You can not select more than 25 topics
Topics must start with a letter or number, can include dashes ('-') and can be up to 35 characters long.
304 lines
9.8 KiB
304 lines
9.8 KiB
# 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 unittest
|
|
import numpy as np
|
|
from op_test import OpTest
|
|
|
|
import paddle.fluid.core as core
|
|
from paddle.fluid.op import Operator
|
|
import paddle.fluid as fluid
|
|
from paddle.fluid import compiler, Program, program_guard
|
|
|
|
|
|
# Situation 1: Attr(shape) is a list(without tensor)
|
|
class TestFillConstantOp1(OpTest):
|
|
def setUp(self):
|
|
'''Test fill_constant op with specified value
|
|
'''
|
|
self.op_type = "fill_constant"
|
|
|
|
self.inputs = {}
|
|
self.attrs = {'shape': [123, 92], 'value': 3.8}
|
|
self.outputs = {'Out': np.full((123, 92), 3.8)}
|
|
|
|
def test_check_output(self):
|
|
self.check_output()
|
|
|
|
|
|
class TestFillConstantOp2(OpTest):
|
|
def setUp(self):
|
|
'''Test fill_constant op with default value
|
|
'''
|
|
self.op_type = "fill_constant"
|
|
|
|
self.inputs = {}
|
|
self.attrs = {'shape': [123, 92]}
|
|
self.outputs = {'Out': np.full((123, 92), 0.0)}
|
|
|
|
def test_check_output(self):
|
|
self.check_output()
|
|
|
|
|
|
class TestFillConstantOp3(OpTest):
|
|
def setUp(self):
|
|
'''Test fill_constant op with specified int64 value
|
|
'''
|
|
self.op_type = "fill_constant"
|
|
|
|
self.inputs = {}
|
|
self.attrs = {'shape': [123, 92], 'value': 10000000000}
|
|
self.outputs = {'Out': np.full((123, 92), 10000000000)}
|
|
|
|
def test_check_output(self):
|
|
self.check_output()
|
|
|
|
|
|
class TestFillConstantOp4(OpTest):
|
|
def setUp(self):
|
|
'''Test fill_constant op with specified int value
|
|
'''
|
|
self.op_type = "fill_constant"
|
|
|
|
self.inputs = {}
|
|
self.attrs = {'shape': [123, 92], 'value': 3}
|
|
self.outputs = {'Out': np.full((123, 92), 3)}
|
|
|
|
def test_check_output(self):
|
|
self.check_output()
|
|
|
|
|
|
class TestFillConstantOpWithSelectedRows(OpTest):
|
|
def check_with_place(self, place):
|
|
scope = core.Scope()
|
|
# create Out Variable
|
|
out = scope.var('Out').get_selected_rows()
|
|
|
|
# create and run fill_constant_op operator
|
|
fill_constant_op = Operator(
|
|
"fill_constant", shape=[123, 92], value=3.8, Out='Out')
|
|
fill_constant_op.run(scope, place)
|
|
|
|
# get result from Out
|
|
result_array = np.array(out.get_tensor())
|
|
full_array = np.full((123, 92), 3.8, 'float32')
|
|
|
|
self.assertTrue(np.array_equal(result_array, full_array))
|
|
|
|
def test_fill_constant_with_selected_rows(self):
|
|
places = [core.CPUPlace()]
|
|
if core.is_compiled_with_cuda():
|
|
places.append(core.CUDAPlace(0))
|
|
|
|
for place in places:
|
|
self.check_with_place(place)
|
|
|
|
|
|
# Situation 2: Attr(shape) is a list(with tensor)
|
|
class TestFillConstantOp1_ShapeTensorList(OpTest):
|
|
def setUp(self):
|
|
'''Test fill_constant op with specified value
|
|
'''
|
|
self.op_type = "fill_constant"
|
|
self.init_data()
|
|
shape_tensor_list = []
|
|
for index, ele in enumerate(self.shape):
|
|
shape_tensor_list.append(("x" + str(index), np.ones(
|
|
(1)).astype('int32') * ele))
|
|
|
|
self.inputs = {"ShapeTensorList": shape_tensor_list}
|
|
self.attrs = {'shape': self.infer_shape, 'value': self.value}
|
|
self.outputs = {'Out': np.full(self.shape, self.value)}
|
|
|
|
def init_data(self):
|
|
self.shape = [123, 92]
|
|
self.infer_shape = [-1, 92]
|
|
self.value = 3.8
|
|
|
|
def test_check_output(self):
|
|
self.check_output()
|
|
|
|
|
|
class TestFillConstantOp2_ShapeTensorList(OpTest):
|
|
def setUp(self):
|
|
'''Test fill_constant op with default value
|
|
'''
|
|
self.op_type = "fill_constant"
|
|
self.init_data()
|
|
shape_tensor_list = []
|
|
for index, ele in enumerate(self.shape):
|
|
shape_tensor_list.append(("x" + str(index), np.ones(
|
|
(1)).astype('int32') * ele))
|
|
|
|
self.inputs = {"ShapeTensorList": shape_tensor_list}
|
|
self.attrs = {'shape': self.infer_shape}
|
|
self.outputs = {'Out': np.full(self.shape, 0.0)}
|
|
|
|
def init_data(self):
|
|
self.shape = [123, 92]
|
|
self.infer_shape = [-1, -1]
|
|
|
|
def test_check_output(self):
|
|
self.check_output()
|
|
|
|
|
|
class TestFillConstantOp3_ShapeTensorList(TestFillConstantOp1_ShapeTensorList):
|
|
def init_data(self):
|
|
self.shape = [123, 92]
|
|
self.infer_shape = [123, -1]
|
|
self.value = 10000000000
|
|
|
|
|
|
class TestFillConstantOp4_ShapeTensorList(TestFillConstantOp1_ShapeTensorList):
|
|
def init_data(self):
|
|
self.shape = [123, 92]
|
|
self.infer_shape = [123, -1]
|
|
self.value = 3
|
|
|
|
|
|
# Situation 3: shape is a tensor
|
|
class TestFillConstantOp1_ShapeTensor(OpTest):
|
|
def setUp(self):
|
|
'''Test fill_constant op with specified value
|
|
'''
|
|
self.op_type = "fill_constant"
|
|
self.init_data()
|
|
|
|
self.inputs = {"ShapeTensor": np.array(self.shape).astype("int32")}
|
|
self.attrs = {'value': self.value}
|
|
self.outputs = {'Out': np.full(self.shape, self.value)}
|
|
|
|
def init_data(self):
|
|
self.shape = [123, 92]
|
|
self.value = 3.8
|
|
|
|
def test_check_output(self):
|
|
self.check_output()
|
|
|
|
|
|
# Test python API
|
|
class TestFillConstantAPI(OpTest):
|
|
def test_api(self):
|
|
positive_2_int32 = fluid.layers.fill_constant([1], "int32", 2)
|
|
|
|
positive_2_int64 = fluid.layers.fill_constant([1], "int64", 2)
|
|
shape_tensor_int32 = fluid.data(
|
|
name="shape_tensor_int32", shape=[2], dtype="int32")
|
|
|
|
shape_tensor_int64 = fluid.data(
|
|
name="shape_tensor_int64", shape=[2], dtype="int64")
|
|
|
|
out_1 = fluid.layers.fill_constant(
|
|
shape=[1, 2], dtype="float32", value=1.1)
|
|
|
|
out_2 = fluid.layers.fill_constant(
|
|
shape=[1, positive_2_int32], dtype="float32", value=1.1)
|
|
|
|
out_3 = fluid.layers.fill_constant(
|
|
shape=[1, positive_2_int64], dtype="float32", value=1.1)
|
|
|
|
out_4 = fluid.layers.fill_constant(
|
|
shape=shape_tensor_int32, dtype="float32", value=1.1)
|
|
|
|
out_5 = fluid.layers.fill_constant(
|
|
shape=shape_tensor_int64, dtype="float32", value=1.1)
|
|
|
|
exe = fluid.Executor(place=fluid.CPUPlace())
|
|
res_1, res_2, res_3, res_4, res_5 = exe.run(
|
|
fluid.default_main_program(),
|
|
feed={
|
|
"shape_tensor_int32": np.array([1, 2]).astype("int32"),
|
|
"shape_tensor_int64": np.array([1, 2]).astype("int64"),
|
|
},
|
|
fetch_list=[out_1, out_2, out_3, out_4, out_5])
|
|
|
|
assert np.array_equal(res_1, np.full([1, 2], 1.1, dtype="float32"))
|
|
assert np.array_equal(res_2, np.full([1, 2], 1.1, dtype="float32"))
|
|
assert np.array_equal(res_3, np.full([1, 2], 1.1, dtype="float32"))
|
|
assert np.array_equal(res_4, np.full([1, 2], 1.1, dtype="float32"))
|
|
assert np.array_equal(res_5, np.full([1, 2], 1.1, dtype="float32"))
|
|
|
|
|
|
class TestFillConstantOpError(OpTest):
|
|
def test_errors(self):
|
|
with program_guard(Program(), Program()):
|
|
#for ci coverage
|
|
x1 = fluid.layers.data(name='x1', shape=[1], dtype="int16")
|
|
self.assertRaises(
|
|
ValueError,
|
|
fluid.layers.fill_constant,
|
|
shape=[1],
|
|
value=5,
|
|
dtype='uint4')
|
|
self.assertRaises(
|
|
TypeError,
|
|
fluid.layers.fill_constant,
|
|
shape=[1],
|
|
value=5,
|
|
dtype='int16',
|
|
out=x1)
|
|
|
|
# The argument dtype of fill_constant_op must be one of bool, float16,
|
|
#float32, float64, int32 or int64
|
|
x2 = fluid.layers.data(name='x2', shape=[1], dtype="int32")
|
|
|
|
self.assertRaises(
|
|
TypeError,
|
|
fluid.layers.fill_constant,
|
|
shape=[1],
|
|
value=5,
|
|
dtype='uint8')
|
|
self.assertRaises(
|
|
TypeError,
|
|
fluid.layers.fill_constant,
|
|
shape=[1],
|
|
value=5,
|
|
dtype='float64',
|
|
out=x2)
|
|
|
|
# The argument shape's type of fill_constant_op must be list, tuple or Variable.
|
|
def test_shape_type():
|
|
fluid.layers.fill_constant(shape=1, dtype="float32", value=1)
|
|
|
|
self.assertRaises(TypeError, test_shape_type)
|
|
|
|
# The argument shape's size of fill_constant_op must not be 0.
|
|
def test_shape_size():
|
|
fluid.layers.fill_constant(shape=[], dtype="float32", value=1)
|
|
|
|
self.assertRaises(AssertionError, test_shape_size)
|
|
|
|
# The shape dtype of fill_constant_op must be int32 or int64.
|
|
def test_shape_tensor_dtype():
|
|
shape = fluid.data(
|
|
name="shape_tensor", shape=[2], dtype="float32")
|
|
fluid.layers.fill_constant(
|
|
shape=shape, dtype="float32", value=1)
|
|
|
|
self.assertRaises(TypeError, test_shape_tensor_dtype)
|
|
|
|
def test_shape_tensor_list_dtype():
|
|
shape = fluid.data(
|
|
name="shape_tensor_list", shape=[1], dtype="bool")
|
|
fluid.layers.fill_constant(
|
|
shape=[shape, 2], dtype="float32", value=1)
|
|
|
|
self.assertRaises(TypeError, test_shape_tensor_list_dtype)
|
|
|
|
|
|
if __name__ == "__main__":
|
|
unittest.main()
|