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96 lines
3.1 KiB
96 lines
3.1 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.core as core
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import paddle.fluid as fluid
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from paddle.fluid import compiler, Program, program_guard
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class TestCastOp1(op_test.OpTest):
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def setUp(self):
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ipt = np.random.random(size=[10, 10])
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self.inputs = {'X': ipt.astype('float32')}
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self.outputs = {'Out': ipt.astype('float64')}
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self.attrs = {
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'in_dtype': int(core.VarDesc.VarType.FP32),
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'out_dtype': int(core.VarDesc.VarType.FP64)
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}
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self.op_type = 'cast'
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def test_check_output(self):
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self.check_output()
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def test_grad(self):
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self.check_grad(['X'], ['Out'])
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class TestCastOp2(op_test.OpTest):
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def setUp(self):
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ipt = np.random.random(size=[10, 10])
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self.inputs = {'X': ipt.astype('float16')}
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self.outputs = {'Out': ipt.astype('float32')}
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self.attrs = {
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'in_dtype': int(core.VarDesc.VarType.FP16),
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'out_dtype': int(core.VarDesc.VarType.FP32)
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}
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self.op_type = 'cast'
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def test_check_output(self):
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self.check_output(atol=1e-3)
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class TestCastOp3(op_test.OpTest):
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def setUp(self):
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ipt = np.random.random(size=[10, 10])
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self.inputs = {'X': ipt.astype('float32')}
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self.outputs = {'Out': ipt.astype('float16')}
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self.attrs = {
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'in_dtype': int(core.VarDesc.VarType.FP32),
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'out_dtype': int(core.VarDesc.VarType.FP16)
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}
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self.op_type = 'cast'
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def test_check_output(self):
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self.check_output(atol=1e-3)
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class TestCastOpError(unittest.TestCase):
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def test_errors(self):
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with program_guard(Program(), Program()):
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# The input type of cast_op must be Variable.
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x1 = fluid.create_lod_tensor(
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np.array([[-1]]), [[1]], fluid.CPUPlace())
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self.assertRaises(TypeError, fluid.layers.cast, x1, 'int32')
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# The input dtype of cast_op must be bool, float16, float32, float64, int32, int64, uint8.
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x2 = fluid.layers.data(name='x2', shape=[4], dtype='int16')
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self.assertRaises(TypeError, fluid.layers.cast, x2, 'int32')
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def test_dtype_type():
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x4 = fluid.layers.data(name='x4', shape=[4], dtype='int32')
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output = fluid.layers.cast(x=x4, dtype='int16')
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self.assertRaises(TypeError, test_dtype_type)
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if __name__ == '__main__':
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paddle.enable_static()
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unittest.main()
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