1# Copyright 2020-2021 Huawei Technologies Co., Ltd 2# 3# Licensed under the Apache License, Version 2.0 (the "License"); 4# you may not use this file except in compliance with the License. 5# You may obtain a copy of the License at 6# 7# http://www.apache.org/licenses/LICENSE-2.0 8# 9# Unless required by applicable law or agreed to in writing, software 10# distributed under the License is distributed on an "AS IS" BASIS, 11# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. 12# See the License for the specific language governing permissions and 13# limitations under the License. 14# ============================================================================ 15 16import numpy as np 17import pytest 18import mindspore.context as context 19from mindspore import Tensor 20from mindspore.nn import Cell 21import mindspore.ops.operations as P 22 23 24class Net(Cell): 25 def __init__(self): 26 super(Net, self).__init__() 27 self.add = P.Add() 28 self.sub = P.Sub() 29 self.mul = P.Mul() 30 self.div = P.RealDiv() 31 self.sqrt = P.Sqrt() 32 self.pow = P.Pow() 33 self.neg = P.Neg() 34 self.reducemin = P.ReduceMin() 35 self.reducesum = P.ReduceSum(keep_dims=True) 36 self.reshape = P.Reshape() 37 38 def construct(self, x, y): 39 add_res1 = self.add(x, 4) 40 add_res2 = self.add(add_res1, 5) 41 sub_res = self.sub(y, 3) 42 mul_res = self.mul(self.sqrt(add_res2), self.sqrt(sub_res)) 43 div_res = self.div(mul_res, self.sqrt(mul_res)) 44 pow_res = self.pow(y, 2) 45 neg_res = self.neg(self.neg(pow_res)) 46 add_res3 = self.add(neg_res, div_res) 47 resh_res = self.reshape(add_res3, (2, 12, 3)) 48 neg_res = self.neg(resh_res) 49 red_res = self.reducesum(neg_res, 0) 50 return self.reducemin(self.reducemin(red_res, 1), 1) 51 52 53class EmptyNet(Cell): 54 def __init__(self): 55 super(EmptyNet, self).__init__() 56 self.add = P.Add() 57 self.neg = P.Neg() 58 59 def construct(self, x, y): 60 add_res1 = self.add(x, y) 61 neg_res1 = self.neg(x) 62 add_res2 = self.add(add_res1, neg_res1) 63 return add_res2 64 65 66def test_basic(): 67 input_x = np.random.normal(0, 1, [2, 3, 4, 3]).astype(np.float32) 68 input_y = np.random.normal(0, 1, [2, 3, 4, 3]).astype(np.float32) 69 input_y = np.abs(input_y) + 3 70 add_res = input_x + 9 71 sub_res = input_y + (-3) 72 mul_res = np.sqrt(add_res * sub_res) 73 div_res = np.sqrt(mul_res) 74 pow_res = input_y * input_y 75 neg_res = pow_res 76 add_res3 = neg_res + div_res 77 neg_res = np.negative(add_res3) 78 red_res = np.sum(neg_res, axis=0, keepdims=True) 79 expect = np.min(red_res, (1, 2, 3)) 80 81 net = Net() 82 result = net(Tensor(input_x), Tensor(input_y)) 83 84 res = np.allclose(expect, result.asnumpy(), rtol=1.e-4, 85 atol=1.e-7, equal_nan=True) 86 assert res 87 88 89def test_empty_graph(): 90 input_x = np.random.normal(0, 1, [2, 3, 4, 3]).astype(np.float32) 91 input_y = np.random.normal(0, 1, [2, 3, 4, 3]).astype(np.float32) 92 expect = input_y 93 94 net = EmptyNet() 95 result = net(Tensor(input_x), Tensor(input_y)) 96 97 res = np.allclose(expect, result.asnumpy(), rtol=1.e-4, 98 atol=1.e-7, equal_nan=True) 99 assert res 100 101 102@pytest.mark.level0 103@pytest.mark.platform_x86_gpu_training 104@pytest.mark.env_onecard 105def test_basic_gpu(): 106 context.set_context(mode=context.GRAPH_MODE, enable_graph_kernel=True, device_target="GPU") 107 test_basic() 108 test_empty_graph() 109 110 111@pytest.mark.level1 112@pytest.mark.platform_arm_ascend_training 113@pytest.mark.platform_x86_ascend_training 114@pytest.mark.env_onecard 115def test_basic_ascend(): 116 context.set_context(mode=context.GRAPH_MODE, enable_graph_kernel=True, device_target="Ascend") 117 test_basic() 118 test_empty_graph() 119