def build_model(model_name):
device = torch.device("cuda:0" if torch.cuda.is_available() else "cpu")
# load pretrained model
model_name = model_name # could be fbresnet152 or inceptionresnetv2
if (model_name == 'senet154'):
model = pretrainedmodels.senet154(pretrained='imagenet')
elif (model_name == 'se_resnet152'):
model = pretrainedmodels.se_resnet152(pretrained='imagenet')
elif (model_name == 'se_resnext101_32x4d'):
model = pretrainedmodels.se_resnext101_32x4d(pretrained='imagenet')
elif (model_name == 'resnet152'):
model = pretrainedmodels.resnet152(pretrained='imagenet')
elif (model_name == 'resnet101'):
model = pretrainedmodels.resnet101(pretrained='imagenet')
elif (model_name == 'densenet201'):
model = pretrainedmodels.densenet201(pretrained='imagenet')
model.to(device)
for param in model.parameters():
param.requires_grad = False
num_ftrs = model.last_linear.in_features
class CustomModel(nn.Module):
def __init__(self, model):
super(CustomModel, self).__init__()
self.features = nn.Sequential(*list(model.children())[:-1])
self.classifier = nn.Sequential(
torch.nn.Linear(num_ftrs, 128),
torch.nn.Dropout(0.3), # drop 50% of the neuron
torch.nn.Linear(128, 7))
def forward(self, x):
x = self.features(x)
x = x.view(x.size(0), -1)
x = self.classifier(x)
return x
model = CustomModel(model)
freeze_layer(model.features)
num_ftrs = list(model.classifier.children())[-1].out_features
model.to(device)
model.name = model_name
PATH = os.path.abspath(os.path.dirname(__file__))
PATH_par = os.path.abspath(os.path.join(PATH, os.pardir))
path_to_model = os.path.join(PATH_par, 'pretrained_model', '128_7')
model.load_state_dict(
torch.load(os.path.join(path_to_model, '%s.pth' % (model_name))))
model.to(device)
for param in model.parameters():
param.requires_grad = False
return model, num_ftrs
def densenet201(input_size=(3, 224, 224), num_classes=1000, pretrained=None):
model = models.densenet201(num_classes=num_classes, pretrained=pretrained)
model = add_instances_to_torchvisionmodel(model)
# Change the First Convol2D layer into new input shape
if input_size != (3, 224, 224):
model.features[0] = nn.Conv2d(input_size[0],
64,
kernel_size=(7, 7),
stride=(2, 2),
padding=(3, 3),
bias=False)
model.input_size = input_size
# calc kernel_size on new_avgpool2d layer
test_tensor = torch.randn((1, input_size[0], input_size[1], input_size[2]))
features = model.features(test_tensor)
#print(features, features.shape[2], features.shape[3])
avg_pool2d_kernel_size = (features.shape[2], features.shape[3])
# calc last linear size
x = F.avg_pool2d(features, kernel_size=avg_pool2d_kernel_size, stride=1)
x = x.view(x.size(0), -1).shape[1]
model.last_linear = nn.Linear(in_features=x, out_features=num_classes)
#del model.logits
#del model.forward
def logits(self, features):
x = F.relu(features, inplace=True)
x = F.avg_pool2d(x, kernel_size=avg_pool2d_kernel_size, stride=1)
x = x.view(x.size(0), -1)
x = self.last_linear(x)
return x
def forward(self, input):
x = self.features(input)
x = self.logits(x)
return x
# Modify methods
model.logits = types.MethodType(logits, model)
model.forward = types.MethodType(forward, model)
return model
def __init__(self, class_names):
super(DenseNet201, self).__init__()
self.base = pretrainedmodels.densenet201()
self.backbone = nn.Sequential(*list(self.base.children())[:-1])
self.head = nn.Linear(self.base.last_linear.in_features,
len(class_names))
def build_model(model_name):
device = torch.device("cuda:0" if torch.cuda.is_available() else "cpu")
# load pretrained model
model_name = model_name # could be fbresnet152 or inceptionresnetv2
if(model_name == 'senet154'):
model = pretrainedmodels.senet154(pretrained='imagenet')
elif(model_name == 'se_resnet152'):
model = pretrainedmodels.se_resnet152(pretrained='imagenet')
elif(model_name == 'se_resnext101_32x4d'):
model = pretrainedmodels.se_resnext101_32x4d(pretrained='imagenet')
elif(model_name == 'resnet152'):
model = pretrainedmodels.resnet152(pretrained='imagenet')
elif(model_name == 'resnet101'):
model = pretrainedmodels.resnet101(pretrained='imagenet')
elif(model_name == 'densenet201'):
model = pretrainedmodels.densenet201(pretrained='imagenet')
model.to(device)
for param in model.parameters():
param.requires_grad = False
num_ftrs = model.last_linear.in_features
class CustomModel(nn.Module):
def __init__(self, model):
super(CustomModel, self).__init__()
self.features = nn.Sequential(*list(model.children())[:-1] )
self.classifier = nn.Sequential(
torch.nn.Linear(num_ftrs, 128),
torch.nn.Dropout(0.3), # drop 50% of the neuron
torch.nn.Linear(128, 7)
)
def forward(self, x):
x = self.features(x)
x = x.view(x.size(0), -1)
x = self.classifier(x)
return x
model = CustomModel(model)
freeze_layer(model.features)
model.to(device)
for param in model.parameters():
param.requires_grad = False
class CustomModel1(nn.Module):
def __init__(self, model):
super(CustomModel1, self).__init__()
self.features = nn.Sequential(*list(model.children())[:-1])
self.classifier = nn.Sequential(
*[list(model.classifier.children())[i] for i in [0]]
)
def forward(self, x):
x = self.features(x)
x = x.view(x.size(0), -1)
x = self.classifier(x)
return x
CustomModel = CustomModel1(model)
num_ftrs = list(CustomModel.classifier.children())[-1].out_features
CustomModel.to(device)
return CustomModel, num_ftrs
