Datasets:

---

mahmud8248

/

robot422_box_pnp_eval

like 0

Error code:   FeaturesError
Exception:    ArrowInvalid
Message:      Schema at index 1 was different: 
dataset: struct<repo_id: string, root: null, episodes: null, image_transforms: struct<enable: bool, max_num_transforms: int64, random_order: bool, tfs: struct<brightness: struct<weight: double, type: string, kwargs: struct<brightness: list<item: double>>>, contrast: struct<weight: double, type: string, kwargs: struct<contrast: list<item: double>>>, saturation: struct<weight: double, type: string, kwargs: struct<saturation: list<item: double>>>, hue: struct<weight: double, type: string, kwargs: struct<hue: list<item: double>>>, sharpness: struct<weight: double, type: string, kwargs: struct<sharpness: list<item: double>>>>>, revision: null, use_imagenet_stats: bool, video_backend: string>
env: null
policy: struct<type: string, n_obs_steps: int64, normalization_mapping: struct<VISUAL: string, STATE: string, ACTION: string>, input_features: struct<observation.state: struct<type: string, shape: list<item: int64>>, observation.images.claw: struct<type: string, shape: list<item: int64>>, observation.images.top: struct<type: string, shape: list<item: int64>>>, output_features: struct<action: struct<type: string, shape: list<item: int64>>>, device: string, use_amp: bool, push_to_hub: bool, repo_id: string, private: null, tags: null, license: null, chunk_size: int64, n_action_steps: int64, vision_backbone: string, pretrained_backbone_weights: string, replace_final_stride_with_dilation: bool, pre_norm: bool, dim_model: int64, n_heads: int64, dim_feedforward: int64, feedforward_activation: string, n_encoder_layers: int64, n_decoder_layers: int64, use_vae: bool, latent_dim: int64, n_vae_encoder_layers: int64, temporal_ensemble_coeff: null, dropout: double, kl_weight: double, optimizer_lr: double, optimizer_weight_decay: double, optimizer_lr_backbone: double>
output_dir: string
job_name: string
resume: bool
seed: int64
num_workers: int64
batch_size: int64
steps: int64
eval_freq: int64
log_freq: int64
save_checkpoint: bool
save_freq: int64
use_policy_training_preset: bool
optimizer: struct<type: string, lr: double, weight_decay: double, grad_clip_norm: double, betas: list<item: double>, eps: double>
scheduler: null
eval: struct<n_episodes: int64, batch_size: int64, use_async_envs: bool>
wandb: struct<enable: bool, disable_artifact: bool, project: string, entity: null, notes: null, run_id: string, mode: null>
vs
dataset: struct<repo_id: string, root: null, episodes: null, image_transforms: struct<enable: bool, max_num_transforms: int64, random_order: bool, tfs: struct<brightness: struct<weight: double, type: string, kwargs: struct<brightness: list<item: double>>>, contrast: struct<weight: double, type: string, kwargs: struct<contrast: list<item: double>>>, saturation: struct<weight: double, type: string, kwargs: struct<saturation: list<item: double>>>, hue: struct<weight: double, type: string, kwargs: struct<hue: list<item: double>>>, sharpness: struct<weight: double, type: string, kwargs: struct<sharpness: list<item: double>>>>>, revision: null, use_imagenet_stats: bool, video_backend: string>
env: null
policy: struct<type: string, n_obs_steps: int64, normalization_mapping: struct<VISUAL: string, STATE: string, ACTION: string>, input_features: struct<observation.state: struct<type: string, shape: list<item: int64>>, observation.images.claw: struct<type: string, shape: list<item: int64>>, observation.images.top: struct<type: string, shape: list<item: int64>>>, output_features: struct<action: struct<type: string, shape: list<item: int64>>>, device: string, use_amp: bool, push_to_hub: bool, repo_id: string, private: null, tags: null, license: null, chunk_size: int64, n_action_steps: int64, vision_backbone: string, pretrained_backbone_weights: string, replace_final_stride_with_dilation: bool, pre_norm: bool, dim_model: int64, n_heads: int64, dim_feedforward: int64, feedforward_activation: string, n_encoder_layers: int64, n_decoder_layers: int64, use_vae: bool, latent_dim: int64, n_vae_encoder_layers: int64, temporal_ensemble_coeff: null, dropout: double, kl_weight: double, optimizer_lr: double, optimizer_weight_decay: double, optimizer_lr_backbone: double>
output_dir: string
job_name: string
resume: bool
checkpoint_path: string
seed: int64
num_workers: int64
batch_size: int64
steps: int64
eval_freq: int64
log_freq: int64
save_checkpoint: bool
save_freq: int64
use_policy_training_preset: bool
optimizer: struct<type: string, lr: double, weight_decay: double, grad_clip_norm: double, betas: list<item: double>, eps: double>
scheduler: null
eval: struct<n_episodes: int64, batch_size: int64, use_async_envs: bool>
wandb: struct<enable: bool, disable_artifact: bool, project: string, entity: null, notes: null, run_id: string, mode: null>
Traceback:    Traceback (most recent call last):
                File "/src/services/worker/src/worker/job_runners/split/first_rows.py", line 228, in compute_first_rows_from_streaming_response
                  iterable_dataset = iterable_dataset._resolve_features()
                File "/src/services/worker/.venv/lib/python3.9/site-packages/datasets/iterable_dataset.py", line 3422, in _resolve_features
                  features = _infer_features_from_batch(self.with_format(None)._head())
                File "/src/services/worker/.venv/lib/python3.9/site-packages/datasets/iterable_dataset.py", line 2187, in _head
                  return next(iter(self.iter(batch_size=n)))
                File "/src/services/worker/.venv/lib/python3.9/site-packages/datasets/iterable_dataset.py", line 2391, in iter
                  for key, example in iterator:
                File "/src/services/worker/.venv/lib/python3.9/site-packages/datasets/iterable_dataset.py", line 1882, in __iter__
                  for key, pa_table in self._iter_arrow():
                File "/src/services/worker/.venv/lib/python3.9/site-packages/datasets/iterable_dataset.py", line 1904, in _iter_arrow
                  yield from self.ex_iterable._iter_arrow()
                File "/src/services/worker/.venv/lib/python3.9/site-packages/datasets/iterable_dataset.py", line 559, in _iter_arrow
                  yield new_key, pa.Table.from_batches(chunks_buffer)
                File "pyarrow/table.pxi", line 4116, in pyarrow.lib.Table.from_batches
                File "pyarrow/error.pxi", line 154, in pyarrow.lib.pyarrow_internal_check_status
                File "pyarrow/error.pxi", line 91, in pyarrow.lib.check_status
              pyarrow.lib.ArrowInvalid: Schema at index 1 was different: 
              dataset: struct<repo_id: string, root: null, episodes: null, image_transforms: struct<enable: bool, max_num_transforms: int64, random_order: bool, tfs: struct<brightness: struct<weight: double, type: string, kwargs: struct<brightness: list<item: double>>>, contrast: struct<weight: double, type: string, kwargs: struct<contrast: list<item: double>>>, saturation: struct<weight: double, type: string, kwargs: struct<saturation: list<item: double>>>, hue: struct<weight: double, type: string, kwargs: struct<hue: list<item: double>>>, sharpness: struct<weight: double, type: string, kwargs: struct<sharpness: list<item: double>>>>>, revision: null, use_imagenet_stats: bool, video_backend: string>
              env: null
              policy: struct<type: string, n_obs_steps: int64, normalization_mapping: struct<VISUAL: string, STATE: string, ACTION: string>, input_features: struct<observation.state: struct<type: string, shape: list<item: int64>>, observation.images.claw: struct<type: string, shape: list<item: int64>>, observation.images.top: struct<type: string, shape: list<item: int64>>>, output_features: struct<action: struct<type: string, shape: list<item: int64>>>, device: string, use_amp: bool, push_to_hub: bool, repo_id: string, private: null, tags: null, license: null, chunk_size: int64, n_action_steps: int64, vision_backbone: string, pretrained_backbone_weights: string, replace_final_stride_with_dilation: bool, pre_norm: bool, dim_model: int64, n_heads: int64, dim_feedforward: int64, feedforward_activation: string, n_encoder_layers: int64, n_decoder_layers: int64, use_vae: bool, latent_dim: int64, n_vae_encoder_layers: int64, temporal_ensemble_coeff: null, dropout: double, kl_weight: double, optimizer_lr: double, optimizer_weight_decay: double, optimizer_lr_backbone: double>
              output_dir: string
              job_name: string
              resume: bool
              seed: int64
              num_workers: int64
              batch_size: int64
              steps: int64
              eval_freq: int64
              log_freq: int64
              save_checkpoint: bool
              save_freq: int64
              use_policy_training_preset: bool
              optimizer: struct<type: string, lr: double, weight_decay: double, grad_clip_norm: double, betas: list<item: double>, eps: double>
              scheduler: null
              eval: struct<n_episodes: int64, batch_size: int64, use_async_envs: bool>
              wandb: struct<enable: bool, disable_artifact: bool, project: string, entity: null, notes: null, run_id: string, mode: null>
              vs
              dataset: struct<repo_id: string, root: null, episodes: null, image_transforms: struct<enable: bool, max_num_transforms: int64, random_order: bool, tfs: struct<brightness: struct<weight: double, type: string, kwargs: struct<brightness: list<item: double>>>, contrast: struct<weight: double, type: string, kwargs: struct<contrast: list<item: double>>>, saturation: struct<weight: double, type: string, kwargs: struct<saturation: list<item: double>>>, hue: struct<weight: double, type: string, kwargs: struct<hue: list<item: double>>>, sharpness: struct<weight: double, type: string, kwargs: struct<sharpness: list<item: double>>>>>, revision: null, use_imagenet_stats: bool, video_backend: string>
              env: null
              policy: struct<type: string, n_obs_steps: int64, normalization_mapping: struct<VISUAL: string, STATE: string, ACTION: string>, input_features: struct<observation.state: struct<type: string, shape: list<item: int64>>, observation.images.claw: struct<type: string, shape: list<item: int64>>, observation.images.top: struct<type: string, shape: list<item: int64>>>, output_features: struct<action: struct<type: string, shape: list<item: int64>>>, device: string, use_amp: bool, push_to_hub: bool, repo_id: string, private: null, tags: null, license: null, chunk_size: int64, n_action_steps: int64, vision_backbone: string, pretrained_backbone_weights: string, replace_final_stride_with_dilation: bool, pre_norm: bool, dim_model: int64, n_heads: int64, dim_feedforward: int64, feedforward_activation: string, n_encoder_layers: int64, n_decoder_layers: int64, use_vae: bool, latent_dim: int64, n_vae_encoder_layers: int64, temporal_ensemble_coeff: null, dropout: double, kl_weight: double, optimizer_lr: double, optimizer_weight_decay: double, optimizer_lr_backbone: double>
              output_dir: string
              job_name: string
              resume: bool
              checkpoint_path: string
              seed: int64
              num_workers: int64
              batch_size: int64
              steps: int64
              eval_freq: int64
              log_freq: int64
              save_checkpoint: bool
              save_freq: int64
              use_policy_training_preset: bool
              optimizer: struct<type: string, lr: double, weight_decay: double, grad_clip_norm: double, betas: list<item: double>, eps: double>
              scheduler: null
              eval: struct<n_episodes: int64, batch_size: int64, use_async_envs: bool>
              wandb: struct<enable: bool, disable_artifact: bool, project: string, entity: null, notes: null, run_id: string, mode: null>