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add resnet50 example #3266

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merged 16 commits into from Aug 3, 2016
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'''This script demonstrates how to build a deep residual network
using the Keras functional API.
get_resne50 returns the deep residual network model (50 layers)
Please visit Kaiming He's GitHub homepage:
https://github.com/KaimingHe
for more information.
The related paper is
"Deep Residual Learning for Image Recognition"
Kaiming He, Xiangyu Zhang, Shaoqing Ren, Jian Sun
http://arxiv.org/abs/1512.03385
Pretrained weights were converted from Kaiming He's caffe model directly.
For now we provide weights for the tensorflow backend only,
thus use 'tf' dim_ordering (e.g. input_shape=(224, 224, 3) for 224*224 color image)
would accelerate the computation, but we also provide weights for 'th' dim_ordering for compatibility.
please donwload them at:
http://pan.baidu.com/s/1o8pO2q2 ('th' dim ordering, For China)
http://pan.baidu.com/s/1pLanuTt ('tf' dim ordering, For China)
https://drive.google.com/open?id=0B4ChsjFJvew3NVQ2U041Q0xHRHM ('th' dim ordering, For other countries)
https://drive.google.com/open?id=0B4ChsjFJvew3NWN5THdxcTdSWmc ('tf' dim ordering, For other countries)
@author: BigMoyan, University of Electronic Science and Technology of China
'''
from keras.layers import merge
from keras.layers.convolutional import Convolution2D, MaxPooling2D, ZeroPadding2D, AveragePooling2D
from keras.layers.core import Dense, Activation, Flatten
from keras.layers.normalization import BatchNormalization
from keras.models import Model
from keras.layers import Input
from keras.preprocessing.image import load_img, img_to_array
import keras.backend as K
import numpy as np
# The names of layers in resnet50 are generated with the following format
# [type][stage][block]_branch[branch][layer]
# type: 'res' for conv layer, 'bn' and 'scale' for BN layer
# stage: from '2' to '5', current stage number
# block: 'a','b','c'... for different blocks in a stage
# branch: '1' for shortcut and '2' for main path
# layer: 'a','b','c'... for different layers in a block
def identity_block(input_tensor, kernel_size, filters, stage, block):
"""
the identity_block is the block that has no conv layer at shortcut
params:
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
"""
dim_ordering = K.image_dim_ordering()
nb_filter1, nb_filter2, nb_filter3 = filters
if dim_ordering == 'tf':
axis = 3
else:
axis = 1
conv_name_base = 'res' + str(stage) + block + '_branch'
bn_name_base = 'bn' + str(stage) + block + '_branch'
out = Convolution2D(nb_filter1, 1, 1, dim_ordering=dim_ordering, name=conv_name_base + '2a')(input_tensor)
out = BatchNormalization(axis=axis, name=bn_name_base + '2a')(out)
out = Activation('relu')(out)
out = out = Convolution2D(nb_filter2, kernel_size, kernel_size, border_mode='same',
dim_ordering=dim_ordering, name=conv_name_base + '2b')(out)
out = BatchNormalization(axis=axis, name=bn_name_base + '2b')(out)
out = Activation('relu')(out)
out = Convolution2D(nb_filter3, 1, 1, dim_ordering=dim_ordering, name=conv_name_base + '2c')(out)
out = BatchNormalization(axis=axis, name=bn_name_base + '2c')(out)
out = merge([out, input_tensor], mode='sum')
out = Activation('relu')(out)
return out
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
params:
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 has subsample=(2,2) as well
"""
nb_filter1, nb_filter2, nb_filter3 = filters
dim_ordering = K.image_dim_ordering()
if dim_ordering == 'tf':
axis = 3
else:
axis = 1
conv_name_base = 'res' + str(stage) + block + '_branch'
bn_name_base = 'bn' + str(stage) + block + '_branch'
out = Convolution2D(nb_filter1, 1, 1, subsample=strides,
dim_ordering=dim_ordering, name=conv_name_base + '2a')(input_tensor)
out = BatchNormalization(axis=axis, name=bn_name_base + '2a')(out)
out = Activation('relu')(out)
out = Convolution2D(nb_filter2, kernel_size, kernel_size, border_mode='same',
dim_ordering=dim_ordering, name=conv_name_base + '2b')(out)
out = BatchNormalization(axis=axis, name=bn_name_base + '2b')(out)
out = Activation('relu')(out)
out = Convolution2D(nb_filter3, 1, 1, dim_ordering=dim_ordering, name=conv_name_base + '2c')(out)
out = BatchNormalization(axis=axis, name=bn_name_base + '2c')(out)
shortcut = Convolution2D(nb_filter3, 1, 1, subsample=strides,
dim_ordering=dim_ordering, name=conv_name_base + '1')(input_tensor)
shortcut = BatchNormalization(axis=axis, name=bn_name_base + '1')(shortcut)
out = merge([out, shortcut], mode='sum')
out = Activation('relu')(out)
return out
def read_img(img_path):
"""
this function returns preprocessed image
"""
dim_ordering = K.image_dim_ordering()
mean = (103.939, 116.779, 123.68)
img = load_img(img_path, target_size=(224, 224))
img = img_to_array(img, dim_ordering=dim_ordering)
# decenterize
img[0, :, :] -= mean[0]
img[1, :, :] -= mean[1]
img[2, :, :] -= mean[2]
# 'RGB'->'BGR'
if dim_ordering == 'th':
img = img[::-1, :, :]
else:
img = img[:, :, ::-1]
# expand dim for test
img = np.expand_dims(img, axis=0)
return img
def get_resnet50():
"""
this function returns the 50-layer residual network model
you should load pretrained weights if you want to use it directly.
Note that since the pretrained weights is converted from caffemodel
the order of channels for input image should be 'BGR' (the channel order of caffe)
"""
if K.image_dim_ordering() == 'tf':
inp = Input(shape=(224, 224, 3))
axis = 3
else:
inp = Input(shape=(3, 224, 224))
axis = 1
dim_ordering = K.image_dim_ordering()
out = ZeroPadding2D((3, 3), dim_ordering=dim_ordering)(inp)
out = Convolution2D(64, 7, 7, subsample=(2, 2), dim_ordering=dim_ordering, name='conv1')(out)
out = BatchNormalization(axis=axis, name='bn_conv1')(out)
out = Activation('relu')(out)
out = MaxPooling2D((3, 3), strides=(2, 2), dim_ordering=dim_ordering)(out)
out = conv_block(out, 3, [64, 64, 256], stage=2, block='a', strides=(1, 1))
out = identity_block(out, 3, [64, 64, 256], stage=2, block='b')
out = identity_block(out, 3, [64, 64, 256], stage=2, block='c')
out = conv_block(out, 3, [128, 128, 512], stage=3, block='a')
out = identity_block(out, 3, [128, 128, 512], stage=3, block='b')
out = identity_block(out, 3, [128, 128, 512], stage=3, block='c')
out = identity_block(out, 3, [128, 128, 512], stage=3, block='d')
out = conv_block(out, 3, [256, 256, 1024], stage=4, block='a')
out = identity_block(out, 3, [256, 256, 1024], stage=4, block='b')
out = identity_block(out, 3, [256, 256, 1024], stage=4, block='c')
out = identity_block(out, 3, [256, 256, 1024], stage=4, block='d')
out = identity_block(out, 3, [256, 256, 1024], stage=4, block='e')
out = identity_block(out, 3, [256, 256, 1024], stage=4, block='f')
out = conv_block(out, 3, [512, 512, 2048], stage=5, block='a')
out = identity_block(out, 3, [512, 512, 2048], stage=5, block='b')
out = identity_block(out, 3, [512, 512, 2048], stage=5, block='c')
out = AveragePooling2D((7, 7), dim_ordering=dim_ordering)(out)
out = Flatten()(out)
out = Dense(1000, activation='softmax', name='fc1000')(out)
model = Model(inp, out)
return model
if __name__ == '__main__':
K.set_image_dim_ordering('tf')
weights_file = K.image_dim_ordering() + '_dim_ordering_resnet50.h5'
resnet_model = get_resnet50()
resnet_model.load_weights(weights_file)
test_img1 = read_img('cat.jpg')
test_img2 = read_img('airplane.jpg')
# you may download synset_words from address given at the begining of this file
class_table = open('synset_words', 'r')
lines = class_table.readlines()
print "result for test 1 is"
print lines[np.argmax(resnet_model.predict(test_img1)[0])]
print "result for test 2 is"
print lines[np.argmax(resnet_model.predict(test_img2)[0])]
class_table.close()