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import tensorflow as tf
from tensorflow.keras import layers
from .skff import selective_kernel_feature_fusion
from .dual_attention import dual_attention_unit_block
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(
up_sampling_module(dual_attention_unit_block(level3_skff))
)
# SKFF 2
skff_ = selective_kernel_feature_fusion(level1_dau_2, level3_dau_2, level3_dau_2)
conv = layers.Conv2D(channels, kernel_size=(3, 3), padding="same")(skff_)
return layers.Add()([input_tensor, conv])
def recursive_residual_group(input_tensor, num_mrb, channels):
conv1 = layers.Conv2D(channels, kernel_size=(3, 3), padding="same")(input_tensor)
for _ in range(num_mrb):
conv1 = multi_scale_residual_block(conv1, channels)
conv2 = layers.Conv2D(channels, kernel_size=(3, 3), padding="same")(conv1)
return layers.Add()([conv2, input_tensor])
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