I reopen this thread because I rework on this implementation.
I want to add some information about the type of transposed convolution that we want to do.
In our tensorflow model we use the Conv2DTranspose like this :
Conv2DTranspose(128, (2, 2), strides=(2, 2), padding=‘same’)
The parameters are as below :
- 128 kernel of 2x2
- Stride of 2
The input of this Conv2DTranspose is obtained by the output of a Conv2D :
Conv2D(256, (3, 3), activation=‘relu’, kernel_initializer=‘he_normal’, padding=‘same’)
Full Code :
c5 = tf.keras.layers.Conv2D(256, (3, 3), activation=‘relu’, kernel_initializer=‘he_normal’, padding=‘same’)(c5)
u6 = tf.keras.layers.Conv2DTranspose(128, (2, 2), strides=(2, 2), padding=‘same’)(c5)
I think I need to use the backward convolution in the cuDNN API, but I’m not sure…
I try to implement a simple transposed convolution first, with only one kernel and small input tensor to understand the implementation but I always have CUDNN_STATUS_BAD_PARAM, and no information…
Someone know examples or tips to implement this type of transposed convolution in cuDNN ?