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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.robomimic_lowdim_policy import RobomimicLowdimPolicy
from diffusion_policy.dataset.base_dataset import BaseLowdimDataset
from diffusion_policy.env_runner.base_lowdim_runner import BaseLowdimRunner
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
OmegaConf.register_new_resolver("eval", eval, replace=True)
class TrainRobomimicLowdimWorkspace(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: RobomimicLowdimPolicy = 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)
# configure dataset
dataset: BaseLowdimDataset
dataset = hydra.utils.instantiate(cfg.task.dataset)
assert isinstance(dataset, BaseLowdimDataset)
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: BaseLowdimRunner
env_runner = hydra.utils.instantiate(
cfg.task.env_runner,
output_dir=self.output_dir)
assert isinstance(env_runner, BaseLowdimRunner)
# 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 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
# training loop
log_path = os.path.join(self.output_dir, 'logs.json.txt')
with JsonLogger(log_path) as json_logger:
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))
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
# 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 = TrainRobomimicLowdimWorkspace(cfg)
workspace.run()
if __name__ == "__main__":
main()
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