Start dataset card

README.md

@@ -1,140 +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).
 
-# 1X World Model Compression Challenge Dataset
-This repository hosts the dataset for the [1X World Model Compression Challenge](https://huggingface.co/spaces/1x-technologies/1X_World_Model_Challenge_Compression).
-
-```bash
-huggingface-cli download 1x-technologies/worldmodel --repo-type dataset --local-dir data
-```
-
-## Updates Since v1.1
-
-- **Train/Val v2.0 (~100 hours)**, replacing v1.1
-- **Test v2.0 dataset for the Compression Challenge**
-- **Faces blurred** for privacy
-- **New raw video dataset** (CC-BY-NC-SA 4.0) at [worldmodel_raw_data](https://huggingface.co/datasets/1x-technologies/worldmodel_raw_data)
-- **Example scripts** now split into:
-  - `cosmos_video_decoder.py` — for decoding Cosmos Tokenized bins
-  - `unpack_data_test.py` — for reading the new test set
-  - `unpack_data_train_val.py` — for reading the train/val sets
-
----
-
-## Train & Val v2.0
-
-### Format
-
-Each split is sharded:  
-- `video_{shard}.bin` — [NVIDIA Cosmos Tokenizer](https://github.com/NVIDIA/Cosmos-Tokenizer) discrete DV8×8×8 tokens at 30 Hz  
-- `segment_idx_{shard}.bin` — segment boundaries  
-- `states_{shard}.bin` — `np.float32` states (see below)  
-- `metadata.json` / `metadata_{shard}.json` — overall vs. per‐shard metadata
-
-
----
-
-## Test v2.0
-
-We provide a 450 sample **test_v2.0** dataset for the [World Model Compression Challenge](https://huggingface.co/spaces/1x-technologies/1X_World_Model_Challenge_Compression) with a similar structure (`video_{shard}.bin`, `states_{shard}.bin`). Use:
-- `unpack_data_test.py` to read the test set
-- `unpack_data_train_val.py` to read train/val
----
-
-### State Index Definition (New)
+Download with:
 ```
- 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 (0= open, 1= closed)
-22: Right hand closure (0= open, 1= closed)
-23: Linear Velocity
-24: Angular Velocity
-```
-
-## Previous v1.1
-
-- `video.bin` — 16×16 patches at 30Hz, quantized
-- `segment_ids.bin` — segment boundaries
-- `actions/` folder storing multiple `.bin`s for states, closures, etc.
-
-### v1.1 Joint Index
-  ```
-   {
-        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
-    }
-  
-A separate `val_v1.1` set is available.
-
----
-
-## Provided Checkpoints
-
-- `magvit2.ckpt` from [MAGVIT2](https://github.com/TencentARC/Open-MAGVIT2) used in v1.1
-- For v2.0, see [NVIDIA Cosmos Tokenizer](https://github.com/NVIDIA/Cosmos-Tokenizer); we supply `cosmos_video_decoder.py`.
-
----
-
-## Directory Structure Example
-
-```
-train_v1.1/
-val_v1.1/
-train_v2.0/
-val_v2.0/
-test_v2.0/
-  ├── video_{shard}.bin
-  ├── states_{shard}.bin
-  ├── ...
-  ├── metadata_{shard}.json
-cosmos_video_decoder.py
-unpack_data_test.py
-unpack_data_train_val.py
-```
-
-**License**: [Apache-2.0](./LICENSE)  
-**Author**: 1X Technologies  
+huggingface-cli download 1x-technologies/worldmodel --repo-type dataset --local-dir data
 ```

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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