wuxuejian
4 years ago
parent
7ab54b5b05
commit
5498103990
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# Copyright 2020 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.composite import GradOperation
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context.set_context(mode=context.GRAPH_MODE, device_target="CPU")
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class Net(nn.Cell):
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def __init__(self, sigma=1.0):
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super(Net, self).__init__()
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self.SmoothL1Loss = P.SmoothL1Loss(sigma)
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def construct(self, pred, gt):
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return self.SmoothL1Loss(pred, gt)
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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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def construct(self, pred, gt, dout):
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return self.grad(self.network)(pred, gt, dout)
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@pytest.mark.level0
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@pytest.mark.platform_x86_cpu
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@pytest.mark.env_onecard
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def test_net():
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pred = np.random.randn(2, 4).astype(np.float32)
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gt = np.random.randn(2, 4).astype(np.float32)
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dout = np.random.randn(2, 4).astype(np.float32)
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smooth_l1_loss_grad = Grad(Net())
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output = smooth_l1_loss_grad(Tensor(pred), Tensor(gt), Tensor(dout))
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print("------------- input ---------------")
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print("predict:\n", pred)
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print("grount truth:\n", gt)
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print("dout:\n", dout)
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print("------------- output ---------------")
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print("predict grad:\n", output[0].asnumpy())
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@ -1,48 +0,0 @@
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# Copyright 2020 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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context.set_context(mode=context.GRAPH_MODE, device_target="CPU")
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class Net(nn.Cell):
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def __init__(self, sigma=1.0):
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super(Net, self).__init__()
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self.SmoothL1Loss = P.SmoothL1Loss(sigma)
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def construct(self, pred, gt):
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return self.SmoothL1Loss(pred, gt)
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@pytest.mark.level0
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@pytest.mark.platform_x86_cpu
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@pytest.mark.env_onecard
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def test_net():
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pred = np.random.randn(2, 4).astype(np.float32)
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gt = np.random.randn(2, 4).astype(np.float32)
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smooth_l1_loss = Net()
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loss = smooth_l1_loss(Tensor(pred), Tensor(gt))
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print("------------- input ---------------")
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print("predict:\n", pred)
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print("grount truth:\n", gt)
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print("------------- output ---------------")
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print("loss:\n", loss.asnumpy())
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@ -0,0 +1,119 @@
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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.ops import composite as C
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def smoothl1loss(beta):
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np.random.seed(42)
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prediction = np.random.randn(20).astype(np.float32)
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target = np.random.randn(20).astype(np.float32)
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net = nn.SmoothL1Loss(beta)
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return net(Tensor(prediction), Tensor(target))
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@pytest.mark.level0
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@pytest.mark.platform_x86_cpu
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@pytest.mark.env_onecard
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def test_smoothl1loss():
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context.set_context(mode=context.GRAPH_MODE, device_target="CPU", save_graphs=True)
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epsilon = 1e-6
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beta = 1.0
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loss = smoothl1loss(beta)
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expect = [0.46941718, 0.00382918, 0.16829303, 2.447778, 0.04812113, 0.05953304,
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2.2302065, 0.07672881, 0.00860204, 0.34798968, 0.00956192, 1.818008,
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0.03262977, 0.36599946, 2.047463, 0.2168481, 0.7216947, 1.7739174,
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0.08826803, 1.109165]
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diff = np.absolute(loss.asnumpy() - np.array(expect))
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assert(diff < epsilon).all()
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beta = 1 / 9
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loss = smoothl1loss(beta)
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expect = [0.9133791, 0.03446258, 0.5246048, 2.8922224, 0.2546738, 0.289504,
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2.674651, 0.33618113, 0.07560876, 0.7786982, 0.08273339, 2.2624524,
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0.19990394, 0.8000138, 2.4919074, 0.6030006, 1.1661391, 2.2183619,
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0.3646064, 1.5536094]
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diff = np.absolute(loss.asnumpy() - np.array(expect))
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assert(diff < epsilon).all()
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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 = C.GradOperation(get_all=True, sens_param=True)
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self.network = network
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def construct(self, x1, x2, sens):
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gout = self.grad(self.network)(x1, x2, sens)
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return gout
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def smoothl1loss_grad(beta):
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np.random.seed(42)
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prediction = np.random.randn(20).astype(np.float32)
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target = np.random.randn(20).astype(np.float32)
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sens = np.random.randn(20).astype(np.float32)
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net = nn.SmoothL1Loss(beta)
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grad = Grad(net)
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return grad(Tensor(prediction), Tensor(target), Tensor(sens))
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@pytest.mark.level0
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@pytest.mark.platform_x86_cpu
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@pytest.mark.env_onecard
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def test_smoothl1loss_grad():
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context.set_context(mode=context.GRAPH_MODE, device_target="CPU", save_graphs=True)
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epsilon = 1e-6
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beta = 1.0
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dx = smoothl1loss_grad(beta)
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dx1_expect = [-0.71552587, 0.01499678, -0.06709455, -0.30110368, -0.45868093,
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0.24838912, -0.46063876, 0.41411355, 0.04507046, -1.4708229,
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0.04481723, 0.38508227, -0.17292616, -0.52333146, -1.0309995,
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0.61330026, 0.83921754, -0.3092124, 0.1391843, -0.9755451]
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dx2_expect = [0.71552587, -0.01499678, 0.06709455, 0.30110368, 0.45868093,
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-0.24838912, 0.46063876, -0.41411355, -0.04507046, 1.4708229,
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-0.04481723, -0.38508227, 0.17292616, 0.52333146, 1.0309995,
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-0.61330026, -0.83921754, 0.3092124, -0.1391843, 0.9755451]
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diff1 = np.absolute(dx[0].asnumpy() - np.array(dx1_expect))
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diff2 = np.absolute(dx[1].asnumpy() - np.array(dx2_expect))
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assert(diff1 < epsilon).all()
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assert(diff2 < epsilon).all()
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beta = 1 / 9
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dx = smoothl1loss_grad(beta)
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dx1_expect = [-0.73846656, 0.13497104, -0.11564828, -0.30110368, -1.478522,
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0.7198442, -0.46063876, 1.0571222, 0.3436183, -1.7630402,
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0.32408398, 0.38508227, -0.676922, -0.6116763, -1.0309995,
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0.93128014, 0.83921754, -0.3092124, 0.33126342, -0.9755451]
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dx2_expect = [0.73846656, -0.13497104, 0.11564828, 0.30110368, 1.478522,
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-0.7198442, 0.46063876, -1.0571222, -0.3436183, 1.7630402,
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-0.32408398, -0.38508227, 0.676922, 0.6116763, 1.0309995,
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-0.93128014, -0.83921754, 0.3092124, -0.33126342, 0.9755451]
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diff1 = np.absolute(dx[0].asnumpy() - np.array(dx1_expect))
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diff2 = np.absolute(dx[1].asnumpy() - np.array(dx2_expect))
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assert(diff1 < epsilon).all()
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assert(diff2 < epsilon).all()
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