SE_ResNeXt_3D
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Python
SE_ResNeXt_3D
The origin of SENet
Code modified from 2d se-resnext
Note
The first layer was changed to ResNet-like structure due to the high memory cost.
- Before:
x = conv_layer(x, filter=64, kernel=[3, 3], stride=1, layer_name=scope+'_conv1')
x = Batch_Normalization(x, training=self.training, scope=scope+'_batch1')
- After:
x = Conv3D(filters=self.init_filters, kernel_size=(7, 7, 7), strides=(2, 2, 2), padding='same')(x)
x = MaxPooling3D(pool_size=(3, 3, 3), strides=(2, 2, 2), padding="same")(x)
x = Batch_Normalization(x, training=self.training, scope=scope+'_batch1')