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# ------------------------------------------------------------------------------------
# minDALL-E
# Copyright (c) 2021 Kakao Brain Corp. All Rights Reserved.
# Licensed under the Apache License, Version 2.0 [see LICENSE for details]
# ------------------------------------------------------------------------------------
import os
import torch
import torch.nn as nn
import pytorch_lightning as pl
from typing import Optional, Tuple
from omegaconf import OmegaConf
from torch.cuda.amp import autocast
from torch.optim.lr_scheduler import CosineAnnealingLR
from torch.nn import functional as F
from .stage1.vqgan import VQGAN
from .stage2.transformer import Transformer1d, iGPT
from .. import utils
from ..utils.config import get_base_config
from ..utils.sampling import sampling, sampling_igpt
from .tokenizer import build_tokenizer
_MODELS = {
'minDALL-E/1.3B': 'https://arena.kakaocdn.net/brainrepo/models/minDALL-E/57b008f02ceaa02b779c8b7463143315/1.3B.tar.gz'
}
class Dalle(nn.Module):
def __init__(self,
config: OmegaConf) -> None:
super().__init__()
self.tokenizer = None
self.stage1 = VQGAN(n_embed=config.stage1.n_embed,
embed_dim=config.stage1.embed_dim,
hparams=config.stage1.hparams)
self.stage2 = Transformer1d(vocab_size_txt=config.stage2.vocab_size_txt,
vocab_size_img=config.stage2.vocab_size_img,
hparams=config.stage2.hparams)
self.config_stage1 = config.stage1
self.config_stage2 = config.stage2
self.config_dataset = config.dataset
@classmethod
def from_pretrained(cls,
path: str) -> nn.Module:
#path = _MODELS[path] if path in _MODELS else path
#path = utils.realpath_url_or_path(path, root=os.path.expanduser(".cache/minDALL-E"))
path = ''
config_base = get_base_config()
config_new = OmegaConf.load(os.path.join(path, '.cache/minDALL-E/1.3B/config.yaml'))
config_update = OmegaConf.merge(config_base, config_new)
model = cls(config_update)
model.tokenizer = build_tokenizer('.cache/minDALL-E/1.3B/tokenizer',
context_length=model.config_dataset.context_length,
lowercase=True,
dropout=None)
model.stage1.from_ckpt('.cache/minDALL-E/1.3B/stage1_last.ckpt')
model.stage2.from_ckpt('.cache/minDALL-E/1.3B/stage2_last.ckpt')
#model.stage1.from_ckpt('https://utexas.box.com/shared/static/rpt9miyj2kikogyekpqnkd6y115xp51i.ckpt')
#model.stage2.from_ckpt('https://utexas.box.com/shared/static/54jc9fw0bious5nx6wvayeqaskcrdgv4.ckpt')
return model
@torch.no_grad()
def sampling(self,
prompt: str,
top_k: int = 256,
top_p: Optional[float] = None,
softmax_temperature: float = 1.0,
num_candidates: int = 96,
device: str = 'cuda:0',
use_fp16: bool = True) -> torch.FloatTensor:
self.stage1.eval()
self.stage2.eval()
tokens = self.tokenizer.encode(prompt)
tokens = torch.LongTensor(tokens.ids)
tokens = torch.repeat_interleave(tokens.unsqueeze(0), num_candidates, dim=0)
# Check if the encoding works as intended
# print(self.tokenizer.decode_batch(tokens.tolist(), skip_special_tokens=True)[0])
tokens = tokens.to(device)
codes = sampling(self.stage2,
tokens,
top_k=top_k,
top_p=top_p,
softmax_temperature=softmax_temperature,
use_fp16=use_fp16)
codes = codes.view(num_candidates, 16, 16) # [B, 16, 16]
pixels = torch.clamp(self.stage1.decode_code(codes) * 0.5 + 0.5, 0, 1) # [B, 256, 256]
return pixels
class ImageGPT(pl.LightningModule):
def __init__(self,
config: OmegaConf) -> None:
super().__init__()
self.stage1 = VQGAN(n_embed=config.stage1.n_embed,
embed_dim=config.stage1.embed_dim,
hparams=config.stage1.hparams)
self.stage2 = iGPT(vocab_size_img=config.stage2.vocab_size_img,
use_cls_cond=config.stage2.use_cls_cond,
hparams=config.stage2.hparams)
self.config = config
self.use_cls_cond = config.stage2.use_cls_cond
# make the parameters in stage 1 not trainable
self.stage1.eval()
for p in self.stage1.parameters():
p.requires_grad = False
@classmethod
def from_pretrained(cls,
path_upstream: str,
path_downstream: str) -> Tuple[nn.Module, OmegaConf]:
config_base = get_base_config(use_default=False)
config_down = OmegaConf.load(path_downstream)
config_down = OmegaConf.merge(config_base, config_down)
model = cls(config_down)
model.stage1.from_ckpt(os.path.join(path_upstream, 'stage1_last.ckpt'), strict=True)
model.stage2.from_ckpt(os.path.join(path_upstream, 'stage2_last.ckpt'), strict=False)
return model, config_down
def sample(self,
cls_idx: Optional[int] = None,
top_k: int = 256,
top_p: Optional[float] = None,
softmax_temperature: float = 1.0,
num_candidates: int = 16,
device: str = 'cuda:0',
use_fp16: bool = True,
is_tqdm: bool = True) -> torch.FloatTensor:
self.stage1.eval()
self.stage2.eval()
if cls_idx is None:
sos = self.stage2.sos.repeat(num_candidates, 1, 1)
else:
sos = torch.LongTensor([cls_idx]).to(device=device)
sos = sos.repeat(num_candidates)
sos = self.stage2.sos(sos).unsqueeze(1)
codes = sampling_igpt(self.stage2,
sos=sos,
top_k=top_k,
top_p=top_p,
softmax_temperature=softmax_temperature,
use_fp16=use_fp16,
is_tqdm=is_tqdm)
codes = codes.view(num_candidates, 16, 16) # [B, 16, 16]
pixels = torch.clamp(self.stage1.decode_code(codes) * 0.5 + 0.5, 0, 1) # [B, 256, 256]
return pixels
def forward(self,
images: torch.FloatTensor,
labels: Optional[torch.LongTensor] = None) -> torch.FloatTensor:
B, C, H, W = images.shape
with torch.no_grad():
with autocast(enabled=False):
codes = self.stage1.get_codes(images).detach()
logits = self.stage2(codes, labels)
return logits, codes
def training_step(self, batch, batch_idx):
images, labels = batch
logits, codes = self(images, labels=labels if self.use_cls_cond else None)
loss = F.cross_entropy(logits.view(-1, logits.shape[-1]), codes.view(-1))
self.log("train/loss", loss, on_step=True, on_epoch=True, prog_bar=False, logger=True)
return loss
def validation_step(self, batch, batch_idx):
images, labels = batch
logits, codes = self(images, labels=labels if self.use_cls_cond else None)
loss = F.cross_entropy(logits.view(-1, logits.shape[-1]), codes.view(-1))
self.log("val/loss", loss, on_step=False, on_epoch=True, prog_bar=False, logger=True)
return loss
def configure_optimizers(self):
assert self.config.optimizer.opt_type == 'adamW'
assert self.config.optimizer.sched_type == 'cosine'
opt = torch.optim.AdamW(self.parameters(),
lr=self.config.optimizer.base_lr,
betas=self.config.optimizer.betas,
weight_decay=self.config.optimizer.weight_decay)
sched = CosineAnnealingLR(opt,
T_max=self.config.optimizer.max_steps,
eta_min=self.config.optimizer.min_lr)
sched = {
'scheduler': sched,
'name': 'cosine'
}
return [opt], [sched]
def optimizer_step(self, epoch, batch_idx, optimizer, optimizer_idx, optimizer_closure,
on_tpu=False, using_native_amp=False, using_lbfgs=False):
optimizer.step(closure=optimizer_closure)
self.lr_schedulers().step()
self.log("lr", self.lr_schedulers().get_last_lr()[0], on_step=True, on_epoch=False, prog_bar=True, logger=True)
def on_epoch_start(self):
self.stage1.eval() |