test_cast_op.py

#   Copyright (c) 2018 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 op_test
import unittest
import numpy as np
import paddle.fluid.core as core
import paddle.fluid as fluid
from paddle.fluid import compiler, Program, program_guard


class TestCastOp1(op_test.OpTest):
    def setUp(self):
        ipt = np.random.random(size=[10, 10])
        self.inputs = {'X': ipt.astype('float32')}
        self.outputs = {'Out': ipt.astype('float64')}
        self.attrs = {
            'in_dtype': int(core.VarDesc.VarType.FP32),
            'out_dtype': int(core.VarDesc.VarType.FP64)
        }
        self.op_type = 'cast'

    def test_check_output(self):
        self.check_output()

    def test_grad(self):
        self.check_grad(['X'], ['Out'])


class TestCastOp2(op_test.OpTest):
    def setUp(self):
        ipt = np.random.random(size=[10, 10])
        # numpy float16 is binded to fluid float16 via uint16
        self.inputs = {'X': ipt.astype('float16').view(np.uint16)}
        self.outputs = {'Out': ipt.astype('float32')}
        self.attrs = {
            'in_dtype': int(core.VarDesc.VarType.FP16),
            'out_dtype': int(core.VarDesc.VarType.FP32)
        }
        self.op_type = 'cast'

    def test_check_output(self):
        self.check_output(atol=1e-3)


class TestCastOp3(op_test.OpTest):
    def setUp(self):
        ipt = np.random.random(size=[10, 10])
        self.inputs = {'X': ipt.astype('float32')}
        self.outputs = {'Out': ipt.astype('float16')}
        self.attrs = {
            'in_dtype': int(core.VarDesc.VarType.FP32),
            'out_dtype': int(core.VarDesc.VarType.FP16)
        }
        self.op_type = 'cast'

    def test_check_output(self):
        self.check_output(atol=1e-3)


class TestCastOpError(op_test.OpTest):
    def test_errors(self):
        with program_guard(Program(), Program()):
            # The input type of cast_op must be Variable.
            x1 = fluid.create_lod_tensor(
                np.array([[-1]]), [[1]], fluid.CPUPlace())
            self.assertRaises(TypeError, fluid.layers.cast, x1, 'int32')
            # The input dtype of cast_op must be bool, float16, float32, float64, int32, int64, uint8.
            x2 = fluid.layers.data(name='x2', shape=[4], dtype='int8')
            self.assertRaises(TypeError, fluid.layers.cast, x2, 'int32')
            x3 = fluid.layers.data(name='x3', shape=[4], dtype='int16')
            self.assertRaises(TypeError, fluid.layers.cast, x3, 'int32')


if __name__ == '__main__':
    unittest.main()