def loading_model(self):
print('Loading %s model' % (self.model_type))
pretrained = None
if self.pretrained:
pretrained = 'imagenet'
if self.model_type == 'inceptionv4':
self.model = inceptionv4(num_classes=1000, pretrained=pretrained)
if self.pretrained:
num_ftrs = self.model.last_linear.in_features
self.model.last_linear = nn.Linear(num_ftrs, self.num_classes)
# free all layers:
for i, param in self.model.named_parameters():
param.requires_grad = False
# unfreeze last layers:
ct = []
for name, child in self.model.features.named_children():
if "4" in ct:
for param in child.parameters():
param.requires_grad = True
ct.append(name)
else:
num_ftrs = self.model.last_linear.in_features
self.model.last_linear = nn.Linear(num_ftrs, self.num_classes)
else:
print('no model')
exit()
# Check gpu and run parallel
if check_gpu() > 0:
self.model = torch.nn.DataParallel(self.model).cuda()
# self.model.cuda()
# define loss function (criterion) and optimizer
self.criterion = nn.CrossEntropyLoss()
if check_gpu() > 0:
self.criterion = nn.CrossEntropyLoss().cuda()
params = list(
filter(lambda p: p.requires_grad, self.model.parameters()))
self.optimizer = optim.SGD(params=params,
lr=self.lr,
momentum=self.momentum,
weight_decay=self.weight_decay)
file = os.path.join(self.data_folder, 'model_best.pth.tar')
if os.path.isfile(file):
print("=> loading checkpoint '{}'".format('model_best.pth.tar'))
checkpoint = torch.load(file)
self.start_epoch = checkpoint['epoch']
self.best_prec1 = checkpoint['best_prec1']
self.model.load_state_dict(checkpoint['state_dict'])
self.optimizer.load_state_dict(checkpoint['optimizer'])
print("=> loaded best model")
else:
print("=> no model best found at ")
exit()
cudnn.benchmark = True
def get_network(args, use_gpu=True):
""" return given network
"""
if args.net == 'vgg16':
from models.vgg import vgg16_bn
net = vgg16_bn()
elif args.net == 'vgg13':
from models.vgg import vgg13_bn
net = vgg13_bn()
elif args.net == 'vgg11':
from models.vgg import vgg11_bn
net = vgg11_bn()
elif args.net == 'vgg19':
from models.vgg import vgg19_bn
net = vgg19_bn()
elif args.net == 'densenet121':
from models.densenet import densenet121
net = densenet121()
elif args.net == 'densenet161':
from models.densenet import densenet161
net = densenet161()
elif args.net == 'densenet169':
from models.densenet import densenet169
net = densenet169()
elif args.net == 'densenet201':
from models.densenet import densenet201
net = densenet201()
elif args.net == 'googlenet':
from models.googlenet import googlenet
net = googlenet()
elif args.net == 'inceptionv3':
from models.inceptionv3 import inceptionv3
net = inceptionv3()
elif args.net == 'inceptionv4':
from models.inceptionv4 import inceptionv4
net = inceptionv4()
elif args.net == 'inceptionresnetv2':
from models.inceptionv4 import inception_resnet_v2
net = inception_resnet_v2()
elif args.net == 'xception':
from models.xception import xception
net = xception()
elif args.net == 'resnet18':
from models.resnet import resnet18
net = resnet18()
elif args.net == 'resnet34':
from models.resnet import resnet34
net = resnet34()
elif args.net == 'resnet50':
from models.resnet import resnet50
net = resnet50()
elif args.net == 'resnet101':
from models.resnet import resnet101
net = resnet101()
elif args.net == 'resnet152':
from models.resnet import resnet152
net = resnet152()
elif args.net == 'preactresnet18':
from models.preactresnet import preactresnet18
net = preactresnet18()
elif args.net == 'preactresnet34':
from models.preactresnet import preactresnet34
net = preactresnet34()
elif args.net == 'preactresnet50':
from models.preactresnet import preactresnet50
net = preactresnet50()
elif args.net == 'preactresnet101':
from models.preactresnet import preactresnet101
net = preactresnet101()
elif args.net == 'preactresnet152':
from models.preactresnet import preactresnet152
net = preactresnet152()
elif args.net == 'resnext50':
from models.resnext import resnext50
net = resnext50()
elif args.net == 'resnext101':
from models.resnext import resnext101
net = resnext101()
elif args.net == 'resnext152':
from models.resnext import resnext152
net = resnext152()
elif args.net == 'shufflenet':
from models.shufflenet import shufflenet
net = shufflenet()
elif args.net == 'shufflenetv2':
from models.shufflenetv2 import shufflenetv2
net = shufflenetv2()
elif args.net == 'squeezenet':
from models.squeezenet import squeezenet
net = squeezenet()
elif args.net == 'mobilenet':
from models.mobilenet import mobilenet
net = mobilenet()
elif args.net == 'mobilenetv2':
from models.mobilenetv2 import mobilenetv2
net = mobilenetv2()
elif args.net == 'nasnet':
from models.nasnet import nasnet
net = nasnet()
elif args.net == 'attention56':
from models.attention import attention56
net = attention56()
elif args.net == 'attention92':
from models.attention import attention92
net = attention92()
elif args.net == 'seresnet18':
from models.senet import seresnet18
net = seresnet18()
elif args.net == 'seresnet34':
from models.senet import seresnet34
net = seresnet34()
elif args.net == 'seresnet50':
from models.senet import seresnet50
net = seresnet50()
elif args.net == 'seresnet101':
from models.senet import seresnet101
net = seresnet101()
elif args.net == 'seresnet152':
from models.senet import seresnet152
net = seresnet152()
else:
print('the network name you have entered is not supported yet')
sys.exit()
if use_gpu:
net = net.cuda()
return net
def get_network(args, use_gpu=True):
""" return given network
"""
if args.net == 'mobilenet':
from models.mobilenet import mobilenet
net = mobilenet(args)
elif args.net == 'mobilenetv2':
from models.mobilenetv2 import mobilenetv2
net = mobilenetv2(args)
elif args.net == 'vgg13':
from models.vgg import vgg13_bn
net = vgg13_bn(args)
elif args.net == 'vgg11':
from models.vgg import vgg11_bn
net = vgg11_bn(args)
elif args.net == 'vgg19':
# from models.vgg import vgg19_bn
# net = vgg19_bn(args)
from torchvision.models import vgg19_bn
import torch.nn as nn
net = vgg19_bn(pretrained=True)
net.classifier[6] = nn.Linear(4096, args.nc)
elif args.net == 'densenet121':
from models.densenet import densenet121
net = densenet121(args)
elif args.net == 'densenet161':
from models.densenet import densenet161
net = densenet161(args)
elif args.net == 'densenet169':
from models.densenet import densenet169
net = densenet169(args)
elif args.net == 'densenet201':
from models.densenet import densenet201
net = densenet201(args)
elif args.net == 'googlenet':
from models.googlenet import googlenet
net = googlenet(args)
elif args.net == 'inceptionv3':
from models.inceptionv3 import inceptionv3
net = inceptionv3(args)
elif args.net == 'inceptionv4':
from models.inceptionv4 import inceptionv4
net = inceptionv4(args)
elif args.net == 'inceptionresnetv2':
from models.inceptionv4 import inception_resnet_v2
net = inception_resnet_v2(args)
elif args.net == 'xception':
from models.xception import xception
net = xception(args)
elif args.net == 'resnet18':
# from models.resnet import resnet18
# net = resnet18(args)
from torchvision.models import resnet18
import torch.nn as nn
net = resnet18(pretrained=True)
net.fc = nn.Linear(512, args.nc)
elif args.net == 'resnet34':
from models.resnet import resnet34
net = resnet34(args)
elif args.net == 'resnet50':
from models.resnet import resnet50
net = resnet50(args)
elif args.net == 'resnet101':
from models.resnet import resnet101
net = resnet101(args)
elif args.net == 'resnet152':
from models.resnet import resnet152
net = resnet152(args)
elif args.net == 'preactresnet18':
from models.preactresnet import preactresnet18
net = preactresnet18(args)
elif args.net == 'preactresnet34':
from models.preactresnet import preactresnet34
net = preactresnet34(args)
elif args.net == 'preactresnet50':
from models.preactresnet import preactresnet50
net = preactresnet50(args)
elif args.net == 'preactresnet101':
from models.preactresnet import preactresnet101
net = preactresnet101(args)
elif args.net == 'preactresnet152':
from models.preactresnet import preactresnet152
net = preactresnet152(args)
elif args.net == 'resnext50':
from models.resnext import resnext50
net = resnext50(args)
elif args.net == 'resnext101':
from models.resnext import resnext101
net = resnext101(args)
elif args.net == 'resnext152':
from models.resnext import resnext152
net = resnext152(args)
elif args.net == 'shufflenet':
from models.shufflenet import shufflenet
net = shufflenet(args)
elif args.net == 'shufflenetv2':
from models.shufflenetv2 import shufflenetv2
net = shufflenetv2(args)
elif args.net == 'squeezenet':
from models.squeezenet import squeezenet
net = squeezenet(args)
elif args.net == 'mobilenet':
from models.mobilenet import mobilenet
net = mobilenet(args)
elif args.net == 'mobilenetv2':
from models.mobilenetv2 import mobilenetv2
net = mobilenetv2(args)
elif args.net == 'mobilenetv3':
from models.mobilenetv3 import mobileNetv3
net = mobileNetv3(args)
elif args.net == 'mobilenetv3_l':
from models.mobilenetv3 import mobileNetv3
net = mobileNetv3(args, mode='large')
elif args.net == 'mobilenetv3_s':
from models.mobilenetv3 import mobileNetv3
net = mobileNetv3(args, mode='small')
elif args.net == 'nasnet':
from models.nasnet import nasnetalarge
net = nasnetalarge(args)
elif args.net == 'attention56':
from models.attention import attention56
net = attention56(args)
elif args.net == 'attention92':
from models.attention import attention92
net = attention92(args)
elif args.net == 'seresnet18':
from models.senet import seresnet18
net = seresnet18(args)
elif args.net == 'seresnet34':
from models.senet import seresnet34
net = seresnet34(args)
elif args.net == 'seresnet50':
from models.senet import seresnet50
net = seresnet50(args)
elif args.net == 'seresnet101':
from models.senet import seresnet101
net = seresnet101(args)
elif args.net == 'seresnet152':
from models.senet import seresnet152
net = seresnet152(args)
elif args.net.lower() == 'sqnxt_23_1x':
from models.SqueezeNext import SqNxt_23_1x
net = SqNxt_23_1x(args)
elif args.net.lower() == 'sqnxt_23_1xv5':
from models.SqueezeNext import SqNxt_23_1x_v5
net = SqNxt_23_1x_v5(args)
elif args.net.lower() == 'sqnxt_23_2x':
from models.SqueezeNext import SqNxt_23_2x
net = SqNxt_23_2x(args)
elif args.net.lower() == 'sqnxt_23_2xv5':
from models.SqueezeNext import SqNxt_23_2x_v5
net = SqNxt_23_2x_v5(args)
elif args.net.lower() == 'mnasnet':
# from models.MnasNet import mnasnet
# net = mnasnet(args)
from models.nasnet_mobile import nasnet_Mobile
net = nasnet_Mobile(args)
elif args.net == 'efficientnet_b0':
from models.efficientnet import efficientnet_b0
net = efficientnet_b0(args)
elif args.net == 'efficientnet_b1':
from models.efficientnet import efficientnet_b1
net = efficientnet_b1(args)
elif args.net == 'efficientnet_b2':
from models.efficientnet import efficientnet_b2
net = efficientnet_b2(args)
elif args.net == 'efficientnet_b3':
from models.efficientnet import efficientnet_b3
net = efficientnet_b3(args)
elif args.net == 'efficientnet_b4':
from models.efficientnet import efficientnet_b4
net = efficientnet_b4(args)
elif args.net == 'efficientnet_b5':
from models.efficientnet import efficientnet_b5
net = efficientnet_b5(args)
elif args.net == 'efficientnet_b6':
from models.efficientnet import efficientnet_b6
net = efficientnet_b6(args)
elif args.net == 'efficientnet_b7':
from models.efficientnet import efficientnet_b7
net = efficientnet_b7(args)
elif args.net == 'mlp':
from models.mlp import MLPClassifier
net = MLPClassifier(args)
elif args.net == 'alexnet':
from torchvision.models import alexnet
import torch.nn as nn
net = alexnet(pretrained=True)
net.classifier[6] = nn.Linear(4096, args.nc)
elif args.net == 'lambda18':
from models._lambda import LambdaResnet18
net = LambdaResnet18(num_classes=args.nc, channels=args.cs)
elif args.net == 'lambda34':
from models._lambda import LambdaResnet34
net = LambdaResnet34(num_classes=args.nc, channels=args.cs)
elif args.net == 'lambda50':
from models._lambda import LambdaResnet50
net = LambdaResnet50(num_classes=args.nc, channels=args.cs)
elif args.net == 'lambda101':
from models._lambda import LambdaResnet101
net = LambdaResnet101(num_classes=args.nc)
elif args.net == 'lambda152':
from models._lambda import LambdaResnet152
net = LambdaResnet152(num_classes=args.nc, channels=args.cs)
else:
print('the network name you have entered is not supported yet')
sys.exit()
if use_gpu:
net = net.cuda()
return net
def get_network(args):
""" return given network
"""
if args.net == 'vgg16':
from models.vgg import vgg16_bn
net = vgg16_bn()
elif args.net == 'vgg13':
from models.vgg import vgg13_bn
net = vgg13_bn()
elif args.net == 'vgg11':
from models.vgg import vgg11_bn
net = vgg11_bn()
elif args.net == 'vgg19':
from models.vgg import vgg19_bn
net = vgg19_bn()
elif args.net == 'densenet121':
from models.densenet import densenet121
net = densenet121()
elif args.net == 'densenet161':
from models.densenet import densenet161
net = densenet161()
elif args.net == 'densenet169':
from models.densenet import densenet169
net = densenet169()
elif args.net == 'densenet201':
from models.densenet import densenet201
net = densenet201()
elif args.net == 'googlenet':
from models.googlenet import googlenet
net = googlenet()
elif args.net == 'inceptionv3':
from models.inceptionv3 import inceptionv3
net = inceptionv3()
elif args.net == 'inceptionv4':
from models.inceptionv4 import inceptionv4
net = inceptionv4()
elif args.net == 'inceptionresnetv2':
from models.inceptionv4 import inception_resnet_v2
net = inception_resnet_v2()
elif args.net == 'xception':
from models.xception import xception
net = xception()
elif args.net == 'resnet18':
from models.resnet import resnet18
net = resnet18()
elif args.net == 'resnet34':
from models.resnet import resnet34
net = resnet34()
elif args.net == 'resnet50':
from models.resnet import resnet50
net = resnet50()
elif args.net == 'resnet101':
from models.resnet import resnet101
net = resnet101()
elif args.net == 'resnet152':
from models.resnet import resnet152
net = resnet152()
elif args.net == 'preactresnet18':
from models.preactresnet import preactresnet18
net = preactresnet18()
elif args.net == 'preactresnet34':
from models.preactresnet import preactresnet34
net = preactresnet34()
elif args.net == 'preactresnet50':
from models.preactresnet import preactresnet50
net = preactresnet50()
elif args.net == 'preactresnet101':
from models.preactresnet import preactresnet101
net = preactresnet101()
elif args.net == 'preactresnet152':
from models.preactresnet import preactresnet152
net = preactresnet152()
elif args.net == 'resnext50':
from models.resnext import resnext50
net = resnext50()
elif args.net == 'resnext101':
from models.resnext import resnext101
net = resnext101()
elif args.net == 'resnext152':
from models.resnext import resnext152
net = resnext152()
elif args.net == 'shufflenet':
from models.shufflenet import shufflenet
net = shufflenet()
elif args.net == 'shufflenetv2':
from models.shufflenetv2 import shufflenetv2
net = shufflenetv2()
elif args.net == 'squeezenet':
from models.squeezenet import squeezenet
net = squeezenet()
elif args.net == 'mobilenet':
from models.mobilenet import mobilenet
net = mobilenet()
elif args.net == 'mobilenetv2':
from models.mobilenetv2 import mobilenetv2
net = mobilenetv2()
elif args.net == 'nasnet':
from models.nasnet import nasnet
net = nasnet()
elif args.net == 'attention56':
from models.attention import attention56
net = attention56()
elif args.net == 'attention92':
from models.attention import attention92
net = attention92()
elif args.net == 'seresnet18':
from models.senet import seresnet18
net = seresnet18()
elif args.net == 'seresnet34':
from models.senet import seresnet34
net = seresnet34()
elif args.net == 'seresnet50':
from models.senet import seresnet50
net = seresnet50()
elif args.net == 'seresnet101':
from models.senet import seresnet101
net = seresnet101()
elif args.net == 'seresnet152':
from models.senet import seresnet152
net = seresnet152()
elif args.net == 'wideresnet':
from models.wideresidual import wideresnet
net = wideresnet()
elif args.net == 'stochasticdepth18':
from models.stochasticdepth import stochastic_depth_resnet18
net = stochastic_depth_resnet18()
elif args.net == 'stochasticdepth34':
from models.stochasticdepth import stochastic_depth_resnet34
net = stochastic_depth_resnet34()
elif args.net == 'stochasticdepth50':
from models.stochasticdepth import stochastic_depth_resnet50
net = stochastic_depth_resnet50()
elif args.net == 'stochasticdepth101':
from models.stochasticdepth import stochastic_depth_resnet101
net = stochastic_depth_resnet101()
elif args.net == 'normal_resnet':
from models.normal_resnet import resnet18
net = resnet18()
elif args.net == 'hyper_resnet':
from models.hypernet_main import Hypernet_Main
net = Hypernet_Main(
encoder="resnet18",
hypernet_params={'vqvae_dict_size': args.dict_size})
elif args.net == 'normal_resnet_wo_bn':
from models.normal_resnet_wo_bn import resnet18
net = resnet18()
elif args.net == 'hyper_resnet_wo_bn':
from models.hypernet_main import Hypernet_Main
net = Hypernet_Main(
encoder="resnet18_wobn",
hypernet_params={'vqvae_dict_size': args.dict_size})
else:
print('the network name you have entered is not supported yet')
sys.exit()
if args.gpu: #use_gpu
net = net.cuda()
return net
def get_network(args):
""" return given network
"""
if args.model == 'vgg16':
from models.vgg import vgg16_bn
model = vgg16_bn()
elif args.model == 'vgg13':
from models.vgg import vgg13_bn
model = vgg13_bn()
elif args.model == 'vgg11':
from models.vgg import vgg11_bn
model = vgg11_bn()
elif args.model == 'vgg19':
from models.vgg import vgg19_bn
model = vgg19_bn()
elif args.model == 'densenet121':
from models.densenet import densenet121
model = densenet121()
elif args.model == 'densenet161':
from models.densenet import densenet161
model = densenet161()
elif args.model == 'densenet169':
from models.densenet import densenet169
model = densenet169()
elif args.model == 'densenet201':
from models.densenet import densenet201
model = densenet201()
elif args.model == 'googlenet':
from models.googlenet import googlenet
model = googlenet()
elif args.model == 'inceptionv3':
from models.inceptionv3 import inceptionv3
model = inceptionv3()
elif args.model == 'inceptionv4':
from models.inceptionv4 import inceptionv4
model = inceptionv4()
elif args.model == 'inceptionresnetv2':
from models.inceptionv4 import inception_resnet_v2
model = inception_resnet_v2()
elif args.model == 'xception':
from models.xception import xception
model = xception()
elif args.model == 'resnet18':
from models.resnet import resnet18
model = resnet18()
elif args.model == 'resnet34':
from models.resnet import resnet34
model = resnet34()
elif args.model == 'resnet50':
from models.resnet import resnet50
model = resnet50()
elif args.model == 'resnet101':
from models.resnet import resnet101
model = resnet101()
elif args.model == 'resnet152':
from models.resnet import resnet152
model = resnet152()
elif args.model == 'preactresnet18':
from models.preactresnet import preactresnet18
model = preactresnet18()
elif args.model == 'preactresnet34':
from models.preactresnet import preactresnet34
model = preactresnet34()
elif args.model == 'preactresnet50':
from models.preactresnet import preactresnet50
model = preactresnet50()
elif args.model == 'preactresnet101':
from models.preactresnet import preactresnet101
model = preactresnet101()
elif args.model == 'preactresnet152':
from models.preactresnet import preactresnet152
model = preactresnet152()
elif args.model == 'resnext50':
from models.resnext import resnext50
model = resnext50()
elif args.model == 'resnext101':
from models.resnext import resnext101
model = resnext101()
elif args.model == 'resnext152':
from models.resnext import resnext152
model = resnext152()
elif args.model == 'shufflenet':
from models.shufflenet import shufflenet
model = shufflenet()
elif args.model == 'shufflenetv2':
from models.shufflenetv2 import shufflenetv2
model = shufflenetv2()
elif args.model == 'squeezenet':
from models.squeezenet import squeezenet
model = squeezenet()
elif args.model == 'mobilenet':
from models.mobilenet import mobilenet
model = mobilenet()
elif args.model == 'mobilenetv2':
from models.mobilenetv2 import mobilenetv2
model = mobilenetv2()
elif args.model == 'nasnet':
from models.nasnet import nasnet
model = nasnet()
elif args.model == 'attention56':
from models.attention import attention56
model = attention56()
elif args.model == 'attention92':
from models.attention import attention92
model = attention92()
elif args.model == 'seresnet18':
from models.senet import seresnet18
model = seresnet18()
elif args.model == 'seresnet34':
from models.senet import seresnet34
model = seresnet34()
elif args.model == 'seresnet50':
from models.senet import seresnet50
model = seresnet50()
elif args.model == 'seresnet101':
from models.senet import seresnet101
model = seresnet101()
elif args.model == 'seresnet152':
from models.senet import seresnet152
model = seresnet152()
elif args.model == 'wideresnet':
from models.wideresidual import wideresnet
model = wideresnet()
elif args.model == 'stochasticdepth18':
from models.stochasticdepth import stochastic_depth_resnet18
model = stochastic_depth_resnet18()
elif args.model == 'stochasticdepth34':
from models.stochasticdepth import stochastic_depth_resnet34
model = stochastic_depth_resnet34()
elif args.model == 'stochasticdepth50':
from models.stochasticdepth import stochastic_depth_resnet50
model = stochastic_depth_resnet50()
elif args.model == 'stochasticdepth101':
from models.stochasticdepth import stochastic_depth_resnet101
model = stochastic_depth_resnet101()
else:
print('the network name you have entered is not supported yet')
sys.exit()
return model
def inception_4(pre):
return children(inceptionv4(pretrained=pre))[0]
def get_network(args, use_gpu=True):
""" return given network
"""
if args.net == 'vgg16':
net = torchvision.models.vgg16_bn(pretrained=args.bool_pretrained)
net.classifier[6] = nn.Linear(4096, args.num_classes, bias=True)
elif args.net == 'vgg13':
net = torchvision.models.vgg13_bn(pretrained=args.bool_pretrained)
net.classifier[6] = nn.Linear(4096, args.num_classes, bias=True)
elif args.net == 'vgg11':
net = torchvision.models.vgg11_bn(pretrained=args.bool_pretrained)
net.classifier[6] = nn.Linear(4096, args.num_classes, bias=True)
elif args.net == 'vgg19':
net = torchvision.models.vgg19_bn(pretrained=args.bool_pretrained)
net.classifier[6] = nn.Linear(4096, args.num_classes, bias=True)
####effcientnet
elif args.net == 'efficientnet-b5':
from efficientnet_pytorch import EfficientNet
if args.bool_pretrained == True:
net = EfficientNet.from_pretrained('efficientnet-b5')
else:
net = EfficientNet.from_name('efficientnet-b5')
net._fc = nn.Linear(2048, args.num_classes, bias=True)
elif args.net == 'densenet121':
from models.densenet import densenet121
net = densenet121()
elif args.net == 'densenet161':
net = torchvision.models.densenet161(pretrained=args.bool_pretrained)
in_features = net.classifier.in_features
net.classifier = nn.Linear(in_features, args.num_classes, bias=True)
elif args.net == 'densenet169':
net = torchvision.models.densenet169(pretrained=args.bool_pretrained)
in_features = net.classifier.in_features
net.classifier = nn.Linear(in_features, args.num_classes, bias=True)
elif args.net == 'densenet201':
net = torchvision.models.densenet201(pretrained=args.bool_pretrained)
in_features = net.classifier.in_features
net.classifier = nn.Linear(in_features, args.num_classes, bias=True)
elif args.net == 'googlenet':
from models.googlenet import googlenet
net = googlenet()
elif args.net == 'inceptionv3':
from models.inceptionv3 import inceptionv3
net = inceptionv3()
elif args.net == 'inceptionv4':
from models.inceptionv4 import inceptionv4
net = inceptionv4()
elif args.net == 'inceptionresnetv2':
from models.inceptionv4 import inception_resnet_v2
net = inception_resnet_v2()
elif args.net == 'xception':
from models.xception import xception
net = xception()
################## ResNet ########################################################
elif args.net == 'resnet18':
from models.resnet import resnet
net = resnet(args.num_classes, 2, args.pretrained, args.net)
elif args.net == 'resnet34':
from models.resnet import resnet
net = resnet(args.num_classes, 2, args.pretrained, args.net)
elif args.net == 'resnet50':
from models.resnet import resnet
net = resnet(args.num_classes, 2, args.pretrained, args.net)
elif args.net == 'resnet101':
from models.resnet import resnet
net = resnet(args.num_classes, 2, args.pretrained, args.net)
elif args.net == 'resnet152':
from models.resnet import resnet
net = resnet(args.num_classes, 2, args.pretrained, args.net)
##################################################################################
elif args.net == 'preactresnet18':
from models.preactresnet import preactresnet18
net = preactresnet18()
elif args.net == 'preactresnet34':
from models.preactresnet import preactresnet34
net = preactresnet34()
elif args.net == 'preactresnet50':
from models.preactresnet import preactresnet50
net = preactresnet50()
elif args.net == 'preactresnet101':
from models.preactresnet import preactresnet101
net = preactresnet101()
elif args.net == 'preactresnet152':
from models.preactresnet import preactresnet152
net = preactresnet152()
##################################################################
elif args.net == 'se_resnext50':
from models.resnext import se_resnext
net = se_resnext(args.num_classes, 2, args.pretrained, args.net)
elif args.net == 'se_resnext101':
from models.resnext import se_resnext
net = se_resnext(args.num_classes, 2, args.pretrained, args.net)
#################################################################
elif args.net == 'shufflenet':
from models.shufflenet import shufflenet
net = shufflenet()
elif args.net == 'shufflenetv2':
from models.shufflenetv2 import shufflenetv2
net = shufflenetv2()
elif args.net == 'squeezenet':
from models.squeezenet import squeezenet
net = squeezenet()
elif args.net == 'mobilenet':
from models.mobilenet import mobilenet
net = mobilenet()
elif args.net == 'mobilenetv2':
from models.mobilenetv2 import mobilenetv2
net = mobilenetv2()
elif args.net == 'nasnet':
from models.nasnet import nasnet
net = nasnet()
elif args.net == 'attention56':
from models.attention import attention56
net = attention56()
elif args.net == 'attention92':
from models.attention import attention92
net = attention92()
#########################################################
elif args.net == 'se_resnet50':
from models.senet import seresnet
net = seresnet(args.num_classes, 2, args.pretrained, args.net)
elif args.net == 'se_resnet101':
from models.senet import seresnet
net = seresnet(args.num_classes, 2, args.pretrained, args.net)
elif args.net == 'se_resnet152':
from models.senet import seresnet
net = seresnet(args.num_classes, 2, args.pretrained, args.net)
else:
print('the network name you have entered is not supported yet')
sys.exit()
if use_gpu:
net = net.cuda()
return net
def get_network(args, use_gpu=True, num_train=0):
""" return given network
"""
if args.dataset == 'cifar-10':
num_classes = 10
elif args.dataset == 'cifar-100':
num_classes = 100
else:
num_classes = 0
if args.ignoring:
if args.net == 'resnet18':
from models.resnet_ign import resnet18_ign
criterion = nn.CrossEntropyLoss(reduction='none')
net = resnet18_ign(criterion, num_classes=num_classes, num_train=num_train,softmax=args.softmax,isalpha=args.isalpha)
else:
if args.net == 'vgg16':
from models.vgg import vgg16_bn
net = vgg16_bn()
elif args.net == 'vgg13':
from models.vgg import vgg13_bn
net = vgg13_bn()
elif args.net == 'vgg11':
from models.vgg import vgg11_bn
net = vgg11_bn()
elif args.net == 'vgg19':
from models.vgg import vgg19_bn
net = vgg19_bn()
elif args.net == 'densenet121':
from models.densenet import densenet121
net = densenet121()
elif args.net == 'densenet161':
from models.densenet import densenet161
net = densenet161()
elif args.net == 'densenet169':
from models.densenet import densenet169
net = densenet169()
elif args.net == 'densenet201':
from models.densenet import densenet201
net = densenet201()
elif args.net == 'googlenet':
from models.googlenet import googlenet
net = googlenet()
elif args.net == 'inceptionv3':
from models.inceptionv3 import inceptionv3
net = inceptionv3()
elif args.net == 'inceptionv4':
from models.inceptionv4 import inceptionv4
net = inceptionv4()
elif args.net == 'inceptionresnetv2':
from models.inceptionv4 import inception_resnet_v2
net = inception_resnet_v2()
elif args.net == 'xception':
from models.xception import xception
net = xception()
elif args.net == 'resnet18':
from models.resnet import resnet18
net = resnet18(num_classes=num_classes)
elif args.net == 'resnet34':
from models.resnet import resnet34
net = resnet34(num_classes=num_classes)
elif args.net == 'resnet50':
from models.resnet import resnet50
net = resnet50(num_classes=num_classes)
elif args.net == 'resnet101':
from models.resnet import resnet101
net = resnet101(num_classes=num_classes)
elif args.net == 'resnet152':
from models.resnet import resnet152
net = resnet152(num_classes=num_classes)
elif args.net == 'preactresnet18':
from models.preactresnet import preactresnet18
net = preactresnet18()
elif args.net == 'preactresnet34':
from models.preactresnet import preactresnet34
net = preactresnet34()
elif args.net == 'preactresnet50':
from models.preactresnet import preactresnet50
net = preactresnet50()
elif args.net == 'preactresnet101':
from models.preactresnet import preactresnet101
net = preactresnet101()
elif args.net == 'preactresnet152':
from models.preactresnet import preactresnet152
net = preactresnet152()
elif args.net == 'resnext50':
from models.resnext import resnext50
net = resnext50()
elif args.net == 'resnext101':
from models.resnext import resnext101
net = resnext101()
elif args.net == 'resnext152':
from models.resnext import resnext152
net = resnext152()
elif args.net == 'shufflenet':
from models.shufflenet import shufflenet
net = shufflenet()
elif args.net == 'shufflenetv2':
from models.shufflenetv2 import shufflenetv2
net = shufflenetv2()
elif args.net == 'squeezenet':
from models.squeezenet import squeezenet
net = squeezenet()
elif args.net == 'mobilenet':
from models.mobilenet import mobilenet
net = mobilenet()
elif args.net == 'mobilenetv2':
from models.mobilenetv2 import mobilenetv2
net = mobilenetv2()
elif args.net == 'nasnet':
from models.nasnet import nasnet
net = nasnet()
elif args.net == 'attention56':
from models.attention import attention56
net = attention56()
elif args.net == 'attention92':
from models.attention import attention92
net = attention92()
elif args.net == 'seresnet18':
from models.senet import seresnet18
net = seresnet18()
elif args.net == 'seresnet34':
from models.senet import seresnet34
net = seresnet34()
elif args.net == 'seresnet50':
from models.senet import seresnet50
net = seresnet50()
elif args.net == 'seresnet101':
from models.senet import seresnet101
net = seresnet101()
elif args.net == 'seresnet152':
from models.senet import seresnet152
net = seresnet152()
else:
print('the network name you have entered is not supported yet')
sys.exit()
if use_gpu:
net = net.cuda()
return net
def get_model(args):
if args.datasets == 'ImageNet':
return models_imagenet.__dict__[args.arch]()
if args.datasets == 'CIFAR10' or args.datasets == 'MNIST':
num_class = 10
elif args.datasets == 'CIFAR100':
num_class = 100
if args.datasets == 'CIFAR100':
if args.arch == 'vgg16':
from models.vgg import vgg16_bn
net = vgg16_bn()
elif args.arch == 'vgg13':
from models.vgg import vgg13_bn
net = vgg13_bn()
elif args.arch == 'vgg11':
from models.vgg import vgg11_bn
net = vgg11_bn()
elif args.arch == 'vgg19':
from models.vgg import vgg19_bn
net = vgg19_bn()
elif args.arch == 'densenet121':
from models.densenet import densenet121
net = densenet121()
elif args.arch == 'densenet161':
from models.densenet import densenet161
net = densenet161()
elif args.arch == 'densenet169':
from models.densenet import densenet169
net = densenet169()
elif args.arch == 'densenet201':
from models.densenet import densenet201
net = densenet201()
elif args.arch == 'googlenet':
from models.googlenet import googlenet
net = googlenet()
elif args.arch == 'inceptionv3':
from models.inceptionv3 import inceptionv3
net = inceptionv3()
elif args.arch == 'inceptionv4':
from models.inceptionv4 import inceptionv4
net = inceptionv4()
elif args.arch == 'inceptionresnetv2':
from models.inceptionv4 import inception_resnet_v2
net = inception_resnet_v2()
elif args.arch == 'xception':
from models.xception import xception
net = xception()
elif args.arch == 'resnet18':
from models.resnet import resnet18
net = resnet18()
elif args.arch == 'resnet34':
from models.resnet import resnet34
net = resnet34()
elif args.arch == 'resnet50':
from models.resnet import resnet50
net = resnet50()
elif args.arch == 'resnet101':
from models.resnet import resnet101
net = resnet101()
elif args.arch == 'resnet152':
from models.resnet import resnet152
net = resnet152()
elif args.arch == 'preactresnet18':
from models.preactresnet import preactresnet18
net = preactresnet18()
elif args.arch == 'preactresnet34':
from models.preactresnet import preactresnet34
net = preactresnet34()
elif args.arch == 'preactresnet50':
from models.preactresnet import preactresnet50
net = preactresnet50()
elif args.arch == 'preactresnet101':
from models.preactresnet import preactresnet101
net = preactresnet101()
elif args.arch == 'preactresnet152':
from models.preactresnet import preactresnet152
net = preactresnet152()
elif args.arch == 'resnext50':
from models.resnext import resnext50
net = resnext50()
elif args.arch == 'resnext101':
from models.resnext import resnext101
net = resnext101()
elif args.arch == 'resnext152':
from models.resnext import resnext152
net = resnext152()
elif args.arch == 'shufflenet':
from models.shufflenet import shufflenet
net = shufflenet()
elif args.arch == 'shufflenetv2':
from models.shufflenetv2 import shufflenetv2
net = shufflenetv2()
elif args.arch == 'squeezenet':
from models.squeezenet import squeezenet
net = squeezenet()
elif args.arch == 'mobilenet':
from models.mobilenet import mobilenet
net = mobilenet()
elif args.arch == 'mobilenetv2':
from models.mobilenetv2 import mobilenetv2
net = mobilenetv2()
elif args.arch == 'nasnet':
from models.nasnet import nasnet
net = nasnet()
elif args.arch == 'attention56':
from models.attention import attention56
net = attention56()
elif args.arch == 'attention92':
from models.attention import attention92
net = attention92()
elif args.arch == 'seresnet18':
from models.senet import seresnet18
net = seresnet18()
elif args.arch == 'seresnet34':
from models.senet import seresnet34
net = seresnet34()
elif args.arch == 'seresnet50':
from models.senet import seresnet50
net = seresnet50()
elif args.arch == 'seresnet101':
from models.senet import seresnet101
net = seresnet101()
elif args.arch == 'seresnet152':
from models.senet import seresnet152
net = seresnet152()
elif args.arch == 'wideresnet':
from models.wideresidual import wideresnet
net = wideresnet()
elif args.arch == 'stochasticdepth18':
from models.stochasticdepth import stochastic_depth_resnet18
net = stochastic_depth_resnet18()
elif args.arch == 'efficientnet':
from models.efficientnet import efficientnet
net = efficientnet(1, 1, 100, bn_momentum=0.9)
elif args.arch == 'stochasticdepth34':
from models.stochasticdepth import stochastic_depth_resnet34
net = stochastic_depth_resnet34()
elif args.arch == 'stochasticdepth50':
from models.stochasticdepth import stochastic_depth_resnet50
net = stochastic_depth_resnet50()
elif args.arch == 'stochasticdepth101':
from models.stochasticdepth import stochastic_depth_resnet101
net = stochastic_depth_resnet101()
else:
net = resnet.__dict__[args.arch](num_classes=num_class)
return net
return resnet.__dict__[args.arch](num_classes=num_class)
def get_network(args):
""" return given network
"""
if args.net == 'vgg16':
from models.vgg import vgg16_bn
net = vgg16_bn()
elif args.net == 'vgg13':
from models.vgg import vgg13_bn
net = vgg13_bn()
elif args.net == 'vgg11':
from models.vgg import vgg11_bn
net = vgg11_bn()
elif args.net == 'vgg19':
from models.vgg import vgg19_bn
net = vgg19_bn()
# elif args.net == 'efficientnet':
# from models.effnetv2 import effnetv2_s
# net = effnetv2_s()
elif args.net == 'densenet121':
from models.densenet import densenet121
net = densenet121()
elif args.net == 'densenet161':
from models.densenet import densenet161
net = densenet161()
elif args.net == 'densenet169':
from models.densenet import densenet169
net = densenet169()
elif args.net == 'densenet201':
from models.densenet import densenet201
net = densenet201()
elif args.net == 'googlenet':
from models.googlenet import googlenet
net = googlenet()
elif args.net == 'inceptionv3':
from models.inceptionv3 import inceptionv3
net = inceptionv3()
elif args.net == 'inceptionv4':
from models.inceptionv4 import inceptionv4
net = inceptionv4()
elif args.net == 'inceptionresnetv2':
from models.inceptionv4 import inception_resnet_v2
net = inception_resnet_v2()
elif args.net == 'xception':
from models.xception import xception
net = xception()
elif args.net == 'resnet18':
from models.resnet import resnet18
net = resnet18()
elif args.net == 'resnet34':
from models.resnet import resnet34
net = resnet34()
elif args.net == 'resnet50':
from models.resnet import resnet50
net = resnet50()
elif args.net == 'resnet101':
from models.resnet import resnet101
net = resnet101()
elif args.net == 'resnet152':
from models.resnet import resnet152
net = resnet152()
elif args.net == 'preactresnet18':
from models.preactresnet import preactresnet18
net = preactresnet18()
elif args.net == 'preactresnet34':
from models.preactresnet import preactresnet34
net = preactresnet34()
elif args.net == 'preactresnet50':
from models.preactresnet import preactresnet50
net = preactresnet50()
elif args.net == 'preactresnet101':
from models.preactresnet import preactresnet101
net = preactresnet101()
elif args.net == 'preactresnet152':
from models.preactresnet import preactresnet152
net = preactresnet152()
elif args.net == 'resnext50':
from models.resnext import resnext50
net = resnext50()
elif args.net == 'resnext101':
from models.resnext import resnext101
net = resnext101()
elif args.net == 'resnext152':
from models.resnext import resnext152
net = resnext152()
elif args.net == 'shufflenet':
from models.shufflenet import shufflenet
net = shufflenet()
elif args.net == 'shufflenetv2':
from models.shufflenetv2 import shufflenetv2
net = shufflenetv2()
elif args.net == 'squeezenet':
from models.squeezenet import squeezenet
net = squeezenet()
elif args.net == 'mobilenet':
from models.mobilenet import mobilenet
net = mobilenet()
elif args.net == 'mobilenetv2':
from models.mobilenetv2 import mobilenetv2
net = mobilenetv2()
elif args.net == 'nasnet':
from models.nasnet import nasnet
net = nasnet()
elif args.net == 'attention56':
from models.attention import attention56
net = attention56()
elif args.net == 'attention92':
from models.attention import attention92
net = attention92()
elif args.net == 'seresnet18':
from models.senet import seresnet18
net = seresnet18()
elif args.net == 'seresnet34':
from models.senet import seresnet34
net = seresnet34()
elif args.net == 'seresnet50':
from models.senet import seresnet50
net = seresnet50()
elif args.net == 'seresnet101':
from models.senet import seresnet101
net = seresnet101()
elif args.net == 'seresnet152':
from models.senet import seresnet152
net = seresnet152()
elif args.net == 'wideresnet':
from models.wideresidual import wideresnet
net = wideresnet()
elif args.net == 'stochasticdepth18':
from models.stochasticdepth import stochastic_depth_resnet18
net = stochastic_depth_resnet18()
elif args.net == 'stochasticdepth34':
from models.stochasticdepth import stochastic_depth_resnet34
net = stochastic_depth_resnet34()
elif args.net == 'stochasticdepth50':
from models.stochasticdepth import stochastic_depth_resnet50
net = stochastic_depth_resnet50()
elif args.net == 'stochasticdepth101':
from models.stochasticdepth import stochastic_depth_resnet101
net = stochastic_depth_resnet101()
elif args.net == 'efficientnetb0':
from models.efficientnet import efficientnetb0
net = efficientnetb0()
elif args.net == 'efficientnetb1':
from models.efficientnet import efficientnetb1
net = efficientnetb1()
elif args.net == 'efficientnetb2':
from models.efficientnet import efficientnetb2
net = efficientnetb2()
elif args.net == 'efficientnetb3':
from models.efficientnet import efficientnetb3
net = efficientnetb3()
elif args.net == 'efficientnetb4':
from models.efficientnet import efficientnetb4
net = efficientnetb4()
elif args.net == 'efficientnetb5':
from models.efficientnet import efficientnetb5
net = efficientnetb5()
elif args.net == 'efficientnetb6':
from models.efficientnet import efficientnetb6
net = efficientnetb6()
elif args.net == 'efficientnetb7':
from models.efficientnet import efficientnetb7
net = efficientnetb7()
elif args.net == 'efficientnetl2':
from models.efficientnet import efficientnetl2
net = efficientnetl2()
elif args.net == 'eff':
from models.efficientnet_pytorch import EfficientNet
net = EfficientNet.from_pretrained('efficientnet-b7', num_classes=2)
else:
print('the network name you have entered is not supported yet')
sys.exit()
if args.gpu: #use_gpu
net = net.cuda()
print("use-gpu")
return net
def get_network(args):
""" return given network
"""
if args.task == 'cifar10':
nclass = 10
elif args.task == 'cifar100':
nclass = 100
#Yang added none bn vggs
if args.net == 'vgg16':
from models.vgg import vgg16
net = vgg16(num_classes=nclass)
elif args.net == 'vgg13':
from models.vgg import vgg13
net = vgg13(num_classes=nclass)
elif args.net == 'vgg11':
from models.vgg import vgg11
net = vgg11(num_classes=nclass)
elif args.net == 'vgg19':
from models.vgg import vgg19
net = vgg19(num_classes=nclass)
elif args.net == 'vgg16bn':
from models.vgg import vgg16_bn
net = vgg16_bn(num_classes=nclass)
elif args.net == 'vgg13bn':
from models.vgg import vgg13_bn
net = vgg13_bn(num_classes=nclass)
elif args.net == 'vgg11bn':
from models.vgg import vgg11_bn
net = vgg11_bn(num_classes=nclass)
elif args.net == 'vgg19bn':
from models.vgg import vgg19_bn
net = vgg19_bn(num_classes=nclass)
elif args.net == 'densenet121':
from models.densenet import densenet121
net = densenet121()
elif args.net == 'densenet161':
from models.densenet import densenet161
net = densenet161()
elif args.net == 'densenet169':
from models.densenet import densenet169
net = densenet169()
elif args.net == 'densenet201':
from models.densenet import densenet201
net = densenet201()
elif args.net == 'googlenet':
from models.googlenet import googlenet
net = googlenet(num_classes=nclass)
elif args.net == 'inceptionv3':
from models.inceptionv3 import inceptionv3
net = inceptionv3()
elif args.net == 'inceptionv4':
from models.inceptionv4 import inceptionv4
net = inceptionv4()
elif args.net == 'inceptionresnetv2':
from models.inceptionv4 import inception_resnet_v2
net = inception_resnet_v2()
elif args.net == 'xception':
from models.xception import xception
net = xception(num_classes=nclass)
elif args.net == 'scnet':
from models.sphereconvnet import sphereconvnet
net = sphereconvnet(num_classes=nclass)
elif args.net == 'sphereresnet18':
from models.sphereconvnet import resnet18
net = resnet18(num_classes=nclass)
elif args.net == 'sphereresnet32':
from models.sphereconvnet import sphereresnet32
net = sphereresnet32(num_classes=nclass)
elif args.net == 'plainresnet32':
from models.sphereconvnet import plainresnet32
net = plainresnet32(num_classes=nclass)
elif args.net == 'ynet18':
from models.ynet import resnet18
net = resnet18(num_classes=nclass)
elif args.net == 'ynet34':
from models.ynet import resnet34
net = resnet34(num_classes=nclass)
elif args.net == 'ynet50':
from models.ynet import resnet50
net = resnet50(num_classes=nclass)
elif args.net == 'ynet101':
from models.ynet import resnet101
net = resnet101(num_classes=nclass)
elif args.net == 'ynet152':
from models.ynet import resnet152
net = resnet152(num_classes=nclass)
elif args.net == 'resnet18':
from models.resnet import resnet18
net = resnet18(num_classes=nclass)
elif args.net == 'resnet34':
from models.resnet import resnet34
net = resnet34(num_classes=nclass)
elif args.net == 'resnet50':
from models.resnet import resnet50
net = resnet50(num_classes=nclass)
elif args.net == 'resnet101':
from models.resnet import resnet101
net = resnet101(num_classes=nclass)
elif args.net == 'resnet152':
from models.resnet import resnet152
net = resnet152(num_classes=nclass)
elif args.net == 'preactresnet18':
from models.preactresnet import preactresnet18
net = preactresnet18(num_classes=nclass)
elif args.net == 'preactresnet34':
from models.preactresnet import preactresnet34
net = preactresnet34(num_classes=nclass)
elif args.net == 'preactresnet50':
from models.preactresnet import preactresnet50
net = preactresnet50(num_classes=nclass)
elif args.net == 'preactresnet101':
from models.preactresnet import preactresnet101
net = preactresnet101(num_classes=nclass)
elif args.net == 'preactresnet152':
from models.preactresnet import preactresnet152
net = preactresnet152(num_classes=nclass)
elif args.net == 'resnext50':
from models.resnext import resnext50
net = resnext50(num_classes=nclass)
elif args.net == 'resnext101':
from models.resnext import resnext101
net = resnext101(num_classes=nclass)
elif args.net == 'resnext152':
from models.resnext import resnext152
net = resnext152(num_classes=nclass)
elif args.net == 'shufflenet':
from models.shufflenet import shufflenet
net = shufflenet()
elif args.net == 'shufflenetv2':
from models.shufflenetv2 import shufflenetv2
net = shufflenetv2()
elif args.net == 'squeezenet':
from models.squeezenet import squeezenet
net = squeezenet()
elif args.net == 'mobilenet':
from models.mobilenet import mobilenet
net = mobilenet(num_classes=nclass)
elif args.net == 'mobilenetv2':
from models.mobilenetv2 import mobilenetv2
net = mobilenetv2(num_classes=nclass)
elif args.net == 'nasnet':
from models.nasnet import nasnet
net = nasnet(num_classes=nclass)
elif args.net == 'attention56':
from models.attention import attention56
net = attention56()
elif args.net == 'attention92':
from models.attention import attention92
net = attention92()
elif args.net == 'seresnet18':
from models.senet import seresnet18
net = seresnet18(num_classes=nclass)
elif args.net == 'seresnet34':
from models.senet import seresnet34
net = seresnet34(num_classes=nclass)
elif args.net == 'seresnet50':
from models.senet import seresnet50
net = seresnet50(num_classes=nclass)
elif args.net == 'seresnet101':
from models.senet import seresnet101
net = seresnet101(num_classes=nclass)
elif args.net == 'seresnet152':
from models.senet import seresnet152
net = seresnet152(num_classes=nclass)
else:
print('the network name you have entered is not supported yet')
sys.exit()
if args.gpu: #use_gpu
net = net.cuda()
return net
def loading_model(self):
print('Loading %s model' % (self.model_type))
pretrained = None
if self.pretrained:
pretrained = 'imagenet'
if self.model_type == 'inceptionv4':
self.model = inceptionv4(num_classes=1000, pretrained=pretrained)
if self.pretrained:
num_ftrs = self.model.last_linear.in_features
self.model.last_linear = nn.Linear(num_ftrs, self.num_classes)
#free all layers:
for i, param in self.model.named_parameters():
param.requires_grad = False
#unfreeze last layers:
ct = []
for name, child in self.model.features.named_children():
if "4" in ct:
for param in child.parameters():
param.requires_grad = True
ct.append(name)
else:
num_ftrs = self.model.last_linear.in_features
self.model.last_linear = nn.Linear(num_ftrs, self.num_classes)
elif self.model_type == 'iresetv2':
self.model = inceptionresnetv2(num_classes=self.num_classes,
pretrained=pretrained)
else:
print('no model')
exit()
cudnn.benchmark = True
# Check gpu and run parallel
if check_gpu() > 0:
self.model = torch.nn.DataParallel(self.model).cuda()
# self.model.cuda()
# define loss function (criterion) and optimizer
self.criterion = nn.CrossEntropyLoss()
if check_gpu() > 0:
self.criterion = nn.CrossEntropyLoss().cuda()
params = self.model.parameters()
if self.pretrained:
params = list(
filter(lambda p: p.requires_grad, self.model.parameters()))
self.optimizer = optim.SGD(params=params,
lr=self.lr,
momentum=self.momentum,
weight_decay=self.weight_decay)
# self.scheduler = optim.lr_scheduler.ReduceLROnPlateau(optimizer=self.optimizer, mode='min', factor=0.1,
# patience=10, verbose=True, min_lr=0)
self.scheduler = optim.lr_scheduler.StepLR(optimizer=self.optimizer,
step_size=10,
gamma=0.1)
# self.optimizer = optim.Adam(params, lr=self.lr)
# optionally resume from a checkpoint
if self.resume:
if os.path.isfile(self.resume):
print("=> loading checkpoint '{}'".format(self.resume))
checkpoint = torch.load(self.resume)
self.start_epoch = checkpoint['epoch']
self.best_prec1 = checkpoint['best_prec1']
self.model.load_state_dict(checkpoint['state_dict'])
self.optimizer.load_state_dict(checkpoint['optimizer'])
print("=> loaded checkpoint (epoch {})".format(
checkpoint['epoch']))
else:
print("=> no checkpoint found at '{}'".format(self.resume))
if self.evaluate:
file_model_best = os.path.join(self.data_folder,
'model_best.pth.tar')
if os.path.isfile(file_model_best):
print(
"=> loading checkpoint '{}'".format('model_best.pth.tar'))
checkpoint = torch.load(file_model_best)
self.start_epoch = checkpoint['epoch']
self.best_prec1 = checkpoint['best_prec1']
self.model.load_state_dict(checkpoint['state_dict'])
self.optimizer.load_state_dict(checkpoint['optimizer'])
print("=> loaded checkpoint (epoch {})".format(
checkpoint['epoch']))
else:
print("=> no checkpoint found at '{}'".format(self.resume))
cudnn.benchmark = True
from models.inceptionv4 import inceptionv4
from iceberg import *
from torch.optim import SGD
lr = 0.0005
mom = 0.9
wd = 1e-4
bsize = 128
model = inceptionv4(1000)
model = torch.nn.DataParallel(model).cuda()
optim = SGD(model.parameters(), lr, momentum=mom, weight_decay=wd)
iceberg = Iceberg(
'./data/train.json',
'./data/inceptionv4_lr5e-4_mom9e-1_wd1e-4_bs{}_pretrained_model.pth'.
format(bsize), model, optim, 10000, bsize)
iceberg.run(
transforms.Compose([
transforms.Resize(320),
transforms.RandomCrop(299),
transforms.RandomHorizontalFlip(),
transforms.ToTensor()
]))
