def broadcast_to_shape_strict(x, shape, name=None):
"""
Broadcast `x` to match `shape`.
This method requires `rank(x)` to be less than or equal to `len(shape)`.
You may use :func:`broadcast_to_shape` instead, to allow the cases where
``rank(x) > len(shape)``.
Args:
x: A tensor.
shape (tuple[int] or tf.Tensor): Broadcast `x` to match this shape.
Returns:
tf.Tensor: The broadcasted tensor.
"""
# check the parameters
x = tf.convert_to_tensor(x)
x_shape = get_static_shape(x)
ns_values = [x]
if is_tensor_object(shape):
shape = tf.convert_to_tensor(shape)
ns_values.append(shape)
else:
shape = tuple(int(s) for s in shape)
with tf.name_scope(name=name or 'broadcast_to_shape', values=ns_values):
cannot_broadcast_msg = (
'`x` cannot be broadcasted to match `shape`: x {!r} vs shape {!r}'.
format(x, shape))
# assert ``rank(x) <= len(shape)``
if isinstance(shape, tuple) and x_shape is not None:
if len(x_shape) > len(shape):
raise ValueError(cannot_broadcast_msg)
elif isinstance(shape, tuple):
with assert_deps([
tf.assert_less_equal(tf.rank(x),
len(shape),
message=cannot_broadcast_msg)
]) as asserted:
if asserted: # pragma: no cover
x = tf.identity(x)
else:
with assert_deps(
[assert_rank(shape, 1,
message=cannot_broadcast_msg)]) as asserted:
if asserted: # pragma: no cover
shape = tf.identity(shape)
with assert_deps([
tf.assert_less_equal(tf.rank(x),
tf.size(shape),
message=cannot_broadcast_msg)
]) as asserted:
if asserted: # pragma: no cover
x = tf.identity(x)
# do broadcast
return broadcast_to_shape(x, shape)
def broadcast_log_det_against_input(log_det, input, value_ndims, name=None):
"""
Broadcast the shape of `log_det` to match the shape of `input`.
Args:
log_det: Tensor, the log-determinant.
input: Tensor, the input.
value_ndims (int): The number of dimensions of each values sample.
Returns:
tf.Tensor: The broadcasted log-determinant.
"""
log_det = tf.convert_to_tensor(log_det)
input = tf.convert_to_tensor(input)
value_ndims = int(value_ndims)
with tf.name_scope(name or 'broadcast_log_det_to_input_shape',
values=[log_det, input]):
shape = get_shape(input)
if value_ndims > 0:
err_msg = (
'Cannot broadcast `log_det` against `input`: log_det is {}, '
'input is {}, value_ndims is {}.'.format(
log_det, input, value_ndims))
with assert_deps(
[assert_rank_at_least(input, value_ndims, message=err_msg)]):
shape = shape[:-value_ndims]
return broadcast_to_shape_strict(log_det, shape)
def multiply_branch():
with assert_deps(assertions):
ones_template = tf.ones(shape, dtype=x.dtype.base_dtype)
try:
return x * ones_template
except ValueError: # pragma: no cover
raise ValueError(cannot_broadcast_msg)
def transform(self, x, compute_y=True, compute_log_det=True, name=None):
"""
Transform `x` into `y`, and compute the log-determinant of `f` at `x`
(i.e., :math:`\\log \\det \\frac{\\partial f(x)}{\\partial x}`).
Args:
x (Tensor): The samples of `x`.
compute_y (bool): Whether or not to compute :math:`y = f(x)`?
Default :obj:`True`.
compute_log_det (bool): Whether or not to compute the
log-determinant? Default :obj:`True`.
name (str): If specified, will use this name as the TensorFlow
operational name scope.
Returns:
(tf.Tensor, tf.Tensor): `y` and the (maybe summed) log-determinant.
The items in the returned tuple might be :obj:`None`
if corresponding `compute_?` argument is set to :obj:`False`.
Raises:
RuntimeError: If both `compute_y` and `compute_log_det` are set
to :obj:`False`.
"""
if not compute_y and not compute_log_det:
raise ValueError('At least one of `compute_y` and '
'`compute_log_det` should be True.')
x = tf.convert_to_tensor(x)
if not self._has_built:
self.build(x)
x = self._x_input_spec.validate('x', x)
with tf.name_scope(name,
default_name=get_default_scope_name(
'transform', self),
values=[x]):
y, log_det = self._transform(x, compute_y, compute_log_det)
if compute_log_det:
with assert_deps([
assert_log_det_shape_matches_input(
log_det=log_det,
input=x,
value_ndims=self.x_value_ndims)
]) as asserted:
if asserted: # pragma: no cover
log_det = tf.identity(log_det)
if y is not None:
maybe_add_histogram(y, 'y')
y = maybe_check_numerics(y, 'y')
if log_det is not None:
maybe_add_histogram(log_det, 'log_det')
log_det = maybe_check_numerics(log_det, 'log_det')
return y, log_det
def _check_scale_or_shift_shape(self, name, tensor, x2):
assert_op = assert_shape_equal(
tensor,
x2,
message='`{}.shape` expected to be {}, but got {}'.format(
name, get_static_shape(x2), get_static_shape(tensor)))
with assert_deps([assert_op]) as asserted:
if asserted: # pragma: no cover
tensor = tf.identity(tensor)
return tensor
def __init__(self, distribution, ndims):
"""
Construct a new :class:`BatchToValueDistribution`.
Args:
distribution (Distribution): The source distribution.
ndims (int): The last few `batch_ndims` to be converted
into `value_ndims`. Must be non-negative.
"""
distribution = as_distribution(distribution)
ndims = int(ndims)
if ndims < 0:
raise ValueError('`ndims` must be non-negative integers: '
'got {!r}'.format(ndims))
with tf.name_scope('BatchToValueDistribution.init'):
# get new batch shape
batch_shape = distribution.batch_shape
batch_static_shape = distribution.get_batch_shape()
if ndims > 0:
# static shape
if batch_static_shape.ndims < ndims:
raise ValueError(
'`distribution.batch_shape.ndims` is less then `ndims`'
': distribution {}, batch_shape.ndims {}, ndims {}'.
format(distribution, batch_static_shape.ndims, ndims))
batch_static_shape = batch_static_shape[:-ndims]
# dynamic shape
batch_shape = batch_shape[:-ndims]
with assert_deps([
tf.assert_greater_equal(
tf.size(distribution.batch_shape), ndims)
]) as asserted:
if asserted: # pragma: no cover
batch_shape = tf.identity(batch_shape)
# get new value ndims
value_ndims = ndims + distribution.value_ndims
self._distribution = distribution
self._ndims = ndims
super(BatchToValueDistribution, self).__init__(
dtype=distribution.dtype,
is_continuous=distribution.is_continuous,
is_reparameterized=distribution.is_reparameterized,
batch_shape=batch_shape,
batch_static_shape=batch_static_shape,
value_ndims=value_ndims,
)
def assert_batch_shape(c, batch_shape):
c_batch_shape = c.batch_shape
with assert_deps([
tf.assert_equal(
tf.reduce_all(
tf.equal(
tf.concat([batch_shape, c_batch_shape], 0),
tf.concat([c_batch_shape, batch_shape], 0))),
True)
]) as asserted:
if asserted: # pragma: no cover
batch_shape = tf.identity(batch_shape)
return batch_shape
def _build(self, input=None):
shape = get_static_shape(input)
dtype = input.dtype.base_dtype
# resolve the split axis
split_axis = self._split_axis
if split_axis < 0:
split_axis += len(shape)
if split_axis < 0 or split_axis < len(shape) - self.x_value_ndims:
raise ValueError(
'`split_axis` out of range, or not covered by `x_value_ndims`: '
'split_axis {}, x_value_ndims {}, input {}'.format(
self._split_axis, self.x_value_ndims, input))
split_axis -= len(shape)
err_msg = (
'The split axis of `input` must be at least 2: input {}, axis {}.'.
format(input, split_axis))
if shape[split_axis] is not None:
x_n_features = shape[split_axis]
if x_n_features < 2:
raise ValueError(err_msg)
x_n_left = x_n_features // 2
x_n_right = x_n_features - x_n_left
else:
x_n_features = get_shape(input)[split_axis]
x_n_left = x_n_features // 2
x_n_right = x_n_features - x_n_left
with assert_deps(
[tf.assert_greater_equal(x_n_features, 2,
message=err_msg)]) as asserted:
if asserted: # pragma: no cover
x_n_left = tf.identity(x_n_left)
x_n_right = tf.identity(x_n_right)
x_n_features = None
self._split_axis = split_axis
self._x_n_left = x_n_left
self._x_n_right = x_n_right
# build the x spec
shape_spec = ['?'] * self.x_value_ndims
if x_n_features is not None:
shape_spec[self._split_axis] = x_n_features
assert (not self.require_batch_dims)
shape_spec = ['...'] + shape_spec
self._x_input_spec = InputSpec(shape=shape_spec, dtype=dtype)
def inverse_transform(self,
y,
compute_x=True,
compute_log_det=True,
name=None):
"""
Transform `y` into `x`, and compute the log-determinant of `f^{-1}` at
`y` (i.e.,
:math:`\\log \\det \\frac{\\partial f^{-1}(y)}{\\partial y}`).
Args:
y (Tensor): The samples of `y`.
compute_x (bool): Whether or not to compute :math:`x = f^{-1}(y)`?
Default :obj:`True`.
compute_log_det (bool): Whether or not to compute the
log-determinant? Default :obj:`True`.
name (str): If specified, will use this name as the TensorFlow
operational name scope.
Returns:
(tf.Tensor, tf.Tensor): `x` and the (maybe summed) log-determinant.
The items in the returned tuple might be :obj:`None`
if corresponding `compute_?` argument is set to :obj:`False`.
Raises:
RuntimeError: If both `compute_x` and `compute_log_det` are set
to :obj:`False`.
RuntimeError: If the flow is not explicitly invertible.
"""
if not self.explicitly_invertible:
raise RuntimeError(
'The flow is not explicitly invertible: {!r}'.format(self))
if not compute_x and not compute_log_det:
raise ValueError('At least one of `compute_x` and '
'`compute_log_det` should be True.')
if not self._has_built:
raise RuntimeError('`inverse_transform` cannot be called before '
'the flow has been built; it can be built by '
'calling `build`, `apply` or `transform`: '
'{!r}'.format(self))
y = tf.convert_to_tensor(y)
y = self._y_input_spec.validate('y', y)
with tf.name_scope(name,
default_name=get_default_scope_name(
'inverse_transform', self),
values=[y]):
x, log_det = self._inverse_transform(y, compute_x, compute_log_det)
if compute_log_det:
with assert_deps([
assert_log_det_shape_matches_input(
log_det=log_det,
input=y,
value_ndims=self.y_value_ndims)
]) as asserted:
if asserted: # pragma: no cover
log_det = tf.identity(log_det)
return x, log_det
def deconv2d(input,
out_channels,
kernel_size,
strides=(1, 1),
padding='same',
channels_last=True,
output_shape=None,
activation_fn=None,
normalizer_fn=None,
weight_norm=False,
gated=False,
gate_sigmoid_bias=2.,
kernel=None,
kernel_initializer=None,
kernel_regularizer=None,
kernel_constraint=None,
use_bias=None,
bias=None,
bias_initializer=tf.zeros_initializer(),
bias_regularizer=None,
bias_constraint=None,
trainable=True,
name=None,
scope=None):
"""
2D deconvolutional layer.
Args:
input (Tensor): The input tensor, at least 4-d.
out_channels (int): The channel numbers of the deconvolution output.
kernel_size (int or (int, int)): Kernel size over spatial dimensions.
strides (int or (int, int)): Strides over spatial dimensions.
padding: One of {"valid", "same"}, case in-sensitive.
channels_last (bool): Whether or not the channel axis is the last
axis in `input`? (i.e., the data format is "NHWC")
output_shape: If specified, use this as the shape of the
deconvolution output; otherwise compute the size of each dimension
by::
output_size = input_size * strides
if padding == 'valid':
output_size += max(kernel_size - strides, 0)
activation_fn: The activation function.
normalizer_fn: The normalizer function.
weight_norm (bool or (tf.Tensor) -> tf.Tensor)):
If :obj:`True`, apply :func:`~tfsnippet.layers.weight_norm` on
`kernel`. `use_scale` will be :obj:`True` if `normalizer_fn`
is not specified, and :obj:`False` otherwise. The axis reduction
will be determined by the layer.
If it is a callable function, then it will be used to normalize
the `kernel` instead of :func:`~tfsnippet.layers.weight_norm`.
The user must ensure the axis reduction is correct by themselves.
gated (bool): Whether or not to use gate on output?
`output = activation_fn(output) * sigmoid(gate)`.
gate_sigmoid_bias (Tensor): The bias added to `gate` before applying
the `sigmoid` activation.
kernel (Tensor): Instead of creating a new variable, use this tensor.
kernel_initializer: The initializer for `kernel`.
Would be ``default_kernel_initializer(...)`` if not specified.
kernel_regularizer: The regularizer for `kernel`.
kernel_constraint: The constraint for `kernel`.
use_bias (bool or None): Whether or not to use `bias`?
If :obj:`True`, will always use bias.
If :obj:`None`, will use bias only if `normalizer_fn` is not given.
If :obj:`False`, will never use bias.
Default is :obj:`None`.
bias (Tensor): Instead of creating a new variable, use this tensor.
bias_initializer: The initializer for `bias`.
bias_regularizer: The regularizer for `bias`.
bias_constraint: The constraint for `bias`.
trainable (bool): Whether or not the parameters are trainable?
Returns:
tf.Tensor: The output tensor.
"""
input, in_channels, data_format = \
validate_conv2d_input(input, channels_last)
out_channels = validate_positive_int_arg('out_channels', out_channels)
dtype = input.dtype.base_dtype
if gated:
out_channels *= 2
# check functional arguments
padding = validate_enum_arg('padding',
str(padding).upper(), ['VALID', 'SAME'])
strides = validate_conv2d_strides_tuple('strides', strides, channels_last)
weight_norm_fn = validate_weight_norm_arg(weight_norm,
axis=-1,
use_scale=normalizer_fn is None)
if use_bias is None:
use_bias = normalizer_fn is None
# get the specification of outputs and parameters
kernel_size = validate_conv2d_size_tuple('kernel_size', kernel_size)
kernel_shape = kernel_size + (out_channels, in_channels)
bias_shape = (out_channels, )
given_h, given_w = None, None
given_output_shape = output_shape
if is_tensor_object(given_output_shape):
given_output_shape = tf.convert_to_tensor(given_output_shape)
elif given_output_shape is not None:
given_h, given_w = given_output_shape
# validate the parameters
if kernel is not None:
kernel_spec = ParamSpec(shape=kernel_shape, dtype=dtype)
kernel = kernel_spec.validate('kernel', kernel)
if kernel_initializer is None:
kernel_initializer = default_kernel_initializer(weight_norm)
if bias is not None:
bias_spec = ParamSpec(shape=bias_shape, dtype=dtype)
bias = bias_spec.validate('bias', bias)
# the main part of the conv2d layer
with tf.variable_scope(scope, default_name=name or 'deconv2d'):
with tf.name_scope('output_shape'):
# detect the input shape and axis arrangements
input_shape = get_static_shape(input)
if channels_last:
c_axis, h_axis, w_axis = -1, -3, -2
else:
c_axis, h_axis, w_axis = -3, -2, -1
output_shape = [None, None, None, None]
output_shape[c_axis] = out_channels
if given_output_shape is None:
if input_shape[h_axis] is not None:
output_shape[h_axis] = get_deconv_output_length(
input_shape[h_axis], kernel_shape[0], strides[h_axis],
padding)
if input_shape[w_axis] is not None:
output_shape[w_axis] = get_deconv_output_length(
input_shape[w_axis], kernel_shape[1], strides[w_axis],
padding)
else:
if not is_tensor_object(given_output_shape):
output_shape[h_axis] = given_h
output_shape[w_axis] = given_w
# infer the batch shape in 4-d
batch_shape = input_shape[:-3]
if None not in batch_shape:
output_shape[0] = int(np.prod(batch_shape))
# now the static output shape is ready
output_static_shape = tf.TensorShape(output_shape)
# prepare for the dynamic batch shape
if output_shape[0] is None:
output_shape[0] = tf.reduce_prod(get_shape(input)[:-3])
# prepare for the dynamic spatial dimensions
if output_shape[h_axis] is None or output_shape[w_axis] is None:
if given_output_shape is None:
input_shape = get_shape(input)
if output_shape[h_axis] is None:
output_shape[h_axis] = get_deconv_output_length(
input_shape[h_axis], kernel_shape[0],
strides[h_axis], padding)
if output_shape[w_axis] is None:
output_shape[w_axis] = get_deconv_output_length(
input_shape[w_axis], kernel_shape[1],
strides[w_axis], padding)
else:
assert (is_tensor_object(given_output_shape))
with assert_deps([
assert_rank(given_output_shape, 1),
assert_scalar_equal(tf.size(given_output_shape), 2)
]):
output_shape[h_axis] = given_output_shape[0]
output_shape[w_axis] = given_output_shape[1]
# compose the final dynamic shape
if any(is_tensor_object(s) for s in output_shape):
output_shape = tf.stack(output_shape)
else:
output_shape = tuple(output_shape)
# create the variables
if kernel is None:
kernel = model_variable('kernel',
shape=kernel_shape,
dtype=dtype,
initializer=kernel_initializer,
regularizer=kernel_regularizer,
constraint=kernel_constraint,
trainable=trainable)
if weight_norm_fn is not None:
kernel = weight_norm_fn(kernel)
maybe_add_histogram(kernel, 'kernel')
kernel = maybe_check_numerics(kernel, 'kernel')
if use_bias and bias is None:
bias = model_variable('bias',
shape=bias_shape,
initializer=bias_initializer,
regularizer=bias_regularizer,
constraint=bias_constraint,
trainable=trainable)
maybe_add_histogram(bias, 'bias')
bias = maybe_check_numerics(bias, 'bias')
# flatten to 4d
output, s1, s2 = flatten_to_ndims(input, 4)
# do convolution or deconvolution
output = tf.nn.conv2d_transpose(value=output,
filter=kernel,
output_shape=output_shape,
strides=strides,
padding=padding,
data_format=data_format)
if output_static_shape is not None:
output.set_shape(output_static_shape)
# add bias
if use_bias:
output = tf.nn.bias_add(output, bias, data_format=data_format)
# apply the normalization function if specified
if normalizer_fn is not None:
output = normalizer_fn(output)
# split into halves if gated
if gated:
output, gate = tf.split(output, 2, axis=c_axis)
# apply the activation function if specified
if activation_fn is not None:
output = activation_fn(output)
# apply the gate if required
if gated:
output = output * tf.sigmoid(gate + gate_sigmoid_bias, name='gate')
# unflatten back to original shape
output = unflatten_from_ndims(output, s1, s2)
maybe_add_histogram(output, 'output')
output = maybe_check_numerics(output, 'output')
return output
def _transform(self, x, compute_y, compute_log_det):
# split the input x
x1, x2 = tf.split(x, [self._x_n_left, self._x_n_right],
axis=self._split_axis)
do_compute_y = compute_y or (self._y_n_left is None)
# apply the left transformation
y1, log_det1 = self._left.transform(x1,
compute_y=do_compute_y,
compute_log_det=compute_log_det)
# apply the right transformation
if self._right is not None:
y2, log_det2 = self._right.transform(
x2, compute_y=do_compute_y, compute_log_det=compute_log_det)
else:
y2, log_det2 = x2, None
# check the outputs
y1_shape = get_static_shape(y1)
y2_shape = get_static_shape(y2)
if len(y1_shape) != len(y2_shape):
raise RuntimeError('`y_left.ndims` != `y_right.ndims`: y_left {} '
'vs y_right {}'.format(y1, y2))
# build the y spec if not built
join_axis = self._join_axis
if self._y_n_left is None:
# resolve the join axis
if join_axis is None:
join_axis = self._split_axis
if join_axis < 0:
join_axis += len(y1_shape)
if join_axis < 0 or join_axis < len(y1_shape) - self.y_value_ndims:
raise ValueError(
'`join_axis` out of range, or not covered by `y_value_ndims'
'`: split_axis {}, y_value_ndims {}, y_left {}, y_right {}'
.format(self._split_axis, self.y_value_ndims, y1, y2))
join_axis -= len(y1_shape)
err_msg = (
'`y_left.shape[join_axis] + y_right.shape[join_axis]` must '
'be at least 2: y_left {}, y_right {} axis {}.'.format(
y1, y2, join_axis))
y_n_left = y1_shape[join_axis]
y_n_right = y2_shape[join_axis]
if y_n_left is not None and y_n_right is not None:
y_n_features = y_n_left + y_n_right
assert (y_n_features >= 2)
else:
y_n_left = get_shape(y1)[join_axis]
y_n_right = get_shape(y2)[join_axis]
y_n_features = None
with assert_deps([
tf.assert_greater_equal(y_n_left + y_n_right,
2,
message=err_msg)
]) as asserted:
if asserted: # pragma: no cover
y_n_left = tf.identity(y_n_left)
y_n_right = tf.identity(y_n_right)
self._join_axis = join_axis
self._y_n_left = y_n_left
self._y_n_right = y_n_right
# build the y spec
dtype = self._x_input_spec.dtype
y_shape_spec = ['?'] * self.y_value_ndims
if y_n_features is not None:
y_shape_spec[self._split_axis] = y_n_features
assert (not self.require_batch_dims)
y_shape_spec = ['...'] + y_shape_spec
self._y_input_spec = InputSpec(shape=y_shape_spec, dtype=dtype)
assert (join_axis is not None)
# compute y
y = None
if compute_y:
y = tf.concat([y1, y2], axis=join_axis)
# compute log_det
log_det = None
if compute_log_det:
log_det = log_det1
if log_det2 is not None:
log_det = sum_log_det([log_det, log_det2])
return y, log_det
def shift(input, shift, name=None):
"""
Shift each axis of `input` according to `shift`, but keep identical size.
The extra content will be discarded if shifted outside the original size.
Zeros will be padded to the front or end of shifted axes.
Args:
input (Tensor): The tensor to be shifted.
shift (Iterable[int]): The shift length for each axes.
It must be equal to the rank of `input`.
For each axis, if its corresponding shift < 0, then the
`input` will be shifted to left by `-shift` at that axis.
If its shift > 0, then the `input` will be shifted to right
by `shift` at that axis.
Returns:
tf.Tensor: The output tensor.
"""
shift = tuple(int(s) for s in shift)
input = tf.convert_to_tensor(input)
shape = get_static_shape(input)
if shape is None:
raise ValueError(
'The rank of `shape` is required to be deterministic: '
'got {}'.format(input))
if len(shift) != len(shape):
raise ValueError('The length of `shift` is required to equal the rank '
'of `input`: shift {} vs input {}'.format(
shift, input))
# cache for the dynamic shape
def get_dynamic_shape():
if cached[0] is None:
cached[0] = get_shape(input)
return cached[0]
cached = [None]
# main routine
with tf.name_scope(name, default_name='shift', values=[input]):
# compute the slicing and padding arguments
has_shift = False
assert_ops = []
slice_begin = []
slice_size = []
paddings = []
err_msg = ('Cannot shift `input`: input {} vs shift {}'.format(
input, shift))
for i, (axis_shift, axis_size) in enumerate(zip(shift, shape)):
# fast approach: shift is zero, no slicing at the axis
if axis_shift == 0:
slice_begin.append(0)
slice_size.append(-1)
paddings.append((0, 0))
continue
# slow approach: shift is not zero, should slice at the axis
axis_shift_abs = abs(axis_shift)
# we first check whether or not the axis size is big enough
if axis_size is None:
dynamic_axis_size = get_dynamic_shape()[i]
assert_ops.append(
tf.assert_greater_equal(dynamic_axis_size,
axis_shift_abs,
message=err_msg))
else:
if axis_size < axis_shift_abs:
raise ValueError(err_msg)
# next, we compose the slicing range
if axis_shift < 0: # shift to left
slice_begin.append(-axis_shift)
slice_size.append(-1)
paddings.append((0, -axis_shift))
else: # shift to right
slice_begin.append(0)
if axis_size is None:
slice_size.append(get_dynamic_shape()[i] - axis_shift)
else:
slice_size.append(axis_size - axis_shift)
paddings.append((axis_shift, 0))
# mark the flag to indicate that we've got any axis to shift
has_shift = True
if assert_ops:
with assert_deps(assert_ops) as asserted:
if asserted:
input = tf.identity(input)
# no axis to shift, directly return the input
if not has_shift:
return input
# do slicing and padding
if any(is_tensor_object(s) for s in slice_size):
slice_size = tf.stack(slice_size, axis=0)
output = tf.slice(input, slice_begin, slice_size)
output = tf.pad(output, paddings)
return output
def reshape_tail(input, ndims, shape, name=None):
"""
Reshape the tail (last) `ndims` into specified `shape`.
Usage::
x = tf.zeros([2, 3, 4, 5, 6])
reshape_tail(x, 3, [-1]) # output: zeros([2, 3, 120])
reshape_tail(x, 1, [3, 2]) # output: zeros([2, 3, 4, 5, 3, 2])
Args:
input (Tensor): The input tensor, at least `ndims` dimensions.
ndims (int): To reshape this number of dimensions at tail.
shape (Iterable[int] or tf.Tensor): The shape of the new tail.
Returns:
tf.Tensor: The reshaped tensor.
"""
input = tf.convert_to_tensor(input)
if not is_tensor_object(shape):
shape = list(int(s) for s in shape)
neg_one_count = 0
for s in shape:
if s <= 0:
if s == -1:
if neg_one_count > 0:
raise ValueError('`shape` is not a valid shape: at '
'most one `-1` can be specified.')
else:
neg_one_count += 1
else:
raise ValueError('`shape` is not a valid shape: {} is '
'not allowed.'.format(s))
with tf.name_scope(name or 'reshape_tail', values=[input]):
# assert the dimension
with assert_deps([
assert_rank_at_least(
input, ndims, message='rank(input) must be at least ndims')
]) as asserted:
if asserted: # pragma: no cover
input = tf.identity(input)
# compute the static shape
static_input_shape = get_static_shape(input)
static_output_shape = None
if static_input_shape is not None:
if ndims > 0:
left_shape = static_input_shape[:-ndims]
right_shape = static_input_shape[-ndims:]
else:
left_shape = static_input_shape
right_shape = ()
# attempt to resolve "-1" in `shape`
if isinstance(shape, list):
if None not in right_shape:
shape_size = int(np.prod([s for s in shape if s != -1]))
right_shape_size = int(np.prod(right_shape))
if (-1 not in shape and shape_size != right_shape_size) or \
(-1 in shape and right_shape_size % shape_size != 0):
raise ValueError(
'Cannot reshape the tail dimensions of '
'`input` into `shape`: input {!r}, ndims '
'{}, shape {}.'.format(input, ndims, shape))
if -1 in shape:
pos = shape.index(-1)
shape[pos] = right_shape_size // shape_size
static_output_shape = left_shape + \
tuple(s if s != -1 else None for s in shape)
static_output_shape = tf.TensorShape(static_output_shape)
# compute the dynamic shape
input_shape = get_shape(input)
if ndims > 0:
output_shape = concat_shapes([input_shape[:-ndims], shape])
else:
output_shape = concat_shapes([input_shape, shape])
# do reshape
output = tf.reshape(input, output_shape)
output.set_shape(static_output_shape)
return output
def broadcast_concat(x, y, axis, name=None):
"""
Broadcast `x` and `y`, then concat them along `axis`.
This method cannot deal with all possible situations yet.
`x` and `y` must have known number of dimensions, and only the deterministic
axes will be broadcasted. You must ensure the non-deterministic axes are
properly broadcasted by yourself.
Args:
x: The tensor `x`.
y: The tensor `y`.
axis: The axis to be concatenated.
Returns:
tf.Tensor: The broadcast and concatenated tensor.
"""
x = tf.convert_to_tensor(x)
y = tf.convert_to_tensor(y)
# check the arguments
x_static_shape = get_static_shape(x)
if x_static_shape is None:
raise ValueError('`x` with non-deterministic shape is not supported.')
y_static_shape = get_static_shape(y)
if y_static_shape is None:
raise ValueError('`y` with non-deterministic shape is not supported.')
x_rank = len(x_static_shape)
y_rank = len(y_static_shape)
out_ndims = max(x_rank, y_rank)
min_axis = -out_ndims
max_axis = out_ndims - 1
if axis < min_axis or axis > max_axis:
raise ValueError('Invalid axis: must >= {} and <= {}, got {}'.format(
min_axis, max_axis, axis))
if axis >= 0:
axis = axis - out_ndims
# compute the broadcast shape
out_static_shape = [None] * out_ndims
x_tile = [1] * out_ndims
y_tile = [1] * out_ndims
assertions = []
dynamic_shape_cache = {}
def get_dynamic_shape(t):
if t not in dynamic_shape_cache:
dynamic_shape_cache[t] = get_shape(t)
return dynamic_shape_cache[t]
def broadcast_axis(i, a, b, a_tile, b_tile, a_tensor, b_tensor):
err_msg = ('`x` and `y` cannot be broadcast concat: {} vs {}'.format(
x, y))
# validate whether or not a == b or can be broadcasted
if a is None and b is None:
# both dynamic, must be equal
a = get_dynamic_shape(a_tensor)[i]
b = get_dynamic_shape(b_tensor)[i]
assertions.append(tf.assert_equal(a, b, message=err_msg))
elif a is not None and b is not None:
# both static, check immediately
if a != 1 and b != 1 and a != b:
raise ValueError(err_msg)
if a == 1:
a_tile[i] = b
elif b == 1:
b_tile[i] = a
out_static_shape[i] = max(a, b)
elif a is None:
# a dynamic, b can be 1 or equal to a
a = get_dynamic_shape(a_tensor)[i]
if b == 1:
b_tile[i] = a
else:
assertions.append(tf.assert_equal(a, b, message=err_msg))
out_static_shape[i] = b
else:
broadcast_axis(i, b, a, b_tile, a_tile, b_tensor, a_tensor)
def maybe_prepend_dims(t, rank, name):
if rank < out_ndims:
t = prepend_dims(t, out_ndims - rank, name=name)
return t
def maybe_tile(t, tile, name):
if any(s != 1 for s in tile):
if any(is_tensor_object(s) for s in tile):
tile = tf.stack(tile, axis=0)
t = tf.tile(t, tile, name=name)
return t
with tf.name_scope(name, default_name='broadcast_concat', values=[x, y]):
# infer the configurations
for i in range(-1, -out_ndims - 1, -1):
a = x_static_shape[i] if i >= -x_rank else 1
b = y_static_shape[i] if i >= -y_rank else 1
if i != axis:
broadcast_axis(i, a, b, x_tile, y_tile, x, y)
else:
if a is not None and b is not None:
out_static_shape[i] = a + b
# do broadcast
x = maybe_tile(maybe_prepend_dims(x, x_rank, name='prepend_dims_to_x'),
x_tile,
name='tile_x')
y = maybe_tile(maybe_prepend_dims(y, y_rank, name='prepend_dims_to_y'),
y_tile,
name='tile_y')
with assert_deps(assertions) as asserted:
if asserted:
x = tf.identity(x)
y = tf.identity(y)
# do concat
ret = tf.concat([x, y], axis=axis)
ret.set_shape(tf.TensorShape(out_static_shape))
return ret
def identity_branch():
with assert_deps(assertions) as asserted:
if asserted:
return tf.identity(x)
else: # pragma: no cover
return x
def broadcast_to_shape(x, shape, name=None):
"""
Broadcast `x` to match `shape`.
If ``rank(x) > len(shape)``, only the tail dimensions will be broadcasted
to match `shape`.
Args:
x: A tensor.
shape (tuple[int] or tf.Tensor): Broadcast `x` to match this shape.
Returns:
tf.Tensor: The broadcasted tensor.
"""
# check the parameters
x = tf.convert_to_tensor(x)
x_shape = get_static_shape(x)
ns_values = [x]
if is_tensor_object(shape):
shape = tf.convert_to_tensor(shape)
ns_values.append(shape)
else:
shape = tuple(int(s) for s in shape)
with tf.name_scope(name=name or 'broadcast_to_shape', values=ns_values):
cannot_broadcast_msg = (
'`x` cannot be broadcasted to match `shape`: x {!r} vs shape {!r}'.
format(x, shape))
# fast routine: shape is tuple[int] and x_shape is all known,
# we can use reshape + tile to do the broadcast, which should be faster
# than using ``x * ones(shape)``.
if isinstance(shape, tuple) and x_shape is not None and \
all(s is not None for s in x_shape):
# reshape to have the same dimension
if len(x_shape) < len(shape):
x_shape = (1, ) * (len(shape) - len(x_shape)) + x_shape
x = tf.reshape(x, x_shape)
# tile to have the same shape
tile = []
i = -1
while i > -len(shape) - 1:
a, b = x_shape[i], shape[i]
if a == 1 and b > 1:
tile.append(b)
elif a != b:
raise ValueError(cannot_broadcast_msg)
else:
tile.append(1)
i -= 1
tile = [1] * (len(x_shape) - len(shape)) + list(reversed(tile))
if any(s > 1 for s in tile):
x = tf.tile(x, tile)
return x
# slow routine: we may need ``x * ones(shape)`` to do the broadcast
assertions = []
post_assert_shape = False
static_shape = tf.TensorShape(None)
if isinstance(shape, tuple) and x_shape is not None:
need_multiply_ones = False
# it should always broadcast if len(x_shape) < len(shape)
if len(x_shape) < len(shape):
need_multiply_ones = True
# check the consistency of x and shape
static_shape_hint = [] # list to gather the static shape hint
axis_to_check = [] # list to gather the axis to check
i = -1
while i >= -len(shape) and i >= -len(x_shape):
a, b = x_shape[i], shape[i]
if a is None:
axis_to_check.append(i)
else:
if a != b:
if a == 1:
need_multiply_ones = True
else:
raise ValueError(cannot_broadcast_msg)
static_shape_hint.append(b)
i -= 1
# compose the static shape hint
if len(shape) < len(x_shape):
static_shape = x_shape[:-len(shape)]
elif len(shape) > len(x_shape):
static_shape = shape[:-len(x_shape)]
else:
static_shape = ()
static_shape = tf.TensorShape(static_shape +
tuple(reversed(static_shape_hint)))
# compose the assertion operations and the multiply flag
if axis_to_check:
need_multiply_flags = []
x_dynamic_shape = tf.shape(x)
for i in axis_to_check:
assertions.append(
tf.assert_equal(tf.logical_or(
tf.equal(x_dynamic_shape[i], shape[i]),
tf.equal(x_dynamic_shape[i], 1),
),
True,
message=cannot_broadcast_msg))
if len(x_shape) >= len(shape):
need_multiply_flags.append(
tf.not_equal(x_dynamic_shape[i], shape[i]))
if not need_multiply_ones:
need_multiply_ones = \
tf.reduce_any(tf.stack(need_multiply_flags))
else:
# we have no ideal about what `shape` is here, thus we need to
# assert the shape after ``x * ones(shape)``.
need_multiply_ones = True
post_assert_shape = True
# do broadcast if `x_shape` != `shape`
def multiply_branch():
with assert_deps(assertions):
ones_template = tf.ones(shape, dtype=x.dtype.base_dtype)
try:
return x * ones_template
except ValueError: # pragma: no cover
raise ValueError(cannot_broadcast_msg)
def identity_branch():
with assert_deps(assertions) as asserted:
if asserted:
return tf.identity(x)
else: # pragma: no cover
return x
t = smart_cond(need_multiply_ones, multiply_branch, identity_branch)
t.set_shape(static_shape)
if post_assert_shape:
post_assert_op = tf.assert_equal(tf.reduce_all(
tf.equal(tf.shape(t)[-tf.size(shape):], shape)),
True,
message=cannot_broadcast_msg)
with assert_deps([post_assert_op]) as asserted:
if asserted:
t = tf.identity(t)
return t