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a01_inception_v4.py
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a01_inception_v4.py
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# coding: utf-8
# Based on: https://github.com/titu1994/Inception-v4/releases
__author__ = 'ZFTurbo: https://kaggle.com/zfturbo'
# Sys
import warnings
# Keras Core
from keras.layers.convolutional import MaxPooling2D, Convolution2D, AveragePooling2D
from keras.layers import Input, merge, Dropout, Dense, Flatten, Activation
from keras.layers.normalization import BatchNormalization
from keras.models import Model
# Backend
from keras import backend as K
# Utils
from keras.utils.layer_utils import convert_all_kernels_in_model
from keras.utils.data_utils import get_file
#########################################################################################
# Implements the Inception Network v4 (http://arxiv.org/pdf/1602.07261v1.pdf) in Keras. #
#########################################################################################
TH_WEIGHTS_PATH = '../weights/inception-v4_weights_th_dim_ordering_th_kernels.h5'
TF_WEIGHTS_PATH = '../weights/inception-v4_weights_tf_dim_ordering_tf_kernels.h5'
def conv2d_bn(x, nb_filter, nb_row, nb_col,
border_mode='same', subsample=(1, 1), bias=False):
"""
Utility function to apply conv + BN.
(Slightly modified from https://github.com/fchollet/keras/blob/master/keras/applications/inception_v3.py)
"""
if K.image_dim_ordering() == "th":
channel_axis = 1
else:
channel_axis = -1
x = Convolution2D(nb_filter, nb_row, nb_col,
subsample=subsample,
border_mode=border_mode,
bias=bias)(x)
x = BatchNormalization(axis=channel_axis)(x)
x = Activation('relu')(x)
return x
def block_inception_a(input):
if K.image_dim_ordering() == "th":
channel_axis = 1
else:
channel_axis = -1
branch_0 = conv2d_bn(input, 96, 1, 1)
branch_1 = conv2d_bn(input, 64, 1, 1)
branch_1 = conv2d_bn(branch_1, 96, 3, 3)
branch_2 = conv2d_bn(input, 64, 1, 1)
branch_2 = conv2d_bn(branch_2, 96, 3, 3)
branch_2 = conv2d_bn(branch_2, 96, 3, 3)
branch_3 = AveragePooling2D((3, 3), strides=(1, 1), border_mode='same')(input)
branch_3 = conv2d_bn(branch_3, 96, 1, 1)
x = merge([branch_0, branch_1, branch_2, branch_3], mode='concat', concat_axis=channel_axis)
return x
def block_reduction_a(input):
if K.image_dim_ordering() == "th":
channel_axis = 1
else:
channel_axis = -1
branch_0 = conv2d_bn(input, 384, 3, 3, subsample=(2, 2), border_mode='valid')
branch_1 = conv2d_bn(input, 192, 1, 1)
branch_1 = conv2d_bn(branch_1, 224, 3, 3)
branch_1 = conv2d_bn(branch_1, 256, 3, 3, subsample=(2, 2), border_mode='valid')
branch_2 = MaxPooling2D((3, 3), strides=(2, 2), border_mode='valid')(input)
x = merge([branch_0, branch_1, branch_2], mode='concat', concat_axis=channel_axis)
return x
def block_inception_b(input):
if K.image_dim_ordering() == "th":
channel_axis = 1
else:
channel_axis = -1
branch_0 = conv2d_bn(input, 384, 1, 1)
branch_1 = conv2d_bn(input, 192, 1, 1)
branch_1 = conv2d_bn(branch_1, 224, 1, 7)
branch_1 = conv2d_bn(branch_1, 256, 7, 1)
branch_2 = conv2d_bn(input, 192, 1, 1)
branch_2 = conv2d_bn(branch_2, 192, 7, 1)
branch_2 = conv2d_bn(branch_2, 224, 1, 7)
branch_2 = conv2d_bn(branch_2, 224, 7, 1)
branch_2 = conv2d_bn(branch_2, 256, 1, 7)
branch_3 = AveragePooling2D((3, 3), strides=(1, 1), border_mode='same')(input)
branch_3 = conv2d_bn(branch_3, 128, 1, 1)
x = merge([branch_0, branch_1, branch_2, branch_3], mode='concat', concat_axis=channel_axis)
return x
def block_reduction_b(input):
if K.image_dim_ordering() == "th":
channel_axis = 1
else:
channel_axis = -1
branch_0 = conv2d_bn(input, 192, 1, 1)
branch_0 = conv2d_bn(branch_0, 192, 3, 3, subsample=(2, 2), border_mode='valid')
branch_1 = conv2d_bn(input, 256, 1, 1)
branch_1 = conv2d_bn(branch_1, 256, 1, 7)
branch_1 = conv2d_bn(branch_1, 320, 7, 1)
branch_1 = conv2d_bn(branch_1, 320, 3, 3, subsample=(2, 2), border_mode='valid')
branch_2 = MaxPooling2D((3, 3), strides=(2, 2), border_mode='valid')(input)
x = merge([branch_0, branch_1, branch_2], mode='concat', concat_axis=channel_axis)
return x
def block_inception_c(input):
if K.image_dim_ordering() == "th":
channel_axis = 1
else:
channel_axis = -1
branch_0 = conv2d_bn(input, 256, 1, 1)
branch_1 = conv2d_bn(input, 384, 1, 1)
branch_10 = conv2d_bn(branch_1, 256, 1, 3)
branch_11 = conv2d_bn(branch_1, 256, 3, 1)
branch_1 = merge([branch_10, branch_11], mode='concat', concat_axis=channel_axis)
branch_2 = conv2d_bn(input, 384, 1, 1)
branch_2 = conv2d_bn(branch_2, 448, 3, 1)
branch_2 = conv2d_bn(branch_2, 512, 1, 3)
branch_20 = conv2d_bn(branch_2, 256, 1, 3)
branch_21 = conv2d_bn(branch_2, 256, 3, 1)
branch_2 = merge([branch_20, branch_21], mode='concat', concat_axis=channel_axis)
branch_3 = AveragePooling2D((3, 3), strides=(1, 1), border_mode='same')(input)
branch_3 = conv2d_bn(branch_3, 256, 1, 1)
x = merge([branch_0, branch_1, branch_2, branch_3], mode='concat', concat_axis=channel_axis)
return x
def inception_v4_base(input):
if K.image_dim_ordering() == "th":
channel_axis = 1
else:
channel_axis = -1
# Input Shape is 299 x 299 x 3 (th) or 3 x 299 x 299 (th)
net = conv2d_bn(input, 32, 3, 3, subsample=(2, 2), border_mode='valid')
net = conv2d_bn(net, 32, 3, 3, border_mode='valid')
net = conv2d_bn(net, 64, 3, 3)
branch_0 = MaxPooling2D((3, 3), strides=(2, 2), border_mode='valid')(net)
branch_1 = conv2d_bn(net, 96, 3, 3, subsample=(2, 2), border_mode='valid')
net = merge([branch_0, branch_1], mode='concat', concat_axis=channel_axis)
branch_0 = conv2d_bn(net, 64, 1, 1)
branch_0 = conv2d_bn(branch_0, 96, 3, 3, border_mode='valid')
branch_1 = conv2d_bn(net, 64, 1, 1)
branch_1 = conv2d_bn(branch_1, 64, 1, 7)
branch_1 = conv2d_bn(branch_1, 64, 7, 1)
branch_1 = conv2d_bn(branch_1, 96, 3, 3, border_mode='valid')
net = merge([branch_0, branch_1], mode='concat', concat_axis=channel_axis)
branch_0 = conv2d_bn(net, 192, 3, 3, subsample=(2, 2), border_mode='valid')
branch_1 = MaxPooling2D((3, 3), strides=(2, 2), border_mode='valid')(net)
net = merge([branch_0, branch_1], mode='concat', concat_axis=channel_axis)
# 35 x 35 x 384
# 4 x Inception-A blocks
for idx in range(4):
net = block_inception_a(net)
# 35 x 35 x 384
# Reduction-A block
net = block_reduction_a(net)
# 17 x 17 x 1024
# 7 x Inception-B blocks
for idx in range(7):
net = block_inception_b(net)
# 17 x 17 x 1024
# Reduction-B block
net = block_reduction_b(net)
# 8 x 8 x 1536
# 3 x Inception-C blocks
for idx in range(3):
net = block_inception_c(net)
return net
def inception_v4(num_classes, dropout_keep_prob, weights):
'''
Creates the inception v4 network
Args:
num_classes: number of classes
dropout_keep_prob: float, the fraction to keep before final layer.
Returns:
logits: the logits outputs of the model.
'''
# Input Shape is 299 x 299 x 3 (tf) or 3 x 299 x 299 (th)
if K.image_dim_ordering() == 'th':
inputs = Input((3, 299, 299))
else:
inputs = Input((299, 299, 3))
# Make inception base
net = inception_v4_base(inputs)
# Final pooling and prediction
# 8 x 8 x 1536
net = AveragePooling2D((8, 8), border_mode='valid')(net)
# 1 x 1 x 1536
net = Dropout(dropout_keep_prob)(net)
net = Flatten()(net)
# 1536
predictions = Dense(output_dim=num_classes, activation='softmax')(net)
model = Model(inputs, predictions, name='inception_v4')
# load weights
if weights == 'imagenet':
if K.image_dim_ordering() == 'th':
model.load_weights(TH_WEIGHTS_PATH, by_name=True)
if K.backend() == 'tensorflow':
warnings.warn('You are using the TensorFlow backend, yet you '
'are using the Theano '
'image dimension ordering convention '
'(`image_dim_ordering="th"`). '
'For best performance, set '
'`image_dim_ordering="tf"` in '
'your Keras config '
'at ~/.keras/keras.json.')
convert_all_kernels_in_model(model)
else:
model.load_weights(TF_WEIGHTS_PATH, by_name=True)
if K.backend() == 'theano':
convert_all_kernels_in_model(model)
print("Loaded Model Weights!")
return model