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Attempt to infer the output shape for Deconvolution2D layer #4

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Attempt to infer the output shape for Deconvolution2D layer #4

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c797487
ENH: Infer output shape for Deconvolution2D
Jul 24, 2016
111060f
BUG: Tensor doesn't have 'shape' property, using K.shape() instead
lukovkin Jul 24, 2016
ab29be6
BUG: Fix subsample index for cols
Jul 24, 2016
b654a2a
BUG: Fix subsample index for cols
Jul 24, 2016
fcc1908
BUG: Fix subsample index for cols
Jul 24, 2016
af03021
Merge branch 'master' of https://github.com/lukovkin/keras
Jul 24, 2016
26b9c44
ENH: Update Deconvolution2D test case in order to test w and w/o outp…
Jul 24, 2016
01fda37
ENH: Update Deconvolution2D test case in order to test w and w/o outp…
lukovkin Jul 24, 2016
c117361
Merge branch 'master' of https://github.com/lukovkin/keras
Jul 24, 2016
4818bff
Merge branch 'master' of https://github.com/yaringal/keras into yarin…
lukovkin Jul 24, 2016
463a550
DOC: Reference for Deconvolution2D added
lukovkin Jul 24, 2016
9ffce28
ENH: Filter rows and cols as variables in Deconvolution2D test case
lukovkin Jul 24, 2016
5d00fb7
Create dummy
lukovkin Jul 25, 2016
5a606c6
BUG: Possible non-compliance between filter_shape passed as a paramet…
lukovkin Jul 25, 2016
810ca65
BUG: Fixed output_shape elements evaluation
lukovkin Jul 25, 2016
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14 changes: 13 additions & 1 deletion keras/backend/theano_backend.py
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Expand Up @@ -1052,7 +1052,19 @@ def deconv2d(x, kernel, output_shape, strides=(1, 1),
kernel = kernel.dimshuffle((1, 0, 2, 3))
th_border_mode = _preprocess_border_mode(border_mode)
np_kernel = kernel.eval()
filter_shape = _preprocess_filter_shape(dim_ordering, filter_shape)
filter_shape = _preprocess_filter_shape(dim_ordering, shape(kernel))
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I think there was a kind of inconsistency here - we receive the filter_shape as a parameter, after that we reshape the kernel itself (shape should change) but leave filter_shape intact and pass it to the AbstractConv2d_gradInputs then.

Probably we could totally remove filter_shape from the parameters, if there are no other reasons to use it.


_t = ()
for v in output_shape:
try:
_v = v.eval()
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Here I stuck probably the same issue that @yaringal mentioned - if we infer output_shape, it becomes the tuple of Theano variables/expressions, which Theano couldn't accept as an input due to some reasons.
So I try to evaluate them, but here I get another error:

MissingInputError: An input of the graph, used to compute Shape(input_25), was not provided and not given a value.Use the Theano flag exception_verbosity='high',for more information on this error.

I'm not an expert in Theano, so have no idea how to fix it quick.

except AttributeError as e:
_v = v
print(e.__str__())
_t = _t + (v, )
output_shape = _t

print("imshp {} kshp {} subsample {} border_mode {} output_shape {}".format(output_shape, filter_shape, strides, th_border_mode, output_shape[2:]))

op = T.nnet.abstract_conv.AbstractConv2d_gradInputs(imshp=output_shape,
kshp=filter_shape,
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19 changes: 13 additions & 6 deletions keras/layers/convolutional.py
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Expand Up @@ -385,8 +385,11 @@ class Deconvolution2D(Convolution2D):
provide the keyword argument `input_shape`
(tuple of integers, does not include the sample axis),
e.g. `input_shape=(3, 128, 128)` for 128x128 RGB pictures.

# References
- [Deconvolutional Networks](http://www.matthewzeiler.com/pubs/cvpr2010/cvpr2010.pdf)
'''
def __init__(self, nb_filter, nb_row, nb_col, output_shape,
def __init__(self, nb_filter, nb_row, nb_col, output_shape=None,
init='glorot_uniform', activation='linear', weights=None,
border_mode='valid', subsample=(1, 1),
dim_ordering=K.image_dim_ordering(),
Expand All @@ -395,7 +398,7 @@ def __init__(self, nb_filter, nb_row, nb_col, output_shape,
bias=True, **kwargs):

if border_mode not in {'valid', 'same'}:
raise Exception('Invalid border mode for AtrousConv2D:', border_mode)
raise Exception('Invalid border mode for Deconvolution2D:', border_mode)

self.output_shape_ = output_shape

Expand All @@ -418,10 +421,12 @@ def get_output_shape_for(self, input_shape):
else:
raise Exception('Invalid dim_ordering: ' + self.dim_ordering)

rows = conv_input_length(rows, self.nb_row,
self.border_mode, self.subsample[0])
cols = conv_input_length(cols, self.nb_col,
self.border_mode, self.subsample[1])
if self.border_mode == 'same':
rows = rows * self.subsample[0]
cols = cols * self.subsample[1]
elif self.border_mode == 'valid':
rows = (rows - 1) * self.subsample[0] + self.nb_row
cols = (cols - 1) * self.subsample[1] + self.nb_col

if self.dim_ordering == 'th':
return (input_shape[0], self.nb_filter, rows, cols)
Expand All @@ -431,6 +436,8 @@ def get_output_shape_for(self, input_shape):
raise Exception('Invalid dim_ordering: ' + self.dim_ordering)

def call(self, x, mask=None):
if not self.output_shape_:
self.output_shape_ = self.get_output_shape_for(K.shape(x))
output = K.deconv2d(x, self.W, self.output_shape_,
strides=self.subsample,
border_mode=self.border_mode,
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1 change: 1 addition & 0 deletions tests/dummy
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@@ -0,0 +1 @@
dummy
62 changes: 37 additions & 25 deletions tests/keras/layers/test_convolutional.py
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Expand Up @@ -96,36 +96,48 @@ def test_deconvolution_2d():
stack_size = 3
nb_row = 10
nb_col = 6
nb_filter_rows = 3
nb_filter_cols = 3

for border_mode in ['valid', 'same']:
for subsample in [(1, 1), (2, 2)]:
if border_mode == 'same' and subsample != (1, 1):
continue
for infer_output_shape in [False, True]:
print("border_mode: {} subsample: {} infer output shape: {}".
format(border_mode, subsample, infer_output_shape))
if border_mode == 'same' and subsample != (1, 1):
continue

rows = conv_input_length(nb_row, 3, border_mode, subsample[0])
cols = conv_input_length(nb_col, 3, border_mode, subsample[1])
layer_test(convolutional.Deconvolution2D,
kwargs={'nb_filter': nb_filter,
'nb_row': 3,
'nb_col': 3,
'output_shape': (nb_samples, nb_filter, rows, cols),
'border_mode': border_mode,
'subsample': subsample},
input_shape=(nb_samples, stack_size, nb_row, nb_col),
fixed_batch_size=True)
# rows = conv_input_length(nb_row, 3, border_mode, subsample[0])
# cols = conv_input_length(nb_col, 3, border_mode, subsample[1])
if border_mode == 'same':
rows = nb_row * subsample[0]
cols = nb_col * subsample[1]
elif border_mode == 'valid':
rows = (nb_row - 1) * subsample[0] + nb_filter_rows
cols = (nb_col - 1) * subsample[1] + nb_filter_cols

layer_test(convolutional.Deconvolution2D,
kwargs={'nb_filter': nb_filter,
'nb_row': 3,
'nb_col': 3,
'output_shape': (nb_samples, nb_filter, rows, cols),
'border_mode': border_mode,
'W_regularizer': 'l2',
'b_regularizer': 'l2',
'activity_regularizer': 'activity_l2',
'subsample': subsample},
input_shape=(nb_samples, stack_size, nb_row, nb_col),
fixed_batch_size=True)
layer_test(convolutional.Deconvolution2D,
kwargs={'nb_filter': nb_filter,
'nb_row': nb_filter_rows,
'nb_col': nb_filter_cols,
'output_shape': None if infer_output_shape else (nb_samples, nb_filter, rows, cols),
'border_mode': border_mode,
'subsample': subsample},
input_shape=(nb_samples, stack_size, nb_row, nb_col),
fixed_batch_size=True)

layer_test(convolutional.Deconvolution2D,
kwargs={'nb_filter': nb_filter,
'nb_row': nb_filter_rows,
'nb_col': nb_filter_cols,
'output_shape': None if infer_output_shape else (nb_samples, nb_filter, rows, cols),
'border_mode': border_mode,
'W_regularizer': 'l2',
'b_regularizer': 'l2',
'activity_regularizer': 'activity_l2',
'subsample': subsample},
input_shape=(nb_samples, stack_size, nb_row, nb_col),
fixed_batch_size=True)


@keras_test
Expand Down