def pool3d(x, pool_size, strides=(1, 1, 1), border_mode='valid',
dim_ordering='th', pool_mode='max'):
# ====== dim ordering ====== #
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))
# ====== border mode ====== #
if border_mode == 'same':
w_pad = pool_size[0] - 2 if pool_size[0] % 2 == 1 else pool_size[0] - 1
h_pad = pool_size[1] - 2 if pool_size[1] % 2 == 1 else pool_size[1] - 1
d_pad = pool_size[2] - 2 if pool_size[2] % 2 == 1 else pool_size[2] - 1
padding = (w_pad, h_pad, d_pad)
elif border_mode == 'valid':
padding = (0, 0, 0)
elif isinstance(border_mode, (tuple, list)):
padding = tuple(border_mode)
else:
raise Exception('Invalid border mode: ' + str(border_mode))
# ====== pooling ====== #
if _on_gpu() and dnn.dnn_available():
pool_out = dnn.dnn_pool(x, pool_size,
stride=strides,
mode=pool_mode,
pad=padding)
else:
padding = padding[:2]
# 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=True,
padding=padding,
mode=pool_mode)
# 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=True,
padding=padding,
mode=pool_mode)
# ====== output ====== #
if dim_ordering == 'tf':
pool_out = pool_out.dimshuffle((0, 2, 3, 4, 1))
return pool_out
# ===========================================================================
# RANDOMNESS
# ===========================================================================
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