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284 lines
8.5 KiB
284 lines
8.5 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 as np
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from op_test import OpTest
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import paddle.fluid as fluid
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from paddle.fluid import compiler, Program, program_guard, core
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class TestSplitOp(OpTest):
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def setUp(self):
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self._set_op_type()
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self.dtype = self.get_dtype()
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axis = 1
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x = np.random.random((4, 5, 6)).astype(self.dtype)
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out = np.split(x, [2, 3], axis)
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self.inputs = {'X': x}
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self.attrs = {'axis': axis, 'sections': [2, 1, 2]}
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self.outputs = {'Out': [('out%d' % i, out[i]) \
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for i in range(len(out))]}
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def get_dtype(self):
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return "float32"
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def _set_op_type(self):
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self.op_type = "split"
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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(['X'], ['out0', 'out1', 'out2'])
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# test with attr(num)
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class TestSplitOp_2(OpTest):
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def setUp(self):
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self._set_op_type()
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self.dtype = self.get_dtype()
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self.init_data()
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self.inputs = {'X': self.x}
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self.attrs = {
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'axis': self.axis,
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'sections': self.sections,
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'num': self.num
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}
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out = np.split(self.x, self.indices_or_sections, self.axis)
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self.outputs = {'Out': [('out%d' % i, out[i]) \
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for i in range(len(out))]}
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def init_data(self):
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self.x = np.random.random((4, 5, 6)).astype(self.dtype)
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self.axis = 2
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self.sections = []
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self.num = 3
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self.indices_or_sections = 3
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def get_dtype(self):
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return "float32"
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def _set_op_type(self):
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self.op_type = "split"
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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(['X'], ['out0', 'out1', 'out2'])
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# attr(axis) is Tensor
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class TestSplitOp_AxisTensor(OpTest):
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def setUp(self):
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self._set_op_type()
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self.dtype = self.get_dtype()
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self.init_data()
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self.inputs = {
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'X': self.x,
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'AxisTensor': np.array([self.axis]).astype("int32")
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}
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self.attrs = {'sections': self.sections, 'num': self.num}
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out = np.split(self.x, self.indices_or_sections, self.axis)
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self.outputs = {'Out': [('out%d' % i, out[i]) \
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for i in range(len(out))]}
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def init_data(self):
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self.x = np.random.random((4, 5, 6)).astype(self.dtype)
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self.axis = 2
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self.sections = []
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self.num = 3
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self.indices_or_sections = 3
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def get_dtype(self):
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return "float32"
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def _set_op_type(self):
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self.op_type = "split"
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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(['X'], ['out0', 'out1', 'out2'])
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# attr(sections) is list containing Tensor
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class TestSplitOp_SectionsTensor(OpTest):
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def setUp(self):
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self._set_op_type()
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self.dtype = self.get_dtype()
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self.init_data()
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self.inputs = {'X': self.x}
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sections_tensor = []
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for index, ele in enumerate(self.sections):
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sections_tensor.append(("x" + str(index), np.ones(
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(1)).astype('int32') * ele))
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self.inputs['SectionsTensorList'] = sections_tensor
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self.attrs = {
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'axis': self.axis,
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'sections': self.sections_infer,
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'num': self.num
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}
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out = np.split(self.x, self.indices_or_sections, self.axis)
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self.outputs = {'Out': [('out%d' % i, out[i]) \
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for i in range(len(out))]}
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def init_data(self):
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self.x = np.random.random((4, 5, 6)).astype(self.dtype)
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self.axis = 1
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self.sections = [2, 1, 2]
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self.sections_infer = [-1, -1, -1]
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self.num = 0
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self.indices_or_sections = [2, 3]
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def get_dtype(self):
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return "float32"
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def _set_op_type(self):
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self.op_type = "split"
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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(['X'], ['out0', 'out1', 'out2'])
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class TestSplitOp_unk_section(OpTest):
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def setUp(self):
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self._set_op_type()
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self.dtype = self.get_dtype()
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self.init_data()
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self.inputs = {'X': self.x}
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self.attrs = {
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'axis': self.axis,
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'sections': self.sections,
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'num': self.num
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}
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out = np.split(self.x, self.indices_or_sections, self.axis)
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self.outputs = {'Out': [('out%d' % i, out[i]) \
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for i in range(len(out))]}
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def init_data(self):
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self.x = np.random.random((4, 5, 6)).astype(self.dtype)
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self.axis = 2
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self.sections = [2, 1, -1]
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self.num = 0
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self.indices_or_sections = [2, 3]
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def get_dtype(self):
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return "float32"
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def _set_op_type(self):
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self.op_type = "split"
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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(['X'], ['out0', 'out1', 'out2'])
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class TestSplitByrefOp(OpTest):
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def _set_op_type(self):
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self.op_type = "split_byref"
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#----------------Split Fp16----------------
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def create_test_fp16(parent):
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@unittest.skipIf(not core.is_compiled_with_cuda(),
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"core is not compiled with CUDA")
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class TestSplitFp16(parent):
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def get_dtype(self):
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return np.float16
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def test_check_grad(self):
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pass
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cls_name = "{0}_{1}".format(parent.__name__, "Fp16")
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TestSplitFp16.__name__ = cls_name
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globals()[cls_name] = TestSplitFp16
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create_test_fp16(TestSplitOp)
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class TestSplitAPI(unittest.TestCase):
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def test_api(self):
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input_1 = np.random.random([4, 5, 6]).astype("int32")
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positive_1_int32 = fluid.layers.fill_constant([1], "int32", 1)
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positive_1_int64 = fluid.layers.fill_constant([1], "int64", 1)
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positive_2_int64 = fluid.layers.fill_constant([1], "int64", 2)
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x_1 = fluid.data(shape=[4, 5, 6], dtype='int32', name='x_1')
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x_2 = fluid.data(shape=[4, 5, None], dtype='int32', name='x_2')
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out_0, out_1, out_2 = fluid.layers.split(
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input=x_1,
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num_or_sections=[positive_2_int64, positive_1_int32, -1],
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dim=positive_1_int64)
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out_3, out_4, out_5 = fluid.layers.split(
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input=x_1, num_or_sections=[2, 1, 2], dim=positive_1_int32)
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fluid.layers.split(input=x_2, num_or_sections=2, dim=2)
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exe = fluid.Executor(place=fluid.CPUPlace())
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[res_0, res_1, res_2, res_3, res_4, res_5] = exe.run(
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fluid.default_main_program(),
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feed={"x_1": input_1,
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"x_2": input_1},
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fetch_list=[out_0, out_1, out_2, out_3, out_4, out_5])
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out = np.split(input_1, [2, 3], 1)
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assert np.array_equal(res_0, out[0])
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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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assert np.array_equal(res_3, out[0])
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assert np.array_equal(res_4, out[1])
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assert np.array_equal(res_5, out[2])
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class TestSplitOpError(unittest.TestCase):
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def test_errors(self):
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with program_guard(Program(), Program()):
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# The type of axis in split_op should be int or Variable.
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def test_axis_type():
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x6 = fluid.layers.data(shape=[4], dtype='float16', name='x3')
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fluid.layers.split(input=x6, num_or_sections=2, dim=3.2)
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self.assertRaises(TypeError, test_axis_type)
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# The type of num_or_sections in split_op should be int, tuple or list.
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def test_num_or_sections_type():
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x6 = fluid.layers.data(shape=[4], dtype='float16', name='x4')
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fluid.layers.split(input=x6, num_or_sections=2.1, dim=3)
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self.assertRaises(TypeError, test_num_or_sections_type)
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if __name__ == '__main__':
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unittest.main()
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