def ResNeXt50(include_top=True, weights='imagenet', input_tensor=None, input_shape=None, pooling=None, classes=1000, **kwargs): """Instantiates the ResNeXt50 architecture.""" def stack_fn(x): x = stack1(x, 64, 3, stride1=1, groups=32, base_width=4, name='conv2') x = stack1(x, 128, 4, groups=32, base_width=4, name='conv3') x = stack1(x, 256, 6, groups=32, base_width=4, name='conv4') return stack1(x, 512, 3, groups=32, base_width=4, name='conv5') return ResNet(stack_fn, False, 'resnext50', include_top, weights, input_tensor, input_shape, pooling, False, None, classes, **kwargs)
def WideResNet101(include_top=True, weights='imagenet', input_tensor=None, input_shape=None, pooling=None, classes=1000, **kwargs): """Instantiates the Wide-ResNet101-2 architecture.""" def stack_fn(x): x = stack1(x, 64, 3, stride1=1, base_width=128, name='conv2') x = stack1(x, 128, 4, base_width=128, name='conv3') x = stack1(x, 256, 23, base_width=128, name='conv4') return stack1(x, 512, 3, base_width=128, name='conv5') return ResNet(stack_fn, False, 'wide_resnet101', include_top, weights, input_tensor, input_shape, pooling, False, None, classes, **kwargs)
def ResNet34(include_top=True, weights='imagenet', input_tensor=None, input_shape=None, pooling=None, classes=1000, **kwargs): """Instantiates the ResNet34 architecture.""" def stack_fn(x): x = stack3(x, 64, 3, stride1=1, conv_shortcut=False, name='conv2') x = stack3(x, 128, 4, name='conv3') x = stack3(x, 256, 6, name='conv4') return stack3(x, 512, 3, name='conv5') return ResNet(stack_fn, False, 'resnet34', include_top, weights, input_tensor, input_shape, pooling, False, None, classes, **kwargs)
def ResNeSt50(include_top=True, weights='imagenet', input_tensor=None, input_shape=None, pooling=None, classes=1000, **kwargs): """Instantiates the ResNeSt50 architecture.""" def stack_fn(x): x = stack2(x, 64, 3, stride1=1, base_width=64, radix=2, is_first=False, name='conv2') x = stack2(x, 128, 4, base_width=64, radix=2, name='conv3') x = stack2(x, 256, 6, base_width=64, radix=2, name='conv4') return stack2(x, 512, 3, base_width=64, radix=2, name='conv5') return ResNet(stack_fn, False, 'resnest50', include_top, weights, input_tensor, input_shape, pooling, True, 32, classes, **kwargs)