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[?1049h(B[?7hEvery 1.0s: squeue --mehkn1993.localdomain: Wed Jun 25 10:23:06 2025JOBID PARTITION NAME USER ST\tTIME NODES NODELIST(REASON)3289577 accelerat train_dy tum_ind3 R 14:46:30\t 1 hkn07123290195 cpuonly mp4_to_n tum_ind3 PD\t0:00\t 1 (Priority)
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import dm_pix as pix\nimport einops\nimport jax\nimport jax.numpy as jnp\n\n\ndef patchify(videos: jax.Array, size: int) -> jax.Array:\n B, T, H, W, C = videos.shape\n x = jnp.pad(videos, ((0, 0), (0, 0), (0, -H % size), (0, -W % size), (0, 0)))\n return einops.rearrange(\n x, "b t (hn hp) (wn wp) c -> b t (hn wn) (hp wp c)", hp=size, wp=size\n )\n\n\ndef unpatchify(patches: jax.Array, size: int, h_out: int, w_out: int) -> jax.Array:\n h_pad = -h_out % size\n hn = (h_out + h_pad) // size\n x = einops.rearrange(\n patches,\n "b t (hn wn) (hp wp c) -> b t (hn hp) (wn wp) c",\n hp=size,\n wp=size,\n hn=hn,\n )\n return x[:, :, :h_out, :w_out]\n
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utils/preprocess_dataset.py
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from dataclasses import dataclass\n\nimport tensorflow as tf\nimport numpy as np\nimport logging\nimport tyro\nfrom pathlib import Path\n\nlogging.basicConfig(level=logging.INFO)\n\n\n@dataclass\nclass Args:\n source_data_dir: str = "data/coinrun_episodes"\n output_tfrecords_dir: str = "data_tfrecords"\n num_shards: int = 50\n\n\nargs = tyro.cli(Args)\n\n\ndef _bytes_feature(value):\n if isinstance(value, type(tf.constant(0))):\n value = value.numpy()\n return tf.train.Feature(bytes_list=tf.train.BytesList(value=[value]))\n\n\ndef _int64_feature(value):\n return tf.train.Feature(int64_list=tf.train.Int64List(value=[value]))\n\n\ndef create_tfrecord_example(episode_numpy_array):\n feature = {\n "height": _int64_feature(episode_numpy_array.shape[1]),\n "width": _int64_feature(episode_numpy_array.shape[2]),\n "channels": _int64_feature(episode_numpy_array.shape[3]),\n "sequence_length": _int64_feature(episode_numpy_array.shape[0]),\n "raw_video": _bytes_feature(episode_numpy_array.tobytes()),\n }\n return tf.train.Example(features=tf.train.Features(feature=feature))\n\n\ndef main_preprocess(data_dir_str, output_dir_str, num_shards):\n data_dir = Path(data_dir_str)\n output_dir = Path(output_dir_str)\n output_dir.mkdir(parents=True, exist_ok=True)\n\n metadata = np.load(data_dir / "metadata.npy", allow_pickle=True)\n episode_source_paths = [Path(item["path"]) for item in metadata]\n num_total_episodes = len(episode_source_paths)\n\n if num_shards <= 0:\n raise ValueError("num_shards must be positive.")\n if num_shards > num_total_episodes:\n logging.warning(\n f"Warning: num_shards ({num_shards}) is greater than total episodes ({num_total_episodes}). "\n f"Setting num_shards to {num_total_episodes}."\n )\n num_shards = num_total_episodes\n\n logging.info(\n f"Preparing to write {num_total_episodes} episodes to {num_shards} TFRecord shards in {output_dir}..."\n )\n\n output_filenames = [\n str(output_dir / f"shard-{i:05d}-of-{num_shards:05d}.tfrecord")\n for i in range(num_shards)\n ]\n writers = [tf.io.TFRecordWriter(filename) for filename in output_filenames]\n\n writer_idx_for_episode = 0\n try:\n for i, npy_path in enumerate(episode_source_paths):\n if i % 100 == 0 and i > 0:\n logging.info(f" Processed {i}/{num_total_episodes} episodes...")\n try:\n episode_data = np.load(npy_path)\n tf_example = create_tfrecord_example(episode_data)\n\n current_writer = writers[writer_idx_for_episode]\n current_writer.write(tf_example.SerializeToString())\n\n writer_idx_for_episode = (writer_idx_for_episode + 1) % num_shards\n\n except Exception as e:\n logging.error(f"Skipping {npy_path} due to error: {e}")\n finally:\n for writer in writers:\n writer.close()\n logging.info(\n f"TFRecord sharding complete. {num_shards} shards written to {output_dir}."\n )\n logging.info("Generated shard files:")\n for fname in output_filenames:\n logging.info(f" {fname}")\n\n\nif __name__ == "__main__":\n if (\n not Path(args.source_data_dir).exists()\n or not (Path(args.source_data_dir) / "metadata.npy").exists()\n ):\n logging.error(f"Please generate data in '{args.source_data_dir}' first.")\n else:\n main_preprocess(\n args.source_data_dir, args.output_tfrecords_dir, args.num_shards\n )\n
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sbatch_scripts/preprocess/mp4_to_npy_6xx copy.sbatch
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#!/usr/bin/env bash\n\n#SBATCH --nodes=1\n#SBATCH --ntasks-per-node=1\n#SBATCH --time=01:00:00\n#SBATCH --partition=cpuonly\n#SBATCH --account=hk-project-p0023960\n#SBATCH --output=logs/logs_preprocessing/%x_%j.log\n#SBATCH --error=logs/logs_preprocessing/%x_%j.log\n#SBATCH --mail-user=avocadoaling@gmail.com\n#SBATCH --job-name=mp4_to_npy_openai_6xx_Jun_29\n#SBATCH --mail-type=ALL\n#!/usr/bin/env bash\n# Log the sbatch script\ncat $0\n\nsource .venv_jafar/bin/activate\n\ninput_path="/hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/data/open_ai_minecraft/all_6xx_Jun_29/"\noutput_path="/hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/data/open_ai_minecraft_npy/all_6xx_Jun_29"\n\nstart_time=$(date +%s) \npython utils/preprocess_video_to_npy.py --input_path $input_path --output_path $output_path\nend_time=$(date +%s)\necho "Time taken: $((end_time - start_time)) seconds"\n\n\n
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#!/usr/bin/env bash\n\n#SBATCH --nodes=1\n#SBATCH --ntasks-per-node=1\n#SBATCH --time=01:00:00\n#SBATCH --partition=cpuonly\n#SBATCH --account=hk-project-p0023960\n#SBATCH --output=logs/logs_preprocessing/%x_%j.log\n#SBATCH --error=logs/logs_preprocessing/%x_%j.log\n#SBATCH --mail-user=avocadoaling@gmail.com\n#SBATCH --job-name=mp4_to_npy_openai_6xx_Jun_29\n#SBATCH --mail-type=ALL\n#!/usr/bin/env bash\n# Log the sbatch script\ncat $0\n\nsource .venv_jafar/bin/activate\n\ninput_path="/hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/data/open_ai_minecraft/all_6xx_Jun_29/"\noutput_path="/hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/data/open_ai_minecraft_npy/all_6xx_Jun_29"\n\nstart_time=$(date +%s) \npython utils/preprocess_video_to_npy.py --input_path $input_path --output_path $output_path\nend_time=$(date +%s)\necho "Time taken: $((end_time - start_time)) seconds"\n\n\n
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