Hi,
I am getting the following error when using images2neibs with neib_shape is
given as variables and used in a function with scan.
python shapebug.py
Using gpu device 0: Quadro K4200 (CNMeM is disabled, cuDNN 5004)
theano version 0.8.2
WARNING (theano.tensor.opt): Failed to infer_shape from Op
Images2Neibs{valid}.
Input shapes: [(TensorConstant{1}, Shape_i{1}.0, Shape_i{2}.0,
Shape_i{3}.0), (TensorConstant{2},), (TensorConstant{2},)]
Exception encountered during infer_shape: <type 'exceptions.ValueError'>
Exception message: length not known: <TensorType(int8, vector)> [id A]
Traceback: Traceback (most recent call last):
File "/usr/local/lib/python2.7/dist-packages/theano/tensor/opt.py", line
917, in get_node_infer_shape
[self.shape_of[r] for r in node.inputs])
File
"/usr/local/lib/python2.7/dist-packages/theano/tensor/nnet/neighbours.py",
line 258, in infer_shape
c, d = node.inputs[1]
File "/usr/local/lib/python2.7/dist-packages/theano/tensor/var.py", line
549, in __iter__
for i in xrange(theano.tensor.basic.get_vector_length(self)):
File "/usr/local/lib/python2.7/dist-packages/theano/tensor/basic.py",
line 4300, in get_vector_length
raise ValueError("length not known: %s" % msg)
ValueError: length not known: <TensorType(int8, vector)> [id A]
The code I am running is:
import theano
import theano.tensor as T
from theano.tensor.nnet.neighbours import images2neibs
import numpy as np
def process_one_input_image(input_image, filter_h, filter_w):
all_windows = images2neibs(input_image.dimshuffle('x', 0, 1, 2),
neib_shape=(filter_h, filter_w), neib_step=(1, 1))
# some other code
return all_windows
def process_all_input_images(input_images, filter_h, filter_w):
s, _ = theano.scan(fn=process_one_input_image,
sequences=[input_images], non_sequences=[filter_h, filter_w])
return s
print 'theano version', theano.__version__
input_images = T.tensor4('input_images')
num_input_images = 3
image_h = 5
image_w = 5
image_ch = 2
filter_h = 2
filter_w = 2
windows = process_all_input_images(input_images, filter_h, filter_w)
f = theano.function([input_images], windows)
images = np.arange(num_input_images * image_h * image_w *
image_ch).reshape((num_input_images, image_ch, image_h,
image_w)).astype('float32')
print f(images)
If I change neib_shape=(filter_h, filter_w) to e.g. neib_shape=(2, 2) the
error goes away.
Is this a bug or am I doing something wrong? How do I get around it?
Best,
Petar Palasek
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