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import os
import math
import random
from PIL import Image
import blobfile as bf
import numpy as np
from torch.utils.data import DataLoader, Dataset
def load_data(
*,
dataset_mode,
data_dir,
batch_size,
image_size,
class_cond=False,
deterministic=False,
random_crop=True,
random_flip=True,
is_train=True,
):
"""
For a dataset, create a generator over (images, kwargs) pairs.
Each images is an NCHW float tensor, and the kwargs dict contains zero or
more keys, each of which map to a batched Tensor of their own.
The kwargs dict can be used for class labels, in which case the key is "y"
and the values are integer tensors of class labels.
:param data_dir: a dataset directory.
:param batch_size: the batch size of each returned pair.
:param image_size: the size to which images are resized.
:param class_cond: if True, include a "y" key in returned dicts for class
label. If classes are not available and this is true, an
exception will be raised.
:param deterministic: if True, yield results in a deterministic order.
:param random_crop: if True, randomly crop the images for augmentation.
:param random_flip: if True, randomly flip the images for augmentation.
"""
if not data_dir:
raise ValueError("unspecified data directory")
if dataset_mode == 'cityscapes':
all_files = _list_image_files_recursively(os.path.join(data_dir, 'leftImg8bit', 'train' if is_train else 'val'))
labels_file = _list_image_files_recursively(os.path.join(data_dir, 'gtFine', 'train' if is_train else 'val'))
classes = [x for x in labels_file if x.endswith('_labelIds.png')]
instances = [x for x in labels_file if x.endswith('_instanceIds.png')]
elif dataset_mode == 'ade20k':
all_files = _list_image_files_recursively(os.path.join(data_dir, 'images', 'training' if is_train else 'validation'))
classes = _list_image_files_recursively(os.path.join(data_dir, 'annotations', 'training' if is_train else 'validation'))
instances = None
elif dataset_mode == 'celeba':
# The edge is computed by the instances.
# However, the edge get from the labels and the instances are the same on CelebA.
# You can take either as instance input
all_files = _list_image_files_recursively(os.path.join(data_dir, 'train' if is_train else 'test', 'images'))
classes = _list_image_files_recursively(os.path.join(data_dir, 'train' if is_train else 'test', 'labels'))
instances = _list_image_files_recursively(os.path.join(data_dir, 'train' if is_train else 'test', 'labels'))
elif dataset_mode == "crack500":
all_files = _list_image_files_recursively(os.path.join(data_dir, 'train' if is_train else 'validation', 'images'))
classes = _list_image_files_recursively(os.path.join(data_dir, 'train' if is_train else 'validation','annotations'))
instances = None
elif dataset_mode == "thincrack":
all_files = _list_image_files_recursively(os.path.join(data_dir, 'train' if is_train else 'train', 'images'))
classes = _list_image_files_recursively(os.path.join(data_dir, 'train' if is_train else 'train','annotations'))
instances = None
else:
raise NotImplementedError('{} not implemented'.format(dataset_mode))
print("Len of Dataset:", len(all_files))
dataset = ImageDataset(
dataset_mode,
image_size,
all_files,
classes=classes,
instances=instances,
random_crop=random_crop,
random_flip=random_flip,
is_train=is_train
)
if deterministic:
loader = DataLoader(
dataset, batch_size=batch_size, shuffle=False, num_workers=1, drop_last=True
)
else:
loader = DataLoader(
dataset, batch_size=batch_size, shuffle=True, num_workers=1, drop_last=True
)
return loader, dataset
def _list_image_files_recursively(data_dir):
results = []
for entry in sorted(bf.listdir(data_dir)):
full_path = bf.join(data_dir, entry)
ext = entry.split(".")[-1]
if "." in entry and ext.lower() in ["jpg", "jpeg", "png", "gif"]:
results.append(full_path)
elif bf.isdir(full_path):
results.extend(_list_image_files_recursively(full_path))
return results
class ImageDataset(Dataset):
def __init__(
self,
dataset_mode,
resolution,
image_paths,
classes=None,
instances=None,
shard=0,
num_shards=1,
random_crop=False,
random_flip=True,
is_train=True
):
super().__init__()
self.is_train = is_train
self.dataset_mode = dataset_mode
self.resolution = resolution
self.local_images = image_paths[shard:][::num_shards]
self.local_classes = None if classes is None else classes[shard:][::num_shards]
self.local_instances = None if instances is None else instances[shard:][::num_shards]
self.random_crop = random_crop
self.random_flip = random_flip
def __len__(self):
return len(self.local_images)
def __getitem__(self, idx):
path = self.local_images[idx]
with bf.BlobFile(path, "rb") as f:
pil_image = Image.open(f)
pil_image.load()
pil_image = pil_image.convert("RGB")
out_dict = {}
class_path = self.local_classes[idx]
with bf.BlobFile(class_path, "rb") as f:
pil_class = Image.open(f)
pil_class.load()
pil_class = pil_class.convert("L")
if self.local_instances is not None:
instance_path = self.local_instances[idx] # DEBUG: from classes to instances, may affect CelebA
with bf.BlobFile(instance_path, "rb") as f:
pil_instance = Image.open(f)
pil_instance.load()
pil_instance = pil_instance.convert("L")
else:
pil_instance = None
if self.dataset_mode == 'cityscapes':
arr_image, arr_class, arr_instance = resize_arr([pil_image, pil_class, pil_instance], self.resolution)
else:
if self.is_train:
if self.random_crop:
arr_image, arr_class, arr_instance = random_crop_arr([pil_image, pil_class, pil_instance], self.resolution)
else:
arr_image, arr_class, arr_instance = center_crop_arr([pil_image, pil_class, pil_instance], self.resolution)
else:
arr_image, arr_class, arr_instance = resize_arr([pil_image, pil_class, pil_instance], self.resolution, keep_aspect=False)
if self.random_flip and random.random() < 0.5:
arr_image = arr_image[:, ::-1].copy()
arr_class = arr_class[:, ::-1].copy()
arr_instance = arr_instance[:, ::-1].copy() if arr_instance is not None else None
arr_image = arr_image.astype(np.float32) / 127.5 - 1
out_dict['path'] = path
out_dict['label_ori'] = arr_class.copy()
if self.dataset_mode == 'ade20k':
arr_class = arr_class - 1
arr_class[arr_class == 255] = 150
elif self.dataset_mode == 'coco':
arr_class[arr_class == 255] = 182
elif self.dataset_mode == 'crack500':
arr_class[arr_class == 255] = 1
elif self.dataset_mode == 'thincrack':
arr_class[arr_class == 255] = 1
out_dict['label'] = arr_class[None, ]
if arr_instance is not None:
out_dict['instance'] = arr_instance[None, ]
return np.transpose(arr_image, [2, 0, 1]), out_dict
def resize_arr(pil_list, image_size, keep_aspect=True):
# We are not on a new enough PIL to support the `reducing_gap`
# argument, which uses BOX downsampling at powers of two first.
# Thus, we do it by hand to improve downsample quality.
pil_image, pil_class, pil_instance = pil_list
while min(*pil_image.size) >= 2 * image_size:
pil_image = pil_image.resize(
tuple(x // 2 for x in pil_image.size), resample=Image.BOX
)
if keep_aspect:
scale = image_size / min(*pil_image.size)
pil_image = pil_image.resize(
tuple(round(x * scale) for x in pil_image.size), resample=Image.BICUBIC
)
else:
pil_image = pil_image.resize((image_size, image_size), resample=Image.BICUBIC)
pil_class = pil_class.resize(pil_image.size, resample=Image.NEAREST)
if pil_instance is not None:
pil_instance = pil_instance.resize(pil_image.size, resample=Image.NEAREST)
arr_image = np.array(pil_image)
arr_class = np.array(pil_class)
arr_instance = np.array(pil_instance) if pil_instance is not None else None
return arr_image, arr_class, arr_instance
def center_crop_arr(pil_list, image_size):
# We are not on a new enough PIL to support the `reducing_gap`
# argument, which uses BOX downsampling at powers of two first.
# Thus, we do it by hand to improve downsample quality.
pil_image, pil_class, pil_instance = pil_list
while min(*pil_image.size) >= 2 * image_size:
pil_image = pil_image.resize(
tuple(x // 2 for x in pil_image.size), resample=Image.BOX
)
scale = image_size / min(*pil_image.size)
pil_image = pil_image.resize(
tuple(round(x * scale) for x in pil_image.size), resample=Image.BICUBIC
)
pil_class = pil_class.resize(pil_image.size, resample=Image.NEAREST)
if pil_instance is not None:
pil_instance = pil_instance.resize(pil_image.size, resample=Image.NEAREST)
arr_image = np.array(pil_image)
arr_class = np.array(pil_class)
arr_instance = np.array(pil_instance) if pil_instance is not None else None
crop_y = (arr_image.shape[0] - image_size) // 2
crop_x = (arr_image.shape[1] - image_size) // 2
return arr_image[crop_y : crop_y + image_size, crop_x : crop_x + image_size],\
arr_class[crop_y: crop_y + image_size, crop_x: crop_x + image_size],\
arr_instance[crop_y : crop_y + image_size, crop_x : crop_x + image_size] if arr_instance is not None else None
def random_crop_arr(pil_list, image_size, min_crop_frac=0.8, max_crop_frac=1.0):
min_smaller_dim_size = math.ceil(image_size / max_crop_frac)
max_smaller_dim_size = math.ceil(image_size / min_crop_frac)
smaller_dim_size = random.randrange(min_smaller_dim_size, max_smaller_dim_size + 1)
# We are not on a new enough PIL to support the `reducing_gap`
# argument, which uses BOX downsampling at powers of two first.
# Thus, we do it by hand to improve downsample quality.
pil_image, pil_class, pil_instance = pil_list
while min(*pil_image.size) >= 2 * smaller_dim_size:
pil_image = pil_image.resize(
tuple(x // 2 for x in pil_image.size), resample=Image.BOX
)
scale = smaller_dim_size / min(*pil_image.size)
pil_image = pil_image.resize(
tuple(round(x * scale) for x in pil_image.size), resample=Image.BICUBIC
)
pil_class = pil_class.resize(pil_image.size, resample=Image.NEAREST)
if pil_instance is not None:
pil_instance = pil_instance.resize(pil_image.size, resample=Image.NEAREST)
arr_image = np.array(pil_image)
arr_class = np.array(pil_class)
arr_instance = np.array(pil_instance) if pil_instance is not None else None
crop_y = random.randrange(arr_image.shape[0] - image_size + 1)
crop_x = random.randrange(arr_image.shape[1] - image_size + 1)
return arr_image[crop_y : crop_y + image_size, crop_x : crop_x + image_size],\
arr_class[crop_y: crop_y + image_size, crop_x: crop_x + image_size],\
arr_instance[crop_y : crop_y + image_size, crop_x : crop_x + image_size] if arr_instance is not None else None
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