Arsey/readme.md

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    ResNet-152 in Keras

This is an Keras implementation of ResNet-152 with ImageNet pre-trained weights. I converted the weights from Caffe provided by the authors of the paper. The implementation supports both Theano and TensorFlow backends. Just in case you are curious about how the conversion is done, you can visit my blog post for more details.
ResNet Paper:
Deep Residual Learning for Image Recognition.
Kaiming He, Xiangyu Zhang, Shaoqing Ren, Jian Sun
arXiv:1512.03385

Fine-tuning

Check this out to see example of fine-tuning ResNet-152 with your own dataset.
Contents

model and usage demo: resnet-152_keras.py
Weights (Theano): resnet152_weights_th.h5
Weights (TensorFlow): resnet152_weights_tf.h5

  
## resnet-152_keras.py
# -*- coding: utf-8 -*-

import cv2
import numpy as np
import copy

from keras.layers import Input, Dense, Convolution2D, MaxPooling2D, AveragePooling2D, ZeroPadding2D, Dropout, Flatten, merge, Reshape, Activation, Lambda, GlobalAveragePooling2D, Merge
from keras.optimizers import SGD
from keras.layers.normalization import BatchNormalization
from keras.models import Model
from keras import initializations
from keras.engine import Layer, InputSpec
from keras import backend as K

import sys
sys.setrecursionlimit(3000)

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 [initializations](../initializations.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
            [initializations](../initializations.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 = initializations.get(beta_init)
        self.gamma_init = initializations.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 = self.gamma_init(shape, name='{}_gamma'.format(self.name))
        self.beta = self.beta_init(shape, name='{}_beta'.format(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 = Convolution2D(nb_filter1, 1, 1, name=conv_name_base + '2a', 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 = Convolution2D(nb_filter2, kernel_size, kernel_size,
                      name=conv_name_base + '2b', 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 = Convolution2D(nb_filter3, 1, 1, name=conv_name_base + '2c', 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 = merge([x, input_tensor], mode='sum', 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 = Convolution2D(nb_filter1, 1, 1, subsample=strides,
                      name=conv_name_base + '2a', 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 = Convolution2D(nb_filter2, kernel_size, kernel_size,
                      name=conv_name_base + '2b', 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 = Convolution2D(nb_filter3, 1, 1, name=conv_name_base + '2c', 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 = Convolution2D(nb_filter3, 1, 1, subsample=strides,
                             name=conv_name_base + '1', 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 = merge([x, shortcut], mode='sum', name='res' + str(stage) + block)
    x = Activation('relu', name='res' + str(stage) + block + '_relu')(x)
    return x

def resnet152_model(weights_path=None):
    '''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
    if K.image_dim_ordering() == 'tf':
      bn_axis = 3
      img_input = Input(shape=(224, 224, 3), name='data')
    else:
      bn_axis = 1
      img_input = Input(shape=(3, 224, 224), name='data')

    x = ZeroPadding2D((3, 3), name='conv1_zeropadding')(img_input)
    x = Convolution2D(64, 7, 7, subsample=(2, 2), name='conv1', 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')

    x_fc = AveragePooling2D((7, 7), name='avg_pool')(x)
    x_fc = Flatten()(x_fc)
    x_fc = Dense(1000, activation='softmax', name='fc1000')(x_fc)

    model = Model(img_input, x_fc)

    # load weights
    if weights_path:
      model.load_weights(weights_path, by_name=True)

    return model

if __name__ == '__main__':

  im = cv2.resize(cv2.imread('cat.jpg'), (224, 224)).astype(np.float32)

  # Remove train image mean
  im[:,:,0] -= 103.939
  im[:,:,1] -= 116.779
  im[:,:,2] -= 123.68

  if K.image_dim_ordering() == 'th':
    # Transpose image dimensions (Theano uses the channels as the 1st dimension)
    im = im.transpose((2,0,1))

    # Use pre-trained weights for Theano backend
    weights_path = 'resnet152_weights_th.h5'
  else:
    # Use pre-trained weights for Tensorflow backend
    weights_path = 'resnet152_weights_tf.h5'

  # Insert a new dimension for the batch_size
  im = np.expand_dims(im, axis=0)

  # Test pretrained model
  model = resnet152_model(weights_path)
  sgd = SGD(lr=1e-2, decay=1e-6, momentum=0.9, nesterov=True)
  model.compile(optimizer=sgd, loss='categorical_crossentropy', metrics=['accuracy'])

  out = model.predict(im)
  print np.argmax(out)
	# -- coding: utf-8 --

	import cv2
	import numpy as np
	import copy

	from keras.layers import Input, Dense, Convolution2D, MaxPooling2D, AveragePooling2D, ZeroPadding2D, Dropout, Flatten, merge, Reshape, Activation, Lambda, GlobalAveragePooling2D, Merge
	from keras.optimizers import SGD
	from keras.layers.normalization import BatchNormalization
	from keras.models import Model
	from keras import initializations
	from keras.engine import Layer, InputSpec
	from keras import backend as K

	import sys
	sys.setrecursionlimit(3000)

	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 [initializations](../initializations.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
	[initializations](../initializations.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 = initializations.get(beta_init)
	self.gamma_init = initializations.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 = self.gamma_init(shape, name='{}_gamma'.format(self.name))
	self.beta = self.beta_init(shape, name='{}_beta'.format(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 = Convolution2D(nb_filter1, 1, 1, name=conv_name_base + '2a', 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 = Convolution2D(nb_filter2, kernel_size, kernel_size,
	name=conv_name_base + '2b', 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 = Convolution2D(nb_filter3, 1, 1, name=conv_name_base + '2c', 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 = merge([x, input_tensor], mode='sum', 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 = Convolution2D(nb_filter1, 1, 1, subsample=strides,
	name=conv_name_base + '2a', 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 = Convolution2D(nb_filter2, kernel_size, kernel_size,
	name=conv_name_base + '2b', 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 = Convolution2D(nb_filter3, 1, 1, name=conv_name_base + '2c', 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 = Convolution2D(nb_filter3, 1, 1, subsample=strides,
	name=conv_name_base + '1', 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 = merge([x, shortcut], mode='sum', name='res' + str(stage) + block)
	x = Activation('relu', name='res' + str(stage) + block + '_relu')(x)
	return x

	def resnet152_model(weights_path=None):
	'''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
	if K.image_dim_ordering() == 'tf':
	bn_axis = 3
	img_input = Input(shape=(224, 224, 3), name='data')
	else:
	bn_axis = 1
	img_input = Input(shape=(3, 224, 224), name='data')

	x = ZeroPadding2D((3, 3), name='conv1_zeropadding')(img_input)
	x = Convolution2D(64, 7, 7, subsample=(2, 2), name='conv1', 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')

	x_fc = AveragePooling2D((7, 7), name='avg_pool')(x)
	x_fc = Flatten()(x_fc)
	x_fc = Dense(1000, activation='softmax', name='fc1000')(x_fc)

	model = Model(img_input, x_fc)

	# load weights
	if weights_path:
	model.load_weights(weights_path, by_name=True)

	return model

	if __name__ == '__main__':

	im = cv2.resize(cv2.imread('cat.jpg'), (224, 224)).astype(np.float32)

	# Remove train image mean
	im[:,:,0] -= 103.939
	im[:,:,1] -= 116.779
	im[:,:,2] -= 123.68

	if K.image_dim_ordering() == 'th':
	# Transpose image dimensions (Theano uses the channels as the 1st dimension)
	im = im.transpose((2,0,1))

	# Use pre-trained weights for Theano backend
	weights_path = 'resnet152_weights_th.h5'
	else:
	# Use pre-trained weights for Tensorflow backend
	weights_path = 'resnet152_weights_tf.h5'

	# Insert a new dimension for the batch_size
	im = np.expand_dims(im, axis=0)

	# Test pretrained model
	model = resnet152_model(weights_path)
	sgd = SGD(lr=1e-2, decay=1e-6, momentum=0.9, nesterov=True)
	model.compile(optimizer=sgd, loss='categorical_crossentropy', metrics=['accuracy'])

	out = model.predict(im)
	print np.argmax(out)