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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 equi_diffpo.workspace.base_workspace import BaseWorkspace
from equi_diffpo.policy.robomimic_image_policy import RobomimicImagePolicy
from equi_diffpo.dataset.base_dataset import BaseImageDataset
from equi_diffpo.env_runner.base_image_runner import BaseImageRunner
from equi_diffpo.common.checkpoint_util import TopKCheckpointManager
from equi_diffpo.common.json_logger import JsonLogger
from equi_diffpo.common.pytorch_util import dict_apply, optimizer_to
OmegaConf.register_new_resolver("eval", eval, replace=True)
class TrainRobomimicImageWorkspace(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: RobomimicImagePolicy = hydra.utils.instantiate(cfg.policy)
# 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)
self.epoch += 1
self.global_step += 1
# 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)
# 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)
# 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')
with JsonLogger(log_path) as json_logger:
while self.epoch < 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
info = self.model.train_on_batch(batch, epoch=self.epoch)
# logging
loss_cpu = info['losses']['action_loss'].item()
tepoch.set_postfix(loss=loss_cpu, refresh=False)
train_losses.append(loss_cpu)
step_log = {
'train_loss': loss_cpu,
'global_step': self.global_step,
'epoch': self.epoch
}
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 ==========
self.model.eval()
# run rollout
if (self.epoch % cfg.training.rollout_every) == 0:
runner_log = env_runner.run(self.model)
# 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))
info = self.model.train_on_batch(batch, epoch=self.epoch, validate=True)
loss = info['losses']['action_loss']
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']
T = gt_action.shape[1]
pred_actions = list()
self.model.reset()
for i in range(T):
result = self.model.predict_action(
dict_apply(obs_dict, lambda x: x[:,[i]])
)
pred_actions.append(result['action'])
pred_actions = torch.cat(pred_actions, dim=1)
mse = torch.nn.functional.mse_loss(pred_actions, gt_action)
step_log['train_action_mse_error'] = mse.item()
del batch
del obs_dict
del gt_action
del result
del pred_actions
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 ==========
self.model.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
@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 = TrainRobomimicImageWorkspace(cfg)
workspace.run()
if __name__ == "__main__":
main()
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