SeparableConvGRU2D
Simple implementation of SeparableConvGRU2D layer in tensorflow keras.
ConvGRU is useful, but parameter size is not small. For more compact model implementation, SeparableConvGRU is suitable.
This module works correctly in my personal project (2D object detection). But, this does not mean "perfect"...
Base code
https://github.com/KoertS/ConvGRU2D
My environment
- ubuntu 18.04
- python 3.8
- tensorflow 2.4.1
Example
import tensorflow as tf
from ConvGRU2D import ConvGRU2D
from SeparableConvGRU2D import SeparableConvGRU2D
steps = 10
height = 32
width = 32
input_channels = 3
output_channels = 6
inputs = tf.keras.Input(shape=(steps, height, width, input_channels))
# ConvGRU
outputs_convgru = ConvGRU2D(filters=output_channels, kernel_size=3)(inputs)
model_convgru = tf.keras.Model(inputs=inputs, outputs=outputs_convgru, name="convgru_model")
model_convgru.summary()
# SeparableConvGRU
outputs_sepconvgru = SeparableConvGRU2D(filters=output_channels, kernel_size=3)(inputs)
model_sepconvgru = tf.keras.Model(inputs=inputs, outputs=outputs_sepconvgru, name="sepconvgru_model")
model_sepconvgru.summary()parameter size
- ConvGRU : 1,476
- SeparableConvGRU : 423