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# Once for All: Train One Network and Specialize it for Efficient Deployment
# Han Cai, Chuang Gan, Tianzhe Wang, Zhekai Zhang, Song Han
# International Conference on Learning Representations (ICLR), 2020.
import warnings
import os
import math
import numpy as np
import torch.utils.data
import torchvision.transforms as transforms
import torchvision.datasets as datasets
from .base_provider import DataProvider
from proard.utils.my_dataloader import MyRandomResizedCrop, MyDistributedSampler
__all__ = ["ImagenetDataProvider"]
class ImagenetDataProvider(DataProvider):
DEFAULT_PATH = "./dataset/imagenet"
def __init__(
self,
save_path=None,
train_batch_size=256,
test_batch_size=512,
valid_size=None,
n_worker=32,
resize_scale=0.08,
distort_color=None,
image_size=224,
num_replicas=None,
rank=None,
):
warnings.filterwarnings("ignore")
self._save_path = save_path
self.image_size = image_size # int or list of int
self.distort_color = "None" if distort_color is None else distort_color
self.resize_scale = resize_scale
self._valid_transform_dict = {}
if not isinstance(self.image_size, int):
from proard.utils.my_dataloader.my_data_loader import MyDataLoader
assert isinstance(self.image_size, list)
self.image_size.sort() # e.g., 160 -> 224
MyRandomResizedCrop.IMAGE_SIZE_LIST = self.image_size.copy()
MyRandomResizedCrop.ACTIVE_SIZE = max(self.image_size)
for img_size in self.image_size:
self._valid_transform_dict[img_size] = self.build_valid_transform(
img_size
)
self.active_img_size = max(self.image_size) # active resolution for test
valid_transforms = self._valid_transform_dict[self.active_img_size]
train_loader_class = MyDataLoader # randomly sample image size for each batch of training image
else:
self.active_img_size = self.image_size
valid_transforms = self.build_valid_transform()
train_loader_class = torch.utils.data.DataLoader
train_dataset = self.train_dataset(self.build_train_transform())
if valid_size is not None:
if not isinstance(valid_size, int):
assert isinstance(valid_size, float) and 0 < valid_size < 1
valid_size = int(len(train_dataset) * valid_size)
valid_dataset = self.train_dataset(valid_transforms)
train_indexes, valid_indexes = self.random_sample_valid_set(
len(train_dataset), valid_size
)
if num_replicas is not None:
train_sampler = MyDistributedSampler(
train_dataset, num_replicas, rank, True, np.array(train_indexes)
)
valid_sampler = MyDistributedSampler(
valid_dataset, num_replicas, rank, True, np.array(valid_indexes)
)
else:
train_sampler = torch.utils.data.sampler.SubsetRandomSampler(
train_indexes
)
valid_sampler = torch.utils.data.sampler.SubsetRandomSampler(
valid_indexes
)
self.train = train_loader_class(
train_dataset,
batch_size=train_batch_size,
sampler=train_sampler,
num_workers=n_worker,
pin_memory=False,
)
self.valid = torch.utils.data.DataLoader(
valid_dataset,
batch_size=test_batch_size,
sampler=valid_sampler,
num_workers=n_worker,
pin_memory=False,
)
else:
if num_replicas is not None:
train_sampler = torch.utils.data.distributed.DistributedSampler(
train_dataset, num_replicas, rank
)
self.train = train_loader_class(
train_dataset,
batch_size=train_batch_size,
sampler=train_sampler,
num_workers=n_worker,
pin_memory=True,
)
else:
self.train = train_loader_class(
train_dataset,
batch_size=train_batch_size,
shuffle=True,
num_workers=n_worker,
pin_memory=False,
)
self.valid = None
test_dataset = self.test_dataset(valid_transforms)
if num_replicas is not None:
test_sampler = torch.utils.data.distributed.DistributedSampler(
test_dataset, num_replicas, rank
)
self.test = torch.utils.data.DataLoader(
test_dataset,
batch_size=test_batch_size,
sampler=test_sampler,
num_workers=n_worker,
pin_memory=False,
)
else:
self.test = torch.utils.data.DataLoader(
test_dataset,
batch_size=test_batch_size,
shuffle=True,
num_workers=n_worker,
pin_memory=False,
)
if self.valid is None:
self.valid = self.test
@staticmethod
def name():
return "imagenet"
@property
def data_shape(self):
return 3, self.active_img_size, self.active_img_size # C, H, W
@property
def n_classes(self):
return 1000
@property
def save_path(self):
if self._save_path is None:
self._save_path = self.DEFAULT_PATH
if not os.path.exists(self._save_path):
self._save_path = os.path.expanduser("~/dataset/imagenet")
return self._save_path
@property
def data_url(self):
raise ValueError("unable to download %s" % self.name())
def train_dataset(self, _transforms):
return datasets.ImageFolder(self.train_path, _transforms)
def test_dataset(self, _transforms):
return datasets.ImageFolder(self.valid_path, _transforms)
@property
def train_path(self):
return os.path.join(self.save_path, "train")
@property
def valid_path(self):
return os.path.join(self.save_path, "val")
@property
def normalize(self):
return transforms.Normalize(
mean=[0.485, 0.456, 0.406], std=[0.229, 0.224, 0.225]
)
def build_train_transform(self, image_size=None, print_log=True):
if image_size is None:
image_size = self.image_size
if print_log:
print(
"Color jitter: %s, resize_scale: %s, img_size: %s"
% (self.distort_color, self.resize_scale, image_size)
)
if isinstance(image_size, list):
resize_transform_class = MyRandomResizedCrop
print(
"Use MyRandomResizedCrop: %s, \t %s"
% MyRandomResizedCrop.get_candidate_image_size(),
"sync=%s, continuous=%s"
% (
MyRandomResizedCrop.SYNC_DISTRIBUTED,
MyRandomResizedCrop.CONTINUOUS,
),
)
else:
resize_transform_class = transforms.RandomResizedCrop
# random_resize_crop -> random_horizontal_flip
train_transforms = [
resize_transform_class(image_size, scale=(self.resize_scale, 1.0)),
transforms.RandomHorizontalFlip(),
]
# color augmentation (optional)
color_transform = None
if self.distort_color == "torch":
color_transform = transforms.ColorJitter(
brightness=0.4, contrast=0.4, saturation=0.4, hue=0.1
)
elif self.distort_color == "tf":
color_transform = transforms.ColorJitter(
brightness=32.0 / 255.0, saturation=0.5
)
if color_transform is not None:
train_transforms.append(color_transform)
train_transforms += [
transforms.ToTensor(),
self.normalize,
]
train_transforms = transforms.Compose(train_transforms)
return train_transforms
def build_valid_transform(self, image_size=None):
if image_size is None:
image_size = self.active_img_size
return transforms.Compose(
[
transforms.Resize(int(math.ceil(image_size / 0.875))),
transforms.CenterCrop(image_size),
transforms.ToTensor(),
self.normalize,
]
)
def assign_active_img_size(self, new_img_size):
self.active_img_size = new_img_size
if self.active_img_size not in self._valid_transform_dict:
self._valid_transform_dict[
self.active_img_size
] = self.build_valid_transform()
# change the transform of the valid and test set
self.valid.dataset.transform = self._valid_transform_dict[self.active_img_size]
self.test.dataset.transform = self._valid_transform_dict[self.active_img_size]
def build_sub_train_loader(
self, n_images, batch_size, num_worker=None, num_replicas=None, rank=None
):
# used for resetting BN running statistics
if self.__dict__.get("sub_train_%d" % self.active_img_size, None) is None:
if num_worker is None:
num_worker = self.train.num_workers
n_samples = len(self.train.dataset)
g = torch.Generator()
g.manual_seed(DataProvider.SUB_SEED)
rand_indexes = torch.randperm(n_samples, generator=g).tolist()
new_train_dataset = self.train_dataset(
self.build_train_transform(
image_size=self.active_img_size, print_log=False
)
)
chosen_indexes = rand_indexes[:n_images]
if num_replicas is not None:
sub_sampler = MyDistributedSampler(
new_train_dataset,
num_replicas,
rank,
True,
np.array(chosen_indexes),
)
else:
sub_sampler = torch.utils.data.sampler.SubsetRandomSampler(
chosen_indexes
)
sub_data_loader = torch.utils.data.DataLoader(
new_train_dataset,
batch_size=batch_size,
sampler=sub_sampler,
num_workers=num_worker,
pin_memory=False,
)
self.__dict__["sub_train_%d" % self.active_img_size] = []
for images, labels in sub_data_loader:
self.__dict__["sub_train_%d" % self.active_img_size].append(
(images, labels)
)
return self.__dict__["sub_train_%d" % self.active_img_size]
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