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252 lines
7.7 KiB
252 lines
7.7 KiB
# Copyright (c) 2018 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 op_test
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import unittest
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import numpy as np
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import paddle
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import paddle.fluid as fluid
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from paddle.static import Program, program_guard
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TEST_META_OP_DATA = [{
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'op_str': 'logical_and',
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'binary_op': True
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}, {
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'op_str': 'logical_or',
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'binary_op': True
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}, {
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'op_str': 'logical_xor',
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'binary_op': True
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}, {
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'op_str': 'logical_not',
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'binary_op': False
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}]
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TEST_META_SHAPE_DATA = {
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'XDimLargerThanYDim1': {
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'x_shape': [2, 3, 4, 5],
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'y_shape': [4, 5]
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},
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'XDimLargerThanYDim2': {
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'x_shape': [2, 3, 4, 5],
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'y_shape': [4, 1]
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},
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'XDimLargerThanYDim3': {
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'x_shape': [2, 3, 4, 5],
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'y_shape': [1, 4, 1]
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},
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'XDimLargerThanYDim4': {
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'x_shape': [2, 3, 4, 5],
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'y_shape': [3, 4, 1]
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},
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'XDimLargerThanYDim5': {
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'x_shape': [2, 3, 1, 5],
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'y_shape': [3, 1, 1]
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},
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'XDimLessThanYDim1': {
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'x_shape': [4, 1],
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'y_shape': [2, 3, 4, 5]
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},
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'XDimLessThanYDim2': {
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'x_shape': [1, 4, 1],
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'y_shape': [2, 3, 4, 5]
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},
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'XDimLessThanYDim3': {
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'x_shape': [3, 4, 1],
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'y_shape': [2, 3, 4, 5]
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},
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'XDimLessThanYDim4': {
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'x_shape': [3, 1, 1],
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'y_shape': [2, 3, 1, 5]
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},
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'XDimLessThanYDim5': {
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'x_shape': [4, 5],
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'y_shape': [2, 3, 4, 5]
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},
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'Axis1InLargerDim': {
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'x_shape': [1, 4, 5],
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'y_shape': [2, 3, 1, 5]
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},
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'EqualDim1': {
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'x_shape': [10, 7],
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'y_shape': [10, 7]
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},
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'EqualDim2': {
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'x_shape': [1, 1, 4, 5],
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'y_shape': [2, 3, 1, 5]
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}
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}
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TEST_META_WRONG_SHAPE_DATA = {
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'ErrorDim1': {
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'x_shape': [2, 3, 4, 5],
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'y_shape': [3, 4]
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},
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'ErrorDim2': {
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'x_shape': [2, 3, 4, 5],
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'y_shape': [4, 3]
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}
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}
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def run_static(x_np, y_np, op_str, use_gpu=False, binary_op=True):
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paddle.enable_static()
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startup_program = fluid.Program()
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main_program = fluid.Program()
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place = paddle.CPUPlace()
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if use_gpu and fluid.core.is_compiled_with_cuda():
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place = paddle.CUDAPlace(0)
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exe = fluid.Executor(place)
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with fluid.program_guard(main_program, startup_program):
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x = paddle.static.data(name='x', shape=x_np.shape, dtype='bool')
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op = getattr(paddle, op_str)
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feed_list = {'x': x_np}
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if not binary_op:
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res = op(x)
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else:
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y = paddle.static.data(name='y', shape=y_np.shape, dtype='bool')
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feed_list['y'] = y_np
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res = op(x, y)
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exe.run(startup_program)
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static_result = exe.run(main_program, feed=feed_list, fetch_list=[res])
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return static_result
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def run_dygraph(x_np, y_np, op_str, use_gpu=False, binary_op=True):
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place = paddle.CPUPlace()
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if use_gpu and fluid.core.is_compiled_with_cuda():
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place = paddle.CUDAPlace(0)
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paddle.disable_static(place)
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op = getattr(paddle, op_str)
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x = paddle.to_tensor(x_np)
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if not binary_op:
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dygraph_result = op(x)
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else:
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y = paddle.to_tensor(y_np)
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dygraph_result = op(x, y)
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return dygraph_result
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def np_data_generator(np_shape, *args, **kwargs):
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return np.random.choice(a=[True, False], size=np_shape).astype(bool)
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def test(unit_test, use_gpu=False, test_error=False):
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for op_data in TEST_META_OP_DATA:
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meta_data = dict(op_data)
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meta_data['use_gpu'] = use_gpu
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np_op = getattr(np, meta_data['op_str'])
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META_DATA = dict(TEST_META_SHAPE_DATA)
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if test_error:
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META_DATA = dict(TEST_META_WRONG_SHAPE_DATA)
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for shape_data in META_DATA.values():
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meta_data['x_np'] = np_data_generator(shape_data['x_shape'])
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meta_data['y_np'] = np_data_generator(shape_data['y_shape'])
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if meta_data['binary_op'] and test_error:
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# catch C++ Exception
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unit_test.assertRaises(BaseException, run_static, **meta_data)
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unit_test.assertRaises(BaseException, run_dygraph, **meta_data)
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continue
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static_result = run_static(**meta_data)
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dygraph_result = run_dygraph(**meta_data)
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if meta_data['binary_op']:
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np_result = np_op(meta_data['x_np'], meta_data['y_np'])
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else:
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np_result = np_op(meta_data['x_np'])
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unit_test.assertTrue((static_result == np_result).all())
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unit_test.assertTrue((dygraph_result.numpy() == np_result).all())
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def test_type_error(unit_test, use_gpu, type_str_map):
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def check_type(op_str, x, y, binary_op):
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op = getattr(paddle, op_str)
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error_type = TypeError
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if isinstance(x, np.ndarray):
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x = paddle.to_tensor(x)
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y = paddle.to_tensor(y)
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error_type = BaseException
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if binary_op:
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if type_str_map['x'] != 'bool' or type_str_map['y'] != 'bool':
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unit_test.assertRaises(error_type, op, x=x, y=y)
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if not fluid.in_dygraph_mode():
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unit_test.assertRaises(error_type, op, x=x, y=y, out=1)
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else:
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if type_str_map['x'] != 'bool':
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unit_test.assertRaises(error_type, op, x=x)
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if not fluid.in_dygraph_mode():
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unit_test.assertRaises(error_type, op, x=x, out=1)
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place = paddle.CPUPlace()
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if use_gpu and fluid.core.is_compiled_with_cuda():
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place = paddle.CUDAPlace(0)
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for op_data in TEST_META_OP_DATA:
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meta_data = dict(op_data)
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binary_op = meta_data['binary_op']
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paddle.disable_static(place)
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x = np.random.choice(a=[0, 1], size=[10]).astype(type_str_map['x'])
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y = np.random.choice(a=[0, 1], size=[10]).astype(type_str_map['y'])
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check_type(meta_data['op_str'], x, y, binary_op)
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paddle.enable_static()
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startup_program = paddle.static.Program()
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main_program = paddle.static.Program()
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with paddle.static.program_guard(main_program, startup_program):
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x = paddle.static.data(
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name='x', shape=[10], dtype=type_str_map['x'])
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y = paddle.static.data(
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name='y', shape=[10], dtype=type_str_map['y'])
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check_type(meta_data['op_str'], x, y, binary_op)
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def type_map_factory():
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x_type_list = ['float32', 'float64', 'int32', 'int64', 'bool']
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y_type_list = ['float32', 'float64', 'int32', 'int64', 'bool']
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return [{
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'x': x_type,
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'y': y_type
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} for x_type in x_type_list for y_type in y_type_list]
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class TestCPU(unittest.TestCase):
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def test(self):
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test(self)
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def test_error(self):
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test(self, False, True)
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def test_type_error(self):
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type_map_list = type_map_factory()
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for type_map in type_map_list:
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test_type_error(self, False, type_map)
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class TestCUDA(unittest.TestCase):
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def test(self):
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test(self, True)
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def test_error(self):
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test(self, True, True)
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def test_type_error(self):
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type_map_list = type_map_factory()
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for type_map in type_map_list:
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test_type_error(self, True, type_map)
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if __name__ == '__main__':
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unittest.main()
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