)) def normalize_2nd_moment(x, dim=1, eps=1e-8): return x * (x.square().mean(dim=dim, keepdim=True) + eps).rsqrt() def get_onehot(y): shape = y.size() _, ind = y.max(dim=-1) y_hard = torch.zeros_like(y).view(-1, shape[-1]) y_hard.scatter_(1, ind.view(-1, 1), 1) y_hard = y_hard.view(*shape) return y_hard def no_same_rows(x): has = False for i in range(len(x)): for j in range(i+1, len(x)): if torch.allclose(x[i], x[j]): has = True return not has def has_enough_concepts(x): has = True for i in range(len(x)): if torch.sum(x[i])<=1: has = False return has if no_same_rows(dhard_masks) and has_enough_concepts(dhard_masks): print('') print('>>>>>>>>>>>>> This version can be used <<<<<<<<<<<<<<') print('') ready = True binary_mask_checker.add(dhard_masks) try: best_mask = binary_mask_checker.check() for i in range(G.mapping.c_dim): print('%40s' % names[i], ' ', best_mask[i].cpu().numpy().astype(np.uint8), ' best') except: pass masks = all_soft_mask hard_masks = all_hard_mask for i in range(G.mapping.i_dim): cur_i_imgs = [] sorted_index = np.argsort(masks[:, i].cpu().numpy(), axis=0)[::-1] for j in sorted_index: if hard_masks[j, i] == 1: cur_i_imgs.append(tmp_imgs[j]) if len(cur_i_imgs) > 0: cur_i_imgs = torch.cat(cur_i_imgs, 0) save_image(cur_i_imgs, os.path.join(run_dir, f'concept_{cur_nimg // 1000:06d}_{i}.jpg'), nrow=grid_size[0], normalize=True, range=(-1, 1)) if True: for i in range(G.mapping.c_dim): if False: M.param_net.data[i] += -1e9*(dsoft_masks[i]<0.05) M_ema.param_net.data[i] += -1e9*(dsoft_masks[i]<0.05) M.use_param[i] = (dsoft_masks[i]<0.05).float() M_ema.use_param[i] = (dsoft_masks[i]<0.05).float() #print(dscores[i], names[i], ' >>>>>>. what fuck ', M.use_param.view(-1), M.param_net[i]) #topk = torch.topk(torch.tensor(dscores), k=5)[1] consistency = version_history_checker.check_history(dhard_masks) version_history_checker.update_history(dhard_masks) for i in range(G.mapping.c_dim): all_sum = torch.sum(dhard_masks, dim=1) target = torch.mode(all_sum)[0] cur_sum = all_sum[i] set_thr = 1.0 cond1 = (dscores[i]>=set_thr) crit = (cur_sum>1 and cur_sum<=target) #cond2 = (dscores[i]>=0.6 and cur_sum>1 and cur_sum<=target and (i in list(topk.cpu()))) cond3 = consistency[i] should_use=True for j in range(G.mapping.c_dim): if dscores[j]> dscores[i] and torch.sum(torch.abs(dhard_masks[i]-dhard_masks[j]))==0 and j!=i: should_use = False if (cond1) and should_use and crit: #M.param_net.data[i] = 1e9*dhard_masks[i] #M.param_net.data[i] += -1e9*(1-dhard_masks[i]) M.target_value[i] = dhard_masks[i] M.use_param[i] = torch.ones_like(M.use_param[i]) #M_ema.param_net.data[i] = 1e9*dhard_masks[i] #M_ema.param_net.data[i] += -1e9*(1-dhard_masks[i]) M_ema.target_value[i] = dhard_masks[i] M_ema.use_param[i] = torch.ones_like(M.use_param[i]) print('>>>>>> replace classss ', names[i], ' ', dscores[i], ' ', M.target_value[i], ' << consistency ', consistency[i]) column_consistency = column_history_cheker.check_history(dhard_masks) column_history_cheker.update_history(dhard_masks) for j in range(G.mapping.i_dim): cur_soft = dsoft_masks[:,j] cur_hard = dhard_masks[:,j] act = cur_soft[cur_hard==1] deact = cur_soft[cur_hard==0] cur_sum = torch.sum(cur_hard) if (act.mean()>0.9 and act.min()>0.6 and cur_sum>1 and cur_tick==5): #M.param_net.data[:,j] = cur_hard*19 #M.param_net.data[:,j] += -1e19*(1-cur_hard) M.use_param[:,j] = torch.ones_like(M.use_param[:,j]) M.target_value[:,j] = cur_hard #M_ema.param_net.data[:,j] = cur_hard #M_ema.param_net.data[:,j] += -1e19*(1-cur_hard) M_ema.target_value[:,j] = cur_hard M_ema.use_param[:,j] = torch.ones_like(M.use_param[:,j]) print('>>>>> replace columns ', j, ' ', M.target_value[:,j].view(-1), ' ', column_consistency[j]) if cur_tick == use_best_binary: best_mask = binary_mask_checker.check() if best_mask is not None: M.use_param = torch.ones_like(M.use_param) M.target_value = best_mask M_ema.use_param = torch.ones_like(M.use_param) M_ema.target_value = best_mask if (cur_tick % 5 ==0 and cur_tick>0) or cur_tick == use_best_binary: for param in M.parameters(): torch.distributed.broadcast(param.data, 0) torch.distributed.broadcast(M.use_param, 0) torch.distributed.broadcast(M_ema.use_param, 0) torch.distributed.broadcast(M.target_value, 0) torch.distributed.broadcast(M_ema.target_value, 0) for param in M_ema.parameters(): torch.distributed.broadcast(param.data, 0) torch.distributed.barrier() #print(M.use_param, ' >>>>>>> m M use_oaramssss bripdcatss ') # Save network snapshot. snapshot_pkl = None snapshot_data = None if (network_snapshot_ticks is not None) and (done or cur_tick % network_snapshot_ticks == 0) and cur_tick>0: snapshot_data = dict(training_set_kwargs=dict(training_set_kwargs)) for name, module in [('G', G), ('D', D), ('G_ema', G_ema), ('augment_pipe', augment_pipe), ('M', M), ('M_ema', M_ema)]: if module is not None: if num_gpus > 1: misc.check_ddp_consistency(module, ignore_regex=r'.*\.w_avg') module = copy.deepcopy(module).eval().requires_grad_(False).cpu() snapshot_data[name] = module del module # conserve memory snapshot_pkl = os.path.join(run_dir, f'network-snapshot-{cur_nimg//1000:06d}.pkl') if rank == 0: #pass with open(snapshot_pkl, 'wb') as f: pickle.dump(snapshot_data, f) # Evaluate metrics. if (snapshot_data is not None) and (len(metrics) > 0): if rank == 0: print('Evaluating metrics...') for metric in metrics: result_dict = metric_main.calc_metric(metric=metric, G=snapshot_data['G_ema'], M=snapshot_data['M_ema'], dataset_kwargs=training_set_kwargs, num_gpus=num_gpus, rank=rank, device=device) if rank == 0: metric_main.report_metric(result_dict, run_dir=run_dir, snapshot_pkl=snapshot_pkl) stats_metrics.update(result_dict.results) del snapshot_data # conserve memory # Collect statistics. for phase in phases: value = [] if (phase.start_event is not None) and (phase.end_event is not None): phase.end_event.synchronize() value = phase.start_event.elapsed_time(phase.end_event) training_stats.report0('Timing/' + phase.name, value) stats_collector.update() stats_dict = stats_collector.as_dict() # Update logs. timestamp = time.time() if stats_jsonl is not None: fields = dict(stats_dict, timestamp=timestamp) stats_jsonl.write(json.dumps(fields) + '\n') stats_jsonl.flush() if stats_tfevents is not None: global_step = int(cur_nimg / 1e3) walltime = timestamp - start_time for name, value in stats_dict.items(): stats_tfevents.add_scalar(name, value.mean, global_step=global_step, walltime=walltime) for name, value in stats_metrics.items(): stats_tfevents.add_scalar(f'Metrics/{name}', value, global_step=global_step, walltime=walltime) stats_tfevents.flush() if progress_fn is not None: progress_fn(cur_nimg // 1000, total_kimg) # Update state. if False and cur_tick%5==0: for paramgroup in M_opt.param_groups: paramgroup['lr'] = paramgroup['lr'] * 0.1 print('>>>>>>>LR decay <<<<<<< %.7f' % paramgroup['lr']) cur_tick += 1 tick_start_nimg = cur_nimg tick_start_time = time.time() maintenance_time = tick_start_time - tick_end_time if done: break # Done. if rank == 0: print() print('Exiting...') #----------------------------------------------------------------------------