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- # Copyright 2020 Huawei Technologies Co., Ltd
- #
- # 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.
- # ============================================================================
-
- import numpy as np
- import pytest
-
- import mindspore.context as context
- import mindspore.nn as nn
- from mindspore.common.parameter import Parameter
- from mindspore.common.initializer import initializer
- from mindspore import Tensor
- from mindspore.ops import operations as P
- from mindspore.ops.operations import _grad_ops as G
-
-
- class Conv2dBpropInputInplace(nn.Cell):
- def __init__(self, w1, w2):
- super(Conv2dBpropInputInplace, self).__init__()
- self.conv2d_1 = P.Conv2DBackpropInput(out_channel=256, kernel_size=1)
- self.w1 = Parameter(initializer(w1, w1.shape), name='w1')
- self.conv2d_2 = P.Conv2DBackpropInput(out_channel=256, kernel_size=1)
- self.w2 = Parameter(initializer(w2, w2.shape), name='w2')
- self.add = P.TensorAdd()
- self.maxpool = P.MaxPool(ksize=3, strides=2, padding='SAME')
- self.maxpool_grad = G.MaxPoolGrad(ksize=3, strides=2, padding='SAME')
- self.shape = (32, 64, 56, 56)
-
- def construct(self, x1, x2, x3):
- dx1 = self.conv2d_1(x1, self.w1, self.shape)
- dx2 = self.conv2d_2(x2, self.w2, self.shape)
-
- dx = self.add(dx1, dx2)
- y = self.maxpool(x3)
- y = self.maxpool_grad(x3, y, dx)
- return y
-
-
- @pytest.mark.level0
- @pytest.mark.platform_x86_gpu_training
- @pytest.mark.env_onecard
- def test_inplace_fusion1():
-
- np.random.seed(42)
- w1_np = np.random.randn(64, 64, 1, 1)
- w2_np = np.random.randn(256, 64, 1, 1)
- x1_np = np.random.randn(32, 64, 56, 56)
- x2_np = np.random.randn(32, 256, 56, 56)
- x3_np = np.random.randn(32, 64, 112, 112)
-
- w1 = Tensor(w1_np.astype(np.float32))
- w2 = Tensor(w2_np.astype(np.float32))
- x1 = Tensor(x1_np.astype(np.float32))
- x2 = Tensor(x2_np.astype(np.float32))
- x3 = Tensor(x3_np.astype(np.float32))
-
- net = Conv2dBpropInputInplace(w1, w2)
- context.set_context(device_target='GPU', mode=context.GRAPH_MODE)
- fusion_output = net(x1, x2, x3)
-
- context.set_context(device_target='GPU', mode=context.PYNATIVE_MODE)
- no_fusion_output = net(x1, x2, x3)
-
- assert np.allclose(fusion_output.asnumpy(), no_fusion_output.asnumpy(), atol=2e-5)
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