def _build(self, input=None):
# check the input.
input = tf.convert_to_tensor(input)
dtype = input.dtype.base_dtype
shape = get_static_shape(input)
# These facts should have been checked in `BaseFlow.build`.
assert (shape is not None)
assert (len(shape) >= self.value_ndims)
# compute var spec and input spec
min_axis = min(self.axis)
shape_spec = [None] * len(shape)
for a in self.axis:
shape_spec[a] = shape[a]
shape_spec = shape_spec[min_axis:]
assert (not not shape_spec)
assert (self.value_ndims >= len(shape_spec))
self._y_input_spec = self._x_input_spec = InputSpec(
shape=(('...', ) + ('?', ) * (self.value_ndims - len(shape_spec)) +
tuple(shape_spec)),
dtype=dtype)
# the shape of variables must only have necessary dimensions,
# such that we can switch freely between `channels_last = True`
# (in which case `input.shape = (..., *,)`, and `channels_last = False`
# (in which case `input.shape = (..., *, 1, 1)`.
self._var_shape = tuple(s for s in shape_spec if s is not None)
# and we still need to compute the aligned variable shape, such that
# we can immediately reshape the variables into this aligned shape,
# then compute `scale * input + bias`.
self._var_shape_aligned = tuple(s or 1 for s in shape_spec)
self._var_spec = ParamSpec(self._var_shape)
# validate the input
self._x_input_spec.validate('input', input)
# build the variables
self._bias = model_variable('bias',
dtype=dtype,
shape=self._var_shape,
regularizer=self._bias_regularizer,
constraint=self._bias_constraint,
trainable=self._trainable)
if self._scale_type == 'exp':
self._pre_scale = model_variable(
'log_scale',
dtype=dtype,
shape=self._var_shape,
regularizer=self._log_scale_regularizer,
constraint=self._log_scale_constraint,
trainable=self._trainable)
else:
self._pre_scale = model_variable(
'scale',
dtype=dtype,
shape=self._var_shape,
regularizer=self._scale_regularizer,
constraint=self._scale_constraint,
trainable=self._trainable)
def _build(self, input=None):
dtype = input.dtype.base_dtype
n_units = get_static_shape(input)[self.axis]
w = model_variable('w',
shape=[1, n_units],
dtype=dtype,
initializer=self._w_initializer,
regularizer=self._w_regularizer,
trainable=self._trainable)
b = model_variable('b',
shape=[1],
dtype=dtype,
initializer=self._b_initializer,
regularizer=self._b_regularizer,
trainable=self._trainable)
u = model_variable('u',
shape=[1, n_units],
dtype=dtype,
initializer=self._u_initializer,
regularizer=self._u_regularizer,
trainable=self._trainable)
wu = tf.matmul(w, u, transpose_b=True) # wu.shape == [1]
u_hat = u + (-1 + tf.nn.softplus(wu) - wu) * \
w / tf.reduce_sum(tf.square(w)) # shape == [1, n_units]
self._w, self._b, self._u, self._u_hat = w, b, u, u_hat
def _build(self, input=None):
n_features = self._n_features = get_static_shape(input)[self.axis]
permutation = np.arange(n_features, dtype=np.int32)
self._random_state.shuffle(permutation)
self._permutation = model_variable(
'permutation', dtype=tf.int32, initializer=permutation,
trainable=False
)
self._inv_permutation = tf.invert_permutation(self._permutation)
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 conv2d(input,
out_channels,
kernel_size,
strides=(1, 1),
dilations=1,
padding='same',
channels_last=True,
activation_fn=None,
normalizer_fn=None,
weight_norm=False,
gated=False,
gate_sigmoid_bias=2.,
kernel=None,
kernel_mask=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 convolutional layer.
Args:
input (Tensor): The input tensor, at least 4-d.
out_channels (int): The channel numbers of the output.
kernel_size (int or (int, int)): Kernel size over spatial dimensions.
strides (int or (int, int)): Strides over spatial dimensions.
dilations (int): The dilation factor 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")
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_mask (Tensor): If specified, multiply this mask onto `kernel`,
i.e., the actual kernel to use will be `kernel * kernel_mask`.
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'])
original_strides = validate_conv2d_size_tuple('strides', strides)
strides = validate_conv2d_strides_tuple('strides', original_strides,
channels_last)
dilations = validate_positive_int_arg('dilations', dilations)
if dilations > 1 and not channels_last:
raise ValueError('`channels_last` == False is incompatible with '
'`dilations` > 1.')
if any(i > 1 for i in strides) and dilations > 1:
raise ValueError('`strides` > 1 is incompatible with `dilations` > 1.')
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 + (in_channels, out_channels)
bias_shape = (out_channels, )
# validate the parameters
if kernel is not None:
kernel_spec = ParamSpec(shape=kernel_shape, dtype=dtype)
kernel = kernel_spec.validate('kernel', kernel)
if kernel_mask is not None:
kernel_mask_spec = InputSpec(dtype=dtype)
kernel_mask = kernel_mask_spec.validate('kernel_mask', kernel_mask)
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 'conv2d'):
c_axis = -1 if channels_last else -3
# 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)
if kernel_mask is not None:
kernel = kernel * kernel_mask
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')
# special optimization: use dense instead of 1x1 conv if possible
if dilations == 1 and kernel_size == (1, 1) and channels_last:
with tf.name_scope('conv2d_1x1'):
conv2d_1x1_kernel = tf.reshape(kernel,
kernel_shape[2:],
name='conv2d_1x1_kernel')
output = input[
..., ::original_strides[0], ::original_strides[1], :]
# flatten to 2d
output, s1, s2 = flatten_to_ndims(output, 2)
output = tf.matmul(output, conv2d_1x1_kernel)
else:
# flatten to 4d
output, s1, s2 = flatten_to_ndims(input, 4)
# do convolution
if dilations > 1:
output = tf.nn.atrous_conv2d(value=output,
filters=kernel,
rate=dilations,
padding=padding)
else:
output = tf.nn.conv2d(input=output,
filter=kernel,
strides=strides,
padding=padding,
data_format=data_format,
dilations=[1] * 4)
# 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 weight_norm(kernel,
axis,
use_scale=True,
scale=None,
scale_initializer=None,
scale_regularizer=None,
scale_constraint=None,
trainable=True,
epsilon=1e-12,
name=None,
scope=None):
"""
Weight normalization proposed by (Salimans & Kingma, 2016).
Roughly speaking, the weight normalization is defined as::
kernel = scale * kernel / tf.sqrt(
tf.reduce_sum(kernel ** 2, axis=<dimensions not in `axis`>,
keepdims=True)
)
This function does not support data-dependent initialization for `scale`.
If you do need this feature, you have to turn off `scale`, and use
:func:`~tfsnippet.layers.act_norm` along with :func:`weight_norm`.
Args:
kernel: Tensor, the weight `w` to be normalized.
axis (int or tuple[int]): The axis to apply weight normalization.
See above description to know what `axis` exactly is.
use_scale (bool): Whether or not to use `scale`. Default :obj:`True`.
scale (Tensor): Instead of creating a new variable, use this tensor.
scale_initializer: The initializer for `scale`.
scale_regularizer: The regularizer for `scale`.
scale_constraint: The constraint for `scale`.
trainable (bool): Whether or not the variables are trainable?
epsilon: Small float number to avoid dividing by zero.
"""
# check the parameters
if not use_scale and scale is not None:
raise ValueError('`use_scale` is False but `scale` is specified.')
axis = validate_int_tuple_arg('axis', axis)
if not axis:
raise ValueError('`axis` cannot be empty.')
kernel = tf.convert_to_tensor(kernel)
kernel_shape = get_static_shape(kernel)
dtype = kernel.dtype.base_dtype
var_spec = ParamSpec(kernel_shape, dtype=dtype)
if scale_initializer is None:
scale_initializer = tf.ones_initializer(dtype=dtype)
if scale is not None:
scale = var_spec.validate('scale', scale)
# any dimension not specified in `axis` should be averaged out
axis = resolve_negative_axis(len(kernel_shape), axis)
reduce_axis = tuple(a for a in range(len(kernel_shape)) if a not in axis)
with tf.variable_scope(scope, default_name=name or 'weight_norm'):
# normalize the kernel
kernel = maybe_check_numerics(
tf.nn.l2_normalize(kernel, axis=reduce_axis, epsilon=epsilon),
'weight-normalized kernel')
# create the scaling variable
if use_scale:
if scale is None:
scale = model_variable('scale',
shape=kernel_shape,
dtype=dtype,
initializer=scale_initializer,
regularizer=scale_regularizer,
constraint=scale_constraint,
trainable=trainable)
scale = maybe_check_numerics(scale, 'scale')
kernel = kernel * scale
# now return the normalized weight
return kernel