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tensorflow/mesh | mesh_tensorflow/transformer/dataset.py | pretokenized_t2t_dataset | def pretokenized_t2t_dataset(dataset_name=gin.REQUIRED,
text2self=False,
data_dir=gin.REQUIRED,
dataset_split="train",
batch_size=gin.REQUIRED,
sequence_length=gin.REQUIRED,
... | python | def pretokenized_t2t_dataset(dataset_name=gin.REQUIRED,
text2self=False,
data_dir=gin.REQUIRED,
dataset_split="train",
batch_size=gin.REQUIRED,
sequence_length=gin.REQUIRED,
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tensorflow/mesh | mesh_tensorflow/transformer/dataset.py | pack_dataset | def pack_dataset(dataset, length, keys=None, use_custom_ops=False):
"""Creates a 'packed' version of a dataset on-the-fly.
Borrowed from the tensor2tensor library.
TODO(noam): make this faster
This is meant to replace the irritation of having to create a separate
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"""Creates a 'packed' version of a dataset on-the-fly.
Borrowed from the tensor2tensor library.
TODO(noam): make this faster
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tensorflow/mesh | mesh_tensorflow/transformer/dataset.py | trim_and_pad_all_features | def trim_and_pad_all_features(features, length):
"""Trim and pad first dimension of all features to size length."""
return {k: _trim_and_pad(v, length) for k, v in features.items()} | python | def trim_and_pad_all_features(features, length):
"""Trim and pad first dimension of all features to size length."""
return {k: _trim_and_pad(v, length) for k, v in features.items()} | [
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tensorflow/mesh | mesh_tensorflow/ops.py | convert_to_dimension | def convert_to_dimension(d):
"""Converts input to a Dimension.
Args:
d: Dimension, tuple (string, int), or None.
Returns:
Dimension or None.
Raises:
ValueError: If d cannot be converted to a Dimension.
"""
if d is None:
return None
if isinstance(d, Dimension):
if not isinstance(d.na... | python | def convert_to_dimension(d):
"""Converts input to a Dimension.
Args:
d: Dimension, tuple (string, int), or None.
Returns:
Dimension or None.
Raises:
ValueError: If d cannot be converted to a Dimension.
"""
if d is None:
return None
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tensorflow/mesh | mesh_tensorflow/ops.py | convert_to_shape | def convert_to_shape(x):
"""Converts input to a Shape.
Args:
x: Shape, str, or None.
Returns:
Shape or None.
Raises:
ValueError: If x cannot be converted to a Shape.
"""
if x is None:
return None
if isinstance(x, Shape):
return x
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x = _parse_string_to_lis... | python | def convert_to_shape(x):
"""Converts input to a Shape.
Args:
x: Shape, str, or None.
Returns:
Shape or None.
Raises:
ValueError: If x cannot be converted to a Shape.
"""
if x is None:
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tensorflow/mesh | mesh_tensorflow/ops.py | convert_to_layout_rules | def convert_to_layout_rules(x):
"""Converts input to a LayoutRules.
Args:
x: LayoutRules, str, or set-like of string pairs.
Returns:
LayoutRules.
"""
if isinstance(x, LayoutRules):
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x = _parse_string_to_list_of_pairs(x)
return LayoutRules(x) | python | def convert_to_layout_rules(x):
"""Converts input to a LayoutRules.
Args:
x: LayoutRules, str, or set-like of string pairs.
Returns:
LayoutRules.
"""
if isinstance(x, LayoutRules):
return x
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tensorflow/mesh | mesh_tensorflow/ops.py | convert_args_to_laid_out_tensors | def convert_args_to_laid_out_tensors(xs):
"""Convert list elements to laid-out-tensors when possible.
Args:
xs: a list
Returns:
a list
"""
ret = []
for x in xs:
if hasattr(x, "to_laid_out_tensor"):
ret.append(x.to_laid_out_tensor())
else:
ret.append(x)
return ret | python | def convert_args_to_laid_out_tensors(xs):
"""Convert list elements to laid-out-tensors when possible.
Args:
xs: a list
Returns:
a list
"""
ret = []
for x in xs:
if hasattr(x, "to_laid_out_tensor"):
ret.append(x.to_laid_out_tensor())
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ret.append(x)
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tensorflow/mesh | mesh_tensorflow/ops.py | slicewise | def slicewise(tf_fn,
xs,
output_shape=None,
output_dtype=None,
splittable_dims=None,
grad_function=None,
name=None):
"""Slice-wise call to any tensorflow function.
The output shape and dtype default to those of the first input.
s... | python | def slicewise(tf_fn,
xs,
output_shape=None,
output_dtype=None,
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grad_function=None,
name=None):
"""Slice-wise call to any tensorflow function.
The output shape and dtype default to those of the first input.
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tensorflow/mesh | mesh_tensorflow/ops.py | cwise | def cwise(tf_fn, xs, output_dtype=None, grad_function=None, name=None):
"""Component-wise operation with no broadcasting.
Args:
tf_fn: a component-wise function taking n tf.Tensor inputs and producing
a tf.Tensor output
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tf_fn: a component-wise function taking n tf.Tensor inputs and producing
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tensorflow/mesh | mesh_tensorflow/ops.py | binary_arguments_to_tensors | def binary_arguments_to_tensors(x1, x2):
"""Convert argument of a binary operation to Tensors.
Args:
x1: a Tensor or something convertible to a tf Scalar
x2: a Tensor or something convertible to a tf Scalar
Returns:
new_x1: a Tensor
new_x2: a Tensor
Raises:
ValueError: on failure
"""
... | python | def binary_arguments_to_tensors(x1, x2):
"""Convert argument of a binary operation to Tensors.
Args:
x1: a Tensor or something convertible to a tf Scalar
x2: a Tensor or something convertible to a tf Scalar
Returns:
new_x1: a Tensor
new_x2: a Tensor
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tensorflow/mesh | mesh_tensorflow/ops.py | minimum | def minimum(x1, x2, output_shape=None, name=None):
"""Binary minimum with broadcsting.
Args:
x1: a Tensor
x2: a Tensor
output_shape: an optional Shape
name: an optional string
Returns:
a Tensor
"""
output_shape = convert_to_shape(output_shape)
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"""Binary minimum with broadcsting.
Args:
x1: a Tensor
x2: a Tensor
output_shape: an optional Shape
name: an optional string
Returns:
a Tensor
"""
output_shape = convert_to_shape(output_shape)
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tensorflow/mesh | mesh_tensorflow/ops.py | split | def split(x, split_dim, num_or_size_splits, name=None):
"""Like tf.split.
Args:
x: a Tensor
split_dim: a Dimension in x.shape.dims
num_or_size_splits: either an integer dividing split_dim.size
or a list of integers adding up to split_dim.size
name: an optional string
Returns:
a list of... | python | def split(x, split_dim, num_or_size_splits, name=None):
"""Like tf.split.
Args:
x: a Tensor
split_dim: a Dimension in x.shape.dims
num_or_size_splits: either an integer dividing split_dim.size
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name: an optional string
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tensorflow/mesh | mesh_tensorflow/ops.py | stack | def stack(xs, dim_name, axis=0, name=None):
"""Stack multiple Tensors to make a new dimension.
Args:
xs: a list of Tensors with identical shapes.
dim_name: a string (name of the new dimension)
axis: an integer (index of the new dimension in the output shape)
name: an optional string
Returns:
... | python | def stack(xs, dim_name, axis=0, name=None):
"""Stack multiple Tensors to make a new dimension.
Args:
xs: a list of Tensors with identical shapes.
dim_name: a string (name of the new dimension)
axis: an integer (index of the new dimension in the output shape)
name: an optional string
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tensorflow/mesh | mesh_tensorflow/ops.py | cumsum | def cumsum(x, dim, exclusive=False):
"""Cumulative sum.
Args:
x: a Tensor
dim: a Dimension
exclusive: a boolean
Returns:
a Tensor with the same shape as x.
"""
with tf.variable_scope("cumsum"):
new_name = "tmp_dim_cumsum"
new_dim = Dimension(new_name, dim.size)
new_shape = x.shap... | python | def cumsum(x, dim, exclusive=False):
"""Cumulative sum.
Args:
x: a Tensor
dim: a Dimension
exclusive: a boolean
Returns:
a Tensor with the same shape as x.
"""
with tf.variable_scope("cumsum"):
new_name = "tmp_dim_cumsum"
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tensorflow/mesh | mesh_tensorflow/ops.py | shift | def shift(x, offset, dim, wrap, name=None):
"""Shift operation.
Shift x right by +offset in dimension dim.
Args:
x: a Tensor
offset: an integer. If negative, shift left instead of right.
dim: a Dimension of x
wrap: a boolean - whether to wrap (True) or pad with zeros (False).
name: an option... | python | def shift(x, offset, dim, wrap, name=None):
"""Shift operation.
Shift x right by +offset in dimension dim.
Args:
x: a Tensor
offset: an integer. If negative, shift left instead of right.
dim: a Dimension of x
wrap: a boolean - whether to wrap (True) or pad with zeros (False).
name: an option... | [
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tensorflow/mesh | mesh_tensorflow/ops.py | import_laid_out_tensor | def import_laid_out_tensor(mesh, laid_out_tensor, shape, name=None):
"""Import a laid_out_tensor.
For expert users.
The input must be laid out appropriately given the eventual MeshImpl,
and layout.
Args:
mesh: a Mesh
laid_out_tensor: a LaidOutTensor
shape: a mtf.Shape
name: an optional strin... | python | def import_laid_out_tensor(mesh, laid_out_tensor, shape, name=None):
"""Import a laid_out_tensor.
For expert users.
The input must be laid out appropriately given the eventual MeshImpl,
and layout.
Args:
mesh: a Mesh
laid_out_tensor: a LaidOutTensor
shape: a mtf.Shape
name: an optional strin... | [
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tensorflow/mesh | mesh_tensorflow/ops.py | get_variable | def get_variable(mesh, name, shape, dtype=tf.float32,
master_dtype=None, slice_dtype=None, activation_dtype=None,
initializer=None, trainable=True,
**kwargs):
"""Create a new variable or retrieve an already-created one.
Args:
mesh: a Mesh
name: a string (u... | python | def get_variable(mesh, name, shape, dtype=tf.float32,
master_dtype=None, slice_dtype=None, activation_dtype=None,
initializer=None, trainable=True,
**kwargs):
"""Create a new variable or retrieve an already-created one.
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mesh: a Mesh
name: a string (u... | [
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tensorflow/mesh | mesh_tensorflow/ops.py | assign | def assign(var, new_val, assign_fn=assign_slice):
"""Assign a new value to a variable.
Args:
var: either a Variable operation or its output Tensor.
new_val: a Tensor
assign_fn: a function from
(mtf.Variable, tf.Variable, tf.Tensor) -> tf.Operation
Returns:
an Operation
Raises:
Value... | python | def assign(var, new_val, assign_fn=assign_slice):
"""Assign a new value to a variable.
Args:
var: either a Variable operation or its output Tensor.
new_val: a Tensor
assign_fn: a function from
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Returns:
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Raises:
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tensorflow/mesh | mesh_tensorflow/ops.py | Print | def Print(x, data, message, **kwargs): # pylint: disable=invalid-name
"""Call tf.Print.
Args:
x: a Tensor.
data: a list of Tensor
message: a string
**kwargs: keyword arguments to tf.Print
Returns:
a Tensor which is identical in value to x
"""
return PrintOperation(x, data, message, **kwa... | python | def Print(x, data, message, **kwargs): # pylint: disable=invalid-name
"""Call tf.Print.
Args:
x: a Tensor.
data: a list of Tensor
message: a string
**kwargs: keyword arguments to tf.Print
Returns:
a Tensor which is identical in value to x
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tensorflow/mesh | mesh_tensorflow/ops.py | rename_dimension | def rename_dimension(x, old_name, new_name):
"""Reshape a Tensor, renaming one dimension.
Args:
x: a Tensor
old_name: a string
new_name: a string
Returns:
a Tensor
"""
return reshape(x, x.shape.rename_dimension(old_name, new_name)) | python | def rename_dimension(x, old_name, new_name):
"""Reshape a Tensor, renaming one dimension.
Args:
x: a Tensor
old_name: a string
new_name: a string
Returns:
a Tensor
"""
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tensorflow/mesh | mesh_tensorflow/ops.py | replace_dimensions | def replace_dimensions(tensor_or_shape, old_dim_or_dims, new_dim_or_dims):
"""Replace dimensions in a Tensor or Shape.
old_dim_or_dims consists of a single dimension or a list of dimensions
that must occur consecutively in the input shape. They are replaced
by the dimensions in new_dim_or_dims.
Args:
t... | python | def replace_dimensions(tensor_or_shape, old_dim_or_dims, new_dim_or_dims):
"""Replace dimensions in a Tensor or Shape.
old_dim_or_dims consists of a single dimension or a list of dimensions
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tensorflow/mesh | mesh_tensorflow/ops.py | einsum | def einsum(xs, output_shape=None, reduced_dims=None, name=None):
"""Einstein summation.
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to the union of all of their shapes, multiplying them componentwise,
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One common case of this is matrix mu... | python | def einsum(xs, output_shape=None, reduced_dims=None, name=None):
"""Einstein summation.
einsum(xs, output_shape) is equivalent to broadcasting all inputs
to the union of all of their shapes, multiplying them componentwise,
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tensorflow/mesh | mesh_tensorflow/ops.py | _reduction_output_shape | def _reduction_output_shape(x, output_shape, reduced_dim):
"""Helper function to reduce_sum, etc."""
if output_shape is None:
if reduced_dim is None:
return Shape([])
else:
if reduced_dim not in x.shape.dims:
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"""Helper function to reduce_sum, etc."""
if output_shape is None:
if reduced_dim is None:
return Shape([])
else:
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tensorflow/mesh | mesh_tensorflow/ops.py | top_1 | def top_1(x, reduced_dim, dtype=tf.int32, name=None):
"""Argmax and Max.
Args:
x: a Tensor
reduced_dim: a Dimension in x.shape.dims
dtype: a tf.dtype (for the output)
name: an optional string
Returns:
indices: a Tensor with given dtype
values: optional Tensor equal to mtf.reduce_max(x, re... | python | def top_1(x, reduced_dim, dtype=tf.int32, name=None):
"""Argmax and Max.
Args:
x: a Tensor
reduced_dim: a Dimension in x.shape.dims
dtype: a tf.dtype (for the output)
name: an optional string
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indices: a Tensor with given dtype
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tensorflow/mesh | mesh_tensorflow/ops.py | top_k | def top_k(x, reduced_dim, new_dim, dtype=tf.int32, name=None):
"""Like tf.top_k.
This operation returns two tensors with the same shape. The output shape
is identical to the shape of x, except that reduced_dim is replaced by
new_dim.
Args:
x: a Tensor
reduced_dim: a Dimension in x.shape.dims.
n... | python | def top_k(x, reduced_dim, new_dim, dtype=tf.int32, name=None):
"""Like tf.top_k.
This operation returns two tensors with the same shape. The output shape
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x: a Tensor
reduced_dim: a Dimension in x.shape.dims.
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tensorflow/mesh | mesh_tensorflow/ops.py | add | def add(x1, x2, output_shape=None, name=None):
"""Binary addition with broadcsting.
Args:
x1: a Tensor
x2: a Tensor
output_shape: an optional Shape
name: an optional string
Returns:
a Tensor
"""
output_shape = convert_to_shape(output_shape)
if not isinstance(x2, Tensor):
return Scal... | python | def add(x1, x2, output_shape=None, name=None):
"""Binary addition with broadcsting.
Args:
x1: a Tensor
x2: a Tensor
output_shape: an optional Shape
name: an optional string
Returns:
a Tensor
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tensorflow/mesh | mesh_tensorflow/ops.py | sub | def sub(x1, x2, output_shape=None, name=None):
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Args:
x1: a Tensor
x2: a Tensor
output_shape: an optional Shape
name: an optional string
Returns:
a Tensor
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"""Binary subtraction with broadcsting.
Args:
x1: a Tensor
x2: a Tensor
output_shape: an optional Shape
name: an optional string
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a Tensor
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tensorflow/mesh | mesh_tensorflow/ops.py | multiply | def multiply(x1, x2, output_shape=None, name=None):
"""Binary multiplication with broadcasting.
Args:
x1: a Tensor
x2: a Tensor
output_shape: an optional Shape
name: an optional string
Returns:
a Tensor
"""
if not isinstance(x2, Tensor):
return ScalarMultiplyOperation(x1, x2).outputs[... | python | def multiply(x1, x2, output_shape=None, name=None):
"""Binary multiplication with broadcasting.
Args:
x1: a Tensor
x2: a Tensor
output_shape: an optional Shape
name: an optional string
Returns:
a Tensor
"""
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tensorflow/mesh | mesh_tensorflow/ops.py | divide | def divide(x1, x2, output_shape=None, name=None):
"""Binary division with broadcasting.
Args:
x1: a Tensor
x2: a Tensor
output_shape: an optional Shape
name: an optional string
Returns:
a Tensor
"""
output_shape = convert_to_shape(output_shape)
if not isinstance(x2, Tensor):
return ... | python | def divide(x1, x2, output_shape=None, name=None):
"""Binary division with broadcasting.
Args:
x1: a Tensor
x2: a Tensor
output_shape: an optional Shape
name: an optional string
Returns:
a Tensor
"""
output_shape = convert_to_shape(output_shape)
if not isinstance(x2, Tensor):
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tensorflow/mesh | mesh_tensorflow/ops.py | one_hot | def one_hot(indices, output_dim, on_value=1.0,
off_value=0.0, dtype=tf.float32, name=None):
"""One hot operation.
TODO(noam): Is there a good reason we need a special mtf.Operation here?
We could just use some code like this:
cast(equal(indices, mtf_range(indices.mesh, output_dim, dtype=indices.dty... | python | def one_hot(indices, output_dim, on_value=1.0,
off_value=0.0, dtype=tf.float32, name=None):
"""One hot operation.
TODO(noam): Is there a good reason we need a special mtf.Operation here?
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cast(equal(indices, mtf_range(indices.mesh, output_dim, dtype=indices.dty... | [
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tensorflow/mesh | mesh_tensorflow/ops.py | gradients | def gradients(ys, xs, grad_ys=None):
"""Compute gradients in dtf.
Args:
ys: a list of Tensors
xs: a list of Tensors
grad_ys: an optional list of Tensors
Returns:
grad_xs: a list of Tensors
"""
graph = ys[0].graph
if not grad_ys:
grad_ys = [Constant(y.mesh, 1.0, y.shape, y.dtype).output... | python | def gradients(ys, xs, grad_ys=None):
"""Compute gradients in dtf.
Args:
ys: a list of Tensors
xs: a list of Tensors
grad_ys: an optional list of Tensors
Returns:
grad_xs: a list of Tensors
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graph = ys[0].graph
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tensorflow/mesh | mesh_tensorflow/ops.py | _infer_binary_broadcast_shape | def _infer_binary_broadcast_shape(shape1, shape2, given_output_shape=None):
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If the output shape is not given with given_output_shape, then we check
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"""Infer shape of the output of a binary op with broadcasting.
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tensorflow/mesh | mesh_tensorflow/ops.py | _expand_dims | def _expand_dims(x, input_shape, output_shape):
"""Expand dimensions and transpose if necessary.
Args:
x: a tf.Tensor
input_shape: a Shape
output_shape: a Shape whose dimensions are a superset of
those in input_shape
Returns:
a tf.Tensor
"""
verify_no_new_dims([output_shape], input_sha... | python | def _expand_dims(x, input_shape, output_shape):
"""Expand dimensions and transpose if necessary.
Args:
x: a tf.Tensor
input_shape: a Shape
output_shape: a Shape whose dimensions are a superset of
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a tf.Tensor
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tensorflow/mesh | mesh_tensorflow/ops.py | _einsum_equation | def _einsum_equation(input_shapes, output_shape):
"""Turn shapes into an einsum equation.
e.g. "ij,jk->ik"
Args:
input_shapes: a list of Shapes
output_shape: a Shape
Returns:
a string
"""
ret = []
next_letter = ord("a")
dim_to_letter = {}
for shape_num, shape in enumerate(input_shapes + ... | python | def _einsum_equation(input_shapes, output_shape):
"""Turn shapes into an einsum equation.
e.g. "ij,jk->ik"
Args:
input_shapes: a list of Shapes
output_shape: a Shape
Returns:
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"""
ret = []
next_letter = ord("a")
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tensorflow/mesh | mesh_tensorflow/ops.py | is_subsequence | def is_subsequence(short_seq, long_seq):
"""Is short_seq a subsequence of long_seq."""
if not short_seq:
return True
pos = 0
for x in long_seq:
if pos == len(short_seq):
return True
if short_seq[pos] == x:
pos += 1
if pos == len(short_seq):
return True
return False | python | def is_subsequence(short_seq, long_seq):
"""Is short_seq a subsequence of long_seq."""
if not short_seq:
return True
pos = 0
for x in long_seq:
if pos == len(short_seq):
return True
if short_seq[pos] == x:
pos += 1
if pos == len(short_seq):
return True
return False | [
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tensorflow/mesh | mesh_tensorflow/ops.py | verify_no_new_dims | def verify_no_new_dims(input_shapes, output_shape):
"""Verifies that all dimensions in the output are in at least one input.
Args:
input_shapes: a list of Shapes
output_shape: a Shape
Raises:
ValueError: if there are new dimensions in the output.
"""
all_input_dims = set(sum([s.dims for s in inpu... | python | def verify_no_new_dims(input_shapes, output_shape):
"""Verifies that all dimensions in the output are in at least one input.
Args:
input_shapes: a list of Shapes
output_shape: a Shape
Raises:
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all_input_dims = set(sum([s.dims for s in inpu... | [
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tensorflow/mesh | mesh_tensorflow/ops.py | pnum_to_processor_coordinates | def pnum_to_processor_coordinates(mesh_shape, pnum):
"""Coordinates of a processor in the mesh.
Args:
mesh_shape: a Shape
pnum: an integer less than len(mesh_shape)
Returns:
a list of integers with length len(mesh_shape)
"""
ret = []
for dimsize in mesh_shape.to_integer_list[::-1]:
ret.app... | python | def pnum_to_processor_coordinates(mesh_shape, pnum):
"""Coordinates of a processor in the mesh.
Args:
mesh_shape: a Shape
pnum: an integer less than len(mesh_shape)
Returns:
a list of integers with length len(mesh_shape)
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ret = []
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tensorflow/mesh | mesh_tensorflow/ops.py | processor_coordinates_to_pnum | def processor_coordinates_to_pnum(mesh_shape, coord):
"""Inverse of pnum_to_processor_coordinates.
Args:
mesh_shape: a Shape
coord: a list of integers with length len(mesh_shape)
Returns:
an integer less than len(mesh_shape)
"""
ret = 0
multiplier = 1
for c, d in zip(coord[::-1], mesh_shape.... | python | def processor_coordinates_to_pnum(mesh_shape, coord):
"""Inverse of pnum_to_processor_coordinates.
Args:
mesh_shape: a Shape
coord: a list of integers with length len(mesh_shape)
Returns:
an integer less than len(mesh_shape)
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tensorflow/mesh | mesh_tensorflow/ops.py | pnum_to_group | def pnum_to_group(mesh_shape, group_dims, pnum):
"""Group number for grouped allreduce.
Args:
mesh_shape: a Shape
group_dims: a list of integers (the dimensions reduced over)
pnum: an integer
Returns:
an integer
"""
coord = pnum_to_processor_coordinates(mesh_shape, pnum)
remaining_shape = ... | python | def pnum_to_group(mesh_shape, group_dims, pnum):
"""Group number for grouped allreduce.
Args:
mesh_shape: a Shape
group_dims: a list of integers (the dimensions reduced over)
pnum: an integer
Returns:
an integer
"""
coord = pnum_to_processor_coordinates(mesh_shape, pnum)
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tensorflow/mesh | mesh_tensorflow/ops.py | processor_groups | def processor_groups(mesh_shape, group_dims):
"""Groups of processors which differ only in the given dimensions.
Args:
mesh_shape: a Shape
group_dims: a list of integers
Returns:
a list of lists of integers (processor numbers)
"""
group_numbers = [
pnum_to_group(mesh_shape, group_dims, pnu... | python | def processor_groups(mesh_shape, group_dims):
"""Groups of processors which differ only in the given dimensions.
Args:
mesh_shape: a Shape
group_dims: a list of integers
Returns:
a list of lists of integers (processor numbers)
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group_numbers = [
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tensorflow/mesh | mesh_tensorflow/ops.py | mtf_range | def mtf_range(mesh, dim, dtype, name=None):
"""Create a 1d mesh tensor with a range from [0, dim.size).
Call externally as mtf.range()
Args:
mesh: a Mesh
dim: a Dimension
dtype: a tf.DType
name: an optional string
Returns:
a Tensor
"""
dim = convert_to_dimension(dim)
with tf.variabl... | python | def mtf_range(mesh, dim, dtype, name=None):
"""Create a 1d mesh tensor with a range from [0, dim.size).
Call externally as mtf.range()
Args:
mesh: a Mesh
dim: a Dimension
dtype: a tf.DType
name: an optional string
Returns:
a Tensor
"""
dim = convert_to_dimension(dim)
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tensorflow/mesh | mesh_tensorflow/ops.py | pretty_print_counters | def pretty_print_counters(counters):
"""print counters hierarchically.
Each counter is a pair of a string and a number.
The string can have slashes, meaning that the number also counts towards
each prefix. e.g. "parameters/trainable" counts towards both "parameters"
and "parameters/trainable".
Args:
... | python | def pretty_print_counters(counters):
"""print counters hierarchically.
Each counter is a pair of a string and a number.
The string can have slashes, meaning that the number also counts towards
each prefix. e.g. "parameters/trainable" counts towards both "parameters"
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... | [
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tensorflow/mesh | mesh_tensorflow/ops.py | _parse_string_to_list_of_pairs | def _parse_string_to_list_of_pairs(s, seconds_to_int=False):
r"""Parses a string into a list of pairs.
In the input string, each pair is separated by a colon, and the delimiters
between pairs are any of " ,.;".
e.g. "rows:32,cols:32"
Args:
s: str to parse.
seconds_to_int: Boolean. If True, then the... | python | def _parse_string_to_list_of_pairs(s, seconds_to_int=False):
r"""Parses a string into a list of pairs.
In the input string, each pair is separated by a colon, and the delimiters
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tensorflow/mesh | mesh_tensorflow/ops.py | parallel | def parallel(devices, fn, *args, **kwargs):
"""Call a function once on each device.
Args:
devices: a list of n devices
fn: a function
*args: arguments, each of which is a list of length n
**kwargs: keyword-args, each of which is a list of length n
Returns:
a list of length n
Raises:
Val... | python | def parallel(devices, fn, *args, **kwargs):
"""Call a function once on each device.
Args:
devices: a list of n devices
fn: a function
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**kwargs: keyword-args, each of which is a list of length n
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tensorflow/mesh | mesh_tensorflow/ops.py | random_uniform | def random_uniform(mesh, shape, **kwargs):
"""Random uniform.
Args:
mesh: a Mesh
shape: a Shape
**kwargs: keyword args for tf.random.uniform, except seed
Returns:
a Tensor
"""
shape = convert_to_shape(shape)
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"""Random uniform.
Args:
mesh: a Mesh
shape: a Shape
**kwargs: keyword args for tf.random.uniform, except seed
Returns:
a Tensor
"""
shape = convert_to_shape(shape)
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tensorflow/mesh | mesh_tensorflow/ops.py | dropout | def dropout(x, keep_prob, noise_shape=None, name=None):
"""Dropout layer.
Args:
x: a Tensor
keep_prob: a float between 0.0 and 1.0
noise_shape: an optional Shape (a subset of x.shape)
name: an optional string
Returns:
a Tensor
"""
noise_shape = convert_to_shape(noise_shape)
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"""Dropout layer.
Args:
x: a Tensor
keep_prob: a float between 0.0 and 1.0
noise_shape: an optional Shape (a subset of x.shape)
name: an optional string
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tensorflow/mesh | mesh_tensorflow/ops.py | _cumprod | def _cumprod(l):
"""Cumulative product of a list.
Args:
l: a list of integers
Returns:
a list with one more element (starting with 1)
"""
ret = [1]
for item in l:
ret.append(ret[-1] * item)
return ret | python | def _cumprod(l):
"""Cumulative product of a list.
Args:
l: a list of integers
Returns:
a list with one more element (starting with 1)
"""
ret = [1]
for item in l:
ret.append(ret[-1] * item)
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tensorflow/mesh | mesh_tensorflow/ops.py | while_loop | def while_loop(cond_fn, body_fn, inputs, num_loop_vars=None,
has_accumulators=False, **kwargs):
"""While Loop.
See comments above for WhileLoopOperation
num_loop_vars is a hack for the multi-gpu setup. In this case, loops
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tensorflow/mesh | mesh_tensorflow/ops.py | _shape_union | def _shape_union(shapes):
"""A shape containing the union of all dimensions in the input shapes.
Args:
shapes: a list of Shapes
Returns:
a Shape
"""
return Shape(sorted(list(set(sum([s.dims for s in shapes], []))))) | python | def _shape_union(shapes):
"""A shape containing the union of all dimensions in the input shapes.
Args:
shapes: a list of Shapes
Returns:
a Shape
"""
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tensorflow/mesh | mesh_tensorflow/ops.py | _tf_flatten_batch_dims | def _tf_flatten_batch_dims(x, num_nonbatch_dims):
"""Flatten all but last num_nonbatch_dims into one dimension.
Args:
x: a tf.Tensor:
num_nonbatch_dims: an integer
Returns:
a tf.Tensor with 1 + num_nonbatch_dims dimensions.
"""
shape = x.shape.as_list()
assert None not in shape
new_shape = (... | python | def _tf_flatten_batch_dims(x, num_nonbatch_dims):
"""Flatten all but last num_nonbatch_dims into one dimension.
Args:
x: a tf.Tensor:
num_nonbatch_dims: an integer
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tensorflow/mesh | mesh_tensorflow/ops.py | _tf_restore_batch_dims | def _tf_restore_batch_dims(x, num_nonbatch_dims, prototype):
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Un-flatten the first dimension of x to match all but the last
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Args:
x: a tf.Tensor with 1 + num_nonbatch_dims dimensions
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"""Reverse op of _tf_flatten_batch_dims.
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tensorflow/mesh | mesh_tensorflow/ops.py | halo_exchange | def halo_exchange(x, blocks_dim, block_size_dim, halo_size, wrap=False):
"""Concat each block with the margins of adjacent blocks.
Get left and right blocks_dim and concatenate along block_size_dim.
Args:
x: a Tensor.
blocks_dim: a Dimension in x.shape
block_size_dim: a Dimension in x.shape
halo... | python | def halo_exchange(x, blocks_dim, block_size_dim, halo_size, wrap=False):
"""Concat each block with the margins of adjacent blocks.
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x: a Tensor.
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tensorflow/mesh | mesh_tensorflow/ops.py | conv2d_with_blocks | def conv2d_with_blocks(
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padding,
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w_blocks_dim=None,
name=None):
"""conv2d operation with spatial partitioning.
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conv_input,
conv_filter,
strides,
padding,
h_blocks_dim=None,
w_blocks_dim=None,
name=None):
"""conv2d operation with spatial partitioning.
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tensorflow/mesh | mesh_tensorflow/ops.py | tensor_dim_to_mesh_dim_size | def tensor_dim_to_mesh_dim_size(layout, mesh_shape, tensor_dim):
"""How many ways does a tensor dimension get split.
This is used to "cheat" when building the mtf graph and peek at how a
tensor dimension will be split. Returns 1 if the tensor dimension is not
split.
Args:
layout: an input to convert_to... | python | def tensor_dim_to_mesh_dim_size(layout, mesh_shape, tensor_dim):
"""How many ways does a tensor dimension get split.
This is used to "cheat" when building the mtf graph and peek at how a
tensor dimension will be split. Returns 1 if the tensor dimension is not
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tensorflow/mesh | mesh_tensorflow/ops.py | serialize_training_step | def serialize_training_step(features, model_fn, batch_dim, num_splits):
"""Break the training batch into multiple microbatches.
Returns two structures:
grads - a list of Tensors corresponding to the gradients on
graph.trainable_variables. These are summed across all microbatches
outputs - a dictionary ... | python | def serialize_training_step(features, model_fn, batch_dim, num_splits):
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grads - a list of Tensors corresponding to the gradients on
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tensorflow/mesh | mesh_tensorflow/ops.py | Shape.rename_dimension | def rename_dimension(self, old_name, new_name):
"""Returns a copy where one dimension is renamed."""
if old_name not in self.dimension_names:
raise ValueError("Shape %s does not have dimension named %s"
% (self, old_name))
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"""Returns a copy where one dimension is renamed."""
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tensorflow/mesh | mesh_tensorflow/ops.py | Shape.resize_dimension | def resize_dimension(self, name, new_size):
"""Returns a copy where one dimension has a different size."""
if name not in self.dimension_names:
raise ValueError("Shape %s does not have dimension named %s"
% (self, name))
return Shape(
[Dimension(name, new_size) if d.name... | python | def resize_dimension(self, name, new_size):
"""Returns a copy where one dimension has a different size."""
if name not in self.dimension_names:
raise ValueError("Shape %s does not have dimension named %s"
% (self, name))
return Shape(
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tensorflow/mesh | mesh_tensorflow/ops.py | LayoutRules.tensor_layout | def tensor_layout(self, tensor_shape, mesh_shape):
"""Computes TensorLayout given a Tensor Shape and a Mesh Shape.
Args:
tensor_shape: Shape.
mesh_shape: Shape.
Returns:
TensorLayout.
Raises:
ValueError: If two Tensor Dimensions map to the same Mesh Dimensions.
"""
ret... | python | def tensor_layout(self, tensor_shape, mesh_shape):
"""Computes TensorLayout given a Tensor Shape and a Mesh Shape.
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tensor_shape: Shape.
mesh_shape: Shape.
Returns:
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Raises:
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tensorflow/mesh | mesh_tensorflow/ops.py | TensorLayout.mesh_axis_to_tensor_axis | def mesh_axis_to_tensor_axis(self, mesh_ndims):
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Args:
mesh_ndims: int.
Returns:
Tuple of optional integers, with length mesh_ndims.
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"""For each mesh axis, which Tensor axis maps to it.
Args:
mesh_ndims: int.
Returns:
Tuple of optional integers, with length mesh_ndims.
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tensorflow/mesh | mesh_tensorflow/ops.py | Graph.unique_name | def unique_name(self, name, mark_as_used=True):
"""Like tf.Graph.unique_name, returns a unique operation name for `name`.
Args:
name: The name for an operation.
mark_as_used: whether to mark this name as being used.
Returns:
A string to use as the name for the operation.
"""
scop... | python | def unique_name(self, name, mark_as_used=True):
"""Like tf.Graph.unique_name, returns a unique operation name for `name`.
Args:
name: The name for an operation.
mark_as_used: whether to mark this name as being used.
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A string to use as the name for the operation.
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tensorflow/mesh | mesh_tensorflow/ops.py | Graph.combine_assignments | def combine_assignments(self, assignments):
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"""Rewrite the current graph to combine "Assign" operations.
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tensorflow/mesh | mesh_tensorflow/ops.py | MeshImpl.tensor_layout | def tensor_layout(self, arg):
"""Compute TensorLayout for a Tensor or a Shape.
Args:
arg: Tensor or Shape.
Returns:
TensorLayout.
"""
if isinstance(arg, Tensor):
arg = arg.shape
return self.layout_rules.tensor_layout(arg, self.shape) | python | def tensor_layout(self, arg):
"""Compute TensorLayout for a Tensor or a Shape.
Args:
arg: Tensor or Shape.
Returns:
TensorLayout.
"""
if isinstance(arg, Tensor):
arg = arg.shape
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tensorflow/mesh | mesh_tensorflow/ops.py | MeshImpl.mesh_axis_to_cumprod | def mesh_axis_to_cumprod(self, tensor_shape):
"""For each mesh axis, give the product of previous tensor axes.
Args:
tensor_shape: Shape.
Returns:
list with length self.ndims where each element is an integer or None.
"""
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"""For each mesh axis, give the product of previous tensor axes.
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tensor_shape: Shape.
Returns:
list with length self.ndims where each element is an integer or None.
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tensorflow/mesh | mesh_tensorflow/ops.py | MeshImpl.slice_shape | def slice_shape(self, tensor_shape):
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Args:
tensor_shape: Shape.
Returns:
list of integers with length tensor_shape.ndims.
Raises:
ValueError: If a Tensor dimension is not divisible by the corresponding
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"""
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Args:
tensor_shape: Shape.
Returns:
list of integers with length tensor_shape.ndims.
Raises:
ValueError: If a Tensor dimension is not divisible by the corresponding
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tensorflow/mesh | mesh_tensorflow/ops.py | MeshImpl.slice_begin | def slice_begin(self, tensor_shape, pnum):
"""Begin position for the tensor slice for the given processor.
Args:
tensor_shape: Shape.
pnum: int <= self.size.
Returns:
list of integers with length tensor_shape.ndims.
"""
tensor_layout = self.tensor_layout(tensor_shape)
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"""Begin position for the tensor slice for the given processor.
Args:
tensor_shape: Shape.
pnum: int <= self.size.
Returns:
list of integers with length tensor_shape.ndims.
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tensor_layout = self.tensor_layout(tensor_shape)
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tensorflow/mesh | mesh_tensorflow/ops.py | MeshImpl.Print | def Print(self, x, data, message, **kwargs): # pylint: disable=invalid-name
"""Calls tf.Print.
Args:
x: LaidOutTensor.
data: list of LaidOutTensor.
message: str.
**kwargs: keyword arguments to tf.print.
Returns:
LaidOutTensor.
"""
del data, message, kwargs
tf.log... | python | def Print(self, x, data, message, **kwargs): # pylint: disable=invalid-name
"""Calls tf.Print.
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data: list of LaidOutTensor.
message: str.
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tensorflow/mesh | mesh_tensorflow/ops.py | MeshImpl.allsplit | def allsplit(self, x, mesh_axis, split_axis, which=None):
"""Inverse of allconcat - split each slice and keep only one piece of it.
The number of ways to split is the number of processors in the group.
The part that is kept corresponds to the processor's index in the group.
Args:
x: LaidOutTenso... | python | def allsplit(self, x, mesh_axis, split_axis, which=None):
"""Inverse of allconcat - split each slice and keep only one piece of it.
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tensorflow/mesh | mesh_tensorflow/ops.py | MeshImpl.shift_by_n_processors | def shift_by_n_processors(self, x, mesh_axis, offset, wrap):
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Args:
x: a LaidOutTensor
mesh_axis: an integer
offset: an integer
wrap: a boolean. If True, then wrap around. Otherwise, pad with zeros.
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n = self.shape[mesh_axis... | python | def shift_by_n_processors(self, x, mesh_axis, offset, wrap):
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x: a LaidOutTensor
mesh_axis: an integer
offset: an integer
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tensorflow/mesh | mesh_tensorflow/ops.py | MeshImpl.laid_out_pcoord | def laid_out_pcoord(self, mesh_axis):
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mesh_axis: int.
Returns:
LaidOutTensor where each slice is an integer scalar.
"""
divisor = list_product(self.shape.to_integer_list[mesh_axis + 1:])
modulus = self.shape[mesh... | python | def laid_out_pcoord(self, mesh_axis):
"""Returns a LaidOutTensor containing the processor coordinate.
Args:
mesh_axis: int.
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tensorflow/mesh | mesh_tensorflow/ops.py | MeshImpl.laid_out_slice_num | def laid_out_slice_num(self, tensor_shape):
"""A LaidOutTensor with an int32 scalar, identical for identical slices.
This is useful for synchronizing random operations.
Args:
tensor_shape: a TensorShape
Returns:
a LaidOutTensor where each slice is an integer scalar.
"""
ret = self.... | python | def laid_out_slice_num(self, tensor_shape):
"""A LaidOutTensor with an int32 scalar, identical for identical slices.
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tensor_shape: a TensorShape
Returns:
a LaidOutTensor where each slice is an integer scalar.
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tensorflow/mesh | mesh_tensorflow/ops.py | MeshImpl.broadcast_impl | def broadcast_impl(self, old_slices, old_shape, new_shape):
"""Implementation of a broadcast operation.
Args:
old_slices: LaidOutTensor.
old_shape: Shape.
new_shape: Shape.
Returns:
LaidOutTensor.
"""
new_slice_shape = self.slice_shape(new_shape)
def tf_fn(x):
ret... | python | def broadcast_impl(self, old_slices, old_shape, new_shape):
"""Implementation of a broadcast operation.
Args:
old_slices: LaidOutTensor.
old_shape: Shape.
new_shape: Shape.
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tensorflow/mesh | mesh_tensorflow/ops.py | MeshImpl.make_slices | def make_slices(self, tf_tensor, tensor_shape):
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Args:
tf_tensor: tf.Tensor.
tensor_shape: Shape.
Returns:
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tensorflow/mesh | mesh_tensorflow/ops.py | MeshImpl.combine_slices | def combine_slices(self, slices, tensor_shape, device=None):
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tensor_shape: Shape.
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tensorflow/mesh | mesh_tensorflow/ops.py | Operation._initialize_splittable_and_unsplittable_dims | def _initialize_splittable_and_unsplittable_dims(
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default... | python | def _initialize_splittable_and_unsplittable_dims(
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tensorflow/mesh | mesh_tensorflow/ops.py | ReshapeOperation.lower | def lower(self, lowering):
"""Lower the ReshapeOperation.
Reshaping can require collective communication between processors.
We haven't yet implemented all possible reshapes. We try to handle the
common cases here - otherwise we raise a NotImplementedError.
Args:
lowering: a Lowering
Ra... | python | def lower(self, lowering):
"""Lower the ReshapeOperation.
Reshaping can require collective communication between processors.
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common cases here - otherwise we raise a NotImplementedError.
Args:
lowering: a Lowering
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tensorflow/mesh | mesh_tensorflow/transformer/utils.py | get_variable_dtype | def get_variable_dtype(
master_dtype=tf.bfloat16,
slice_dtype=tf.float32,
activation_dtype=tf.float32):
"""Datatypes to use for the run.
Args:
master_dtype: string, datatype for checkpoints
keep this the same between training and eval/inference
slice_dtype: string, datatype for variables ... | python | def get_variable_dtype(
master_dtype=tf.bfloat16,
slice_dtype=tf.float32,
activation_dtype=tf.float32):
"""Datatypes to use for the run.
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master_dtype: string, datatype for checkpoints
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tensorflow/mesh | mesh_tensorflow/transformer/utils.py | build_model | def build_model(model_type="bitransformer",
input_vocab_size=gin.REQUIRED,
output_vocab_size=gin.REQUIRED,
layout_rules=None,
mesh_shape=None):
"""Build a transformer model.
Currently, three types of models are supported:
"bitransformer": The tradi... | python | def build_model(model_type="bitransformer",
input_vocab_size=gin.REQUIRED,
output_vocab_size=gin.REQUIRED,
layout_rules=None,
mesh_shape=None):
"""Build a transformer model.
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tensorflow/mesh | mesh_tensorflow/transformer/utils.py | decode_from_file | def decode_from_file(estimator,
vocabulary,
model_type,
batch_size,
sequence_length,
checkpoint_path="",
input_filename=gin.REQUIRED,
output_filename=gin.REQUIRED,
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input_filename=gin.REQUIRED,
output_filename=gin.REQUIRED,
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tensorflow/mesh | mesh_tensorflow/transformer/utils.py | clean_decodes | def clean_decodes(ids, vocab_size, eos_id=1):
"""Stop at EOS or padding or OOV.
Args:
ids: a list of integers
vocab_size: an integer
eos_id: EOS id
Returns:
a list of integers
"""
ret = []
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break
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break
ret.append(int(... | python | def clean_decodes(ids, vocab_size, eos_id=1):
"""Stop at EOS or padding or OOV.
Args:
ids: a list of integers
vocab_size: an integer
eos_id: EOS id
Returns:
a list of integers
"""
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tensorflow/mesh | mesh_tensorflow/transformer/utils.py | auto_batch_size | def auto_batch_size(sequence_length,
mesh_shape,
layout_rules,
tokens_per_split=2048):
"""Automatically compute batch size.
Args:
sequence_length: an integer
mesh_shape: an input to mtf.convert_to_shape()
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mesh_shape,
layout_rules,
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sequence_length: an integer
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tensorflow/mesh | mesh_tensorflow/transformer/utils.py | evaluate | def evaluate(estimator, eval_args):
"""Runs evaluation on the latest model checkpoint & logs to tensorboard.
Args:
estimator: A tf.Estimator object.
eval_args: Dictionary of {eval_name: (input_fn, eval_steps)} where eval_name
is the name of the evaluation set, e.g. "train" or "val", input_fn is an
... | python | def evaluate(estimator, eval_args):
"""Runs evaluation on the latest model checkpoint & logs to tensorboard.
Args:
estimator: A tf.Estimator object.
eval_args: Dictionary of {eval_name: (input_fn, eval_steps)} where eval_name
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tensorflow/mesh | mesh_tensorflow/simd_mesh_impl.py | _ring_2d | def _ring_2d(m, n):
"""Ring-order of a mxn mesh.
Args:
m: an integer
n: an integer
Returns:
a list of mxn pairs
"""
if m == 1:
return [(0, i) for i in range(n)]
if n == 1:
return [(i, 0) for i in range(m)]
if m % 2 != 0:
tf.logging.warning("Odd dimension")
return [(i % m, i //... | python | def _ring_2d(m, n):
"""Ring-order of a mxn mesh.
Args:
m: an integer
n: an integer
Returns:
a list of mxn pairs
"""
if m == 1:
return [(0, i) for i in range(n)]
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tensorflow/mesh | mesh_tensorflow/simd_mesh_impl.py | tile_2d | def tile_2d(physical_shape, tile_shape,
outer_name="outer",
inner_name="inner",
cores_name=None):
"""2D tiling of a 3d physical mesh.
The "outer" mesh dimension corresponds to which tile.
The "inner" mesh dimension corresponds to the position within a tile
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outer_name="outer",
inner_name="inner",
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"""2D tiling of a 3d physical mesh.
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tensorflow/mesh | mesh_tensorflow/simd_mesh_impl.py | SimdMeshImpl.slice | def slice(self, tf_tensor, tensor_shape):
""""Slice out the corresponding part of tensor given the pnum variable."""
tensor_layout = self.tensor_layout(tensor_shape)
if tensor_layout.is_fully_replicated:
return self.LaidOutTensor([tf_tensor])
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""""Slice out the corresponding part of tensor given the pnum variable."""
tensor_layout = self.tensor_layout(tensor_shape)
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tensorflow/mesh | examples/mnist_dataset.py | read32 | def read32(bytestream):
"""Read 4 bytes from bytestream as an unsigned 32-bit integer."""
dt = np.dtype(np.uint32).newbyteorder('>')
return np.frombuffer(bytestream.read(4), dtype=dt)[0] | python | def read32(bytestream):
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"""Validate that filename corresponds to images for the MNIST dataset."""
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read32(f) # num_images, unused
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tensorflow/mesh | examples/mnist_dataset.py | dataset | def dataset(directory, images_file, labels_file):
"""Download and parse MNIST dataset."""
images_file = download(directory, images_file)
labels_file = download(directory, labels_file)
check_image_file_header(images_file)
check_labels_file_header(labels_file)
def decode_image(image):
# Normalize from ... | python | def dataset(directory, images_file, labels_file):
"""Download and parse MNIST dataset."""
images_file = download(directory, images_file)
labels_file = download(directory, labels_file)
check_image_file_header(images_file)
check_labels_file_header(labels_file)
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mattjj/pyhsmm | pyhsmm/util/stats.py | sample_discrete | def sample_discrete(distn,size=[],dtype=np.int32):
'samples from a one-dimensional finite pmf'
distn = np.atleast_1d(distn)
assert (distn >=0).all() and distn.ndim == 1
if (0 == distn).all():
return np.random.randint(distn.shape[0],size=size)
cumvals = np.cumsum(distn)
return np.sum(np.a... | python | def sample_discrete(distn,size=[],dtype=np.int32):
'samples from a one-dimensional finite pmf'
distn = np.atleast_1d(distn)
assert (distn >=0).all() and distn.ndim == 1
if (0 == distn).all():
return np.random.randint(distn.shape[0],size=size)
cumvals = np.cumsum(distn)
return np.sum(np.a... | [
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mattjj/pyhsmm | pyhsmm/models.py | _HMMBase.used_states | def used_states(self):
'a list of the used states in the order they appear'
c = itertools.count()
canonical_ids = collections.defaultdict(lambda: next(c))
for s in self.states_list:
for state in s.stateseq:
canonical_ids[state]
return list(map(operator... | python | def used_states(self):
'a list of the used states in the order they appear'
c = itertools.count()
canonical_ids = collections.defaultdict(lambda: next(c))
for s in self.states_list:
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mattjj/pyhsmm | pyhsmm/util/plot.py | plot_gaussian_2D | def plot_gaussian_2D(mu, lmbda, color='b', centermarker=True,label='',alpha=1.,ax=None,artists=None):
'''
Plots mean and cov ellipsoid into current axes. Must be 2D. lmbda is a covariance matrix.
'''
assert len(mu) == 2
ax = ax if ax else plt.gca()
# TODO use artists!
t = np.hstack([np.ara... | python | def plot_gaussian_2D(mu, lmbda, color='b', centermarker=True,label='',alpha=1.,ax=None,artists=None):
'''
Plots mean and cov ellipsoid into current axes. Must be 2D. lmbda is a covariance matrix.
'''
assert len(mu) == 2
ax = ax if ax else plt.gca()
# TODO use artists!
t = np.hstack([np.ara... | [
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mattjj/pyhsmm | pyhsmm/basic/abstractions.py | DurationDistribution.resample_with_censoring | def resample_with_censoring(self,data=[],censored_data=[]):
'''
censored_data is full of observations that were censored, meaning a
value of x really could have been anything >= x, so this method samples
them out to be at least that large
'''
filled_in = self._uncensor_da... | python | def resample_with_censoring(self,data=[],censored_data=[]):
'''
censored_data is full of observations that were censored, meaning a
value of x really could have been anything >= x, so this method samples
them out to be at least that large
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mattjj/pyhsmm | pyhsmm/util/general.py | scoreatpercentile | def scoreatpercentile(data,per,axis=0):
'like the function in scipy.stats but with an axis argument and works on arrays'
a = np.sort(data,axis=axis)
idx = per/100. * (data.shape[axis]-1)
if (idx % 1 == 0):
return a[[slice(None) if ii != axis else idx for ii in range(a.ndim)]]
else:
... | python | def scoreatpercentile(data,per,axis=0):
'like the function in scipy.stats but with an axis argument and works on arrays'
a = np.sort(data,axis=axis)
idx = per/100. * (data.shape[axis]-1)
if (idx % 1 == 0):
return a[[slice(None) if ii != axis else idx for ii in range(a.ndim)]]
else:
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lk-geimfari/mimesis | mimesis/providers/internet.py | Internet.content_type | def content_type(self, mime_type: Optional[MimeType] = None) -> str:
"""Get a random HTTP content type.
:return: Content type.
:Example:
Content-Type: application/json
"""
fmt = self.__file.mime_type(type_=mime_type)
return 'Content-Type: {}'.format(fmt) | python | def content_type(self, mime_type: Optional[MimeType] = None) -> str:
"""Get a random HTTP content type.
:return: Content type.
:Example:
Content-Type: application/json
"""
fmt = self.__file.mime_type(type_=mime_type)
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lk-geimfari/mimesis | mimesis/providers/internet.py | Internet.ip_v4 | def ip_v4(self, with_port: bool = False) -> str:
"""Generate a random IPv4 address.
:param with_port: Add port to IP.
:return: Random IPv4 address.
:Example:
19.121.223.58
"""
ip = '.'.join(str(self.random.randint(0, 255)) for _ in range(4))
if with... | python | def ip_v4(self, with_port: bool = False) -> str:
"""Generate a random IPv4 address.
:param with_port: Add port to IP.
:return: Random IPv4 address.
:Example:
19.121.223.58
"""
ip = '.'.join(str(self.random.randint(0, 255)) for _ in range(4))
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lk-geimfari/mimesis | mimesis/providers/internet.py | Internet.ip_v6 | def ip_v6(self) -> str:
"""Generate a random IPv6 address.
:return: Random IPv6 address.
:Example:
2001:c244:cf9d:1fb1:c56d:f52c:8a04:94f3
"""
ipv6 = IPv6Address(
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retu... | python | def ip_v6(self) -> str:
"""Generate a random IPv6 address.
:return: Random IPv6 address.
:Example:
2001:c244:cf9d:1fb1:c56d:f52c:8a04:94f3
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ipv6 = IPv6Address(
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lk-geimfari/mimesis | mimesis/providers/internet.py | Internet.mac_address | def mac_address(self) -> str:
"""Generate a random MAC address.
:return: Random MAC address.
:Example:
00:16:3e:25:e7:b1
"""
mac_hex = [
0x00, 0x16, 0x3e,
self.random.randint(0x00, 0x7f),
self.random.randint(0x00, 0xff),
... | python | def mac_address(self) -> str:
"""Generate a random MAC address.
:return: Random MAC address.
:Example:
00:16:3e:25:e7:b1
"""
mac_hex = [
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lk-geimfari/mimesis | mimesis/providers/internet.py | Internet.image_placeholder | def image_placeholder(width: Union[int, str] = 1920,
height: Union[int, str] = 1080) -> str:
"""Generate a link to the image placeholder.
:param width: Width of image.
:param height: Height of image.
:return: URL to image placeholder.
"""
url = ... | python | def image_placeholder(width: Union[int, str] = 1920,
height: Union[int, str] = 1080) -> str:
"""Generate a link to the image placeholder.
:param width: Width of image.
:param height: Height of image.
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lk-geimfari/mimesis | mimesis/providers/internet.py | Internet.hashtags | def hashtags(self, quantity: int = 4) -> Union[str, list]:
"""Generate a list of hashtags.
:param quantity: The quantity of hashtags.
:return: The list of hashtags.
:raises NonEnumerableError: if category is not in Hashtag.
:Example:
['#love', '#sky', '#nice']
... | python | def hashtags(self, quantity: int = 4) -> Union[str, list]:
"""Generate a list of hashtags.
:param quantity: The quantity of hashtags.
:return: The list of hashtags.
:raises NonEnumerableError: if category is not in Hashtag.
:Example:
['#love', '#sky', '#nice']
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"... | Generate a list of hashtags.
:param quantity: The quantity of hashtags.
:return: The list of hashtags.
:raises NonEnumerableError: if category is not in Hashtag.
:Example:
['#love', '#sky', '#nice'] | [
"Generate",
"a",
"list",
"of",
"hashtags",
"."
] | 4b16ee7a8dba6281a904654a88dbb4b052869fc5 | https://github.com/lk-geimfari/mimesis/blob/4b16ee7a8dba6281a904654a88dbb4b052869fc5/mimesis/providers/internet.py#L191-L207 | train |
lk-geimfari/mimesis | mimesis/providers/internet.py | Internet.home_page | def home_page(self, tld_type: Optional[TLDType] = None) -> str:
"""Generate a random home page.
:param tld_type: TLD type.
:return: Random home page.
:Example:
http://www.fontir.info
"""
resource = self.random.choice(USERNAMES)
domain = self.top_leve... | python | def home_page(self, tld_type: Optional[TLDType] = None) -> str:
"""Generate a random home page.
:param tld_type: TLD type.
:return: Random home page.
:Example:
http://www.fontir.info
"""
resource = self.random.choice(USERNAMES)
domain = self.top_leve... | [
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:param tld_type: TLD type.
:return: Random home page.
:Example:
http://www.fontir.info | [
"Generate",
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"random",
"home",
"page",
"."
] | 4b16ee7a8dba6281a904654a88dbb4b052869fc5 | https://github.com/lk-geimfari/mimesis/blob/4b16ee7a8dba6281a904654a88dbb4b052869fc5/mimesis/providers/internet.py#L209-L224 | train |
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