def _format_as_impl(self, is_numeric, batch, space):
if isinstance(space, VectorSequenceSpace):
if self.max_labels == space.dim:
mode = 'merge'
elif self.dim * self.max_labels == space.dim:
mode = 'concatenate'
else:
raise ValueError("There is a bug. Couldn't format to a "
"VectorSequenceSpace because it had an "
"incorrect size, but this should've been "
"caught in "
"IndexSequenceSpace._check_sizes().")
format_func = (self.formatter.format
if is_numeric else self.formatter.theano_expr)
return _cast(format_func(batch, mode=mode), space.dtype)
elif isinstance(space, IndexSequenceSpace):
if space.dim != self.dim or space.max_labels != self.max_labels:
raise ValueError("The two IndexSequenceSpaces' dim and "
"max_labels values don't match. This should "
"have been caught by "
"IndexSequenceSpace._check_sizes().")
return _cast(batch, space.dtype)
else:
raise ValueError("Can't convert %s to %s" % (self, space))
def _format_as_impl(self, is_numeric, batch, space):
if isinstance(space, VectorSequenceSpace):
if self.max_labels == space.window_dim:
mode = 'merge'
elif self.window_dim * self.max_labels == space.window_dim:
mode = 'concatenate'
else:
raise ValueError("There is a bug. Couldn't format to a "
"VectorSequenceSpace because it had an "
"incorrect size, but this should've been "
"caught in "
"IndexSequenceSpace._check_sizes().")
format_func = (self.formatter.format if is_numeric else
self.formatter.theano_expr)
return _cast(format_func(batch, mode=mode), space.dtype)
elif isinstance(space, IndexSequenceSpace):
if space.window_dim != self.window_dim or space.max_labels != self.max_labels:
raise ValueError("The two IndexSequenceSpaces' window_dim and "
"max_labels values don't match. This should "
"have been caught by "
"IndexSequenceSpace._check_sizes().")
return _cast(batch, space.dtype)
else:
raise ValueError("Can't convert %s to %s"
% (self, space))
def _format_as_impl(self, is_numeric, batch, space):
if space == self:
return batch
else:
if isinstance(space, SequenceDataSpace):
if is_numeric:
formatted_batch = np.transpose(np.asarray([
self.space._format_as_impl(is_numeric, sample,
space.space)
for sample in np.transpose(batch, (1, 0, 2))
]), (1, 0, 2))
else:
formatted_batch, _ = scan(
fn=lambda elem: self.space._format_as_impl(
is_numeric, elem, space.space),
sequences=[batch]
)
return formatted_batch
elif isinstance(space, space.VectorSpace):
row = batch.shape[0] * batch.shape[1]
col = self.dim
result = tensor.reshape(batch,
newshape=[row, col],
ndim=2)
return space._cast(result, space.dtype)
elif isinstance(space, space.Conv2DSpace):
result = batch.dimshuffle(1, 0, 'x', 2)
# Newly added part
b01c_shape = [result.shape[0],
space.shape[0],
space.shape[1],
space.num_channels]
result = result.flatten()
result = tensor.reshape(result,
newshape=b01c_shape,
ndim=4)
# end of newly added part
return space._cast(result, space.dtype)
else:
print('Unexpected space', space)
raise NotImplementedError
def _format_as_impl(self, is_numeric, batch, space):
if space == self:
return batch
else:
if isinstance(space, SequenceDataSpace):
if is_numeric:
formatted_batch = np.transpose(np.asarray([
self.space._format_as_impl(is_numeric, sample,
space.space)
for sample in np.transpose(batch, (1, 0, 2))
]), (1, 0, 2))
else:
formatted_batch, _ = scan(
fn=lambda elem: self.space._format_as_impl(
is_numeric, elem, space.space),
sequences=[batch]
)
return formatted_batch
elif isinstance(space, space.VectorSpace):
row = batch.shape[0] * batch.shape[1]
col = self.dim
result = tensor.reshape(batch,
newshape=[row, col],
ndim=2)
return space._cast(result, space.dtype)
elif isinstance(space, space.Conv2DSpace):
result = batch.dimshuffle(1, 0, 'x', 2)
# Newly added part
b01c_shape = [result.shape[0],
space.shape[0],
space.shape[1],
space.num_channels]
result = result.flatten()
result = tensor.reshape(result,
newshape=b01c_shape,
ndim=4)
# end of newly added part
return space._cast(result, space.dtype)
else:
print('Unexpected space', space)
raise NotImplementedError
def _format_as_impl(self, is_numeric, batch, space):
if isinstance(space, VectorSequenceSpace):
if space.window_dim != self.window_dim:
raise ValueError("The two VectorSequenceSpaces' window_dim "
"values don't match. This should have been "
"caught by "
"VectorSequenceSpace._check_sizes().")
return _cast(batch, space.dtype)
else:
raise ValueError("Can't convert %s to %s" % (self, space))
def _format_as_impl(self, is_numeric, batch, space):
if isinstance(space, VectorSequenceSpace):
if space.dim != self.dim:
raise ValueError("The two VectorSequenceSpaces' dim "
"values don't match. This should have been "
"caught by "
"VectorSequenceSpace._check_sizes().")
return _cast(batch, space.dtype)
else:
raise ValueError("Can't convert %s to %s" % (self, space))
def _format_as_impl(self, is_numeric, batch, space):
if isinstance(space, VectorSpace):
# We need to ensure that the resulting batch will always be
# the same in `space`, no matter what the axes of `self` are.
if self.axes != self.default_axes:
# The batch index goes on the first axis
assert self.default_axes[0] == "b"
batch = batch.transpose(*[self.axes.index(axis) for axis in self.default_axes])
result = batch.reshape((batch.shape[0], self.get_total_dimension()))
if space.sparse:
result = _dense_to_sparse(result)
elif isinstance(space, Conv3DSpace):
result = Conv3DSpace.convert(batch, self.axes, space.axes)
else:
raise NotImplementedError("%s doesn't know how to format as %s" % (str(self), str(space)))
return _cast(result, space.dtype)
def _format_as_impl(self, is_numeric, batch, space):
if isinstance(space, VectorSpace):
# We need to ensure that the resulting batch will always be
# the same in `space`, no matter what the axes of `self` are.
if self.axes != self.default_axes:
# The batch index goes on the first axis
assert self.default_axes[0] == 'b'
batch = batch.transpose(
*[self.axes.index(axis) for axis in self.default_axes])
result = batch.reshape(
(batch.shape[0], self.get_total_dimension()))
if space.sparse:
result = _dense_to_sparse(result)
elif isinstance(space, Conv3DSpace):
result = Conv3DSpace.convert(batch, self.axes, space.axes)
else:
raise NotImplementedError("%s doesn't know how to format as %s" %
(str(self), str(space)))
return _cast(result, space.dtype)
