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436 lines
14 KiB
436 lines
14 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
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import paddle.fluid.core as core
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from paddle.fluid.op import Operator
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import paddle.fluid as fluid
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import numpy as np
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from paddle.fluid import compiler, Program, program_guard
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# Situation 1: Attr(shape) is a list(without tensor)
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class TestFillConstantOp1(OpTest):
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def setUp(self):
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'''Test fill_constant op with specified value
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'''
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self.op_type = "fill_constant"
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self.inputs = {}
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self.attrs = {'shape': [123, 92], 'value': 3.8}
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self.outputs = {'Out': np.full((123, 92), 3.8)}
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def test_check_output(self):
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self.check_output()
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class TestFillConstantOp2(OpTest):
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def setUp(self):
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'''Test fill_constant op with default value
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'''
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self.op_type = "fill_constant"
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self.inputs = {}
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self.attrs = {'shape': [123, 92]}
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self.outputs = {'Out': np.full((123, 92), 0.0)}
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def test_check_output(self):
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self.check_output()
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class TestFillConstantOp3(OpTest):
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def setUp(self):
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'''Test fill_constant op with specified int64 value
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'''
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self.op_type = "fill_constant"
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self.inputs = {}
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self.attrs = {'shape': [123, 92], 'value': 10000000000}
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self.outputs = {'Out': np.full((123, 92), 10000000000)}
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def test_check_output(self):
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self.check_output()
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class TestFillConstantOp4(OpTest):
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def setUp(self):
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'''Test fill_constant op with specified int value
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'''
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self.op_type = "fill_constant"
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self.inputs = {}
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self.attrs = {'shape': [123, 92], 'value': 3}
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self.outputs = {'Out': np.full((123, 92), 3)}
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def test_check_output(self):
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self.check_output()
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class TestFillConstantOpWithSelectedRows(unittest.TestCase):
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def check_with_place(self, place):
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scope = core.Scope()
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# create Out Variable
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out = scope.var('Out').get_selected_rows()
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# create and run fill_constant_op operator
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fill_constant_op = Operator(
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"fill_constant", shape=[123, 92], value=3.8, Out='Out')
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fill_constant_op.run(scope, place)
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# get result from Out
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result_array = np.array(out.get_tensor())
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full_array = np.full((123, 92), 3.8, 'float32')
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self.assertTrue(np.array_equal(result_array, full_array))
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def test_fill_constant_with_selected_rows(self):
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places = [core.CPUPlace()]
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if core.is_compiled_with_cuda():
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places.append(core.CUDAPlace(0))
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for place in places:
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self.check_with_place(place)
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# Situation 2: Attr(shape) is a list(with tensor)
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class TestFillConstantOp1_ShapeTensorList(OpTest):
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def setUp(self):
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'''Test fill_constant op with specified value
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'''
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self.op_type = "fill_constant"
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self.init_data()
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shape_tensor_list = []
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for index, ele in enumerate(self.shape):
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shape_tensor_list.append(("x" + str(index), np.ones(
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(1)).astype('int32') * ele))
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self.inputs = {"ShapeTensorList": shape_tensor_list}
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self.attrs = {'shape': self.infer_shape, 'value': self.value}
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self.outputs = {'Out': np.full(self.shape, self.value)}
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def init_data(self):
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self.shape = [123, 92]
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self.infer_shape = [-1, 92]
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self.value = 3.8
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def test_check_output(self):
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self.check_output()
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class TestFillConstantOp2_ShapeTensorList(OpTest):
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def setUp(self):
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'''Test fill_constant op with default value
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'''
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self.op_type = "fill_constant"
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self.init_data()
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shape_tensor_list = []
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for index, ele in enumerate(self.shape):
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shape_tensor_list.append(("x" + str(index), np.ones(
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(1)).astype('int32') * ele))
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self.inputs = {"ShapeTensorList": shape_tensor_list}
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self.attrs = {'shape': self.infer_shape}
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self.outputs = {'Out': np.full(self.shape, 0.0)}
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def init_data(self):
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self.shape = [123, 92]
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self.infer_shape = [-1, -1]
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def test_check_output(self):
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self.check_output()
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class TestFillConstantOp3_ShapeTensorList(TestFillConstantOp1_ShapeTensorList):
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def init_data(self):
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self.shape = [123, 92]
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self.infer_shape = [123, -1]
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self.value = 10000000000
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class TestFillConstantOp4_ShapeTensorList(TestFillConstantOp1_ShapeTensorList):
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def init_data(self):
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self.shape = [123, 92]
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self.infer_shape = [123, -1]
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self.value = 3
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# Situation 3: shape is a tensor
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class TestFillConstantOp1_ShapeTensor(OpTest):
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def setUp(self):
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'''Test fill_constant op with specified value
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'''
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self.op_type = "fill_constant"
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self.init_data()
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self.inputs = {"ShapeTensor": np.array(self.shape).astype("int32")}
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self.attrs = {'value': self.value}
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self.outputs = {'Out': np.full(self.shape, self.value)}
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def init_data(self):
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self.shape = [123, 92]
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self.value = 3.8
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def test_check_output(self):
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self.check_output()
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# Situation 4: value is a tensor
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class TestFillConstantOp1_ValueTensor(OpTest):
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def setUp(self):
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'''Test fill_constant op with specified value
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'''
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self.op_type = "fill_constant"
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self.init_data()
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self.inputs = {
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"ShapeTensor": np.array(self.shape).astype("int32"),
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'ValueTensor': np.array([self.value]).astype("float32")
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}
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self.attrs = {'value': self.value + 1.0}
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self.outputs = {'Out': np.full(self.shape, self.value)}
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def init_data(self):
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self.shape = [123, 92]
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self.value = 3.8
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self.dtype = np.float32
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def test_check_output(self):
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self.check_output()
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# Situation 5: value is a tensor
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class TestFillConstantOp2_ValueTensor(OpTest):
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def setUp(self):
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'''Test fill_constant op with specified value
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'''
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self.op_type = "fill_constant"
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self.init_data()
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self.inputs = {
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"ShapeTensor": np.array(self.shape).astype("int32"),
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'ValueTensor': np.array([self.value]).astype("int32")
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}
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self.attrs = {'value': self.value, 'dtype': 2}
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self.outputs = {'Out': np.full(self.shape, self.value)}
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def init_data(self):
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self.shape = [123, 92]
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self.value = 3
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self.dtype = np.int32
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def test_check_output(self):
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self.check_output()
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# Test python API
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class TestFillConstantAPI(unittest.TestCase):
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def test_api(self):
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positive_2_int32 = fluid.layers.fill_constant([1], "int32", 2)
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positive_2_int64 = fluid.layers.fill_constant([1], "int64", 2)
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shape_tensor_int32 = fluid.data(
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name="shape_tensor_int32", shape=[2], dtype="int32")
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shape_tensor_int64 = fluid.data(
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name="shape_tensor_int64", shape=[2], dtype="int64")
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out_1 = fluid.layers.fill_constant(
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shape=[1, 2], dtype="float32", value=1.1)
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out_2 = fluid.layers.fill_constant(
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shape=[1, positive_2_int32], dtype="float32", value=1.1)
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out_3 = fluid.layers.fill_constant(
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shape=[1, positive_2_int64], dtype="float32", value=1.1)
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out_4 = fluid.layers.fill_constant(
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shape=shape_tensor_int32, dtype="float32", value=1.1)
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out_5 = fluid.layers.fill_constant(
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shape=shape_tensor_int64, dtype="float32", value=1.1)
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out_6 = fluid.layers.fill_constant(
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shape=shape_tensor_int64, dtype=np.float32, value=1.1)
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val1 = fluid.layers.fill_constant(
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shape=[1], dtype=np.float32, value=1.1)
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val2 = fluid.layers.fill_constant(
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shape=[1], dtype=np.float64, value=1.1)
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out_7 = fluid.layers.fill_constant(
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shape=shape_tensor_int64, dtype=np.float32, value=val1)
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out_8 = fluid.layers.fill_constant(
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shape=shape_tensor_int64, dtype=np.float32, value=val2)
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exe = fluid.Executor(place=fluid.CPUPlace())
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res_1, res_2, res_3, res_4, res_5, res_6, res_7, res_8 = exe.run(
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fluid.default_main_program(),
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feed={
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"shape_tensor_int32": np.array([1, 2]).astype("int32"),
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"shape_tensor_int64": np.array([1, 2]).astype("int64"),
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},
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fetch_list=[
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out_1, out_2, out_3, out_4, out_5, out_6, out_7, out_8
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])
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assert np.array_equal(res_1, np.full([1, 2], 1.1, dtype="float32"))
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assert np.array_equal(res_2, np.full([1, 2], 1.1, dtype="float32"))
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assert np.array_equal(res_3, np.full([1, 2], 1.1, dtype="float32"))
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assert np.array_equal(res_4, np.full([1, 2], 1.1, dtype="float32"))
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assert np.array_equal(res_5, np.full([1, 2], 1.1, dtype="float32"))
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assert np.array_equal(res_6, np.full([1, 2], 1.1, dtype="float32"))
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assert np.array_equal(res_7, np.full([1, 2], 1.1, dtype="float32"))
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assert np.array_equal(res_8, np.full([1, 2], 1.1, dtype="float32"))
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class TestFillConstantImperative(unittest.TestCase):
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def test_api(self):
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with fluid.dygraph.guard():
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data1 = np.array([1, 2]).astype('int32')
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data2 = np.array([1.1]).astype('float32')
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data3 = np.array([88]).astype('int32')
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shape = fluid.dygraph.to_variable(data1)
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val = fluid.dygraph.to_variable(data2)
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value = fluid.dygraph.to_variable(data3)
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res1 = fluid.layers.fill_constant(
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shape=[1, 2], dtype='float32', value=1.1)
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res2 = fluid.layers.fill_constant(
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shape=shape, dtype='float32', value=1.1)
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res3 = fluid.layers.fill_constant(
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shape=shape, dtype='float32', value=val)
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res4 = fluid.layers.fill_constant(
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shape=shape, dtype='int32', value=value)
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assert np.array_equal(
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res1.numpy(), np.full(
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[1, 2], 1.1, dtype="float32"))
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assert np.array_equal(
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res2.numpy(), np.full(
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[1, 2], 1.1, dtype="float32"))
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assert np.array_equal(
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res3.numpy(), np.full(
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[1, 2], 1.1, dtype="float32"))
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assert np.array_equal(
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res4.numpy(), np.full(
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[1, 2], 88, dtype="int32"))
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def test_nan(self):
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with fluid.dygraph.guard():
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res = fluid.layers.fill_constant([1], 'float32', np.nan)
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self.assertTrue(np.isnan(res.numpy().item(0)))
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def test_inf(self):
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with fluid.dygraph.guard():
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res = fluid.layers.fill_constant([1], 'float32', np.inf)
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self.assertTrue(np.isinf(res.numpy().item(0)))
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def test_ninf(self):
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with fluid.dygraph.guard():
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res = fluid.layers.fill_constant([1], 'float32', np.NINF)
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self.assertTrue(np.isinf(res.numpy().item(0)))
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self.assertEqual(np.NINF, res.numpy().item(0))
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class TestFillConstantOpError(unittest.TestCase):
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def test_errors(self):
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with program_guard(Program(), Program()):
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#for ci coverage
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x1 = fluid.layers.data(name='x1', shape=[1], dtype="int16")
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self.assertRaises(
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TypeError,
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fluid.layers.fill_constant,
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shape=[1],
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value=5,
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dtype='uint4')
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self.assertRaises(
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TypeError,
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fluid.layers.fill_constant,
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shape=[1],
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value=5,
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dtype='int16',
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out=x1)
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self.assertRaises(
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TypeError,
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fluid.layers.fill_constant,
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shape=[1.1],
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value=5,
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dtype='float32',
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out=x1)
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# The argument dtype of fill_constant_op must be one of bool, float16,
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#float32, float64, int32 or int64
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x2 = fluid.layers.data(name='x2', shape=[1], dtype="int32")
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self.assertRaises(
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TypeError,
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fluid.layers.fill_constant,
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shape=[1],
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value=5,
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dtype='uint8')
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self.assertRaises(
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TypeError,
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fluid.layers.fill_constant,
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shape=[1],
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value=5,
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dtype='float64',
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out=x2)
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x3 = np.random.randn(100, 100).astype('int32')
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self.assertRaises(
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TypeError,
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fluid.layers.fill_constant,
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shape=[100, 100],
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value=5,
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dtype='float64',
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out=x3)
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# The argument shape's type of fill_constant_op must be list, tuple or Variable.
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def test_shape_type():
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fluid.layers.fill_constant(shape=1, dtype="float32", value=1)
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self.assertRaises(TypeError, test_shape_type)
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# The argument shape's size of fill_constant_op must not be 0.
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def test_shape_size():
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fluid.layers.fill_constant(shape=[], dtype="float32", value=1)
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self.assertRaises(AssertionError, test_shape_size)
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# The shape dtype of fill_constant_op must be int32 or int64.
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def test_shape_tensor_dtype():
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shape = fluid.data(
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name="shape_tensor", shape=[2], dtype="float32")
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fluid.layers.fill_constant(
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shape=shape, dtype="float32", value=1)
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self.assertRaises(TypeError, test_shape_tensor_dtype)
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def test_shape_tensor_list_dtype():
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shape = fluid.data(
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name="shape_tensor_list", shape=[1], dtype="bool")
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fluid.layers.fill_constant(
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shape=[shape, 2], dtype="float32", value=1)
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self.assertRaises(TypeError, test_shape_tensor_list_dtype)
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if __name__ == "__main__":
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unittest.main()
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