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Parent(s):
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Upload folder using huggingface_hub
Browse files- README.md +62 -0
- config.json +53 -0
- config.yaml +148 -0
- eval_info.json +3523 -0
- model.safetensors +3 -0
- replay.mp4 +0 -0
README.md
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---
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license: apache-2.0
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datasets:
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- lerobot/pusht_keypoints
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pipeline_tag: robotics
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---
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# Model Card for Diffusion Policy / PushT (keypoints)
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Diffusion Policy (as per [Diffusion Policy: Visuomotor Policy
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Learning via Action Diffusion](https://arxiv.org/abs/2303.04137)) trained for the `PushT` environment from [gym-pusht](https://github.com/huggingface/gym-pusht) with keypoint-only observations.
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## How to Get Started with the Model
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Use `python lerobot/scripts/eval.py -p lerobot/diffusion_pusht` to evaluate for 50 episodes with the outputs sent to `outputs/eval`.
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For further information, please see the [LeRobot library](https://github.com/huggingface/lerobot) (particularly the [evaluation script](https://github.com/huggingface/lerobot/blob/main/lerobot/scripts/eval.py)).
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## Training Details
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Trained with TODO commit hash
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The model was trained using [LeRobot's training script](https://github.com/huggingface/lerobot/blob/d747195c5733c4f68d4bfbe62632d6fc1b605712/lerobot/scripts/train.py) and with the [pusht_keypoints](https://huggingface.co/datasets/lerobot/pusht_keypoints/tree/v1.5) dataset, using this command:
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```bash
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python lerobot/scripts/train.py \
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hydra.job.name=diffusion_pusht_keypoints \
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hydra.run.dir=outputs/train/2024-07-03/13-52-44_diffusion_pusht_keypoints \
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env=pusht_keypoints \
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policy=diffusion_pusht_keypoints \
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training.save_checkpoint=true \
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training.offline_steps=200000 \
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training.save_freq=20000 \
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training.eval_freq=10000 \
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training.log_freq=50 \
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training.num_workers=4 \
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eval.n_episodes=50 \
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eval.batch_size=50 \
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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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use_amp=true
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```
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The training curves may be found at https://wandb.ai/alexander-soare/lerobot/runs/5z9d8q9q/overview.
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This took about 5 hours to train on an Nvida RTX H100.
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## Evaluation
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The model was evaluated on the `PushT` environment from [gym-pusht](https://github.com/huggingface/gym-pusht). There are two evaluation metrics on a per-episode basis:
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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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Metric|Average over 500 episodes
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-|-|-
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Average max. overlap ratio | 0.97
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Success rate (%) | 71.0
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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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config.json
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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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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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"horizon": 16,
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"input_normalization_modes": {
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"observation.environment_state": "min_max",
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"observation.state": "min_max"
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},
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"input_shapes": {
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"observation.environment_state": [
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16
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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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"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": "DDIM",
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"num_inference_steps": 10,
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"num_train_timesteps": 100,
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"output_normalization_modes": {
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"action": "min_max"
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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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"prediction_type": "epsilon",
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"pretrained_backbone_weights": null,
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"spatial_softmax_num_keypoints": 32,
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"use_film_scale_modulation": true,
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"use_group_norm": true,
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"vision_backbone": "resnet18"
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}
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config.yaml
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resume: false
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device: cuda
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use_amp: true
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seed: 100000
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dataset_repo_id: alexandersoare/pusht_keypoints_only
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video_backend: pyav
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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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log_freq: 50
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save_checkpoint: true
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save_freq: 20000
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num_workers: 4
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batch_size: 64
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image_transforms:
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enable: false
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max_num_transforms: 3
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random_order: false
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brightness:
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weight: 1
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min_max:
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- 0.8
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- 1.2
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contrast:
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weight: 1
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min_max:
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- 0.8
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- 1.2
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saturation:
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weight: 1
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min_max:
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- 0.5
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- 1.5
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hue:
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weight: 1
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min_max:
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- -0.05
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- 0.05
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sharpness:
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weight: 1
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min_max:
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- 0.8
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- 1.2
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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.environment_state:
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- -0.1
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- 0.0
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observation.state:
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- -0.1
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- 0.0
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action:
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- -0.1
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- 0.0
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- 0.1
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- 0.2
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- 0.3
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- 0.4
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- 0.5
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- 0.6
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- 0.7
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- 0.8
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- 0.9
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- 1.0
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- 1.1
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- 1.2
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- 1.3
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- 1.4
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drop_n_last_frames: 7
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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: environment_state_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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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.environment_state:
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- 16
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observation.state:
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- ${env.state_dim}
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output_shapes:
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action:
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- ${env.action_dim}
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input_normalization_modes:
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observation.environment_state: min_max
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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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- 84
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- 84
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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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- 512
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- 1024
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- 2048
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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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noise_scheduler_type: DDIM
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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: 10
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do_mask_loss_for_padding: false
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eval_info.json
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