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convert_imagenet_to_h5.py
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import os | |
import torch | |
import h5py as h5 | |
from torchvision import datasets | |
from tqdm import tqdm | |
""" | |
Usage: this script creates `imagenet_train.h5` in `data_path`. | |
If you need to create `imagenet_val.h5`, set `is_train = False` at L26. | |
Assume `data_path` has the following folders | |
data_path | |
train/ | |
class1/ | |
img1.jpeg | |
class2/ | |
img2.jpeg | |
val/ | |
class1/ | |
img3.jpeg | |
class/2 | |
img4.jpeg | |
""" | |
data_path = '/mnt/scratch07/datasets/ILSVRC2012' | |
is_train = True | |
tag = 'train' if is_train else 'val' | |
root = os.path.join(data_path, tag) | |
dataset = datasets.ImageFolder(root, transform=None) | |
h5_file = os.path.join(data_path, f'imagenet_{tag}.h5') | |
print(f'* Writing {h5_file}:') | |
with h5.File(h5_file, 'w') as f: | |
for j, (x, y) in enumerate(tqdm(dataset)): | |
grp = f.create_group(f'{j}') | |
grp.create_dataset('data', data=x) | |
grp.create_dataset('target', data=y) |
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# Copyright 2021 Samsung Electronics Co., Ltd. | |
# | |
# Licensed under the Apache License, Version 2.0 (the "License"); | |
# you may not use this file except in compliance with the License. | |
# You may obtain a copy of the License at | |
# http://www.apache.org/licenses/LICENSE-2.0 | |
# Unless required by applicable law or agreed to in writing, software | |
# distributed under the License is distributed on an "AS IS" BASIS, | |
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. | |
# See the License for the specific language governing permissions and | |
# limitations under the License. | |
# ============================================================================= | |
import h5py | |
import numpy as np | |
from PIL import Image | |
import torch | |
from torch.utils.data import Dataset, DataLoader | |
class H5Dataset(Dataset): | |
def __init__(self, h5_path, transform=None): | |
self.h5_path = h5_path | |
self.h5_file = None | |
self.length = len(h5py.File(h5_path, 'r')) | |
self.transform = transform | |
def __getitem__(self, index): | |
#loading in getitem allows us to use multiple processes for data loading | |
#because hdf5 files aren't pickelable so can't transfer them across processes | |
# https://discuss.pytorch.org/t/hdf5-a-data-format-for-pytorch/40379 | |
# https://discuss.pytorch.org/t/dataloader-when-num-worker-0-there-is-bug/25643/16 | |
# TODO possible look at __getstate__ and __setstate__ as a more elegant solution | |
if self.h5_file is None: | |
self.h5_file = h5py.File(self.h5_path, 'r') | |
record = self.h5_file[str(index)] | |
if self.transform: | |
x = Image.fromarray(record['data'][()]) | |
x = self.transform(x) | |
else: | |
x = torch.from_numpy(record['data'][()]) | |
y = record['target'][()] | |
y = torch.from_numpy(np.asarray(y)) | |
return (x,y) | |
def __len__(self): | |
return self.length |
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