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Added the mode "bilinear" in the upscaling2D layer. #10994

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Merged
merged 15 commits into from
Aug 30, 2018
Merged

Added the mode "bilinear" in the upscaling2D layer. #10994

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0855745
bilinear upsampling initial implementation
ahundt Feb 4, 2018
4b03e6a
bilinear interpolation docstrings added
ahundt Feb 4, 2018
b7c10bd
thano_backend.py fix bilinear interpolation ratio
ahundt Feb 5, 2018
104f209
theano_backend.py pep8 & padding fix
ahundt Feb 6, 2018
73f0311
theano_backend.py remove border_mode as per review
ahundt Feb 14, 2018
dcc9dfb
Merge branch 'bilinear_upsampling' of github.com:ahundt/keras into up…
gabrieldemarmiesse Aug 25, 2018
bd1b3b4
Added the mode "bilinear" in the upscaling2D layer. Theano only supports
gabrieldemarmiesse Aug 25, 2018
2833ee4
Added some details about what was possible with bilinear interpolation.
gabrieldemarmiesse Aug 25, 2018
8b6baf3
Made the lines shorted in the resize implementation of tensorflow.
gabrieldemarmiesse Aug 25, 2018
c4b781e
Added a more meaningful error message for bilinear and CNTK.
gabrieldemarmiesse Aug 26, 2018
9d155e2
Used ' instead of " for consistency.
gabrieldemarmiesse Aug 26, 2018
7770684
Parametrized the bilinear test.
Aug 27, 2018
2528906
Removed unused import.
Aug 27, 2018
70654da
Fix error messages.
fchollet Aug 28, 2018
dac12e9
Parametrized tests.
Aug 29, 2018
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theano_backend.py pep8 & padding fix
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@ahundt
ahundt authored Feb 6, 2018
commit 104f209a3ab41503182502b54d2fc488e0e909b0
6 changes: 4 additions & 2 deletions keras/backend/theano_backend.py
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Expand Up @@ -904,8 +904,10 @@ def resize_images(x, height_factor, width_factor, data_format, interpolation='ne
output = repeat_elements(x, height_factor, axis=axis_1)
output = repeat_elements(output, width_factor, axis=axis_2)
elif interpolation == 'bilinear':
ratio = height_factor/width_factor
output = theano.tensor.nnet.abstract_conv.bilinear_upsampling(x, ratio=ratio)
ratio = height_factor / width_factor
th_padding = _preprocess_padding('same')
output = theano.tensor.nnet.abstract_conv.bilinear_upsampling(
x, ratio=ratio, border_mode=th_padding)
if hasattr(x, '_keras_shape'):
output._keras_shape = list(x._keras_shape)
repeat_dim_1 = x._keras_shape[axis_1]
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