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KerasCodeExamples.txt

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max_pooling = tf.reduce_mean(input_tensor, axis=-1)
max_pooling = tf.expand_dims(max_pooling, axis=-1)
concatenated = layers.Concatenate(axis=-1)([average_pooling, max_pooling])
feature_map = layers.Conv2D(1, kernel_size=(1, 1))(concatenated)
feature_map = tf.nn.sigmoid(feature_map)
return input_tensor * feature_map
def channel_attention_block(input_tensor):
channels = list(input_tensor.shape)[-1]
average_pooling = layers.GlobalAveragePooling2D()(input_tensor)
feature_descriptor = tf.reshape(average_pooling, shape=(-1, 1, 1, channels))
feature_activations = layers.Conv2D(
filters=channels // 8, kernel_size=(1, 1), activation=\"relu\"
)(feature_descriptor)
feature_activations = layers.Conv2D(
filters=channels, kernel_size=(1, 1), activation=\"sigmoid\"
)(feature_activations)
return input_tensor * feature_activations
def dual_attention_unit_block(input_tensor):
channels = list(input_tensor.shape)[-1]
feature_map = layers.Conv2D(
channels, kernel_size=(3, 3), padding=\"same\", activation=\"relu\"
)(input_tensor)
feature_map = layers.Conv2D(channels, kernel_size=(3, 3), padding=\"same\")(
feature_map
)
channel_attention = channel_attention_block(feature_map)
spatial_attention = spatial_attention_block(feature_map)
concatenation = layers.Concatenate(axis=-1)([channel_attention, spatial_attention])
concatenation = layers.Conv2D(channels, kernel_size=(1, 1))(concatenation)
return layers.Add()([input_tensor, concatenation])
Multi-Scale Residual Block
The Multi-Scale Residual Block is capable of generating a spatially-precise output by maintaining high-resolution representations, while receiving rich contextual information from low-resolutions. The MRB consists of multiple (three in this paper) fully-convolutional streams connected in parallel. It allows information...
# Recursive Residual Modules
def down_sampling_module(input_tensor):
channels = list(input_tensor.shape)[-1]
main_branch = layers.Conv2D(channels, kernel_size=(1, 1), activation=\"relu\")(
input_tensor
)
main_branch = layers.Conv2D(
channels, kernel_size=(3, 3), padding=\"same\", activation=\"relu\"
)(main_branch)
main_branch = layers.MaxPooling2D()(main_branch)
main_branch = layers.Conv2D(channels * 2, kernel_size=(1, 1))(main_branch)
skip_branch = layers.MaxPooling2D()(input_tensor)
skip_branch = layers.Conv2D(channels * 2, kernel_size=(1, 1))(skip_branch)
return layers.Add()([skip_branch, main_branch])
def up_sampling_module(input_tensor):
channels = list(input_tensor.shape)[-1]
main_branch = layers.Conv2D(channels, kernel_size=(1, 1), activation=\"relu\")(
input_tensor
)
main_branch = layers.Conv2D(
channels, kernel_size=(3, 3), padding=\"same\", activation=\"relu\"
)(main_branch)
main_branch = layers.UpSampling2D()(main_branch)
main_branch = layers.Conv2D(channels // 2, kernel_size=(1, 1))(main_branch)
skip_branch = layers.UpSampling2D()(input_tensor)
skip_branch = layers.Conv2D(channels // 2, kernel_size=(1, 1))(skip_branch)
return layers.Add()([skip_branch, main_branch])
# MRB Block
def multi_scale_residual_block(input_tensor, channels):
# features
level1 = input_tensor
level2 = down_sampling_module(input_tensor)
level3 = down_sampling_module(level2)
# DAU
level1_dau = dual_attention_unit_block(level1)
level2_dau = dual_attention_unit_block(level2)
level3_dau = dual_attention_unit_block(level3)
# SKFF
level1_skff = selective_kernel_feature_fusion(
level1_dau,
up_sampling_module(level2_dau),
up_sampling_module(up_sampling_module(level3_dau)),
)
level2_skff = selective_kernel_feature_fusion(
down_sampling_module(level1_dau), level2_dau, up_sampling_module(level3_dau)
)
level3_skff = selective_kernel_feature_fusion(
down_sampling_module(down_sampling_module(level1_dau)),
down_sampling_module(level2_dau),
level3_dau,
)
# DAU 2
level1_dau_2 = dual_attention_unit_block(level1_skff)
level2_dau_2 = up_sampling_module((dual_attention_unit_block(level2_skff)))
level3_dau_2 = up_sampling_module(