I've just started to learn Tensorflow (2.1.0), Keras (2.3.7) with Python 3.7.7.
I'm trying an encoder-decoder network using VGG16.
I need to Upsample a layer from (12, 12, ...) to (25, 25, ...) to make conv7_1 has the same shape as conv4_3 layer. The layer with the 'problem' is upsp2:
conv4_3 (Conv2D) (None, 25, 25, 512) 2359808
_________________________________________________________________
pool_4 (MaxPooling2D) (None, 12, 12, 512) 0
_________________________________________________________________
conv5_1 (Conv2D) (None, 12, 12, 512) 2359808
_________________________________________________________________
conv5_2 (Conv2D) (None, 12, 12, 512) 2359808
_________________________________________________________________
conv5_3 (Conv2D) (None, 12, 12, 512) 2359808
_________________________________________________________________
pool_5 (MaxPooling2D) (None, 6, 6, 512) 0
_________________________________________________________________
upsp1 (UpSampling2D) (None, 12, 12, 512) 0
_________________________________________________________________
conv6_1 (Conv2D) (None, 12, 12, 512) 2359808
_________________________________________________________________
conv6_2 (Conv2D) (None, 12, 12, 512) 2359808
_________________________________________________________________
conv6_3 (Conv2D) (None, 12, 12, 512) 2359808
_________________________________________________________________
upsp2 (UpSampling2D) (None, 24, 24, 512) 0
_________________________________________________________________
conv7_1 (Conv2D) (None, 24, 24, 512) 2359808
I have tried this:
#################################
# Decoder
#################################
#conv1 = Conv2DTranspose(512, (2, 2), strides = 2, name = 'conv1')(pool5)
upsp1 = UpSampling2D(size = (2,2), name = 'upsp1')(pool5)
conv6 = Conv2D(512, 3, activation = 'relu', padding = 'same', name = 'conv6_1')(upsp1)
conv6 = Conv2D(512, 3, activation = 'relu', padding = 'same', name = 'conv6_2')(conv6)
conv6 = Conv2D(512, 3, activation = 'relu', padding = 'same', name = 'conv6_3')(conv6)
zero1 = ZeroPadding2D(padding = (1,1), data_format = 'channels_last', name='zero1')(conv6)
upsp2 = UpSampling2D(size = (2,2), name = 'upsp2')(zero1)
But I get that shape (12, 12, ...) gets into (14, 14, ...) at zero1 layer:
conv6_3 (Conv2D) (None, 12, 12, 512) 2359808
_________________________________________________________________
zero1 (ZeroPadding2D) (None, 14, 14, 512) 0
_________________________________________________________________
upsp2 (UpSampling2D) (None, 28, 28, 512) 0
_________________________________________________________________
How can I upsample (12,12,512) to (25,25,512)?
I did it using padding as a tuple of 2 tuples of 2 ints: interpreted as ((top_pad, bottom_pad), (left_pad, right_pad)). And setting ZeroPadding2D at the end of convolution 7 layer:
#################################
# Decoder
#################################
#conv1 = Conv2DTranspose(512, (2, 2), strides = 2, name = 'conv1')(pool5)
upsp1 = UpSampling2D(size = (2,2), name = 'upsp1')(pool5)
conv6 = Conv2D(512, 3, activation = 'relu', padding = 'same', name = 'conv6_1')(upsp1)
conv6 = Conv2D(512, 3, activation = 'relu', padding = 'same', name = 'conv6_2')(conv6)
conv6 = Conv2D(512, 3, activation = 'relu', padding = 'same', name = 'conv6_3')(conv6)
upsp2 = UpSampling2D(size = (2,2), name = 'upsp2')(conv6)
conv7 = Conv2D(512, 3, activation = 'relu', padding = 'same', name = 'conv7_1')(upsp2)
conv7 = Conv2D(512, 3, activation = 'relu', padding = 'same', name = 'conv7_2')(conv7)
conv7 = Conv2D(512, 3, activation = 'relu', padding = 'same', name = 'conv7_3')(conv7)
zero1 = ZeroPadding2D(padding = ((1, 0), (1, 0)), data_format = 'channels_last', name='zero1')(conv7)