208 lines
7.3 KiB
Python
208 lines
7.3 KiB
Python
# 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.ops import operations as P
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from mindspore.ops import composite as C
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from mindspore.common import dtype as mstype
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class PReLUOpNet(nn.Cell):
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def __init__(self):
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super(PReLUOpNet, self).__init__()
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self.prelu = P.PReLU()
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def construct(self, x, weight):
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return self.prelu(x, weight)
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class PReLUOpGradNet(nn.Cell):
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def __init__(self, net):
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super(PReLUOpGradNet, self).__init__()
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self.forward = net
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self.grad = C.GradOperation(get_all=True, sens_param=False)
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def construct(self, x, weight):
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return self.grad(self.forward)(x, weight)
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def judge_result_correct(result, expect):
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result = result.asnumpy()
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expect = expect.asnumpy()
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assert result.dtype == expect.dtype
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assert result.shape == expect.shape
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assert np.allclose(result, expect, rtol=1.e-2)
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def prelu_test(x, weight, expect_forward, expect_dx, expect_dw):
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prelu_forward = PReLUOpNet()
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prelu_backward = PReLUOpGradNet(prelu_forward)
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forward_output = prelu_forward(x, weight)
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judge_result_correct(forward_output, expect_forward)
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backward_output = prelu_backward(x, weight)
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assert len(backward_output) == 2
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judge_result_correct(backward_output[0], expect_dx)
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judge_result_correct(backward_output[1], expect_dw)
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context.set_context(device_target="GPU", mode=context.GRAPH_MODE)
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dtypes = [mstype.float16, mstype.float32]
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@pytest.mark.level1
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@pytest.mark.platform_x86_gpu_training
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@pytest.mark.env_onecard
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def test_prelu_single_weight():
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x = np.arange(-10, 26).reshape((2, 3, 2, 3)) * 0.7
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weight = np.array([0.6])
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expect_forward = np.where(x >= 0, x, weight * x)
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expect_dx = np.where(x > 0, 1, weight)
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expect_dw = np.sum(np.where(x >= 0, 0, x)).reshape((1,))
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for dtype in dtypes:
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x = Tensor(x, dtype)
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weight = Tensor(weight, dtype)
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expect_forward = Tensor(expect_forward, dtype)
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expect_dx = Tensor(expect_dx, dtype)
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expect_dw = Tensor(expect_dw, dtype)
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prelu_test(x, weight, expect_forward, expect_dx, expect_dw)
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@pytest.mark.level1
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@pytest.mark.platform_x86_gpu_training
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@pytest.mark.env_onecard
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def test_prelu_multiple_weight():
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x = np.arange(-10, 26).reshape((2, 3, 2, 3)) * 0.6
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weight = np.array([0.2, 0.3, 0.4])
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expect_forward = np.array([[[[-1.20, -1.08, -0.96],
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[-0.84, -0.72, -0.60]],
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[[-0.72, -0.54, -0.36],
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[-0.18, 0.00, 0.60]],
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[[1.20, 1.80, 2.40],
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[3.00, 3.60, 4.20]]],
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[[[4.80, 5.40, 6.00],
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[6.60, 7.20, 7.80]],
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[[8.40, 9.00, 9.60],
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[10.20, 10.80, 11.40]],
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[[12.00, 12.60, 13.20],
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[13.80, 14.40, 15.00]]]])
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expect_dx = np.array([[[[0.2, 0.2, 0.2],
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[0.2, 0.2, 0.2]],
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[[0.3, 0.3, 0.3],
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[0.3, 0.3, 1.0]],
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[[1.0, 1.0, 1.0],
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[1.0, 1.0, 1.0]]],
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[[[1.0, 1.0, 1.0],
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[1.0, 1.0, 1.0]],
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[[1.0, 1.0, 1.0],
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[1.0, 1.0, 1.0]],
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[[1.0, 1.0, 1.0],
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[1.0, 1.0, 1.0]]]])
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expect_dw = np.array([-27.0, -6.0, 0.0])
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for dtype in dtypes:
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x = Tensor(x, dtype)
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weight = Tensor(weight, dtype)
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expect_forward = Tensor(expect_forward, dtype)
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expect_dx = Tensor(expect_dx, dtype)
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expect_dw = Tensor(expect_dw, dtype)
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prelu_test(x, weight, expect_forward, expect_dx, expect_dw)
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@pytest.mark.level1
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@pytest.mark.platform_x86_gpu_training
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@pytest.mark.env_onecard
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def test_prelu_single_weight_0_D():
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x = np.array(-0.8)
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weight = np.array([0.6])
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expect_forward = np.array(-0.48)
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expect_dx = np.array(0.6)
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expect_dw = np.array([-0.8])
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for dtype in dtypes:
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x = Tensor(x, dtype)
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weight = Tensor(weight, dtype)
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expect_forward = Tensor(expect_forward, dtype)
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expect_dx = Tensor(expect_dx, dtype)
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expect_dw = Tensor(expect_dw, dtype)
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prelu_test(x, weight, expect_forward, expect_dx, expect_dw)
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@pytest.mark.level1
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@pytest.mark.platform_x86_gpu_training
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@pytest.mark.env_onecard
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def test_prelu_single_weight_1_D():
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x = np.arange(-10, 26).reshape((36,)) * 0.7
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weight = np.array([0.6])
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expect_forward = np.where(x >= 0, x, weight * x)
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expect_dx = np.where(x > 0, 1, weight)
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expect_dw = np.sum(np.where(x >= 0, 0, x)).reshape((1,))
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for dtype in dtypes:
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x = Tensor(x, dtype)
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weight = Tensor(weight, dtype)
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expect_forward = Tensor(expect_forward, dtype)
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expect_dx = Tensor(expect_dx, dtype)
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expect_dw = Tensor(expect_dw, dtype)
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prelu_test(x, weight, expect_forward, expect_dx, expect_dw)
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@pytest.mark.level1
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@pytest.mark.platform_x86_gpu_training
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@pytest.mark.env_onecard
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def test_prelu_single_weight_2_D():
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x = np.arange(-10, 26).reshape((4, 9)) * 0.7
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weight = np.array([0.6])
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expect_forward = np.where(x >= 0, x, weight * x)
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expect_dx = np.where(x > 0, 1, weight)
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expect_dw = np.sum(np.where(x >= 0, 0, x)).reshape((1,))
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for dtype in dtypes:
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x = Tensor(x, dtype)
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weight = Tensor(weight, dtype)
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expect_forward = Tensor(expect_forward, dtype)
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expect_dx = Tensor(expect_dx, dtype)
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expect_dw = Tensor(expect_dw, dtype)
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prelu_test(x, weight, expect_forward, expect_dx, expect_dw)
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@pytest.mark.level1
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@pytest.mark.platform_x86_gpu_training
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@pytest.mark.env_onecard
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def test_prelu_multiple_weight_2_D():
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x = np.arange(-6, 6).reshape((3, 4)) * 0.6
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weight = np.array([0.2, 0.4, 0.7, 0.9])
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expect_forward = np.array([[-0.72, -1.20, -1.68, -1.62],
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[-0.24, -0.24, 0.00, 0.60],
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[1.20, 1.80, 2.40, 3.00]])
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expect_dx = np.array([[0.2, 0.4, 0.7, 0.9],
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[0.2, 0.4, 0.7, 1.0],
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[1.0, 1.0, 1.0, 1.0]])
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expect_dw = np.array([-4.8, -3.6, -2.4, -1.8])
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for dtype in dtypes:
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x = Tensor(x, dtype)
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weight = Tensor(weight, dtype)
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expect_forward = Tensor(expect_forward, dtype)
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expect_dx = Tensor(expect_dx, dtype)
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expect_dw = Tensor(expect_dw, dtype)
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prelu_test(x, weight, expect_forward, expect_dx, expect_dw)
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