This repo includes Zhang2019's CLSTM implemented using keras(tensorflow2).
Zhang, Haokui, et al. "Exploiting temporal consistency for real-time video depth estimation."
Proceedings of the IEEE International Conference on Computer Vision. 2019.
Author's implimentation using pytorch is here.
https://github.com/hkzhang91/ST-CLSTM/blob/master/CLSTM_Depth_Estimation-master/models_CLTSM/R_CLSTM_modules_2.py
OS Windows10
CUDA Toolkit 10.1 update2
cuDNN v7.6.5 (November 5th, 2019), for CUDA 10.1
Python 3.6.6 (anaconda3)
tensorflow 2.3.0
keras 2.4.3
Zhang2019_custom_CLSTM_layer/
┣━━ README.md ... this doc.
┣━━ convolutional_recurrent.py ... for calling keras's ConvRNN2D locally
┗━━ STConvLSTM2DCell.py ... main CLSTM cell
Note that convolutional_recurrent.py is wget from keras v2.3.0.
$ wget https://raw.githubusercontent.com/tensorflow/tensorflow/v2.3.0/tensorflow/python/keras/layers/convolutional_recurrent.pygit clone
$ git clone https://github.com/catdance124/Zhang2019_custom_CLSTM_layer.gitThe following is an example of how to call a custom CLSTM.
from tensorflow.keras.layers import Input
from tensorflow.keras.models import Model
from convolutional_recurrent import ConvRNN2D
from STConvLSTM2DCell import STConvLSTM2DCell
inputs = Input((None, 256, 256, 3))
x = ConvRNN2D(STConvLSTM2DCell(8, kernel_size=3, padding='same',
activation='tanh', recurrent_activation='hard_sigmoid',
kernel_initializer='glorot_uniform', recurrent_initializer='orthogonal'),
return_sequences=True, name='STConvLSTM2D')(inputs)
model = Model(inputs=inputs, outputs=x)
model.summary()
# _________________________________________________________________
# Layer (type) Output Shape Param #
# =================================================================
# input_2 (InputLayer) [(None, None, 256, 256, 3 0
# _________________________________________________________________
# STConvLSTM2D (ConvRNN2D) (None, None, 256, 256, 1) 9914
# =================================================================
# Total params: 9,914
# Trainable params: 9,870
# Non-trainable params: 44
# _________________________________________________________________In Japanese: 好きな構造のconvlutional RNNを組み立てる(tensorflow2/keras)
https://catdance124.hatenablog.jp/entry/2020/10/04/211805
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