I am having a hard time understanding the output shape of keras.layers.Conv2DTranspose
Here is the prototype:
keras.layers.Conv2DTranspose(
filters,
kernel_size,
strides=(1, 1),
padding='valid',
output_padding=None,
data_format=None,
dilation_rate=(1, 1),
activation=None,
use_bias=True,
kernel_initializer='glorot_uniform',
bias_initializer='zeros',
kernel_regularizer=None,
bias_regularizer=None,
activity_regularizer=None,
kernel_constraint=None,
bias_constraint=None
)
In the documentation (https://keras.io/layers/convolutional/), I read:
If output_padding is set to None (default), the output shape is inferred.
In the code (https://github.com/keras-team/keras/blob/master/keras/layers/convolutional.py), I read:
out_height = conv_utils.deconv_length(height,
stride_h, kernel_h,
self.padding,
out_pad_h,
self.dilation_rate[0])
out_width = conv_utils.deconv_length(width,
stride_w, kernel_w,
self.padding,
out_pad_w,
self.dilation_rate[1])
if self.data_format == 'channels_first':
output_shape = (batch_size, self.filters, out_height, out_width)
else:
output_shape = (batch_size, out_height, out_width, self.filters)
and (https://github.com/keras-team/keras/blob/master/keras/utils/conv_utils.py):
def deconv_length(dim_size, stride_size, kernel_size, padding, output_padding, dilation=1):
"""Determines output length of a transposed convolution given input length.
# Arguments
dim_size: Integer, the input length.
stride_size: Integer, the stride along the dimension of `dim_size`.
kernel_size: Integer, the kernel size along the dimension of `dim_size`.
padding: One of `"same"`, `"valid"`, `"full"`.
output_padding: Integer, amount of padding along the output dimension, can be set to `None` in which case the output length is inferred.
dilation: dilation rate, integer.
# Returns
The output length (integer).
"""
assert padding in {'same', 'valid', 'full'}
if dim_size is None:
return None
# Get the dilated kernel size
kernel_size = kernel_size + (kernel_size - 1) * (dilation - 1)
# Infer length if output padding is None, else compute the exact length
if output_padding is None:
if padding == 'valid':
dim_size = dim_size * stride_size + max(kernel_size - stride_size, 0)
elif padding == 'full':
dim_size = dim_size * stride_size - (stride_size + kernel_size - 2)
elif padding == 'same':
dim_size = dim_size * stride_size
else:
if padding == 'same':
pad = kernel_size // 2
elif padding == 'valid':
pad = 0
elif padding == 'full':
pad = kernel_size - 1
dim_size = ((dim_size - 1) * stride_size + kernel_size - 2 * pad + output_padding)
return dim_size
I understand that Conv2DTranspose is kind of a Conv2D, but reversed.
Since applying a Conv2D with kernel_size = (3, 3), strides = (10, 10) and padding = "same" to a 200x200 image will output a 20x20 image, I assume that applying a Conv2DTranspose with kernel_size = (3, 3), strides = (10, 10) and padding = "same" to a 20x20 image will output a 200x200 image.
Also, applying a Conv2D with kernel_size = (3, 3), strides = (10, 10) and padding = "same" to a 195x195 image will also output a 20x20 image.
So, I understand that there is kind of an ambiguity on the output shape when applying a Conv2DTranspose with kernel_size = (3, 3), strides = (10, 10) and padding = "same" (user might want output to be 195x195, or 200x200, or many other compatible shapes).
I assume that "the output shape is inferred." means that a default output shape is computed according to the parameters of the layer, and I assume that there is a mechanism to specify an output shape differnet from the default one, if necessary.
This said, I do not really understand
the meaning of the "output_padding" parameter
the interactions between parameters "padding" and "output_padding"
the various formulas in the function keras.conv_utils.deconv_length
Could someone explain this?
Many thanks,
Julien
