def pool3d(x, pool_size, strides=(1, 1, 1), border_mode='valid',
dim_ordering='th', pool_mode='max'):
if border_mode == 'same':
# TODO: add implementation for border_mode="same"
raise Exception('border_mode="same" not supported with Theano.')
elif border_mode == 'valid':
ignore_border = True
padding = (0, 0)
else:
raise Exception('Invalid border mode: ' + str(border_mode))
if dim_ordering not in {'th', 'tf'}:
raise Exception('Unknown dim_ordering ' + str(dim_ordering))
if dim_ordering == 'tf':
x = x.dimshuffle((0, 4, 1, 2, 3))
if pool_mode == 'max':
# pooling over conv_dim2, conv_dim1 (last two channels)
output = pool.pool_2d(input=x.dimshuffle(0, 1, 4, 3, 2),
ds=(pool_size[1], pool_size[0]),
st=(strides[1], strides[0]),
ignore_border=ignore_border,
padding=padding,
mode='max')
# pooling over conv_dim3
pool_out = pool.pool_2d(input=output.dimshuffle(0, 1, 4, 3, 2),
ds=(1, pool_size[2]),
st=(1, strides[2]),
ignore_border=ignore_border,
padding=padding,
mode='max')
elif pool_mode == 'avg':
# pooling over conv_dim2, conv_dim1 (last two channels)
output = pool.pool_2d(input=x.dimshuffle(0, 1, 4, 3, 2),
ds=(pool_size[1], pool_size[0]),
st=(strides[1], strides[0]),
ignore_border=ignore_border,
padding=padding,
mode='average_exc_pad')
# pooling over conv_dim3
pool_out = pool.pool_2d(input=output.dimshuffle(0, 1, 4, 3, 2),
ds=(1, pool_size[2]),
st=(1, strides[2]),
ignore_border=ignore_border,
padding=padding,
mode='average_exc_pad')
else:
raise Exception('Invalid pooling mode: ' + str(pool_mode))
if dim_ordering == 'tf':
pool_out = pool_out.dimshuffle((0, 2, 3, 4, 1))
return pool_out
# RANDOMNESS
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