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1# Copyright 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
18
19import mindspore.context as context
20import mindspore.nn as nn
21from mindspore import Tensor
22from mindspore.common.api import ms_function
23from mindspore.ops import functional as F
24from mindspore.ops.composite import GradOperation
25context.set_context(mode=context.GRAPH_MODE, device_target="GPU")
26
27class Grad(nn.Cell):
28    def __init__(self, network):
29        super(Grad, self).__init__()
30        self.grad = GradOperation(get_all=True, sens_param=True)
31        self.network = network
32
33    @ms_function
34    def construct(self, input_x, grad):
35        return self.grad(self.network)(input_x, grad)
36
37class Net(nn.Cell):
38    def __init__(self, n):
39        super(Net, self).__init__()
40        self.ops = nn.BatchNorm2d(n, use_batch_statistics=True, gamma_init=0.5, beta_init=0.5)
41
42    def construct(self, x):
43        shape = F.shape(x)
44        return F.reshape(self.ops(F.reshape(x, (1, -1, shape[2], shape[3]))), shape)
45
46@pytest.mark.level0
47@pytest.mark.platform_x86_gpu_training
48@pytest.mark.env_onecard
49def test_InstanceNorm2d_fp32():
50    x_np = np.random.randn(3, 3, 2, 2).astype(np.float32)
51    bn_instance_comp = Net(3 * 3)
52    bn_instance_op = nn.InstanceNorm2d(3, gamma_init=0.5, beta_init=0.5)
53    comp_out = bn_instance_comp(Tensor(x_np))
54    op_out = bn_instance_op(Tensor(x_np))
55    assert np.allclose(comp_out.asnumpy(), op_out.asnumpy())
56
57    sens = np.random.randn(3, 3, 2, 2).astype(np.float32)
58    bn_comp_backward_net = Grad(bn_instance_comp)
59    bn_op_backward_net = Grad(bn_instance_op)
60    output1 = bn_comp_backward_net(Tensor(x_np), Tensor(sens))
61    output2 = bn_op_backward_net(Tensor(x_np), Tensor(sens))
62    assert np.allclose(output1[0].asnumpy(), output2[0].asnumpy())
63