def apply(self, image, location, scale):
matrices = self.compute_crop_matrices(location, scale)
patch = image
for axis, matrix in enumerate(matrices):
patch = util.batched_tensordot(patch, matrix, [[2], [1]])
self.add_auxiliary_variable(T.constant(0.), name="mean_savings")
return patch
def apply(self, image, location, scale):
matrices = self.compute_crop_matrices(location, scale)
patch = image
for axis, matrix in enumerate(matrices):
patch = util.batched_tensordot(patch, matrix, [[2], [1]])
self.add_auxiliary_variable(T.constant(0.), name="mean_savings")
return patch
def apply_inner(self, image, location, scale, a, b):
slices = [theano.gradient.disconnected_grad(T.arange(a[i], b[i]))
for i in xrange(self.n_spatial_dims)]
hardcrop = image[
np.index_exp[:, :] +
tuple(slice(a[i], b[i])
for i in range(self.n_spatial_dims))]
matrices = self.compute_crop_matrices(location, scale, slices)
patch = hardcrop
for axis, matrix in enumerate(matrices):
patch = util.batched_tensordot(patch, matrix, [[2], [1]])
return patch
def apply_inner(self, image, location, scale, a, b):
slices = [theano.gradient.disconnected_grad(T.arange(a[i], b[i]))
for i in xrange(self.n_spatial_dims)]
hardcrop = util.subtensor(
image,
[(T.arange(image.shape[0]), 0),
(T.arange(image.shape[1]), 1)]
+ [(slice, 2 + i) for i, slice in enumerate(slices)])
matrices = self.compute_crop_matrices(location, scale, slices)
patch = hardcrop
for axis, matrix in enumerate(matrices):
patch = util.batched_tensordot(patch, matrix, [[2], [1]])
return patch
def apply_inner(self, image, location, scale, a, b):
slices = [
theano.gradient.disconnected_grad(T.arange(a[i], b[i]))
for i in xrange(self.n_spatial_dims)
]
hardcrop = util.subtensor(image, [(T.arange(image.shape[0]), 0),
(T.arange(image.shape[1]), 1)] +
[(slice, 2 + i)
for i, slice in enumerate(slices)])
matrices = self.compute_crop_matrices(location, scale, slices)
patch = hardcrop
for axis, matrix in enumerate(matrices):
patch = util.batched_tensordot(patch, matrix, [[2], [1]])
return patch
def apply(self, image, location, scale):
matrices = self.compute_crop_matrices(location, scale)
patch = image
for axis, matrix in enumerate(matrices):
patch = util.batched_tensordot(patch, matrix, [[2], [1]])
return patch, T.constant(0)