/
bincount_ops.py
529 lines (458 loc) · 20.6 KB
/
bincount_ops.py
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# Copyright 2020 The TensorFlow Authors. All Rights Reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# maxlengthations under the License.
# ==============================================================================
"""bincount ops."""
from tensorflow.python.framework import constant_op
from tensorflow.python.framework import dtypes
from tensorflow.python.framework import ops
from tensorflow.python.framework import sparse_tensor
from tensorflow.python.ops import array_ops
from tensorflow.python.ops import check_ops
from tensorflow.python.ops import gen_count_ops
from tensorflow.python.ops import gen_math_ops
from tensorflow.python.ops import math_ops
from tensorflow.python.ops.ragged import ragged_tensor
from tensorflow.python.util import deprecation
from tensorflow.python.util.tf_export import tf_export
@tf_export("math.bincount", v1=[])
def bincount(arr,
weights=None,
minlength=None,
maxlength=None,
dtype=dtypes.int32,
name=None,
axis=None,
binary_output=False):
"""Counts the number of occurrences of each value in an integer array.
If `minlength` and `maxlength` are not given, returns a vector with length
`tf.reduce_max(arr) + 1` if `arr` is non-empty, and length 0 otherwise.
If `weights` are non-None, then index `i` of the output stores the sum of the
value in `weights` at each index where the corresponding value in `arr` is
`i`.
```python
values = tf.constant([1,1,2,3,2,4,4,5])
tf.math.bincount(values) #[0 2 2 1 2 1]
```
Vector length = Maximum element in vector `values` is 5. Adding 1, which is 6
will be the vector length.
Each bin value in the output indicates number of occurrences of the particular
index. Here, index 1 in output has a value 2. This indicates value 1 occurs
two times in `values`.
```python
values = tf.constant([1,1,2,3,2,4,4,5])
weights = tf.constant([1,5,0,1,0,5,4,5])
tf.math.bincount(values, weights=weights) #[0 6 0 1 9 5]
```
Bin will be incremented by the corresponding weight instead of 1.
Here, index 1 in output has a value 6. This is the summation of weights
corresponding to the value in `values`.
**Bin-counting on a certain axis**
This example takes a 2 dimensional input and returns a `Tensor` with
bincounting on each sample.
>>> data = np.array([[1, 2, 3, 0], [0, 0, 1, 2]], dtype=np.int32)
>>> tf.math.bincount(data, axis=-1)
<tf.Tensor: shape=(2, 4), dtype=int32, numpy=
array([[1, 1, 1, 1],
[2, 1, 1, 0]], dtype=int32)>
**Bin-counting with binary_output**
This example gives binary output instead of counting the occurrence.
>>> data = np.array([[1, 2, 3, 0], [0, 0, 1, 2]], dtype=np.int32)
>>> tf.math.bincount(data, axis=-1, binary_output=True)
<tf.Tensor: shape=(2, 4), dtype=int32, numpy=
array([[1, 1, 1, 1],
[1, 1, 1, 0]], dtype=int32)>
Args:
arr: A Tensor, RaggedTensor, or SparseTensor whose values should be counted.
These tensors must have a rank of 2 if `axis=-1`.
weights: If non-None, must be the same shape as arr. For each value in
`arr`, the bin will be incremented by the corresponding weight instead of
1.
minlength: If given, ensures the output has length at least `minlength`,
padding with zeros at the end if necessary.
maxlength: If given, skips values in `arr` that are equal or greater than
`maxlength`, ensuring that the output has length at most `maxlength`.
dtype: If `weights` is None, determines the type of the output bins.
name: A name scope for the associated operations (optional).
axis: The axis to slice over. Axes at and below `axis` will be flattened
before bin counting. Currently, only `0`, and `-1` are supported. If None,
all axes will be flattened (identical to passing `0`).
binary_output: If True, this op will output 1 instead of the number of times
a token appears (equivalent to one_hot + reduce_any instead of one_hot +
reduce_add). Defaults to False.
Returns:
A vector with the same dtype as `weights` or the given `dtype`. The bin
values.
Raises:
`InvalidArgumentError` if negative values are provided as an input.
"""
name = "bincount" if name is None else name
with ops.name_scope(name):
# Somehow forward compatible needs to be False.
if not binary_output and axis is None:
arr = ops.convert_to_tensor(arr, name="arr", dtype=dtypes.int32)
array_is_nonempty = math_ops.reduce_prod(array_ops.shape(arr)) > 0
output_size = math_ops.cast(array_is_nonempty, dtypes.int32) * (
math_ops.reduce_max(arr) + 1)
if minlength is not None:
minlength = ops.convert_to_tensor(
minlength, name="minlength", dtype=dtypes.int32)
output_size = gen_math_ops.maximum(minlength, output_size)
if maxlength is not None:
maxlength = ops.convert_to_tensor(
maxlength, name="maxlength", dtype=dtypes.int32)
output_size = gen_math_ops.minimum(maxlength, output_size)
if weights is not None:
weights = ops.convert_to_tensor(weights, name="weights")
return gen_math_ops.unsorted_segment_sum(weights, arr, output_size)
weights = constant_op.constant([], dtype)
return gen_math_ops.bincount(arr, output_size, weights)
if not isinstance(arr, sparse_tensor.SparseTensor):
arr = ragged_tensor.convert_to_tensor_or_ragged_tensor(arr, name="arr")
if weights is not None:
if not isinstance(weights, sparse_tensor.SparseTensor):
weights = ragged_tensor.convert_to_tensor_or_ragged_tensor(
weights, name="weights")
if weights is not None and binary_output:
raise ValueError("Arguments `binary_output` and `weights` are mutually "
"exclusive. Please specify only one.")
if not arr.dtype.is_integer:
arr = math_ops.cast(arr, dtypes.int32)
if axis is None:
axis = 0
if axis not in [0, -1]:
raise ValueError(f"Unsupported value for argument axis={axis}. Only 0 and"
" -1 are currently supported.")
if isinstance(arr, ragged_tensor.RaggedTensor):
array_is_nonempty = math_ops.reduce_prod(array_ops.shape(arr.values)) > 0
else:
array_is_nonempty = math_ops.reduce_prod(array_ops.shape(arr)) > 0
if isinstance(arr, sparse_tensor.SparseTensor):
output_size = math_ops.cast(array_is_nonempty, arr.dtype) * (
math_ops.reduce_max(arr.values) + 1)
else:
output_size = math_ops.cast(array_is_nonempty, arr.dtype) * (
math_ops.reduce_max(arr) + 1)
if minlength is not None:
minlength = ops.convert_to_tensor(
minlength, name="minlength", dtype=arr.dtype)
output_size = gen_math_ops.maximum(minlength, output_size)
if maxlength is not None:
maxlength = ops.convert_to_tensor(
maxlength, name="maxlength", dtype=arr.dtype)
output_size = gen_math_ops.minimum(maxlength, output_size)
if axis == 0:
if isinstance(arr, sparse_tensor.SparseTensor):
if weights is not None:
weights = validate_sparse_weights(arr, weights, dtype)
arr = arr.values
elif isinstance(arr, ragged_tensor.RaggedTensor):
if weights is not None:
weights = validate_ragged_weights(arr, weights, dtype)
arr = arr.values
else:
if weights is not None:
weights = array_ops.reshape(weights, [-1])
arr = array_ops.reshape(arr, [-1])
if isinstance(arr, sparse_tensor.SparseTensor):
weights = validate_sparse_weights(arr, weights, dtype)
return gen_math_ops.sparse_bincount(
indices=arr.indices,
values=arr.values,
dense_shape=arr.dense_shape,
size=output_size,
weights=weights,
binary_output=binary_output)
elif isinstance(arr, ragged_tensor.RaggedTensor):
weights = validate_ragged_weights(arr, weights, dtype)
return gen_math_ops.ragged_bincount(
splits=arr.row_splits,
values=arr.values,
size=output_size,
weights=weights,
binary_output=binary_output)
else:
weights = validate_dense_weights(arr, weights, dtype)
return gen_math_ops.dense_bincount(
input=arr,
size=output_size,
weights=weights,
binary_output=binary_output)
@tf_export(v1=["math.bincount", "bincount"])
@deprecation.deprecated_endpoints("bincount")
def bincount_v1(arr,
weights=None,
minlength=None,
maxlength=None,
dtype=dtypes.int32):
"""Counts the number of occurrences of each value in an integer array.
If `minlength` and `maxlength` are not given, returns a vector with length
`tf.reduce_max(arr) + 1` if `arr` is non-empty, and length 0 otherwise.
If `weights` are non-None, then index `i` of the output stores the sum of the
value in `weights` at each index where the corresponding value in `arr` is
`i`.
Args:
arr: An int32 tensor of non-negative values.
weights: If non-None, must be the same shape as arr. For each value in
`arr`, the bin will be incremented by the corresponding weight instead of
1.
minlength: If given, ensures the output has length at least `minlength`,
padding with zeros at the end if necessary.
maxlength: If given, skips values in `arr` that are equal or greater than
`maxlength`, ensuring that the output has length at most `maxlength`.
dtype: If `weights` is None, determines the type of the output bins.
Returns:
A vector with the same dtype as `weights` or the given `dtype`. The bin
values.
"""
return bincount(arr, weights, minlength, maxlength, dtype)
@tf_export("sparse.bincount")
def sparse_bincount(values,
weights=None,
axis=0,
minlength=None,
maxlength=None,
binary_output=False,
name=None):
"""Count the number of times an integer value appears in a tensor.
This op takes an N-dimensional `Tensor`, `RaggedTensor`, or `SparseTensor`,
and returns an N-dimensional int64 SparseTensor where element
`[i0...i[axis], j]` contains the number of times the value `j` appears in
slice `[i0...i[axis], :]` of the input tensor. Currently, only N=0 and
N=-1 are supported.
Args:
values: A Tensor, RaggedTensor, or SparseTensor whose values should be
counted. These tensors must have a rank of 2 if `axis=-1`.
weights: If non-None, must be the same shape as arr. For each value in
`value`, the bin will be incremented by the corresponding weight instead
of 1.
axis: The axis to slice over. Axes at and below `axis` will be flattened
before bin counting. Currently, only `0`, and `-1` are supported. If None,
all axes will be flattened (identical to passing `0`).
minlength: If given, ensures the output has length at least `minlength`,
padding with zeros at the end if necessary.
maxlength: If given, skips values in `values` that are equal or greater than
`maxlength`, ensuring that the output has length at most `maxlength`.
binary_output: If True, this op will output 1 instead of the number of times
a token appears (equivalent to one_hot + reduce_any instead of one_hot +
reduce_add). Defaults to False.
name: A name for this op.
Returns:
A SparseTensor with `output.shape = values.shape[:axis] + [N]`, where `N` is
* `maxlength` (if set);
* `minlength` (if set, and `minlength > reduce_max(values)`);
* `0` (if `values` is empty);
* `reduce_max(values) + 1` otherwise.
Examples:
**Bin-counting every item in individual batches**
This example takes an input (which could be a Tensor, RaggedTensor, or
SparseTensor) and returns a SparseTensor where the value of (i,j) is the
number of times value j appears in batch i.
>>> data = np.array([[10, 20, 30, 20], [11, 101, 11, 10001]], dtype=np.int64)
>>> output = tf.sparse.bincount(data, axis=-1)
>>> print(output)
SparseTensor(indices=tf.Tensor(
[[ 0 10]
[ 0 20]
[ 0 30]
[ 1 11]
[ 1 101]
[ 1 10001]], shape=(6, 2), dtype=int64),
values=tf.Tensor([1 2 1 2 1 1], shape=(6,), dtype=int64),
dense_shape=tf.Tensor([ 2 10002], shape=(2,), dtype=int64))
**Bin-counting with defined output shape**
This example takes an input (which could be a Tensor, RaggedTensor, or
SparseTensor) and returns a SparseTensor where the value of (i,j) is the
number of times value j appears in batch i. However, all values of j
above 'maxlength' are ignored. The dense_shape of the output sparse tensor
is set to 'minlength'. Note that, while the input is identical to the
example above, the value '10001' in batch item 2 is dropped, and the
dense shape is [2, 500] instead of [2,10002] or [2, 102].
>>> minlength = maxlength = 500
>>> data = np.array([[10, 20, 30, 20], [11, 101, 11, 10001]], dtype=np.int64)
>>> output = tf.sparse.bincount(
... data, axis=-1, minlength=minlength, maxlength=maxlength)
>>> print(output)
SparseTensor(indices=tf.Tensor(
[[ 0 10]
[ 0 20]
[ 0 30]
[ 1 11]
[ 1 101]], shape=(5, 2), dtype=int64),
values=tf.Tensor([1 2 1 2 1], shape=(5,), dtype=int64),
dense_shape=tf.Tensor([ 2 500], shape=(2,), dtype=int64))
**Binary bin-counting**
This example takes an input (which could be a Tensor, RaggedTensor, or
SparseTensor) and returns a SparseTensor where (i,j) is 1 if the value j
appears in batch i at least once and is 0 otherwise. Note that, even though
some values (like 20 in batch 1 and 11 in batch 2) appear more than once,
the 'values' tensor is all 1s.
>>> data = np.array([[10, 20, 30, 20], [11, 101, 11, 10001]], dtype=np.int64)
>>> output = tf.sparse.bincount(data, binary_output=True, axis=-1)
>>> print(output)
SparseTensor(indices=tf.Tensor(
[[ 0 10]
[ 0 20]
[ 0 30]
[ 1 11]
[ 1 101]
[ 1 10001]], shape=(6, 2), dtype=int64),
values=tf.Tensor([1 1 1 1 1 1], shape=(6,), dtype=int64),
dense_shape=tf.Tensor([ 2 10002], shape=(2,), dtype=int64))
**Weighted bin-counting**
This example takes two inputs - a values tensor and a weights tensor. These
tensors must be identically shaped, and have the same row splits or indices
in the case of RaggedTensors or SparseTensors. When performing a weighted
count, the op will output a SparseTensor where the value of (i, j) is the
sum of the values in the weight tensor's batch i in the locations where
the values tensor has the value j. In this case, the output dtype is the
same as the dtype of the weights tensor.
>>> data = np.array([[10, 20, 30, 20], [11, 101, 11, 10001]], dtype=np.int64)
>>> weights = [[2, 0.25, 15, 0.5], [2, 17, 3, 0.9]]
>>> output = tf.sparse.bincount(data, weights=weights, axis=-1)
>>> print(output)
SparseTensor(indices=tf.Tensor(
[[ 0 10]
[ 0 20]
[ 0 30]
[ 1 11]
[ 1 101]
[ 1 10001]], shape=(6, 2), dtype=int64),
values=tf.Tensor([2. 0.75 15. 5. 17. 0.9], shape=(6,), dtype=float32),
dense_shape=tf.Tensor([ 2 10002], shape=(2,), dtype=int64))
"""
with ops.name_scope(name, "count", [values, weights]):
if not isinstance(values, sparse_tensor.SparseTensor):
values = ragged_tensor.convert_to_tensor_or_ragged_tensor(
values, name="values")
if weights is not None:
if not isinstance(weights, sparse_tensor.SparseTensor):
weights = ragged_tensor.convert_to_tensor_or_ragged_tensor(
weights, name="weights")
if weights is not None and binary_output:
raise ValueError("Arguments `binary_output` and `weights` are mutually "
"exclusive. Please specify only one.")
if axis is None:
axis = 0
if axis not in [0, -1]:
raise ValueError(f"Unsupported value for argument axis={axis}. Only 0 and"
" -1 are currently supported.")
minlength_value = minlength if minlength is not None else -1
maxlength_value = maxlength if maxlength is not None else -1
if axis == 0:
if isinstance(values, sparse_tensor.SparseTensor):
if weights is not None:
weights = validate_sparse_weights(values, weights)
values = values.values
elif isinstance(values, ragged_tensor.RaggedTensor):
if weights is not None:
weights = validate_ragged_weights(values, weights)
values = values.values
else:
if weights is not None:
weights = array_ops.reshape(weights, [-1])
values = array_ops.reshape(values, [-1])
if isinstance(values, sparse_tensor.SparseTensor):
weights = validate_sparse_weights(values, weights)
c_ind, c_val, c_shape = gen_count_ops.sparse_count_sparse_output(
values.indices,
values.values,
values.dense_shape,
weights,
minlength=minlength_value,
maxlength=maxlength_value,
binary_output=binary_output)
elif isinstance(values, ragged_tensor.RaggedTensor):
weights = validate_ragged_weights(values, weights)
c_ind, c_val, c_shape = gen_count_ops.ragged_count_sparse_output(
values.row_splits,
values.values,
weights,
minlength=minlength_value,
maxlength=maxlength_value,
binary_output=binary_output)
else:
weights = validate_dense_weights(values, weights)
c_ind, c_val, c_shape = gen_count_ops.dense_count_sparse_output(
values,
weights=weights,
minlength=minlength_value,
maxlength=maxlength_value,
binary_output=binary_output)
return sparse_tensor.SparseTensor(c_ind, c_val, c_shape)
def validate_dense_weights(values, weights, dtype=None):
"""Validates the passed weight tensor or creates an empty one."""
if weights is None:
if dtype:
return array_ops.constant([], dtype=dtype)
return array_ops.constant([], dtype=values.dtype)
if not isinstance(weights, ops.Tensor):
raise ValueError(
"Argument `weights` must be a tf.Tensor if `values` is a tf.Tensor. "
f"Received weights={weights} of type: {type(weights).__name__}")
return weights
def validate_sparse_weights(values, weights, dtype=None):
"""Validates the passed weight tensor or creates an empty one."""
if weights is None:
if dtype:
return array_ops.constant([], dtype=dtype)
return array_ops.constant([], dtype=values.values.dtype)
if not isinstance(weights, sparse_tensor.SparseTensor):
raise ValueError(
"Argument `weights` must be a SparseTensor if `values` is a "
f"SparseTensor. Received weights={weights} of type: "
f"{type(weights).__name__}")
checks = []
if weights.dense_shape is not values.dense_shape:
checks.append(
check_ops.assert_equal(
weights.dense_shape,
values.dense_shape,
message="'weights' and 'values' must have the same dense shape."))
if weights.indices is not values.indices:
checks.append(
check_ops.assert_equal(
weights.indices,
values.indices,
message="'weights' and 'values' must have the same indices.")
)
if checks:
with ops.control_dependencies(checks):
weights = array_ops.identity(weights.values)
else:
weights = weights.values
return weights
def validate_ragged_weights(values, weights, dtype=None):
"""Validates the passed weight tensor or creates an empty one."""
if weights is None:
if dtype:
return array_ops.constant([], dtype=dtype)
return array_ops.constant([], dtype=values.values.dtype)
if not isinstance(weights, ragged_tensor.RaggedTensor):
raise ValueError(
"`weights` must be a RaggedTensor if `values` is a RaggedTensor. "
f"Received argument weights={weights} of type: "
f"{type(weights).__name__}.")
checks = []
if weights.row_splits is not values.row_splits:
checks.append(
check_ops.assert_equal(
weights.row_splits,
values.row_splits,
message="'weights' and 'values' must have the same row splits."))
if checks:
with ops.control_dependencies(checks):
weights = array_ops.identity(weights.values)
else:
weights = weights.values
return weights