if __name__ == "__main__": import sys import os import pathlib ROOT_DIR = str(pathlib.Path(__file__).parent.parent.parent) sys.path.append(ROOT_DIR) os.chdir(ROOT_DIR) import os import hydra import torch from omegaconf import OmegaConf import pathlib from torch.utils.data import DataLoader import copy import random import wandb import tqdm import numpy as np import shutil from diffusion_policy.workspace.base_workspace import BaseWorkspace from diffusion_policy.policy.diffusion_unet_hybrid_image_policy import DiffusionUnetHybridImagePolicy from diffusion_policy.dataset.base_dataset import BaseImageDataset from diffusion_policy.env_runner.base_image_runner import BaseImageRunner from diffusion_policy.common.checkpoint_util import TopKCheckpointManager from diffusion_policy.common.json_logger import JsonLogger from diffusion_policy.common.pytorch_util import dict_apply, optimizer_to from diffusion_policy.model.diffusion.ema_model import EMAModel from diffusion_policy.model.common.lr_scheduler import get_scheduler OmegaConf.register_new_resolver("eval", eval, replace=True) class TrainDiffusionUnetHybridWorkspace(BaseWorkspace): include_keys = ['global_step', 'epoch'] def __init__(self, cfg: OmegaConf, output_dir=None): super().__init__(cfg, output_dir=output_dir) # set seed seed = cfg.training.seed torch.manual_seed(seed) np.random.seed(seed) random.seed(seed) # configure model self.model: DiffusionUnetHybridImagePolicy = hydra.utils.instantiate(cfg.policy) self.ema_model: DiffusionUnetHybridImagePolicy = None if cfg.training.use_ema: self.ema_model = copy.deepcopy(self.model) # configure training state self.optimizer = hydra.utils.instantiate( cfg.optimizer, params=self.model.parameters()) # configure training state self.global_step = 0 self.epoch = 0 def run(self): cfg = copy.deepcopy(self.cfg) # resume training if cfg.training.resume: lastest_ckpt_path = self.get_checkpoint_path() if lastest_ckpt_path.is_file(): print(f"Resuming from checkpoint {lastest_ckpt_path}") self.load_checkpoint(path=lastest_ckpt_path) # configure dataset dataset: BaseImageDataset dataset = hydra.utils.instantiate(cfg.task.dataset) assert isinstance(dataset, BaseImageDataset) train_dataloader = DataLoader(dataset, **cfg.dataloader) normalizer = dataset.get_normalizer() # configure validation dataset val_dataset = dataset.get_validation_dataset() val_dataloader = DataLoader(val_dataset, **cfg.val_dataloader) self.model.set_normalizer(normalizer) if cfg.training.use_ema: self.ema_model.set_normalizer(normalizer) # configure lr scheduler lr_scheduler = get_scheduler( cfg.training.lr_scheduler, optimizer=self.optimizer, num_warmup_steps=cfg.training.lr_warmup_steps, num_training_steps=( len(train_dataloader) * cfg.training.num_epochs) \ // cfg.training.gradient_accumulate_every, # pytorch assumes stepping LRScheduler every epoch # however huggingface diffusers steps it every batch last_epoch=self.global_step-1 ) # configure ema ema: EMAModel = None if cfg.training.use_ema: ema = hydra.utils.instantiate( cfg.ema, model=self.ema_model) # configure env env_runner: BaseImageRunner env_runner = hydra.utils.instantiate( cfg.task.env_runner, output_dir=self.output_dir) assert isinstance(env_runner, BaseImageRunner) # configure logging wandb_run = wandb.init( dir=str(self.output_dir), config=OmegaConf.to_container(cfg, resolve=True), **cfg.logging ) wandb.config.update( { "output_dir": self.output_dir, } ) # configure checkpoint topk_manager = TopKCheckpointManager( save_dir=os.path.join(self.output_dir, 'checkpoints'), **cfg.checkpoint.topk ) # device transfer device = torch.device(cfg.training.device) self.model.to(device) if self.ema_model is not None: self.ema_model.to(device) optimizer_to(self.optimizer, device) # save batch for sampling train_sampling_batch = None if cfg.training.debug: cfg.training.num_epochs = 2 cfg.training.max_train_steps = 3 cfg.training.max_val_steps = 3 cfg.training.rollout_every = 1 cfg.training.checkpoint_every = 1 cfg.training.val_every = 1 cfg.training.sample_every = 1 # training loop log_path = os.path.join(self.output_dir, 'logs.json.txt') json_logger = JsonLogger(log_path) json_logger.start() for local_epoch_idx in range(cfg.training.num_epochs): step_log = dict() # ========= train for this epoch ========== train_losses = list() with tqdm.tqdm(train_dataloader, desc=f"Training epoch {self.epoch}", leave=False, mininterval=cfg.training.tqdm_interval_sec) as tepoch: for batch_idx, batch in enumerate(tepoch): # device transfer batch = dict_apply(batch, lambda x: x.to(device, non_blocking=True)) if train_sampling_batch is None: train_sampling_batch = batch # compute loss raw_loss = self.model.compute_loss(batch) loss = raw_loss / cfg.training.gradient_accumulate_every loss.backward() # step optimizer if self.global_step % cfg.training.gradient_accumulate_every == 0: self.optimizer.step() self.optimizer.zero_grad() lr_scheduler.step() # update ema if cfg.training.use_ema: ema.step(self.model) # logging raw_loss_cpu = raw_loss.item() tepoch.set_postfix(loss=raw_loss_cpu, refresh=False) train_losses.append(raw_loss_cpu) step_log = { 'train_loss': raw_loss_cpu, 'global_step': self.global_step, 'epoch': self.epoch, 'lr': lr_scheduler.get_last_lr()[0] } is_last_batch = (batch_idx == (len(train_dataloader)-1)) if not is_last_batch: # log of last step is combined with validation and rollout wandb_run.log(step_log, step=self.global_step) json_logger.log(step_log) self.global_step += 1 if (cfg.training.max_train_steps is not None) \ and batch_idx >= (cfg.training.max_train_steps-1): break # at the end of each epoch # replace train_loss with epoch average train_loss = np.mean(train_losses) step_log['train_loss'] = train_loss # ========= eval for this epoch ========== policy = self.model if cfg.training.use_ema: policy = self.ema_model policy.eval() # run rollout if (self.epoch % cfg.training.rollout_every) == 0: runner_log = env_runner.run(policy) # log all step_log.update(runner_log) # run validation if (self.epoch % cfg.training.val_every) == 0: with torch.no_grad(): val_losses = list() with tqdm.tqdm(val_dataloader, desc=f"Validation epoch {self.epoch}", leave=False, mininterval=cfg.training.tqdm_interval_sec) as tepoch: for batch_idx, batch in enumerate(tepoch): batch = dict_apply(batch, lambda x: x.to(device, non_blocking=True)) loss = self.model.compute_loss(batch) val_losses.append(loss) if (cfg.training.max_val_steps is not None) \ and batch_idx >= (cfg.training.max_val_steps-1): break if len(val_losses) > 0: val_loss = torch.mean(torch.tensor(val_losses)).item() # log epoch average validation loss step_log['val_loss'] = val_loss # run diffusion sampling on a training batch if (self.epoch % cfg.training.sample_every) == 0: with torch.no_grad(): # sample trajectory from training set, and evaluate difference batch = dict_apply(train_sampling_batch, lambda x: x.to(device, non_blocking=True)) obs_dict = batch['obs'] gt_action = batch['action'] result = policy.predict_action(obs_dict) pred_action = result['action_pred'] mse = torch.nn.functional.mse_loss(pred_action, gt_action) step_log['train_action_mse_error'] = mse.item() del batch del obs_dict del gt_action del result del pred_action del mse # checkpoint if (self.epoch % cfg.training.checkpoint_every) == 0: # checkpointing if cfg.checkpoint.save_last_ckpt: self.save_checkpoint() if cfg.checkpoint.save_last_snapshot: self.save_snapshot() # sanitize metric names metric_dict = dict() for key, value in step_log.items(): new_key = key.replace('/', '_') metric_dict[new_key] = value # We can't copy the last checkpoint here # since save_checkpoint uses threads. # therefore at this point the file might have been empty! topk_ckpt_path = topk_manager.get_ckpt_path(metric_dict) if topk_ckpt_path is not None: self.save_checkpoint(path=topk_ckpt_path) # ========= eval end for this epoch ========== policy.train() # end of epoch # log of last step is combined with validation and rollout wandb_run.log(step_log, step=self.global_step) json_logger.log(step_log) self.global_step += 1 self.epoch += 1 json_logger.stop() @hydra.main( version_base=None, config_path=str(pathlib.Path(__file__).parent.parent.joinpath("config")), config_name=pathlib.Path(__file__).stem) def main(cfg): workspace = TrainDiffusionUnetHybridWorkspace(cfg) workspace.run() if __name__ == "__main__": main()