Resnet-101 pre-trained model in Keras

readme.md

ResNet-101 in Keras

This is an Keras implementation of ResNet-101 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-101 with your own dataset.

Contents

model and usage demo: resnet-101_keras.py

Weights (Theano): resnet101_weights_th.h5

Weights (TensorFlow): resnet101_weights_tf.h5

resnet-101_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):
    '''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.
        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 resnet101_model(weights_path=None):
    '''Instantiate the ResNet101 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(1,3):
      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(1,23):
      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

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

    # Use pre-trained weights for Theano backend
    weights_path = 'resnet101_weights_th.h5'
  else:
    # Use pre-trained weights for Tensorflow backend
    weights_path = 'resnet101_weights_tf.h5'

  # Insert a new dimension for the batch_size
  im = np.expand_dims(im, axis=0)
  
  # Test pretrained model
  model = resnet101_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)

Hi! I'd love to use your pre-trained model for my experiment, but when I wanted to download the Theano/TensorFlow weights, it says I needed request to access?