feat: switch pusht transformer logging to swanlab

diffusion_policy/env_runner/pusht_image_runner.py

@@ -1,21 +1,37 @@
-import wandb
 import numpy as np
 import torch
 import collections
-import pathlib
 import tqdm
 import dill
 import math
-import wandb.sdk.data_types.video as wv
 from diffusion_policy.env.pusht.pusht_image_env import PushTImageEnv
 from diffusion_policy.gym_util.sync_vector_env import SyncVectorEnv
 from diffusion_policy.gym_util.multistep_wrapper import MultiStepWrapper
-from diffusion_policy.gym_util.video_recording_wrapper import VideoRecordingWrapper, VideoRecorder
 
 from diffusion_policy.policy.base_image_policy import BaseImagePolicy
 from diffusion_policy.common.pytorch_util import dict_apply
 from diffusion_policy.env_runner.base_image_runner import BaseImageRunner
 
+
+def summarize_rollout_metrics(env_seeds, env_prefixs, all_rewards, all_video_paths=None):
+    del all_video_paths
+
+    max_rewards = collections.defaultdict(list)
+    log_data = dict()
+    for seed, prefix, rewards in zip(env_seeds, env_prefixs, all_rewards):
+        max_reward = np.max(rewards)
+        max_rewards[prefix].append(max_reward)
+        log_data[prefix + f'sim_max_reward_{seed}'] = max_reward
+
+    aggregate_key_map = {
+        'train/': 'train_mean_score',
+        'test/': 'test_mean_score',
+    }
+    for prefix, value in max_rewards.items():
+        log_data[aggregate_key_map.get(prefix, prefix + 'mean_score')] = np.mean(value)
+
+    return log_data
+
 class PushTImageRunner(BaseImageRunner):
     def __init__(self,
             output_dir,
@@ -40,24 +56,11 @@ class PushTImageRunner(BaseImageRunner):
         if n_envs is None:
             n_envs = n_train + n_test
 
-        steps_per_render = max(10 // fps, 1)
         def env_fn():
             return MultiStepWrapper(
-                VideoRecordingWrapper(
-                    PushTImageEnv(
-                        legacy=legacy_test,
-                        render_size=render_size
-                    ),
-                    video_recoder=VideoRecorder.create_h264(
-                        fps=fps,
-                        codec='h264',
-                        input_pix_fmt='rgb24',
-                        crf=crf,
-                        thread_type='FRAME',
-                        thread_count=1
-                    ),
-                    file_path=None,
-                    steps_per_render=steps_per_render
+                PushTImageEnv(
+                    legacy=legacy_test,
+                    render_size=render_size
                 ),
                 n_obs_steps=n_obs_steps,
                 n_action_steps=n_action_steps,
@@ -71,21 +74,8 @@ class PushTImageRunner(BaseImageRunner):
         # train
         for i in range(n_train):
             seed = train_start_seed + i
-            enable_render = i < n_train_vis
-
-            def init_fn(env, seed=seed, enable_render=enable_render):
-                # setup rendering
-                # video_wrapper
-                assert isinstance(env.env, VideoRecordingWrapper)
-                env.env.video_recoder.stop()
-                env.env.file_path = None
-                if enable_render:
-                    filename = pathlib.Path(output_dir).joinpath(
-                        'media', wv.util.generate_id() + ".mp4")
-                    filename.parent.mkdir(parents=False, exist_ok=True)
-                    filename = str(filename)
-                    env.env.file_path = filename
 
+            def init_fn(env, seed=seed):
                 # set seed
                 assert isinstance(env, MultiStepWrapper)
                 env.seed(seed)
@@ -97,21 +87,8 @@ class PushTImageRunner(BaseImageRunner):
         # test
         for i in range(n_test):
             seed = test_start_seed + i
-            enable_render = i < n_test_vis
-
-            def init_fn(env, seed=seed, enable_render=enable_render):
-                # setup rendering
-                # video_wrapper
-                assert isinstance(env.env, VideoRecordingWrapper)
-                env.env.video_recoder.stop()
-                env.env.file_path = None
-                if enable_render:
-                    filename = pathlib.Path(output_dir).joinpath(
-                        'media', wv.util.generate_id() + ".mp4")
-                    filename.parent.mkdir(parents=False, exist_ok=True)
-                    filename = str(filename)
-                    env.env.file_path = filename
 
+            def init_fn(env, seed=seed):
                 # set seed
                 assert isinstance(env, MultiStepWrapper)
                 env.seed(seed)
@@ -154,7 +131,6 @@ class PushTImageRunner(BaseImageRunner):
         n_chunks = math.ceil(n_inits / n_envs)
 
         # allocate data
-        all_video_paths = [None] * n_inits
         all_rewards = [None] * n_inits
 
         for chunk_idx in range(n_chunks):
@@ -214,39 +190,16 @@ class PushTImageRunner(BaseImageRunner):
                 pbar.update(action.shape[1])
             pbar.close()
 
-            all_video_paths[this_global_slice] = env.render()[this_local_slice]
             all_rewards[this_global_slice] = env.call('get_attr', 'reward')[this_local_slice]
-        # clear out video buffer
+        # reset env state between evaluation calls
         _ = env.reset()
 
-        # log
-        max_rewards = collections.defaultdict(list)
-        log_data = dict()
-        # results reported in the paper are generated using the commented out line below
-        # which will only report and average metrics from first n_envs initial condition and seeds
-        # fortunately this won't invalidate our conclusion since
-        # 1. This bug only affects the variance of metrics, not their mean
-        # 2. All baseline methods are evaluated using the same code
-        # to completely reproduce reported numbers, uncomment this line:
-        # for i in range(len(self.env_fns)):
-        # and comment out this line
-        for i in range(n_inits):
-            seed = self.env_seeds[i]
-            prefix = self.env_prefixs[i]
-            max_reward = np.max(all_rewards[i])
-            max_rewards[prefix].append(max_reward)
-            log_data[prefix+f'sim_max_reward_{seed}'] = max_reward
-
-            # visualize sim
-            video_path = all_video_paths[i]
-            if video_path is not None:
-                sim_video = wandb.Video(video_path)
-                log_data[prefix+f'sim_video_{seed}'] = sim_video
-
-        # log aggregate metrics
-        for prefix, value in max_rewards.items():
-            name = prefix+'mean_score'
-            value = np.mean(value)
-            log_data[name] = value
-
-        return log_data
+        # results reported in the paper are generated using the commented out
+        # line below, which would only report and average metrics from the
+        # first n_envs initial conditions and seeds. We keep the full n_inits
+        # behavior here.
+        return summarize_rollout_metrics(
+            env_seeds=self.env_seeds[:n_inits],
+            env_prefixs=self.env_prefixs[:n_inits],
+            all_rewards=all_rewards[:n_inits],
+        )

diffusion_policy/workspace/train_diffusion_transformer_hybrid_workspace.py

@@ -8,6 +8,8 @@ if __name__ == "__main__":
     os.chdir(ROOT_DIR)
 
 import os
+import contextlib
+import importlib
 import hydra
 import torch
 from omegaconf import OmegaConf
@@ -15,7 +17,6 @@ import pathlib
 from torch.utils.data import DataLoader
 import copy
 import random
-import wandb
 import tqdm
 import numpy as np
 import shutil
@@ -31,6 +32,111 @@ from diffusion_policy.model.common.lr_scheduler import get_scheduler
 
 OmegaConf.register_new_resolver("eval", eval, replace=True)
 
+
+class _LoggingBackend:
+    def log(self, payload, step=None):
+        raise NotImplementedError
+
+    def finish(self):
+        raise NotImplementedError
+
+
+class _WandbLoggingBackend(_LoggingBackend):
+    def __init__(self, run):
+        self.run = run
+
+    def log(self, payload, step=None):
+        self.run.log(payload, step=step)
+
+    def finish(self):
+        self.run.finish()
+
+
+class _SwanLabLoggingBackend(_LoggingBackend):
+    def __init__(self, run):
+        self.run = run
+
+    def log(self, payload, step=None):
+        self.run.log(payload, step=step)
+
+    def finish(self):
+        self.run.finish()
+
+
+def _load_wandb():
+    try:
+        return importlib.import_module('wandb')
+    except ImportError as exc:
+        raise ImportError(
+            "wandb is required when cfg.logging.backend == 'wandb' or missing"
+        ) from exc
+
+
+def _load_swanlab():
+    try:
+        return importlib.import_module('swanlab')
+    except ImportError:
+        return None
+
+
+def init_logging_backend(cfg: OmegaConf, output_dir):
+    backend = OmegaConf.select(cfg, 'logging.backend', default='wandb')
+    if backend == 'swanlab':
+        swanlab = _load_swanlab()
+        if swanlab is None:
+            raise ImportError("swanlab is required when cfg.logging.backend == 'swanlab'")
+        logging_cfg = cfg.logging
+        mode = logging_cfg.mode
+        if mode == 'online':
+            mode = 'cloud'
+        run = swanlab.init(
+            project=logging_cfg.project,
+            experiment_name=logging_cfg.name,
+            group=logging_cfg.group,
+            tags=logging_cfg.tags,
+            id=logging_cfg.id,
+            resume=logging_cfg.resume,
+            mode=mode,
+            logdir=str(pathlib.Path(output_dir) / 'swanlog'),
+            config=OmegaConf.to_container(cfg, resolve=True),
+        )
+        return _SwanLabLoggingBackend(run)
+
+    if backend not in (None, 'wandb'):
+        raise ValueError(f"Unknown logging backend: {backend}")
+
+    wandb = _load_wandb()
+    logging_kwargs = OmegaConf.to_container(cfg.logging, resolve=True)
+    logging_kwargs.pop('backend', None)
+    run = wandb.init(
+        dir=str(output_dir),
+        config=OmegaConf.to_container(cfg, resolve=True),
+        **logging_kwargs
+    )
+    wandb.config.update(
+        {
+            "output_dir": str(output_dir),
+        }
+    )
+    return _WandbLoggingBackend(run)
+
+
+@contextlib.contextmanager
+def logging_backend_session(cfg: OmegaConf, output_dir):
+    logging_backend = init_logging_backend(cfg=cfg, output_dir=output_dir)
+    primary_error = None
+    try:
+        yield logging_backend
+    except BaseException as exc:
+        primary_error = exc
+        raise
+    finally:
+        try:
+            logging_backend.finish()
+        except BaseException:
+            if primary_error is None:
+                raise
+
 class TrainDiffusionTransformerHybridWorkspace(BaseWorkspace):
     include_keys = ['global_step', 'epoch']
 
@@ -109,18 +215,6 @@ class TrainDiffusionTransformerHybridWorkspace(BaseWorkspace):
             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'),
@@ -148,140 +242,141 @@ class TrainDiffusionTransformerHybridWorkspace(BaseWorkspace):
 
         # 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))
-                        if train_sampling_batch is None:
-                            train_sampling_batch = batch
+        with logging_backend_session(cfg=cfg, output_dir=self.output_dir) as logging_backend:
+            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))
+                            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()
+                            # 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()
+                            # 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)
+                            # 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]
-                        }
+                            # 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
+                            is_last_batch = (batch_idx == (len(train_dataloader)-1))
+                            if not is_last_batch:
+                                # log of last step is combined with validation and rollout
+                                logging_backend.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
+                            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
+                    # 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()
+                    # ========= 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 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 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']
+                    # 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
+                            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()
+                    # 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
+                        # 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)
+                        # 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()
+                        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
+                    # end of epoch
+                    # log of last step is combined with validation and rollout
+                    logging_backend.log(step_log, step=self.global_step)
+                    json_logger.log(step_log)
+                    self.global_step += 1
+                    self.epoch += 1
 
 @hydra.main(
     version_base=None,

image_pusht_diffusion_policy_dit.yaml

@@ -0,0 +1,28 @@
+defaults:
+  - diffusion_policy/config/train_diffusion_transformer_hybrid_workspace@_here_
+  - override /diffusion_policy/config/task@task: pusht_image
+  - _self_
+
+exp_name: pusht_image_dit
+
+policy:
+  _target_: diffusion_policy.policy.diffusion_transformer_hybrid_image_policy.DiffusionTransformerHybridImagePolicy
+
+logging:
+  backend: swanlab
+  mode: online
+  tags: ["${name}", "${task_name}", "${exp_name}", "swanlab"]
+  id: ${now:%Y%m%d%H%M%S}_${name}_${task_name}
+  group: ${exp_name}
+
+dataloader:
+  num_workers: 0
+
+val_dataloader:
+  num_workers: 0
+
+task:
+  env_runner:
+    n_envs: 1
+    n_test_vis: 0
+    n_train_vis: 0

requirements-pusht-5090.txt

@@ -36,3 +36,4 @@ av==14.0.1
 pygame==2.5.2
 robomimic==0.2.0
 opencv-python-headless==4.10.0.84
+swanlab

tests/test_pusht_image_runner_metrics.py

@@ -0,0 +1,110 @@
+import pathlib
+import sys
+
+import gym
+from gym import spaces
+import numpy as np
+import pytest
+import torch
+
+ROOT_DIR = pathlib.Path(__file__).resolve().parents[1]
+if str(ROOT_DIR) not in sys.path:
+    sys.path.append(str(ROOT_DIR))
+
+import diffusion_policy.env_runner.pusht_image_runner as runner_module
+from diffusion_policy.env_runner.pusht_image_runner import summarize_rollout_metrics
+
+
+class FakePushTImageEnv(gym.Env):
+    metadata = {'render.modes': ['rgb_array']}
+
+    def __init__(self, legacy=False, render_size=96):
+        del legacy, render_size
+        self.observation_space = spaces.Dict({
+            'image': spaces.Box(low=0, high=255, shape=(3, 4, 4), dtype=np.uint8),
+        })
+        self.action_space = spaces.Box(low=-1.0, high=1.0, shape=(2,), dtype=np.float32)
+        self.seed_value = 0
+        self.step_count = 0
+
+    def seed(self, seed=None):
+        self.seed_value = 0 if seed is None else seed
+
+    def reset(self):
+        self.step_count = 0
+        return {'image': np.zeros((3, 4, 4), dtype=np.uint8)}
+
+    def step(self, action):
+        del action
+        self.step_count += 1
+        reward = 0.1 if self.seed_value < 10000 else 0.9
+        done = self.step_count >= 1
+        obs = {'image': np.full((3, 4, 4), self.step_count, dtype=np.uint8)}
+        return obs, reward, done, {}
+
+    def render(self, *args, **kwargs):
+        raise AssertionError('render should not be called for scalar-only PushT image rollouts')
+
+
+class FakePolicy:
+    device = torch.device('cpu')
+    dtype = torch.float32
+
+    def reset(self):
+        return None
+
+    def predict_action(self, obs_dict):
+        n_envs = next(iter(obs_dict.values())).shape[0]
+        return {
+            'action': torch.zeros((n_envs, 2, 2), dtype=torch.float32),
+        }
+
+
+def test_summarize_rollout_metrics_keeps_scalar_rewards_renames_means_and_omits_videos():
+    log_data = summarize_rollout_metrics(
+        env_seeds=[11, 12, 101],
+        env_prefixs=['train/', 'train/', 'test/'],
+        all_rewards=[
+            [0.2, 0.8],
+            [0.1, 0.4],
+            [0.5, 0.9],
+        ],
+        all_video_paths=[
+            '/tmp/train-11.mp4',
+            '/tmp/train-12.mp4',
+            '/tmp/test-101.mp4',
+        ],
+    )
+
+    assert log_data['train/sim_max_reward_11'] == 0.8
+    assert log_data['train/sim_max_reward_12'] == 0.4
+    assert log_data['test/sim_max_reward_101'] == 0.9
+    assert log_data['train_mean_score'] == pytest.approx(0.6)
+    assert log_data['test_mean_score'] == pytest.approx(0.9)
+    assert not any(key.startswith('train/sim_video_') for key in log_data)
+    assert not any(key.startswith('test/sim_video_') for key in log_data)
+
+
+def test_runner_ignores_vis_flags_and_never_emits_sim_videos(tmp_path, monkeypatch):
+    monkeypatch.setattr(runner_module, 'PushTImageEnv', FakePushTImageEnv)
+
+    runner = runner_module.PushTImageRunner(
+        output_dir=tmp_path,
+        n_train=1,
+        n_train_vis=1,
+        n_test=1,
+        n_test_vis=1,
+        n_envs=2,
+        max_steps=2,
+        n_obs_steps=2,
+        n_action_steps=2,
+        tqdm_interval_sec=0.0,
+    )
+
+    log_data = runner.run(FakePolicy())
+
+    assert log_data['train/sim_max_reward_0'] == pytest.approx(0.1)
+    assert log_data['test/sim_max_reward_10000'] == pytest.approx(0.9)
+    assert log_data['train_mean_score'] == pytest.approx(0.1)
+    assert log_data['test_mean_score'] == pytest.approx(0.9)
+    assert not any('sim_video' in key for key in log_data)

tests/test_train_diffusion_transformer_workspace_logging.py

@@ -0,0 +1,198 @@
+import importlib
+import pathlib
+import sys
+
+import pytest
+from omegaconf import OmegaConf
+
+ROOT_DIR = pathlib.Path(__file__).resolve().parents[1]
+if str(ROOT_DIR) not in sys.path:
+    sys.path.append(str(ROOT_DIR))
+
+MODULE_NAME = 'diffusion_policy.workspace.train_diffusion_transformer_hybrid_workspace'
+
+
+def load_workspace_module(monkeypatch, *, wandb_missing=False):
+    sys.modules.pop(MODULE_NAME, None)
+    if wandb_missing:
+        monkeypatch.setitem(sys.modules, 'wandb', None)
+    return importlib.import_module(MODULE_NAME)
+
+
+def test_init_logger_uses_swanlab_backend_mapping_without_loading_wandb(tmp_path, monkeypatch):
+    workspace_module = load_workspace_module(monkeypatch, wandb_missing=True)
+    events = []
+
+    class FakeRun:
+        def log(self, payload, step=None):
+            events.append(('log', payload, step))
+
+        def finish(self):
+            events.append(('finish',))
+
+    class FakeSwanLab:
+        def init(self, **kwargs):
+            events.append(('init', kwargs))
+            return FakeRun()
+
+    monkeypatch.setattr(workspace_module, '_load_swanlab', lambda: FakeSwanLab())
+    monkeypatch.setattr(
+        workspace_module,
+        '_load_wandb',
+        lambda: pytest.fail('wandb should not be loaded for the SwanLab backend'),
+    )
+
+    cfg = OmegaConf.create({
+        'logging': {
+            'backend': 'swanlab',
+            'project': 'demo-project',
+            'name': 'demo-run',
+            'group': 'demo-group',
+            'tags': ['pusht', 'dit'],
+            'id': 'run-123',
+            'resume': True,
+            'mode': 'online',
+        }
+    })
+
+    logger = workspace_module.init_logging_backend(cfg=cfg, output_dir=tmp_path)
+    logger.log({'metric': 1.0}, step=7)
+    logger.finish()
+
+    assert events[0][0] == 'init'
+    init_kwargs = events[0][1]
+    assert init_kwargs['project'] == 'demo-project'
+    assert init_kwargs['experiment_name'] == 'demo-run'
+    assert init_kwargs['group'] == 'demo-group'
+    assert init_kwargs['tags'] == ['pusht', 'dit']
+    assert init_kwargs['id'] == 'run-123'
+    assert init_kwargs['resume'] is True
+    assert init_kwargs['mode'] == 'cloud'
+    assert init_kwargs['logdir'] == str(tmp_path / 'swanlog')
+    assert ('log', {'metric': 1.0}, 7) in events
+    assert events.count(('finish',)) == 1
+
+
+def test_init_logger_defaults_to_legacy_wandb_path_when_backend_missing(tmp_path, monkeypatch):
+    workspace_module = load_workspace_module(monkeypatch)
+    events = []
+
+    class FakeRun:
+        def log(self, payload, step=None):
+            events.append(('log', payload, step))
+
+        def finish(self):
+            events.append(('finish',))
+
+    class FakeConfig:
+        def update(self, payload):
+            events.append(('config.update', payload))
+
+    class FakeWandb:
+        def __init__(self):
+            self.config = FakeConfig()
+
+        def init(self, **kwargs):
+            events.append(('init', kwargs))
+            return FakeRun()
+
+    monkeypatch.setattr(workspace_module, '_load_wandb', lambda: FakeWandb())
+
+    cfg = OmegaConf.create({
+        'logging': {
+            'project': 'demo-project',
+            'name': 'demo-run',
+            'group': None,
+            'tags': ['shared'],
+            'id': None,
+            'resume': True,
+            'mode': 'online',
+        }
+    })
+
+    logger = workspace_module.init_logging_backend(cfg=cfg, output_dir=tmp_path)
+    logger.log({'metric': 2.0}, step=3)
+    logger.finish()
+
+    assert events[0][0] == 'init'
+    init_kwargs = events[0][1]
+    assert init_kwargs['dir'] == str(tmp_path)
+    assert init_kwargs['project'] == 'demo-project'
+    assert init_kwargs['name'] == 'demo-run'
+    assert init_kwargs['mode'] == 'online'
+    assert ('config.update', {'output_dir': str(tmp_path)}) in events
+    assert ('log', {'metric': 2.0}, 3) in events
+    assert events.count(('finish',)) == 1
+
+
+def test_init_logger_rejects_unknown_backends(tmp_path, monkeypatch):
+    workspace_module = load_workspace_module(monkeypatch)
+    cfg = OmegaConf.create({
+        'logging': {
+            'backend': 'tensorboard',
+            'project': 'demo-project',
+            'name': 'demo-run',
+            'mode': 'offline',
+        }
+    })
+
+    with pytest.raises(ValueError, match='Unknown logging backend'):
+        workspace_module.init_logging_backend(cfg=cfg, output_dir=tmp_path)
+
+
+
+
+def test_logging_backend_session_preserves_primary_exception_when_finish_fails(tmp_path, monkeypatch):
+    workspace_module = load_workspace_module(monkeypatch)
+    events = []
+
+    class FakeBackend:
+        def log(self, payload, step=None):
+            events.append(('log', payload, step))
+
+        def finish(self):
+            events.append(('finish',))
+            raise RuntimeError('finish boom')
+
+    monkeypatch.setattr(
+        workspace_module,
+        'init_logging_backend',
+        lambda cfg, output_dir: FakeBackend(),
+    )
+
+    cfg = OmegaConf.create({'logging': {'mode': 'offline'}})
+
+    with pytest.raises(ValueError, match='primary boom'):
+        with workspace_module.logging_backend_session(cfg=cfg, output_dir=tmp_path) as logger:
+            logger.log({'metric': 6.0}, step=12)
+            raise ValueError('primary boom')
+
+    assert ('log', {'metric': 6.0}, 12) in events
+    assert events.count(('finish',)) == 1
+
+def test_logging_backend_session_finishes_on_exception(tmp_path, monkeypatch):
+    workspace_module = load_workspace_module(monkeypatch)
+    events = []
+
+    class FakeBackend:
+        def log(self, payload, step=None):
+            events.append(('log', payload, step))
+
+        def finish(self):
+            events.append(('finish',))
+
+    monkeypatch.setattr(
+        workspace_module,
+        'init_logging_backend',
+        lambda cfg, output_dir: FakeBackend(),
+    )
+
+    cfg = OmegaConf.create({'logging': {'mode': 'offline'}})
+
+    with pytest.raises(RuntimeError, match='boom'):
+        with workspace_module.logging_backend_session(cfg=cfg, output_dir=tmp_path) as logger:
+            logger.log({'metric': 5.0}, step=11)
+            raise RuntimeError('boom')
+
+    assert ('log', {'metric': 5.0}, 11) in events
+    assert events.count(('finish',)) == 1