105 lines
4.1 KiB
Python
105 lines
4.1 KiB
Python
# SPDX-License-Identifier: Apache-2.0
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# schema.py
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"""
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Unified data schema and format for saving and loading image/video data after
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preprocessing.
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It uses apache arrow in-memory format that can be consumed by modern data
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frameworks that can handle parquet or lance file.
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"""
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import pyarrow as pa
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pyarrow_schema_i2v = pa.schema([
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pa.field("id", pa.string()),
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# --- Image/Video VAE latents ---
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# Tensors are stored as raw bytes with shape and dtype info for loading
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pa.field("vae_latent_bytes", pa.binary()),
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# e.g., [C, T, H, W] or [C, H, W]
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pa.field("vae_latent_shape", pa.list_(pa.int64())),
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# e.g., 'float32'
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pa.field("vae_latent_dtype", pa.string()),
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# --- Text encoder output tensor ---
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# Tensors are stored as raw bytes with shape and dtype info for loading
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pa.field("text_embedding_bytes", pa.binary()),
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# e.g., [SeqLen, Dim]
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pa.field("text_embedding_shape", pa.list_(pa.int64())),
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# e.g., 'bfloat16' or 'float32'
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pa.field("text_embedding_dtype", pa.string()),
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#I2V
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pa.field("clip_feature_bytes", pa.binary()),
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pa.field("clip_feature_shape", pa.list_(pa.int64())),
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pa.field("clip_feature_dtype", pa.string()),
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pa.field("first_frame_latent_bytes", pa.binary()),
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pa.field("first_frame_latent_shape", pa.list_(pa.int64())),
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pa.field("first_frame_latent_dtype", pa.string()),
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# I2V Validation
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pa.field("pil_image_bytes", pa.binary()),
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pa.field("pil_image_shape", pa.list_(pa.int64())),
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pa.field("pil_image_dtype", pa.string()),
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# --- Metadata ---
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pa.field("file_name", pa.string()),
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pa.field("caption", pa.string()),
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pa.field("media_type", pa.string()), # 'image' or 'video'
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pa.field("width", pa.int64()),
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pa.field("height", pa.int64()),
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# -- Video-specific (can be null/default for images) ---
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# Number of frames processed (e.g., 1 for image, N for video)
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pa.field("num_frames", pa.int64()),
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pa.field("duration_sec", pa.float64()),
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pa.field("fps", pa.float64()),
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])
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pyarrow_schema_t2v = pa.schema([
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pa.field("id", pa.string()),
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# --- Image/Video VAE latents ---
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# Tensors are stored as raw bytes with shape and dtype info for loading
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pa.field("vae_latent_bytes", pa.binary()),
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# e.g., [C, T, H, W] or [C, H, W]
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pa.field("vae_latent_shape", pa.list_(pa.int64())),
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# e.g., 'float32'
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pa.field("vae_latent_dtype", pa.string()),
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# --- Text encoder output tensor ---
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# Tensors are stored as raw bytes with shape and dtype info for loading
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pa.field("text_embedding_bytes", pa.binary()),
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# e.g., [SeqLen, Dim]
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pa.field("text_embedding_shape", pa.list_(pa.int64())),
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# e.g., 'bfloat16' or 'float32'
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pa.field("text_embedding_dtype", pa.string()),
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# --- Metadata ---
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pa.field("file_name", pa.string()),
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pa.field("caption", pa.string()),
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pa.field("media_type", pa.string()), # 'image' or 'video'
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pa.field("width", pa.int64()),
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pa.field("height", pa.int64()),
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# -- Video-specific (can be null/default for images) ---
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# Number of frames processed (e.g., 1 for image, N for video)
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pa.field("num_frames", pa.int64()),
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pa.field("duration_sec", pa.float64()),
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pa.field("fps", pa.float64()),
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])
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pyarrow_schema_ode_trajectory_text_only = pa.schema([
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pa.field("id", pa.string()),
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# --- Text encoder output tensor ---
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# Tensors are stored as raw bytes with shape and dtype info for loading
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pa.field("text_embedding_bytes", pa.binary()),
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# e.g., [SeqLen, Dim]
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pa.field("text_embedding_shape", pa.list_(pa.int64())),
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# e.g., 'bfloat16' or 'float32'
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pa.field("text_embedding_dtype", pa.string()),
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# --- ODE Trajectory ---
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pa.field("trajectory_latents_bytes", pa.binary()),
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pa.field("trajectory_latents_shape", pa.list_(pa.int64())),
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pa.field("trajectory_latents_dtype", pa.string()),
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pa.field("trajectory_timesteps_bytes", pa.binary()),
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pa.field("trajectory_timesteps_shape", pa.list_(pa.int64())),
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pa.field("trajectory_timesteps_dtype", pa.string()),
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# --- Metadata ---
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pa.field("file_name", pa.string()),
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pa.field("caption", pa.string()),
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pa.field("media_type", pa.string()), # Always 'text' for text-only
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])
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