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184 lines
6.5 KiB
184 lines
6.5 KiB
# Copyright (c) 2020 PaddlePaddle Authors. All Rights Reserved.
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#
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# Licensed under the Apache License, Version 2.0 (the "License");
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# you may not use this file except in compliance with the License.
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# You may obtain a copy of the License at
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#
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# http://www.apache.org/licenses/LICENSE-2.0
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#
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# Unless required by applicable law or agreed to in writing, software
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# distributed under the License is distributed on an "AS IS" BASIS,
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# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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# See the License for the specific language governing permissions and
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# limitations under the License.
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from __future__ import print_function
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import unittest
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import numpy as np
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from op_test import OpTest, skip_check_grad_ci
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import paddle.fluid as fluid
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import paddle
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from paddle.fluid import compiler, Program, program_guard, core
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class TestMeshgridOp(OpTest):
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def setUp(self):
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self.op_type = "meshgrid"
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self.dtype = self.get_dtype()
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ins, outs = self.init_test_data()
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self.inputs = {'X': [('x%d' % i, ins[i]) for i in range(len(ins))]}
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self.outputs = {
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'Out': [('out%d' % i, outs[i]) for i in range(len(outs))]
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}
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def get_dtype(self):
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return "float64"
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def test_check_output(self):
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self.check_output()
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def test_check_grad(self):
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self.check_grad(['x0'], ['out0'])
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self.check_grad(['x1'], ['out1'])
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def init_test_data(self):
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self.shape = self.get_x_shape()
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ins = []
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outs = []
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for i in range(len(self.shape)):
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ins.append(np.random.random((self.shape[i], )).astype(self.dtype))
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for i in range(len(self.shape)):
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out_reshape = [1] * len(self.shape)
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out_reshape[i] = self.shape[i]
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out_temp = np.reshape(ins[i], out_reshape)
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outs.append(np.broadcast_to(out_temp, self.shape))
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return ins, outs
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def get_x_shape(self):
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return [100, 200]
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class TestMeshgridOp2(TestMeshgridOp):
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def get_x_shape(self):
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return [100, 300]
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class TestMeshgridOp3(unittest.TestCase):
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def test_api(self):
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x = fluid.data(shape=[100], dtype='int32', name='x')
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y = fluid.data(shape=[200], dtype='int32', name='y')
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input_1 = np.random.randint(0, 100, [100, ]).astype('int32')
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input_2 = np.random.randint(0, 100, [200, ]).astype('int32')
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out_1 = np.reshape(input_1, [100, 1])
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out_1 = np.broadcast_to(out_1, [100, 200])
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out_2 = np.reshape(input_2, [1, 200])
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out_2 = np.broadcast_to(out_2, [100, 200])
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exe = fluid.Executor(place=fluid.CPUPlace())
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grid_x, grid_y = paddle.tensor.meshgrid(x, y)
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res_1, res_2 = exe.run(fluid.default_main_program(),
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feed={'x': input_1,
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'y': input_2},
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fetch_list=[grid_x, grid_y])
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assert np.array_equal(res_1, out_1)
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assert np.array_equal(res_2, out_2)
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class TestMeshgridOp4(unittest.TestCase):
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def test_list_input(self):
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x = fluid.data(shape=[100], dtype='int32', name='x')
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y = fluid.data(shape=[200], dtype='int32', name='y')
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input_1 = np.random.randint(0, 100, [100, ]).astype('int32')
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input_2 = np.random.randint(0, 100, [200, ]).astype('int32')
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out_1 = np.reshape(input_1, [100, 1])
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out_1 = np.broadcast_to(out_1, [100, 200])
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out_2 = np.reshape(input_2, [1, 200])
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out_2 = np.broadcast_to(out_2, [100, 200])
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exe = fluid.Executor(place=fluid.CPUPlace())
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grid_x, grid_y = paddle.tensor.meshgrid([x, y])
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res_1, res_2 = exe.run(fluid.default_main_program(),
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feed={'x': input_1,
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'y': input_2},
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fetch_list=[grid_x, grid_y])
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assert np.array_equal(res_1, out_1)
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assert np.array_equal(res_2, out_2)
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class TestMeshgridOp5(unittest.TestCase):
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def test_tuple_input(self):
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x = fluid.data(shape=[100], dtype='int32', name='x')
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y = fluid.data(shape=[200], dtype='int32', name='y')
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input_1 = np.random.randint(0, 100, [100, ]).astype('int32')
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input_2 = np.random.randint(0, 100, [200, ]).astype('int32')
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out_1 = np.reshape(input_1, [100, 1])
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out_1 = np.broadcast_to(out_1, [100, 200])
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out_2 = np.reshape(input_2, [1, 200])
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out_2 = np.broadcast_to(out_2, [100, 200])
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exe = fluid.Executor(place=fluid.CPUPlace())
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grid_x, grid_y = paddle.tensor.meshgrid((x, y))
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res_1, res_2 = exe.run(fluid.default_main_program(),
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feed={'x': input_1,
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'y': input_2},
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fetch_list=[grid_x, grid_y])
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assert np.array_equal(res_1, out_1)
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assert np.array_equal(res_2, out_2)
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class TestMeshgridOp6(unittest.TestCase):
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def test_api_with_dygraph(self):
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input_3 = np.random.randint(0, 100, [100, ]).astype('int32')
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input_4 = np.random.randint(0, 100, [200, ]).astype('int32')
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with fluid.dygraph.guard():
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tensor_3 = fluid.dygraph.to_variable(input_3)
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tensor_4 = fluid.dygraph.to_variable(input_4)
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res_3, res_4 = paddle.tensor.meshgrid(tensor_3, tensor_4)
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assert np.array_equal(res_3.shape, [100, 200])
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assert np.array_equal(res_4.shape, [100, 200])
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class TestMeshgridOp7(unittest.TestCase):
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def test_api_with_dygraph_list_input(self):
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input_3 = np.random.randint(0, 100, [100, ]).astype('int32')
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input_4 = np.random.randint(0, 100, [200, ]).astype('int32')
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with fluid.dygraph.guard():
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tensor_3 = fluid.dygraph.to_variable(input_3)
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tensor_4 = fluid.dygraph.to_variable(input_4)
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res_3, res_4 = paddle.tensor.meshgrid([tensor_3, tensor_4])
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assert np.array_equal(res_3.shape, [100, 200])
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assert np.array_equal(res_4.shape, [100, 200])
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class TestMeshgridOp7(unittest.TestCase):
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def test_api_with_dygraph_tuple_input(self):
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input_3 = np.random.randint(0, 100, [100, ]).astype('int32')
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input_4 = np.random.randint(0, 100, [200, ]).astype('int32')
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with fluid.dygraph.guard():
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tensor_3 = fluid.dygraph.to_variable(input_3)
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tensor_4 = fluid.dygraph.to_variable(input_4)
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res_3, res_4 = paddle.tensor.meshgrid((tensor_3, tensor_4))
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assert np.array_equal(res_3.shape, [100, 200])
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assert np.array_equal(res_4.shape, [100, 200])
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if __name__ == '__main__':
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unittest.main()
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