def layer(self, input_layer, pool_dims, mode='max', implementation='fast'):
from lasagne.layers import SpatialPyramidPoolingLayer
if implementation != 'kaiming':
try:
import theano.tensor as T
from lasagne.layers.pool import pool_2d
pool_2d(T.tensor4(),
ws=T.ivector(),
stride=T.ivector(),
ignore_border=True,
pad=None)
except ValueError:
pytest.skip('Old theano version')
return SpatialPyramidPoolingLayer(input_layer,
pool_dims=pool_dims,
mode=mode,
implementation=implementation)
def layer(self, input_layer, pool_dims, mode='max', implementation='fast'):
from lasagne.layers import SpatialPyramidPoolingLayer
if implementation != 'kaiming':
try:
import theano.tensor as T
from lasagne.layers.pool import pool_2d
pool_2d(T.tensor4(),
ws=T.ivector(),
stride=T.ivector(),
ignore_border=True,
pad=None)
except ValueError:
pytest.skip('Old theano version')
return SpatialPyramidPoolingLayer(input_layer,
pool_dims=pool_dims,
mode=mode,
implementation=implementation)
def get_output_for(self, input, **kwargs):
#Add one additional dimension in the rigth and give the 5d tensor for the
#pooling. the 2d pooling will do the polling in the last 2 dimensiotns
#the rows and the new additional one. then we kick it out in the return [:,:,:,:,0]
input_5d = T.shape_padright(input, 1)
pool=pool_2d(input_5d,
ws=(self.pool_size[0], 1),
stride=(self.stride[0], 1),
ignore_border=self.ignore_border,
pad=(self.pad[0], 0),
mode=self.mode,
)
return pool[:,:, :, :, 0]
def get_output_for(self, input, **kwargs):
""" Uses pool_2d to pool each dimension."""
input_shape = input.shape
n = self.n
if n % 2 == 1:
n += 1
input = T.shape_padright(input, 1)
# aggregate axis for clearer code?
n_axis = input.ndim - n
# map_shape = input.shape[axis:]
# new_shape = T.cast(T.join(0, T.as_tensor([-1]), map_shape), 'int64')
# input = T.reshape(input, new_shape, n+1)
# loop reshape -> pool for n trailing axis
for i in np.arange(0, n, 2):
# extract parameters for the corresponding axes
i1 = (n - 2 + i) % n
i2 = (n - 1 + i) % n
# pool last 2 axis
input = pool.pool_2d(
input,
ds=(self.pool_size[i1], self.pool_size[i2]),
st=(self.stride[i1], self.stride[i2]),
ignore_border=self.ignore_border,
padding=(self.pad[i1], self.pad[i2]),
mode=self.mode,
)
# Get next permutation, which shifts by 2 (+1 is for first axis)
fixed = tuple(np.arange(n_axis))
perm = tuple((np.arange(2, n + 2) % n) + n_axis)
# include the first axis from input
shuffle = fixed + perm
# shuffle
input = input.dimshuffle(shuffle)
# restore original shape
input = input.reshape(self.get_output_shape_for(input_shape))
return input
def get_output_for(self, input, **kwargs):
""" Uses pool_2d to pool each dimension."""
input_shape = input.shape
n = self.n
if n % 2 == 1:
n += 1
input = T.shape_padright(input, 1)
# aggregate axis for clearer code?
n_axis = input.ndim - n
# map_shape = input.shape[axis:]
# new_shape = T.cast(T.join(0, T.as_tensor([-1]), map_shape), 'int64')
# input = T.reshape(input, new_shape, n+1)
# loop reshape -> pool for n trailing axis
for i in np.arange(0, n, 2):
# extract parameters for the corresponding axes
i1 = (n-2 + i) % n
i2 = (n-1 + i) % n
# pool last 2 axis
input = pool.pool_2d(input,
ds=(self.pool_size[i1], self.pool_size[i2]),
st=(self.stride[i1], self.stride[i2]),
ignore_border=self.ignore_border,
padding=(self.pad[i1], self.pad[i2]),
mode=self.mode, )
# Get next permutation, which shifts by 2 (+1 is for first axis)
fixed = tuple(np.arange(n_axis))
perm = tuple((np.arange(2, n+2) % n) + n_axis)
# include the first axis from input
shuffle = fixed + perm
# shuffle
input = input.dimshuffle(shuffle)
# restore original shape
input = input.reshape(self.get_output_shape_for(input_shape))
return input