def test(): x = torch.randn(4, 3, 256, 256) tests = { "unet_vgg11": unet_vgg11(), "unet_vgg11_bn": unet_vgg11_bn(), "unet_vgg13": unet_vgg13(), "unet_vgg13_bn": unet_vgg13_bn(), "unet_vgg16": unet_vgg16(), "unet_vgg16_bn": unet_vgg16_bn(), "unet_vgg19": unet_vgg19(), "unet_vgg19_bn": unet_vgg19_bn(), "unet_mobilenetv2": unet_mobilenetv2(), "unet_resnet18": unet_resnet18(), "unet_resnet34": unet_resnet34(), "unet_resnet50": unet_resnet50(), "unet_resnet101": unet_resnet101(), "unet_resnet152": unet_resnet152(), } for key, value in tests.items(): model = tests[key] y = model(x) print("Model name: ", model.name) print("Trainable parameters: ", get_num_parameters(model)) print("in shape: ", x.shape, ", out shape: ", y.shape) # test()
def test(): x = torch.randn(4, 3, 224, 224) model = mobilenet_v3_small() y = model(x) print("Trainable parameters: ", get_num_parameters(model)) print("in shape: ", x.shape, ", out shape: ", y.shape)
def test(): x = torch.randn(1, 3, 224, 224) model = SqueezeNet() y = model(x) print("Trainable parameters: ", get_num_parameters(model)) print("in shape: ", x.shape, ", out shape: ", y.shape)
def test(): x = torch.randn(1, 3, 224, 224) model = shufflenet_v2_x0_5() y = model(x) print("Trainable parameters: ", get_num_parameters(model)) print("in shape: ", x.shape, ", out shape: ", y.shape)
def test(): x = torch.randn(1, 3, 224, 224) model = alexnet(n_classes=3) y = model(x) print("Trainable parameters: ", get_num_parameters(model)) print("in shape: ", x.shape, ", out shape: ", y.shape) # test()
def test(convnet="resnet18"): x = torch.randn(1, 3, 224, 224) tests = { "resnet18": resnet18(), "resnet34": resnet34(), "resnet50": resnet50(), "resnet101": resnet101(), "resnet152": resnet152() } model = tests[convnet] y = model(x) print("Trainable parameters: ", get_num_parameters(model, True)) print("in shape: ", x.shape, ", out shape: ", y.shape)
def test(convnet="vgg11"): x = torch.randn(1, 3, 224, 224) tests = { "vgg11": vgg11(), "vgg11_bn": vgg11_bn(), "vgg13": vgg13(), "vgg13_bn": vgg13_bn(), "vgg16": vgg16(), "vgg16_bn": vgg16_bn(), "vgg19": vgg19(), "vgg19_bn": vgg19_bn() } model = tests[convnet] y = model(x) print("Trainable parameters: ", get_num_parameters(model)) print("in shape: ", x.shape, ", out shape: ", y.shape)
def test(): x = torch.randn(4, 3, 256, 256) tests = { "deeplabv3_mobilenetv2": deeplabv3_mobilenetv2(), "deeplabv3_resnet18": deeplabv3_resnet18(), "deeplabv3_resnet34": deeplabv3_resnet34(), "deeplabv3_resnet50": deeplabv3_resnet50(), "deeplabv3_resnet101": deeplabv3_resnet101(), "deeplabv3_resnet152": deeplabv3_resnet152(), } for key, value in tests.items(): model = tests[key] y = model(x) print("Model name: ", model.name) print("Trainable parameters: ", get_num_parameters(model)) print("in shape: ", x.shape, ", out shape: ", y.shape) # test()