import numpy as np import copy from keras.layers import Input, Dense, Conv2D, MaxPooling2D, AveragePooling2D, ZeroPadding2D, Flatten, Activation, add, GlobalAveragePooling2D from keras.optimizers import SGD from keras.layers.normalization import BatchNormalization from keras.models import Model from keras import initializers from keras.engine import Layer, InputSpec from keras import backend as K class Scale(Layer): '''Custom Layer for ResNet used for BatchNormalization. Learns a set of weights and biases used for scaling the input data. the output consists simply in an element-wise multiplication of the input and a sum of a set of constants: out = in * gamma + beta, where 'gamma' and 'beta' are the weights and biases larned. # Arguments axis: integer, axis along which to normalize in mode 0. For instance, if your input tensor has shape (samples, channels, rows, cols), set axis to 1 to normalize per feature map (channels axis). momentum: momentum in the computation of the exponential average of the mean and standard deviation of the data, for feature-wise normalization. weights: Initialization weights. List of 2 Numpy arrays, with shapes: `[(input_shape,), (input_shape,)]` beta_init: name of initialization function for shift parameter (see [initializers](../initializers.md)), or alternatively, Theano/TensorFlow function to use for weights initialization. This parameter is only relevant if you don't pass a `weights` argument. gamma_init: name of initialization function for scale parameter (see [initializers](../initializers.md)), or alternatively, Theano/TensorFlow function to use for weights initialization. This parameter is only relevant if you don't pass a `weights` argument. ''' def __init__(self, weights=None, axis=-1, momentum = 0.9, beta_init='zero', gamma_init='one', **kwargs): self.momentum = momentum self.axis = axis self.beta_init = initializers.get(beta_init) self.gamma_init = initializers.get(gamma_init) self.initial_weights = weights super(Scale, self).__init__(**kwargs) def build(self, input_shape): self.input_spec = [InputSpec(shape=input_shape)] shape = (int(input_shape[self.axis]),) self.gamma = K.variable(self.gamma_init(shape), name='%s_gamma'%self.name) self.beta = K.variable(self.beta_init(shape), name='%s_beta'%self.name) self.trainable_weights = [self.gamma, self.beta] if self.initial_weights is not None: self.set_weights(self.initial_weights) del self.initial_weights def call(self, x, mask=None): input_shape = self.input_spec[0].shape broadcast_shape = [1] * len(input_shape) broadcast_shape[self.axis] = input_shape[self.axis] out = K.reshape(self.gamma, broadcast_shape) * x + K.reshape(self.beta, broadcast_shape) return out def get_config(self): config = {"momentum": self.momentum, "axis": self.axis} base_config = super(Scale, self).get_config() return dict(list(base_config.items()) + list(config.items())) def identity_block(input_tensor, kernel_size, filters, stage, block): '''The identity_block is the block that has no conv layer at shortcut # Arguments input_tensor: input tensor kernel_size: defualt 3, the kernel size of middle conv layer at main path filters: list of integers, the nb_filters of 3 conv layer at main path stage: integer, current stage label, used for generating layer names block: 'a','b'..., current block label, used for generating layer names ''' eps = 1.1e-5 nb_filter1, nb_filter2, nb_filter3 = filters conv_name_base = 'res' + str(stage) + block + '_branch' bn_name_base = 'bn' + str(stage) + block + '_branch' scale_name_base = 'scale' + str(stage) + block + '_branch' x = Conv2D(nb_filter1, (1, 1), name=conv_name_base + '2a', use_bias=False)(input_tensor) x = BatchNormalization(epsilon=eps, axis=bn_axis, name=bn_name_base + '2a')(x) x = Scale(axis=bn_axis, name=scale_name_base + '2a')(x) x = Activation('relu', name=conv_name_base + '2a_relu')(x) x = ZeroPadding2D((1, 1), name=conv_name_base + '2b_zeropadding')(x) x = Conv2D(nb_filter2, (kernel_size, kernel_size), name=conv_name_base + '2b', use_bias=False)(x) x = BatchNormalization(epsilon=eps, axis=bn_axis, name=bn_name_base + '2b')(x) x = Scale(axis=bn_axis, name=scale_name_base + '2b')(x) x = Activation('relu', name=conv_name_base + '2b_relu')(x) x = Conv2D(nb_filter3, (1, 1), name=conv_name_base + '2c', use_bias=False)(x) x = BatchNormalization(epsilon=eps, axis=bn_axis, name=bn_name_base + '2c')(x) x = Scale(axis=bn_axis, name=scale_name_base + '2c')(x) x = add([x, input_tensor], name='res' + str(stage) + block) x = Activation('relu', name='res' + str(stage) + block + '_relu')(x) return x def conv_block(input_tensor, kernel_size, filters, stage, block, strides=(2, 2)): '''conv_block is the block that has a conv layer at shortcut # Arguments input_tensor: input tensor kernel_size: defualt 3, the kernel size of middle conv layer at main path filters: list of integers, the nb_filters of 3 conv layer at main path stage: integer, current stage label, used for generating layer names block: 'a','b'..., current block label, used for generating layer names Note that from stage 3, the first conv layer at main path is with subsample=(2,2) And the shortcut should have subsample=(2,2) as well ''' eps = 1.1e-5 nb_filter1, nb_filter2, nb_filter3 = filters conv_name_base = 'res' + str(stage) + block + '_branch' bn_name_base = 'bn' + str(stage) + block + '_branch' scale_name_base = 'scale' + str(stage) + block + '_branch' x = Conv2D(nb_filter1, (1, 1), strides=strides, name=conv_name_base + '2a', use_bias=False)(input_tensor) x = BatchNormalization(epsilon=eps, axis=bn_axis, name=bn_name_base + '2a')(x) x = Scale(axis=bn_axis, name=scale_name_base + '2a')(x) x = Activation('relu', name=conv_name_base + '2a_relu')(x) x = ZeroPadding2D((1, 1), name=conv_name_base + '2b_zeropadding')(x) x = Conv2D(nb_filter2, (kernel_size, kernel_size), name=conv_name_base + '2b', use_bias=False)(x) x = BatchNormalization(epsilon=eps, axis=bn_axis, name=bn_name_base + '2b')(x) x = Scale(axis=bn_axis, name=scale_name_base + '2b')(x) x = Activation('relu', name=conv_name_base + '2b_relu')(x) x = Conv2D(nb_filter3, (1, 1), name=conv_name_base + '2c', use_bias=False)(x) x = BatchNormalization(epsilon=eps, axis=bn_axis, name=bn_name_base + '2c')(x) x = Scale(axis=bn_axis, name=scale_name_base + '2c')(x) shortcut = Conv2D(nb_filter3, (1, 1), strides=strides, name=conv_name_base + '1', use_bias=False)(input_tensor) shortcut = BatchNormalization(epsilon=eps, axis=bn_axis, name=bn_name_base + '1')(shortcut) shortcut = Scale(axis=bn_axis, name=scale_name_base + '1')(shortcut) x = add([x, shortcut], name='res' + str(stage) + block) x = Activation('relu', name='res' + str(stage) + block + '_relu')(x) return x def ResNet152(include_top, weights, input_shape, pooling, bottleneck_features=16384): '''Instantiate the ResNet152 architecture, # Arguments weights_path: path to pretrained weight file # Returns A Keras model instance. ''' eps = 1.1e-5 # Handle Dimension Ordering for different backends global bn_axis bn_axis = 3 img_input = Input(shape=input_shape, name='data') x = ZeroPadding2D((3, 3), name='conv1_zeropadding')(img_input) x = Conv2D(64, (7, 7), strides=(2, 2), name='conv1', use_bias=False)(x) x = BatchNormalization(epsilon=eps, axis=bn_axis, name='bn_conv1')(x) x = Scale(axis=bn_axis, name='scale_conv1')(x) x = Activation('relu', name='conv1_relu')(x) x = MaxPooling2D((3, 3), strides=(2, 2), name='pool1')(x) x = conv_block(x, 3, [64, 64, 256], stage=2, block='a', strides=(1, 1)) x = identity_block(x, 3, [64, 64, 256], stage=2, block='b') x = identity_block(x, 3, [64, 64, 256], stage=2, block='c') x = conv_block(x, 3, [128, 128, 512], stage=3, block='a') for i in range(1,8): x = identity_block(x, 3, [128, 128, 512], stage=3, block='b'+str(i)) x = conv_block(x, 3, [256, 256, 1024], stage=4, block='a') for i in range(1,36): x = identity_block(x, 3, [256, 256, 1024], stage=4, block='b'+str(i)) x = conv_block(x, 3, [512, 512, 2048], stage=5, block='a') x = identity_block(x, 3, [512, 512, 2048], stage=5, block='b') x = identity_block(x, 3, [512, 512, 2048], stage=5, block='c') # new if bottleneck_features != 2048: x = conv_block(x, 3, [1024, 1024, bottleneck_features], stage=6, block='a') x = identity_block(x, 3, [1024, 1024, bottleneck_features], stage=6, block='b') x = identity_block(x, 3, [1024, 1024, bottleneck_features], stage=6, block='c') #x = AveragePooling2D((7, 7), name='avg_pool')(x) x = GlobalAveragePooling2D(name='avg_pool')(x) if include_top: x = Flatten()(x) x = Dense(1000, activation='softmax', name='fc1000')(x) model = Model(img_input, x) # load weights if weights == 'imagenet': model.load_weights('resnet152_weights_tf.h5', by_name=True) return model def preprocess_input(im): # Remove train image mean im[:,:,0] -= 103.939 im[:,:,1] -= 116.779 im[:,:,2] -= 123.68 return im