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113 lines
3.7 KiB
113 lines
3.7 KiB
# 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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""" test loss """
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import numpy as np
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import pytest
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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.nn.loss.loss import _Loss
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from mindspore.nn.loss.loss import L1Loss
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import mindspore.context as context
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class WeightedLoss(_Loss):
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def __init__(self, reduction='mean', weights=1.0):
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super(WeightedLoss, self).__init__(reduction)
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self.abs = P.Abs()
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self.weights = weights
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def construct(self, base, target):
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x = self.abs(base - target)
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return self.get_loss(x, self.weights)
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def weighted_loss(nptype):
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context.set_context(mode=context.GRAPH_MODE, device_target='GPU')
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loss = WeightedLoss()
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input_data = Tensor(np.array([[1, 2, 3], [2, 3, 4]]).astype(nptype))
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target_data = Tensor(np.array([[0, 2, 5], [3, 1, 1]]).astype(nptype))
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output_data = loss(input_data, target_data)
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error_range = np.ones(shape=output_data.shape) * 10e-6
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loss = WeightedLoss(weights=2.0)
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test_output = loss(input_data, target_data)
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diff = test_output - output_data * 2.0
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assert np.all(abs(diff.asnumpy()) < error_range)
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loss = WeightedLoss(weights=3)
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test_output = loss(input_data, target_data)
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diff = test_output - output_data * 3
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assert np.all(abs(diff.asnumpy()) < error_range)
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loss = WeightedLoss(weights=Tensor(np.array([[0.7, 0.3], [0.7, 0.3]]).astype(nptype)))
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y_true = Tensor(np.array([[0., 1.], [0., 0.]]).astype(nptype))
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y_pred = Tensor(np.array([[1., 1.], [1., 0.]]).astype(nptype))
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test_data = 0.35
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output = loss(y_true, y_pred)
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diff = test_data - output.asnumpy()
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assert np.all(abs(diff) < error_range)
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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_weighted_loss_float32():
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weighted_loss(np.float32)
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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_weighted_loss_float64():
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weighted_loss(np.float64)
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class CustomLoss(_Loss):
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def __init__(self, reduction='mean'):
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super(CustomLoss, self).__init__(reduction)
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self.abs = P.Abs()
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def construct(self, base, target):
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x = self.abs(base - target)
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return self.get_loss(x, weights=2.0)
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def custom_loss(nptype):
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loss = L1Loss()
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input_data = Tensor(np.array([[1, 2, 3], [2, 3, 4]]).astype(nptype))
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target_data = Tensor(np.array([[0, 2, 5], [3, 1, 1]]).astype(nptype))
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output_data = loss(input_data, target_data)
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error_range = np.ones(shape=output_data.shape) * 10e-6
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customloss = CustomLoss()
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test_output = customloss(input_data, target_data)
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diff = test_output - output_data * 2.0
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assert np.all(abs(diff.asnumpy()) < error_range)
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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_custom_loss_float16():
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custom_loss(np.float16)
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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_custom_loss_float32():
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custom_loss(np.float32)
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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_custom_loss_float64():
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custom_loss(np.float64)
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