wehub-resource-sync
58 lines
1.7 KiB
Python
58 lines
1.7 KiB
Python
import torch
|
|
import numpy as np
|
|
from typing import Dict, Union, Callable
|
|
from PIL import Image
|
|
|
|
from constants import DatasetKey
|
|
from image_classification.imagenet import (
|
|
get_transform,
|
|
IMAGENET_WNID_TO_ID,
|
|
)
|
|
|
|
|
|
IMAGENET_JPEG_SPLIT_S3_ROOT = "s3://anyscale-imagenet/ILSVRC/Data/CLS-LOC"
|
|
IMAGENET_JPEG_SPLIT_S3_DIRS = {
|
|
DatasetKey.TRAIN: f"{IMAGENET_JPEG_SPLIT_S3_ROOT}/train",
|
|
DatasetKey.VALID: f"{IMAGENET_JPEG_SPLIT_S3_ROOT}/val",
|
|
DatasetKey.TEST: f"{IMAGENET_JPEG_SPLIT_S3_ROOT}/test",
|
|
}
|
|
|
|
|
|
def get_preprocess_map_fn(
|
|
random_transforms: bool = True,
|
|
) -> Callable[[Dict[str, Union[np.ndarray, str]]], Dict[str, torch.Tensor]]:
|
|
"""Get a map function that transforms a row to the format expected by the training loop.
|
|
|
|
Args:
|
|
random_transforms: Whether to use random transforms for training
|
|
|
|
Returns:
|
|
A function that takes a row dict with:
|
|
- "image": numpy array in HWC format
|
|
- "class": WNID string
|
|
|
|
The output is a dict with "image" and "label" keys.
|
|
"""
|
|
crop_resize_transform = get_transform(
|
|
to_torch_tensor=True, random_transforms=random_transforms
|
|
)
|
|
|
|
def map_fn(row: Dict[str, Union[np.ndarray, str]]) -> Dict[str, torch.Tensor]:
|
|
"""Process a single row into the expected format.
|
|
|
|
Args:
|
|
row: Dict containing "image" and "class" keys
|
|
|
|
Returns:
|
|
Dict with "image" and "label" keys
|
|
"""
|
|
# Convert NumPy HWC image to PIL
|
|
image_pil = Image.fromarray(row["image"])
|
|
# Apply transform (includes ToTensor + Normalize)
|
|
image = crop_resize_transform(image_pil)
|
|
# Convert label
|
|
label = IMAGENET_WNID_TO_ID[row["class"]]
|
|
return {"image": image, "label": label}
|
|
|
|
return map_fn
|