add norm 2.0 api, test=develop

python/paddle/fluid/tests/unittests/test_batch_norm_op_v2.py

@@ -0,0 +1,173 @@
+# Copyright (c) 2020 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 os
+import unittest
+import numpy as np
+import paddle.fluid.core as core
+from paddle.fluid.op import Operator
+import paddle.fluid as fluid
+from op_test import OpTest, _set_use_system_allocator
+from paddle.fluid.framework import grad_var_name
+import paddle.fluid as fluid
+from paddle.fluid import Program, program_guard
+import paddle
+
+
+class TestBatchNorm(unittest.TestCase):
+    def test_functional_dygraph(self):
+        places = [fluid.CPUPlace()]
+        if core.is_compiled_with_cuda() and core.op_support_gpu("batch_norm"):
+            places.append(fluid.CUDAPlace(0))
+        for p in places:
+            with fluid.dygraph.guard(p):
+                x_data = np.random.random(size=(2, 1, 2, 3)).astype('float32')
+                running_mean = np.random.random(size=1).astype('float32')
+                running_variance = np.random.random(size=1).astype('float32')
+                x = fluid.dygraph.to_variable(x_data)
+                rm = fluid.dygraph.to_variable(running_mean)
+                rv = fluid.dygraph.to_variable(running_variance)
+                batch_norm_out = paddle.nn.functional.batch_norm(x, rm, rv)
+
+    def test_functional_static(self):
+        places = [fluid.CPUPlace()]
+        if core.is_compiled_with_cuda() and core.op_support_gpu("batch_norm"):
+            places.append(fluid.CUDAPlace(0))
+        for p in places:
+            exe = fluid.Executor(p)
+            with program_guard(Program(), Program()):
+                x_data = np.random.random(size=(2, 1, 2, 3)).astype('float32')
+                running_mean = np.random.random(size=1).astype('float32')
+                running_variance = np.random.random(size=1).astype('float32')
+                x = fluid.data(name='x', shape=x_data.shape, dtype=x_data.dtype)
+                rm = fluid.data(
+                    name='rm',
+                    shape=running_mean.shape,
+                    dtype=running_mean.dtype)
+                rv = fluid.data(
+                    name='rv',
+                    shape=running_variance.shape,
+                    dtype=running_variance.dtype)
+                batch_norm_out = paddle.nn.functional.batch_norm(x, rm, rv)
+                exe.run(fluid.default_startup_program())
+                r = exe.run(feed={
+                    'x': x_data,
+                    'rm': running_mean,
+                    'rv': running_variance
+                },
+                            fetch_list=[batch_norm_out])[0]
+
+                batch_norm_out = paddle.nn.functional.batch_norm(x, rm, rv)
+
+    def test_name(self):
+        places = [fluid.CPUPlace()]
+        if core.is_compiled_with_cuda() and core.op_support_gpu("batch_norm"):
+            places.append(fluid.CUDAPlace(0))
+        for p in places:
+            with fluid.dygraph.guard(p):
+                batch_norm1d = paddle.nn.BatchNorm1d(1, name="test")
+
+    def test_error(self):
+        places = [fluid.CPUPlace()]
+        if core.is_compiled_with_cuda() and core.op_support_gpu("batch_norm"):
+            places.append(fluid.CUDAPlace(0))
+        for p in places:
+            #paddle.disable_static()
+            x_data_4 = np.random.random(size=(2, 1, 3, 3)).astype('float32')
+            x_data_3 = np.random.random(size=(2, 1, 3)).astype('float32')
+
+            def error1d():
+                x_data_4 = np.random.random(size=(2, 1, 3, 3)).astype('float32')
+                batch_norm1d = paddle.nn.BatchNorm1d(1)
+                batch_norm1d(fluid.dygraph.to_variable(x_data_4))
+
+            def error2d():
+                x_data_3 = np.random.random(size=(2, 1, 3)).astype('float32')
+                batch_norm1d = paddle.nn.BatchNorm2d(1)
+                batch_norm1d(fluid.dygraph.to_variable(x_data_3))
+
+            def error3d():
+                x_data_4 = np.random.random(size=(2, 1, 3, 3)).astype('float32')
+                batch_norm1d = paddle.nn.BatchNorm3d(1)
+                batch_norm1d(fluid.dygraph.to_variable(x_data_4))
+
+            with fluid.dygraph.guard(p):
+                self.assertRaises(ValueError, error1d)
+                self.assertRaises(ValueError, error2d)
+                self.assertRaises(ValueError, error3d)
+
+    def test_dygraph(self):
+        places = [fluid.CPUPlace()]
+        if core.is_compiled_with_cuda() and core.op_support_gpu("batch_norm"):
+            places.append(fluid.CUDAPlace(0))
+        for p in places:
+            shape = [4, 10, 4, 4]
+
+            def compute_v1(x, is_test, trainable_statistics):
+                with fluid.dygraph.guard(p):
+                    bn = fluid.dygraph.BatchNorm(
+                        shape[1],
+                        is_test=is_test,
+                        trainable_statistics=trainable_statistics)
+                    y = bn(fluid.dygraph.to_variable(x))
+                return y.numpy()
+
+            def compute_v2(x):
+                with fluid.dygraph.guard(p):
+                    bn = paddle.nn.BatchNorm2d(shape[1])
+                    y = bn(fluid.dygraph.to_variable(x))
+                return y.numpy()
+
+            x = np.random.randn(*shape).astype("float32")
+            y1 = compute_v1(x, False, False)
+            y2 = compute_v2(x)
+            self.assertTrue(np.allclose(y1, y2))
+
+    def test_static(self):
+        places = [fluid.CPUPlace()]
+        if core.is_compiled_with_cuda() and core.op_support_gpu("batch_norm"):
+            places.append(fluid.CUDAPlace(0))
+        for p in places:
+            exe = fluid.Executor(p)
+            shape = [4, 10, 16, 16]
+
+            def compute_v1(x_np, is_test, trainable_statistics):
+                with program_guard(Program(), Program()):
+                    bn = fluid.dygraph.BatchNorm(
+                        shape[1],
+                        is_test=is_test,
+                        trainable_statistics=trainable_statistics)
+                    x = fluid.data(name='x', shape=x_np.shape, dtype=x_np.dtype)
+                    y = bn(x)
+                    exe.run(fluid.default_startup_program())
+                    r = exe.run(feed={'x': x_np}, fetch_list=[y])[0]
+                return r
+
+            def compute_v2(x_np):
+                with program_guard(Program(), Program()):
+                    bn = paddle.nn.BatchNorm2d(shape[1])
+                    x = fluid.data(name='x', shape=x_np.shape, dtype=x_np.dtype)
+                    y = bn(x)
+                    exe.run(fluid.default_startup_program())
+                    r = exe.run(feed={'x': x_np}, fetch_list=[y])[0]
+                return r
+
+            x = np.random.randn(*shape).astype("float32")
+            y1 = compute_v1(x, False, False)
+            y2 = compute_v2(x)
+            self.assertTrue(np.allclose(y1, y2))
+
+
+if __name__ == '__main__':
+    unittest.main()

python/paddle/nn/__init__.py

@@ -107,6 +107,12 @@
 from .layer.norm import LayerNorm  #DEFINE_ALIAS
 from .layer.norm import SpectralNorm  #DEFINE_ALIAS
 from .layer.norm import InstanceNorm  #DEFINE_ALIAS
+from .layer.norm import InstanceNorm1d  #DEFINE_ALIAS
+from .layer.norm import InstanceNorm2d  #DEFINE_ALIAS
+from .layer.norm import InstanceNorm3d  #DEFINE_ALIAS
+from .layer.norm import BatchNorm1d  #DEFINE_ALIAS
+from .layer.norm import BatchNorm2d  #DEFINE_ALIAS
+from .layer.norm import BatchNorm3d  #DEFINE_ALIAS
 # from .layer.rnn import RNNCell        #DEFINE_ALIAS
 # from .layer.rnn import GRUCell        #DEFINE_ALIAS
 # from .layer.rnn import LSTMCell        #DEFINE_ALIAS

python/paddle/nn/functional/__init__.py

@@ -144,12 +144,11 @@
 from .loss import square_error_cost  #DEFINE_ALIAS
 from .loss import ssd_loss  #DEFINE_ALIAS
 from .loss import teacher_student_sigmoid_loss  #DEFINE_ALIAS
-# from .norm import batch_norm        #DEFINE_ALIAS
+from .norm import batch_norm  #DEFINE_ALIAS
 # from .norm import data_norm        #DEFINE_ALIAS
-# from .norm import group_norm        #DEFINE_ALIAS
-# from .norm import instance_norm        #DEFINE_ALIAS
-from .norm import l2_normalize  #DEFINE_ALIAS
-# from .norm import layer_norm        #DEFINE_ALIAS
+# from .norm import group_norm        #DEFINE_ALIAS# from .norm import instance_norm        #DEFINE_ALIAS
+from .norm import instance_norm  #DEFINE_ALIAS
+from .norm import layer_norm  #DEFINE_ALIAS
 from .norm import lrn  #DEFINE_ALIAS
 from .norm import normalize  #DEFINE_ALIAS
 # from .norm import spectral_norm        #DEFINE_ALIAS