Solve conflicts + pre-commit run -a

README.md

@@ -108,7 +108,10 @@ eval_episodes=7
 
 **Style**
 ```
+# install if needed
 pre-commit install
+# apply style and linter checks before git commit
+pre-commit run -a
 ```
 
 **Tests**

lerobot/common/datasets/pusht.py

@@ -9,19 +9,14 @@ import pymunk
 import torch
 import torchrl
 import tqdm
-from tensordict import TensorDict
-from torchrl.data.datasets.utils import _get_root_dir
-from torchrl.data.replay_buffers.replay_buffers import (
-    TensorDictReplayBuffer,
-)
-from torchrl.data.replay_buffers.samplers import (
-    Sampler,
-)
-from torchrl.data.replay_buffers.storages import TensorStorage, _collate_id
-from torchrl.data.replay_buffers.writers import ImmutableDatasetWriter, Writer
-
 from diffusion_policy.common.replay_buffer import ReplayBuffer
 from diffusion_policy.env.pusht.pusht_env import pymunk_to_shapely
+from tensordict import TensorDict
+from torchrl.data.datasets.utils import _get_root_dir
+from torchrl.data.replay_buffers.replay_buffers import TensorDictReplayBuffer
+from torchrl.data.replay_buffers.samplers import Sampler
+from torchrl.data.replay_buffers.storages import TensorStorage, _collate_id
+from torchrl.data.replay_buffers.writers import ImmutableDatasetWriter, Writer
 
 # as define in env
 SUCCESS_THRESHOLD = 0.95  # 95% coverage,

lerobot/common/datasets/simxarm.py

@@ -8,9 +8,7 @@ import torchrl
 import tqdm
 from tensordict import TensorDict
 from torchrl.data.datasets.utils import _get_root_dir
-from torchrl.data.replay_buffers.replay_buffers import (
-    TensorDictReplayBuffer,
-)
+from torchrl.data.replay_buffers.replay_buffers import TensorDictReplayBuffer
 from torchrl.data.replay_buffers.samplers import (
     Sampler,
     SliceSampler,

lerobot/common/logger.py

@@ -1,13 +1,12 @@
 import contextlib
-import datetime
 import os
 from pathlib import Path
 
 import numpy as np
-import pandas as pd
 from omegaconf import OmegaConf
 from termcolor import colored
 
+
 def make_dir(dir_path):
     """Create directory if it does not already exist."""
     with contextlib.suppress(OSError):

lerobot/common/policies/diffusion.py

@@ -5,7 +5,6 @@ import hydra
 import torch
 import torch.nn as nn
 from diffusers.schedulers.scheduling_ddpm import DDPMScheduler
-
 from diffusion_policy.model.common.lr_scheduler import get_scheduler
 from diffusion_policy.model.vision.model_getter import get_resnet
 from diffusion_policy.model.vision.multi_image_obs_encoder import MultiImageObsEncoder
@@ -128,12 +127,8 @@ class DiffusionPolicy(nn.Module):
 
             out = {
                 "obs": {
-                    "image": batch["observation", "image"].to(
-                        self.device, non_blocking=True
-                    ),
-                    "agent_pos": batch["observation", "state"].to(
-                        self.device, non_blocking=True
-                    ),
+                    "image": batch["observation", "image"].to(self.device, non_blocking=True),
+                    "agent_pos": batch["observation", "state"].to(self.device, non_blocking=True),
                 },
                 "action": batch["action"].to(self.device, non_blocking=True),
             }

lerobot/common/utils.py

@@ -33,7 +33,7 @@ def init_logging():
     logging.getLogger().addHandler(console_handler)
 
 
-def format_number_KMB(num):
+def format_big_number(num):
     suffixes = ["", "K", "M", "B", "T", "Q"]
     divisor = 1000.0
 

lerobot/scripts/train.py

@@ -1,5 +1,4 @@
 import logging
-import time
 
 import hydra
 import numpy as np
@@ -13,7 +12,7 @@ from lerobot.common.datasets.factory import make_offline_buffer
 from lerobot.common.envs.factory import make_env
 from lerobot.common.logger import Logger
 from lerobot.common.policies.factory import make_policy
-from lerobot.common.utils import format_number_KMB, init_logging, set_seed
+from lerobot.common.utils import format_big_number, init_logging, set_seed
 from lerobot.scripts.eval import eval_policy
 
 
@@ -49,11 +48,11 @@ def log_train_info(logger, info, step, cfg, offline_buffer, is_offline):
     num_episodes = num_samples / avg_samples_per_ep
     num_epochs = num_samples / offline_buffer.num_samples
     log_items = [
-        f"step:{format_number_KMB(step)}",
+        f"step:{format_big_number(step)}",
         # number of samples seen during training
-        f"smpl:{format_number_KMB(num_samples)}",
+        f"smpl:{format_big_number(num_samples)}",
         # number of episodes seen during training
-        f"ep:{format_number_KMB(num_episodes)}",
+        f"ep:{format_big_number(num_episodes)}",
         # number of time all unique samples are seen
         f"epch:{num_epochs:.2f}",
         f"loss:{loss:.3f}",
@@ -86,11 +85,11 @@ def log_eval_info(logger, info, step, cfg, offline_buffer, is_offline):
     num_episodes = num_samples / avg_samples_per_ep
     num_epochs = num_samples / offline_buffer.num_samples
     log_items = [
-        f"step:{format_number_KMB(step)}",
+        f"step:{format_big_number(step)}",
         # number of samples seen during training
-        f"smpl:{format_number_KMB(num_samples)}",
+        f"smpl:{format_big_number(num_samples)}",
         # number of episodes seen during training
-        f"ep:{format_number_KMB(num_episodes)}",
+        f"ep:{format_big_number(num_episodes)}",
         # number of time all unique samples are seen
         f"epch:{num_epochs:.2f}",
         f"∑rwrd:{avg_sum_reward:.3f}",
@@ -156,7 +155,6 @@ def train(cfg: dict, out_dir=None, job_name=None):
 
     logger = Logger(out_dir, job_name, cfg)
 
-    online_ep_idx = 0
     step = 0  # number of policy update
 
     is_offline = True
@@ -200,7 +198,8 @@ def train(cfg: dict, out_dir=None, job_name=None):
                 auto_cast_to_device=True,
             )
         assert len(rollout) <= cfg.env.episode_length
-        rollout["episode"] = torch.tensor([online_ep_idx] * len(rollout), dtype=torch.int)
+        # set same episode index for all time steps contained in this rollout
+        rollout["episode"] = torch.tensor([env_step] * len(rollout), dtype=torch.int)
         online_buffer.extend(rollout)
 
         ep_sum_reward = rollout["next", "reward"].sum()
@@ -210,12 +209,10 @@ def train(cfg: dict, out_dir=None, job_name=None):
             "avg_sum_reward": np.nanmean(ep_sum_reward),
             "avg_max_reward": np.nanmean(ep_max_reward),
             "pc_success": np.nanmean(ep_success) * 100,
-            "online_ep_idx": online_ep_idx,
+            "env_step": env_step,
             "ep_length": len(rollout),
         }
 
-        online_ep_idx += 1
-
         for _ in range(cfg.policy.utd):
             train_info = policy.update(
                 online_buffer,
@@ -233,7 +230,7 @@ def train(cfg: dict, out_dir=None, job_name=None):
                     num_episodes=cfg.eval_episodes,
                     return_first_video=True,
                 )
-                log_eval_info(L, eval_info, step, cfg, offline_buffer, is_offline)
+                log_eval_info(logger, eval_info, step, cfg, offline_buffer, is_offline)
                 if cfg.wandb.enable:
                     logger.log_video(first_video, step, mode="eval")
 

lerobot/scripts/visualize_dataset.py

@@ -3,9 +3,7 @@ from pathlib import Path
 import hydra
 import imageio
 import torch
-from torchrl.data.replay_buffers import (
-    SliceSamplerWithoutReplacement,
-)
+from torchrl.data.replay_buffers import SliceSamplerWithoutReplacement
 
 from lerobot.common.datasets.factory import make_offline_buffer