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113 lines
3.5 KiB
113 lines
3.5 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 unittest
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import numpy
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import op_test
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import paddle.fluid as fluid
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import paddle.fluid.framework as framework
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import paddle.fluid.layers as layers
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class TestAssignValueOp(op_test.OpTest):
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def setUp(self):
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self.op_type = "assign_value"
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self.inputs = {}
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self.attrs = {}
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self.init_data()
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self.attrs["shape"] = self.value.shape
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self.attrs["dtype"] = framework.convert_np_dtype_to_dtype_(
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self.value.dtype)
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self.outputs = {"Out": self.value}
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def init_data(self):
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self.value = numpy.random.random(size=(2, 5)).astype(numpy.float32)
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self.attrs["fp32_values"] = [float(v) for v in self.value.flat]
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def test_forward(self):
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self.check_output()
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class TestAssignValueOp2(TestAssignValueOp):
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def init_data(self):
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self.value = numpy.random.random(size=(2, 5)).astype(numpy.int32)
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self.attrs["int32_values"] = [int(v) for v in self.value.flat]
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class TestAssignValueOp3(TestAssignValueOp):
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def init_data(self):
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self.value = numpy.random.random(size=(2, 5)).astype(numpy.int64)
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self.attrs["int64_values"] = [int(v) for v in self.value.flat]
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class TestAssignValueOp4(TestAssignValueOp):
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def init_data(self):
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self.value = numpy.random.choice(
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a=[False, True], size=(2, 5)).astype(numpy.bool)
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self.attrs["bool_values"] = [bool(v) for v in self.value.flat]
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class TestAssignApi(unittest.TestCase):
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def setUp(self):
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self.init_dtype()
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self.value = (
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-100 + 200 * numpy.random.random(size=(2, 5))).astype(self.dtype)
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self.place = fluid.CUDAPlace(0) if fluid.is_compiled_with_cuda(
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) else fluid.CPUPlace()
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def init_dtype(self):
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self.dtype = "float32"
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def test_assign(self):
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main_program = fluid.Program()
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with fluid.program_guard(main_program):
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x = layers.create_tensor(dtype=self.dtype)
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layers.assign(input=self.value, output=x)
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exe = fluid.Executor(self.place)
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[fetched_x] = exe.run(main_program, feed={}, fetch_list=[x])
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self.assertTrue(
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numpy.array_equal(fetched_x, self.value),
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"fetch_x=%s val=%s" % (fetched_x, self.value))
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self.assertEqual(fetched_x.dtype, self.value.dtype)
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class TestAssignApi2(TestAssignApi):
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def init_dtype(self):
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self.dtype = "int32"
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class TestAssignApi3(TestAssignApi):
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def init_dtype(self):
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self.dtype = "int64"
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class TestAssignApi4(TestAssignApi):
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def setUp(self):
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self.init_dtype()
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self.value = numpy.random.choice(
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a=[False, True], size=(2, 5)).astype(numpy.bool)
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self.place = fluid.CUDAPlace(0) if fluid.is_compiled_with_cuda(
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) else fluid.CPUPlace()
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def init_dtype(self):
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self.dtype = "bool"
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if __name__ == '__main__':
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unittest.main()
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