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# -*- coding: utf-8 -*- |
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import cv2 |
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import numpy as np |
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import copy |
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from keras.layers import Input, Dense, Convolution2D, MaxPooling2D, AveragePooling2D, ZeroPadding2D, Dropout, Flatten, merge, Reshape, Activation, Lambda, GlobalAveragePooling2D, Merge |
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from keras.optimizers import SGD |
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from keras.layers.normalization import BatchNormalization |
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from keras.models import Model |
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from keras import initializations |
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from keras.engine import Layer, InputSpec |
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from keras import backend as K |
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import sys |
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sys.setrecursionlimit(3000) |
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class Scale(Layer): |
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'''Learns a set of weights and biases used for scaling the input data. |
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the output consists simply in an element-wise multiplication of the input |
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and a sum of a set of constants: |
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out = in * gamma + beta, |
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where 'gamma' and 'beta' are the weights and biases larned. |
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# Arguments |
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axis: integer, axis along which to normalize in mode 0. For instance, |
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if your input tensor has shape (samples, channels, rows, cols), |
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set axis to 1 to normalize per feature map (channels axis). |
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momentum: momentum in the computation of the |
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exponential average of the mean and standard deviation |
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of the data, for feature-wise normalization. |
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weights: Initialization weights. |
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List of 2 Numpy arrays, with shapes: |
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`[(input_shape,), (input_shape,)]` |
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beta_init: name of initialization function for shift parameter |
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(see [initializations](../initializations.md)), or alternatively, |
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Theano/TensorFlow function to use for weights initialization. |
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This parameter is only relevant if you don't pass a `weights` argument. |
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gamma_init: name of initialization function for scale parameter (see |
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[initializations](../initializations.md)), or alternatively, |
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Theano/TensorFlow function to use for weights initialization. |
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This parameter is only relevant if you don't pass a `weights` argument. |
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gamma_init: name of initialization function for scale parameter (see |
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[initializations](../initializations.md)), or alternatively, |
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Theano/TensorFlow function to use for weights initialization. |
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This parameter is only relevant if you don't pass a `weights` argument. |
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''' |
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def __init__(self, weights=None, axis=-1, momentum = 0.9, beta_init='zero', gamma_init='one', **kwargs): |
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self.momentum = momentum |
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self.axis = axis |
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self.beta_init = initializations.get(beta_init) |
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self.gamma_init = initializations.get(gamma_init) |
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self.initial_weights = weights |
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super(Scale, self).__init__(**kwargs) |
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def build(self, input_shape): |
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self.input_spec = [InputSpec(shape=input_shape)] |
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shape = (int(input_shape[self.axis]),) |
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self.gamma = self.gamma_init(shape, name='{}_gamma'.format(self.name)) |
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self.beta = self.beta_init(shape, name='{}_beta'.format(self.name)) |
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self.trainable_weights = [self.gamma, self.beta] |
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if self.initial_weights is not None: |
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self.set_weights(self.initial_weights) |
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del self.initial_weights |
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def call(self, x, mask=None): |
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input_shape = self.input_spec[0].shape |
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broadcast_shape = [1] * len(input_shape) |
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broadcast_shape[self.axis] = input_shape[self.axis] |
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out = K.reshape(self.gamma, broadcast_shape) * x + K.reshape(self.beta, broadcast_shape) |
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return out |
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def get_config(self): |
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config = {"momentum": self.momentum, "axis": self.axis} |
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base_config = super(Scale, self).get_config() |
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return dict(list(base_config.items()) + list(config.items())) |
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def identity_block(input_tensor, kernel_size, filters, stage, block): |
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'''The identity_block is the block that has no conv layer at shortcut |
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# Arguments |
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input_tensor: input tensor |
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kernel_size: defualt 3, the kernel size of middle conv layer at main path |
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filters: list of integers, the nb_filters of 3 conv layer at main path |
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stage: integer, current stage label, used for generating layer names |
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block: 'a','b'..., current block label, used for generating layer names |
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''' |
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eps = 1.1e-5 |
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nb_filter1, nb_filter2, nb_filter3 = filters |
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conv_name_base = 'res' + str(stage) + block + '_branch' |
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bn_name_base = 'bn' + str(stage) + block + '_branch' |
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scale_name_base = 'scale' + str(stage) + block + '_branch' |
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x = Convolution2D(nb_filter1, 1, 1, name=conv_name_base + '2a', bias=False)(input_tensor) |
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x = BatchNormalization(epsilon=eps, axis=bn_axis, name=bn_name_base + '2a')(x) |
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x = Scale(axis=bn_axis, name=scale_name_base + '2a')(x) |
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x = Activation('relu', name=conv_name_base + '2a_relu')(x) |
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x = ZeroPadding2D((1, 1), name=conv_name_base + '2b_zeropadding')(x) |
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x = Convolution2D(nb_filter2, kernel_size, kernel_size, |
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name=conv_name_base + '2b', bias=False)(x) |
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x = BatchNormalization(epsilon=eps, axis=bn_axis, name=bn_name_base + '2b')(x) |
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x = Scale(axis=bn_axis, name=scale_name_base + '2b')(x) |
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x = Activation('relu', name=conv_name_base + '2b_relu')(x) |
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x = Convolution2D(nb_filter3, 1, 1, name=conv_name_base + '2c', bias=False)(x) |
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x = BatchNormalization(epsilon=eps, axis=bn_axis, name=bn_name_base + '2c')(x) |
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x = Scale(axis=bn_axis, name=scale_name_base + '2c')(x) |
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x = merge([x, input_tensor], mode='sum', name='res' + str(stage) + block) |
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x = Activation('relu', name='res' + str(stage) + block + '_relu')(x) |
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return x |
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def conv_block(input_tensor, kernel_size, filters, stage, block, strides=(2, 2)): |
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'''conv_block is the block that has a conv layer at shortcut |
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# Arguments |
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input_tensor: input tensor |
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kernel_size: defualt 3, the kernel size of middle conv layer at main path |
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filters: list of integers, the nb_filters of 3 conv layer at main path |
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stage: integer, current stage label, used for generating layer names |
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block: 'a','b'..., current block label, used for generating layer names |
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Note that from stage 3, the first conv layer at main path is with subsample=(2,2) |
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And the shortcut should have subsample=(2,2) as well |
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''' |
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eps = 1.1e-5 |
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nb_filter1, nb_filter2, nb_filter3 = filters |
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conv_name_base = 'res' + str(stage) + block + '_branch' |
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bn_name_base = 'bn' + str(stage) + block + '_branch' |
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scale_name_base = 'scale' + str(stage) + block + '_branch' |
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x = Convolution2D(nb_filter1, 1, 1, subsample=strides, |
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name=conv_name_base + '2a', bias=False)(input_tensor) |
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x = BatchNormalization(epsilon=eps, axis=bn_axis, name=bn_name_base + '2a')(x) |
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x = Scale(axis=bn_axis, name=scale_name_base + '2a')(x) |
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x = Activation('relu', name=conv_name_base + '2a_relu')(x) |
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x = ZeroPadding2D((1, 1), name=conv_name_base + '2b_zeropadding')(x) |
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x = Convolution2D(nb_filter2, kernel_size, kernel_size, |
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name=conv_name_base + '2b', bias=False)(x) |
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x = BatchNormalization(epsilon=eps, axis=bn_axis, name=bn_name_base + '2b')(x) |
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x = Scale(axis=bn_axis, name=scale_name_base + '2b')(x) |
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x = Activation('relu', name=conv_name_base + '2b_relu')(x) |
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x = Convolution2D(nb_filter3, 1, 1, name=conv_name_base + '2c', bias=False)(x) |
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x = BatchNormalization(epsilon=eps, axis=bn_axis, name=bn_name_base + '2c')(x) |
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x = Scale(axis=bn_axis, name=scale_name_base + '2c')(x) |
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shortcut = Convolution2D(nb_filter3, 1, 1, subsample=strides, |
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name=conv_name_base + '1', bias=False)(input_tensor) |
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shortcut = BatchNormalization(epsilon=eps, axis=bn_axis, name=bn_name_base + '1')(shortcut) |
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shortcut = Scale(axis=bn_axis, name=scale_name_base + '1')(shortcut) |
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x = merge([x, shortcut], mode='sum', name='res' + str(stage) + block) |
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x = Activation('relu', name='res' + str(stage) + block + '_relu')(x) |
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return x |
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def resnet101_model(weights_path=None): |
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'''Instantiate the ResNet101 architecture, |
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# Arguments |
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weights_path: path to pretrained weight file |
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# Returns |
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A Keras model instance. |
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''' |
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eps = 1.1e-5 |
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# Handle Dimension Ordering for different backends |
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global bn_axis |
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if K.image_dim_ordering() == 'tf': |
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bn_axis = 3 |
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img_input = Input(shape=(224, 224, 3), name='data') |
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else: |
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bn_axis = 1 |
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img_input = Input(shape=(3, 224, 224), name='data') |
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x = ZeroPadding2D((3, 3), name='conv1_zeropadding')(img_input) |
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x = Convolution2D(64, 7, 7, subsample=(2, 2), name='conv1', bias=False)(x) |
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x = BatchNormalization(epsilon=eps, axis=bn_axis, name='bn_conv1')(x) |
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x = Scale(axis=bn_axis, name='scale_conv1')(x) |
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x = Activation('relu', name='conv1_relu')(x) |
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x = MaxPooling2D((3, 3), strides=(2, 2), name='pool1')(x) |
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x = conv_block(x, 3, [64, 64, 256], stage=2, block='a', strides=(1, 1)) |
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x = identity_block(x, 3, [64, 64, 256], stage=2, block='b') |
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x = identity_block(x, 3, [64, 64, 256], stage=2, block='c') |
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x = conv_block(x, 3, [128, 128, 512], stage=3, block='a') |
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for i in range(1,3): |
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x = identity_block(x, 3, [128, 128, 512], stage=3, block='b'+str(i)) |
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x = conv_block(x, 3, [256, 256, 1024], stage=4, block='a') |
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for i in range(1,23): |
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x = identity_block(x, 3, [256, 256, 1024], stage=4, block='b'+str(i)) |
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x = conv_block(x, 3, [512, 512, 2048], stage=5, block='a') |
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x = identity_block(x, 3, [512, 512, 2048], stage=5, block='b') |
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x = identity_block(x, 3, [512, 512, 2048], stage=5, block='c') |
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x_fc = AveragePooling2D((7, 7), name='avg_pool')(x) |
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x_fc = Flatten()(x_fc) |
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x_fc = Dense(1000, activation='softmax', name='fc1000')(x_fc) |
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model = Model(img_input, x_fc) |
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# load weights |
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if weights_path: |
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model.load_weights(weights_path, by_name=True) |
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return model |
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if __name__ == '__main__': |
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im = cv2.resize(cv2.imread('cat.jpg'), (224, 224)).astype(np.float32) |
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# Remove train image mean |
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im[:,:,0] -= 103.939 |
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im[:,:,1] -= 116.779 |
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im[:,:,2] -= 123.68 |
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# Transpose image dimensions (Theano uses the channels as the 1st dimension) |
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if K.image_dim_ordering() == 'th': |
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im = im.transpose((2,0,1)) |
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# Use pre-trained weights for Theano backend |
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weights_path = 'resnet101_weights_th.h5' |
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else: |
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# Use pre-trained weights for Tensorflow backend |
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weights_path = 'resnet101_weights_tf.h5' |
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# Insert a new dimension for the batch_size |
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im = np.expand_dims(im, axis=0) |
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# Test pretrained model |
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model = resnet101_model(weights_path) |
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sgd = SGD(lr=1e-2, decay=1e-6, momentum=0.9, nesterov=True) |
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model.compile(optimizer=sgd, loss='categorical_crossentropy', metrics=['accuracy']) |
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out = model.predict(im) |
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print np.argmax(out) |
Good job, it seems useful and runs correctly using your pretrained tf model. But as the upgrade of keras and tensorflow, some places need to be modified.
Thank you very much, it helps me a lot.