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Paddle
未验证 提交 9a9e0aa0 编写于 作者: X xiaoguoguo626807 提交者： GitHub
浏览文件
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【fluid api clear】Move batch norm1 (#47965) 

* modify slice infershape

* code style

* modify slice_unittest

* temp fix

* batch_norm api move

* code_style

* codestyle

* ci_static

* add __init__

* reset other change

* revert .cc

* add import batchnorm

* conflict and revert

* fix bug

* fix third conflict one day

* fix conflict

* fix conflict bug

* fix conflict bug

* modify api

* code_style

* modify doc

* add lost doc stable

* fix conflict bug

* ci lack of gpu
上级 1a3d2592
隐藏空白更改
内联 并排
Showing with 450 addition and 428 deletion
python/paddle/fluid/contrib/layers/nn.py
浏览文件 @ 9a9e0aa0
......@@ -1963,8 +1963,11 @@ def fused_bn_add_act(
Examples:
.. code-block:: python
import paddle
import paddle.fluid as fluid
paddle.enable_static()
# required: gpu
def build_program(main_program, startup_program):
with fluid.program_guard(main_program, startup_program):
x = fluid.layers.data(name='x', shape=[1, 28, 28], dtype='float32')
......@@ -1987,7 +1990,7 @@ def fused_bn_add_act(
act=None,
bias_attr=False,
data_format='NHWC')
bn = fluid.layers.batch_norm(
bn = paddle.static.nn.batch_norm(
input=conv1_1,
act=None,
data_layout='NHWC')
......
python/paddle/fluid/contrib/slim/tests/test_graph.py
浏览文件 @ 9a9e0aa0
......@@ -37,7 +37,7 @@ def conv_block():
pool_stride=2,
act="relu",
)
conv_pool_1 = fluid.layers.batch_norm(conv_pool_1)
conv_pool_1 = paddle.static.nn.batch_norm(conv_pool_1)
conv_pool_2 = fluid.nets.simple_img_conv_pool(
input=conv_pool_1,
filter_size=5,
......
python/paddle/fluid/contrib/slim/tests/test_quantization_mkldnn_pass.py
浏览文件 @ 9a9e0aa0
......@@ -37,7 +37,7 @@ def conv_net(img, label):
pool_stride=2,
act="relu",
)
conv_pool_1 = fluid.layers.batch_norm(conv_pool_1)
conv_pool_1 = paddle.static.nn.batch_norm(conv_pool_1)
conv_pool_2 = fluid.nets.simple_img_conv_pool(
input=conv_pool_1,
filter_size=5,
......
python/paddle/fluid/contrib/slim/tests/test_quantization_pass.py
浏览文件 @ 9a9e0aa0
......@@ -57,7 +57,7 @@ def residual_block(num, quant_skip_pattern=None):
act=None,
bias_attr=bias_attr,
)
return fluid.layers.batch_norm(input=tmp, act=act)
return paddle.static.nn.batch_norm(input=tmp, act=act)
data = fluid.layers.data(
name='image',
......@@ -102,7 +102,7 @@ def conv_net(img, label, quant_skip_pattern):
pool_type='max',
act="relu",
)
conv_pool_1 = fluid.layers.batch_norm(conv_pool_1)
conv_pool_1 = paddle.static.nn.batch_norm(conv_pool_1)
conv_pool_2 = fluid.nets.simple_img_conv_pool(
input=conv_pool_1,
filter_size=5,
......@@ -712,7 +712,7 @@ def quant_dequant_residual_block(num, quant_skip_pattern=None):
act=None,
bias_attr=bias_attr,
)
return fluid.layers.batch_norm(input=tmp, act=act)
return paddle.static.nn.batch_norm(input=tmp, act=act)
data1 = fluid.layers.data(name='image', shape=[1, 32, 32], dtype='float32')
data2 = fluid.layers.data(
......
python/paddle/fluid/contrib/slim/tests/test_quantization_scale_pass.py
浏览文件 @ 9a9e0aa0
......@@ -43,7 +43,7 @@ def conv_net(img, label):
pool_type='max',
act="relu",
)
conv_pool_1 = fluid.layers.batch_norm(conv_pool_1)
conv_pool_1 = paddle.static.nn.batch_norm(conv_pool_1)
conv_pool_2 = fluid.nets.simple_img_conv_pool(
input=conv_pool_1,
filter_size=5,
......
python/paddle/fluid/contrib/slim/tests/test_user_defined_quantization.py
浏览文件 @ 9a9e0aa0
......@@ -45,7 +45,7 @@ def conv_net(img, label):
pool_type='max',
act="relu",
)
conv_pool_1 = fluid.layers.batch_norm(conv_pool_1)
conv_pool_1 = paddle.static.nn.batch_norm(conv_pool_1)
conv_pool_2 = fluid.nets.simple_img_conv_pool(
input=conv_pool_1,
filter_size=5,
......
python/paddle/fluid/contrib/tests/test_image_classification_fp16.py
浏览文件 @ 9a9e0aa0
......@@ -41,7 +41,7 @@ def resnet_cifar10(input, depth=32):
act=None,
bias_attr=bias_attr,
)
return fluid.layers.batch_norm(input=tmp, act=act)
return paddle.static.nn.batch_norm(input=tmp, act=act)
def shortcut(input, ch_in, ch_out, stride):
if ch_in != ch_out:
......@@ -97,7 +97,7 @@ def vgg16_bn_drop(input):
drop = fluid.layers.dropout(x=conv5, dropout_prob=0.5)
fc1 = fluid.layers.fc(input=drop, size=4096, act=None)
bn = fluid.layers.batch_norm(input=fc1, act='relu')
bn = paddle.static.nn.batch_norm(input=fc1, act='relu')
drop2 = fluid.layers.dropout(x=bn, dropout_prob=0.5)
fc2 = fluid.layers.fc(input=drop2, size=4096, act=None)
return fc2
......
python/paddle/fluid/contrib/tests/test_multi_precision_fp16_train.py
浏览文件 @ 9a9e0aa0
......@@ -59,7 +59,7 @@ def resnet_cifar10(input, depth=32):
act=None,
bias_attr=bias_attr,
)
return fluid.layers.batch_norm(input=tmp, act=act)
return paddle.static.nn.batch_norm(input=tmp, act=act)
def shortcut(input, ch_in, ch_out, stride):
if ch_in != ch_out:
......
python/paddle/fluid/contrib/tests/test_quantize_transpiler.py
浏览文件 @ 9a9e0aa0
......@@ -48,7 +48,7 @@ def residual_block(num):
act=None,
bias_attr=bias_attr,
)
return fluid.layers.batch_norm(input=tmp, act=act)
return paddle.static.nn.batch_norm(input=tmp, act=act)
data = fluid.layers.data(name='image', shape=[1, 32, 32], dtype='float32')
label = fluid.layers.data(name='label', shape=[1], dtype='int64')
......@@ -72,7 +72,7 @@ def conv_net(img, label):
pool_stride=2,
act="relu",
)
conv_pool_1 = fluid.layers.batch_norm(conv_pool_1)
conv_pool_1 = paddle.static.nn.batch_norm(conv_pool_1)
conv_pool_2 = fluid.nets.simple_img_conv_pool(
input=conv_pool_1,
filter_size=5,
......
python/paddle/fluid/layers/nn.py
浏览文件 @ 9a9e0aa0
......@@ -69,7 +69,6 @@ __all__ = [
'crf_decoding',
'conv2d',
'pool2d',
'batch_norm',
'dropout',
'split',
'l2_normalize',
......@@ -1681,328 +1680,6 @@ def pool2d(
return pool_out
def batch_norm(
input,
act=None,
is_test=False,
momentum=0.9,
epsilon=1e-05,
param_attr=None,
bias_attr=None,
data_layout='NCHW',
in_place=False,
name=None,
moving_mean_name=None,
moving_variance_name=None,
do_model_average_for_mean_and_var=True,
use_global_stats=False,
):
r"""
:api_attr: Static Graph
**Batch Normalization Layer**
Can be used as a normalizer function for convolution or fully_connected operations.
The required data format for this layer is one of the following:
1. NHWC `[batch, in_height, in_width, in_channels]`
2. NCHW `[batch, in_channels, in_height, in_width]`
Refer to `Batch Normalization: Accelerating Deep Network Training by Reducing
Internal Covariate Shift <https://arxiv.org/pdf/1502.03167.pdf>`_
for more details.
:math:`input` is the input features over a mini-batch.
.. 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 \\\\
\\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
moving\_mean = moving\_mean * momentum + mini-batch\_mean * (1. - momentum) \\\\
moving\_var = moving\_var * momentum + mini-batch\_var * (1. - momentum)
moving_mean is global mean and moving_var is global variance.
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. (It usually got from the
pre-trained model.)
The training and testing (or inference) have the same behavior:
.. math::
\\hat{x_i} &\\gets \\frac{x_i - \\mu_\\beta} {\\sqrt{\\
\\sigma_{\\beta}^{2} + \\epsilon}} \\\\
y_i &\\gets \\gamma \\hat{x_i} + \\beta
Note:
if build_strategy.sync_batch_norm=True, the batch_norm in network will use
sync_batch_norm automatically.
`is_test = True` can only be used in test program and inference program, `is_test` CANNOT be set to True in train program, if you want to use global status from pre_train model in train program, please set `use_global_stats = True`.
Args:
input(Tensor): The rank of input Tensor can be 2, 3, 4, 5. The data type
is float16 or float32 or float64.
act(string, Default None): Activation type, linear|relu|prelu|...
is_test (bool, Default False): A flag indicating whether it is in
test phrase or not.
momentum(float|Tensor, Default 0.9): The value used for the moving_mean and
moving_var computation. This should be a float number or a Tensor with
shape [1] and data type as float32. The updated formula is:
:math:`moving\_mean = moving\_mean * momentum + new\_mean * (1. - momentum)`
:math:`moving\_var = moving\_var * momentum + new\_var * (1. - momentum)`
Default is 0.9.
epsilon(float, Default 1e-05): A value added to the denominator for
numerical stability. Default is 1e-5.
param_attr(ParamAttr|None): 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, the name of scale can be set in ParamAttr.
If the Initializer of the param_attr is not set, the parameter is initialized
with Xavier. Default: None.
bias_attr(ParamAttr|None): 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, the name of bias can be set in ParamAttr.
If the Initializer of the bias_attr is not set, the bias is initialized zero.
Default: None.
data_layout (str, optional): Specify the data format of the input, and the data format of the output
will be consistent with that of the input. An optional string from: `"NCHW"`, `"NHWC"`.
The default is `"NCHW"`. When it is `"NCHW"`, the data is stored in the order of:
`[batch_size, input_channels, input_height, input_width]`.
in_place(bool, Default False): Make the input and output of batch norm reuse memory.
name(str|None): For detailed information, please refer to :ref:`api_guide_Name`.
Usually name is no need to set and None by default.
moving_mean_name(str, Default None): The name of moving_mean which store the global Mean. If it
is set to None, batch_norm will save global mean with a random name, otherwise, batch_norm
will save global mean with the string.
moving_variance_name(str, Default None): The name of the moving_variance which store the global Variance.
If it is set to None, batch_norm will save global variance with a random name, otherwise, batch_norm
will save global variance with the string.
do_model_average_for_mean_and_var(bool, Default True): Whether parameter mean and variance should do model
average when model average is enabled.
use_global_stats(bool, Default False): 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.
Returns:
A Tensor which is the result after applying batch normalization on the input,
has same shape and data type with input.
Examples:
.. code-block:: python
import paddle
paddle.enable_static()
x = paddle.static.data(name='x', shape=[3, 7, 3, 7], dtype='float32')
hidden1 = paddle.static.nn.fc(x=x, size=200)
print(hidden1.shape)
# [3, 200]
hidden2 = paddle.static.nn.batch_norm(input=hidden1)
print(hidden2.shape)
# [3, 200]
"""
assert (
bias_attr is not False
), "bias_attr should not be False in batch_norm."
helper = LayerHelper('batch_norm', **locals())
check_variable_and_dtype(
input, 'input', ['float16', 'float32', 'float64'], 'batch_norm'
)
dtype = helper.input_dtype()
# use fp32 for bn parameter
if dtype == core.VarDesc.VarType.FP16:
dtype = core.VarDesc.VarType.FP32
input_shape = input.shape
if data_layout == 'NCHW':
channel_num = input_shape[1]
else:
if data_layout == 'NHWC':
channel_num = input_shape[-1]
else:
raise ValueError("unsupported data layout:" + data_layout)
param_shape = [channel_num]
# create parameter
scale = helper.create_parameter(
attr=helper.param_attr,
shape=param_shape,
dtype=dtype,
default_initializer=Constant(1.0),
)
bias = helper.create_parameter(
attr=helper.bias_attr, shape=param_shape, dtype=dtype, is_bias=True
)
mean = helper.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=dtype,
)
mean.stop_gradient = True
variance = helper.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=dtype,
)
variance.stop_gradient = True
# create output
# mean and mean_out share the same memory
mean_out = mean
# variance and variance_out share the same memory
variance_out = variance
if in_dygraph_mode():
inputs_has_MomemtumTensor = False
attrs_has_momentum = False
tmp_tensor_type = core.eager.Tensor
if isinstance(momentum, tmp_tensor_type):
inputs_has_MomemtumTensor = True
else:
attrs_has_momentum = True
attrs_ = ()
if attrs_has_momentum:
attrs_ = (
'momentum',
momentum,
'epsilon',
epsilon,
'is_test',
is_test,
'data_layout',
data_layout,
'use_mkldnn',
False,
'fuse_with_relu',
False,
'use_global_stats',
use_global_stats,
)
else:
attrs_ = (
'epsilon',
epsilon,
'is_test',
is_test,
'data_layout',
data_layout,
'use_mkldnn',
False,
'fuse_with_relu',
False,
'use_global_stats',
use_global_stats,
)
if inputs_has_MomemtumTensor:
batch_norm_out, _, _, _, _, _ = _legacy_C_ops.batch_norm(
input,
scale,
bias,
mean,
variance,
momentum,
mean_out,
variance_out,
*attrs_,
)
else:
batch_norm_out, _, _, _, _, _ = _legacy_C_ops.batch_norm(
input,
scale,
bias,
mean,
variance,
None,
mean_out,
variance_out,
*attrs_,
)
return dygraph_utils._append_activation_in_dygraph(
batch_norm_out, act=act, use_mkldnn=False
)
saved_mean = helper.create_variable_for_type_inference(
dtype=dtype, stop_gradient=True
)
saved_variance = helper.create_variable_for_type_inference(
dtype=dtype, stop_gradient=True
)
reserve_space = None
if not is_test:
reserve_space = helper.create_variable_for_type_inference(
dtype=helper.input_dtype(), stop_gradient=True
)
batch_norm_out = (
input if in_place else helper.create_variable_for_type_inference(dtype)
)
inputs = {
"X": input,
"Scale": scale,
"Bias": bias,
"Mean": mean,
"Variance": variance,
"MeanOut": mean_out,
"VarianceOut": variance_out,
}
attrs = {
"epsilon": epsilon,
"is_test": is_test,
"data_layout": data_layout,
"use_mkldnn": False,
"fuse_with_relu": False,
"use_global_stats": use_global_stats,
}
if isinstance(momentum, Variable):
inputs['MomemtumTensor'] = momentum
else:
attrs['momentum'] = momentum
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
helper.append_op(
type="batch_norm", inputs=inputs, outputs=outputs, attrs=attrs
)
return helper.append_activation(batch_norm_out)
@templatedoc()
def layer_norm(
input,
......
python/paddle/fluid/nets.py
浏览文件 @ 9a9e0aa0
......@@ -253,7 +253,7 @@ def img_conv_group(
)
if conv_with_batchnorm[i]:
tmp = layers.batch_norm(input=tmp, act=conv_act)
tmp = paddle.static.nn.batch_norm(input=tmp, act=conv_act)
drop_rate = conv_batchnorm_drop_rate[i]
if abs(drop_rate) > 1e-5:
tmp = layers.dropout(x=tmp, dropout_prob=drop_rate)
......
python/paddle/fluid/tests/book/test_image_classification.py
浏览文件 @ 9a9e0aa0
......@@ -40,7 +40,7 @@ def resnet_cifar10(input, depth=32):
act=None,
bias_attr=bias_attr,
)
return fluid.layers.batch_norm(input=tmp, act=act)
return paddle.static.nn.batch_norm(input=tmp, act=act)
def shortcut(input, ch_in, ch_out, stride):
if ch_in != ch_out:
......@@ -96,7 +96,7 @@ def vgg16_bn_drop(input):
drop = fluid.layers.dropout(x=conv5, dropout_prob=0.5)
fc1 = fluid.layers.fc(input=drop, size=4096, act=None)
bn = fluid.layers.batch_norm(input=fc1, act='relu')
bn = paddle.static.nn.batch_norm(input=fc1, act='relu')
drop2 = fluid.layers.dropout(x=bn, dropout_prob=0.5)
fc2 = fluid.layers.fc(input=drop2, size=4096, act=None)
return fc2
......
python/paddle/fluid/tests/book/test_recognize_digits.py
浏览文件 @ 9a9e0aa0
......@@ -51,7 +51,7 @@ def conv_net(img, label):
pool_stride=2,
act="relu",
)
conv_pool_1 = fluid.layers.batch_norm(conv_pool_1)
conv_pool_1 = paddle.static.nn.batch_norm(conv_pool_1)
conv_pool_2 = fluid.nets.simple_img_conv_pool(
input=conv_pool_1,
filter_size=5,
......
python/paddle/fluid/tests/unittests/dist_se_resnext.py
浏览文件 @ 9a9e0aa0
......@@ -182,7 +182,7 @@ class SE_ResNeXt:
),
bias_attr=False,
)
return fluid.layers.batch_norm(input=conv, act=act)
return paddle.static.nn.batch_norm(input=conv, act=act)
def squeeze_excitation(self, input, num_channels, reduction_ratio):
pool = fluid.layers.pool2d(
......
python/paddle/fluid/tests/unittests/ipu/test_batch_norm_op_ipu.py
浏览文件 @ 9a9e0aa0
......@@ -58,7 +58,7 @@ class TestBase(IPUOpTest):
x = paddle.static.nn.conv2d(
x, num_filters=3, filter_size=3, bias_attr=False
)
x = paddle.fluid.layers.batch_norm(x, **self.attrs)
x = paddle.static.nn.batch_norm(x, **self.attrs)
self.fetch_list = [x.name]
def run_model(self, exec_mode):
......
python/paddle/fluid/tests/unittests/ir/inference/test_trt_activation_pass.py
浏览文件 @ 9a9e0aa0
......@@ -22,6 +22,7 @@ from inference_pass_test import InferencePassTest
import paddle
import paddle.fluid as fluid
import paddle.fluid.core as core
import paddle.static.nn as nn
from paddle.fluid.core import AnalysisConfig, PassVersionChecker
......@@ -39,7 +40,7 @@ class TensorRTSubgraphPassActivationTest(InferencePassTest):
name="data", shape=[-1, 6, 32, 32], dtype="float32"
)
act_out = self.append_act(data)
out = fluid.layers.batch_norm(act_out, is_test=True)
out = nn.batch_norm(act_out, is_test=True)
self.feeds = {
"data": np.random.random([1, 6, 32, 32]).astype("float32"),
}
......
python/paddle/fluid/tests/unittests/ir/inference/test_trt_anchor_generator_op.py
浏览文件 @ 9a9e0aa0
......@@ -20,6 +20,7 @@ from inference_pass_test import InferencePassTest
import paddle
import paddle.fluid as fluid
import paddle.fluid.core as core
import paddle.static.nn as nn
from paddle.fluid.core import AnalysisConfig, PassVersionChecker
......@@ -67,7 +68,7 @@ class TRTAnchorGeneratorBaseTest(InferencePassTest):
)
if self.dynamic_shape_params is not None:
anchor = paddle.transpose(anchor, [2, 3, 0, 1])
out = fluid.layers.batch_norm(anchor, is_test=True)
out = nn.batch_norm(anchor, is_test=True)
self.fetch_list = [out, var]
......
python/paddle/fluid/tests/unittests/ir/inference/test_trt_elementwise_op.py
浏览文件 @ 9a9e0aa0
......@@ -21,6 +21,7 @@ from inference_pass_test import InferencePassTest
import paddle.fluid as fluid
import paddle.fluid.core as core
import paddle.static.nn as nn
from paddle.fluid.core import AnalysisConfig, PassVersionChecker
......@@ -34,7 +35,7 @@ class TensorRTSubgraphPassElementwiseBroadcastTest(InferencePassTest):
name="data2", shape=[-1, 3, 64, 1], dtype="float32"
)
eltwise_out = self.append_eltwise(data1, data2)
out = fluid.layers.batch_norm(eltwise_out, is_test=True)
out = nn.batch_norm(eltwise_out, is_test=True)
self.feeds = {
"data1": np.random.random([1, 3, 64, 64]).astype("float32"),
"data2": np.random.random([1, 3, 64, 1]).astype("float32"),
......
python/paddle/fluid/tests/unittests/ir/inference/test_trt_flatten_op.py
浏览文件 @ 9a9e0aa0
......@@ -20,6 +20,7 @@ from inference_pass_test import InferencePassTest
import paddle
import paddle.fluid as fluid
import paddle.fluid.core as core
import paddle.static.nn as nn
from paddle.fluid.core import AnalysisConfig, PassVersionChecker
......@@ -30,7 +31,7 @@ class TRTFlattenTest(InferencePassTest):
name="data", shape=[-1, 6, 64, 64], dtype="float32"
)
flatten_out = self.append_flatten(data)
out = fluid.layers.batch_norm(flatten_out, is_test=True)
out = nn.batch_norm(flatten_out, is_test=True)
self.feeds = {
"data": np.random.random([1, 6, 64, 64]).astype("float32"),
}
......@@ -59,7 +60,7 @@ class TRTFlattenDynamicTest(InferencePassTest):
name="data", shape=[-1, 6, 64, 64], dtype="float32"
)
flatten_out = self.append_flatten(data)
out = fluid.layers.batch_norm(flatten_out, is_test=True)
out = nn.batch_norm(flatten_out, is_test=True)
self.feeds = {
"data": np.random.random([2, 6, 64, 64]).astype("float32"),
}
......
python/paddle/fluid/tests/unittests/ir/inference/test_trt_gather_nd_op.py
浏览文件 @ 9a9e0aa0
......@@ -20,6 +20,7 @@ from inference_pass_test import InferencePassTest
import paddle
import paddle.fluid as fluid
import paddle.fluid.core as core
import paddle.static.nn as nn
from paddle.fluid.core import AnalysisConfig, PassVersionChecker
......@@ -29,7 +30,7 @@ class TRTGatherNdTest(InferencePassTest):
data = fluid.data(name="data", shape=[-1, 3, 4], dtype="float32")
index = fluid.data(name="index", shape=[-1, 2, 2], dtype="int32")
gather_nd = paddle.gather_nd(data, index)
out = fluid.layers.batch_norm(gather_nd, is_test=True)
out = nn.batch_norm(gather_nd, is_test=True)
self.feeds = {
"data": np.random.random([2, 3, 4]).astype("float32"),
......@@ -66,7 +67,7 @@ class TRTGatherNdFp16Test(InferencePassTest):
)
index = fluid.data(name="index", shape=[-1, 1028, 2], dtype="int32")
gather_nd = paddle.gather_nd(data, index)
out = fluid.layers.batch_norm(gather_nd, is_test=True)
out = nn.batch_norm(gather_nd, is_test=True)
index_data = np.zeros((1, 1028, 2), dtype='int32')
self.feeds = {
......
python/paddle/fluid/tests/unittests/ir/inference/test_trt_inspector.py
浏览文件 @ 9a9e0aa0
......@@ -37,7 +37,7 @@ class TensorRTInspectorTest(InferencePassTest):
transpose_y=self.transpose_y,
)
matmul_out = paddle.scale(matmul_out, scale=self.alpha)
out = fluid.layers.batch_norm(matmul_out, is_test=True)
out = paddle.static.nn.batch_norm(matmul_out, is_test=True)
self.feeds = {
"data": np.ones([1, 16, 16]).astype("float32"),
......
python/paddle/fluid/tests/unittests/ir/inference/test_trt_instance_norm_op.py
浏览文件 @ 9a9e0aa0
......@@ -20,9 +20,9 @@ import unittest
import numpy as np
from inference_pass_test import InferencePassTest
import paddle
import paddle.fluid as fluid
import paddle.fluid.core as core
import paddle.static.nn as nn
from paddle.fluid.core import AnalysisConfig, PassVersionChecker
......@@ -44,8 +44,8 @@ class TRTInstanceNormTest(InferencePassTest):
with fluid.program_guard(self.main_program, self.startup_program):
shape = [-1, self.channel, self.height, self.width]
data = fluid.data(name='in', shape=shape, dtype='float32')
instance_norm_out = paddle.static.nn.instance_norm(data)
out = fluid.layers.batch_norm(instance_norm_out, is_test=True)
instance_norm_out = nn.instance_norm(data)
out = nn.batch_norm(instance_norm_out, is_test=True)
shape[0] = self.bs
self.feeds = {
......
python/paddle/fluid/tests/unittests/ir/inference/test_trt_matmul.py
浏览文件 @ 9a9e0aa0
......@@ -20,6 +20,7 @@ from inference_pass_test import InferencePassTest
import paddle
import paddle.fluid as fluid
import paddle.fluid.core as core
import paddle.static.nn as nn
from paddle.fluid.core import AnalysisConfig, PassVersionChecker
......@@ -35,7 +36,7 @@ class TensorRTMatMulDims2Test(InferencePassTest):
transpose_y=self.transpose_y,
)
matmul_out = paddle.scale(matmul_out, scale=self.alpha)
out = fluid.layers.batch_norm(matmul_out, is_test=True)
out = nn.batch_norm(matmul_out, is_test=True)
self.feeds = {
"data": np.ones([24, 24]).astype("float32"),
......@@ -74,7 +75,7 @@ class TensorRTMatMulTest(InferencePassTest):
transpose_y=self.transpose_y,
)
matmul_out = paddle.scale(matmul_out, scale=self.alpha)
out = fluid.layers.batch_norm(matmul_out, is_test=True)
out = nn.batch_norm(matmul_out, is_test=True)
self.feeds = {
"data": np.ones([1, 6, 24, 24]).astype("float32"),
......@@ -136,7 +137,7 @@ class TensorRTMatMulBroadcastTest(InferencePassTest):
transpose_y=self.transpose_y,
)
matmul_out = paddle.scale(matmul_out, scale=self.alpha)
out = fluid.layers.batch_norm(matmul_out, is_test=True)
out = nn.batch_norm(matmul_out, is_test=True)
self.feeds = {
"data_x": np.ones([2, 6, 24]).astype("float32"),
......
python/paddle/fluid/tests/unittests/ir/inference/test_trt_matmul_quant_dequant.py
浏览文件 @ 9a9e0aa0
......@@ -135,7 +135,7 @@ class TensorRTMatMulQuantDequantDims4Test(QuantDequantTest):
transpose_y=self.transpose_y,
)
matmul_out = paddle.scale(matmul_out, scale=self.alpha)
out = fluid.layers.batch_norm(matmul_out, is_test=True)
out = paddle.static.nn.batch_norm(matmul_out, is_test=True)
fc_out = fluid.layers.fc(
input=matmul_out,
size=10,
......@@ -231,7 +231,7 @@ class TensorRTMatMulQuantDequantDims3DynamicTest(QuantDequantTest):
transpose_y=self.transpose_y,
)
matmul_out = paddle.scale(matmul_out, scale=self.alpha)
out = fluid.layers.batch_norm(matmul_out, is_test=True)
out = paddle.static.nn.batch_norm(matmul_out, is_test=True)
fc_out = fluid.layers.fc(
input=matmul_out,
size=10,
......
python/paddle/fluid/tests/unittests/ir/inference/test_trt_multiclass_nms3_op.py
浏览文件 @ 9a9e0aa0
......@@ -21,6 +21,7 @@ from inference_pass_test import InferencePassTest
import paddle
import paddle.fluid as fluid
import paddle.fluid.core as core
import paddle.static.nn as nn
from paddle.fluid.core import AnalysisConfig, PassVersionChecker
from paddle.fluid.framework import in_dygraph_mode
from paddle.fluid.layer_helper import LayerHelper
......@@ -242,7 +243,7 @@ class TensorRTMultiClassNMS3Test(InferencePassTest):
[self.bs, 1, self.keep_top_k, 6],
name='reshape',
)
out = fluid.layers.batch_norm(multiclass_nms_out, is_test=True)
out = nn.batch_norm(multiclass_nms_out, is_test=True)
boxes_data = (
np.arange(self.num_boxes * 4)
......
python/paddle/fluid/tests/unittests/ir/inference/test_trt_multiclass_nms_op.py
浏览文件 @ 9a9e0aa0
......@@ -21,6 +21,7 @@ from inference_pass_test import InferencePassTest
import paddle
import paddle.fluid as fluid
import paddle.fluid.core as core
import paddle.static.nn as nn
from paddle.fluid.core import AnalysisConfig, PassVersionChecker
......@@ -69,7 +70,7 @@ class TensorRTMultiClassNMSTest(InferencePassTest):
[self.bs, 1, self.keep_top_k, 6],
name='reshape',
)
out = fluid.layers.batch_norm(multiclass_nms_out, is_test=True)
out = nn.batch_norm(multiclass_nms_out, is_test=True)
boxes_data = (
np.arange(self.num_boxes * 4)
......
python/paddle/fluid/tests/unittests/ir/inference/test_trt_nearest_interp_op.py
浏览文件 @ 9a9e0aa0
......@@ -20,6 +20,7 @@ from inference_pass_test import InferencePassTest
import paddle
import paddle.fluid as fluid
import paddle.fluid.core as core
import paddle.static.nn as nn
from paddle.fluid.core import AnalysisConfig, PassVersionChecker
......@@ -44,7 +45,7 @@ class TRTNearestInterpTest(InferencePassTest):
]
data = fluid.data(name='data', shape=shape, dtype='float32')
resize_out = self.append_nearest_interp(data)
out = fluid.layers.batch_norm(resize_out, is_test=True)
out = nn.batch_norm(resize_out, is_test=True)
if self.data_layout == 'NCHW':
shape = [
......
python/paddle/fluid/tests/unittests/ir/inference/test_trt_nearest_interp_v2_op.py
浏览文件 @ 9a9e0aa0
......@@ -19,6 +19,7 @@ from inference_pass_test import InferencePassTest
import paddle.fluid.core as core
import paddle.nn.functional as F
import paddle.static.nn as nn
from paddle import fluid
from paddle.fluid.core import AnalysisConfig, PassVersionChecker
......@@ -44,7 +45,7 @@ class TRTNearestInterpTest(InferencePassTest):
]
data = fluid.data(name='data', shape=shape, dtype='float32')
resize_out = self.append_nearest_interp(data)
out = fluid.layers.batch_norm(resize_out, is_test=True)
out = nn.batch_norm(resize_out, is_test=True)
if self.data_layout == 'NCHW':
shape = [
......
python/paddle/fluid/tests/unittests/ir/inference/test_trt_pad_op.py
浏览文件 @ 9a9e0aa0
......@@ -20,6 +20,7 @@ from inference_pass_test import InferencePassTest
import paddle
import paddle.fluid as fluid
import paddle.fluid.core as core
import paddle.static.nn as nn
from paddle.fluid.core import AnalysisConfig
......@@ -32,7 +33,7 @@ class PadOpTRTTest(InferencePassTest):
pad_out = paddle.nn.functional.pad(
x=data, pad=[0, 0, 0, 0, 0, 1, 1, 2], value=0.0
)
out = fluid.layers.batch_norm(pad_out, is_test=True)
out = nn.batch_norm(pad_out, is_test=True)
self.feeds = {
"data": np.random.random((1, 3, 128, 128)).astype("float32")
......
python/paddle/fluid/tests/unittests/ir/inference/test_trt_pool3d_op.py
浏览文件 @ 9a9e0aa0
......@@ -80,7 +80,7 @@ class TensorRTPool3dTest(InferencePassTest):
ceil_mode=self.ceil_mode,
exclusive=self.exclusive,
)
# out = fluid.layers.batch_norm(pool_out, is_test=True)
# out = paddle.static.nn.batch_norm(pool_out, is_test=True)
self.fetch_list = [pool_out]
def check_output(self):
......@@ -198,7 +198,7 @@ class TensorRTAdaptiveAvgPool3DTest(InferencePassTest):
pool_out = paddle.nn.functional.adaptive_avg_pool3d(
x=data, output_size=[3, 3, 3]
)
# out = fluid.layers.batch_norm(pool_out, is_test=True)
# out = paddle.static.nn.batch_norm(pool_out, is_test=True)
self.fetch_list = [pool_out]
def check_output(self):
......@@ -298,7 +298,7 @@ class TensorRTAdaptiveMaxPool3DTest(InferencePassTest):
pool_out = paddle.nn.functional.adaptive_max_pool3d(
x=data, output_size=[3, 3, 3]
)
# out = fluid.layers.batch_norm(pool_out, is_test=True)
# out = paddle.static.nn.batch_norm(pool_out, is_test=True)
self.fetch_list = [pool_out]
def check_output(self):
......
python/paddle/fluid/tests/unittests/ir/inference/test_trt_pool_op.py
浏览文件 @ 9a9e0aa0
......@@ -22,6 +22,7 @@ from inference_pass_test import InferencePassTest
import paddle.fluid as fluid
import paddle.fluid.core as core
import paddle.static.nn as nn
from paddle.fluid.core import AnalysisConfig, PassVersionChecker
......@@ -72,7 +73,7 @@ class TensorRTPoolTest(InferencePassTest):
ceil_mode=self.ceil_mode,
exclusive=self.exclusive,
)
out = fluid.layers.batch_norm(pool_out, is_test=True)
out = nn.batch_norm(pool_out, is_test=True)
self.fetch_list = [out]
def check_output(self):
......
python/paddle/fluid/tests/unittests/ir/inference/test_trt_reduce_sum_op.py
浏览文件 @ 9a9e0aa0
......@@ -20,6 +20,7 @@ from inference_pass_test import InferencePassTest
import paddle
import paddle.fluid as fluid
import paddle.fluid.core as core
import paddle.static.nn as nn
from paddle.fluid.core import AnalysisConfig, PassVersionChecker
......@@ -30,7 +31,7 @@ class TRTReduceSumTest(InferencePassTest):
name="data", shape=[-1, 3, 10, 192], dtype="float32"
)
reduce_sum = paddle.sum(data, axis=[2, -1], keepdim=True)
out = fluid.layers.batch_norm(reduce_sum, is_test=True)
out = nn.batch_norm(reduce_sum, is_test=True)
self.feeds = {
"data": np.random.random([3, 3, 10, 192]).astype("float32"),
......@@ -63,7 +64,7 @@ class TRTReduceSumAllTest(InferencePassTest):
name="data", shape=[-1, 3, 10, 192], dtype="float32"
)
reduce_sum = paddle.sum(data, keepdim=True)
out = fluid.layers.batch_norm(reduce_sum, is_test=True)
out = nn.batch_norm(reduce_sum, is_test=True)
self.feeds = {
"data": np.random.random([3, 3, 10, 192]).astype("float32"),
......
python/paddle/fluid/tests/unittests/ir/inference/test_trt_reshape_op.py
浏览文件 @ 9a9e0aa0
......@@ -20,6 +20,7 @@ from inference_pass_test import InferencePassTest
import paddle
import paddle.fluid as fluid
import paddle.fluid.core as core
import paddle.static.nn as nn
from paddle.fluid.core import AnalysisConfig, PassVersionChecker
......@@ -39,7 +40,7 @@ class TRTReshapeTest(InferencePassTest):
name='data', shape=self.data_shape, dtype='float32'
)
reshape_out = self.append_reshape(data, self.reshape)
out = fluid.layers.batch_norm(reshape_out, is_test=True)
out = nn.batch_norm(reshape_out, is_test=True)
self.feeds = {
'data': np.random.random(self.data_shape).astype('float32'),
}
......@@ -77,7 +78,7 @@ class TRTReshapeTest1(TRTReshapeTest):
name='data', shape=self.data_shape, dtype='float32'
)
reshape_out = self.append_reshape(data, self.reshape)
out = fluid.layers.batch_norm(reshape_out, is_test=True)
out = nn.batch_norm(reshape_out, is_test=True)
self.feeds = {
'data': np.random.random(self.data_shape).astype('float32'),
}
......@@ -104,7 +105,7 @@ class TRTReshapeTest2(TRTReshapeTest):
name='data', shape=self.data_shape, dtype='float32'
)
reshape_out = paddle.reshape(x=data, shape=self.reshape)
out = fluid.layers.batch_norm(reshape_out, is_test=True)
out = nn.batch_norm(reshape_out, is_test=True)
self.feeds = {
'data': np.random.random(self.data_shape).astype('float32')
}
......@@ -130,7 +131,7 @@ class TRTReshapeTest3(TRTReshapeTest):
data = fluid.data(
name='data', shape=self.data_shape, dtype='float32'
)
bn_out = fluid.layers.batch_norm(data, is_test=True)
bn_out = nn.batch_norm(data, is_test=True)
out = self.append_reshape(bn_out, self.reshape)
self.feeds = {
'data': np.random.random(self.data_shape).astype('float32'),
......
python/paddle/fluid/tests/unittests/ir/inference/test_trt_scale_op.py
浏览文件 @ 9a9e0aa0
......@@ -20,6 +20,7 @@ from inference_pass_test import InferencePassTest
import paddle
import paddle.fluid as fluid
import paddle.fluid.core as core
import paddle.static.nn as nn
from paddle.fluid.core import AnalysisConfig, PassVersionChecker
......@@ -28,7 +29,7 @@ class TRTScaleTest(InferencePassTest):
with fluid.program_guard(self.main_program, self.startup_program):
data = fluid.data(name="data", shape=[-1, 512], dtype="float32")
scale_out = self.append_scale(data)
out = fluid.layers.batch_norm(scale_out, is_test=True)
out = nn.batch_norm(scale_out, is_test=True)
self.feeds = {
"data": np.random.random([1, 512]).astype("float32"),
......@@ -60,7 +61,7 @@ class TRTScaleShape2Test(InferencePassTest):
name="data", shape=[-1, 512, 512], dtype="float32"
)
scale_out = self.append_scale(data)
out = fluid.layers.batch_norm(scale_out, is_test=True)
out = nn.batch_norm(scale_out, is_test=True)
self.feeds = {
"data": np.random.random([1, 512, 512]).astype("float32"),
......
python/paddle/fluid/tests/unittests/ir/inference/test_trt_shuffle_channel_detect_pass.py
浏览文件 @ 9a9e0aa0
......@@ -19,6 +19,7 @@ from inference_pass_test import InferencePassTest
import paddle
import paddle.fluid as fluid
import paddle.static.nn as nn
from paddle.fluid.core import AnalysisConfig, PassVersionChecker
......@@ -31,8 +32,7 @@ class ShuffleChannelFuseTRTPassTest(InferencePassTest):
reshape1 = paddle.reshape(x=data, shape=[-1, 2, 3, 64, 64])
trans = paddle.transpose(x=reshape1, perm=[0, 2, 1, 3, 4])
reshape2 = paddle.reshape(x=trans, shape=[-1, 6, 64, 64])
out = fluid.layers.batch_norm(reshape2, is_test=True)
out = nn.batch_norm(reshape2, is_test=True)
self.feeds = {
"data": np.random.random([1, 6, 64, 64]).astype("float32"),
......
python/paddle/fluid/tests/unittests/ir/inference/test_trt_slice_plugin.py
浏览文件 @ 9a9e0aa0
......@@ -20,6 +20,7 @@ from inference_pass_test import InferencePassTest
import paddle
import paddle.fluid as fluid
import paddle.fluid.core as core
import paddle.static.nn as nn
from paddle.fluid.core import AnalysisConfig
......@@ -45,7 +46,7 @@ class SlicePluginTRTTest(InferencePassTest):
starts = self.params_starts
ends = self.params_ends
slice_out = paddle.slice(data, axes=axes, starts=starts, ends=ends)
out = fluid.layers.batch_norm(slice_out, is_test=True)
out = nn.batch_norm(slice_out, is_test=True)
self.feeds = {
"data": np.random.random((3, 3, 3, 3)).astype("float32"),
......@@ -115,7 +116,7 @@ class SlicePluginTRTTestInt32(SlicePluginTRTTest):
ends = self.params_ends
slice_out = paddle.slice(data, axes=axes, starts=starts, ends=ends)
cast_out = fluid.layers.cast(slice_out, 'float32')
out = fluid.layers.batch_norm(cast_out, is_test=True)
out = nn.batch_norm(cast_out, is_test=True)
self.feeds = {
"data": np.random.random((3, 3, 3, 3)).astype("int32"),
......@@ -140,7 +141,7 @@ class StaticSlicePluginTRTTestInt32(SlicePluginTRTTest):
ends = self.params_ends
slice_out = paddle.slice(data, axes=axes, starts=starts, ends=ends)
cast_out = fluid.layers.cast(slice_out, 'float32')
out = fluid.layers.batch_norm(cast_out, is_test=True)
out = nn.batch_norm(cast_out, is_test=True)
self.feeds = {
"data": np.random.random((3, 3, 3, 3)).astype("int32"),
......
python/paddle/fluid/tests/unittests/ir/inference/test_trt_subgraph_pass.py
浏览文件 @ 9a9e0aa0
......@@ -22,6 +22,7 @@ from inference_pass_test import InferencePassTest
import paddle
import paddle.fluid as fluid
import paddle.fluid.core as core
import paddle.static.nn as nn
from paddle.fluid.core import AnalysisConfig, PassVersionChecker
......@@ -62,7 +63,7 @@ class TensorRTSubgraphPassConcatTest(InferencePassTest):
name="data2", shape=[-1, 3, 64, 64], dtype="float32"
)
concat_out = fluid.layers.concat([data1, data2], axis=2)
out = fluid.layers.batch_norm(concat_out, is_test=True)
out = nn.batch_norm(concat_out, is_test=True)
self.feeds = {
"data1": np.random.random([1, 3, 64, 64]).astype("float32"),
"data2": np.random.random([1, 3, 64, 64]).astype("float32"),
......@@ -89,7 +90,7 @@ class TensorRTSubgraphPassSplitTest(InferencePassTest):
name="data", shape=[-1, 3, 64, 64], dtype="float32"
)
split_out = fluid.layers.split(data, dim=-1, num_or_sections=2)
out = fluid.layers.batch_norm(split_out[0], is_test=True)
out = nn.batch_norm(split_out[0], is_test=True)
self.feeds = {
"data": np.random.random([1, 3, 64, 64]).astype("float32"),
}
......@@ -115,7 +116,7 @@ class TensorRTSubgraphPassSplitSerializeTest(InferencePassTest):
name="data", shape=[-1, 3, 64, 64], dtype="float32"
)
split_out = fluid.layers.split(data, dim=-1, num_or_sections=2)
out = fluid.layers.batch_norm(split_out[0], is_test=True)
out = nn.batch_norm(split_out[0], is_test=True)
self.feeds = {
"data": np.random.random([1, 3, 64, 64]).astype("float32"),
}
......@@ -143,7 +144,7 @@ class TensorRTSubgraphPassDynamicSplitFp16SerializeTest(InferencePassTest):
name="data", shape=[-1, 3, 64, 64], dtype="float32"
)
split_out = fluid.layers.split(data, dim=-1, num_or_sections=2)
out = fluid.layers.batch_norm(split_out[0], is_test=True)
out = nn.batch_norm(split_out[0], is_test=True)
self.feeds = {
"data": np.random.random([1, 3, 64, 64]).astype("float32"),
}
......@@ -216,7 +217,7 @@ class TensorRTSubgraphPassTransposeTest(InferencePassTest):
name="data", shape=[-1, 6, 64, 64], dtype="float32"
)
transpose_out = self.append_transpose(data)
out = fluid.layers.batch_norm(transpose_out, is_test=True)
out = nn.batch_norm(transpose_out, is_test=True)
self.feeds = {
"data": np.random.random([1, 6, 64, 64]).astype("float32"),
}
......@@ -366,7 +367,7 @@ class TensorRTSubgraphPassElementwiseTest(InferencePassTest):
name="data2", shape=[-1, 3, 64, 64], dtype="float32"
)
eltwise_out = self.append_eltwise(data1, data2)
out = fluid.layers.batch_norm(eltwise_out, is_test=True)
out = nn.batch_norm(eltwise_out, is_test=True)
self.feeds = {
"data1": np.random.random([1, 3, 64, 64]).astype("float32"),
"data2": np.random.random([1, 3, 64, 64]).astype("float32"),
......@@ -419,7 +420,7 @@ class TensorRTSubgraphPassElementwiseBroadcastDynamicTest(InferencePassTest):
)
data2 = fluid.data(name="data2", shape=[64, 64], dtype="float32")
eltwise_out = self.append_eltwise(data1, data2)
out = fluid.layers.batch_norm(eltwise_out, is_test=True)
out = nn.batch_norm(eltwise_out, is_test=True)
self.feeds = {
"data1": np.random.random([1, 3, 64, 64]).astype("float32"),
"data2": np.random.random([64, 64]).astype("float32"),
......
python/paddle/fluid/tests/unittests/ir/inference/test_trt_tile_op.py
浏览文件 @ 9a9e0aa0
......@@ -30,7 +30,7 @@ class TRTTileTest(InferencePassTest):
name="data", shape=[4, 3, 224, 256], dtype="float32"
)
tile_out = paddle.tile(x=data, repeat_times=[1, 1, 1, 1])
out = fluid.layers.batch_norm(tile_out, is_test=True)
out = paddle.static.nn.batch_norm(tile_out, is_test=True)
self.feeds = {
"data": np.random.random([4, 3, 224, 256]).astype("float32"),
......@@ -55,7 +55,7 @@ class TRTTileExpandTest(InferencePassTest):
with fluid.program_guard(self.main_program, self.startup_program):
data = fluid.data(name="data", shape=[1, 1, 1, 1], dtype="float32")
tile_out = paddle.tile(x=data, repeat_times=[1, 4, 1080, 1920])
out = fluid.layers.batch_norm(tile_out, is_test=True)
out = paddle.static.nn.batch_norm(tile_out, is_test=True)
self.feeds = {
"data": np.random.random([1, 1, 1, 1]).astype("float32"),
......@@ -80,7 +80,7 @@ class TRTTileExpandStaticTest(InferencePassTest):
with fluid.program_guard(self.main_program, self.startup_program):
data = fluid.data(name="data", shape=[1, 1, 1, 1], dtype="float32")
tile_out = paddle.tile(x=data, repeat_times=[1, 4, 1080, 1920])
out = fluid.layers.batch_norm(tile_out, is_test=True)
out = paddle.static.nn.batch_norm(tile_out, is_test=True)
self.feeds = {
"data": np.random.random([1, 1, 1, 1]).astype("float32"),
......@@ -105,7 +105,7 @@ class TRTTileExpandHalfTest(InferencePassTest):
with fluid.program_guard(self.main_program, self.startup_program):
data = fluid.data(name="data", shape=[1, 1, 1, 1], dtype="float32")
tile_out = paddle.tile(x=data, repeat_times=[1, 4, 1080, 1920])
out = fluid.layers.batch_norm(tile_out, is_test=True)
out = paddle.static.nn.batch_norm(tile_out, is_test=True)
self.feeds = {
"data": np.random.random([1, 1, 1, 1]).astype("float32"),
......
python/paddle/fluid/tests/unittests/ir/inference/test_trt_transpose_flatten_concat_fuse_pass.py
浏览文件 @ 9a9e0aa0
......@@ -42,7 +42,7 @@ class TransposeFlattenConcatFusePassTRTTest(InferencePassTest):
# There is no parameters for above structure.
# Hence, append a batch_norm to avoid failure caused by load_combined.
reshape_out = paddle.reshape(concat_out, [-1, 0, 1, 1])
out = fluid.layers.batch_norm(reshape_out, is_test=True)
out = paddle.static.nn.batch_norm(reshape_out, is_test=True)
self.feeds = {
"data1": np.random.random([8, 32, 128]).astype("float32"),
......
python/paddle/fluid/tests/unittests/mlu/sync_batch_norm_op_mlu.py
浏览文件 @ 9a9e0aa0
......@@ -86,7 +86,7 @@ class TestSyncBatchNormOpTraining(TestSyncBatchNormRunnerBase):
)
if self.bn_dtype == np.float16:
conv = fluid.layers.cast(conv, 'float16')
bn = fluid.layers.batch_norm(
bn = paddle.static.nn.batch_norm(
conv,
param_attr=fluid.ParamAttr(name='bn_scale'),
bias_attr=fluid.ParamAttr(name='bn_bias'),
......
python/paddle/fluid/tests/unittests/mlu/test_batch_norm_op_mlu.py
浏览文件 @ 9a9e0aa0
......@@ -742,12 +742,12 @@ class TestBatchNormOpError(unittest.TestCase):
x1 = fluid.create_lod_tensor(
np.array([-1, 3, 5, 5]), [[1, 1, 1, 1]], fluid.CPUPlace()
)
self.assertRaises(TypeError, fluid.layers.batch_norm, x1)
self.assertRaises(TypeError, paddle.static.nn.batch_norm, x1)
# the input dtype of batch_norm must be float16 or float32 or float64
# float16 only can be set on GPU place
x2 = fluid.layers.data(name='x2', shape=[3, 4, 5, 6], dtype="int32")
self.assertRaises(TypeError, fluid.layers.batch_norm, x2)
self.assertRaises(TypeError, paddle.static.nn.batch_norm, x2)
class TestDygraphBatchNormAPIError(unittest.TestCase):
......
python/paddle/fluid/tests/unittests/npu/sync_batch_norm_op_npu.py
浏览文件 @ 9a9e0aa0
......@@ -88,7 +88,7 @@ class TestSyncBatchNormOpTraining(TestSyncBatchNormRunnerBase):
bias_attr=False,
use_cudnn=use_cudnn,
)
bn = fluid.layers.batch_norm(
bn = paddle.static.nn.batch_norm(
conv,
param_attr=fluid.ParamAttr(name='bn_scale'),
bias_attr=fluid.ParamAttr(name='bn_bias'),
......
python/paddle/fluid/tests/unittests/seresnext_net.py
浏览文件 @ 9a9e0aa0
......@@ -80,7 +80,7 @@ def conv_bn_layer(
return (
conv
if remove_bn
else fluid.layers.batch_norm(input=conv, act=act, momentum=0.1)
else paddle.static.nn.batch_norm(input=conv, act=act, momentum=0.1)
)
......
python/paddle/fluid/tests/unittests/simple_nets.py
浏览文件 @ 9a9e0aa0
......@@ -53,7 +53,7 @@ def batchnorm_fc_with_inputs(img, label, class_num=10):
),
)
hidden = fluid.layers.batch_norm(input=hidden)
hidden = paddle.static.nn.batch_norm(input=hidden)
prediction = fluid.layers.fc(hidden, size=class_num, act='softmax')
loss = fluid.layers.cross_entropy(input=prediction, label=label)
......
python/paddle/fluid/tests/unittests/test_async_ssa_graph_executor_mnist.py
浏览文件 @ 9a9e0aa0
......@@ -46,7 +46,7 @@ def convolutional_neural_network(use_py_reader):
pool_stride=2,
act="relu",
)
conv_pool_1 = fluid.layers.batch_norm(conv_pool_1)
conv_pool_1 = paddle.static.nn.batch_norm(conv_pool_1)
conv_pool_2 = fluid.nets.simple_img_conv_pool(
input=conv_pool_1,
filter_size=5,
......
python/paddle/fluid/tests/unittests/test_batch_norm_op.py
浏览文件 @ 9a9e0aa0
......@@ -759,12 +759,12 @@ class TestBatchNormOpError(unittest.TestCase):
x1 = fluid.create_lod_tensor(
np.array([-1, 3, 5, 5]), [[1, 1, 1, 1]], fluid.CPUPlace()
)
self.assertRaises(TypeError, fluid.layers.batch_norm, x1)
self.assertRaises(TypeError, paddle.static.nn.batch_norm, x1)
# the input dtype of batch_norm must be float16 or float32 or float64
# float16 only can be set on GPU place
x2 = fluid.layers.data(name='x2', shape=[3, 4, 5, 6], dtype="int32")
self.assertRaises(TypeError, fluid.layers.batch_norm, x2)
self.assertRaises(TypeError, paddle.static.nn.batch_norm, x2)
class TestDygraphBatchNormAPIError(unittest.TestCase):
......
python/paddle/fluid/tests/unittests/test_fetch_unmerged.py
浏览文件 @ 9a9e0aa0
......@@ -34,7 +34,7 @@ class TestFetchUnmerged(unittest.TestCase):
pool_type='max',
act="relu",
)
conv_pool_1 = fluid.layers.batch_norm(conv_pool_1)
conv_pool_1 = paddle.static.nn.batch_norm(conv_pool_1)
conv_pool_2 = fluid.nets.simple_img_conv_pool(
input=conv_pool_1,
filter_size=5,
......
python/paddle/fluid/tests/unittests/test_fuse_bn_act_pass.py
浏览文件 @ 9a9e0aa0
......@@ -41,7 +41,7 @@ class TestFuseBatchNormActPass(unittest.TestCase):
name='batch_norm_b',
initializer=fluid.initializer.Constant(value=0.0),
)
hidden2 = fluid.layers.batch_norm(
hidden2 = paddle.static.nn.batch_norm(
input=hidden1,
param_attr=param_attr,
bias_attr=bias_attr,
......@@ -49,7 +49,7 @@ class TestFuseBatchNormActPass(unittest.TestCase):
data_layout='NHWC',
)
hidden3 = fluid.layers.fc(input=hidden2, size=32, act='relu')
hidden4 = fluid.layers.batch_norm(
hidden4 = paddle.static.nn.batch_norm(
input=hidden3, act='relu', data_layout='NHWC'
)
prediction = fluid.layers.fc(input=hidden4, size=10, act='softmax')
......
python/paddle/fluid/tests/unittests/test_fuse_bn_add_act_pass.py
浏览文件 @ 9a9e0aa0
......@@ -87,7 +87,7 @@ class TestFusedBnAddActAPI(unittest.TestCase):
bias_attr=False,
data_format='NHWC',
)
bn = fluid.layers.batch_norm(
bn = paddle.static.nn.batch_norm(
input=conv1_1,
param_attr=self.bn_param_attr1,
bias_attr=self.bn_bias_attr1,
......@@ -133,7 +133,7 @@ class TestFusedBnAddActAPI(unittest.TestCase):
bias_attr=False,
data_format='NHWC',
)
bn1 = fluid.layers.batch_norm(
bn1 = paddle.static.nn.batch_norm(
input=conv1_1,
param_attr=self.bn_param_attr1,
bias_attr=self.bn_bias_attr1,
......@@ -150,7 +150,7 @@ class TestFusedBnAddActAPI(unittest.TestCase):
bias_attr=False,
data_format='NHWC',
)
bn2 = fluid.layers.batch_norm(
bn2 = paddle.static.nn.batch_norm(
input=conv1_1,
param_attr=self.bn_param_attr2,
bias_attr=self.bn_bias_attr2,
......
python/paddle/fluid/tests/unittests/test_fuse_relu_depthwise_conv_pass.py
浏览文件 @ 9a9e0aa0
......@@ -23,7 +23,7 @@ import paddle.fluid.core as core
def norm(*args, **kargs):
return fluid.layers.batch_norm(*args, **kargs)
return paddle.static.nn.batch_norm(*args, **kargs)
def sep_conv(input, channel, stride, filter, dilation=1, act=None):
......
python/paddle/fluid/tests/unittests/test_image_classification_layer.py
浏览文件 @ 9a9e0aa0
......@@ -42,9 +42,9 @@ class TestLayer(unittest.TestCase):
images = fluid.layers.data(
name='pixel', shape=[3, 48, 48], dtype='float32'
)
hidden1 = fluid.layers.batch_norm(input=images)
hidden1 = paddle.static.nn.batch_norm(input=images)
hidden2 = fluid.layers.fc(input=hidden1, size=128, act='relu')
fluid.layers.batch_norm(input=hidden2)
paddle.static.nn.batch_norm(input=hidden2)
print(str(main_program))
......
python/paddle/fluid/tests/unittests/test_imperative_load_static_param.py
浏览文件 @ 9a9e0aa0
......@@ -55,8 +55,8 @@ class TestDygraphLoadStatic(unittest.TestCase):
batchnorm_in = fluid.data(
name="batchnorm_in", shape=[None, 10], dtype='float32'
)
batchnorm_out_1 = fluid.layers.batch_norm(batchnorm_in)
batchnorm_out_2 = fluid.layers.batch_norm(batchnorm_in)
batchnorm_out_1 = paddle.static.nn.batch_norm(batchnorm_in)
batchnorm_out_2 = paddle.static.nn.batch_norm(batchnorm_in)
emb_in = fluid.data(name='emb_in', shape=[None, 10], dtype='int64')
emb_out_1 = fluid.embedding(emb_in, [1000, 100])
......
python/paddle/fluid/tests/unittests/test_imperative_static_runner_mnist.py
浏览文件 @ 9a9e0aa0
......@@ -33,7 +33,7 @@ def convolutional_neural_network(img):
pool_stride=2,
act="relu",
)
conv_pool_1 = fluid.layers.batch_norm(conv_pool_1)
conv_pool_1 = paddle.static.nn.batch_norm(conv_pool_1)
conv_pool_2 = fluid.nets.simple_img_conv_pool(
input=conv_pool_1,
filter_size=5,
......
python/paddle/fluid/tests/unittests/test_inplace_abn_op.py
浏览文件 @ 9a9e0aa0
......@@ -56,7 +56,7 @@ class TestInplaceANBOpTraining(unittest.TestCase):
stop_gradient=False,
)
bn = fluid.layers.batch_norm(
bn = paddle.static.nn.batch_norm(
data,
param_attr=fluid.ParamAttr(name='bn_scale'),
bias_attr=fluid.ParamAttr(name='bn_bias'),
......
python/paddle/fluid/tests/unittests/test_ir_inplace_pass.py
浏览文件 @ 9a9e0aa0
......@@ -38,7 +38,7 @@ def fc_with_batchnorm(use_feed):
),
)
hidden = fluid.layers.batch_norm(input=hidden)
hidden = paddle.static.nn.batch_norm(input=hidden)
prediction = fluid.layers.fc(hidden, size=10, act='softmax')
loss = fluid.layers.cross_entropy(input=prediction, label=label)
loss = paddle.mean(loss)
......
python/paddle/fluid/tests/unittests/test_layers.py
浏览文件 @ 9a9e0aa0
......@@ -2863,7 +2863,7 @@ class TestBook(LayerTest):
data = self._get_data(
name='data', shape=[32, 128, 128], dtype="float32"
)
out = layers.batch_norm(data)
out = paddle.static.nn.batch_norm(data)
return out
def make_batch_norm_momentum_variable(self):
......@@ -2879,7 +2879,7 @@ class TestBook(LayerTest):
dtype='float32',
append_batch_size=False,
)
out = layers.batch_norm(data, momentum=momentum)
out = paddle.static.nn.batch_norm(data, momentum=momentum)
return out
def make_range(self):
......
python/paddle/fluid/tests/unittests/test_load_state_dict_from_old_format.py
浏览文件 @ 9a9e0aa0
......@@ -33,7 +33,7 @@ def convolutional_neural_network(img):
pool_stride=2,
act="relu",
)
conv_pool_1 = fluid.layers.batch_norm(conv_pool_1)
conv_pool_1 = paddle.static.nn.batch_norm(conv_pool_1)
conv_pool_2 = fluid.nets.simple_img_conv_pool(
input=conv_pool_1,
filter_size=5,
......
python/paddle/fluid/tests/unittests/test_mix_precision_all_reduce_fuse.py
浏览文件 @ 9a9e0aa0
......@@ -45,7 +45,7 @@ def conv_net(use_feed):
pool_stride=2,
act="relu",
)
conv_pool_1 = fluid.layers.batch_norm(conv_pool_1)
conv_pool_1 = paddle.static.nn.batch_norm(conv_pool_1)
conv_pool_1 = fluid.layers.cast(conv_pool_1, np.float32)
conv_pool_2 = fluid.nets.simple_img_conv_pool(
......
python/paddle/fluid/tests/unittests/test_norm_nn_grad.py
浏览文件 @ 9a9e0aa0
......@@ -171,7 +171,7 @@ class TestBatchNormDoubleGradCheck(unittest.TestCase):
eps = 0.005
atol = 1e-4
x = paddle.create_parameter(dtype=dtype, shape=self.shape, name='x')
z = fluid.layers.batch_norm(
z = paddle.static.nn.batch_norm(
input=x,
data_layout=self.data_layout,
use_global_stats=self.use_global_stats,
......@@ -251,7 +251,7 @@ class TestBatchNormDoubleGradCheckCase5(TestBatchNormDoubleGradCheck):
self.shape[1] if self.data_layout == 'NCHW' else self.shape[-1]
)
x = paddle.create_parameter(dtype=dtype, shape=self.shape, name='x')
z = fluid.layers.batch_norm(
z = paddle.static.nn.batch_norm(
input=x,
data_layout=self.data_layout,
use_global_stats=self.use_global_stats,
......
python/paddle/fluid/tests/unittests/test_parallel_executor_fetch_feed.py
浏览文件 @ 9a9e0aa0
......@@ -26,10 +26,10 @@ from paddle.fluid import compiler
def Lenet(data, class_dim):
conv1 = fluid.layers.conv2d(data, 4, 5, 1, act=None)
bn1 = fluid.layers.batch_norm(conv1, act='relu')
bn1 = paddle.static.nn.batch_norm(conv1, act='relu')
pool1 = fluid.layers.pool2d(bn1, 2, 'max', 2)
conv2 = fluid.layers.conv2d(pool1, 16, 5, 1, act=None)
bn2 = fluid.layers.batch_norm(conv2, act='relu')
bn2 = paddle.static.nn.batch_norm(conv2, act='relu')
pool2 = fluid.layers.pool2d(bn2, 2, 'max', 2)
fc1 = fluid.layers.fc(pool2, size=50, act='relu')
......
python/paddle/fluid/tests/unittests/test_parallel_executor_mnist.py
浏览文件 @ 9a9e0aa0
......@@ -58,7 +58,7 @@ def fc_with_batchnorm(use_feed):
),
)
hidden = fluid.layers.batch_norm(input=hidden)
hidden = paddle.static.nn.batch_norm(input=hidden)
with fluid.name_scope("fc_layer"):
prediction = fluid.layers.fc(hidden, size=10, act='softmax')
with fluid.name_scope("loss"):
......
python/paddle/fluid/tests/unittests/test_set_bool_attr.py
浏览文件 @ 9a9e0aa0
......@@ -12,8 +12,10 @@
# See the License for the specific language governing permissions and
# limitations under the License.
import unittest
import paddle
import paddle.fluid as fluid
......@@ -28,7 +30,7 @@ class TestAttrSet(unittest.TestCase):
name='batch_norm_b',
initializer=fluid.initializer.Constant(value=0.0),
)
bn = fluid.layers.batch_norm(
bn = paddle.static.nn.batch_norm(
input=x, param_attr=param_attr, bias_attr=bias_attr
)
block = fluid.default_main_program().desc.block(0)
......
python/paddle/fluid/tests/unittests/test_sync_batch_norm_op.py
浏览文件 @ 9a9e0aa0
......@@ -81,7 +81,7 @@ class TestSyncBatchNormOpTraining(unittest.TestCase):
bias_attr=False,
use_cudnn=use_cudnn,
)
bn = fluid.layers.batch_norm(
bn = paddle.static.nn.batch_norm(
conv,
param_attr=fluid.ParamAttr(name='bn_scale'),
bias_attr=fluid.ParamAttr(name='bn_bias'),
......
python/paddle/static/__init__.py
浏览文件 @ 9a9e0aa0
......@@ -74,11 +74,13 @@ from ..fluid.layers import create_global_var # noqa: F401
from ..fluid.contrib.layers import ctr_metric_bundle # noqa: F401
from ..fluid.layers import exponential_decay # noqa: F401
from .nn.common import batch_norm # noqa: F401
from paddle.static.nn.metric import auc # noqa: F401
from paddle.static.nn.metric import accuracy # noqa: F401
__all__ = [ # noqa
'append_backward',
'batch_norm',
'gradients',
'Executor',
'global_scope',
......
python/paddle/static/nn/__init__.py
浏览文件 @ 9a9e0aa0
......@@ -13,6 +13,7 @@
# limitations under the License.
from .common import fc # noqa: F401
from .common import batch_norm # noqa: F401
from .common import instance_norm # noqa: F401
from .common import data_norm # noqa: F401
from .common import group_norm # noqa: F401
......@@ -22,9 +23,7 @@ from .common import conv2d_transpose # noqa: F401
from .common import conv3d_transpose # noqa: F401
from .common import bilinear_tensor_product # noqa: F401
from .common import py_func # noqa: F401
from ...tensor.creation import create_parameter # noqa: F401
from ...fluid.layers import batch_norm # noqa: F401
from ...fluid.layers import case # noqa: F401
from ...fluid.layers import cond # noqa: F401
from ...fluid.layers import conv2d # noqa: F401
......
python/paddle/static/nn/common.py
浏览文件 @ 9a9e0aa0
......@@ -2160,6 +2160,328 @@ def bilinear_tensor_product(
return helper.append_activation(out)
def batch_norm(
input,
act=None,
is_test=False,
momentum=0.9,
epsilon=1e-05,
param_attr=None,
bias_attr=None,
data_layout='NCHW',
in_place=False,
name=None,
moving_mean_name=None,
moving_variance_name=None,
do_model_average_for_mean_and_var=True,
use_global_stats=False,
):
r"""
**Batch Normalization Layer**
Can be used as a normalizer function for convolution or fully_connected operations.
The required data format for this layer is one of the following:
1. NHWC `[batch, in_height, in_width, in_channels]`
2. NCHW `[batch, in_channels, in_height, in_width]`
Refer to `Batch Normalization: Accelerating Deep Network Training by Reducing
Internal Covariate Shift <https://arxiv.org/pdf/1502.03167.pdf>`_
for more details.
:math:input is the input features over a mini-batch.
.. 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 \\\\
\\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
moving\_mean = moving\_mean * momentum + mini-batch\_mean * (1. - momentum) \\\\
moving\_var = moving\_var * momentum + mini-batch\_var * (1. - momentum)
moving_mean is global mean and moving_var is global variance.
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. (It usually got from the
pre-trained model.)
The training and testing (or inference) have the same behavior:
.. math::
\\hat{x_i} &\\gets \\frac{x_i - \\mu_\\beta} {\\sqrt{\\
\\sigma_{\\beta}^{2} + \\epsilon}} \\\\
y_i &\\gets \\gamma \\hat{x_i} + \\beta
Note:
if build_strategy.sync_batch_norm=True, the batch_norm in network will use
sync_batch_norm automatically.
`is_test = True` can only be used in test program and inference program, `is_test` CANNOT be set to True in train program, if you want to use global status from pre_train model in train program, please set `use_global_stats = True`.
Args:
input(Tensor): The rank of input Tensor can be 2, 3, 4, 5. The data type
is float16 or float32 or float64.
act(string, Default None): Activation type, linear|relu|prelu|...
is_test (bool, Default False): A flag indicating whether it is in
test phrase or not.
momentum(float|Tensor, Default 0.9): The value used for the moving_mean and
moving_var computation. This should be a float number or a Tensor with
shape [1] and data type as float32. The updated formula is:
:math:`moving\_mean = moving\_mean * momentum + new\_mean * (1. - momentum)`
:math:`moving\_var = moving\_var * momentum + new\_var * (1. - momentum)`
Default is 0.9.
epsilon(float, Default 1e-05): A value added to the denominator for
numerical stability. Default is 1e-5.
param_attr(ParamAttr|None): 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, the name of scale can be set in ParamAttr.
If the Initializer of the param_attr is not set, the parameter is initialized
with Xavier. Default: None.
bias_attr(ParamAttr|None): 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, the name of bias can be set in ParamAttr.
If the Initializer of the bias_attr is not set, the bias is initialized zero.
Default: None.
data_layout (str, optional): Specify the data format of the input, and the data format of the output
will be consistent with that of the input. An optional string from: `"NCHW"`, `"NHWC"`.
The default is `"NCHW"`. When it is `"NCHW"`, the data is stored in the order of:
`[batch_size, input_channels, input_height, input_width]`.
in_place(bool, Default False): Make the input and output of batch norm reuse memory.
name(str|None): For detailed information, please refer to :ref:`api_guide_Name`.
Usually name is no need to set and None by default.
moving_mean_name(str, Default None): The name of moving_mean which store the global Mean. If it
is set to None, batch_norm will save global mean with a random name, otherwise, batch_norm
will save global mean with the string.
moving_variance_name(str, Default None): The name of the moving_variance which store the global Variance.
If it is set to None, batch_norm will save global variance with a random name, otherwise, batch_norm
will save global variance with the string.
do_model_average_for_mean_and_var(bool, Default True): Whether parameter mean and variance should do model
average when model average is enabled.
use_global_stats(bool, Default False): 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.
Returns:
A Tensor which is the result after applying batch normalization on the input,
has same shape and data type with input.
Examples:
.. code-block:: python
import paddle
paddle.enable_static()
x = paddle.static.data(name='x', shape=[3, 7, 3, 7], dtype='float32')
hidden1 = paddle.static.nn.fc(x=x, size=200)
print(hidden1.shape)
# [3, 200]
hidden2 = paddle.static.nn.batch_norm(input=hidden1)
print(hidden2.shape)
# [3, 200]
"""
assert (
bias_attr is not False
), "bias_attr should not be False in batch_norm."
helper = LayerHelper('batch_norm', **locals())
check_variable_and_dtype(
input, 'input', ['float16', 'float32', 'float64'], 'batch_norm'
)
dtype = helper.input_dtype()
# use fp32 for bn parameter
if dtype == core.VarDesc.VarType.FP16:
dtype = core.VarDesc.VarType.FP32
input_shape = input.shape
if data_layout == 'NCHW':
channel_num = input_shape[1]
else:
if data_layout == 'NHWC':
channel_num = input_shape[-1]
else:
raise ValueError("unsupported data layout:" + data_layout)
param_shape = [channel_num]
# create parameter
scale = helper.create_parameter(
attr=helper.param_attr,
shape=param_shape,
dtype=dtype,
default_initializer=paddle.fluid.initializer.Constant(1.0),
)
bias = helper.create_parameter(
attr=helper.bias_attr, shape=param_shape, dtype=dtype, is_bias=True
)
mean = helper.create_parameter(
attr=paddle.ParamAttr(
name=moving_mean_name,
initializer=paddle.fluid.initializer.Constant(0.0),
trainable=False,
do_model_average=do_model_average_for_mean_and_var,
),
shape=param_shape,
dtype=dtype,
)
mean.stop_gradient = True
variance = helper.create_parameter(
attr=paddle.ParamAttr(
name=moving_variance_name,
initializer=paddle.fluid.initializer.Constant(1.0),
trainable=False,
do_model_average=do_model_average_for_mean_and_var,
),
shape=param_shape,
dtype=dtype,
)
variance.stop_gradient = True
# create output
# mean and mean_out share the same memory
mean_out = mean
# variance and variance_out share the same memory
variance_out = variance
if _non_static_mode():
inputs_has_MomemtumTensor = False
attrs_has_momentum = False
tmp_tensor_type = core.eager.Tensor
if isinstance(momentum, tmp_tensor_type):
inputs_has_MomemtumTensor = True
else:
attrs_has_momentum = True
attrs_ = ()
if attrs_has_momentum:
attrs_ = (
'momentum',
momentum,
'epsilon',
epsilon,
'is_test',
is_test,
'data_layout',
data_layout,
'use_mkldnn',
False,
'fuse_with_relu',
False,
'use_global_stats',
use_global_stats,
)
else:
attrs_ = (
'epsilon',
epsilon,
'is_test',
is_test,
'data_layout',
data_layout,
'use_mkldnn',
False,
'fuse_with_relu',
False,
'use_global_stats',
use_global_stats,
)
if inputs_has_MomemtumTensor:
batch_norm_out, _, _, _, _, _ = paddle._legacy_C_ops.batch_norm(
input,
scale,
bias,
mean,
variance,
momentum,
mean_out,
variance_out,
*attrs_,
)
else:
batch_norm_out, _, _, _, _, _ = paddle._legacy_C_ops.batch_norm(
input,
scale,
bias,
mean,
variance,
None,
mean_out,
variance_out,
*attrs_,
)
return paddle.fluid.dygraph_utils._append_activation_in_dygraph(
batch_norm_out, act=act, use_mkldnn=False
)
saved_mean = helper.create_variable_for_type_inference(
dtype=dtype, stop_gradient=True
)
saved_variance = helper.create_variable_for_type_inference(
dtype=dtype, stop_gradient=True
)
reserve_space = None
if not is_test:
reserve_space = helper.create_variable_for_type_inference(
dtype=helper.input_dtype(), stop_gradient=True
)
batch_norm_out = (
input if in_place else helper.create_variable_for_type_inference(dtype)
)
inputs = {
"X": input,
"Scale": scale,
"Bias": bias,
"Mean": mean,
"Variance": variance,
"MeanOut": mean_out,
"VarianceOut": variance_out,
}
attrs = {
"epsilon": epsilon,
"is_test": is_test,
"data_layout": data_layout,
"use_mkldnn": False,
"fuse_with_relu": False,
"use_global_stats": use_global_stats,
}
if isinstance(momentum, paddle.static.Variable):
inputs['MomemtumTensor'] = momentum
else:
attrs['momentum'] = momentum
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
helper.append_op(
type="batch_norm", inputs=inputs, outputs=outputs, attrs=attrs
)
return helper.append_activation(batch_norm_out)
@static_only
def prelu(x, mode, param_attr=None, data_format="NCHW", name=None):
r"""
......
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