Start dataset card

README.md

@@ -1,104 +1,12 @@
----
-license: apache-2.0
-pretty_name: 1X World Model Challenge Dataset
-size_categories:
-- 10M<n<100M
-viewer: false
----
+---
+license: apache-2.0
+pretty_name: 1X World Model Challenge Dataset
+size_categories:
+- 10M<n<100M
+---
 Dataset for the [1X World Model Challenge](https://github.com/1x-technologies/1xgpt).
 
 Download with:
 ```
 huggingface-cli download 1x-technologies/worldmodel --repo-type dataset --local-dir data
-```
-
-Changes from v1.1:
-- New train and val dataset of 100 hours, replacing the v1.1 datasets
-- Blur applied to faces
-- Shared a new raw video dataset under CC-BY-NC-SA 4.0: https://huggingface.co/datasets/1x-technologies/worldmodel_raw_data
-- Example scripts to decode Cosmos Tokenized bins `cosmos_video_decoder.py` and load in frame data `unpack_data.py`
-
-Contents of train/val_v2.0:
-
-The training dataset is shareded into 100 independent shards. The definitions are as follows:
-
-- **video_{shard}.bin**: 8x8x8 image patches at 30hz, with 17 frame temporal window, encoded using [NVIDIA Cosmos Tokenizer](https://github.com/NVIDIA/Cosmos-Tokenizer) "Cosmos-Tokenizer-DV8x8x8".
-- **segment_idx_{shard}.bin** - Maps each frame `i` to its corresponding segment index. You may want to use this to separate non-contiguous frames from different videos (transitions).
-- **states_{shard}.bin** - States arrays (defined below in `Index-to-State Mapping`) stored in `np.float32` format. For frame `i`, the corresponding state is represented by `states_{shard}[i]`.
-- **metadata** - The `metadata.json` file provides high-level information about the entire dataset, while `metadata_{shard}.json` files contain specific details for each shard. 
-
-  #### Index-to-State Mapping (NEW)
-  ```
-   {
-        0: HIP_YAW
-        1: HIP_ROLL
-        2: HIP_PITCH
-        3: KNEE_PITCH
-        4: ANKLE_ROLL
-        5: ANKLE_PITCH
-        6: LEFT_SHOULDER_PITCH
-        7: LEFT_SHOULDER_ROLL
-        8: LEFT_SHOULDER_YAW
-        9: LEFT_ELBOW_PITCH
-        10: LEFT_ELBOW_YAW
-        11: LEFT_WRIST_PITCH
-        12: LEFT_WRIST_ROLL
-        13: RIGHT_SHOULDER_PITCH
-        14: RIGHT_SHOULDER_ROLL
-        15: RIGHT_SHOULDER_YAW
-        16: RIGHT_ELBOW_PITCH
-        17: RIGHT_ELBOW_YAW
-        18: RIGHT_WRIST_PITCH
-        19: RIGHT_WRIST_ROLL
-        20: NECK_PITCH
-        21: Left hand closure state (0 = open, 1 = closed)
-        22: Right hand closure state (0 = open, 1 = closed)
-        23: Linear Velocity
-        24: Angular Velocity
-    }
-
-
-Previous version: v1.1
-
-- **magvit2.ckpt** - weights for [MAGVIT2](https://github.com/TencentARC/Open-MAGVIT2) image tokenizer we used. We provide the encoder (tokenizer) and decoder (de-tokenizer) weights.
-
-Contents of train/val_v1.1:
-- **video.bin** - 16x16 image patches at 30hz, each patch is vector-quantized into 2^18 possible integer values. These can be decoded into 256x256 RGB images using the provided `magvig2.ckpt` weights.
-- **segment_ids.bin** - for each frame `segment_ids[i]` uniquely points to the segment index that frame `i` came from. You may want to use this to separate non-contiguous frames from different videos (transitions).
-- **actions/** - a folder of action arrays stored in `np.float32` format. For frame `i`, the corresponding action is given by `joint_pos[i]`, `driving_command[i]`, `neck_desired[i]`, and so on. The shapes and definitions of the arrays are as follows (N is the number of frames):
-  - **joint_pos** `(N, 21)`: Joint positions. See `Index-to-Joint Mapping` below.  
-  - **driving_command** `(N, 2)`: Linear and angular velocities.
-  - **neck_desired** `(N, 1)`: Desired neck pitch.
-  - **l_hand_closure** `(N, 1)`: Left hand closure state (0 = open, 1 = closed).
-  - **r_hand_closure** `(N, 1)`: Right hand closure state (0 = open, 1 = closed).
-  #### Index-to-Joint Mapping (OLD)
-  ```
-   {
-        0: HIP_YAW
-        1: HIP_ROLL
-        2: HIP_PITCH
-        3: KNEE_PITCH
-        4: ANKLE_ROLL
-        5: ANKLE_PITCH
-        6: LEFT_SHOULDER_PITCH
-        7: LEFT_SHOULDER_ROLL
-        8: LEFT_SHOULDER_YAW
-        9: LEFT_ELBOW_PITCH
-        10: LEFT_ELBOW_YAW
-        11: LEFT_WRIST_PITCH
-        12: LEFT_WRIST_ROLL
-        13: RIGHT_SHOULDER_PITCH
-        14: RIGHT_SHOULDER_ROLL
-        15: RIGHT_SHOULDER_YAW
-        16: RIGHT_ELBOW_PITCH
-        17: RIGHT_ELBOW_YAW
-        18: RIGHT_WRIST_PITCH
-        19: RIGHT_WRIST_ROLL
-        20: NECK_PITCH
-    }
-  
-  ```
-
-  
-
-We also provide a small `val_v1.1` data split containing held-out examples not seen in the training set, in case you want to try evaluating your model on held-out frames.
+```

cosmos_video_decoder.py

@@ -1,93 +0,0 @@
-"""
-NOTE: Download the Cosmos-Tokenizer repository and pre-trained model weights before running this script.
-For full installation and setup instructions, please refer to:
-https://github.com/NVIDIA/Cosmos-Tokenizer#readme
-"""
-
-import math
-from pathlib import Path
-
-import av
-import numpy as np
-import torch
-
-from cosmos_tokenizer.utils import tensor2numpy
-from cosmos_tokenizer.video_lib import CausalVideoTokenizer
-
-input_dir = Path("../worldmodel/val_v2.0")
-output_dir = Path("/tmp/reconst_1xgpt/")
-model_name = "Cosmos-Tokenizer-DV8x8x8"
-decoder_path = Path("pretrained_ckpts") / model_name / "decoder.jit"
-
-print(f"Output directory exists: {input_dir.exists()}")
-print(f"Decoder path exists: {decoder_path.exists()}")
-
-rank = 0
-metadata_path = input_dir / f"metadata_{rank}.json"
-if not metadata_path.exists():
-    raise FileNotFoundError(f"Metadata file not found at {metadata_path}")
-
-with open(metadata_path, "r") as f:
-    metadata_shard = json.load(f)
-
-total_frames = metadata_shard["shard_num_frames"]
-print(f"Total frames: {total_frames}")
-
-encoded_video_dataset = np.memmap(input_dir / f"video_{rank}.bin", dtype=np.int32, mode="r", shape=(math.ceil(total_frames / 17), 3, 32, 32))
-
-print(f"Encoded video dataset shape: {encoded_video_dataset.shape}")
-
-indices = torch.tensor(encoded_video_dataset, device="cuda") if not isinstance(encoded_video_dataset, torch.Tensor) else encoded_video_dataset
-
-try:
-    decoder = CausalVideoTokenizer(checkpoint_dec=str(decoder_path))
-    if decoder._dec_model is None:
-        raise RuntimeError(f"Failed to load decoder model from {decoder_path}")
-    print("Decoder initialized successfully.")
-except Exception as e:
-    raise RuntimeError(f"Error loading decoder: {str(e)}") from e
-
-batch_size = 1
-fps = 30
-output_file = output_dir / "reconstructed_video.mp4"
-
-first_batch = torch.from_numpy(encoded_video_dataset[0:1]).cuda()
-with torch.no_grad():
-    first_output = decoder.decode(first_batch).float()
-    _, _, height, width = first_output.shape[-4:]
-
-print(f"Output video dimensions: {width}x{height}")
-
-
-ec = av.open(str(output_file), mode="w")
-es = ec.add_stream("hevc_nvenc", rate=30)
-es.width = 256
-es.height = 256
-
-
-num_batches = math.ceil(len(encoded_video_dataset) / batch_size)
-for i in range(num_batches):
-    start_idx = i * batch_size
-    end_idx = min((i + 1) * batch_size, len(encoded_video_dataset))
-
-    batch = torch.from_numpy(encoded_video_dataset[start_idx:end_idx]).cuda()
-    with torch.no_grad():
-        # [B, 3, 17, 256, 256]
-        reconstructed_batch = decoder.decode(batch)
-
-    # (B, 17, 256, 256, 3)
-    reconstructed_batch = tensor2numpy(reconstructed_batch)
-
-    # frame: 17, 256, 256, 3
-    for this_batch in reconstructed_batch:
-        for single_frame in this_batch:  # Temporal dimension
-            # 256, 256, 3
-            for ep in es.encode(av.VideoFrame.from_ndarray(single_frame, format="rgb24")):
-                ec.mux(ep)
-
-    print(f"Processed batch {i + 1}/{num_batches}", flush=True)
-    if i == 100:
-        break
-
-ec.close()
-print(f"Video saved to: {output_file}")

magvit2.ckpt

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train_v1.1/actions/l_hand_closure.bin → train_v0/actions.bin

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train_v0/metadata.json

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+{"unet": "1x-technologies/worldmodel_unet_v0", "query":"","num_images": 11873739, "s": 20, "vocab_size": 1000}

{train_v1.1 → train_v0}/segment_ids.bin

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train_v0/size.txt

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+11873739

{val_v1.1 → train_v0}/video.bin

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train_v1.1/actions/driving_command.bin

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train_v1.1/actions/joint_pos.bin

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train_v1.1/actions/neck_desired.bin

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train_v1.1/actions/r_hand_closure.bin

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train_v1.1/metadata.json

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-{"token_dtype": "uint32", "s": 16, "h": 16, "w": 16, "vocab_size": 262144, "hz": 30, "tokenizer_ckpt": "data/magvit2.ckpt", "num_images": 10832584}

train_v1.1/video.bin

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train_v2.0/metadata.json

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train_v2.0/metadata/metadata_0.json

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