def mnasnet1_3(pretrained: bool,
progress: bool = True,
requires_grad: bool = True):
model = models.mnasnet1_3(pretrained=pretrained, progress=progress)
for params in model.parameters():
params.requires_grad = requires_grad
return model
def eval(model_name: str) -> None:
if model_name == "mnasnet0_5":
model = models.mnasnet0_5(num_classes=1000, pretrained=True).cuda()
elif model_name == "mnasnet0_75":
model = models.mnasnet0_75(num_classes=1000).cuda()
elif model_name == "mnasnet1_0":
model = models.mnasnet1_0(num_classes=1000, pretrained=True).cuda()
elif model_name == "mnasnet1_3":
model = models.mnasnet1_3(num_classes=1000).cuda()
else:
raise ValueError("Don't know how to evaluate {}".format(model_name))
loss = torch.nn.CrossEntropyLoss().cuda()
val_dataset = imagenet.validation(IMAGENET_DIR)
val_loader = torch.utils.data.DataLoader(val_dataset,
batch_size=512,
shuffle=False,
num_workers=8,
pin_memory=True)
all_metrics = metrics.default()
model.eval()
with torch.no_grad():
val_losses = []
metric_dict = collections.defaultdict(list)
for batch_index, (inputs, truth) in enumerate(tqdm.tqdm(val_loader)):
outputs = model(inputs.cuda()).cpu()
val_losses.append(loss(outputs, truth).item())
for name, metric_fn in all_metrics:
metric_dict[name].append(metric_fn(outputs, truth))
print(
numpy.mean(val_losses),
list([(name, numpy.mean(vals))
for name, vals in metric_dict.items()]))
def get_mnasnet1_3(class_num):
model = models.mnasnet1_3(pretrained=True)
set_parameter_requires_grad(model)
model.name = 'mnasnet1_3'
n_inputs = model.classifier[1].in_features
model.classifier[1] = nn.Linear(n_inputs, class_num)
return model, 224
def DefaultBackBone(self,pretrained=False, progress=True):
"""
MNASNet with depth multiplier of 1.3 from “MnasNet: Platform-Aware Neural Architecture Search for Mobile”.
:param
pretrained: If True, returns a model pre-trained on ImageNet
:type pretrained: bool
:param
progress: If True, displays a progress bar of the download to stderr
:type progress: bool
"""
self.model=models.mnasnet1_3(pretrained=pretrained, progress=progress)
def train(model_name: str) -> None:
if model_name == "mnasnet0_5":
model = models.mnasnet0_5(num_classes=1000).cuda()
elif model_name == "mnasnet0_75":
model = models.mnasnet0_75(num_classes=1000).cuda()
elif model_name == "mnasnet1_0":
model = models.mnasnet1_0(num_classes=1000).cuda()
elif model_name == "mnasnet1_3":
model = models.mnasnet1_3(num_classes=1000).cuda()
else:
raise ValueError("Don't know how to train {}".format(model_name))
params = TRAINING_PARAMS[model_name]
if torch.cuda.device_count() > 1:
model = torch.nn.DataParallel(model)
optimizer = torch.optim.SGD(model.parameters(),
lr=params["base_lr"],
momentum=params["momentum"],
weight_decay=params["weight_decay"],
nesterov=True)
loss = torch.nn.CrossEntropyLoss().cuda()
lr_schedule = CosineWithWarmup(optimizer, WARMUP, 0.1,
params["num_epochs"])
train_dataset = imagenet.training(IMAGENET_DIR)
val_dataset = imagenet.validation(IMAGENET_DIR)
message = "Training {}, cosine annealing with warmup. Parameters: {}".format(
model_name, params)
train = trainer.Trainer(".",
message,
"multiclass_classification",
True,
model,
optimizer,
loss,
lr_schedule,
metrics.default(),
cudnn_autotune=True)
train.fit(train_dataset,
val_dataset,
num_epochs=params["num_epochs"],
batch_size=params["batch_size"],
num_workers=multiprocessing.cpu_count())
elif TopModelName=='resnext101_32x8d':
pre_model = models.resnext101_32x8d(pretrained=True, progress=True)
elif TopModelName=="wide_resnet50_2":
pre_model = models.wide_resnet50_2(pretrained=True, progress=True)
elif TopModelName=='wide_resnet101_2':
pre_model = models.wide_resnet101_2(pretrained=True, progress=True)
##############################
elif TopModelName=="mnasnet0_5":
pre_model = models.mnasnet0_5(pretrained=True, progress=True)
elif TopModelName=="mnasnet0_75":
pre_model = models.mnasnet0_75(pretrained=True, progress=True)
elif TopModelName=="mnasnet1_0":
pre_model = models.mnasnet1_0(pretrained=True, progress=True)
elif TopModelName=="mnasnet1_3":
pre_model = models.mnasnet1_3(pretrained=True, progress=True)
elif TopModelName=="efficientnet-b0":
from efficientnet_pytorch import EfficientNet
pre_model = EfficientNet.from_pretrained('efficientnet-b0')
######################### Testing Models #################################
elif TopModelName=="resnext50_32x4d_test":
model = models.resnext50_32x4d(pretrained=True, progress=True)
model.conv1=nn.Conv2d(1, 64, kernel_size=(7, 7), stride=(2, 2), padding=(3, 3), bias=False)
model.fc.out_features=2
test=True
elif TopModelName=="vgg16_test":
model=models.vgg16(pretrained=True, progress=True)
model.features[0] = nn.Conv2d(1, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
model.classifier[6].out_features = 2
test=True
def test_mnasnet1_3(self):
process_model(models.mnasnet1_3(self.pretrained), self.image,
_C_tests.forward_mnasnet1_3, 'MNASNet1_3')
def modeldict(mmd):
if mmd == 'alexnet_pt': out_model = models.alexnet(pretrained=True)
elif mmd == 'alexnet': out_model = models.alexnet(pretrained=False)
elif mmd == 'vgg11_pt': out_model = models.vgg11(pretrained=True)
elif mmd == 'vgg11': out_model = models.vgg11(pretrained=False)
elif mmd == 'vgg11bn_pt': out_model = models.vgg11_bn(pretrained=True)
elif mmd == 'vgg11bn': out_model = models.vgg11_bn(pretrained=False)
elif mmd == 'vgg13_pt': out_model = models.vgg13(pretrained=True)
elif mmd == 'vgg13': out_model = models.vgg13(pretrained=False)
elif mmd == 'vgg13bn_pt': out_model = models.vgg13_bn(pretrained=True)
elif mmd == 'vgg13bn': out_model = models.vgg13_bn(pretrained=False)
elif mmd == 'vgg16_pt': out_model = models.vgg16(pretrained=True)
elif mmd == 'vgg16': out_model = models.vgg16(pretrained=False)
elif mmd == 'vgg16bn_pt': out_model = models.vgg16_bn(pretrained=True)
elif mmd == 'vgg16bn': out_model = models.vgg16_bn(pretrained=False)
elif mmd == 'vgg19_pt': out_model = models.vgg19(pretrained=True)
elif mmd == 'vgg19': out_model = models.vgg19(pretrained=False)
elif mmd == 'vgg19bn_pt': out_model = models.vgg19_bn(pretrained=True)
elif mmd == 'vgg19bn': out_model = models.vgg19_bn(pretrained=False)
elif mmd == 'resnet18': out_model = models.resnet18(pretrained=False)
elif mmd == 'resnet18_pt': out_model = models.resnet18(pretrained=True)
elif mmd == 'resnet34': out_model = models.resnet34(pretrained=False)
elif mmd == 'resnet34_pt': out_model = models.resnet34(pretrained=True)
elif mmd == 'resnet50': out_model = models.resnet50(pretrained=False)
elif mmd == 'resnet50_pt': out_model = models.resnet50(pretrained=True)
elif mmd == 'resnet101': out_model = models.resnet101(pretrained=False)
elif mmd == 'resnet101_pt': out_model = models.resnet101(pretrained=True)
elif mmd == 'resnet152': out_model = models.resnet152(pretrained=False)
elif mmd == 'resnet152_pt': out_model = models.resnet152(pretrained=True)
elif mmd == 'squeezenet10_pt':
out_model = models.squeezenet1_0(pretrained=True)
elif mmd == 'squeezenet10':
out_model = models.squeezenet1_0(pretrained=False)
elif mmd == 'squeezenet11_pt':
out_model = models.squeezenet1_1(pretrained=True)
elif mmd == 'squeezenet11':
out_model = models.squeezenet1_1(pretrained=False)
elif mmd == 'densenet121_pt':
out_model = models.densenet121(pretrained=True)
elif mmd == 'densenet121':
out_model = models.densenet121(pretrained=False)
elif mmd == 'densenet161_pt':
out_model = models.densenet161(pretrained=True)
elif mmd == 'densenet161':
out_model = models.densenet161(pretrained=False)
elif mmd == 'densenet169_pt':
out_model = models.densenet169(pretrained=True)
elif mmd == 'densenet169':
out_model = models.densenet169(pretrained=False)
elif mmd == 'densenet201_pt':
out_model = models.densenet201(pretrained=True)
elif mmd == 'densenet201':
out_model = models.densenet201(pretrained=False)
elif mmd == 'inception_pt':
out_model = models.inception_v3(pretrained=True, aux_logits=False)
elif mmd == 'inception':
out_model = models.inception_v3(pretrained=False, aux_logits=False)
elif mmd == 'inceptionv4_pt':
out_model = pretrainedmodels.__dict__['inceptionv4'](
num_classes=1000, pretrained='imagenet')
elif mmd == 'inceptionv4':
out_model = pretrainedmodels.__dict__['inceptionv4'](num_classes=1000,
pretrained=None)
elif mmd == 'inceptionresnetv2_pt':
out_model = pretrainedmodels.__dict__['inceptionresnetv2'](
num_classes=1000, pretrained='imagenet')
elif mmd == 'inceptionresnetv2':
out_model = pretrainedmodels.__dict__['inceptionresnetv2'](
num_classes=1000, pretrained=None)
elif mmd == 'googlenet_pt':
out_model = models.googlenet(pretrained=True, aux_logits=False)
elif mmd == 'googlenet':
out_model = models.googlenet(pretrained=False, aux_logits=False)
elif mmd == 'shufflenet05_pt':
out_model = models.shufflenet_v2_x0_5(pretrained=True)
elif mmd == 'shufflenet05':
out_model = models.shufflenet_v2_x0_5(pretrained=False)
elif mmd == 'shufflenet10_pt':
out_model = models.shufflenet_v2_x1_0(pretrained=True)
elif mmd == 'shufflenet10':
out_model = models.shufflenet_v2_x1_0(pretrained=False)
elif mmd == 'shufflenet20':
out_model = models.shufflenet_v2_x2_0(pretrained=False)
elif mmd == 'mobilenet_pt':
out_model = models.mobilenet_v2(pretrained=True)
elif mmd == 'mobilenet':
out_model = models.mobilenet_v2(pretrained=False)
elif mmd == 'resnext50_32x4d_pt':
out_model = models.resnext50_32x4d(pretrained=True)
elif mmd == 'resnext50_32x4d':
out_model = models.resnext50_32x4d(pretrained=False)
elif mmd == 'resnext101_32x8d_pt':
out_model = models.resnext101_32x8d(pretrained=True)
elif mmd == 'resnext101_32x8d':
out_model = models.resnext101_32x8d(pretrained=False)
elif mmd == 'wide_resnet50_2_pt':
out_model = models.wide_resnet50_2(pretrained=True)
elif mmd == 'wide_resnet50_2':
out_model = models.wide_resnet50_2(pretrained=False)
elif mmd == 'wide_resnet101_2_pt':
out_model = models.wide_resnet101_2(pretrained=True)
elif mmd == 'wide_resnet101_2':
out_model = models.wide_resnet101_2(pretrained=False)
elif mmd == 'mnasnet05_pt':
out_model = models.mnasnet0_5(pretrained=True)
elif mmd == 'mnasnet05':
out_model = models.mnasnet0_5(pretrained=False)
elif mmd == 'mnasnet075':
out_model = models.mnasnet0_75(pretrained=False)
elif mmd == 'mnasnet10_pt':
out_model = models.mnasnet1_0(pretrained=True)
elif mmd == 'mnasnet10':
out_model = models.mnasnet1_0(pretrained=False)
elif mmd == 'mnasnet13':
out_model = models.mnasnet1_3(pretrained=False)
elif mmd == 'xception_pt':
out_model = pretrainedmodels.__dict__['xception'](
num_classes=1000, pretrained='imagenet')
elif mmd == 'xception':
out_model = pretrainedmodels.__dict__['xception'](num_classes=1000,
pretrained=None)
elif mmd == 'dpn68_pt':
out_model = pretrainedmodels.__dict__['dpn68'](num_classes=1000,
pretrained='imagenet')
elif mmd == 'dpn68':
out_model = pretrainedmodels.__dict__['dpn68'](num_classes=1000,
pretrained=None)
elif mmd == 'dpn98_pt':
out_model = pretrainedmodels.__dict__['dpn98'](num_classes=1000,
pretrained='imagenet')
elif mmd == 'dpn98':
out_model = pretrainedmodels.__dict__['dpn98'](num_classes=1000,
pretrained=None)
elif mmd == 'dpn131_pt':
out_model = pretrainedmodels.__dict__['dpn131'](num_classes=1000,
pretrained='imagenet')
elif mmd == 'dpn131':
out_model = pretrainedmodels.__dict__['dpn131'](num_classes=1000,
pretrained=None)
elif mmd == 'dpn68b_pt':
out_model = pretrainedmodels.__dict__['dpn68b'](
num_classes=1000, pretrained='imagenet+5k')
elif mmd == 'dpn68b':
out_model = pretrainedmodels.__dict__['dpn68b'](num_classes=1000,
pretrained=None)
elif mmd == 'dpn92_pt':
out_model = pretrainedmodels.__dict__['dpn92'](
num_classes=1000, pretrained='imagenet+5k')
elif mmd == 'dpn92':
out_model = pretrainedmodels.__dict__['dpn92'](num_classes=1000,
pretrained=None)
elif mmd == 'dpn107_pt':
out_model = pretrainedmodels.__dict__['dpn107'](
num_classes=1000, pretrained='imagenet+5k')
elif mmd == 'dpn107':
out_model = pretrainedmodels.__dict__['dpn107'](num_classes=1000,
pretrained=None)
elif mmd == 'fbresnet152_pt':
out_model = pretrainedmodels.__dict__['fbresnet152'](
num_classes=1000, pretrained='imagenet')
elif mmd == 'fbresnet152':
out_model = pretrainedmodels.__dict__['fbresnet152'](num_classes=1000,
pretrained=None)
else:
out_model = None
print('Invalid model name. Terminated.')
exit(0)
return out_model
def main(train_loader, train_datasets, valid_loader):
'''args: model_arch, train_loader, train_datasets, valid_loader, epochs, learning_rate, hidden_units, device, save_dir
saves checkpoint under save_dir
returns: checkpoint with {'epoch_tot',
'model',
'criterion',
'optimizer',
'optimizer.state_dict',
'model.state_dict',
'model.class_to_idx'}'''
arg = input_args()
model_arch = arg.arch
hidden_units = arg.hidden_units
learning_rate = arg.learning_rate
device = arg.device
epochs = arg.epochs
save_dir = arg.save_dir
if model_arch == 'alexnet':
gs_vgg = models.alexnet(pretrained=True)
elif model_arch == 'vgg11':
gs_vgg = models.vgg11(pretrained=True)
elif model_arch == 'vgg11_bn':
gs_vgg = models.vgg11_bn(pretrained=True)
elif model_arch == 'vgg13':
gs_vgg = models.vgg13(pretrained=True)
elif model_arch == 'vgg13_bn':
gs_vgg = models.vgg13_bn(pretrained=True)
elif model_arch == 'vgg16':
gs_vgg = models.vgg16(pretrained=True)
elif model_arch == 'vgg16_bn':
gs_vgg = models.vgg16_bn(pretrained=True)
elif model_arch == 'vgg19':
gs_vgg = models.vgg19(pretrained=True)
elif model_arch == 'vgg19_bn':
gs_vgg = models.vgg19_bn(pretrained=True)
elif model_arch == 'resnet18':
gs_vgg = models.resnet18(pretrained=True)
elif model_arch == 'resnet34':
gs_vgg = models.resnet34(pretrained=True)
elif model_arch == 'resnet50':
gs_vgg = models.resnet50(pretrained=True)
elif model_arch == 'resnet101':
gs_vgg = models.resnet101(pretrained=True)
elif model_arch == 'resnet152':
gs_vgg = models.resnet152(pretrained=True)
elif model_arch == 'squeezenet1_0':
gs_vgg = models.squeezenet1_0(pretrained=True)
elif model_arch == 'squeezenet1_1':
gs_vgg = models.squeezenet1_1(pretrained=True)
elif model_arch == 'densenet121':
gs_vgg = models.densenet121(pretrained=True)
elif model_arch == 'densenet169':
gs_vgg = models.densenet169(pretrained=True)
elif model_arch == 'densenet161':
gs_vgg = models.densenet161(pretrained=True)
elif model_arch == 'densenet201':
gs_vgg = models.densenet201(pretrained=True)
elif model_arch == 'inception_v3':
gs_vgg = models.inception_v3(pretrained=True)
elif model_arch == 'googlenet':
gs_vgg = models.googlenet(pretrained=True)
elif model_arch == 'shufflenet_v2_x0_5':
gs_vgg = models.shufflenet_v2_x0_5(pretrained=True)
elif model_arch == 'shufflenet_v2_x1_0':
gs_vgg = models.shufflenet_v2_x1_0(pretrained=True)
elif model_arch == 'shufflenet_v2_x1_5':
gs_vgg = models.shufflenet_v2_x1_5(pretrained=True)
elif model_arch == 'shufflenet_v2_x2_0':
gs_vgg = models.shufflenet_v2_x2_0(pretrained=True)
elif model_arch == 'mobilenet_v2':
gs_vgg = models.mobilenet_v2(pretrained=True)
elif model_arch == 'resnext50_32x4d':
gs_vgg = models.resnext50_32x4d(pretrained=True)
elif model_arch == 'resnext101_32x8d':
gs_vgg = models.resnext101_32x8d(pretrained=True)
elif model_arch == 'wide_resnet50_2':
gs_vgg = models.wide_resnet50_2(pretrained=True)
elif model_arch == 'wide_resnet101_2':
gs_vgg = models.wide_resnet101_2(pretrained=True)
elif model_arch == 'mnasnet0_5':
gs_vgg = models.mnasnet0_5(pretrained=True)
elif model_arch == 'mnasnet0_75':
gs_vgg = models.mnasnet0_75(pretrained=True)
elif model_arch == 'mnasnet1_0':
gs_vgg = models.mnasnet1_0(pretrained=True)
elif model_arch == 'mnasnet1_3':
gs_vgg = models.mnasnet1_3(pretrained=True)
epoch_tot = 0
for parameters in gs_vgg.parameters():
parameters.requires_grad = False
try:
input_layer = gs_vgg.classifier[0].in_features
except:
input_layer = gs_vgg.classifier.in_features
hidden_layers = [(int(hidden_units * 0.68)), (int(hidden_units * 0.32))]
output_layer = len(train_loader)
gs_vgg.classifier = nn.Sequential(
nn.Linear(input_layer, hidden_layers[0]),
nn.ReLU(),
nn.Dropout(p=0.3),
nn.Linear(hidden_layers[0], hidden_layers[1]),
nn.ReLU(),
#nn.Linear(hidden_layers[1], output_layer),
nn.Linear(hidden_layers[1], 102),
nn.LogSoftmax(dim=1))
criterion = nn.NLLLoss()
optimizer = optim.Adam(gs_vgg.classifier.parameters(), lr=learning_rate)
gs_vgg.to(device)
step_num = 0
epoch = epochs
running_loss = 0
print_every = 10
for e in range(epoch):
epoch_tot += 1
for images, labels in train_loader:
gs_vgg.train()
images, labels = images.to(device), labels.to(device)
optimizer.zero_grad()
output = gs_vgg.forward(images)
loss = criterion(output, labels)
loss.backward()
optimizer.step()
running_loss += loss.item()
step_num += 1
if step_num % print_every == 0:
test_loss = 0
accuracy = 0
gs_vgg.eval()
for images, labels in valid_loader:
images, labels = images.to(device), labels.to(device)
output = gs_vgg.forward(images)
loss = criterion(output, labels)
test_loss += loss.item()
prob = torch.exp(output)
top_p, top_class = prob.topk(1, dim=1)
equals = top_class == labels.view(*top_class.shape)
accuracy += torch.mean(equals.type(
torch.FloatTensor)).item()
print(
f"Total Epochs: {epoch_tot}.. "
f"Train loss: {running_loss/print_every:.3f}.. "
f"Test loss: {test_loss/len(valid_loader):.3f}.. "
f"Test accuracy: {(accuracy/len(valid_loader))*100:.1f}%")
running_loss = 0
gs_vgg.train()
gs_vgg.class_to_idx = train_datasets.class_to_idx
gs_checkpoint = {
'epoch_tot': epoch_tot,
'model': gs_vgg,
'criterion': criterion,
'optimizer': optimizer,
'optimizer.state_dict': optimizer.state_dict(),
'model.state_dict': gs_vgg.state_dict(),
'model.class_to_idx': gs_vgg.class_to_idx
}
torch.save(gs_checkpoint, save_dir)
return gs_checkpoint
def get_model(name, device):
if name == "normal_cnn":
model = Net().to(device)
return model
elif name == "alexnet":
model = models.alexnet().to(device)
num_features = model.classifier[6].in_features
model.classifier[6] = nn.Linear(num_features, 1).to(device)
return model
elif name == "vgg11":
model = models.vgg11().to(device)
num_features = model.classifier[6].in_features
model.classifier[6] = nn.Linear(num_features, 1).to(device)
return model
elif name == "vgg13":
model = models.vgg13().to(device)
num_features = model.classifier[6].in_features
model.classifier[6] = nn.Linear(num_features, 1).to(device)
return model
elif name == "vgg16":
model = models.vgg16().to(device)
num_features = model.classifier[6].in_features
model.classifier[6] = nn.Linear(num_features, 1).to(device)
return model
elif name == "vgg19":
model = models.vgg19().to(device)
num_features = model.classifier[6].in_features
model.classifier[6] = nn.Linear(num_features, 1).to(device)
return model
elif name == "vgg11_bn":
model = models.vgg11_bn().to(device)
num_features = model.classifier[6].in_features
model.classifier[6] = nn.Linear(num_features, 1).to(device)
return model
elif name == "vgg13_bn":
model = models.vgg13_bn().to(device)
num_features = model.classifier[6].in_features
model.classifier[6] = nn.Linear(num_features, 1).to(device)
return model
elif name == "vgg16_bn":
model = models.vgg16_bn().to(device)
num_features = model.classifier[6].in_features
model.classifier[6] = nn.Linear(num_features, 1).to(device)
return model
elif name == "vgg19_bn":
model = models.vgg19_bn().to(device)
num_features = model.classifier[6].in_features
model.classifier[6] = nn.Linear(num_features, 1).to(device)
return model
elif name == "densenet121":
model = models.densenet121().to(device)
num_features = model.classifier.in_features
model.classifier = nn.Linear(num_features, 1).to(device)
return model
elif name == "densenet161":
model = models.densenet161().to(device)
num_features = model.classifier.in_features
model.classifier = nn.Linear(num_features, 1).to(device)
return model
elif name == "densenet169":
model = models.densenet169().to(device)
num_features = model.classifier.in_features
model.classifier = nn.Linear(num_features, 1).to(device)
return model
elif name == "densenet201":
model = models.densenet201().to(device)
num_features = model.classifier.in_features
model.classifier = nn.Linear(num_features, 1).to(device)
return model
elif name == "resnet18":
model = models.resnet18().to(device)
num_features = model.fc.in_features
model.fc = nn.Linear(num_features, 1).to(device)
return model
elif name == "resnet34":
model = models.resnet34().to(device)
num_features = model.fc.in_features
model.fc = nn.Linear(num_features, 1).to(device)
return model
elif name == "resnet50":
model = models.resnet50().to(device)
num_features = model.fc.in_features
model.fc = nn.Linear(num_features, 1).to(device)
return model
elif name == "resnet101":
model = models.resnet101().to(device)
num_features = model.fc.in_features
model.fc = nn.Linear(num_features, 1).to(device)
return model
elif name == "resnet152":
model = models.resnet152().to(device)
num_features = model.fc.in_features
model.fc = nn.Linear(num_features, 1).to(device)
return model
elif name == "googlenet":
model = models.googlenet(aux_logits=False).to(device)
num_features = model.fc.in_features
model.fc = nn.Linear(num_features, 1).to(device)
return model
elif name == "inception_v3":
model = models.inception_v3(aux_logits=False).to(device)
num_features = model.fc.in_features
model.fc = nn.Linear(num_features, 1).to(device)
return model
elif name == "shufflenet_v2_x0_5":
model = models.shufflenet_v2_x0_5().to(device)
num_features = model.fc.in_features
model.fc = nn.Linear(num_features, 1).to(device)
return model
elif name == "shufflenet_v2_x1_0":
model = models.shufflenet_v2_x1_0().to(device)
num_features = model.fc.in_features
model.fc = nn.Linear(num_features, 1).to(device)
return model
elif name == "shufflenet_v2_x1_5":
model = models.shufflenet_v2_x1_5().to(device)
num_features = model.fc.in_features
model.fc = nn.Linear(num_features, 1).to(device)
return model
elif name == "shufflenet_v2_x2_0":
model = models.shufflenet_v2_x2_0().to(device)
num_features = model.fc.in_features
model.fc = nn.Linear(num_features, 1).to(device)
return model
elif name == "mobilenet_v2":
model = models.mobilenet_v2().to(device)
num_features = model.classifier[1].in_features
model.classifier[1] = nn.Linear(num_features, 1).to(device)
return model
elif name == "resnext50_32x4d":
model = models.resnext50_32x4d().to(device)
num_features = model.fc.in_features
model.fc = nn.Linear(num_features, 1).to(device)
return model
elif name == "resnext101_32x8d":
model = models.resnext101_32x8d().to(device)
num_features = model.fc.in_features
model.fc = nn.Linear(num_features, 1).to(device)
return model
elif name == "wide_resnet50_2":
model = models.wide_resnet50_2().to(device)
num_features = model.fc.in_features
model.fc = nn.Linear(num_features, 1).to(device)
return model
elif name == "wide_resnet101_2":
model = models.wide_resnet101_2().to(device)
num_features = model.fc.in_features
model.fc = nn.Linear(num_features, 1).to(device)
return model
elif name == "mnasnet0_5":
model = models.mnasnet0_5().to(device)
num_features = model.classifier[1].in_features
model.classifier[1] = nn.Linear(num_features, 1).to(device)
return model
elif name == "mnasnet0_75":
model = models.mnasnet0_75().to(device)
num_features = model.classifier[1].in_features
model.classifier[1] = nn.Linear(num_features, 1).to(device)
return model
elif name == "mnasnet1_0":
model = models.mnasnet1_0().to(device)
num_features = model.classifier[1].in_features
model.classifier[1] = nn.Linear(num_features, 1).to(device)
return model
elif name == "mnasnet1_3":
model = models.mnasnet1_3().to(device)
num_features = model.classifier[1].in_features
model.classifier[1] = nn.Linear(num_features, 1).to(device)
return model
def test_mnasnet1_3(self):
process_model(models.mnasnet1_3(), self.image,
_C_tests.forward_mnasnet1_3, "MNASNet1_3")
def get_model(model_id, use_pretrained):
model_ft = None
if model_id == PyTorchModelsEnum.ALEXNET:
model_ft = models.alexnet(pretrained=use_pretrained)
elif model_id == PyTorchModelsEnum.DENSENET121:
model_ft = models.densenet121(pretrained=use_pretrained)
elif model_id == PyTorchModelsEnum.DENSENET161:
model_ft = models.densenet161(pretrained=use_pretrained)
elif model_id == PyTorchModelsEnum.DENSENET169:
model_ft = models.densenet169(pretrained=use_pretrained)
elif model_id == PyTorchModelsEnum.DENSENET201:
model_ft = models.densenet201(pretrained=use_pretrained)
elif model_id == PyTorchModelsEnum.GOOGLENET:
model_ft = models.googlenet(pretrained=use_pretrained)
elif model_id == PyTorchModelsEnum.INCEPTION_V3:
model_ft = models.inception_v3(pretrained=use_pretrained)
elif model_id == PyTorchModelsEnum.MOBILENET_V2:
model_ft = models.mobilenet_v2(pretrained=use_pretrained)
elif model_id == PyTorchModelsEnum.MNASNET_0_5:
model_ft = models.mnasnet0_5(pretrained=use_pretrained)
elif model_id == PyTorchModelsEnum.MNASNET_0_75: # no pretrained
model_ft = models.mnasnet0_75(pretrained=use_pretrained)
elif model_id == PyTorchModelsEnum.MNASNET_1_0:
model_ft = models.mnasnet1_0(pretrained=use_pretrained)
elif model_id == PyTorchModelsEnum.MNASNET_1_3:
model_ft = models.mnasnet1_3(pretrained=use_pretrained)
elif model_id == PyTorchModelsEnum.RESNET18:
model_ft = models.resnet18(pretrained=use_pretrained)
elif model_id == PyTorchModelsEnum.RESNET34:
model_ft = models.resnet34(pretrained=use_pretrained)
elif model_id == PyTorchModelsEnum.RESNET50:
model_ft = models.resnet50(pretrained=use_pretrained)
elif model_id == PyTorchModelsEnum.RESNET101:
model_ft = models.resnet101(pretrained=use_pretrained)
elif model_id == PyTorchModelsEnum.RESNET152:
model_ft = models.resnet152(pretrained=use_pretrained)
elif model_id == PyTorchModelsEnum.RESNEXT50:
model_ft = models.resnext50_32x4d(pretrained=use_pretrained)
elif model_id == PyTorchModelsEnum.RESNEXT101:
model_ft = models.resnext101_32x8d(pretrained=use_pretrained)
elif model_id == PyTorchModelsEnum.SHUFFLENET_V2_0_5:
model_ft = models.shufflenet_v2_x0_5(pretrained=use_pretrained)
elif model_id == PyTorchModelsEnum.SHUFFLENET_V2_1_0:
model_ft = models.shufflenet_v2_x1_0(pretrained=use_pretrained)
elif model_id == PyTorchModelsEnum.SHUFFLENET_V2_1_5:
model_ft = models.shufflenet_v2_x1_5(pretrained=use_pretrained)
elif model_id == PyTorchModelsEnum.SHUFFLENET_V2_2_0:
model_ft = models.shufflenet_v2_x2_0(pretrained=use_pretrained)
elif model_id == PyTorchModelsEnum.SQUEEZENET1_0:
model_ft = models.squeezenet1_0(pretrained=use_pretrained)
elif model_id == PyTorchModelsEnum.SQUEEZENET1_1:
model_ft = models.squeezenet1_1(pretrained=use_pretrained)
elif model_id == PyTorchModelsEnum.VGG11:
model_ft = models.vgg11(pretrained=use_pretrained)
elif model_id == PyTorchModelsEnum.VGG11_BN:
model_ft = models.vgg11_bn(pretrained=use_pretrained)
elif model_id == PyTorchModelsEnum.VGG13:
model_ft = models.vgg13(pretrained=use_pretrained)
elif model_id == PyTorchModelsEnum.VGG13_BN:
model_ft = models.vgg13_bn(pretrained=use_pretrained)
elif model_id == PyTorchModelsEnum.VGG16:
model_ft = models.vgg16(pretrained=use_pretrained)
elif model_id == PyTorchModelsEnum.VGG16_BN:
model_ft = models.vgg16_bn(pretrained=use_pretrained)
elif model_id == PyTorchModelsEnum.VGG19:
model_ft = models.vgg19(pretrained=use_pretrained)
elif model_id == PyTorchModelsEnum.VGG19_BN:
model_ft = models.vgg19_bn(pretrained=use_pretrained)
elif model_id == PyTorchModelsEnum.WIDE_RESNET50:
model_ft = models.wide_resnet50_2(pretrained=use_pretrained)
elif model_id == PyTorchModelsEnum.WIDE_RESNET101:
model_ft = models.wide_resnet101_2(pretrained=use_pretrained)
return model_ft
'batch_size':
batch_size,
'network':
lambda: models.shufflenet_v2_x1_0(),
'criterion':
lambda: nn.CrossEntropyLoss(),
'optimizer':
lambda parameters: optim.SGD(parameters, lr=0.001, momentum=0.9)
},
{
'skip':
True,
'name': {
'folder': 'MnasNet1.3_CrossEntropy_SGDMomentum',
'network': 'MnasNet1.3',
'criterion': 'CrossEntropyLoss',
'optimizer': 'SGD with momentum',
},
'epochs':
epochs,
'batch_size':
batch_size,
'network':
lambda: models.mnasnet1_3(),
'criterion':
lambda: nn.CrossEntropyLoss(),
'optimizer':
lambda parameters: optim.SGD(parameters, lr=0.001, momentum=0.9)
},
]
