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未验证 提交 9ffc760f 编写于 作者: H heyanru 提交者： GitHub
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[Fluid Clean] remove paddle.fluid.layers.nn.reduce_mean (#48196)

上级 b9421dc1
隐藏空白更改
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Showing with 176 addition and 520 deletion
python/paddle/fluid/contrib/slim/quantization/adaround.py
浏览文件 @ 9ffc760f
......@@ -64,7 +64,7 @@ class AdaRoundLoss:
square_cost = fluid.layers.square_error_cost(
ada_quantized_output, orig_output
)
recon_loss = fluid.layers.reduce_mean(paddle.sum(square_cost, axis=-1))
recon_loss = paddle.mean(paddle.sum(square_cost, axis=-1))
return recon_loss
def compute_round_loss(self, alpha_v, warm_start, beta):
......
python/paddle/fluid/contrib/slim/tests/test_moving_average_abs_max_scale_op.py
浏览文件 @ 9ffc760f
......@@ -53,7 +53,7 @@ class TestMovingAverageAbsMaxScaleOp(unittest.TestCase):
cross_entropy = fluid.layers.softmax_with_cross_entropy(
fc_tmp, label
)
loss = fluid.layers.reduce_mean(cross_entropy)
loss = paddle.mean(cross_entropy)
sgd = fluid.optimizer.SGD(learning_rate=1e-3)
sgd.minimize(loss)
......
python/paddle/fluid/contrib/tests/test_correlation.py
浏览文件 @ 9ffc760f
......@@ -122,7 +122,7 @@ class TestCorrelationOp(unittest.TestCase):
stride2=1,
)
loss = fluid.layers.reduce_mean(out)
loss = paddle.mean(out)
optimizer = fluid.optimizer.Momentum(0.0001, 0.9)
optimizer.minimize(loss)
......
python/paddle/fluid/layers/nn.py
浏览文件 @ 9ffc760f
......@@ -71,7 +71,6 @@ __all__ = [
'softmax',
'pool2d',
'batch_norm',
'reduce_mean',
'reduce_all',
'reduce_any',
'dropout',
......@@ -2506,63 +2505,6 @@ def reduce_sum(input, dim=None, keep_dim=False, name=None):
return out
@deprecated(since="2.0.0", update_to="paddle.mean")
def reduce_mean(input, dim=None, keep_dim=False, name=None):
"""
Computes the mean of the input tensor's elements along the given dimension.
Args:
input (Variable): The input variable which is a Tensor, the data type is float32,
float64, int32, int64.
dim (list|int, optional): The dimension along which the mean is computed. If
`None`, compute the mean over all elements of :attr:`input`
and return a variable with a single element, otherwise it
must be in the range :math:`[-rank(input), rank(input))`. If
:math:`dim[i] < 0`, the dimension to reduce is
:math:`rank(input) + dim[i]`.
keep_dim (bool, optional): Whether to reserve the reduced dimension in the
output Tensor. The result tensor will have one fewer dimension
than the :attr:`input` unless :attr:`keep_dim` is true, default
value is False.
name(str, optional): The default value is None. Normally there is no need for
user to set this property. For more information, please refer to :ref:`api_guide_Name`
Returns:
Variable: Tensor, results of average on the specified dim of input tensor,
it's data type is the same as input's Tensor.
Raises:
TypeError, if out data type is different with the input data type.
Examples:
.. code-block:: python
import paddle
import paddle.fluid as fluid
paddle.enable_static()
# x is a Tensor variable with following elements:
# [[0.2, 0.3, 0.5, 0.9]
# [0.1, 0.2, 0.6, 0.7]]
# Each example is followed by the corresponding output tensor.
x = fluid.data(name='x', shape=[2, 4], dtype='float32')
fluid.layers.reduce_mean(x) # [0.4375]
fluid.layers.reduce_mean(x, dim=0) # [0.15, 0.25, 0.55, 0.8]
fluid.layers.reduce_mean(x, dim=-1) # [0.475, 0.4]
fluid.layers.reduce_mean(x, dim=1, keep_dim=True) # [[0.475], [0.4]]
# y is a Tensor variable with shape [2, 2, 2] and elements as below:
# [[[1.0, 2.0], [3.0, 4.0]],
# [[5.0, 6.0], [7.0, 8.0]]]
# Each example is followed by the corresponding output tensor.
y = fluid.data(name='y', shape=[2, 2, 2], dtype='float32')
fluid.layers.reduce_mean(y, dim=[1, 2]) # [2.5, 6.5]
fluid.layers.reduce_mean(y, dim=[0, 1]) # [4.0, 5.0]
"""
return paddle.mean(x=input, axis=dim, keepdim=keep_dim, name=name)
def reduce_all(input, dim=None, keep_dim=False, name=None):
"""
......
python/paddle/fluid/tests/unittests/auto_checkpoint_utils.py
浏览文件 @ 9ffc760f
......@@ -17,6 +17,7 @@ import unittest
import numpy as np
import paddle
import paddle.fluid as fluid
import paddle.fluid.incubate.checkpoint.auto_checkpoint as acp
from paddle.fluid import unique_name
......@@ -71,7 +72,7 @@ class AutoCheckpointBase(unittest.TestCase):
cross_entropy = fluid.layers.softmax_with_cross_entropy(
fc_tmp, label
)
loss = fluid.layers.reduce_mean(cross_entropy)
loss = paddle.mean(cross_entropy)
sgd = fluid.optimizer.SGD(learning_rate=1e-3)
if minimize:
sgd.minimize(loss)
......
python/paddle/fluid/tests/unittests/auto_parallel/test_dist_embedding.py
浏览文件 @ 9ffc760f
......@@ -39,7 +39,7 @@ def make_program_lookup_table_v1_mp_dp():
dtype="float32",
is_sparse=False,
)
loss = paddle.fluid.layers.reduce_mean(emb_out)
loss = paddle.mean(emb_out)
auto.shard_tensor(
src_ids,
......
python/paddle/fluid/tests/unittests/dygraph_to_static/ifelse_simple_func.py
浏览文件 @ 9ffc760f
......@@ -57,7 +57,7 @@ def dyfunc_with_if_else2(x, col=100):
# `x` is Tensor, `col` is not Tensor, and `col` is the return value of `true_fn` after transformed.
# col = -1
col = fluid.layers.fill_constant(shape=[1], value=-1, dtype="int64")
if fluid.layers.reduce_mean(x).numpy()[0] > x.numpy()[row][col]:
if paddle.mean(x).numpy()[0] > x.numpy()[row][col]:
y = fluid.layers.relu(x)
else:
x_pow = paddle.pow(x, 2)
......
python/paddle/fluid/tests/unittests/dygraph_to_static/seq2seq_dygraph_model.py
浏览文件 @ 9ffc760f
......@@ -303,7 +303,7 @@ class BaseModel(fluid.dygraph.Layer):
tar_sequence_length, maxlen=max_tar_seq_len, dtype='float32'
)
loss = loss * tar_mask
loss = fluid.layers.reduce_mean(loss, dim=[0])
loss = paddle.mean(loss, axis=[0])
loss = paddle.sum(loss)
return loss
......@@ -837,7 +837,7 @@ class AttentionModel(fluid.dygraph.Layer):
tar_sequence_length, maxlen=max_tar_seq_len, dtype='float32'
)
loss = loss * tar_mask
loss = fluid.layers.reduce_mean(loss, dim=[0])
loss = paddle.sum(loss)
loss = paddle.mean(loss, axis=[0])
loss = fluid.layers.reduce_sum(loss)
return loss
python/paddle/fluid/tests/unittests/dygraph_to_static/simnet_dygraph_model.py
浏览文件 @ 9ffc760f
......@@ -114,7 +114,7 @@ class ReduceMeanLayer:
"""
operation
"""
mean = fluid.layers.reduce_mean(input)
mean = paddle.mean(input)
return mean
......
python/paddle/fluid/tests/unittests/dygraph_to_static/test_bmn.py
浏览文件 @ 9ffc760f
......@@ -331,11 +331,11 @@ def bmn_loss_func(
epsilon = 0.000001
# temp = paddle.log(pred_score + epsilon)
loss_pos = paddle.multiply(paddle.log(pred_score + epsilon), pmask)
loss_pos = coef_1 * fluid.layers.reduce_mean(loss_pos)
loss_pos = coef_1 * paddle.mean(loss_pos)
loss_neg = paddle.multiply(
paddle.log(1.0 - pred_score + epsilon), (1.0 - pmask)
)
loss_neg = coef_0 * fluid.layers.reduce_mean(loss_neg)
loss_neg = coef_0 * paddle.mean(loss_neg)
loss = -1 * (loss_pos + loss_neg)
return loss
......
python/paddle/fluid/tests/unittests/dygraph_to_static/test_cycle_gan.py
浏览文件 @ 9ffc760f
......@@ -93,8 +93,8 @@ class Cycle_Gan(fluid.dygraph.Layer):
diff_A = paddle.abs(paddle.subtract(x=input_A, y=cyc_A))
diff_B = paddle.abs(paddle.subtract(x=input_B, y=cyc_B))
cyc_A_loss = fluid.layers.reduce_mean(diff_A) * lambda_A
cyc_B_loss = fluid.layers.reduce_mean(diff_B) * lambda_B
cyc_A_loss = paddle.mean(diff_A) * lambda_A
cyc_B_loss = paddle.mean(diff_B) * lambda_B
cyc_loss = cyc_A_loss + cyc_B_loss
fake_rec_A = self.build_gen_discriminator_a(fake_B)
......@@ -105,8 +105,8 @@ class Cycle_Gan(fluid.dygraph.Layer):
G = g_A_loss + g_B_loss
idt_A = self.build_generator_resnet_9blocks_a(input_B)
idt_loss_A = (
fluid.layers.reduce_mean(
paddle.abs(paddle.subtract(x=input_B, y=idt_A))
paddle.mean(
paddle.abs(fluid.layers.elementwise_sub(x=input_B, y=idt_A))
)
* lambda_B
* lambda_identity
......@@ -114,8 +114,8 @@ class Cycle_Gan(fluid.dygraph.Layer):
idt_B = self.build_generator_resnet_9blocks_b(input_A)
idt_loss_B = (
fluid.layers.reduce_mean(
paddle.abs(paddle.subtract(x=input_A, y=idt_B))
paddle.mean(
paddle.abs(fluid.layers.elementwise_sub(x=input_A, y=idt_B))
)
* lambda_A
* lambda_identity
......@@ -648,7 +648,7 @@ def train(args, to_static):
d_loss_A = (
paddle.square(fake_pool_rec_B) + paddle.square(rec_B - 1)
) / 2.0
d_loss_A = fluid.layers.reduce_mean(d_loss_A)
d_loss_A = paddle.mean(d_loss_A)
d_loss_A.backward()
optimizer2.minimize(d_loss_A)
......@@ -661,7 +661,7 @@ def train(args, to_static):
d_loss_B = (
paddle.square(fake_pool_rec_A) + paddle.square(rec_A - 1)
) / 2.0
d_loss_B = fluid.layers.reduce_mean(d_loss_B)
d_loss_B = paddle.mean(d_loss_B)
d_loss_B.backward()
optimizer3.minimize(d_loss_B)
......
python/paddle/fluid/tests/unittests/dygraph_to_static/test_ptb_lm.py
浏览文件 @ 9ffc760f
......@@ -220,7 +220,7 @@ class PtbModel(fluid.Layer):
logits=projection, label=label, soft_label=False
)
loss = paddle.reshape(loss, shape=[-1, self.num_steps])
loss = fluid.layers.reduce_mean(loss, dim=[0])
loss = paddle.mean(loss, axis=[0])
loss = paddle.sum(loss)
return loss, last_hidden, last_cell
......
python/paddle/fluid/tests/unittests/dygraph_to_static/test_word2vec.py
浏览文件 @ 9ffc760f
......@@ -265,7 +265,7 @@ class SkipGram(fluid.dygraph.Layer):
loss = paddle.nn.functional.binary_cross_entropy_with_logits(
word_sim, label
)
loss = fluid.layers.reduce_mean(loss)
loss = paddle.mean(loss)
return pred, loss
......
python/paddle/fluid/tests/unittests/dygraph_to_static/yolov3.py
浏览文件 @ 9ffc760f
......@@ -325,7 +325,7 @@ class YOLOv3(fluid.dygraph.Layer):
downsample_ratio=self.downsample,
use_label_smooth=cfg.label_smooth,
)
self.losses.append(fluid.layers.reduce_mean(loss))
self.losses.append(paddle.mean(loss))
else:
mask_anchors = []
......
python/paddle/fluid/tests/unittests/ipu/test_reduce_x_op_ipu.py
浏览文件 @ 9ffc760f
......@@ -28,7 +28,7 @@ class TestMean(IPUOpTest):
self.set_test_op()
def set_test_op(self):
self.op = paddle.fluid.layers.reduce_mean
self.op = paddle.mean
def set_feed_attr(self):
self.feed_shape = [x.shape for x in self.feed_fp32.values()]
......
python/paddle/fluid/tests/unittests/ir/inference/CMakeLists.txt
浏览文件 @ 9ffc760f
......@@ -144,7 +144,6 @@ if(WITH_GPU AND TENSORRT_FOUND)
test_trt_pool3d_op
PROPERTIES ENVIRONMENT FLAGS_fraction_of_gpu_memory_to_use=0.1 TIMEOUT 45)
endif()
set_tests_properties(test_trt_reduce_mean_op PROPERTIES TIMEOUT 60)
set_tests_properties(test_trt_tile_op PROPERTIES TIMEOUT 60)
set_tests_properties(test_trt_fc_fuse_quant_dequant_pass PROPERTIES TIMEOUT
100)
......
python/paddle/fluid/tests/unittests/ir/inference/test_trt_reduce_mean_op.py 已删除 100644 → 0
浏览文件 @ b9421dc1
# Copyright (c) 2021 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 unittest
import numpy as np
from inference_pass_test import InferencePassTest
import paddle.fluid as fluid
import paddle.fluid.core as core
from paddle.fluid.core import AnalysisConfig, PassVersionChecker
class TRTReduceMeanTest(InferencePassTest):
def setUp(self):
with fluid.program_guard(self.main_program, self.startup_program):
data = fluid.data(
name="data", shape=[-1, 3, -1, -1], dtype="float32"
)
reduce_mean = fluid.layers.reduce_mean(
data, dim=[2, -1], keep_dim=True
)
out = fluid.layers.batch_norm(reduce_mean, is_test=True)
self.feeds = {
"data": np.random.random([3, 3, 56, 56]).astype("float32"),
}
self.enable_trt = True
self.trt_parameters = TRTReduceMeanTest.TensorRTParam(
1 << 30, 32, 1, AnalysisConfig.Precision.Float32, False, False
)
self.fetch_list = [out]
self.dynamic_shape_params = TRTReduceMeanTest.DynamicShapeParam(
{'data': [1, 3, 16, 16]},
{'data': [3, 3, 56, 56]},
{'data': [3, 3, 56, 56]},
False,
)
def test_check_output(self):
if core.is_compiled_with_cuda():
use_gpu = True
self.check_output_with_option(use_gpu, flatten=True)
self.assertTrue(
PassVersionChecker.IsCompatible('tensorrt_subgraph_pass')
)
class TRTReduceMeanAllNoBatchTest(InferencePassTest):
def setUp(self):
with fluid.program_guard(self.main_program, self.startup_program):
data = fluid.data(
name="data", shape=[-1, 3, -1, -1], dtype="float32"
)
reduce_mean = fluid.layers.reduce_mean(data, keep_dim=True)
out = fluid.layers.batch_norm(reduce_mean, is_test=True)
self.feeds = {
"data": np.random.random([3, 3, 56, 56]).astype("float32"),
}
self.enable_trt = True
self.trt_parameters = TRTReduceMeanAllNoBatchTest.TensorRTParam(
1 << 30, 32, 1, AnalysisConfig.Precision.Float32, False, False
)
self.fetch_list = [out]
self.dynamic_shape_params = (
TRTReduceMeanAllNoBatchTest.DynamicShapeParam(
{'data': [1, 3, 16, 16]},
{'data': [3, 3, 56, 56]},
{'data': [3, 3, 56, 56]},
False,
)
)
def test_check_output(self):
if core.is_compiled_with_cuda():
use_gpu = True
self.check_output_with_option(use_gpu, flatten=True)
self.assertTrue(
PassVersionChecker.IsCompatible('tensorrt_subgraph_pass')
)
class TRTReduceMeanTestFP16(InferencePassTest):
def setUp(self):
with fluid.program_guard(self.main_program, self.startup_program):
data = fluid.data(
name="data", shape=[-1, 3, -1, -1], dtype="float32"
)
reduce_mean = fluid.layers.reduce_mean(
data, dim=[2, -1], keep_dim=True
)
out = fluid.layers.batch_norm(reduce_mean, is_test=True)
self.feeds = {
"data": np.random.random([3, 3, 56, 56]).astype("float32"),
}
self.enable_trt = True
self.trt_parameters = TRTReduceMeanTestFP16.TensorRTParam(
1 << 30, 32, 1, AnalysisConfig.Precision.Half, False, False
)
self.fetch_list = [out]
self.dynamic_shape_params = TRTReduceMeanTestFP16.DynamicShapeParam(
{'data': [1, 3, 16, 16]},
{'data': [3, 3, 56, 56]},
{'data': [3, 3, 56, 56]},
False,
)
def test_check_output(self):
if core.is_compiled_with_cuda():
use_gpu = True
self.check_output_with_option(use_gpu, flatten=True)
self.assertTrue(
PassVersionChecker.IsCompatible('tensorrt_subgraph_pass')
)
class TRTReduceMeanAllTest(InferencePassTest):
def setUp(self):
with fluid.program_guard(self.main_program, self.startup_program):
data = fluid.data(
name="data", shape=[-1, 3, 56, 56], dtype="float32"
)
reduce_mean = fluid.layers.reduce_mean(data, keep_dim=True)
out = fluid.layers.batch_norm(reduce_mean, is_test=True)
self.feeds = {
"data": np.random.random([3, 3, 56, 56]).astype("float32"),
}
self.enable_trt = True
self.trt_parameters = TRTReduceMeanAllTest.TensorRTParam(
1 << 30, 32, 1, AnalysisConfig.Precision.Float32, False, False
)
self.fetch_list = [out]
self.dynamic_shape_params = TRTReduceMeanAllTest.DynamicShapeParam(
{'data': [1, 3, 56, 56]},
{'data': [3, 3, 56, 56]},
{'data': [3, 3, 56, 56]},
False,
)
def test_check_output(self):
if core.is_compiled_with_cuda():
use_gpu = True
self.check_output_with_option(use_gpu, flatten=True)
self.assertTrue(
PassVersionChecker.IsCompatible('tensorrt_subgraph_pass')
)
class TRTReduceMeanTestStatic(InferencePassTest):
def setUp(self):
with fluid.program_guard(self.main_program, self.startup_program):
data = fluid.data(
name="data", shape=[3, 3, 56, 56], dtype="float32"
)
reduce_mean = fluid.layers.reduce_mean(
data, dim=[2, -1], keep_dim=True
)
out = fluid.layers.batch_norm(reduce_mean, is_test=True)
self.feeds = {
"data": np.random.random([3, 3, 56, 56]).astype("float32"),
}
self.enable_trt = True
self.trt_parameters = TRTReduceMeanTestStatic.TensorRTParam(
1 << 30, 32, 1, AnalysisConfig.Precision.Float32, False, False
)
self.fetch_list = [out]
def test_check_output(self):
if core.is_compiled_with_cuda():
use_gpu = True
self.check_output_with_option(use_gpu, flatten=True)
self.assertTrue(
PassVersionChecker.IsCompatible('tensorrt_subgraph_pass')
)
class TRTReduceMeanStaticAllTest(InferencePassTest):
def setUp(self):
with fluid.program_guard(self.main_program, self.startup_program):
data = fluid.data(
name="data", shape=[4, 3, 56, 56], dtype="float32"
)
reduce_mean = fluid.layers.reduce_mean(data, keep_dim=True)
out = fluid.layers.batch_norm(reduce_mean, is_test=True)
self.feeds = {
"data": np.random.random([4, 3, 56, 56]).astype("float32"),
}
self.enable_trt = True
self.trt_parameters = TRTReduceMeanStaticAllTest.TensorRTParam(
1 << 30, 32, 1, AnalysisConfig.Precision.Float32, False, False
)
self.fetch_list = [out]
def test_check_output(self):
if core.is_compiled_with_cuda():
use_gpu = True
self.check_output_with_option(use_gpu, flatten=True)
self.assertTrue(
PassVersionChecker.IsCompatible('tensorrt_subgraph_pass')
)
class TRTReduceMeanStaticFP16(InferencePassTest):
def setUp(self):
with fluid.program_guard(self.main_program, self.startup_program):
data = fluid.data(
name="data", shape=[4, 3, 56, 56], dtype="float32"
)
reduce_mean = fluid.layers.reduce_mean(data, keep_dim=True)
out = fluid.layers.batch_norm(reduce_mean, is_test=True)
self.feeds = {
"data": np.random.random([4, 3, 56, 56]).astype("float32"),
}
self.enable_trt = True
self.trt_parameters = TRTReduceMeanStaticFP16.TensorRTParam(
1 << 30, 32, 1, AnalysisConfig.Precision.Half, False, False
)
self.fetch_list = [out]
def test_check_output(self):
if core.is_compiled_with_cuda():
use_gpu = True
self.check_output_with_option(
use_gpu, flatten=True, atol=1e-3, rtol=1e-3
)
self.assertTrue(
PassVersionChecker.IsCompatible('tensorrt_subgraph_pass')
)
class TRTReduceMeanFP16Static(InferencePassTest):
def setUp(self):
with fluid.program_guard(self.main_program, self.startup_program):
data = fluid.data(
name="data", shape=[4, 3, 56, 56], dtype="float32"
)
reduce_mean = fluid.layers.reduce_mean(data, keep_dim=True)
out = fluid.layers.batch_norm(reduce_mean, is_test=True)
self.feeds = {
"data": np.random.random([4, 3, 56, 56]).astype("float32"),
}
self.enable_trt = True
self.trt_parameters = TRTReduceMeanFP16Static.TensorRTParam(
1 << 30, 32, 1, AnalysisConfig.Precision.Half, True, False
)
self.fetch_list = [out]
def test_check_output(self):
if core.is_compiled_with_cuda():
use_gpu = True
self.check_output_with_option(
use_gpu, flatten=True, atol=1e-3, rtol=1e-3
)
self.assertTrue(
PassVersionChecker.IsCompatible('tensorrt_subgraph_pass')
)
if __name__ == "__main__":
unittest.main()
python/paddle/fluid/tests/unittests/mlu/test_adam_op_mlu.py
浏览文件 @ 9ffc760f
......@@ -264,7 +264,7 @@ class TestNet(unittest.TestCase):
prediction = fluid.layers.fc(input=fc_1, size=2, act='softmax')
cost = fluid.layers.cross_entropy(input=prediction, label=label)
loss = fluid.layers.reduce_mean(cost)
loss = paddle.mean(cost)
adam = fluid.optimizer.Adam(learning_rate=0.01)
adam.minimize(loss)
......
python/paddle/fluid/tests/unittests/mlu/test_adamw_op_mlu.py
浏览文件 @ 9ffc760f
......@@ -215,7 +215,7 @@ class TestNet(unittest.TestCase):
prediction = fluid.layers.fc(input=fc_1, size=2, act='softmax')
cost = fluid.layers.cross_entropy(input=prediction, label=label)
loss = fluid.layers.reduce_mean(cost)
loss = paddle.mean(cost)
adam = paddle.optimizer.AdamW(learning_rate=0.01, weight_decay=0.02)
adam.minimize(loss)
......
python/paddle/fluid/tests/unittests/mlu/test_elementwise_max_op_mlu.py
浏览文件 @ 9ffc760f
......@@ -344,7 +344,7 @@ class TestElementwiseMaxNet(unittest.TestCase):
prediction = fluid.layers.fc(input=fc_1, size=2, act='softmax')
cost = fluid.layers.cross_entropy(input=prediction, label=label)
loss = fluid.layers.reduce_mean(cost)
loss = paddle.mean(cost)
sgd = fluid.optimizer.SGD(learning_rate=0.01)
sgd.minimize(loss)
......
python/paddle/fluid/tests/unittests/mlu/test_elementwise_min_op_mlu.py
浏览文件 @ 9ffc760f
......@@ -190,7 +190,7 @@ class TestElementwiseMinOpNet(unittest.TestCase):
prediction = fluid.layers.fc(input=fc_1, size=2, act='softmax')
cost = fluid.layers.cross_entropy(input=prediction, label=label)
loss = fluid.layers.reduce_mean(cost)
loss = paddle.mean(cost)
sgd = fluid.optimizer.SGD(learning_rate=0.01)
sgd.minimize(loss)
......
python/paddle/fluid/tests/unittests/mlu/test_gelu_op_mlu.py
浏览文件 @ 9ffc760f
......@@ -113,7 +113,7 @@ class TestGeluNet(unittest.TestCase):
prediction = fluid.layers.fc(input=fc_1_gelu, size=2, act='softmax')
cost = fluid.layers.cross_entropy(input=prediction, label=label)
loss = fluid.layers.reduce_mean(cost)
loss = paddle.mean(cost)
sgd = fluid.optimizer.SGD(learning_rate=0.01)
sgd.minimize(loss)
......
python/paddle/fluid/tests/unittests/mlu/test_leaky_relu_op_mlu.py
浏览文件 @ 9ffc760f
......@@ -107,7 +107,7 @@ class TestLeakyReluNet(unittest.TestCase):
prediction = fluid.layers.fc(input=fc_1, size=2, act='softmax')
cost = fluid.layers.cross_entropy(input=prediction, label=label)
loss = fluid.layers.reduce_mean(cost)
loss = paddle.mean(cost)
sgd = fluid.optimizer.SGD(learning_rate=0.01)
sgd.minimize(loss)
......
python/paddle/fluid/tests/unittests/mlu/test_relu6_op_mlu.py
浏览文件 @ 9ffc760f
......@@ -126,7 +126,7 @@ class TestRelu6Net(unittest.TestCase):
prediction = fluid.layers.fc(input=fc_1, size=2, act='softmax')
cost = fluid.layers.cross_entropy(input=prediction, label=label)
loss = fluid.layers.reduce_mean(cost)
loss = paddle.mean(cost)
sgd = fluid.optimizer.SGD(learning_rate=0.01)
sgd.minimize(loss)
......
python/paddle/fluid/tests/unittests/mlu/test_relu_op_mlu.py
浏览文件 @ 9ffc760f
......@@ -127,7 +127,7 @@ class TestReluNet(unittest.TestCase):
prediction = fluid.layers.fc(input=fc_1, size=2, act='softmax')
cost = fluid.layers.cross_entropy(input=prediction, label=label)
loss = fluid.layers.reduce_mean(cost)
loss = paddle.mean(cost)
sgd = fluid.optimizer.SGD(learning_rate=0.01)
sgd.minimize(loss)
......
python/paddle/fluid/tests/unittests/mlu/test_softmax_with_cross_entropy_op_mlu.py
浏览文件 @ 9ffc760f
......@@ -127,7 +127,7 @@ class TestPowNet(unittest.TestCase):
prediction = fluid.layers.fc(input=fc_1, size=2)
cost = fluid.layers.softmax_with_cross_entropy(prediction, label)
loss = fluid.layers.reduce_mean(cost)
loss = paddle.mean(cost)
sgd = fluid.optimizer.SGD(learning_rate=0.01)
sgd.minimize(loss)
......
python/paddle/fluid/tests/unittests/mlu/test_tanh_op_mlu.py
浏览文件 @ 9ffc760f
......@@ -108,7 +108,7 @@ class TestTanhNet(unittest.TestCase):
prediction = fluid.layers.fc(input=fc_1, size=2, act='softmax')
cost = fluid.layers.cross_entropy(input=prediction, label=label)
loss = fluid.layers.reduce_mean(cost)
loss = paddle.mean(cost)
sgd = fluid.optimizer.SGD(learning_rate=0.01)
sgd.minimize(loss)
......
python/paddle/fluid/tests/unittests/npu/test_adam_op_npu.py
浏览文件 @ 9ffc760f
......@@ -264,7 +264,7 @@ class TestNet(unittest.TestCase):
prediction = fluid.layers.fc(input=fc_1, size=2, act='softmax')
cost = fluid.layers.cross_entropy(input=prediction, label=label)
loss = fluid.layers.reduce_mean(cost)
loss = paddle.mean(cost)
adam = fluid.optimizer.Adam(learning_rate=0.01)
adam.minimize(loss)
......@@ -349,7 +349,7 @@ class TestNetWithEpsilonTensor(unittest.TestCase):
)
cost = fluid.layers.cross_entropy(input=prediction, label=label)
loss = fluid.layers.reduce_mean(cost)
loss = paddle.mean(cost)
beta1_init = 0.9
beta2_init = 0.999
epsilon_init = 1e-8
......
python/paddle/fluid/tests/unittests/npu/test_adamw_op_npu.py
浏览文件 @ 9ffc760f
......@@ -215,7 +215,7 @@ class TestNet(unittest.TestCase):
prediction = fluid.layers.fc(input=fc_1, size=2, act='softmax')
cost = fluid.layers.cross_entropy(input=prediction, label=label)
loss = fluid.layers.reduce_mean(cost)
loss = paddle.mean(cost)
adam = paddle.optimizer.AdamW(learning_rate=0.01, weight_decay=0.02)
adam.minimize(loss)
......
python/paddle/fluid/tests/unittests/npu/test_cos_op_npu.py
浏览文件 @ 9ffc760f
......@@ -105,7 +105,7 @@ class TestCosNet(unittest.TestCase):
prediction = fluid.layers.fc(input=fc_1, size=2, act='softmax')
cost = fluid.layers.cross_entropy(input=prediction, label=label)
loss = fluid.layers.reduce_mean(cost)
loss = paddle.mean(cost)
sgd = fluid.optimizer.SGD(learning_rate=0.01)
sgd.minimize(loss)
......
python/paddle/fluid/tests/unittests/npu/test_elementwise_div_op_npu.py
浏览文件 @ 9ffc760f
......@@ -139,7 +139,7 @@ class TestElementwiseDivNet(unittest.TestCase):
prediction = fluid.layers.fc(input=fc_1, size=2, act='softmax')
cost = fluid.layers.cross_entropy(input=prediction, label=label)
loss = fluid.layers.reduce_mean(cost)
loss = paddle.mean(cost)
sgd = fluid.optimizer.SGD(learning_rate=0.01)
sgd.minimize(loss)
......
python/paddle/fluid/tests/unittests/npu/test_elementwise_max_op_npu.py
浏览文件 @ 9ffc760f
......@@ -303,7 +303,7 @@ class TestElementwiseMaxNet(unittest.TestCase):
prediction = fluid.layers.fc(input=fc_1, size=2, act='softmax')
cost = fluid.layers.cross_entropy(input=prediction, label=label)
loss = fluid.layers.reduce_mean(cost)
loss = paddle.mean(cost)
sgd = fluid.optimizer.SGD(learning_rate=0.01)
sgd.minimize(loss)
......
python/paddle/fluid/tests/unittests/npu/test_elementwise_min_op_npu.py
浏览文件 @ 9ffc760f
......@@ -190,7 +190,7 @@ class TestElementwiseMinOpNet(unittest.TestCase):
prediction = fluid.layers.fc(input=fc_1, size=2, act='softmax')
cost = fluid.layers.cross_entropy(input=prediction, label=label)
loss = fluid.layers.reduce_mean(cost)
loss = paddle.mean(cost)
sgd = fluid.optimizer.SGD(learning_rate=0.01)
sgd.minimize(loss)
......
python/paddle/fluid/tests/unittests/npu/test_elementwise_pow_op_npu.py
浏览文件 @ 9ffc760f
......@@ -314,7 +314,7 @@ class TestElementwisePowNet(unittest.TestCase):
prediction = fluid.layers.fc(input=fc_1, size=2, act='softmax')
cost = fluid.layers.cross_entropy(input=prediction, label=label)
loss = fluid.layers.reduce_mean(cost)
loss = paddle.mean(cost)
sgd = fluid.optimizer.SGD(learning_rate=0.01)
sgd.minimize(loss)
......
python/paddle/fluid/tests/unittests/npu/test_elementwise_sub_op_npu.py
浏览文件 @ 9ffc760f
......@@ -195,7 +195,7 @@ class TestSubtractNet(unittest.TestCase):
prediction = fluid.layers.fc(input=fc_1, size=2, act='softmax')
cost = fluid.layers.cross_entropy(input=prediction, label=label)
loss = fluid.layers.reduce_mean(cost)
loss = paddle.mean(cost)
sgd = fluid.optimizer.SGD(learning_rate=0.01)
sgd.minimize(loss)
......
python/paddle/fluid/tests/unittests/npu/test_gather_op_npu.py
浏览文件 @ 9ffc760f
......@@ -134,7 +134,7 @@ class TestGatherGrad(unittest.TestCase):
a.stop_gradient = False
b = paddle.gather(a, index)
loss = fluid.layers.reduce_mean(b)
loss = paddle.mean(b)
sgd = fluid.optimizer.SGD(learning_rate=0.01)
sgd.minimize(loss)
......
python/paddle/fluid/tests/unittests/npu/test_gelu_op_npu.py
浏览文件 @ 9ffc760f
......@@ -113,7 +113,7 @@ class TestGeluNet(unittest.TestCase):
prediction = fluid.layers.fc(input=fc_1_gelu, size=2, act='softmax')
cost = fluid.layers.cross_entropy(input=prediction, label=label)
loss = fluid.layers.reduce_mean(cost)
loss = paddle.mean(cost)
sgd = fluid.optimizer.SGD(learning_rate=0.01)
sgd.minimize(loss)
......
python/paddle/fluid/tests/unittests/npu/test_leaky_relu_op_npu.py
浏览文件 @ 9ffc760f
......@@ -107,7 +107,7 @@ class TestLeakyReluNet(unittest.TestCase):
prediction = fluid.layers.fc(input=fc_1, size=2, act='softmax')
cost = fluid.layers.cross_entropy(input=prediction, label=label)
loss = fluid.layers.reduce_mean(cost)
loss = paddle.mean(cost)
sgd = fluid.optimizer.SGD(learning_rate=0.01)
sgd.minimize(loss)
......
python/paddle/fluid/tests/unittests/npu/test_log_op_npu.py
浏览文件 @ 9ffc760f
......@@ -105,7 +105,7 @@ class TestLogNet(unittest.TestCase):
prediction = fluid.layers.fc(input=fc_1, size=2, act='softmax')
cost = fluid.layers.cross_entropy(input=prediction, label=label)
loss = fluid.layers.reduce_mean(cost)
loss = paddle.mean(cost)
sgd = fluid.optimizer.SGD(learning_rate=0.01)
sgd.minimize(loss)
......
python/paddle/fluid/tests/unittests/npu/test_mul_op_npu.py
浏览文件 @ 9ffc760f
......@@ -248,7 +248,7 @@ class TestMulNet(unittest.TestCase):
prediction = fluid.layers.fc(input=fc_1, size=2, act='softmax')
cost = fluid.layers.cross_entropy(input=prediction, label=label)
loss = fluid.layers.reduce_mean(cost)
loss = paddle.mean(cost)
sgd = fluid.optimizer.SGD(learning_rate=0.01)
sgd.minimize(loss)
......@@ -325,7 +325,7 @@ class TestMulNet3_2(unittest.TestCase):
prediction = fluid.layers.fc(input=fc_1, size=2, act='softmax')
cost = fluid.layers.cross_entropy(input=prediction, label=label)
loss = fluid.layers.reduce_mean(cost)
loss = paddle.mean(cost)
sgd = fluid.optimizer.SGD(learning_rate=0.01)
sgd.minimize(loss)
......@@ -405,7 +405,7 @@ class TestMulNet3_2_xc2(unittest.TestCase):
prediction = fluid.layers.fc(input=fc_1, size=2, act='softmax')
cost = fluid.layers.cross_entropy(input=prediction, label=label)
loss = fluid.layers.reduce_mean(cost)
loss = paddle.mean(cost)
sgd = fluid.optimizer.SGD(learning_rate=0.01)
sgd.minimize(loss)
......@@ -486,7 +486,7 @@ class TestMulNet4_2(unittest.TestCase):
prediction = fluid.layers.fc(input=result, size=2, act='softmax')
cost = fluid.layers.cross_entropy(input=prediction, label=label)
loss = fluid.layers.reduce_mean(cost)
loss = paddle.mean(cost)
sgd = fluid.optimizer.SGD(learning_rate=0.01)
sgd.minimize(loss)
......
python/paddle/fluid/tests/unittests/npu/test_pow_op_npu.py
浏览文件 @ 9ffc760f
......@@ -105,7 +105,7 @@ class TestPowNet(unittest.TestCase):
prediction = fluid.layers.fc(input=fc_1, size=2, act='softmax')
cost = fluid.layers.cross_entropy(input=prediction, label=label)
loss = fluid.layers.reduce_mean(cost)
loss = paddle.mean(cost)
sgd = fluid.optimizer.SGD(learning_rate=0.01)
sgd.minimize(loss)
......
python/paddle/fluid/tests/unittests/npu/test_reduce_sum_op_npu.py
浏览文件 @ 9ffc760f
......@@ -113,7 +113,7 @@ class TestReduceSumNet(unittest.TestCase):
prediction = fluid.layers.fc(input=z_1, size=2, act='softmax')
cost = fluid.layers.cross_entropy(input=prediction, label=label)
loss = fluid.layers.reduce_mean(cost)
loss = paddle.mean(cost)
sgd = fluid.optimizer.SGD(learning_rate=0.01)
sgd.minimize(loss)
......
python/paddle/fluid/tests/unittests/npu/test_relu6_op_npu.py
浏览文件 @ 9ffc760f
......@@ -126,7 +126,7 @@ class TestRelu6Net(unittest.TestCase):
prediction = fluid.layers.fc(input=fc_1, size=2, act='softmax')
cost = fluid.layers.cross_entropy(input=prediction, label=label)
loss = fluid.layers.reduce_mean(cost)
loss = paddle.mean(cost)
sgd = fluid.optimizer.SGD(learning_rate=0.01)
sgd.minimize(loss)
......
python/paddle/fluid/tests/unittests/npu/test_relu_op_npu.py
浏览文件 @ 9ffc760f
......@@ -119,7 +119,7 @@ class TestReluNet(unittest.TestCase):
prediction = fluid.layers.fc(input=fc_1, size=2, act='softmax')
cost = fluid.layers.cross_entropy(input=prediction, label=label)
loss = fluid.layers.reduce_mean(cost)
loss = paddle.mean(cost)
sgd = fluid.optimizer.SGD(learning_rate=0.01)
sgd.minimize(loss)
......
python/paddle/fluid/tests/unittests/npu/test_rmsprop_op_npu.py
浏览文件 @ 9ffc760f
......@@ -53,7 +53,7 @@ class TestNet(unittest.TestCase):
prediction = fluid.layers.fc(input=fc_1, size=2, act='softmax')
cost = fluid.layers.cross_entropy(input=prediction, label=label)
loss = fluid.layers.reduce_mean(cost)
loss = paddle.mean(cost)
rmsprop = fluid.optimizer.RMSProp(learning_rate=0.01)
rmsprop.minimize(loss)
......@@ -116,7 +116,7 @@ class TestCenteredNet(unittest.TestCase):
prediction = fluid.layers.fc(input=fc_1, size=2, act='softmax')
cost = fluid.layers.cross_entropy(input=prediction, label=label)
loss = fluid.layers.reduce_mean(cost)
loss = paddle.mean(cost)
rmsprop = fluid.optimizer.RMSProp(learning_rate=0.01, centered=True)
rmsprop.minimize(loss)
......
python/paddle/fluid/tests/unittests/npu/test_sgd_op_npu.py
浏览文件 @ 9ffc760f
......@@ -78,7 +78,7 @@ class TestNet(unittest.TestCase):
prediction = fluid.layers.fc(input=fc_1, size=2, act='softmax')
cost = fluid.layers.cross_entropy(input=prediction, label=label)
loss = fluid.layers.reduce_mean(cost)
loss = paddle.mean(cost)
sgd = fluid.optimizer.SGD(learning_rate=0.01)
sgd.minimize(loss)
......
python/paddle/fluid/tests/unittests/npu/test_softmax_with_cross_entropy_op_npu.py
浏览文件 @ 9ffc760f
......@@ -125,7 +125,7 @@ class TestPowNet(unittest.TestCase):
prediction = fluid.layers.fc(input=fc_1, size=2)
cost = fluid.layers.softmax_with_cross_entropy(prediction, label)
loss = fluid.layers.reduce_mean(cost)
loss = paddle.mean(cost)
sgd = fluid.optimizer.SGD(learning_rate=0.01)
sgd.minimize(loss)
......
python/paddle/fluid/tests/unittests/npu/test_sqrt_op_npu.py
浏览文件 @ 9ffc760f
......@@ -108,7 +108,7 @@ class TestSqrtNet(unittest.TestCase):
prediction = fluid.layers.fc(input=fc_1, size=2, act='softmax')
cost = fluid.layers.cross_entropy(input=prediction, label=label)
loss = fluid.layers.reduce_mean(cost)
loss = paddle.mean(cost)
sgd = fluid.optimizer.SGD(learning_rate=0.01)
sgd.minimize(loss)
......
python/paddle/fluid/tests/unittests/npu/test_square_op_npu.py
浏览文件 @ 9ffc760f
......@@ -105,7 +105,7 @@ class TestSquareNet(unittest.TestCase):
prediction = fluid.layers.fc(input=fc_1, size=2, act='softmax')
cost = fluid.layers.cross_entropy(input=prediction, label=label)
loss = fluid.layers.reduce_mean(cost)
loss = paddle.mean(cost)
sgd = fluid.optimizer.SGD(learning_rate=0.01)
sgd.minimize(loss)
......
python/paddle/fluid/tests/unittests/npu/test_tanh_op_npu.py
浏览文件 @ 9ffc760f
......@@ -108,7 +108,7 @@ class TestTanhNet(unittest.TestCase):
prediction = fluid.layers.fc(input=fc_1, size=2, act='softmax')
cost = fluid.layers.cross_entropy(input=prediction, label=label)
loss = fluid.layers.reduce_mean(cost)
loss = paddle.mean(cost)
sgd = fluid.optimizer.SGD(learning_rate=0.01)
sgd.minimize(loss)
......
python/paddle/fluid/tests/unittests/parallel_dygraph_sparse_embedding.py
浏览文件 @ 9ffc760f
......@@ -72,7 +72,7 @@ class SimpleNet(fluid.Layer):
logits=projection, label=label, soft_label=False
)
loss = paddle.reshape(loss, shape=[-1, self.num_steps])
loss = fluid.layers.reduce_mean(loss, dim=[0])
loss = paddle.mean(loss, axis=[0])
loss = paddle.sum(loss)
return loss
......
python/paddle/fluid/tests/unittests/seresnext_net.py
浏览文件 @ 9ffc760f
......@@ -51,7 +51,7 @@ def squeeze_excitation(input, num_channels, reduction_ratio):
conv = input
shape = conv.shape
reshape = paddle.reshape(x=conv, shape=[-1, shape[1], shape[2] * shape[3]])
pool = fluid.layers.reduce_mean(input=reshape, dim=2)
pool = paddle.mean(x=reshape, axis=2)
squeeze = fluid.layers.fc(
input=pool, size=num_channels // reduction_ratio, act='relu'
......@@ -162,7 +162,7 @@ def SE_ResNeXt50Small(use_feed):
shape = conv.shape
reshape = paddle.reshape(x=conv, shape=[-1, shape[1], shape[2] * shape[3]])
pool = fluid.layers.reduce_mean(input=reshape, dim=2)
pool = paddle.mean(x=reshape, axis=2)
dropout = (
pool
if remove_dropout
......
python/paddle/fluid/tests/unittests/test_adam_op.py
浏览文件 @ 9ffc760f
......@@ -614,7 +614,7 @@ class TestAdamOpV2(unittest.TestCase):
with fluid.unique_name.guard():
data = fluid.data(name="data", shape=shape)
conv = fluid.layers.conv2d(data, 8, 3)
loss = fluid.layers.reduce_mean(conv)
loss = paddle.mean(conv)
beta1 = fluid.layers.create_global_var(
shape=[1], value=0.85, dtype='float32', persistable=True
......@@ -807,7 +807,7 @@ class TestAdamOptimizer(unittest.TestCase):
)
cost = fluid.layers.cross_entropy(input=prediction, label=label)
loss = fluid.layers.reduce_mean(cost)
loss = paddle.mean(cost)
beta1_init = 0.9
beta2_init = 0.999
epsilon_init = 1e-8
......@@ -965,7 +965,7 @@ class TestAdamOptimizer(unittest.TestCase):
prediction = fluid.layers.fc(input=fc_1, size=2, act='softmax')
cost = fluid.layers.cross_entropy(input=prediction, label=label)
loss = fluid.layers.reduce_mean(cost)
loss = paddle.mean(cost)
adam = fluid.optimizer.Adam(use_global_beta_pow=True)
adam.minimize(loss)
self.assertRaises(Exception, adam._get_global_accumulator, 'tmp')
......
python/paddle/fluid/tests/unittests/test_dataloader_early_reset.py
浏览文件 @ 9ffc760f
......@@ -12,11 +12,10 @@
# See the License for the specific language governing permissions and
# limitations under the License.
import unittest
import numpy as np
import paddle
import paddle.fluid as fluid
import numpy as np
import unittest
def infinite_reader():
......@@ -33,7 +32,7 @@ class TestDataLoaderEarlyReset(unittest.TestCase):
def build_network(self):
y = fluid.layers.fc(self.x, size=10)
loss = fluid.layers.reduce_mean(y)
loss = paddle.mean(y)
optimizer = fluid.optimizer.SGD(learning_rate=1e-3)
optimizer.minimize(loss)
......
python/paddle/fluid/tests/unittests/test_dataloader_keep_order.py
浏览文件 @ 9ffc760f
......@@ -17,6 +17,7 @@ import unittest
import numpy as np
import paddle
import paddle.fluid as fluid
......@@ -48,7 +49,7 @@ class DataLoaderKeepOrderTestBase(unittest.TestCase):
)
fc = fluid.layers.fc(input_data, size=10)
loss = fluid.layers.reduce_mean(fc)
loss = paddle.mean(fc)
loader.set_batch_generator(
create_reader(self.shape, self.batch_num),
......
python/paddle/fluid/tests/unittests/test_dataloader_unkeep_order.py
浏览文件 @ 9ffc760f
......@@ -17,6 +17,7 @@ import unittest
import numpy as np
import paddle
import paddle.fluid as fluid
from paddle.fluid.reader import keep_data_loader_order
......@@ -54,7 +55,7 @@ class DataLoaderKeepOrderTestBase(unittest.TestCase):
)
fc = fluid.layers.fc(input_data, size=10)
loss = fluid.layers.reduce_mean(fc)
loss = paddle.mean(fc)
loader.set_batch_generator(
create_reader(self.shape, self.batch_num),
......
python/paddle/fluid/tests/unittests/test_dist_sparse_load_ps0.py
浏览文件 @ 9ffc760f
......@@ -55,7 +55,7 @@ class SparseLoadOp(unittest.TestCase):
),
),
)
loss = fluid.layers.reduce_mean(fc1)
loss = paddle.mean(fc1)
return loss
def save_origin_model(self, emb_array, fc_array):
......
python/paddle/fluid/tests/unittests/test_dist_sparse_tensor_load_sgd.py
浏览文件 @ 9ffc760f
......@@ -52,7 +52,7 @@ class TestSparseLoadProgram(unittest.TestCase):
)
fc1 = fluid.layers.fc(input=emb, size=128, act="relu")
fc2 = fluid.layers.fc(input=fc1, size=64, act="relu")
loss = fluid.layers.reduce_mean(fc2)
loss = paddle.mean(fc2)
return scope, train_program, startup_program, loss
......
python/paddle/fluid/tests/unittests/test_dist_transpiler.py
浏览文件 @ 9ffc760f
......@@ -437,7 +437,7 @@ class TestFakeInit(TranspilerTest):
paddle.sum(true_xent, axis=1),
paddle.sum(neg_xent, axis=1),
)
avg_cost = fluid.layers.reduce_mean(cost)
avg_cost = paddle.mean(cost)
sgd_optimizer = fluid.optimizer.SGD(
learning_rate=fluid.layers.exponential_decay(
......
python/paddle/fluid/tests/unittests/test_dynamic_rnn_stop_gradient.py
浏览文件 @ 9ffc760f
......@@ -57,7 +57,7 @@ def build_and_run_program(place, batch_size, beam_size, stop_gradient=False):
layers.assign(length_cond, cond)
out = layers.tensor_array_to_tensor(scores, axis=0, use_stack=True)[0]
loss = layers.reduce_mean(out)
loss = paddle.mean(out)
opt = fluid.optimizer.Adam(0.01)
opt.minimize(loss)
exe = fluid.Executor(place)
......
python/paddle/fluid/tests/unittests/test_eager_deletion_padding_rnn.py
浏览文件 @ 9ffc760f
......@@ -468,7 +468,7 @@ def lm_model(
)
loss = paddle.reshape(loss, shape=[-1, num_steps])
loss = layers.reduce_mean(loss, dim=[0])
loss = paddle.mean(loss, axis=[0])
loss = paddle.sum(loss)
loss.persistable = True
......
python/paddle/fluid/tests/unittests/test_gradient_clip.py
浏览文件 @ 9ffc760f
......@@ -412,7 +412,7 @@ class TestDygraphGradientClip(unittest.TestCase):
[16, 5], min=-10, max=10
).astype('float32')
out = linear(fluid.dygraph.to_variable(inputs))
loss = fluid.layers.reduce_mean(out)
loss = paddle.mean(out)
loss.backward()
sgd_optimizer = fluid.optimizer.SGD(
learning_rate=0.0,
......@@ -557,7 +557,7 @@ class TestDygraphGradientClipFP16(unittest.TestCase):
).astype('float32')
with paddle.amp.auto_cast(level='O2'):
out = model(fluid.dygraph.to_variable(inputs))
loss = fluid.layers.reduce_mean(out)
loss = paddle.mean(out)
scaled = scaler.scale(loss)
scaled.backward()
scaler.unscale_(sgd_optimizer)
......@@ -605,7 +605,7 @@ class TestDygraphGradientClipFP64(unittest.TestCase):
).astype('float32')
linear = paddle.nn.Linear(5, 5)
out = linear(fluid.dygraph.to_variable(inputs))
loss = fluid.layers.reduce_mean(out)
loss = paddle.mean(out)
loss.backward()
# before clip
params_grads = []
......
python/paddle/fluid/tests/unittests/test_hsigmoid_op.py
浏览文件 @ 9ffc760f
......@@ -321,7 +321,7 @@ class TestHSigmoidOpWithSparseGrad(unittest.TestCase):
path_code=path_code,
)
avg_cost = fluid.layers.reduce_mean(cost)
avg_cost = paddle.mean(cost)
return avg_cost, data_list
......
python/paddle/fluid/tests/unittests/test_imperative_auto_prune.py
浏览文件 @ 9ffc760f
......@@ -45,7 +45,7 @@ class AutoPruneLayer0(fluid.Layer):
a = self.linear1(x)
b = self.linear2(y)
c = fluid.layers.mul(a, b)
d = fluid.layers.reduce_mean(c)
d = paddle.mean(c)
return d
......@@ -74,7 +74,7 @@ class AutoPruneLayer1(fluid.Layer):
b = self.linear2(y)
b.stop_gradient = True
c = fluid.layers.mul(a, b)
d = fluid.layers.reduce_mean(c)
d = paddle.mean(c)
return d
......@@ -124,15 +124,15 @@ class MyLayer(fluid.Layer):
def forward(self, x):
# this method involves only the linear layers
loss = fluid.layers.reduce_mean(self.linear_0(x) + self.linear_1(x))
loss = paddle.mean(self.linear_0(x) + self.linear_1(x))
return loss
def linear0(self, x):
loss = fluid.layers.reduce_mean(self.linear_0(x))
loss = paddle.mean(self.linear_0(x))
return loss
def embed_linear0(self, x):
loss = fluid.layers.reduce_mean(self.linear_0(self.embed0(x)))
loss = paddle.mean(self.linear_0(self.embed0(x)))
return loss
......@@ -147,18 +147,18 @@ class MyLayer2(fluid.Layer):
def forward(self, indices):
# mind the difference with MyLayer
# In this example, the forward method involes all params
loss = fluid.layers.reduce_mean(
loss = paddle.mean(
self.linear_0(self.embed0(indices))
+ self.linear_1(self.embed1(indices))
)
return loss
def linear0(self, x):
loss = fluid.layers.reduce_mean(self.linear_0(x))
loss = paddle.mean(self.linear_0(x))
return loss
def embed_linear0(self, x):
loss = fluid.layers.reduce_mean(self.linear_0(self.embed0(x)))
loss = paddle.mean(self.linear_0(self.embed0(x)))
return loss
......
python/paddle/fluid/tests/unittests/test_imperative_container_parameterlist.py
浏览文件 @ 9ffc760f
......@@ -50,7 +50,7 @@ class TestImperativeContainerParameterList(unittest.TestCase):
self.assertEqual(len(model.params), num_stacked_param)
res = model(x)
self.assertListEqual(res.shape, [5, 2])
loss = fluid.layers.reduce_mean(res)
loss = paddle.mean(res)
loss.backward()
model.params[num_stacked_param - 1] = fluid.layers.create_parameter(
......@@ -64,7 +64,7 @@ class TestImperativeContainerParameterList(unittest.TestCase):
self.assertEqual(len(model.params), num_stacked_param + 1)
res = model(x)
self.assertListEqual(res.shape, [5, 4])
loss = fluid.layers.reduce_mean(res)
loss = paddle.mean(res)
loss.backward()
def test_paramter_list(self):
......
python/paddle/fluid/tests/unittests/test_imperative_container_sequential.py
浏览文件 @ 9ffc760f
......@@ -16,6 +16,7 @@ import unittest
import numpy as np
import paddle
import paddle.fluid as fluid
from paddle.fluid.framework import _test_eager_guard
from paddle.nn import Linear
......@@ -32,7 +33,7 @@ class TestImperativeContainerSequential(unittest.TestCase):
model1[1] = Linear(1, 3)
res1 = model1(data)
self.assertListEqual(res1.shape, [5, 3])
loss1 = fluid.layers.reduce_mean(res1)
loss1 = paddle.mean(res1)
loss1.backward()
l1 = Linear(10, 1)
......@@ -53,7 +54,7 @@ class TestImperativeContainerSequential(unittest.TestCase):
res2 = model2(data)
self.assertListEqual(res2.shape, [5, 4])
loss2 = fluid.layers.reduce_mean(res2)
loss2 = paddle.mean(res2)
loss2.backward()
def test_sequential(self):
......@@ -71,7 +72,7 @@ class TestImperativeContainerSequential(unittest.TestCase):
model1[1] = Linear(1, 3)
res1 = model1(data)
self.assertListEqual(res1.shape, [5, 3])
loss1 = fluid.layers.reduce_mean(res1)
loss1 = paddle.mean(res1)
loss1.backward()
l1 = Linear(10, 1)
......@@ -92,7 +93,7 @@ class TestImperativeContainerSequential(unittest.TestCase):
res2 = model2(data)
self.assertListEqual(res2.shape, [5, 4])
loss2 = fluid.layers.reduce_mean(res2)
loss2 = paddle.mean(res2)
loss2.backward()
def test_sequential_list_params(self):
......
python/paddle/fluid/tests/unittests/test_imperative_double_grad.py
浏览文件 @ 9ffc760f
......@@ -322,7 +322,7 @@ class TestDygraphDoubleGrad(TestCase):
z = y1 + y2
w = z * z
w_mean = fluid.layers.reduce_mean(w)
w_mean = paddle.mean(w)
del y1, z, w
(dx_actual,) = self.grad(
......@@ -440,7 +440,7 @@ class TestDygraphDoubleGrad(TestCase):
z = y + 1
w = z * z
w_mean = fluid.layers.reduce_mean(w)
w_mean = paddle.mean(w)
del y, z, w
(dx_actual,) = self.grad([w_mean], [x], create_graph=True)
......@@ -454,7 +454,7 @@ class TestDygraphDoubleGrad(TestCase):
).astype('float32')
np.testing.assert_allclose(dx_actual.numpy(), dx_expected, rtol=1e-05)
loss = fluid.layers.reduce_mean(dx_actual * dx_actual + x * x)
loss = paddle.mean(dx_actual * dx_actual + x * x)
loss.backward(retain_graph=True)
x_grad_actual = x.gradient()
......@@ -494,7 +494,7 @@ class TestDygraphDoubleGrad(TestCase):
z = y1 + y2
w = z * z
w_mean = fluid.layers.reduce_mean(w)
w_mean = paddle.mean(w)
del y1, z, w
(dx_actual,) = self.grad(
......@@ -517,7 +517,7 @@ class TestDygraphDoubleGrad(TestCase):
).astype('float32')
np.testing.assert_allclose(dx_actual.numpy(), dx_expected, rtol=1e-05)
loss = fluid.layers.reduce_mean(dx_actual * dx_actual + x * x)
loss = paddle.mean(dx_actual * dx_actual + x * x)
loss.backward()
x_grad_actual = x.gradient()
......@@ -544,7 +544,7 @@ class TestDygraphDoubleGrad(TestCase):
z = y + 1
w = z * z
w_mean = fluid.layers.reduce_mean(w)
w_mean = paddle.mean(w)
del y, z, w
(dx_actual,) = self.grad([w_mean], [x], create_graph=False)
......@@ -558,7 +558,7 @@ class TestDygraphDoubleGrad(TestCase):
np.testing.assert_allclose(dx_actual.numpy(), dx_expected, rtol=1e-05)
loss = fluid.layers.reduce_mean(dx_actual * dx_actual + x * x)
loss = paddle.mean(dx_actual * dx_actual + x * x)
loss.backward()
x_grad_actual = x.gradient()
......@@ -644,7 +644,7 @@ class TestRaiseNoDoubleGradOp(TestCase):
outputs=[y], inputs=[x], create_graph=True, retain_graph=True
)[0]
loss = fluid.layers.reduce_mean(dx)
loss = paddle.mean(dx)
loss.backward()
def test_raise(self):
......
python/paddle/fluid/tests/unittests/test_imperative_gan.py
浏览文件 @ 9ffc760f
......@@ -79,7 +79,7 @@ class TestDygraphGAN(unittest.TestCase):
)
d_real = discriminator(img)
d_loss_real = fluid.layers.reduce_mean(
d_loss_real = paddle.mean(
paddle.nn.functional.binary_cross_entropy_with_logits(
logit=d_real,
label=fluid.layers.fill_constant(
......@@ -89,7 +89,7 @@ class TestDygraphGAN(unittest.TestCase):
)
d_fake = discriminator(generator(noise))
d_loss_fake = fluid.layers.reduce_mean(
d_loss_fake = paddle.mean(
paddle.nn.functional.binary_cross_entropy_with_logits(
logit=d_fake,
label=fluid.layers.fill_constant(
......@@ -112,7 +112,7 @@ class TestDygraphGAN(unittest.TestCase):
)
d_fake = discriminator(generator(noise))
g_loss = fluid.layers.reduce_mean(
g_loss = paddle.mean(
paddle.nn.functional.binary_cross_entropy_with_logits(
logit=d_fake,
label=fluid.layers.fill_constant(
......@@ -164,7 +164,7 @@ class TestDygraphGAN(unittest.TestCase):
)
d_real = discriminator(to_variable(np.ones([2, 1], np.float32)))
d_loss_real = fluid.layers.reduce_mean(
d_loss_real = paddle.mean(
paddle.nn.functional.binary_cross_entropy_with_logits(
logit=d_real, label=to_variable(np.ones([2, 1], np.float32))
)
......@@ -173,7 +173,7 @@ class TestDygraphGAN(unittest.TestCase):
d_fake = discriminator(
generator(to_variable(np.ones([2, 2], np.float32)))
)
d_loss_fake = fluid.layers.reduce_mean(
d_loss_fake = paddle.mean(
paddle.nn.functional.binary_cross_entropy_with_logits(
logit=d_fake,
label=to_variable(np.zeros([2, 1], np.float32)),
......@@ -189,7 +189,7 @@ class TestDygraphGAN(unittest.TestCase):
d_fake = discriminator(
generator(to_variable(np.ones([2, 2], np.float32)))
)
g_loss = fluid.layers.reduce_mean(
g_loss = paddle.mean(
paddle.nn.functional.binary_cross_entropy_with_logits(
logit=d_fake, label=to_variable(np.ones([2, 1], np.float32))
)
......@@ -219,7 +219,7 @@ class TestDygraphGAN(unittest.TestCase):
)
d_real2 = discriminator2(to_variable(np.ones([2, 1], np.float32)))
d_loss_real2 = fluid.layers.reduce_mean(
d_loss_real2 = paddle.mean(
paddle.nn.functional.binary_cross_entropy_with_logits(
logit=d_real2,
label=to_variable(np.ones([2, 1], np.float32)),
......@@ -229,7 +229,7 @@ class TestDygraphGAN(unittest.TestCase):
d_fake2 = discriminator2(
generator2(to_variable(np.ones([2, 2], np.float32)))
)
d_loss_fake2 = fluid.layers.reduce_mean(
d_loss_fake2 = paddle.mean(
paddle.nn.functional.binary_cross_entropy_with_logits(
logit=d_fake2,
label=to_variable(np.zeros([2, 1], np.float32)),
......@@ -245,7 +245,7 @@ class TestDygraphGAN(unittest.TestCase):
d_fake2 = discriminator2(
generator2(to_variable(np.ones([2, 2], np.float32)))
)
g_loss2 = fluid.layers.reduce_mean(
g_loss2 = paddle.mean(
paddle.nn.functional.binary_cross_entropy_with_logits(
logit=d_fake2,
label=to_variable(np.ones([2, 1], np.float32)),
......
python/paddle/fluid/tests/unittests/test_imperative_lod_tensor_to_selected_rows.py
浏览文件 @ 9ffc760f
......@@ -73,7 +73,7 @@ class SimpleNet(fluid.Layer):
logits=projection, label=label, soft_label=False
)
loss = paddle.reshape(loss, shape=[-1, self.num_steps])
loss = fluid.layers.reduce_mean(loss, dim=[0])
loss = paddle.mean(loss, axis=[0])
loss = paddle.sum(loss)
return loss
......
python/paddle/fluid/tests/unittests/test_imperative_optimizer.py
浏览文件 @ 9ffc760f
......@@ -141,7 +141,7 @@ class TestImperativeOptimizerBase(unittest.TestCase):
img = paddle.reshape(img, shape=[batch_size, -1])
cost = mlp(img)
avg_loss = fluid.layers.reduce_mean(cost)
avg_loss = paddle.mean(cost)
dy_out = avg_loss.numpy()
if batch_id == 0:
......@@ -180,7 +180,7 @@ class TestImperativeOptimizerBase(unittest.TestCase):
label = fluid.layers.data(name='label', shape=[1], dtype='int64')
img = paddle.reshape(img, shape=[batch_size, 784])
cost = mlp(img)
avg_loss = fluid.layers.reduce_mean(cost)
avg_loss = paddle.mean(cost)
optimizer.minimize(avg_loss)
# initialize params and fetch them
......@@ -478,7 +478,7 @@ class TestOptimizerLearningRate(unittest.TestCase):
b = linear(a)
loss = fluid.layers.reduce_mean(b)
loss = paddle.mean(b)
adam = fluid.optimizer.Adam(
0.001, parameter_list=linear.parameters()
......@@ -509,7 +509,7 @@ class TestOptimizerLearningRate(unittest.TestCase):
b = linear(a)
loss = fluid.layers.reduce_mean(b)
loss = paddle.mean(b)
bd = [2, 4, 6, 8]
value = [0.2, 0.4, 0.6, 0.8, 1.0]
......@@ -545,7 +545,7 @@ class TestOptimizerLearningRate(unittest.TestCase):
b = linear(a)
loss = fluid.layers.reduce_mean(b)
loss = paddle.mean(b)
base_lr = 1.0
adam = fluid.optimizer.Adam(
......@@ -584,7 +584,7 @@ class TestOptimizerLearningRate(unittest.TestCase):
b = linear(a)
loss = fluid.layers.reduce_mean(b)
loss = paddle.mean(b)
adam = fluid.optimizer.Adam(0.1, parameter_list=linear.parameters())
......@@ -965,7 +965,7 @@ class TestImperativeOptimizerList(unittest.TestCase):
y = linear_1(in_data)
y = linear_2(y)
loss = fluid.layers.reduce_mean(y)
loss = paddle.mean(y)
loss.backward()
sgd.minimize(loss)
......
python/paddle/fluid/tests/unittests/test_imperative_optimizer_v2.py
浏览文件 @ 9ffc760f
......@@ -139,7 +139,7 @@ class TestImperativeOptimizerBase(unittest.TestCase):
img = paddle.reshape(img, shape=[batch_size, -1])
cost = mlp(img)
avg_loss = fluid.layers.reduce_mean(cost)
avg_loss = paddle.mean(cost)
dy_out = avg_loss.numpy()
if batch_id == 0:
......@@ -189,7 +189,7 @@ class TestImperativeOptimizerBase(unittest.TestCase):
label = fluid.layers.data(name='label', shape=[1], dtype='int64')
img = paddle.reshape(img, shape=[batch_size, 784])
cost = mlp(img)
avg_loss = fluid.layers.reduce_mean(cost)
avg_loss = paddle.mean(cost)
optimizer.minimize(avg_loss)
# initialize params and fetch them
......@@ -616,7 +616,7 @@ class TestOptimizerLearningRate(unittest.TestCase):
b = linear(a)
loss = fluid.layers.reduce_mean(b)
loss = paddle.mean(b)
adam = paddle.optimizer.Adam(0.001, parameters=linear.parameters())
......@@ -645,7 +645,7 @@ class TestOptimizerLearningRate(unittest.TestCase):
b = linear(a)
loss = fluid.layers.reduce_mean(b)
loss = paddle.mean(b)
bd = [2, 4, 6, 8]
value = [0.2, 0.4, 0.6, 0.8, 1.0]
......@@ -677,7 +677,7 @@ class TestOptimizerLearningRate(unittest.TestCase):
a = fluid.dygraph.to_variable(a)
b = linear(a)
loss = fluid.layers.reduce_mean(b)
loss = paddle.mean(b)
base_lr = 1.0
scheduler = paddle.optimizer.lr.NaturalExpDecay(1.0, gamma=0.5)
......@@ -709,7 +709,7 @@ class TestOptimizerLearningRate(unittest.TestCase):
b = linear(a)
loss = fluid.layers.reduce_mean(b)
loss = paddle.mean(b)
adam = paddle.optimizer.Adam(0.1, parameters=linear.parameters())
......@@ -1085,7 +1085,7 @@ class TestImperativeOptimizerList(unittest.TestCase):
y = linear_1(in_data)
y = linear_2(y)
loss = fluid.layers.reduce_mean(y)
loss = paddle.mean(y)
loss.backward()
sgd.minimize(loss)
......
python/paddle/fluid/tests/unittests/test_imperative_partitial_backward.py
浏览文件 @ 9ffc760f
......@@ -31,7 +31,7 @@ class TestImperativePartitialBackward(unittest.TestCase):
y = linear1(x[:, :2])
z = linear2(x[:, 2:])
loss = fluid.layers.reduce_mean(y)
loss = paddle.mean(y)
loss.backward()
for param in linear1.parameters():
......
python/paddle/fluid/tests/unittests/test_imperative_ptb_rnn.py
浏览文件 @ 9ffc760f
......@@ -232,7 +232,7 @@ class PtbModel(fluid.Layer):
logits=projection, label=label, soft_label=False
)
loss = paddle.reshape(loss, shape=[-1, self.num_steps])
loss = fluid.layers.reduce_mean(loss, dim=[0])
loss = paddle.mean(loss, axis=[0])
loss = paddle.sum(loss)
return loss, last_hidden, last_cell
......
python/paddle/fluid/tests/unittests/test_imperative_save_load.py
浏览文件 @ 9ffc760f
......@@ -228,7 +228,7 @@ class PtbModel(fluid.Layer):
logits=projection, label=label, soft_label=False
)
loss = paddle.reshape(loss, shape=[-1, self.num_steps])
loss = fluid.layers.reduce_mean(loss, dim=[0])
loss = paddle.mean(loss, axis=[0])
loss = paddle.sum(loss)
return loss, last_hidden, last_cell
......
python/paddle/fluid/tests/unittests/test_imperative_save_load_v2.py
浏览文件 @ 9ffc760f
......@@ -229,7 +229,7 @@ class PtbModel(fluid.Layer):
logits=projection, label=label, soft_label=False
)
loss = paddle.reshape(loss, shape=[-1, self.num_steps])
loss = fluid.layers.reduce_mean(loss, dim=[0])
loss = paddle.mean(loss, axis=[0])
loss = paddle.sum(loss)
return loss, last_hidden, last_cell
......
python/paddle/fluid/tests/unittests/test_imperative_selected_rows_to_lod_tensor.py
浏览文件 @ 9ffc760f
......@@ -82,7 +82,7 @@ class SimpleNet(fluid.Layer):
logits=projection, label=label, soft_label=False
)
loss = paddle.reshape(loss, shape=[-1, self.num_steps])
loss = fluid.layers.reduce_mean(loss, dim=[0])
loss = paddle.mean(loss, axis=[0])
loss = paddle.sum(loss)
return loss
......
python/paddle/fluid/tests/unittests/test_imperative_star_gan_with_gradient_penalty.py
浏览文件 @ 9ffc760f
......@@ -445,9 +445,7 @@ def get_generator_loss(
):
fake_img = generator(image_real, label_trg)
rec_img = generator(fake_img, label_org)
g_loss_rec = fluid.layers.reduce_mean(
paddle.abs(paddle.subtract(image_real, rec_img))
)
g_loss_rec = paddle.mean(paddle.abs(paddle.subtract(image_real, rec_img)))
pred_fake, cls_fake = discriminator(fake_img)
......
python/paddle/fluid/tests/unittests/test_imperative_trace_non_persistable_inputs.py
浏览文件 @ 9ffc760f
......@@ -72,7 +72,7 @@ class TestTracedLayerRecordNonPersistableInput(unittest.TestCase):
static_out = traced_layer([in_x])[0]
np.testing.assert_array_equal(dygraph_out_numpy, static_out)
loss = fluid.layers.reduce_mean(dygraph_out)
loss = paddle.mean(dygraph_out)
loss.backward()
optimizer.minimize(loss)
......
python/paddle/fluid/tests/unittests/test_learning_rate_scheduler.py
浏览文件 @ 9ffc760f
......@@ -151,7 +151,7 @@ class TestLearningRateDecayDygraph(unittest.TestCase):
for epoch in range(10):
out = linear(input)
loss = fluid.layers.reduce_mean(out)
loss = paddle.mean(out)
loss.backward()
adam1.minimize(loss)
adam2.minimize(loss)
......
python/paddle/fluid/tests/unittests/test_mean_op.py
浏览文件 @ 9ffc760f
......@@ -383,7 +383,7 @@ class TestMeanAPI(unittest.TestCase):
def test_fluid_api(self):
with fluid.program_guard(fluid.Program(), fluid.Program()):
x = fluid.data("x", shape=[10, 10], dtype="float32")
out = fluid.layers.reduce_mean(input=x, dim=1)
out = paddle.mean(x=x, axis=1)
place = fluid.CPUPlace()
exe = fluid.Executor(place)
x_np = np.random.rand(10, 10).astype(np.float32)
......@@ -393,7 +393,7 @@ class TestMeanAPI(unittest.TestCase):
with fluid.dygraph.guard():
x_np = np.random.rand(10, 10).astype(np.float32)
x = fluid.dygraph.to_variable(x_np)
out = fluid.layers.reduce_mean(input=x, dim=1)
out = paddle.mean(x=x, axis=1)
np.testing.assert_allclose(
out.numpy(), np.mean(x_np, axis=1), rtol=1e-05
)
......
python/paddle/fluid/tests/unittests/test_memory_reuse_exclude_feed_var.py
浏览文件 @ 9ffc760f
......@@ -16,6 +16,7 @@ import unittest
import numpy as np
import paddle
import paddle.fluid as fluid
......@@ -29,7 +30,7 @@ class TestMemoryReuseExcludeFeedVar(unittest.TestCase):
name='image', shape=self.image_shape, dtype='float32'
)
relu_image = fluid.layers.relu(image)
loss = fluid.layers.reduce_mean(relu_image)
loss = paddle.mean(relu_image)
build_strategy = fluid.BuildStrategy()
build_strategy.enable_inplace = True
......
python/paddle/fluid/tests/unittests/test_multiprocess_dataloader_dynamic.py
浏览文件 @ 9ffc760f
......@@ -16,6 +16,7 @@ import sys
import time
import unittest
import paddle
import numpy as np
from test_multiprocess_dataloader_static import (
BATCH_SIZE,
......@@ -100,7 +101,7 @@ class TestDygraphDataLoader(unittest.TestCase):
for image, label in dataloader():
out = fc_net(image)
loss = fluid.layers.cross_entropy(out, label)
avg_loss = fluid.layers.reduce_mean(loss)
avg_loss = paddle.mean(loss)
avg_loss.backward()
optimizer.minimize(avg_loss)
fc_net.clear_gradients()
......@@ -170,7 +171,7 @@ class TestDygraphDataLoaderWithBatchedDataset(TestDygraphDataLoader):
for image, label in dataloader():
out = fc_net(image)
loss = fluid.layers.cross_entropy(out, label)
avg_loss = fluid.layers.reduce_mean(loss)
avg_loss = paddle.mean(loss)
avg_loss.backward()
optimizer.minimize(avg_loss)
fc_net.clear_gradients()
......
python/paddle/fluid/tests/unittests/test_multiprocess_dataloader_iterable_dataset_dynamic.py
浏览文件 @ 9ffc760f
......@@ -100,7 +100,7 @@ class TestDygraphDataLoader(unittest.TestCase):
for image, label in dataloader():
out = fc_net(image)
loss = fluid.layers.cross_entropy(out, label)
avg_loss = fluid.layers.reduce_mean(loss)
avg_loss = paddle.mean(loss)
avg_loss.backward()
optimizer.minimize(avg_loss)
fc_net.clear_gradients()
......@@ -168,7 +168,7 @@ class TestDygraphDataLoaderWithBatchedDataset(TestDygraphDataLoader):
for image, label in dataloader():
out = fc_net(image)
loss = fluid.layers.cross_entropy(out, label)
avg_loss = fluid.layers.reduce_mean(loss)
avg_loss = paddle.mean(loss)
avg_loss.backward()
optimizer.minimize(avg_loss)
fc_net.clear_gradients()
......
python/paddle/fluid/tests/unittests/test_multiprocess_dataloader_iterable_dataset_static.py
浏览文件 @ 9ffc760f
......@@ -18,6 +18,7 @@ import unittest
import numpy as np
import paddle
import paddle.fluid as fluid
from paddle.io import DataLoader, IterableDataset
......@@ -78,7 +79,7 @@ def simple_fc_net_static():
param_attr=param_attr,
bias_attr=bias_attr,
)
loss = fluid.layers.reduce_mean(
loss = paddle.mean(
fluid.layers.cross_entropy(input=predict_label, label=label)
)
......
python/paddle/fluid/tests/unittests/test_multiprocess_dataloader_static.py
浏览文件 @ 9ffc760f
......@@ -79,7 +79,7 @@ def simple_fc_net_static():
param_attr=param_attr,
bias_attr=bias_attr,
)
loss = fluid.layers.reduce_mean(
loss = paddle.mean(
fluid.layers.cross_entropy(input=predict_label, label=label)
)
......
python/paddle/fluid/tests/unittests/test_nn_grad.py
浏览文件 @ 9ffc760f
......@@ -75,7 +75,7 @@ class TestReduceMeanWithDimDoubleGradCheck(unittest.TestCase):
x = layers.data('x', shape, False, dtype)
x.persistable = True
y = layers.reduce_mean(x, dim=0)
y = paddle.mean(x, axis=0)
x_arr = np.random.uniform(-1, 1, shape).astype(dtype)
gradient_checker.double_grad_check(
......
python/paddle/fluid/tests/unittests/test_optimizer.py
浏览文件 @ 9ffc760f
......@@ -1169,7 +1169,7 @@ class TestRecomputeOptimizer(unittest.TestCase):
input=[drop_res], size=2, act='softmax'
)
cost = fluid.layers.cross_entropy(input=prediction, label=input_y)
sum_cost = fluid.layers.reduce_mean(cost)
sum_cost = paddle.mean(cost)
return drop_res, prediction, sum_cost
main_program = Program()
......@@ -1226,7 +1226,7 @@ class TestRecomputeOptimizerCUDA(unittest.TestCase):
input=[drop_res], size=2, act='softmax'
)
cost = fluid.layers.cross_entropy(input=prediction, label=input_y)
sum_cost = fluid.layers.reduce_mean(cost)
sum_cost = paddle.mean(cost)
return drop_res, prediction, sum_cost
main_program = Program()
......
python/paddle/fluid/tests/unittests/test_paddle_imperative_double_grad.py
浏览文件 @ 9ffc760f
......@@ -239,7 +239,7 @@ class TestDygraphDoubleGrad(TestCase):
z = y + 1
w = z * z
w_mean = fluid.layers.reduce_mean(w)
w_mean = paddle.mean(w)
del y, z, w
(dx_actual,) = self.grad([w_mean], [x], create_graph=True)
......@@ -256,7 +256,7 @@ class TestDygraphDoubleGrad(TestCase):
if not _in_legacy_dygraph():
pass
else:
loss = fluid.layers.reduce_mean(dx_actual * dx_actual + x * x)
loss = paddle.mean(dx_actual * dx_actual + x * x)
loss.backward()
x_grad_actual = x.gradient()
......@@ -286,7 +286,7 @@ class TestDygraphDoubleGrad(TestCase):
z = y1 + y2
w = z * z
w_mean = fluid.layers.reduce_mean(w)
w_mean = paddle.mean(w)
del y1, z, w
(dx_actual,) = self.grad(
......@@ -308,7 +308,7 @@ class TestDygraphDoubleGrad(TestCase):
if not _in_legacy_dygraph():
pass
else:
loss = fluid.layers.reduce_mean(dx_actual * dx_actual + x * x)
loss = paddle.mean(dx_actual * dx_actual + x * x)
loss.backward()
x_grad_actual = x.gradient()
......@@ -337,7 +337,7 @@ class TestDygraphDoubleGrad(TestCase):
z = y + 1
w = z * z
w_mean = fluid.layers.reduce_mean(w)
w_mean = paddle.mean(w)
del y, z, w
(dx_actual,) = self.grad([w_mean], [x], create_graph=False)
......@@ -354,7 +354,7 @@ class TestDygraphDoubleGrad(TestCase):
if not _in_legacy_dygraph():
pass
else:
loss = fluid.layers.reduce_mean(dx_actual * dx_actual + x * x)
loss = paddle.mean(dx_actual * dx_actual + x * x)
loss.backward()
x_grad_actual = x.gradient()
......
python/paddle/fluid/tests/unittests/test_paddle_save_load.py
浏览文件 @ 9ffc760f
......@@ -213,7 +213,7 @@ class TestSaveLoadAny(unittest.TestCase):
)
z = paddle.static.nn.fc(x, 10)
z = paddle.static.nn.fc(z, 10, bias_attr=False)
loss = fluid.layers.reduce_mean(z)
loss = paddle.mean(z)
opt = Adam(learning_rate=1e-3)
opt.minimize(loss)
place = paddle.CPUPlace()
......@@ -382,7 +382,7 @@ class TestSaveLoadAny(unittest.TestCase):
name="x", shape=[None, IMAGE_SIZE], dtype='float32'
)
z = paddle.static.nn.fc(x, 128)
loss = fluid.layers.reduce_mean(z)
loss = paddle.mean(z)
place = (
fluid.CPUPlace()
if not paddle.fluid.core.is_compiled_with_cuda()
......@@ -640,7 +640,7 @@ class TestSaveLoadAny(unittest.TestCase):
)
z = paddle.static.nn.fc(x, 10, bias_attr=False)
z = paddle.static.nn.fc(z, 128, bias_attr=False)
loss = fluid.layers.reduce_mean(z)
loss = paddle.mean(z)
place = (
fluid.CPUPlace()
if not paddle.fluid.core.is_compiled_with_cuda()
......@@ -915,7 +915,7 @@ class TestSaveLoadToMemory(unittest.TestCase):
)
z = paddle.static.nn.fc(x, 10, bias_attr=False)
z = paddle.static.nn.fc(z, 128, bias_attr=False)
loss = fluid.layers.reduce_mean(z)
loss = paddle.mean(z)
place = (
fluid.CPUPlace()
if not paddle.fluid.core.is_compiled_with_cuda()
......
python/paddle/fluid/tests/unittests/test_paddle_save_load_binary.py
浏览文件 @ 9ffc760f
......@@ -79,7 +79,7 @@ class TestSaveLoadBinaryFormat(unittest.TestCase):
)
z = paddle.static.nn.fc(x, 10, bias_attr=False)
z = paddle.static.nn.fc(z, 128, bias_attr=False)
loss = fluid.layers.reduce_mean(z)
loss = paddle.mean(z)
place = (
fluid.CPUPlace()
if not paddle.fluid.core.is_compiled_with_cuda()
......
python/paddle/fluid/tests/unittests/test_parallel_executor_fetch_isolated_var.py
浏览文件 @ 9ffc760f
......@@ -31,7 +31,7 @@ class TestParallelExecutorFetchIsolatedVarBase(unittest.TestCase):
x = fluid.data(name='x', shape=[-1, 10], dtype='float32')
y = fluid.data(name='y', shape=[-1, 10], dtype='float32')
fc = fluid.layers.fc(x, size=30, bias_attr=False)
loss = fluid.layers.reduce_mean(fc)
loss = paddle.mean(fc)
if is_training:
adam = fluid.optimizer.Adam(learning_rate=1e-3)
adam.minimize(loss)
......
python/paddle/fluid/tests/unittests/test_parallel_executor_inference_feed_partial_data.py
浏览文件 @ 9ffc760f
......@@ -16,6 +16,7 @@ import unittest
import numpy as np
import paddle
import paddle.fluid as fluid
......@@ -183,7 +184,7 @@ class TestInferencePartialFeedUsingDataLoader(unittest.TestCase):
feed_list=[x], capacity=16, iterable=iterable, drop_last=drop_last
)
y = fluid.layers.fc(x, size=10)
loss = fluid.layers.reduce_mean(y)
loss = paddle.mean(y)
exe = fluid.Executor(places[0])
exe.run(fluid.default_startup_program())
......
python/paddle/fluid/tests/unittests/test_rnn_cell_api.py
浏览文件 @ 9ffc760f
......@@ -640,7 +640,7 @@ def def_seq2seq_model(
target_length, maxlen=max_tar_seq_len, dtype="float32"
)
loss = loss * tar_mask
loss = layers.reduce_mean(loss, dim=[0])
loss = paddle.mean(loss, axis=[0])
loss = paddle.sum(loss)
# optimizer
......
python/paddle/fluid/tests/unittests/test_rnn_decode_api.py
浏览文件 @ 9ffc760f
......@@ -319,7 +319,7 @@ class PolicyGradient:
cost = (
(paddle.sum(cost) / paddle.sum(length))
if length is not None
else layers.reduce_mean(cost)
else paddle.mean(cost)
)
optimizer = fluid.optimizer.Adam(self.lr)
optimizer.minimize(cost)
......@@ -405,7 +405,7 @@ class MLE:
max_seq_len = layers.shape(probs)[1]
mask = layers.sequence_mask(length, maxlen=max_seq_len, dtype="float32")
loss = loss * mask
loss = layers.reduce_mean(loss, dim=[0])
loss = paddle.mean(loss, axis=[0])
loss = paddle.sum(loss)
optimizer = fluid.optimizer.Adam(self.lr)
optimizer.minimize(loss)
......
python/paddle/fluid/tests/unittests/test_static_save_load.py
浏览文件 @ 9ffc760f
......@@ -241,7 +241,7 @@ class PtbModel(fluid.Layer):
logits=projection, label=label, soft_label=False
)
loss = paddle.reshape(loss, shape=[-1, self.num_steps])
loss = fluid.layers.reduce_mean(loss, dim=[0])
loss = paddle.mean(loss, axis=[0])
loss = paddle.sum(loss)
return loss, last_hidden, last_cell
......
python/paddle/fluid/tests/unittests/test_traced_layer_err_msg.py
浏览文件 @ 9ffc760f
......@@ -223,7 +223,7 @@ class TestTracedLayerErrMsg(unittest.TestCase):
).astype('float32')
)
dygraph_out = layer(in_x)
loss = fluid.layers.reduce_mean(dygraph_out)
loss = paddle.mean(dygraph_out)
loss.backward()
optimizer.minimize(loss)
return layer
......
python/paddle/nn/layer/loss.py
浏览文件 @ 9ffc760f
......@@ -522,24 +522,16 @@ class MSELoss(Layer):
r"""
**Mean Square Error Loss**
Computes the mean square error (squared L2 norm) of given input and label.
If :attr:`reduction` is set to ``'none'``, loss is calculated as:
.. math::
Out = (input - label)^2
If :attr:`reduction` is set to ``'mean'``, loss is calculated as:
.. math::
Out = \operatorname{mean}((input - label)^2)
If :attr:`reduction` is set to ``'sum'``, loss is calculated as:
.. math::
Out = \operatorname{sum}((input - label)^2)
where `input` and `label` are `float32` tensors of same shape.
Parameters:
reduction (string, optional): The reduction method for the output,
could be 'none' | 'mean' | 'sum'.
......@@ -547,17 +539,13 @@ class MSELoss(Layer):
If :attr:`size_average` is ``'sum'``, the reduced sum loss is returned.
If :attr:`reduction` is ``'none'``, the unreduced loss is returned.
Default is ``'mean'``.
Shape:
input (Tensor): Input tensor, the data type is float32 or float64
label (Tensor): Label tensor, the data type is float32 or float64
output (Tensor): output tensor storing the MSE loss of input and label, the data type is same as input.
Examples:
.. code-block:: python
import paddle
mse_loss = paddle.nn.loss.MSELoss()
input = paddle.to_tensor([1.5])
label = paddle.to_tensor([1.7])
......@@ -596,7 +584,7 @@ class MSELoss(Layer):
square_out = paddle.sum(square_out)
return square_out
return getattr(fluid.layers, reduce_op)(square_out)
return paddle.mean(square_out)
class L1Loss(Layer):
......
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