def pool(self, x, mode, pool_size, strides, padding=(0,0)):
if strides is None:
strides = pool_size
assert len(strides)==len(pool_size)
do2D = len(pool_size)==2
if mode=='avg':
mode='average_exc_pad'
# theano requires symmetric padding
# We pad the larger on when two sides' padding are unequal
max_padding = list(padding)
for i, p in enumerate(padding):
if isinstance(p, tuple):
assert p[1]==p[0]+1
max_padding[i] = p[1]
else:
max_padding[i] = p
if do2D:
pool_out = pool.pool_2d(x, ds=pool_size, st=strides,
ignore_border=True,
padding=max_padding,
mode=mode)
else:
# pool over HW
pool_out = pool.pool_2d(x.dimshuffle(0,1,4,2,3),
ds=pool_size[:2],
st=strides[:2],
ignore_border=True,
padding=max_padding[:2],
mode=mode)
# pool over Z
pool_out = pool.pool_2d(pool_out.dimshuffle(0,1,3,4,2),
ds=(1,pool_size[2]),
st=(1,strides[2]),
ignore_border=True,
padding=(0, max_padding[2]),
mode=mode)
# theano might output more than expected output shape (due to max padding). We truncate them here
exp_l = []
for i in range(len(strides)):
l = T.ceil(self.cast(x.shape[i+2], _FLOATX)/strides[i])
exp_l.append(self.cast(l, 'int32'))
if do2D:
return pool_out[:, :, :exp_l[0], :exp_l[1]]
else:
return pool_out[:, :, :exp_l[0], :exp_l[1], :exp_l[2]]
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