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63 lines
2.4 KiB
63 lines
2.4 KiB
4 years ago
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# Copyright 2021 Huawei Technologies Co., Ltd
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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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# ============================================================================
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import numpy as np
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import pytest
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import mindspore.context as context
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import mindspore.nn as nn
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from mindspore import Tensor
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from mindspore.common.api import ms_function
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from mindspore.ops import functional as F
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from mindspore.ops.composite import GradOperation
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context.set_context(mode=context.GRAPH_MODE, device_target="GPU")
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class Grad(nn.Cell):
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def __init__(self, network):
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super(Grad, self).__init__()
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self.grad = GradOperation(get_all=True, sens_param=True)
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self.network = network
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@ms_function
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def construct(self, input_x, grad):
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return self.grad(self.network)(input_x, grad)
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class Net(nn.Cell):
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def __init__(self, n):
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super(Net, self).__init__()
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self.ops = nn.BatchNorm2d(n, use_batch_statistics=True, gamma_init=0.5, beta_init=0.5)
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def construct(self, x):
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shape = F.shape(x)
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return F.reshape(self.ops(F.reshape(x, (1, -1, shape[2], shape[3]))), shape)
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@pytest.mark.level0
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@pytest.mark.platform_x86_gpu_training
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@pytest.mark.env_onecard
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def test_InstanceNorm2d_fp32():
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x_np = np.random.randn(3, 3, 2, 2).astype(np.float32)
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bn_instance_comp = Net(3 * 3)
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bn_instance_op = nn.InstanceNorm2d(3, use_batch_statistics=True, gamma_init=0.5, beta_init=0.5)
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comp_out = bn_instance_comp(Tensor(x_np))
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op_out = bn_instance_op(Tensor(x_np))
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assert np.allclose(comp_out.asnumpy(), op_out.asnumpy())
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sens = np.random.randn(3, 3, 2, 2).astype(np.float32)
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bn_comp_backward_net = Grad(bn_instance_comp)
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bn_op_backward_net = Grad(bn_instance_op)
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output1 = bn_comp_backward_net(Tensor(x_np), Tensor(sens))
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output2 = bn_op_backward_net(Tensor(x_np), Tensor(sens))
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assert np.allclose(output1[0].asnumpy(), output2[0].asnumpy())
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