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""" Dataset reader that wraps Hugging Face datasets
Hacked together by / Copyright 2022 Ross Wightman
"""
import io
import math
from typing import Optional
import torch
import torch.distributed as dist
from PIL import Image
try:
import datasets
except ImportError as e:
print("Please install Hugging Face datasets package `pip install datasets`.")
raise e
from .class_map import load_class_map
from .reader import Reader
def get_class_labels(info, label_key='label'):
if 'label' not in info.features:
return {}
class_label = info.features[label_key]
class_to_idx = {n: class_label.str2int(n) for n in class_label.names}
return class_to_idx
class ReaderHfds(Reader):
def __init__(
self,
name: str,
root: Optional[str] = None,
split: str = 'train',
class_map: dict = None,
input_key: str = 'image',
target_key: str = 'label',
download: bool = False,
trust_remote_code: bool = False
):
"""
"""
super().__init__()
self.root = root
self.split = split
self.dataset = datasets.load_dataset(
name, # 'name' maps to path arg in hf datasets
split=split,
cache_dir=self.root, # timm doesn't expect hidden cache dir for datasets, specify a path if root set
trust_remote_code=trust_remote_code
)
# leave decode for caller, plus we want easy access to original path names...
self.dataset = self.dataset.cast_column(input_key, datasets.Image(decode=False))
self.image_key = input_key
self.label_key = target_key
self.remap_class = False
if class_map:
self.class_to_idx = load_class_map(class_map)
self.remap_class = True
else:
self.class_to_idx = get_class_labels(self.dataset.info, self.label_key)
self.split_info = self.dataset.info.splits[split]
self.num_samples = self.split_info.num_examples
def __getitem__(self, index):
item = self.dataset[index]
image = item[self.image_key]
if 'bytes' in image and image['bytes']:
image = io.BytesIO(image['bytes'])
else:
assert 'path' in image and image['path']
image = open(image['path'], 'rb')
label = item[self.label_key]
if self.remap_class:
label = self.class_to_idx[label]
return image, label
def __len__(self):
return len(self.dataset)
def _filename(self, index, basename=False, absolute=False):
item = self.dataset[index]
return item[self.image_key]['path']
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