flyyufelix · August 5, 2022 15:20 · Apr 14, 2017 · Apr 13, 2017 · Apr 13, 2017
diff --git a/readme.md b/readme.md
@@ -12,7 +12,7 @@ arXiv:1512.03385
 
 ## Fine-tuning
 
-Check [this](https://github.com/flyyufelix/cnn_finetune/blob/master/resnet152.py) out to see example of fine-tuning ResNet-152 with your own dataset.
+Check [this](https://github.com/flyyufelix/cnn_finetune/blob/master/resnet_152.py) out to see example of fine-tuning ResNet-152 with your own dataset.
 
 ## Contents
 

diff --git a/resnet-152_keras.py b/resnet-152_keras.py
@@ -0,0 +1,240 @@
+# -*- 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)
diff --git a/readme.md b/readme.md
@@ -0,0 +1,23 @@
+## ResNet-152 in Keras
+
+This is an [Keras](https://keras.io/) implementation of ResNet-152 with [ImageNet](http://www.image-net.org/) pre-trained weights. I converted the weights from [Caffe](http://caffe.berkeleyvision.org/) provided by the authors of the paper. The implementation supports both [Theano](http://deeplearning.net/software/theano/) and [TensorFlow](https://www.tensorflow.org/) backends. Just in case you are curious about how the conversion is done, you can visit my [blog post](https://flyyufelix.github.io/2017/03/23/caffe-to-keras.html) 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](https://github.com/flyyufelix/cnn_finetune/blob/master/resnet152.py) 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](https://drive.google.com/file/d/0Byy2AcGyEVxfZHhUT3lWVWxRN28/view?usp=sharing)
+
+Weights (TensorFlow): [resnet152_weights_tf.h5](https://drive.google.com/file/d/0Byy2AcGyEVxfeXExMzNNOHpEODg/view?usp=sharing)