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未验证 提交 0111d012 编写于 作者: R risemeup1 提交者： GitHub
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delete batch_norm (#49396) 

* delete batch_norm

* test

* test

* test

* test

* test

* recover cmake_gen

* debug
上级 b2f41825
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Showing with 1 addition and 328 deletion
python/paddle/fluid/__init__.py
浏览文件 @ 0111d012
......@@ -99,7 +99,6 @@ from . import compiler
from .compiler import *
from paddle.fluid.layers.math_op_patch import monkey_patch_variable
from . import install_check
from .dygraph.nn import *
from .dygraph.layers import *
from .dygraph.base import enable_dygraph, disable_dygraph
from .io import save, load, load_program_state, set_program_state
......
python/paddle/fluid/dygraph/__init__.py
浏览文件 @ 0111d012
......@@ -21,9 +21,6 @@ from .layers import *
from . import container
from .container import *
from . import nn
from .nn import *
from . import tracer
from .tracer import *
......@@ -45,7 +42,6 @@ __all__ = []
__all__ += layers.__all__
__all__ += base.__all__
__all__ += container.__all__
__all__ += nn.__all__
__all__ += parallel.__all__
__all__ += checkpoint.__all__
__all__ += learning_rate_scheduler.__all__
......
python/paddle/fluid/dygraph/nn.py 已删除 100644 → 0
浏览文件 @ b2f41825
# Copyright (c) 2018 PaddlePaddle 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
# limitations under the License.
import paddle
from .. import core
from ..layers import utils
from ..layers import nn as F
from .. import dygraph_utils
from . import layers
from ..framework import (
Variable,
OpProtoHolder,
Parameter,
_dygraph_tracer,
_varbase_creator,
default_main_program,
_global_flags,
in_dygraph_mode,
)
from ..data_feeder import (
convert_dtype,
check_variable_and_dtype,
check_type,
check_dtype,
)
from ..param_attr import ParamAttr
from ..initializer import Normal, Constant, NumpyArrayInitializer
from .. import unique_name
from .layer_object_helper import LayerObjectHelper
from ..data_feeder import check_variable_and_dtype, check_type
import numpy as np
import numbers
import logging
import os
import paddle.utils.deprecated as deprecated
from paddle import _C_ops, _legacy_C_ops
__all__ = []
class BatchNorm(layers.Layer):
r"""
This interface is used to construct a callable object of the ``BatchNorm`` class.
For more details, refer to code examples.
It implements the function of the Batch Normalization Layer and can be used
as a normalizer function for conv2d and fully connected operations.
The data is normalized by the mean and variance of the channel based on the current batch data.
Refer to `Batch Normalization: Accelerating Deep Network Training by Reducing
Internal Covariate Shift <https://arxiv.org/pdf/1502.03167.pdf>`_
for more details.
When use_global_stats = False, the :math:`\mu_{\beta}`
and :math:`\sigma_{\beta}^{2}` are the statistics of one mini-batch.
Calculated as follows:
.. math::
\mu_{\beta} &\gets \frac{1}{m} \sum_{i=1}^{m} x_i \qquad &
//\ mini-batch\ mean \\
\sigma_{\beta}^{2} &\gets \frac{1}{m} \sum_{i=1}^{m}(x_i - \mu_{\beta})^2 \qquad &
//\ mini-batch\ variance \\
- :math:`x` : mini-batch data
- :math:`m` : the size of the mini-batch data
When use_global_stats = True, the :math:`\\mu_{\\beta}`
and :math:`\\sigma_{\\beta}^{2}` are not the statistics of one mini-batch.
They are global or running statistics (moving_mean and moving_variance). It usually got from the
pre-trained model. Calculated as follows:
.. math::
moving\_mean = moving\_mean * momentum + \mu_{\beta} * (1. - momentum) \quad &// global mean \\
moving\_variance = moving\_variance * momentum + \sigma_{\beta}^{2} * (1. - momentum) \quad &// global variance \\
The normalization function formula is as follows:
.. math::
\hat{x_i} &\gets \frac{x_i - \mu_\beta} {\sqrt{\
\sigma_{\beta}^{2} + \epsilon}} \qquad &//\ normalize \\
y_i &\gets \gamma \hat{x_i} + \beta \qquad &//\ scale\ and\ shift
- :math:`\epsilon` : add a smaller value to the variance to prevent division by zero
- :math:`\gamma` : trainable proportional parameter
- :math:`\beta` : trainable deviation parameter
Parameters:
num_channels(int): Indicate the number of channels of the input ``Tensor``.
act(str, optional): Activation to be applied to the output of batch normalization. Default: None.
is_test (bool, optional): A flag indicating whether it is in test phrase or not.
This flag only has effect on static graph mode. For dygraph mode, please use ``eval()``.
Default: False.
momentum(float, optional): The value used for the moving_mean and moving_var computation. Default: 0.9.
epsilon(float, optional): The small value added to the variance to prevent division by zero. Default: 1e-5.
param_attr(ParamAttr, optional): The parameter attribute for Parameter `scale`
of batch_norm. If it is set to None or one attribute of ParamAttr, batch_norm
will create ParamAttr as param_attr. If the Initializer of the param_attr
is not set, the parameter is initialized with Xavier. Default: None.
bias_attr(ParamAttr, optional): The parameter attribute for the bias of batch_norm.
If it is set to None or one attribute of ParamAttr, batch_norm
will create ParamAttr as bias_attr. If the Initializer of the bias_attr
is not set, the bias is initialized zero. Default: None.
dtype(str, optional): Indicate the data type of the input ``Tensor``,
which can be float32 or float64. Default: float32.
data_layout(str, optional): Specify the input data format, the data format can be "NCHW" or "NHWC", where `N` is batch size, `C` is the number of the feature map, `H` is the height of the feature map, `W` is the width of the feature map. Default: NCHW.
in_place(bool, optional): Make the input and output of batch norm reuse memory. Default: False.
moving_mean_name(str, optional): The name of moving_mean which store the global Mean. Default: None.
moving_variance_name(str, optional): The name of the moving_variance which store the global Variance. Default: None.
do_model_average_for_mean_and_var(bool, optional): Whether parameter mean and variance should do model
average when model average is enabled. Default: True.
use_global_stats(bool, optional): Whether to use global mean and
variance. In inference or test mode, set use_global_stats to true
or is_test to true, and the behavior is equivalent.
In train mode, when setting use_global_stats True, the global mean
and variance are also used during train period. Default: False.
trainable_statistics(bool, optional): Whether to calculate mean and var in eval mode. In eval mode, when
setting trainable_statistics True, mean and variance will be calculated by current batch statistics.
Default: False.
Returns:
None
Examples:
.. code-block:: python
import paddle
import paddle.fluid as fluid
from paddle.fluid.dygraph.base import to_variable
x = paddle.rand([3, 10, 3, 7], 'float32')
with fluid.dygraph.guard():
x = to_variable(x)
batch_norm = fluid.BatchNorm(10)
hidden1 = batch_norm(x)
"""
def __init__(
self,
num_channels,
act=None,
is_test=False,
momentum=0.9,
epsilon=1e-05,
param_attr=None,
bias_attr=None,
dtype='float32',
data_layout='NCHW',
in_place=False,
moving_mean_name=None,
moving_variance_name=None,
do_model_average_for_mean_and_var=True,
use_global_stats=False,
trainable_statistics=False,
):
super().__init__()
self._param_attr = param_attr
self._bias_attr = bias_attr
self._act = act
self._use_mkldnn = _global_flags()["FLAGS_use_mkldnn"]
assert (
bias_attr is not False
), "bias_attr should not be False in batch_norm."
if dtype == "float16":
self._dtype = "float32"
else:
self._dtype = dtype
param_shape = [num_channels]
# create parameter
self.weight = self.create_parameter(
attr=self._param_attr,
shape=param_shape,
dtype=self._dtype,
default_initializer=Constant(1.0),
)
self.weight.stop_gradient = (
use_global_stats and self._param_attr.learning_rate == 0.0
)
self.bias = self.create_parameter(
attr=self._bias_attr,
shape=param_shape,
dtype=self._dtype,
is_bias=True,
)
self.bias.stop_gradient = (
use_global_stats and self._param_attr.learning_rate == 0.0
)
self._mean = self.create_parameter(
attr=ParamAttr(
name=moving_mean_name,
initializer=Constant(0.0),
trainable=False,
do_model_average=do_model_average_for_mean_and_var,
),
shape=param_shape,
dtype=self._dtype,
)
self._mean.stop_gradient = True
self._variance = self.create_parameter(
attr=ParamAttr(
name=moving_variance_name,
initializer=Constant(1.0),
trainable=False,
do_model_average=do_model_average_for_mean_and_var,
),
shape=param_shape,
dtype=self._dtype,
)
self._variance.stop_gradient = True
self._in_place = in_place
self._data_layout = data_layout
self._momentum = momentum
self._epsilon = epsilon
self._is_test = is_test
self._fuse_with_relu = False
self._use_global_stats = use_global_stats
self._trainable_statistics = trainable_statistics
def forward(self, input):
# create output
# mean and mean_out share the same memory
mean_out = self._mean
# variance and variance out share the same memory
variance_out = self._variance
if in_dygraph_mode():
batch_norm_out, t1, t2, t3, t4, _ = _C_ops.batch_norm(
input,
self._mean,
self._variance,
self.weight,
self.bias,
not self.training,
self._momentum,
self._epsilon,
self._data_layout,
self._use_global_stats,
self._trainable_statistics,
)
return dygraph_utils._append_activation_in_dygraph(
batch_norm_out, act=self._act, use_mkldnn=self._use_mkldnn
)
else:
check_variable_and_dtype(
input, 'input', ['float16', 'float32', 'float64'], 'BatchNorm'
)
attrs = {
"momentum": self._momentum,
"epsilon": self._epsilon,
"is_test": self._is_test,
"data_layout": self._data_layout,
"use_mkldnn": False,
"fuse_with_relu": self._fuse_with_relu,
"use_global_stats": self._use_global_stats,
"trainable_statistics": self._trainable_statistics,
}
inputs = {
"X": [input],
"Scale": [self.weight],
"Bias": [self.bias],
"Mean": [self._mean],
"Variance": [self._variance],
}
saved_mean = self._helper.create_variable_for_type_inference(
dtype=self._dtype, stop_gradient=True
)
saved_variance = self._helper.create_variable_for_type_inference(
dtype=self._dtype, stop_gradient=True
)
reserve_space = self._helper.create_variable_for_type_inference(
dtype=self._helper.input_dtype(input), stop_gradient=True
)
batch_norm_out = (
input
if self._in_place
else self._helper.create_variable_for_type_inference(
self._dtype
)
)
outputs = {
"Y": [batch_norm_out],
"MeanOut": [mean_out],
"VarianceOut": [variance_out],
"SavedMean": [saved_mean],
"SavedVariance": [saved_variance],
}
if reserve_space is not None:
outputs["ReserveSpace"] = [reserve_space]
self._helper.append_op(
type="batch_norm", inputs=inputs, outputs=outputs, attrs=attrs
)
# Currently, we don't support inplace in dygraph mode
return self._helper.append_activation(batch_norm_out, self._act)
python/paddle/fluid/install_check.py
浏览文件 @ 0111d012
......@@ -25,7 +25,7 @@ from .param_attr import ParamAttr
from .initializer import Constant
from . import layers
from . import backward
from .dygraph import Layer, nn
from .dygraph import Layer
from . import executor
from . import optimizer
from . import core
......
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