[Clean Fluid] replace accuracy and auc and remove get_places, distributions(#48554)

* mv accuracy and auc

* rm distributions

* rm get_places

* replace metric

python/paddle/distributed/fleet/metrics/metric.py

@@ -148,8 +148,8 @@ def auc(stat_pos, stat_neg, scope=None, util=None):
     distributed auc in fleet
 
     Args:
-        stat_pos(numpy.array|Variable|string): stat_pos in output of fluid.layers.auc
-        stat_neg(numpy.array|Variable|string): stat_neg in output of fluid.layers.auc
+        stat_pos(numpy.array|Variable|string): stat_pos in output of paddle.static.auc
+        stat_neg(numpy.array|Variable|string): stat_neg in output of paddle.static.auc
         scope(Scope): specific scope
 
     Returns:
@@ -163,7 +163,7 @@ def auc(stat_pos, stat_neg, scope=None, util=None):
           binary_predict = fluid.layers.concat(
               input=[fluid.layers.elementwise_sub(fluid.layers.ceil(similarity_norm), similarity_norm), similarity_norm], axis=1)
           self.auc, batch_auc, [batch_stat_pos, batch_stat_neg, stat_pos, stat_neg] =
-              fluid.layers.auc(input=binary_predict, label=label, curve='ROC', num_thresholds=4096)
+              paddle.static.auc(input=binary_predict, label=label, curve='ROC', num_thresholds=4096)
 
           # in train.py, after train or infer
           pos = np.array(scope.find_var(stat_pos.name).get_tensor())

python/paddle/fluid/contrib/slim/tests/test_imperative_ptq.py

@@ -150,8 +150,8 @@ class TestImperativePTQ(unittest.TestCase):
             label = paddle.to_tensor(y_data)
 
             out = model(img)
-            acc_top1 = fluid.layers.accuracy(input=out, label=label, k=1)
-            acc_top5 = fluid.layers.accuracy(input=out, label=label, k=5)
+            acc_top1 = paddle.static.accuracy(input=out, label=label, k=1)
+            acc_top5 = paddle.static.accuracy(input=out, label=label, k=5)
             eval_acc_top1_list.append(float(acc_top1.numpy()))
 
             if batch_id % 50 == 0:

python/paddle/fluid/contrib/slim/tests/test_imperative_qat.py

@@ -129,7 +129,7 @@ class TestImperativeQat(unittest.TestCase):
                     img = fluid.dygraph.to_variable(x_data)
                     label = fluid.dygraph.to_variable(y_data)
                     out = lenet(img)
-                    acc = fluid.layers.accuracy(out, label)
+                    acc = paddle.static.accuracy(out, label)
                     loss = fluid.layers.cross_entropy(out, label)
                     avg_loss = paddle.mean(loss)
                     avg_loss.backward()
@@ -160,10 +160,10 @@ class TestImperativeQat(unittest.TestCase):
                     label = fluid.dygraph.to_variable(y_data)
 
                     out = lenet(img)
-                    acc_top1 = fluid.layers.accuracy(
+                    acc_top1 = paddle.static.accuracy(
                         input=out, label=label, k=1
                     )
-                    acc_top5 = fluid.layers.accuracy(
+                    acc_top5 = paddle.static.accuracy(
                         input=out, label=label, k=5
                     )
 
@@ -200,7 +200,7 @@ class TestImperativeQat(unittest.TestCase):
             label = fluid.dygraph.to_variable(y_data)
             lenet.eval()
             fp32_out = lenet(test_img)
-            fp32_acc = fluid.layers.accuracy(fp32_out, label).numpy()
+            fp32_acc = paddle.static.accuracy(fp32_out, label).numpy()
 
         with tempfile.TemporaryDirectory(prefix="qat_save_path_") as tmpdir:
             # save inference quantized model
@@ -237,7 +237,7 @@ class TestImperativeQat(unittest.TestCase):
             )
             paddle.disable_static()
             quant_out = fluid.dygraph.to_variable(quant_out)
-            quant_acc = fluid.layers.accuracy(quant_out, label).numpy()
+            quant_acc = paddle.static.accuracy(quant_out, label).numpy()
             paddle.enable_static()
             delta_value = fp32_acc - quant_acc
             self.assertLessEqual(delta_value, self.diff_threshold)

python/paddle/fluid/contrib/slim/tests/test_imperative_qat_amp.py

@@ -118,7 +118,7 @@ class TestImperativeQatAmp(unittest.TestCase):
             if use_amp:
                 with paddle.amp.auto_cast():
                     out = model(img)
-                    acc = fluid.layers.accuracy(out, label)
+                    acc = paddle.static.accuracy(out, label)
                     loss = fluid.layers.cross_entropy(out, label)
                     avg_loss = paddle.mean(loss)
                 scaled_loss = scaler.scale(avg_loss)
@@ -128,7 +128,7 @@ class TestImperativeQatAmp(unittest.TestCase):
                 adam.clear_gradients()
             else:
                 out = model(img)
-                acc = fluid.layers.accuracy(out, label)
+                acc = paddle.static.accuracy(out, label)
                 loss = fluid.layers.cross_entropy(out, label)
                 avg_loss = paddle.mean(loss)
                 avg_loss.backward()
@@ -167,8 +167,8 @@ class TestImperativeQatAmp(unittest.TestCase):
 
             with paddle.amp.auto_cast(use_amp):
                 out = model(img)
-                acc_top1 = fluid.layers.accuracy(input=out, label=label, k=1)
-                acc_top5 = fluid.layers.accuracy(input=out, label=label, k=5)
+                acc_top1 = paddle.static.accuracy(input=out, label=label, k=1)
+                acc_top5 = paddle.static.accuracy(input=out, label=label, k=5)
 
             acc_top1_list.append(float(acc_top1.numpy()))
             if batch_id % 100 == 0:

python/paddle/fluid/contrib/slim/tests/test_imperative_qat_lsq.py

@@ -170,7 +170,7 @@ class TestImperativeQatLSQ(unittest.TestCase):
                 img = fluid.dygraph.to_variable(x_data)
                 label = fluid.dygraph.to_variable(y_data)
                 out = lenet(img)
-                acc = fluid.layers.accuracy(out, label)
+                acc = paddle.static.accuracy(out, label)
                 loss = fluid.layers.cross_entropy(out, label)
                 avg_loss = paddle.mean(loss)
 
@@ -202,10 +202,10 @@ class TestImperativeQatLSQ(unittest.TestCase):
                     label = fluid.dygraph.to_variable(y_data)
 
                     out = lenet(img)
-                    acc_top1 = fluid.layers.accuracy(
+                    acc_top1 = paddle.static.accuracy(
                         input=out, label=label, k=1
                     )
-                    acc_top5 = fluid.layers.accuracy(
+                    acc_top5 = paddle.static.accuracy(
                         input=out, label=label, k=5
                     )
 

python/paddle/fluid/contrib/tests/test_image_classification_fp16.py

@@ -131,7 +131,7 @@ def train(net_type, use_cuda, save_dirname, is_local):
             logits, label, return_softmax=True
         )
         avg_cost = paddle.mean(cost)
-        acc = fluid.layers.accuracy(input=predict, label=label)
+        acc = paddle.static.accuracy(input=predict, label=label)
 
         # Test program
         test_program = train_program.clone(for_test=True)

python/paddle/fluid/incubate/fleet/tests/fleet_deep_ctr.py

@@ -146,8 +146,8 @@ def model():
     merge_layer = fluid.layers.concat(input=[dnn_out, lr_pool], axis=1)
 
     predict = fluid.layers.fc(input=merge_layer, size=2, act='softmax')
-    acc = fluid.layers.accuracy(input=predict, label=label)
-    auc_var, batch_auc_var, auc_states = fluid.layers.auc(
+    acc = paddle.static.accuracy(input=predict, label=label)
+    auc_var, batch_auc_var, auc_states = paddle.static.auc(
         input=predict, label=label
     )
     cost = fluid.layers.cross_entropy(input=predict, label=label)

python/paddle/fluid/incubate/fleet/utils/fleet_util.py

@@ -192,7 +192,7 @@ class FleetUtil:
                       fluid.layers.ceil(similarity_norm), similarity_norm),\
                   similarity_norm], axis=1)
               auc, batch_auc, [batch_stat_pos, batch_stat_neg, stat_pos, \
-                  stat_neg] = fluid.layers.auc(input=binary_predict,\
+                  stat_neg] = paddle.static.auc(input=binary_predict,\
                                                label=label, curve='ROC',\
                                                num_thresholds=4096)
 
@@ -1381,7 +1381,7 @@ class FleetUtil:
                       fluid.layers.ceil(similarity_norm), similarity_norm),\
                   similarity_norm], axis=1)
               auc, batch_auc, [batch_stat_pos, batch_stat_neg, stat_pos, \
-                  stat_neg] = fluid.layers.auc(input=binary_predict,\
+                  stat_neg] = paddle.static.auc(input=binary_predict,\
                                                label=label, curve='ROC',\
                                                num_thresholds=4096)
               local_sqrerr, local_abserr, local_prob, local_q, local_pos_ins,\
@@ -1581,7 +1581,7 @@ class FleetUtil:
                       fluid.layers.ceil(similarity_norm), similarity_norm),\
                   similarity_norm], axis=1)
               auc, batch_auc, [batch_stat_pos, batch_stat_neg, stat_pos, \
-                  stat_neg] = fluid.layers.auc(input=binary_predict,\
+                  stat_neg] = paddle.static.auc(input=binary_predict,\
                                                label=label, curve='ROC',\
                                                num_thresholds=4096)
               local_sqrerr, local_abserr, local_prob, local_q, local_pos_ins, \

python/paddle/fluid/layers/__init__.py

@@ -20,19 +20,14 @@ from . import tensor
 from .tensor import *
 from . import control_flow
 from .control_flow import *
-from . import device
-from .device import *
 from . import math_op_patch
 from .math_op_patch import *
 from . import loss
 from .loss import *
 from . import detection
 from .detection import *
-from . import metric_op
-from .metric_op import *
 from .learning_rate_scheduler import *
 from .collective import *
-from .distributions import *
 from .sequence_lod import *
 from . import rnn
 
@@ -41,11 +36,8 @@ __all__ += nn.__all__
 __all__ += io.__all__
 __all__ += tensor.__all__
 __all__ += control_flow.__all__
-__all__ += device.__all__
 __all__ += detection.__all__
-__all__ += metric_op.__all__
 __all__ += learning_rate_scheduler.__all__
-__all__ += distributions.__all__
 __all__ += sequence_lod.__all__
 __all__ += loss.__all__
 __all__ += rnn.__all__

python/paddle/fluid/layers/device.py

-#   Copyright (c) 2018 PaddlePaddle Authors. All Rights Reserved.
-#
-# Licensed under the Apache License, Version 2.0 (the "License");
-# you may not use this file except in compliance with the License.
-# You may obtain a copy of the License at
-#
-#     http://www.apache.org/licenses/LICENSE-2.0
-#
-# Unless required by applicable law or agreed to in writing, software
-# distributed under the License is distributed on an "AS IS" BASIS,
-# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-# See the License for the specific language governing permissions and
-# limitations under the License.
-"""
-All util layers.
-"""
-
-from .layer_function_generator import autodoc
-from ..framework import unique_name
-from ..layer_helper import LayerHelper
-from paddle.utils import deprecated
-
-__all__ = []
-
-
-@deprecated(since='0.15.0', update_to="paddle.fluid.ParallelExecutor")
-@autodoc()
-def get_places(device_count=None, device_type=None):
-    helper = LayerHelper('get_places', **locals())
-    out_places = helper.create_variable(
-        name=unique_name.generate_with_ignorable_key(helper.name + ".out")
-    )
-    attrs = dict()
-    if device_count is not None:
-        attrs['device_count'] = int(device_count)
-    if device_type is not None:
-        attrs['device_type'] = str(device_type)
-
-    helper.append_op(
-        type='get_places', outputs={"Out": [out_places]}, attrs=attrs
-    )
-
-    return out_places

python/paddle/fluid/metrics.py

@@ -714,7 +714,7 @@ class Auc(MetricBase):
     The auc metric is for binary classification.
     Refer to https://en.wikipedia.org/wiki/Receiver_operating_characteristic#Area_under_the_curve.
     Please notice that the auc metric is implemented with python, which may be a little bit slow.
-    If you concern the speed, please use the fluid.layers.auc instead.
+    If you concern the speed, please use the paddle.static.auc instead.
 
     The `auc` function creates four local variables, `true_positives`,
     `true_negatives`, `false_positives` and `false_negatives` that are used to

python/paddle/fluid/tests/book/notest_understand_sentiment.py

@@ -49,7 +49,7 @@ def convolution_net(
     )
     cost = fluid.layers.cross_entropy(input=prediction, label=label)
     avg_cost = paddle.mean(cost)
-    accuracy = fluid.layers.accuracy(input=prediction, label=label)
+    accuracy = paddle.static.accuracy(input=prediction, label=label)
     return avg_cost, accuracy, prediction
 
 
@@ -84,7 +84,7 @@ def stacked_lstm_net(
     )
     cost = fluid.layers.cross_entropy(input=prediction, label=label)
     avg_cost = paddle.mean(cost)
-    accuracy = fluid.layers.accuracy(input=prediction, label=label)
+    accuracy = paddle.static.accuracy(input=prediction, label=label)
     return avg_cost, accuracy, prediction
 
 

python/paddle/fluid/tests/book/test_image_classification.py

@@ -121,7 +121,7 @@ def train(net_type, use_cuda, save_dirname, is_local):
     predict = fluid.layers.fc(input=net, size=classdim, act='softmax')
     cost = fluid.layers.cross_entropy(input=predict, label=label)
     avg_cost = paddle.mean(cost)
-    acc = fluid.layers.accuracy(input=predict, label=label)
+    acc = paddle.static.accuracy(input=predict, label=label)
 
     # Test program
     test_program = fluid.default_main_program().clone(for_test=True)

python/paddle/fluid/tests/book/test_recognize_digits.py

@@ -32,7 +32,7 @@ def loss_net(hidden, label):
     prediction = fluid.layers.fc(input=hidden, size=10, act='softmax')
     loss = fluid.layers.cross_entropy(input=prediction, label=label)
     avg_loss = paddle.mean(loss)
-    acc = fluid.layers.accuracy(input=prediction, label=label)
+    acc = paddle.static.accuracy(input=prediction, label=label)
     return prediction, avg_loss, acc
 
 

python/paddle/fluid/tests/unittests/check_nan_inf_base.py

@@ -66,7 +66,7 @@ def net():
     cost, y_predict = fluid.layers.softmax_with_cross_entropy(
         hidden, y, return_softmax=True
     )
-    acc_top1 = fluid.layers.accuracy(input=y_predict, label=y, k=1)
+    acc_top1 = paddle.static.accuracy(input=y_predict, label=y, k=1)
     avg_cost = paddle.mean(cost)
 
     sgd_optimizer = fluid.optimizer.SGD(learning_rate=0.05)

python/paddle/fluid/tests/unittests/collective/fleet/dist_mnist_gradient_merge.py

@@ -39,7 +39,7 @@ class TestDistMnist2x2(TestDistRunnerBase):
 
         # Evaluator
         batch_size_tensor = fluid.layers.create_tensor(dtype='int64')
-        batch_acc = fluid.layers.accuracy(
+        batch_acc = paddle.static.accuracy(
             input=predict, label=label, total=batch_size_tensor
         )
 

python/paddle/fluid/tests/unittests/collective/fleet/pipeline_mnist.py

@@ -106,7 +106,7 @@ class TestDistMnist2x2(TestDistRunnerBase):
         # Evaluator
         with fluid.device_guard("gpu:1"):
             batch_size_tensor = fluid.layers.create_tensor(dtype='int64')
-            batch_acc = fluid.layers.accuracy(
+            batch_acc = paddle.static.accuracy(
                 input=predict, label=label, total=batch_size_tensor
             )
 

python/paddle/fluid/tests/unittests/collective/fleet/pipeline_mnist_multi_device.py

@@ -106,7 +106,7 @@ class TestDistMnist2x2(TestDistRunnerBase):
         # Evaluator
         with fluid.device_guard("gpu:1"):
             batch_size_tensor = fluid.layers.create_tensor(dtype='int64')
-            batch_acc = fluid.layers.accuracy(
+            batch_acc = paddle.static.accuracy(
                 input=predict, label=label, total=batch_size_tensor
             )
 

python/paddle/fluid/tests/unittests/collective/fleet/pipeline_mnist_one_device.py

@@ -98,7 +98,7 @@ class TestDistMnist2x2(TestDistRunnerBase):
         # Evaluator
         with fluid.device_guard("gpu:0"):
             batch_size_tensor = fluid.layers.create_tensor(dtype='int64')
-            batch_acc = fluid.layers.accuracy(
+            batch_acc = paddle.static.accuracy(
                 input=predict, label=label, total=batch_size_tensor
             )
 

python/paddle/fluid/tests/unittests/dist_allreduce_op.py

@@ -82,7 +82,7 @@ class TestDistMnist2x2(TestDistRunnerBase):
 
         # Evaluator
         batch_size_tensor = fluid.layers.create_tensor(dtype='int64')
-        batch_acc = fluid.layers.accuracy(
+        batch_acc = paddle.static.accuracy(
             input=predict, label=label, total=batch_size_tensor
         )
 

python/paddle/fluid/tests/unittests/dist_ctr.py

@@ -99,8 +99,8 @@ class TestDistCTR2x2(TestDistRunnerBase):
         merge_layer = fluid.layers.concat(input=[dnn_out, lr_pool], axis=1)
 
         predict = fluid.layers.fc(input=merge_layer, size=2, act='softmax')
-        acc = fluid.layers.accuracy(input=predict, label=label)
-        auc_var, batch_auc_var, auc_states = fluid.layers.auc(
+        acc = paddle.static.accuracy(input=predict, label=label)
+        auc_var, batch_auc_var, auc_states = paddle.static.auc(
             input=predict, label=label
         )
         cost = fluid.layers.cross_entropy(input=predict, label=label)

python/paddle/fluid/tests/unittests/dist_fleet_ctr.py

@@ -148,9 +148,9 @@ class TestDistCTR2x2(FleetDistRunnerBase):
         merge_layer = fluid.layers.concat(input=[dnn_out, lr_pool], axis=1)
 
         predict = fluid.layers.fc(input=merge_layer, size=2, act='softmax')
-        acc = fluid.layers.accuracy(input=predict, label=label)
+        acc = paddle.static.accuracy(input=predict, label=label)
 
-        auc_var, batch_auc_var, auc_states = fluid.layers.auc(
+        auc_var, batch_auc_var, auc_states = paddle.static.auc(
             input=predict, label=label
         )
 

python/paddle/fluid/tests/unittests/dist_fleet_raw_program_optimizer.py

@@ -84,7 +84,7 @@ class TestFleetMetaOptimizerPrecision(TestDistRunnerBase):
 
         # Evaluator
         batch_size_tensor = fluid.layers.create_tensor(dtype='int64')
-        batch_acc = fluid.layers.accuracy(
+        batch_acc = paddle.static.accuracy(
             input=predict, label=label, total=batch_size_tensor
         )
 

python/paddle/fluid/tests/unittests/dist_fleet_raw_program_optimizer_fuse_allreduce.py

@@ -84,7 +84,7 @@ class TestFleetMetaOptimizerFuseAllReducePrecision(TestDistRunnerBase):
 
         # Evaluator
         batch_size_tensor = fluid.layers.create_tensor(dtype='int64')
-        batch_acc = fluid.layers.accuracy(
+        batch_acc = paddle.static.accuracy(
             input=predict, label=label, total=batch_size_tensor
         )
 

python/paddle/fluid/tests/unittests/dist_fleet_sparse_embedding_ctr.py

@@ -138,8 +138,8 @@ class TestDistCTR2x2(FleetDistRunnerBase):
         merge_layer = fluid.layers.concat(input=[dnn_out, lr_pool], axis=1)
         predict = fluid.layers.fc(input=merge_layer, size=2, act='softmax')
 
-        acc = fluid.layers.accuracy(input=predict, label=label)
-        auc_var, _, _ = fluid.layers.auc(input=predict, label=label)
+        acc = paddle.static.accuracy(input=predict, label=label)
+        auc_var, _, _ = paddle.static.auc(input=predict, label=label)
         cost = fluid.layers.cross_entropy(input=predict, label=label)
         avg_cost = paddle.mean(x=cost)
 

python/paddle/fluid/tests/unittests/dist_mnist.py

@@ -83,7 +83,7 @@ class TestDistMnist2x2(TestDistRunnerBase):
 
         # Evaluator
         batch_size_tensor = fluid.layers.create_tensor(dtype='int64')
-        batch_acc = fluid.layers.accuracy(
+        batch_acc = paddle.static.accuracy(
             input=predict, label=label, total=batch_size_tensor
         )
 

python/paddle/fluid/tests/unittests/dist_mnist_batch_merge.py

@@ -48,7 +48,7 @@ class TestDistMnist2x2(TestDistRunnerBase):
 
         # Evaluator
         batch_size_tensor = fluid.layers.create_tensor(dtype='int64')
-        batch_acc = fluid.layers.accuracy(
+        batch_acc = paddle.static.accuracy(
             input=predict, label=label, total=batch_size_tensor
         )
 

python/paddle/fluid/tests/unittests/dist_mnist_fp16_allreduce.py

@@ -42,7 +42,7 @@ class TestDistMnist2x2(TestDistRunnerBase):
 
         # Evaluator
         batch_size_tensor = fluid.layers.create_tensor(dtype='int64')
-        batch_acc = fluid.layers.accuracy(
+        batch_acc = paddle.static.accuracy(
             input=predict, label=label, total=batch_size_tensor
         )
 

python/paddle/fluid/tests/unittests/dist_mnist_lars.py

@@ -39,7 +39,7 @@ class TestDistMnist2x2(TestDistRunnerBase):
 
         # Evaluator
         batch_size_tensor = fluid.layers.create_tensor(dtype='int64')
-        batch_acc = fluid.layers.accuracy(
+        batch_acc = paddle.static.accuracy(
             input=predict, label=label, total=batch_size_tensor
         )
 

python/paddle/fluid/tests/unittests/dist_se_resnext.py

@@ -224,8 +224,8 @@ class DistSeResneXt2x2(TestDistRunnerBase):
         cost = fluid.layers.cross_entropy(input=out, label=label)
 
         avg_cost = paddle.mean(x=cost)
-        acc_top1 = fluid.layers.accuracy(input=out, label=label, k=1)
-        acc_top5 = fluid.layers.accuracy(input=out, label=label, k=5)
+        acc_top1 = paddle.static.accuracy(input=out, label=label, k=1)
+        acc_top5 = paddle.static.accuracy(input=out, label=label, k=5)
 
         # Evaluator
         test_program = fluid.default_main_program().clone(for_test=True)

python/paddle/fluid/tests/unittests/dist_text_classification.py

@@ -134,7 +134,7 @@ class TestDistTextClassification2x2(TestDistRunnerBase):
         predict = conv_net(data, dict_dim)
         cost = fluid.layers.cross_entropy(input=predict, label=label)
         avg_cost = paddle.mean(x=cost)
-        acc = fluid.layers.accuracy(input=predict, label=label)
+        acc = paddle.static.accuracy(input=predict, label=label)
         inference_program = fluid.default_main_program().clone()
 
         # Optimization

python/paddle/fluid/tests/unittests/distribution/test_distribution.py

@@ -12,18 +12,6 @@
 # See the License for the specific language governing permissions and
 # limitations under the License.
 
-import unittest
-
-import config
-import numpy as np
-import parameterize
-
-import paddle
-from paddle.distribution import Categorical, Normal, Uniform
-from paddle.fluid import layers
-
-paddle.enable_static()
-
 
 class DistributionNumpy:
     def sample(self):
@@ -40,153 +28,3 @@ class DistributionNumpy:
 
     def probs(self, value):
         raise NotImplementedError
-
-
-class DistributionTestName(unittest.TestCase):
-    def get_prefix(self, string):
-        return string.split('.')[0]
-
-    def test_normal_name(self):
-        name = 'test_normal'
-        normal1 = Normal(0.0, 1.0, name=name)
-        self.assertEqual(normal1.name, name)
-
-        normal2 = Normal(0.0, 1.0)
-        self.assertEqual(normal2.name, 'Normal')
-
-        paddle.enable_static()
-
-        sample = normal1.sample([2])
-        self.assertEqual(self.get_prefix(sample.name), name + '_sample')
-
-        entropy = normal1.entropy()
-        self.assertEqual(self.get_prefix(entropy.name), name + '_entropy')
-
-        value_npdata = np.array([0.8], dtype="float32")
-        value_tensor = layers.create_tensor(dtype="float32")
-        layers.assign(value_npdata, value_tensor)
-
-        lp = normal1.log_prob(value_tensor)
-        self.assertEqual(self.get_prefix(lp.name), name + '_log_prob')
-
-        p = normal1.probs(value_tensor)
-        self.assertEqual(self.get_prefix(p.name), name + '_probs')
-
-        kl = normal1.kl_divergence(normal2)
-        self.assertEqual(self.get_prefix(kl.name), name + '_kl_divergence')
-
-    def test_uniform_name(self):
-        name = 'test_uniform'
-        uniform1 = Uniform(0.0, 1.0, name=name)
-        self.assertEqual(uniform1.name, name)
-
-        uniform2 = Uniform(0.0, 1.0)
-        self.assertEqual(uniform2.name, 'Uniform')
-
-        paddle.enable_static()
-
-        sample = uniform1.sample([2])
-        self.assertEqual(self.get_prefix(sample.name), name + '_sample')
-
-        entropy = uniform1.entropy()
-        self.assertEqual(self.get_prefix(entropy.name), name + '_entropy')
-
-        value_npdata = np.array([0.8], dtype="float32")
-        value_tensor = layers.create_tensor(dtype="float32")
-        layers.assign(value_npdata, value_tensor)
-
-        lp = uniform1.log_prob(value_tensor)
-        self.assertEqual(self.get_prefix(lp.name), name + '_log_prob')
-
-        p = uniform1.probs(value_tensor)
-        self.assertEqual(self.get_prefix(p.name), name + '_probs')
-
-    def test_categorical_name(self):
-        name = 'test_categorical'
-        categorical1 = Categorical([0.4, 0.6], name=name)
-        self.assertEqual(categorical1.name, name)
-
-        categorical2 = Categorical([0.5, 0.5])
-        self.assertEqual(categorical2.name, 'Categorical')
-
-        paddle.enable_static()
-
-        sample = categorical1.sample([2])
-        self.assertEqual(self.get_prefix(sample.name), name + '_sample')
-
-        entropy = categorical1.entropy()
-        self.assertEqual(self.get_prefix(entropy.name), name + '_entropy')
-
-        kl = categorical1.kl_divergence(categorical2)
-        self.assertEqual(self.get_prefix(kl.name), name + '_kl_divergence')
-
-        value_npdata = np.array([0], dtype="int64")
-        value_tensor = layers.create_tensor(dtype="int64")
-        layers.assign(value_npdata, value_tensor)
-
-        p = categorical1.probs(value_tensor)
-        self.assertEqual(self.get_prefix(p.name), name + '_probs')
-
-        lp = categorical1.log_prob(value_tensor)
-        self.assertEqual(self.get_prefix(lp.name), name + '_log_prob')
-
-
-@parameterize.place(config.DEVICES)
-@parameterize.parameterize_cls(
-    (parameterize.TEST_CASE_NAME, 'batch_shape', 'event_shape'),
-    [
-        ('test-tuple', (10, 20), (10, 20)),
-        ('test-list', [100, 100], [100, 200, 300]),
-        ('test-null-eventshape', (100, 100), ()),
-    ],
-)
-class TestDistributionShape(unittest.TestCase):
-    def setUp(self):
-        paddle.disable_static()
-        self.dist = paddle.distribution.Distribution(
-            batch_shape=self.batch_shape, event_shape=self.event_shape
-        )
-
-    def tearDown(self):
-        paddle.enable_static()
-
-    def test_batch_shape(self):
-        self.assertTrue(isinstance(self.dist.batch_shape, tuple))
-        self.assertTrue(self.dist.batch_shape == tuple(self.batch_shape))
-
-    def test_event_shape(self):
-        self.assertTrue(isinstance(self.dist.event_shape, tuple))
-        self.assertTrue(self.dist.event_shape == tuple(self.event_shape))
-
-    def test_prob(self):
-        with self.assertRaises(NotImplementedError):
-            self.dist.prob(paddle.to_tensor(parameterize.xrand()))
-
-    def test_extend_shape(self):
-        shapes = [(34, 20), (56,), ()]
-        for shape in shapes:
-            self.assertTrue(
-                self.dist._extend_shape(shape),
-                shape + self.dist.batch_shape + self.dist.event_shape,
-            )
-
-
-class TestDistributionException(unittest.TestCase):
-    def setUp(self):
-        self._d = paddle.distribution.Distribution()
-
-    def test_mean(self):
-        with self.assertRaises(NotImplementedError):
-            self._d.mean
-
-    def test_variance(self):
-        with self.assertRaises(NotImplementedError):
-            self._d.variance
-
-    def test_rsample(self):
-        with self.assertRaises(NotImplementedError):
-            self._d.rsample(())
-
-
-if __name__ == '__main__':
-    unittest.main()

python/paddle/fluid/tests/unittests/dygraph_to_static/bert_dygraph_model.py

@@ -424,7 +424,7 @@ class PretrainModelLayer(Layer):
             logits=next_sent_fc_out, label=labels, return_softmax=True
         )
 
-        next_sent_acc = fluid.layers.accuracy(
+        next_sent_acc = paddle.static.accuracy(
             input=next_sent_softmax, label=labels
         )
 

python/paddle/fluid/tests/unittests/dygraph_to_static/test_mnist.py

@@ -110,7 +110,7 @@ class MNIST(fluid.dygraph.Layer):
     def forward(self, inputs, label=None):
         x = self.inference(inputs)
         if label is not None:
-            acc = fluid.layers.accuracy(input=x, label=label)
+            acc = paddle.static.accuracy(input=x, label=label)
             loss = fluid.layers.cross_entropy(x, label)
             avg_loss = paddle.mean(loss)
 

python/paddle/fluid/tests/unittests/dygraph_to_static/test_mobile_net.py

@@ -540,8 +540,8 @@ def train_mobilenet(args, to_static):
                     input=softmax_out, label=label
                 )
                 avg_loss = paddle.mean(x=loss)
-                acc_top1 = fluid.layers.accuracy(input=out, label=label, k=1)
-                acc_top5 = fluid.layers.accuracy(input=out, label=label, k=5)
+                acc_top1 = paddle.static.accuracy(input=out, label=label, k=1)
+                acc_top5 = paddle.static.accuracy(input=out, label=label, k=5)
                 t_start_back = time.time()
 
                 loss_data.append(avg_loss.numpy())

python/paddle/fluid/tests/unittests/dygraph_to_static/test_resnet.py

@@ -276,10 +276,10 @@ class ResNetHelper:
                     pred = resnet(img)
                     loss = fluid.layers.cross_entropy(input=pred, label=label)
                     avg_loss = paddle.mean(x=loss)
-                    acc_top1 = fluid.layers.accuracy(
+                    acc_top1 = paddle.static.accuracy(
                         input=pred, label=label, k=1
                     )
-                    acc_top5 = fluid.layers.accuracy(
+                    acc_top5 = paddle.static.accuracy(
                         input=pred, label=label, k=5
                     )
 

python/paddle/fluid/tests/unittests/dygraph_to_static/test_resnet_amp.py

@@ -76,8 +76,8 @@ def train(to_static, build_strategy=None):
                     # If we remove it, the loss between dygraph and dy2stat is exactly same.
                     loss = fluid.layers.cross_entropy(input=pred, label=label)
                 avg_loss = paddle.mean(x=pred)
-                acc_top1 = fluid.layers.accuracy(input=pred, label=label, k=1)
-                acc_top5 = fluid.layers.accuracy(input=pred, label=label, k=5)
+                acc_top1 = paddle.static.accuracy(input=pred, label=label, k=1)
+                acc_top5 = paddle.static.accuracy(input=pred, label=label, k=5)
 
                 scaled = scaler.scale(avg_loss)
                 scaled.backward()

python/paddle/fluid/tests/unittests/dygraph_to_static/test_resnet_pure_fp16.py

@@ -77,8 +77,8 @@ def train(to_static, build_strategy=None):
                 pred = resnet(img)
                 loss = fluid.layers.cross_entropy(input=pred, label=label)
             avg_loss = paddle.mean(x=pred)
-            acc_top1 = fluid.layers.accuracy(input=pred, label=label, k=1)
-            acc_top5 = fluid.layers.accuracy(input=pred, label=label, k=5)
+            acc_top1 = paddle.static.accuracy(input=pred, label=label, k=1)
+            acc_top5 = paddle.static.accuracy(input=pred, label=label, k=5)
 
             scaled = scaler.scale(avg_loss)
             scaled.backward()

python/paddle/fluid/tests/unittests/dygraph_to_static/test_se_resnet.py

@@ -347,8 +347,8 @@ class SeResNeXt(fluid.dygraph.Layer):
         loss = fluid.layers.cross_entropy(input=softmax_out, label=label)
         avg_loss = paddle.mean(x=loss)
 
-        acc_top1 = fluid.layers.accuracy(input=softmax_out, label=label, k=1)
-        acc_top5 = fluid.layers.accuracy(input=softmax_out, label=label, k=5)
+        acc_top1 = paddle.static.accuracy(input=softmax_out, label=label, k=1)
+        acc_top5 = paddle.static.accuracy(input=softmax_out, label=label, k=5)
         return out, avg_loss, acc_top1, acc_top5
 
 

python/paddle/fluid/tests/unittests/dygraph_to_static/test_sentiment.py

@@ -109,7 +109,7 @@ class CNN(fluid.dygraph.Layer):
 
         cost = fluid.layers.cross_entropy(input=prediction, label=label)
         avg_cost = paddle.mean(x=cost)
-        acc = fluid.layers.accuracy(input=prediction, label=label)
+        acc = paddle.static.accuracy(input=prediction, label=label)
         return avg_cost, prediction, acc
 
 
@@ -152,7 +152,7 @@ class BOW(fluid.dygraph.Layer):
 
         cost = fluid.layers.cross_entropy(input=prediction, label=label)
         avg_cost = paddle.mean(x=cost)
-        acc = fluid.layers.accuracy(input=prediction, label=label)
+        acc = paddle.static.accuracy(input=prediction, label=label)
         return avg_cost, prediction, acc
 
 
@@ -198,7 +198,7 @@ class GRU(fluid.dygraph.Layer):
         prediction = paddle.nn.functional.softmax(prediction)
         cost = fluid.layers.cross_entropy(input=prediction, label=label)
         avg_cost = paddle.mean(x=cost)
-        acc = fluid.layers.accuracy(input=prediction, label=label)
+        acc = paddle.static.accuracy(input=prediction, label=label)
         return avg_cost, prediction, acc
 
 
@@ -257,7 +257,7 @@ class BiGRU(fluid.dygraph.Layer):
         # if label is not None:
         cost = fluid.layers.cross_entropy(input=prediction, label=label)
         avg_cost = paddle.mean(x=cost)
-        acc = fluid.layers.accuracy(input=prediction, label=label)
+        acc = paddle.static.accuracy(input=prediction, label=label)
         return avg_cost, prediction, acc
         # else:
         #     return prediction

python/paddle/fluid/tests/unittests/dygraph_to_static/test_tsm.py

@@ -334,10 +334,10 @@ def train(args, fake_data_reader, to_static):
                     input=outputs, label=labels, ignore_index=-1
                 )
                 avg_loss = paddle.mean(loss)
-                acc_top1 = fluid.layers.accuracy(
+                acc_top1 = paddle.static.accuracy(
                     input=outputs, label=labels, k=1
                 )
-                acc_top5 = fluid.layers.accuracy(
+                acc_top5 = paddle.static.accuracy(
                     input=outputs, label=labels, k=5
                 )
 

python/paddle/fluid/tests/unittests/mlu/test_accuracy_op_mlu.py

@@ -74,14 +74,14 @@ class TestAccuracyOpError(unittest.TestCase):
             label = fluid.layers.data(
                 name='label', shape=[-1, 1], dtype="int32"
             )
-            self.assertRaises(TypeError, fluid.layers.accuracy, x1, label)
+            self.assertRaises(TypeError, paddle.static.accuracy, x1, label)
             self.assertRaises(TypeError, paddle.metric.accuracy, x1, label)
             # The input dtype of accuracy_op must be float32 or float64.
             x2 = fluid.layers.data(name='x2', shape=[4], dtype="int32")
-            self.assertRaises(TypeError, fluid.layers.accuracy, x2, label)
+            self.assertRaises(TypeError, paddle.static.accuracy, x2, label)
             self.assertRaises(TypeError, paddle.metric.accuracy, x2, label)
             x3 = fluid.layers.data(name='input', shape=[-1, 2], dtype="float16")
-            fluid.layers.accuracy(input=x3, label=label)
+            paddle.static.accuracy(input=x3, label=label)
             paddle.metric.accuracy(input=x3, label=label)
 
 

python/paddle/fluid/tests/unittests/test_accuracy_op.py

@@ -69,14 +69,14 @@ class TestAccuracyOpError(unittest.TestCase):
             label = fluid.layers.data(
                 name='label', shape=[-1, 1], dtype="int32"
             )
-            self.assertRaises(TypeError, fluid.layers.accuracy, x1, label)
+            self.assertRaises(TypeError, paddle.static.accuracy, x1, label)
             self.assertRaises(TypeError, paddle.metric.accuracy, x1, label)
             # The input dtype of accuracy_op must be float32 or float64.
             x2 = fluid.layers.data(name='x2', shape=[4], dtype="int32")
-            self.assertRaises(TypeError, fluid.layers.accuracy, x2, label)
+            self.assertRaises(TypeError, paddle.static.accuracy, x2, label)
             self.assertRaises(TypeError, paddle.metric.accuracy, x2, label)
             x3 = fluid.layers.data(name='input', shape=[-1, 2], dtype="float16")
-            fluid.layers.accuracy(input=x3, label=label)
+            paddle.static.accuracy(input=x3, label=label)
             paddle.metric.accuracy(input=x3, label=label)
 
 

python/paddle/fluid/tests/unittests/test_async_ssa_graph_executor_mnist.py

@@ -59,7 +59,7 @@ def convolutional_neural_network(use_py_reader):
         prediction = fluid.layers.fc(input=conv_pool_2, size=10, act='softmax')
         loss = fluid.layers.cross_entropy(input=prediction, label=label)
         avg_loss = paddle.mean(loss)
-        acc = fluid.layers.accuracy(input=prediction, label=label)
+        acc = paddle.static.accuracy(input=prediction, label=label)
         i = fluid.layers.zeros(shape=[1], dtype='int64')
         array = fluid.layers.array_write(x=prediction, i=i)
         fluid.layers.increment(i)

python/paddle/fluid/tests/unittests/test_auc_op.py

@@ -158,7 +158,7 @@ class TestAucOpError(unittest.TestCase):
                     name="input2", shape=[-1, 2], dtype="float32"
                 )
                 label2 = fluid.data(name="label2", shape=[-1], dtype="float32")
-                result2 = fluid.layers.auc(input=data2, label=label2)
+                result2 = paddle.static.auc(input=data2, label=label2)
 
             self.assertRaises(TypeError, test_type2)
 

python/paddle/fluid/tests/unittests/test_desc_clone.py

@@ -78,7 +78,7 @@ def get_model(batch_size):
 
     # Evaluator
     batch_size_tensor = fluid.layers.create_tensor(dtype='int64')
-    batch_acc = fluid.layers.accuracy(
+    batch_acc = paddle.static.accuracy(
         input=predict, label=label, total=batch_size_tensor
     )
 

python/paddle/fluid/tests/unittests/test_get_places_op.py

-#   Copyright (c) 2018 PaddlePaddle Authors. All Rights Reserved.
-#
-# Licensed under the Apache License, Version 2.0 (the "License");
-# you may not use this file except in compliance with the License.
-# You may obtain a copy of the License at
-#
-#     http://www.apache.org/licenses/LICENSE-2.0
-#
-# Unless required by applicable law or agreed to in writing, software
-# distributed under the License is distributed on an "AS IS" BASIS,
-# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-# See the License for the specific language governing permissions and
-# limitations under the License.
-
-import unittest
-
-from decorator_helper import prog_scope
-
-import paddle.fluid as fluid
-import paddle.fluid.core as core
-from paddle.fluid.layers.device import get_places
-
-
-class TestGetPlaces(unittest.TestCase):
-    @prog_scope()
-    def check_get_cpu_places(self):
-        places = get_places()
-        cpu = fluid.CPUPlace()
-        exe = fluid.Executor(cpu)
-        exe.run(fluid.default_main_program())
-        self.assertEqual(places.type, fluid.core.VarDesc.VarType.PLACE_LIST)
-
-    @prog_scope()
-    def check_get_gpu_places(self):
-        places = get_places(device_type='CUDA')
-        gpu = fluid.CUDAPlace(0)
-        exe = fluid.Executor(gpu)
-        exe.run(fluid.default_main_program())
-        self.assertEqual(places.type, fluid.core.VarDesc.VarType.PLACE_LIST)
-
-    def test_main(self):
-        if core.is_compiled_with_cuda():
-            self.check_get_gpu_places()
-        self.check_get_cpu_places()
-
-
-if __name__ == '__main__':
-    unittest.main()

python/paddle/fluid/tests/unittests/test_inference_model_io.py

@@ -174,8 +174,8 @@ class TestSaveInferenceModel(unittest.TestCase):
             x = layers.data(name='x', shape=[2], dtype='float32')
             y = layers.data(name='y', shape=[1], dtype='int32')
             predict = fluid.layers.fc(input=x, size=2, act='softmax')
-            acc = fluid.layers.accuracy(input=predict, label=y)
-            auc_var, batch_auc_var, auc_states = fluid.layers.auc(
+            acc = paddle.static.accuracy(input=predict, label=y)
+            auc_var, batch_auc_var, auc_states = paddle.static.auc(
                 input=predict, label=y
             )
             cost = fluid.layers.cross_entropy(input=predict, label=y)

python/paddle/fluid/tests/unittests/test_layers.py

@@ -34,7 +34,6 @@ from paddle.fluid.framework import (
     program_guard,
 )
 from paddle.fluid.initializer import Constant
-from paddle.fluid.layers.device import get_places
 from paddle.fluid.param_attr import ParamAttr
 from paddle.tensor import random
 
@@ -2895,7 +2894,7 @@ class TestLayer(LayerTest):
             label = fluid.data(name="label", shape=[-1, 1], dtype="int")
             fc_out = fluid.layers.fc(input=data, size=10)
             predict = fluid.layers.softmax(input=fc_out)
-            result = fluid.layers.accuracy(input=predict, label=label, k=5)
+            result = paddle.static.accuracy(input=predict, label=label, k=5)
             place = fluid.CPUPlace()
             exe = fluid.Executor(place)
 
@@ -2911,7 +2910,9 @@ class TestLayer(LayerTest):
             label = base.to_variable(y)
             fc_out = fluid.layers.fc(data, size=10)
             predict = fluid.layers.softmax(fc_out)
-            dynamic_out = fluid.layers.accuracy(input=predict, label=label, k=5)
+            dynamic_out = paddle.static.accuracy(
+                input=predict, label=label, k=5
+            )
 
         np.testing.assert_array_equal(static_out[0], dynamic_out.numpy())
 
@@ -2954,7 +2955,6 @@ class TestBook(LayerTest):
                     )
 
                 else:
-                    assert method.__name__ in ('make_get_places')
                     continue
             if method.__name__ in self.only_static_set:
                 continue
@@ -3201,12 +3201,6 @@ class TestBook(LayerTest):
             hid = layers.fc(input=data, size=20)
             return layers.softmax(hid, axis=1)
 
-    def make_get_places(self):
-        with program_guard(
-            fluid.default_main_program(), fluid.default_startup_program()
-        ):
-            get_places(device_count=1)
-
     @prog_scope()
     def make_nce(self):
         window_size = 5

python/paddle/fluid/tests/unittests/test_profiler.py

@@ -60,7 +60,7 @@ class TestProfiler(unittest.TestCase):
             cost = fluid.layers.cross_entropy(input=predict, label=label)
             avg_cost = paddle.mean(cost)
             batch_size = fluid.layers.create_tensor(dtype='int64')
-            batch_acc = fluid.layers.accuracy(
+            batch_acc = paddle.static.accuracy(
                 input=predict, label=label, total=batch_size
             )
 

python/paddle/fluid/tests/unittests/test_program_prune_backward.py

@@ -76,7 +76,7 @@ def simple_fc_net_with_accuracy(use_feed):
     prediction = fluid.layers.fc(hidden, size=10, act='softmax')
     loss = fluid.layers.cross_entropy(input=prediction, label=label)
     loss = paddle.mean(loss)
-    accuracy_out = fluid.layers.accuracy(input=prediction, label=label, k=5)
+    accuracy_out = paddle.static.accuracy(input=prediction, label=label, k=5)
     return loss
 
 

python/paddle/static/__init__.py

@@ -68,11 +68,12 @@ from ..fluid.io import batch  # noqa: F401
 
 from ..fluid.layers import create_parameter  # noqa: F401
 from ..fluid.layers import create_global_var  # noqa: F401
-from ..fluid.layers.metric_op import auc  # noqa: F401
-from ..fluid.layers.metric_op import accuracy  # noqa: F401
 from ..fluid.contrib.layers import ctr_metric_bundle  # noqa: F401
 from ..fluid.layers import exponential_decay  # noqa: F401
 
+from paddle.static.nn.metric import auc  # noqa: F401
+from paddle.static.nn.metric import accuracy  # noqa: F401
+
 __all__ = [  # noqa
     'append_backward',
     'gradients',

python/paddle/fluid/layers/metric_op.py → python/paddle/static/nn/metric.py

@@ -15,22 +15,16 @@
 All layers just related to metric.
 """
 
-import warnings
-from ..layer_helper import LayerHelper
-from ..initializer import Normal, Constant
-from ..framework import (
+from paddle.fluid.layer_helper import LayerHelper
+from paddle.fluid.initializer import Constant
+from paddle.fluid.framework import (
     Variable,
     _non_static_mode,
     _varbase_creator,
-    _in_legacy_dygraph,
-    in_dygraph_mode,
 )
-from .. import core
-from ..param_attr import ParamAttr
-from . import nn
-from . import tensor
-from ..data_feeder import check_variable_and_dtype
-from paddle import _C_ops, _legacy_C_ops
+from paddle.fluid.layers import tensor
+from paddle.fluid.data_feeder import check_variable_and_dtype
+from paddle import _legacy_C_ops
 
 __all__ = ['accuracy', 'auc']