# 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 import paddle.fluid.core as core from paddle.fluid.op import Operator import paddle.fluid as fluid import numpy as np 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(unittest.TestCase): 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() # Situation 4: value is a tensor class TestFillConstantOp1_ValueTensor(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"), 'ValueTensor': np.array([self.value]).astype("float32") } self.attrs = {'value': self.value + 1.0} self.outputs = {'Out': np.full(self.shape, self.value)} def init_data(self): self.shape = [123, 92] self.value = 3.8 self.dtype = np.float32 def test_check_output(self): self.check_output() # Situation 5: value is a tensor class TestFillConstantOp2_ValueTensor(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"), 'ValueTensor': np.array([self.value]).astype("int32") } self.attrs = {'value': self.value, 'dtype': 2} self.outputs = {'Out': np.full(self.shape, self.value)} def init_data(self): self.shape = [123, 92] self.value = 3 self.dtype = np.int32 def test_check_output(self): self.check_output() # Test python API class TestFillConstantAPI(unittest.TestCase): 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) out_6 = fluid.layers.fill_constant( shape=shape_tensor_int64, dtype=np.float32, value=1.1) val1 = fluid.layers.fill_constant( shape=[1], dtype=np.float32, value=1.1) val2 = fluid.layers.fill_constant( shape=[1], dtype=np.float64, value=1.1) out_7 = fluid.layers.fill_constant( shape=shape_tensor_int64, dtype=np.float32, value=val1) out_8 = fluid.layers.fill_constant( shape=shape_tensor_int64, dtype=np.float32, value=val2) exe = fluid.Executor(place=fluid.CPUPlace()) res_1, res_2, res_3, res_4, res_5, res_6, res_7, res_8 = 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, out_6, out_7, out_8 ]) 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")) assert np.array_equal(res_6, np.full([1, 2], 1.1, dtype="float32")) assert np.array_equal(res_7, np.full([1, 2], 1.1, dtype="float32")) assert np.array_equal(res_8, np.full([1, 2], 1.1, dtype="float32")) class TestFillConstantImperative(unittest.TestCase): def test_api(self): with fluid.dygraph.guard(): data1 = np.array([1, 2]).astype('int32') data2 = np.array([1.1]).astype('float32') data3 = np.array([88]).astype('int32') shape = fluid.dygraph.to_variable(data1) val = fluid.dygraph.to_variable(data2) value = fluid.dygraph.to_variable(data3) res1 = fluid.layers.fill_constant( shape=[1, 2], dtype='float32', value=1.1) res2 = fluid.layers.fill_constant( shape=shape, dtype='float32', value=1.1) res3 = fluid.layers.fill_constant( shape=shape, dtype='float32', value=val) res4 = fluid.layers.fill_constant( shape=shape, dtype='int32', value=value) assert np.array_equal( res1.numpy(), np.full( [1, 2], 1.1, dtype="float32")) assert np.array_equal( res2.numpy(), np.full( [1, 2], 1.1, dtype="float32")) assert np.array_equal( res3.numpy(), np.full( [1, 2], 1.1, dtype="float32")) assert np.array_equal( res4.numpy(), np.full( [1, 2], 88, dtype="int32")) class TestFillConstantOpError(unittest.TestCase): def test_errors(self): with program_guard(Program(), Program()): #for ci coverage x1 = fluid.layers.data(name='x1', shape=[1], dtype="int16") self.assertRaises( TypeError, 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) x3 = np.random.randn(100, 100).astype('int32') self.assertRaises( TypeError, fluid.layers.fill_constant, shape=[100, 100], value=5, dtype='float64', out=x3) # 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()