def conv_block(kernel_size, filters, stage, block, weight_decay=0., strides=(2, 2), batch_momentum=0.99):
'''conv_block is the block that has a conv layer at shortcut
# Arguments
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 strides=(2,2)
And the shortcut should have strides=(2,2) as well
'''
def f(input_tensor):
nb_filter1, nb_filter2, nb_filter3 = filters
if K.image_data_format() == 'channels_last':
bn_axis = 3
else:
bn_axis = 1
conv_name_base = 'res' + str(stage) + block + '_branch'
bn_name_base = 'bn' + str(stage) + block + '_branch'
x = Conv2D(nb_filter1, (1, 1), strides=strides,
name=conv_name_base + '2a', kernel_regularizer=l2(weight_decay))(input_tensor)
x = BatchNormalization(axis=bn_axis, name=bn_name_base + '2a', momentum=batch_momentum)(x)
x = Activation('relu')(x)
x = Conv2D(nb_filter2, (kernel_size, kernel_size), padding='same',
name=conv_name_base + '2b', kernel_regularizer=l2(weight_decay))(x)
x = BatchNormalization(axis=bn_axis, name=bn_name_base + '2b', momentum=batch_momentum)(x)
x = Activation('relu')(x)
x = Conv2D(nb_filter3, (1, 1), name=conv_name_base + '2c', kernel_regularizer=l2(weight_decay))(x)
x = BatchNormalization(axis=bn_axis, name=bn_name_base + '2c', momentum=batch_momentum)(x)
shortcut = Conv2D(nb_filter3, (1, 1), strides=strides,
name=conv_name_base + '1', kernel_regularizer=l2(weight_decay))(input_tensor)
shortcut = BatchNormalization(axis=bn_axis, name=bn_name_base + '1', momentum=batch_momentum)(shortcut)
x = Add()([x, shortcut])
x = Activation('relu')(x)
return x
return f
# Atrous-Convolution version of residual blocks
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