forked from mindspore-Ecosystem/mindspore
144 lines
4.5 KiB
Python
144 lines
4.5 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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import mindspore as ms
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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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class L2LossNet(nn.Cell):
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def __init__(self):
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super(L2LossNet, self).__init__()
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self.l2_loss = P.L2Loss()
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def construct(self, x):
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return self.l2_loss(x)
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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_l2loss_pynative_fp32_2x2():
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context.set_context(mode=context.PYNATIVE_MODE, device_target="CPU")
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error = 1e-4
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x = Tensor(np.array([[1., 2.], [3., 4.]]), ms.float32)
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expect = np.array(15, np.float32)
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output = P.L2Loss()(x)
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diff = output.asnumpy() - expect
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assert np.all(diff < error)
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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_l2loss_pynative_fp16_2x2():
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context.set_context(mode=context.PYNATIVE_MODE, device_target="CPU")
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error = 1e-4
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x = Tensor(np.array([[1., 2.], [3., 4.]]), ms.float16)
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expect = np.array(15, np.float16)
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output = P.L2Loss()(x)
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diff = output.asnumpy() - expect
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assert np.all(diff < error)
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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_l2loss_pynative_fp32_1x4():
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context.set_context(mode=context.PYNATIVE_MODE, device_target="CPU")
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error = 1e-4
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x = Tensor(np.array([1., 2., 3., 4.]), ms.float32)
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expect = np.array(15, np.float32)
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output = P.L2Loss()(x)
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diff = output.asnumpy() - expect
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assert np.all(diff < error)
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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_l2loss_pynative_fp16_1x4():
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context.set_context(mode=context.PYNATIVE_MODE, device_target="CPU")
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error = 1e-4
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x = Tensor(np.array([1., 2., 3., 4.]), ms.float16)
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expect = np.array(15, np.float16)
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output = P.L2Loss()(x)
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diff = output.asnumpy() - expect
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assert np.all(diff < error)
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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_l2loss_graph_fp32_1x4():
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context.set_context(mode=context.GRAPH_MODE, device_target="CPU")
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error = 1e-4
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x = Tensor(np.array([1., 2., 3., 4.]), ms.float32)
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expect = np.array(15, np.float32)
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l2_loss = L2LossNet()
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output = l2_loss(x)
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diff = output.asnumpy() - expect
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assert np.all(diff < error)
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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_l2loss_graph_fp16_1x4():
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context.set_context(mode=context.GRAPH_MODE, device_target="CPU")
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error = 1e-4
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x = Tensor(np.array([1., 2., 3., 4.]), ms.float16)
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expect = np.array(15, np.float16)
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l2_loss = L2LossNet()
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output = l2_loss(x)
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diff = output.asnumpy() - expect
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assert np.all(diff < error)
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class GradNet(nn.Cell):
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def __init__(self, net):
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super(GradNet, self).__init__()
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self.net = net
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self.grad_op = C.GradOperation(get_all=True)
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def construct(self, x):
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gradient_function = self.grad_op(self.net)
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return gradient_function(x)
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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_l2loss_grad_fp32():
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context.set_context(mode=context.GRAPH_MODE, device_target="CPU")
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x = Tensor(np.array([2.4, 3.2, 1.2, 5.9, 9.]).astype(np.float32))
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error = 1e-4
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net = L2LossNet()
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output = GradNet(net)(x)[0]
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expect = x
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diff = output.asnumpy() - expect
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assert np.all(diff < error)
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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_l2loss_grad_fp16():
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context.set_context(mode=context.GRAPH_MODE, device_target="CPU")
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x = Tensor(np.array([[2.4, 3.2, 4.8], [1.2, 5.9, 9.]]).astype(np.float16))
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error = 1e-4
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net = L2LossNet()
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output = GradNet(net)(x)[0]
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expect = x
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diff = output.asnumpy() - expect
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assert np.all(diff < error)
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