remove_hydra (#2)
Browse files- Upload folder using huggingface_hub (b06c88b1bf33993b7f25b2cd64dba79923eda85b)
- Upload eval_info.json with huggingface_hub (ba8a9926fc085c3785f144e2b676bc35ca053a5c)
- Delete config.yaml (81e6614b6692f4552ff46beea8dc97d1ba4e9314)
- Update README (21573fe81522899ec7144da09e14c34034c13e6d)
- Use 175k steps checkpoint (d3d143b0342488252497853815b27ce3c0384c6b)
- Cleanup, update training curves (6d8bb7de11d55b2100c12ca4b73a6a72e2754369)
- Update results (f322b4fdce74e7d3c4e0636fb1135814996eabf2)
- README.md +35 -24
- config.json +59 -38
- config.yaml +0 -141
- eval_avg_max_reward.csv +0 -21
- eval_info.json +0 -0
- eval_pc_success.csv +0 -21
- model.safetensors +2 -2
- train_config.json +233 -0
- train_loss.csv +0 -801
- training_curves.png +0 -0
README.md
CHANGED
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license: apache-2.0
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datasets:
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- lerobot/pusht
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pipeline_tag: robotics
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---
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# Model Card for Diffusion Policy / PushT
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## Training Details
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```bash
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python lerobot/scripts/train.py \
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wandb.enable=true \
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wandb.disable_artifact=true \
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device=cuda
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```
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The training curves may be found at https://wandb.ai/
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This took about 7 hours to train on an Nvida RTX 3090.
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_Note: At the time of training, [this PR](https://github.com/huggingface/lerobot/pull/129) was also incorporated._
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## Evaluation
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- Maximum overlap with target (seen as `eval/avg_max_reward` in the charts above). This ranges in [0, 1].
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- Success: whether or not the maximum overlap is at least 95%.
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Here are the metrics for 500 episodes worth of evaluation.
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<blank>|Ours|Theirs
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-|-|-
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Average max. overlap ratio | 0.
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Success rate for 500 episodes (%) |
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Beta distribution lower/mean/upper (%) | 61.6 / 63.7 / 65.9 | 62.0 / 64.1 / 66.3
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The results of each of the individual rollouts may be found in [eval_info.json](eval_info.json).
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license: apache-2.0
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datasets:
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- lerobot/pusht
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tags:
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- diffusion-policy
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- model_hub_mixin
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- pytorch_model_hub_mixin
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- robotics
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pipeline_tag: robotics
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---
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# Model Card for Diffusion Policy / PushT
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## Training Details
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Trained with [LeRobot@3c0a209](https://github.com/huggingface/lerobot/tree/3c0a209f9fac4d2a57617e686a7f2a2309144ba2).
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The model was trained using [LeRobot's training script](https://github.com/huggingface/lerobot/blob/main/lerobot/scripts/train.py) and with the [pusht](https://huggingface.co/datasets/lerobot/pusht) dataset, using this command:
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```bash
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python lerobot/scripts/train.py \
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--output_dir=outputs/train/diffusion_pusht \
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--policy.type=diffusion \
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--dataset.repo_id=lerobot/pusht \
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--seed=100000 \
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--env.type=pusht \
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--batch_size=64 \
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--offline.steps=200000 \
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--eval_freq=25000 \
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--save_freq=25000 \
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--wandb.enable=true
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```
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The training curves may be found at https://wandb.ai/aliberts/lerobot/runs/s7elvf4r.
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The current model corresponds to the checkpoint at 175k steps.
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## Evaluation
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- Maximum overlap with target (seen as `eval/avg_max_reward` in the charts above). This ranges in [0, 1].
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- Success: whether or not the maximum overlap is at least 95%.
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Here are the metrics for 500 episodes worth of evaluation. The "Theirs" column is for an equivalent model trained on the original Diffusion Policy repository and evaluated on LeRobot (the model weights may be found in the [`original_dp_repo`](https://huggingface.co/lerobot/diffusion_pusht/tree/original_dp_repo) branch of this respository).
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<blank>|Ours|Theirs
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-|-|-
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Average max. overlap ratio | 0.955 | 0.957
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Success rate for 500 episodes (%) | 65.4 | 64.2
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The results of each of the individual rollouts may be found in [eval_info.json](eval_info.json).
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It was produced after training with this command:
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```bash
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python lerobot/scripts/eval.py \
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--policy.path=outputs/train/diffusion_pusht/checkpoints/175000/pretrained_model \
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--output_dir=outputs/eval/diffusion_pusht/175000 \
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--env.type=pusht \
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--eval.n_episodes=500 \
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--eval.batch_size=50 \
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--device=cuda \
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--use_amp=false
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```
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config.json
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{
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96,
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],
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"observation.state": [
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2
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]
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},
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"output_shapes": {
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"action": [
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2
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]
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},
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"input_normalization_modes": {
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"observation.image": "mean_std",
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"observation.state": "min_max"
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},
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"output_normalization_modes": {
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"action": "min_max"
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},
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"vision_backbone": "resnet18",
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"crop_shape": [
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84,
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],
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"
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"
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"use_group_norm": true,
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"spatial_softmax_num_keypoints": 32,
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"down_dims": [
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512,
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1024,
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2048
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],
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"kernel_size": 5,
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"n_groups": 8,
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"
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"num_train_timesteps": 100,
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"prediction_type": "epsilon",
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}
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{
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"beta_end": 0.02,
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"beta_schedule": "squaredcos_cap_v2",
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"beta_start": 0.0001,
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"clip_sample": true,
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"clip_sample_range": 1.0,
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"crop_is_random": true,
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"crop_shape": [
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84,
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],
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"diffusion_step_embed_dim": 128,
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"do_mask_loss_for_padding": false,
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"down_dims": [
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512,
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1024,
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2048
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],
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"drop_n_last_frames": 7,
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"horizon": 16,
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"input_features": {
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"observation.image": {
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"shape": [
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3,
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],
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"type": "VISUAL"
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},
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"observation.state": {
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"shape": [
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],
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"type": "STATE"
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}
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},
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"kernel_size": 5,
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"n_action_steps": 8,
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"n_groups": 8,
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"n_obs_steps": 2,
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"noise_scheduler_type": "DDPM",
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"normalization_mapping": {
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"ACTION": "MIN_MAX",
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"STATE": "MIN_MAX",
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"VISUAL": "MEAN_STD"
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},
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"num_inference_steps": null,
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"num_train_timesteps": 100,
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"optimizer_betas": [
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0.95,
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0.999
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],
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"optimizer_eps": 1e-08,
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"optimizer_lr": 0.0001,
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"optimizer_weight_decay": 1e-06,
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"output_features": {
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"action": {
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"shape": [
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],
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"type": "ACTION"
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}
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},
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"prediction_type": "epsilon",
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"pretrained_backbone_weights": null,
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"scheduler_name": "cosine",
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"scheduler_warmup_steps": 500,
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"spatial_softmax_num_keypoints": 32,
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"type": "diffusion",
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"use_film_scale_modulation": true,
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"use_group_norm": true,
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"use_separate_rgb_encoder_per_camera": false,
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"vision_backbone": "resnet18"
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}
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config.yaml
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device: cuda
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use_amp: false
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seed: 100000
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dataset_repo_id: lerobot/pusht
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training:
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offline_steps: 200000
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online_steps: 0
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online_steps_between_rollouts: 1
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online_sampling_ratio: 0.5
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online_env_seed: ???
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eval_freq: 10000
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save_freq: 20000
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log_freq: 250
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save_model: true
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batch_size: 64
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grad_clip_norm: 10
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lr: 0.0001
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lr_scheduler: cosine
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lr_warmup_steps: 500
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adam_betas:
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- 0.95
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- 0.999
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adam_eps: 1.0e-08
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adam_weight_decay: 1.0e-06
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delta_timestamps:
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observation.image:
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observation.state:
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action:
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n_end_keyframes_dropped: ${policy.horizon} - ${policy.n_action_steps} - ${policy.n_obs_steps}
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+ 1
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eval:
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n_episodes: 50
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batch_size: 50
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use_async_envs: false
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wandb:
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enable: true
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disable_artifact: true
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project: lerobot
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notes: ''
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fps: 10
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env:
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name: pusht
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task: PushT-v0
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image_size: 96
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state_dim: 2
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action_dim: 2
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fps: ${fps}
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episode_length: 300
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gym:
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obs_type: pixels_agent_pos
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render_mode: rgb_array
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visualization_width: 384
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visualization_height: 384
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override_dataset_stats:
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observation.image:
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mean:
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- - - 0.5
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- - - 0.5
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- - - 0.5
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std:
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observation.state:
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min:
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- 32.938293
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max:
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- 496.14618
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- 510.9579
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action:
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min:
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max:
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policy:
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name: diffusion
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n_obs_steps: 2
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horizon: 16
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n_action_steps: 8
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input_shapes:
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observation.image:
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- 3
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observation.state:
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output_shapes:
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action:
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input_normalization_modes:
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observation.image: mean_std
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observation.state: min_max
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output_normalization_modes:
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action: min_max
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vision_backbone: resnet18
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crop_shape:
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crop_is_random: true
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pretrained_backbone_weights: null
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use_group_norm: true
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spatial_softmax_num_keypoints: 32
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down_dims:
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kernel_size: 5
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n_groups: 8
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diffusion_step_embed_dim: 128
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use_film_scale_modulation: true
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num_train_timesteps: 100
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beta_schedule: squaredcos_cap_v2
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beta_start: 0.0001
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beta_end: 0.02
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prediction_type: epsilon
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clip_sample: true
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clip_sample_range: 1.0
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num_inference_steps: 100
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training_curves.png
CHANGED