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Resnet-152 pre-trained model in TF Keras 2.x
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# -*- coding: utf-8 -*- | |
import cv2 | |
import numpy as np | |
from tensorflow.keras.layers import Input, Dense, Conv2D, MaxPool2D, AvgPool2D, Activation | |
from tensorflow.keras.layers import Layer, BatchNormalization, ZeroPadding2D, Flatten, add | |
from tensorflow.keras.optimizers import SGD | |
from tensorflow.keras.models import Model | |
from tensorflow.keras import initializers | |
from tensorflow.python.keras.layers import InputSpec | |
from tensorflow.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. | |
beta_init: name of initialization function for shift parameter | |
(see [initializers](../initializers.md)). | |
gamma_init: name of initialization function for scale parameter (see | |
[initializers](../initializers.md)). | |
''' | |
def __init__(self, axis=-1, momentum = 0.9, beta_init='zero', gamma_init='one', **kwargs): | |
self.momentum = momentum | |
self.axis = axis | |
self.beta_initializer = initializers.get(beta_init) | |
self.gamma_initializer = initializers.get(gamma_init) | |
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.add_weight( | |
name='%s_gamma'%self.name, | |
shape=shape, | |
initializer=self.gamma_initializer, | |
trainable=True, | |
dtype=self.dtype) | |
self.beta = self.add_weight( | |
name='%s_beta'%self.name, | |
shape=shape, | |
initializer=self.beta_initializer, | |
trainable=True, | |
dtype=self.dtype) | |
self.built = True | |
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, name=bn_name_base + '2a')(x) | |
x = Scale(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, name=bn_name_base + '2b')(x) | |
x = Scale(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, name=bn_name_base + '2c')(x) | |
x = Scale(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, name=bn_name_base + '2a')(x) | |
x = Scale(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, name=bn_name_base + '2b')(x) | |
x = Scale(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, name=bn_name_base + '2c')(x) | |
x = Scale(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, name=bn_name_base + '1')(shortcut) | |
shortcut = Scale(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_model(input_shape=(224, 224, 3), weights_path=None): | |
'''Instantiate the ResNet152 architecture, | |
# Arguments | |
input_shape: shape of the model input | |
weights_path: path to pretrained weight file | |
# Returns | |
A Keras model instance. | |
''' | |
eps = 1.1e-5 | |
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, name='bn_conv1')(x) | |
x = Scale(name='scale_conv1')(x) | |
x = Activation('relu', name='conv1_relu')(x) | |
x = MaxPool2D((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 = AvgPool2D((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__': | |
input_shape = (224, 224, 3) | |
weights_path = 'resnet152_weights_tf.h5' | |
image_path = 'cat.jpg' | |
im = cv2.resize(cv2.imread(image_path), input_shape[0:2]).astype(np.float32) | |
# Remove train image mean | |
im -= [103.939, 116.779, 123.68] | |
# Insert a new dimension for the batch_size | |
im = np.expand_dims(im, axis=0) | |
# Test pretrained model | |
model = resnet152_model(input_shape, weights_path) | |
sgd = SGD(lr=1e-2, decay=1e-6, momentum=0.9, nesterov=True) | |
model.compile(optimizer=sgd, loss='categorical_crossentropy', metrics=['accuracy']) | |
preds = model.predict(im) | |
print(np.argmax(preds)) |
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Code is bases on https://gist.github.com/flyyufelix/7e2eafb149f72f4d38dd661882c554a6. You can find the weights there...