ops.py 3.6 KB
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#   Copyright (c) 2018 PaddlePaddle Authors. All Rights Reserved.
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#
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# 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
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#
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#     http://www.apache.org/licenses/LICENSE-2.0
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#
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# 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
# limitations under the License.
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from __future__ import print_function
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import os
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from .layer_function_generator import generate_layer_fn, generate_activation_fn
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from .. import core
from ..framework import convert_np_dtype_to_dtype_
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__activations_noattr__ = [
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    'sigmoid',
    'logsigmoid',
    'exp',
    'tanh',
    'tanh_shrink',
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    'softshrink',
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    'sqrt',
    'abs',
    'ceil',
    'floor',
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    'cos',
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    'sin',
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    'round',
    'reciprocal',
    'square',
    'softplus',
    'softsign',
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]

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__all__ = []
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for _OP in set(__all__):
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    globals()[_OP] = generate_layer_fn(_OP)
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# It is a hot fix in some unittest using:
#   fluid.layers.scale(x=x, scale=10.0, out=out_var)
# e.g.: test_program_code.py, test_dist_train.py
globals()['_scale'] = generate_layer_fn('scale')

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globals()['_elementwise_div'] = generate_layer_fn('elementwise_div')

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__all__ += __activations_noattr__

for _OP in set(__activations_noattr__):
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    globals()[_OP] = generate_activation_fn(_OP)
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__all__ += ["uniform_random"]

_uniform_random_ = generate_layer_fn('uniform_random')


def uniform_random(shape, dtype=None, min=None, max=None, seed=None):
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    locals_var = locals().keys()
    if not isinstance(dtype, core.VarDesc.VarType):
        dtype = convert_np_dtype_to_dtype_(dtype)
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    kwargs = dict()
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    for name in locals_var:
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        val = locals()[name]
        if val is not None:
            kwargs[name] = val
    return _uniform_random_(**kwargs)

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uniform_random.__doc__ = _uniform_random_.__doc__ + """
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Examples:

    >>> result = fluid.layers.uniform_random(shape=[32, 784])
"""
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__all__ += ['hard_shrink']

_hard_shrink_ = generate_layer_fn('hard_shrink')


def hard_shrink(x, threshold=None):
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    locals_var = locals().keys()
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    kwargs = dict()
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    for name in locals_var:
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        val = locals()[name]
        if val is not None:
            kwargs[name] = val
    return _hard_shrink_(**kwargs)


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hard_shrink.__doc__ = _hard_shrink_.__doc__ + """
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Examples:

    >>> data = fluid.layers.data(name="input", shape=[784])
    >>> result = fluid.layers.hard_shrink(x=data, threshold=0.3)
"""
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__all__ += ['cumsum']

_cum_sum_ = generate_layer_fn('cumsum')


def cumsum(x, axis=None, exclusive=None, reverse=None):
    locals_var = locals().keys()
    kwargs = dict()
    for name in locals_var:
        val = locals()[name]
        if val is not None:
            kwargs[name] = val
    return _cum_sum_(**kwargs)


cumsum.__doc__ = _cum_sum_.__doc__ + """
Examples:

    >>> data = fluid.layers.data(name="input", shape=[32, 784])
    >>> result = fluid.layers.cumsum(data, axis=0)
"""
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__all__ += ['thresholded_relu']

_thresholded_relu_ = generate_layer_fn('thresholded_relu')


def thresholded_relu(x, threshold=None):
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    locals_var = locals().keys()
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    kwargs = dict()
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    for name in locals_var:
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        val = locals()[name]
        if val is not None:
            kwargs[name] = val

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    return _thresholded_relu_(**kwargs)
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thresholded_relu.__doc__ = _thresholded_relu_.__doc__ + """
Examples:

    >>> data = fluid.layers.data(name="input", shape=[1])
    >>> result = fluid.layers.thresholded_relu(data, threshold=0.4)
"""