update the location of if not paddle.framework.use_pir_api()

test/legacy_test/.ipynb_checkpoints/test_nn_functional_hot_op-checkpoint.py

@@ -0,0 +1,207 @@
+#   Copyright (c) 2019 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 op_test import OpTest
+
+import paddle
+from paddle import base
+from paddle.base import core
+from paddle.nn import functional
+from paddle.pir_utils import test_with_pir_api
+
+
+class TestOneHotOp(OpTest):
+    def setUp(self):
+        self.op_type = 'one_hot_v2'
+        depth = 10
+        depth_np = np.array(10).astype('int32')
+        dimension = 12
+        x_lod = [[4, 1, 3, 3]]
+        x = [np.random.randint(0, depth - 1) for i in range(sum(x_lod[0]))]
+        x = np.array(x).astype('int32').reshape([sum(x_lod[0])])
+
+        out = np.zeros(shape=(np.prod(x.shape), depth)).astype('float32')
+
+        for i in range(np.prod(x.shape)):
+            out[i, x[i]] = 1.0
+
+        self.inputs = {'X': (x, x_lod), 'depth_tensor': depth_np}
+        self.attrs = {'dtype': int(core.VarDesc.VarType.FP32)}
+        self.outputs = {'Out': (out, x_lod)}
+
+    def test_check_output(self):
+        self.check_output(check_dygraph=False)
+
+
+class TestOneHotOp_attr(OpTest):
+    def setUp(self):
+        self.op_type = 'one_hot_v2'
+        depth = 10
+        dimension = 12
+        x_lod = [[4, 1, 3, 3]]
+        x = [np.random.randint(0, depth - 1) for i in range(sum(x_lod[0]))]
+        x = np.array(x).astype('int32').reshape([sum(x_lod[0]), 1])
+
+        out = np.zeros(shape=(np.prod(x.shape[:-1]), 1, depth)).astype(
+            'float32'
+        )
+
+        for i in range(np.prod(x.shape)):
+            out[i, 0, x[i]] = 1.0
+
+        self.inputs = {'X': (x, x_lod)}
+        self.attrs = {'dtype': int(core.VarDesc.VarType.FP32), 'depth': depth}
+        self.outputs = {'Out': (out, x_lod)}
+
+    def test_check_output(self):
+        self.check_output(check_dygraph=False)
+
+
+class TestOneHotOp_default_dtype(OpTest):
+    def setUp(self):
+        self.op_type = 'one_hot_v2'
+        depth = 10
+        depth_np = np.array(10).astype('int32')
+        dimension = 12
+        x_lod = [[4, 1, 3, 3]]
+        x = [np.random.randint(0, depth - 1) for i in range(sum(x_lod[0]))]
+        x = np.array(x).astype('int32').reshape([sum(x_lod[0])])
+
+        out = np.zeros(shape=(np.prod(x.shape), depth)).astype('float32')
+
+        for i in range(np.prod(x.shape)):
+            out[i, x[i]] = 1.0
+
+        self.inputs = {'X': (x, x_lod), 'depth_tensor': depth_np}
+        self.attrs = {}
+        self.outputs = {'Out': (out, x_lod)}
+
+    def test_check_output(self):
+        self.check_output(check_dygraph=False)
+
+
+class TestOneHotOp_default_dtype_attr(OpTest):
+    def setUp(self):
+        self.op_type = 'one_hot_v2'
+        depth = 10
+        dimension = 12
+        x_lod = [[4, 1, 3, 3]]
+        x = [np.random.randint(0, depth - 1) for i in range(sum(x_lod[0]))]
+        x = np.array(x).astype('int32').reshape([sum(x_lod[0]), 1])
+
+        out = np.zeros(shape=(np.prod(x.shape[:-1]), 1, depth)).astype(
+            'float32'
+        )
+
+        for i in range(np.prod(x.shape)):
+            out[i, 0, x[i]] = 1.0
+
+        self.inputs = {'X': (x, x_lod)}
+        self.attrs = {'depth': depth}
+        self.outputs = {'Out': (out, x_lod)}
+
+    def test_check_output(self):
+        self.check_output(check_dygraph=False)
+
+
+class TestOneHotOpApi(unittest.TestCase):
+    @test_with_pir_api
+    def test_api(self):
+        main = paddle.static.Program()
+        startup = paddle.static.Program()
+        with paddle.static.program_guard(main, startup):
+            num_classes = 10
+            label = paddle.static.data(
+                name="label", shape=[-1, 1], dtype="int64"
+            )
+            one_hot_label = functional.one_hot(x=label, num_classes=num_classes)
+
+            place = base.CPUPlace()
+            label_data = np.array(
+                [np.random.randint(0, 10 - 1) for i in range(6)]
+            ).reshape([6, 1])
+            label_data = label_data.astype('int64')
+
+            exe = base.Executor(place)
+            exe.run(startup)
+            ret = exe.run(
+                feed={
+                    'label': label_data,
+                },
+                fetch_list=[one_hot_label],
+                return_numpy=False,
+            )
+
+    @test_with_pir_api
+    def test_api_with_depthTensor(self):
+        main = paddle.static.Program()
+        startup = paddle.static.Program()
+        with paddle.static.program_guard(main, startup):
+            num_classes = paddle.assign(np.array([10], dtype=np.int32))
+            label = paddle.static.data(
+                name="label", shape=[-1, 1], dtype="int64"
+            )
+            one_hot_label = functional.one_hot(x=label, num_classes=num_classes)
+
+            place = base.CPUPlace()
+            label_data = np.array(
+                [np.random.randint(0, 10 - 1) for i in range(6)]
+            ).reshape([6, 1])
+            label_data = label_data.astype('int64')
+
+            exe = base.Executor(place)
+            exe.run(startup)
+            ret = exe.run(
+                feed={
+                    'label': label_data,
+                },
+                fetch_list=[one_hot_label],
+                return_numpy=False,
+            )
+
+    def test_api_with_dygraph(self):
+        num_classes = 10
+        label = np.array(
+            [np.random.randint(0, num_classes - 1) for i in range(6)]
+        ).reshape([6, 1])
+        with base.dygraph.guard():
+            one_hot_label = functional.one_hot(
+                x=paddle.to_tensor(label), num_classes=num_classes
+            )
+
+
+class BadInputTestOnehotV2(unittest.TestCase):
+    def test_error(self):
+        with base.program_guard(base.Program()):
+
+            def test_bad_x():
+                label = paddle.static.data(
+                    name="label",
+                    shape=[4],
+                    dtype="float32",
+                )
+
+                if not paddle.framework.use_pir_api():
+                    label.desc.set_need_check_feed(False)
+                one_hot_label = functional.one_hot(x=label, num_classes=4)
+
+            self.assertRaises(TypeError, test_bad_x)
+
+
+if __name__ == '__main__':
+    paddle.enable_static()
+    unittest.main()

test/legacy_test/test_nn_functional_hot_op.py

@@ -186,20 +186,20 @@ def test_api_with_dygraph(self):
 
 class BadInputTestOnehotV2(unittest.TestCase):
     def test_error(self):
-        if not paddle.framework.use_pir_api():
-            with base.program_guard(base.Program()):
+        with base.program_guard(base.Program()):
 
-                def test_bad_x():
-                    label = paddle.static.data(
-                        name="label",
-                        shape=[4],
-                        dtype="float32",
-                    )
+            def test_bad_x():
+                label = paddle.static.data(
+                    name="label",
+                    shape=[4],
+                    dtype="float32",
+                )
 
+                if not paddle.framework.use_pir_api():
                     label.desc.set_need_check_feed(False)
-                    one_hot_label = functional.one_hot(x=label, num_classes=4)
+                one_hot_label = functional.one_hot(x=label, num_classes=4)
 
-                self.assertRaises(TypeError, test_bad_x)
+            self.assertRaises(TypeError, test_bad_x)
 
 
 if __name__ == '__main__':