未验证 提交 557be6fc 编写于 作者: X Xin Pan 提交者: GitHub

Merge pull request #12902 from PaddlePaddle/revert-12736

Revert "Disable in_place in batch_norm API. (#12736)"
......@@ -135,7 +135,7 @@ class BatchNormOpMaker : public framework::OpProtoAndCheckerMaker {
AddInput("Variance",
"The global variance (for training) "
"or estimated Variance (for testing)");
AddOutput("Y", "result after normalization");
AddOutput("Y", "result after normalization").Reuse("X");
AddOutput("MeanOut",
"Share memory with Mean. "
"Store the global mean when training")
......
......@@ -27,7 +27,6 @@ from . import utils
import random
from .. import unique_name
from functools import reduce
import warnings
__all__ = [
'fc',
......@@ -2048,7 +2047,7 @@ def batch_norm(input,
param_attr(ParamAttr): The parameter attribute for Parameter `scale`.
bias_attr(ParamAttr): The parameter attribute for Parameter `bias`.
data_layout(string, default NCHW): NCHW|NHWC
in_place(bool, Default False): This argument is deprecated since 0.15.0.
in_place(bool, Default False): Make the input and output of batch norm reuse memory.
use_mkldnn(bool, Default false): ${use_mkldnn_comment}
name(string, Default None): A name for this layer(optional). If set None, the layer
will be named automatically.
......@@ -2070,10 +2069,6 @@ def batch_norm(input,
helper = LayerHelper('batch_norm', **locals())
dtype = helper.input_dtype()
if in_place:
raise warnings.warn("The argument in_place is deprecated since 0.15.0, "
"please do not set it True.")
input_shape = input.shape
if data_layout == 'NCHW':
channel_num = input_shape[1]
......@@ -2123,7 +2118,7 @@ def batch_norm(input,
saved_mean = helper.create_tmp_variable(dtype=dtype, stop_gradient=True)
saved_variance = helper.create_tmp_variable(dtype=dtype, stop_gradient=True)
batch_norm_out = helper.create_tmp_variable(dtype)
batch_norm_out = input if in_place else helper.create_tmp_variable(dtype)
helper.append_op(
type="batch_norm",
......
......@@ -229,7 +229,7 @@ def img_conv_group(input,
use_mkldnn=use_mkldnn)
if conv_with_batchnorm[i]:
tmp = layers.batch_norm(input=tmp, act=conv_act)
tmp = layers.batch_norm(input=tmp, act=conv_act, in_place=True)
drop_rate = conv_batchnorm_drop_rate[i]
if abs(drop_rate) > 1e-5:
tmp = layers.dropout(x=tmp, dropout_prob=drop_rate)
......
......@@ -256,10 +256,7 @@ def main(net_type, use_cuda, is_local=True):
save_dirname = "image_classification_" + net_type + ".inference.model"
train(net_type, use_cuda, save_dirname, is_local)
# There is bug in fluid.InferenceTranspiler for VGG.
if net_type == "resnet":
infer(use_cuda, save_dirname)
infer(use_cuda, save_dirname)
class TestImageClassification(unittest.TestCase):
......
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