def correct_pad(backend, inputs, kernel_size): """Returns a tuple for zero-padding for 2D convolution with downsampling. # Arguments input_size: An integer or tuple/list of 2 integers. kernel_size: An integer or tuple/list of 2 integers. # Returns A tuple. """ img_dim = 2 if backend.image_data_format() == 'channels_first' else 1 input_size = backend.int_shape(inputs)[img_dim:(img_dim + 2)] if isinstance(kernel_size, int): kernel_size = (kernel_size, kernel_size) if input_size[0] is None: adjust = (1, 1) else: adjust = (1 - input_size[0] % 2, 1 - input_size[1] % 2) correct = (kernel_size[0] // 2, kernel_size[1] // 2) return ((correct[0] - adjust[0], correct[0]), (correct[1] - adjust[1], correct[1]))
def transition_block(x, reduction, name): """A transition block. # Arguments x: input tensor. reduction: float, compression rate at transition layers. name: string, block label. # Returns output tensor for the block. """ bn_axis = 3 if backend.image_data_format() == 'channels_last' else 1 x = layers.BatchNormalization(axis=bn_axis, epsilon=1.001e-5, name=name + '_bn')(x) x = layers.Activation('relu', name=name + '_relu')(x) x = layers.Conv2D(int(backend.int_shape(x)[bn_axis] * reduction), 1, use_bias=False, name=name + '_conv')(x) x = layers.AveragePooling2D(2, strides=2, name=name + '_pool')(x) return x