import os import numpy as np import torch import torch.random from torch.optim import AdamW from torch.utils.data import DataLoader import pytorch_lightning as pl from pytorch_lightning import seed_everything from pytorch_lightning.trainer import Trainer from dataloader import CellLoader from celle import VQGanVAE, CELLE from omegaconf import OmegaConf import argparse, os, sys, datetime, glob from celle.celle import gumbel_sample, top_k torch.random.manual_seed(42) np.random.seed(42) from celle_taming_main import ( instantiate_from_config, nondefault_trainer_args, get_parser, ) class CellDataModule(pl.LightningDataModule): def __init__( self, data_csv, dataset, sequence_mode="standard", vocab="bert", crop_size=256, resize=600, batch_size=1, threshold="median", text_seq_len=1000, num_workers=1, **kwargs, ): super().__init__() self.data_csv = data_csv self.dataset = dataset self.protein_sequence_length = 0 self.image_folders = [] self.crop_size = crop_size self.resize = resize self.batch_size = batch_size self.sequence_mode = sequence_mode self.threshold = threshold self.text_seq_len = int(text_seq_len) self.vocab = vocab self.num_workers = num_workers if num_workers is not None else batch_size * 2 def setup(self, stage=None): # called on every GPU self.cell_dataset_train = CellLoader( data_csv=self.data_csv, dataset=self.dataset, crop_size=self.crop_size, resize=self.resize, split_key="train", crop_method="random", sequence_mode=self.sequence_mode, vocab=self.vocab, text_seq_len=self.text_seq_len, threshold=self.threshold, ) self.cell_dataset_val = CellLoader( data_csv=self.data_csv, dataset=self.dataset, crop_size=self.crop_size, resize=self.resize, crop_method="center", split_key="val", sequence_mode=self.sequence_mode, vocab=self.vocab, text_seq_len=self.text_seq_len, threshold=self.threshold, ) def prepare_data(self): pass def train_dataloader(self): return DataLoader( self.cell_dataset_train, num_workers=self.num_workers, shuffle=True, batch_size=self.batch_size, ) def val_dataloader(self): return DataLoader( self.cell_dataset_val, num_workers=self.num_workers, batch_size=self.batch_size, ) # def test_dataloader(self): # transforms = ... # return DataLoader(self.test, batch_size=64) class CELLE_trainer(pl.LightningModule): def __init__( self, vqgan_model_path, vqgan_config_path, ckpt_path=None, image_key="threshold", condition_model_path=None, condition_config_path=None, num_images=2, dim=2, num_text_tokens=30, text_seq_len=1000, depth=16, heads=16, dim_head=64, attn_dropout=0.1, ff_dropout=0.1, attn_types="full", loss_img_weight=7, stable=False, rotary_emb=True, text_embedding="bert", fixed_embedding=True, loss_cond_weight=1, learning_rate=3e-4, monitor="val_loss", ): super().__init__() vae = VQGanVAE( vqgan_model_path=vqgan_model_path, vqgan_config_path=vqgan_config_path ) self.image_key = image_key if condition_config_path: condition_vae = VQGanVAE( vqgan_model_path=condition_model_path, vqgan_config_path=condition_config_path, ) else: condition_vae = None self.celle = CELLE( dim=dim, vae=vae, # automatically infer (1) image sequence length and (2) number of image tokens condition_vae=condition_vae, num_images=num_images, num_text_tokens=num_text_tokens, # vocab size for text text_seq_len=text_seq_len, # text sequence length depth=depth, # should aim to be 64 heads=heads, # attention heads dim_head=dim_head, # attention head dimension attn_dropout=attn_dropout, # attention dropout ff_dropout=ff_dropout, # feedforward dropout loss_img_weight=loss_img_weight, stable=stable, rotary_emb=rotary_emb, text_embedding=text_embedding, fixed_embedding=fixed_embedding, loss_cond_weight=loss_cond_weight, ) self.learning_rate = learning_rate self.num_text_tokens = num_text_tokens self.num_images = num_images if monitor is not None: self.monitor = monitor ignore_keys = [] if condition_model_path: ignore_keys.append("celle.condition_vae") if vqgan_model_path: ignore_keys.append("celle.vae") 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"] ckpt = sd.copy() for k in sd.keys(): for ik in ignore_keys: if k.startswith(ik): # print("Deleting key {} from state_dict.".format(k)) del ckpt[k] self.load_state_dict(ckpt, strict=False) print(f"Restored from {path}") def forward(self, text, condition, target, return_loss=True): return self.celle( text=text, condition=condition, image=target, return_loss=return_loss ) def get_input(self, batch): text = batch["sequence"].squeeze(1) condition = batch["nucleus"] target = batch[self.image_key] return text, condition, target def get_image_from_logits(self, logits, temperature=0.9): filtered_logits = top_k(logits, thres=0.5) sample = gumbel_sample(filtered_logits, temperature=temperature, dim=-1) self.celle.vae.eval() out = self.celle.vae.decode( sample[:, self.celle.text_seq_len + self.celle.condition_seq_len :] - (self.celle.num_text_tokens + self.celle.num_condition_tokens) ) return out def get_loss(self, text, condition, target): loss_dict = {} loss, loss_dict, logits = self(text, condition, target, return_loss=True) return loss, loss_dict def total_loss( self, loss, loss_dict, mode="train", ): loss_dict = {f"{mode}/{key}": value for key, value in loss_dict.items()} for key, value in loss_dict.items(): self.log( key, value, prog_bar=True, logger=True, on_step=True, on_epoch=True, sync_dist=True, ) return loss def training_step(self, batch, batch_idx): text, condition, target = self.get_input(batch) loss, log_dict = self.get_loss(text, condition, target) loss = self.total_loss(loss, log_dict, mode="train") return loss def validation_step(self, batch, batch_idx): with torch.no_grad(): text, condition, target = self.get_input(batch) loss, log_dict = self.get_loss(text, condition, target) loss = self.total_loss(loss, log_dict, mode="val") return loss def configure_optimizers(self): optimizer = AdamW(self.parameters(), lr=self.learning_rate, betas=(0.9, 0.95)) return optimizer def scale_image(self, image): for tensor in image: if torch.min(tensor) < 0: tensor += -torch.min(tensor) else: tensor -= torch.min(tensor) tensor /= torch.max(tensor) return image @torch.no_grad() def log_images(self, batch, **kwargs): log = [] text, condition, target = self.get_input(batch) text = text.squeeze(1).to(self.device) condition = condition.to(self.device) out = self.celle.generate_images(text=text, condition=condition) log["condition"] = self.scale_image(condition) log["output"] = self.scale_image(out) if self.image_key == "threshold": log["threshold"] = self.scale_image(target) log["target"] = self.scale_image(batch["target"]) else: log["target"] = self.scale_image(target) return log # from https://github.com/CompVis/taming-transformers/blob/master/celle_main.py if __name__ == "__main__": # custom parser to specify config files, train, test and debug mode, # postfix, resume. # `--key value` arguments are interpreted as arguments to the trainer. # `nested.key=value` arguments are interpreted as config parameters. # configs are merged from left-to-right followed by command line parameters. # model: # learning_rate: float # target: path to lightning module # params: # key: value # data: # target: celle_main.DataModuleFromConfig # params: # batch_size: int # wrap: bool # train: # target: path to train dataset # params: # key: value # validation: # target: path to validation dataset # params: # key: value # test: # target: path to test dataset # params: # key: value # lightning: (optional, has sane defaults and can be specified on cmdline) # trainer: # additional arguments to trainer # logger: # logger to instantiate # modelcheckpoint: # modelcheckpoint to instantiate # callbacks: # callback1: # target: importpath # params: # key: value now = datetime.datetime.now().strftime("%Y-%m-%dT%H-%M-%S") # add cwd for convenience and to make classes in this file available when # running as `python celle_main.py` # (in particular `celle_main.DataModuleFromConfig`) sys.path.append(os.getcwd()) parser = get_parser() parser = Trainer.add_argparse_args(parser) opt, unknown = parser.parse_known_args() if opt.name and opt.resume: raise ValueError( "-n/--name and -r/--resume cannot be specified both." "If you want to resume training in a new log folder, " "use -n/--name in combination with --resume_from_checkpoint" ) if opt.resume: if not os.path.exists(opt.resume): raise ValueError("Cannot find {}".format(opt.resume)) if os.path.isfile(opt.resume): paths = opt.resume.split("/") idx = len(paths) - paths[::-1].index("logs") + 1 logdir = "/".join(paths[:idx]) ckpt = opt.resume else: assert os.path.isdir(opt.resume), opt.resume logdir = opt.resume.rstrip("/") ckpt = os.path.join(logdir, "checkpoints", "last.ckpt") opt.resume_from_checkpoint = ckpt base_configs = sorted(glob.glob(os.path.join(logdir, "configs/*.yaml"))) opt.base = base_configs + opt.base _tmp = logdir.split("/") nowname = _tmp[_tmp.index("logs") + 1] else: if opt.name: name = "_" + opt.name elif opt.base: cfg_fname = os.path.split(opt.base[0])[-1] cfg_name = os.path.splitext(cfg_fname)[0] name = "_" + cfg_name else: name = "" nowname = now + name + opt.postfix logdir = os.path.join("logs", nowname) ckptdir = os.path.join(logdir, "checkpoints") cfgdir = os.path.join(logdir, "configs") seed_everything(opt.seed) try: # init and save configs configs = [OmegaConf.load(cfg) for cfg in opt.base] cli = OmegaConf.from_dotlist(unknown) config = OmegaConf.merge(*configs, cli) lightning_config = config.pop("lightning", OmegaConf.create()) # merge trainer cli with config trainer_config = lightning_config.get("trainer", OmegaConf.create()) # default to ddp # trainer_config["distributed_backend"] = "ddp" for k in nondefault_trainer_args(opt): trainer_config[k] = getattr(opt, k) if not "gpus" in trainer_config: del trainer_config["distributed_backend"] cpu = True else: gpuinfo = trainer_config["gpus"] print(f"Running on GPUs {gpuinfo}") cpu = False trainer_opt = argparse.Namespace(**trainer_config) lightning_config.trainer = trainer_config # model # model = instantiate_from_config(config.model) model = instantiate_from_config(config.model) # trainer and callbacks trainer_kwargs = dict() # default logger configs # NOTE wandb < 0.10.0 interferes with shutdown # wandb >= 0.10.0 seems to fix it but still interferes with pudb # debugging (wrongly sized pudb ui) # thus prefer testtube for now default_logger_cfgs = { "wandb": { "target": "pytorch_lightning.loggers.WandbLogger", "params": { "name": nowname, "save_dir": logdir, "offline": opt.debug, "id": nowname, }, }, "testtube": { # "target": "pytorch_lightning.loggers.TestTubeLogger", "target": "pytorch_lightning.loggers.TensorBoardLogger", "params": { "name": "testtube", "save_dir": logdir, }, }, } default_logger_cfg = default_logger_cfgs["testtube"] # logger_cfg = lightning_config.logger or OmegaConf.create() try: logger_cfg = lightning_config.logger except: logger_cfg = OmegaConf.create() logger_cfg = OmegaConf.merge(default_logger_cfg, logger_cfg) trainer_kwargs["logger"] = instantiate_from_config(logger_cfg) # modelcheckpoint - use TrainResult/EvalResult(checkpoint_on=metric) to # specify which metric is used to determine best models default_modelckpt_cfg = { "checkpoint_callback": { "target": "pytorch_lightning.callbacks.ModelCheckpoint", "params": { "dirpath": ckptdir, "filename": "{epoch:06}", "verbose": True, "save_last": True, }, } } if hasattr(model, "monitor"): print(f"Monitoring {model.monitor} as checkpoint metric.") default_modelckpt_cfg["checkpoint_callback"]["params"][ "monitor" ] = model.monitor default_modelckpt_cfg["checkpoint_callback"]["params"]["save_top_k"] = 3 try: modelckpt_cfg = lightning_config.modelcheckpoint except: modelckpt_cfg = OmegaConf.create() modelckpt_cfg = OmegaConf.merge(default_modelckpt_cfg, modelckpt_cfg) # trainer_kwargs["checkpoint_callback"] = instantiate_from_config(modelckpt_cfg) # add callback which sets up log directory default_callbacks_cfg = { "setup_callback": { "target": "celle_taming_main.SetupCallback", "params": { "resume": opt.resume, "now": now, "logdir": logdir, "ckptdir": ckptdir, "cfgdir": cfgdir, "config": config, "lightning_config": lightning_config, }, }, # "image_logger": { # "target": "celle_taming_main.ImageLogger", # "params": { # "batch_frequency": 0, # "max_images": 0, # "clamp": False, # "increase_log_steps": False, # }, # }, # "learning_rate_logger": { # "target": "celle_taming_main.LearningRateMonitor", # "params": { # "logging_interval": "step", # # "log_momentum": True # }, # }, } try: callbacks_cfg = lightning_config.callbacks except: callbacks_cfg = OmegaConf.create() callbacks_cfg = OmegaConf.merge(default_callbacks_cfg, callbacks_cfg) callbacks_cfg = OmegaConf.merge(modelckpt_cfg, callbacks_cfg) trainer_kwargs["callbacks"] = [ instantiate_from_config(callbacks_cfg[k]) for k in callbacks_cfg ] trainer = Trainer.from_argparse_args( trainer_opt, **trainer_kwargs, profiler="simple" ) # data data = instantiate_from_config(config.data) # NOTE according to https://pytorch-lightning.readthedocs.io/en/latest/datamodules.html # calling these ourselves should not be necessary but it is. # lightning still takes care of proper multiprocessing though data.setup() data.prepare_data() # configure learning rate bs, lr = config.data.params.batch_size, config.model.learning_rate if not cpu: ngpu = len(lightning_config.trainer.gpus.strip(",").split(",")) else: ngpu = 1 try: accumulate_grad_batches = lightning_config.trainer.accumulate_grad_batches except: accumulate_grad_batches = 1 print(f"accumulate_grad_batches = {accumulate_grad_batches}") lightning_config.trainer.accumulate_grad_batches = accumulate_grad_batches model.learning_rate = accumulate_grad_batches * ngpu * bs * lr print( "Setting learning rate to {:.2e} = {} (accumulate_grad_batches) * {} (num_gpus) * {} (batchsize) * {:.2e} (lr)".format( model.learning_rate, accumulate_grad_batches, ngpu, bs, lr ) ) # allow checkpointing via USR1 def melk(*args, **kwargs): # run all checkpoint hooks if trainer.global_rank == 0: print("Summoning checkpoint.") ckpt_path = os.path.join(ckptdir, "last.ckpt") trainer.save_checkpoint(ckpt_path) def divein(*args, **kwargs): if trainer.global_rank == 0: import pudb pudb.set_trace() import signal signal.signal(signal.SIGUSR1, melk) signal.signal(signal.SIGUSR2, divein) # run if opt.train: try: # model = torch.compile(model, mode="reduce_overhead") torch.compile(trainer.fit(model, data), mode="max-autotune") except Exception: melk() raise if not opt.no_test and not trainer.interrupted: trainer.test(model, data) except Exception: if opt.debug and trainer.global_rank == 0: try: import pudb as debugger except ImportError: import pdb as debugger debugger.post_mortem() raise finally: # move newly created debug project to debug_runs if opt.debug and not opt.resume and trainer.global_rank == 0: dst, name = os.path.split(logdir) dst = os.path.join(dst, "debug_runs", name) os.makedirs(os.path.split(dst)[0], exist_ok=True) os.rename(logdir, dst)