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registry.py
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from engine.models import classifiers, deeplab
from torchvision import datasets, transforms as T
from engine.utils import sync_transforms as sT
from PIL import PngImagePlugin
LARGE_ENOUGH_NUMBER = 100
PngImagePlugin.MAX_TEXT_CHUNK = LARGE_ENOUGH_NUMBER * (1024**2)
import os
import torch
import torchvision
import engine
import torch.nn as nn
from PIL import Image
NORMALIZE_DICT = {
# In-domain data
'cifar100': dict( mean=(0.5071, 0.4867, 0.4408), std=(0.2675, 0.2565, 0.2761) ),
# Out-of-domain data
'cifar10': dict( mean=(0.4914, 0.4822, 0.4465), std=(0.2023, 0.1994, 0.2010) ),
'imagenet': dict( mean=[0.485, 0.456, 0.406], std=[0.229, 0.224, 0.225]),
'svhn': dict( mean=(0.5, 0.5, 0.5), std=(0.5, 0.5, 0.5) ),
'places365_32x32': dict( mean=(0.5, 0.5, 0.5), std=(0.5, 0.5, 0.5) ),
'imagenet_32x32': dict( mean=(0.5, 0.5, 0.5), std=(0.5, 0.5, 0.5) ),
}
MODEL_DICT = {
'wrn16_1': classifiers.wresnet.wrn_16_1,
'wrn16_2': classifiers.wresnet.wrn_16_2,
'wrn40_1': classifiers.wresnet.wrn_40_1,
'wrn40_2': classifiers.wresnet.wrn_40_2,
'resnet18': classifiers.resnet.resnet18,
'resnet34': classifiers.resnet.resnet34,
'vgg11': classifiers.vgg.vgg11_bn,
#'resnet8': classifiers.resnet_tiny.resnet8,
#'resnet20': classifiers.resnet_tiny.resnet20,
#'resnet32': classifiers.resnet_tiny.resnet32,
#'resnet56': classifiers.resnet_tiny.resnet56,
#'resnet110': classifiers.resnet_tiny.resnet110,
#'resnet8x4': classifiers.resnet_tiny.resnet8x4,
#'resnet32x4': classifiers.resnet_tiny.resnet32x4,
#'resnet50': classifiers.resnet.resnet50,
#'vgg8': classifiers.vgg.vgg8_bn,
#'vgg13': classifiers.vgg.vgg13_bn,
#'shufflenetv2': classifiers.shufflenetv2.shuffle_v2,
#'mobilenetv2': classifiers.mobilenetv2.mobilenet_v2,
}
def get_model(name: str, num_classes, pretrained=False, **kwargs):
model = MODEL_DICT[name](num_classes=num_classes)
return model
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=='cifar10':
num_classes = 10
train_transform = T.Compose([
#T.Resize((224, 224), Image.BICUBIC),
T.RandomCrop(32, padding=4),
T.RandomHorizontalFlip(),
T.ToTensor(),
T.Normalize( **NORMALIZE_DICT[name] ),
])
val_transform = T.Compose([
#T.Resize((224, 224), Image.BICUBIC),
T.ToTensor(),
T.Normalize( **NORMALIZE_DICT[name] ),
])
data_root = os.path.join( data_root, 'torchdata' )
train_dst = datasets.CIFAR10(data_root, train=True, download=True, transform=train_transform)
val_dst = datasets.CIFAR10(data_root, train=False, download=True, 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, 'torchdata' )
train_dst = datasets.CIFAR100(data_root, train=True, download=True, transform=train_transform)
val_dst = datasets.CIFAR100(data_root, train=False, download=True, 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 = datasets.SVHN(data_root, split='train', download=True, transform=train_transform)
val_dst = datasets.SVHN(data_root, split='test', download=True, transform=val_transform)
elif name=='imagenet_32x32':
num_classes=1000
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, 'ImageNet_32x32' )
train_dst = datasets.ImageFolder(os.path.join(data_root, 'train'), transform=train_transform)
val_dst = datasets.ImageFolder(os.path.join(data_root, 'val'), transform=val_transform)
elif name=='places365_32x32':
num_classes=365
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, 'Places365_32x32' )
train_dst = datasets.ImageFolder(os.path.join(data_root, 'train'), transform=train_transform)
val_dst = datasets.ImageFolder(os.path.join(data_root, 'val'), transform=val_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