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registry.py
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import os
import paddle
import paddle.nn as nn
import paddle.vision.transforms as T
from PIL import Image
from paddle.io import Dataset, DataLoader
import pdb
from datafree.models import classifiers_p
NORMALIZE_DICT = {
'mnist': dict( mean=(0.5,), std=(0.5,) ),
'fmnist': dict(mean=(0.5,), std=(0.5,)),
'cifar10': dict(mean=(0.5, 0.5, 0.5), std=(0.5, 0.5, 0.5)),
'cifar100': dict(mean=(0.5, 0.5, 0.5), std=(0.5, 0.5, 0.5)),
'tiny': dict(mean=(0.5, 0.5, 0.5), std=(0.5, 0.5, 0.5)),
'svhn': dict( mean=(0.5, 0.5, 0.5), std=(0.5, 0.5, 0.5) ),
}
MODEL_DICT = {
'lenet': classifiers_p.lenet.LeNet5,
}
def get_dataset(name: str, data_root: str='data', return_transform=False, split=['A', 'B', 'C', 'D']):
name = name.lower()
data_root = os.path.expanduser(data_root)
if name == 'mnist':
num_classes = 10
train_transform = T.Compose([
T.Resize((32, 32)),
T.ToTensor(),
T.Normalize(**NORMALIZE_DICT[name]),
])
val_transform = T.Compose([
T.Resize((32, 32)),
T.ToTensor(),
T.Normalize(**NORMALIZE_DICT[name]),
])
data_root = os.path.join(data_root)
# pdb.set_trace()
train_dst = paddle.vision.datasets.MNIST(mode='train', transform=train_transform)
val_dst = paddle.vision.datasets.MNIST(mode='test', transform=val_transform)
elif name == 'fmnist':
num_classes = 10
train_transform = T.Compose([
T.Resize((32, 32)),
T.ToTensor(),
T.Normalize(**NORMALIZE_DICT[name]),
])
val_transform = T.Compose([
T.Resize((32, 32)),
T.ToTensor(),
T.Normalize(**NORMALIZE_DICT[name]),
])
data_root = os.path.join(data_root)
train_dst = paddle.vision.datasets.FashionMNIST(data_root, mode='train', transform=train_transform)
val_dst = paddle.vision.datasets.FashionMNIST(data_root, mode='test', transform=val_transform)
elif name == 'cifar10':
num_classes = 10
train_transform = T.Compose([
T.RandomCrop(32, padding=4),
T.RandomHorizontalFlip(),
T.ToTensor(),
T.Normalize(**NORMALIZE_DICT[name]),
])
val_transform = T.Compose([
T.ToTensor(),
T.Normalize(**NORMALIZE_DICT[name]),
])
data_root = os.path.join(data_root)
train_dst = paddle.vision.datasets.Cifar10(data_root, mode='train', transform=train_transform)
val_dst = paddle.vision.datasets.Cifar10(data_root, mode='test', transform=val_transform)
elif name == 'cifar100':
num_classes = 100
train_transform = T.Compose([
T.RandomCrop(32, padding=4),
T.RandomHorizontalFlip(),
T.ToTensor(),
T.Normalize(**NORMALIZE_DICT[name]),
])
val_transform = T.Compose([
T.ToTensor(),
T.Normalize(**NORMALIZE_DICT[name]),
])
data_root = os.path.join(data_root)
train_dst = paddle.vision.datasets.Cifar100(data_root, mode='train', transform=train_transform)
val_dst = paddle.vision.datasets.Cifar100(data_root, mode='test', transform=val_transform)
elif name == 'svhn':
num_classes = 10
train_transform = T.Compose([
T.ToTensor(),
T.Normalize(**NORMALIZE_DICT[name]),
])
val_transform = T.Compose([
T.ToTensor(),
T.Normalize(**NORMALIZE_DICT[name]),
])
data_root = os.path.join(data_root, 'torchdata')
train_dst = paddle.vision.datasets.SVHN(data_root, split='train', transform=train_transform)
val_dst = paddle.vision.datasets.SVHN(data_root, split='test', transform=val_transform)
elif name == "tiny":
num_classes = 200
transform = T.Compose([T.Resize(64),
T.ToTensor(),
T.Normalize(mean=[0.5, 0.5, 0.5],
std=[0.5, 0.5, 0.5])])
root_dir = "/gdata/dairong/fedsam/Data/Raw/tiny-imagenet-200/"
trn_img_list, trn_lbl_list, tst_img_list, tst_lbl_list = [], [], [], []
trn_file = os.path.join(root_dir, 'train_list.txt')
tst_file = os.path.join(root_dir, 'val_list.txt')
with open(trn_file) as f:
line_list = f.readlines()
for line in line_list:
img, lbl = line.strip().split()
trn_img_list.append(img)
trn_lbl_list.append(int(lbl))
with open(tst_file) as f:
line_list = f.readlines()
for line in line_list:
img, lbl = line.strip().split()
tst_img_list.append(img)
tst_lbl_list.append(int(lbl))
train_dst = DatasetFromDir(img_root=root_dir, img_list=trn_img_list, label_list=trn_lbl_list,
transformer=transform)
val_dst = DatasetFromDir(img_root=root_dir, img_list=tst_img_list, label_list=tst_lbl_list,
transformer=transform)
else:
raise NotImplementedError
if return_transform:
return num_classes, train_dst, val_dst, train_transform, val_transform
return num_classes, train_dst, val_dst
def get_model(name: str, num_classes, pretrained=False, **kwargs):
if 'imagenet' in name:
model = IMAGENET_MODEL_DICT[name](pretrained=pretrained)
if num_classes!=1000:
model.fc = nn.Linear(model.fc.in_features, num_classes)
elif 'deeplab' in name:
model = SEGMENTATION_MODEL_DICT[name](num_classes=num_classes, pretrained_backbone=kwargs.get('pretrained_backbone', False))
else:
model = MODEL_DICT[name](num_classes=num_classes)
return model
class DatasetFromDir(Dataset):
def __init__(self, img_root, img_list, label_list, transformer):
super(DatasetFromDir, self).__init__()
self.root_dir = img_root
self.img_list = img_list
self.label_list = label_list
self.size = len(self.img_list)
self.transform = transformer
def __getitem__(self, index):
img_name = self.img_list[index % self.size]
img_path = os.path.join(self.root_dir, img_name)
img_id = self.label_list[index % self.size]
img_raw = Image.open(img_path).convert('RGB')
img = self.transform(img_raw)
return img, img_id
def __len__(self):
return len(self.img_list)