chore: import upstream snapshot with attribution

test/legacy_test/test_lod_tensor_array.py

@@ -0,0 +1,96 @@
+#   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
+
+import numpy as np
+
+import paddle
+from paddle.base import core
+
+
+class TestDenseTensorArray(unittest.TestCase):
+    def test_get_set(self):
+        scope = core.Scope()
+        arr = scope.var('tmp_lod_tensor_array')
+        tensor_array = arr.get_dense_tensor_array()
+        self.assertEqual(0, len(tensor_array))
+        cpu = core.CPUPlace()
+        for i in range(10):
+            t = core.DenseTensor()
+            t.set(np.array([i], dtype='float32'), cpu)
+            tensor_array.append(t)
+
+        self.assertEqual(10, len(tensor_array))
+
+        for i in range(10):
+            t = tensor_array[i]
+            self.assertEqual(np.array(t), np.array([i], dtype='float32'))
+
+            t = core.DenseTensor()
+            t.set(np.array([i + 10], dtype='float32'), cpu)
+            tensor_array[i] = t
+            t = tensor_array[i]
+            self.assertEqual(np.array(t), np.array([i + 10], dtype='float32'))
+
+
+class TestCreateArray(unittest.TestCase):
+    def setUp(self):
+        self.place = paddle.CPUPlace()
+        self.shapes = [[10, 4], [8, 12], [1]]
+
+    def test_initialized_list_and_error(self):
+        paddle.disable_static()
+        init_data = [
+            np.random.random(shape).astype('float32') for shape in self.shapes
+        ]
+        array = paddle.tensor.create_array(
+            'float32', [paddle.to_tensor(x) for x in init_data]
+        )
+        for res, gt in zip(array, init_data):
+            np.testing.assert_array_equal(res, gt)
+
+        # test for None
+        array = paddle.tensor.create_array('float32')
+        self.assertTrue(isinstance(array, list))
+        self.assertEqual(len(array), 0)
+
+        # test error
+        with self.assertRaises(TypeError):
+            paddle.tensor.create_array('float32', 'str')
+
+    def test_static(self):
+        paddle.enable_static()
+        init_data = [
+            paddle.ones(shape, dtype='float32') for shape in self.shapes
+        ]
+        array = paddle.tensor.create_array('float32', init_data)
+        for res, gt in zip(array, init_data):
+            self.assertTrue(res.shape, gt.shape)
+
+        # test error with nest list
+        with self.assertRaises(TypeError):
+            paddle.tensor.create_array(
+                'float32', [init_data[0], [init_data[1]]]
+            )
+
+        # test error with not variable
+        with self.assertRaises(TypeError):
+            paddle.tensor.create_array('float32', ("str"))
+
+        paddle.enable_static()
+
+
+if __name__ == '__main__':
+    unittest.main()