# 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. from __future__ import print_function import unittest import numpy as np from op_test import OpTest import paddle.fluid as fluid import paddle class TestGatherNdOpWithEmptyIndex(OpTest): # Index has empty element, which means copy entire tensor def setUp(self): self.op_type = "gather_nd" self.python_api = paddle.gather_nd xnp = np.random.random((5, 20)).astype("float64") self.inputs = {'X': xnp, 'Index': np.array([[], []]).astype("int32")} self.outputs = { 'Out': np.vstack((xnp[np.newaxis, :], xnp[np.newaxis, :])) } def test_check_output(self): self.check_output(check_eager=False) def test_check_grad(self): self.check_grad(['X'], 'Out', check_eager=False) class TestGatherNdOpWithIndex1(OpTest): def setUp(self): self.op_type = "gather_nd" self.python_api = paddle.gather_nd xnp = np.random.random((5, 20)).astype("float64") self.inputs = {'X': xnp, 'Index': np.array([1]).astype("int32")} self.outputs = {'Out': self.inputs["X"][self.inputs["Index"]]} def test_check_output(self): self.check_output(check_eager=False) def test_check_grad(self): self.check_grad(['X'], 'Out', check_eager=False) class TestGatherNdOpWithLowIndex(OpTest): #Index has low rank, X has high rank def setUp(self): self.op_type = "gather_nd" self.python_api = paddle.gather_nd xnp = np.random.uniform(0, 100, (10, 10)).astype("float64") index = np.array([[1], [2]]).astype("int64") self.inputs = {'X': xnp, 'Index': index} self.outputs = {'Out': xnp[tuple(index.T)]} #[[14, 25, 1], [76, 22, 3]] def test_check_output(self): self.check_output(check_eager=False) def test_check_grad(self): self.check_grad(['X'], 'Out', check_eager=False) class TestGatherNdOpIndex1(OpTest): #Index has low rank, X has high rank def setUp(self): self.op_type = "gather_nd" self.python_api = paddle.gather_nd xnp = np.random.uniform(0, 100, (10, 10)).astype("float64") index = np.array([1, 2]).astype("int32") self.inputs = {'X': xnp, 'Index': index} self.outputs = {'Out': xnp[tuple(index.T)]} def test_check_output(self): self.check_output(check_eager=False) def test_check_grad(self): self.check_grad(['X'], 'Out', check_eager=False) class TestGatherNdOpWithSameIndexAsX(OpTest): #Index has same rank as X's rank def setUp(self): self.op_type = "gather_nd" self.python_api = paddle.gather_nd xnp = np.random.uniform(0, 100, (10, 10)).astype("float64") index = np.array([[1, 1], [2, 1]]).astype("int64") self.inputs = {'X': xnp, 'Index': index} self.outputs = {'Out': xnp[tuple(index.T)]} #[25, 22] def test_check_output(self): self.check_output(check_eager=False) def test_check_grad(self): self.check_grad(['X'], 'Out', check_eager=False) class TestGatherNdOpWithHighRankSame(OpTest): #Both Index and X have high rank, and Rank(Index) = Rank(X) def setUp(self): self.op_type = "gather_nd" self.python_api = paddle.gather_nd shape = (5, 2, 3, 1, 10) xnp = np.random.rand(*shape).astype("float64") index = np.vstack([np.random.randint(0, s, size=2) for s in shape]).T self.inputs = {'X': xnp, 'Index': index.astype("int32")} self.outputs = {'Out': xnp[tuple(index.T)]} def test_check_output(self): self.check_output(check_eager=False) def test_check_grad(self): self.check_grad(['X'], 'Out', check_eager=False) class TestGatherNdOpWithHighRankDiff(OpTest): #Both Index and X have high rank, and Rank(Index) < Rank(X) def setUp(self): self.op_type = "gather_nd" self.python_api = paddle.gather_nd shape = (2, 3, 4, 1, 10) xnp = np.random.rand(*shape).astype("float64") index = np.vstack([np.random.randint(0, s, size=200) for s in shape]).T index_re = index.reshape([20, 5, 2, 5]) self.inputs = {'X': xnp, 'Index': index_re.astype("int32")} self.outputs = {'Out': xnp[tuple(index.T)].reshape([20, 5, 2])} def test_check_output(self): self.check_output(check_eager=False) def test_check_grad(self): self.check_grad(['X'], 'Out', check_eager=False) #Test Python API class TestGatherNdOpAPI(unittest.TestCase): def test_case1(self): x1 = fluid.layers.data( name='x1', shape=[30, 40, 50, 60], dtype='float32') index1 = fluid.layers.data(name='index1', shape=[2, 4], dtype='int32') output1 = fluid.layers.gather_nd(x1, index1) def test_case2(self): x2 = fluid.layers.data(name='x2', shape=[30, 40, 50], dtype='float32') index2 = fluid.layers.data(name='index2', shape=[2, 2], dtype='int64') output2 = fluid.layers.gather_nd(x2, index2) def test_case3(self): x3 = fluid.layers.data(name='x3', shape=[3, 4, 5], dtype='float32') index3 = fluid.layers.data(name='index3', shape=[2, 1], dtype='int32') output3 = fluid.layers.gather_nd(x3, index3, name="gather_nd_layer") #Test Raise Index Error class TestGatherNdOpRaise(unittest.TestCase): def test_check_raise(self): def check_raise_is_test(): try: x = fluid.layers.data( name='x', shape=[3, 4, 5], dtype='float32') index = fluid.layers.data( name='index', shape=[2, 10], dtype='int32') output = fluid.layers.gather_nd(x, index) except Exception as e: t = \ "Input(Index).shape[-1] should be no greater than Input(X).rank" if t in str(e): raise IndexError self.assertRaises(IndexError, check_raise_is_test) class TestGatherNdError(unittest.TestCase): def test_error(self): with paddle.static.program_guard(paddle.static.Program(), paddle.static.Program()): shape = [8, 9, 6] x = paddle.fluid.data(shape=shape, dtype='float32', name='x') index = paddle.fluid.data(shape=shape, dtype='bool', name='index') index_float = paddle.fluid.data( shape=shape, dtype='float32', name='index_float') np_x = np.random.random(shape).astype('float32') np_index = np.array(np.random.randint(2, size=shape, dtype=bool)) def test_x_type(): paddle.gather_nd(np_x, index) self.assertRaises(TypeError, test_x_type) def test_index_type(): paddle.gather_nd(x, np_index) self.assertRaises(TypeError, test_index_type) def test_index_dtype(): paddle.gather_nd(x, index_float) self.assertRaises(TypeError, test_index_dtype) class TestGatherNdAPI2(unittest.TestCase): def test_static(self): with fluid.program_guard(fluid.Program(), fluid.Program()): data1 = fluid.layers.data('data1', shape=[-1, 2], dtype='float64') index = fluid.layers.data('index', shape=[-1, 1], dtype='int32') out = paddle.gather_nd(data1, index) place = fluid.CPUPlace() exe = fluid.Executor(place) input = np.array([[1, 2], [3, 4], [5, 6]]) index_1 = np.array([[1]]) result, = exe.run(feed={"data1": input, "index": index_1}, fetch_list=[out]) expected_output = np.array([[3, 4]]) self.assertTrue(np.allclose(result, expected_output)) def test_imperative(self): paddle.disable_static() input_1 = np.array([[1, 2], [3, 4], [5, 6]]) index_1 = np.array([[1]]) input = fluid.dygraph.to_variable(input_1) index = fluid.dygraph.to_variable(index_1) output = paddle.fluid.layers.gather(input, index) output_np = output.numpy() expected_output = np.array([3, 4]) self.assertTrue(np.allclose(output_np, expected_output)) paddle.enable_static() if __name__ == "__main__": paddle.enable_static() unittest.main()