forked from mindspore-Ecosystem/mindspore
167 lines
6.9 KiB
Python
167 lines
6.9 KiB
Python
# Copyright 2022 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.operations import _grad_ops as G
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from mindspore.ops.functional import vmap
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class LrnNet(nn.Cell):
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def __init__(self):
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super(LrnNet, self).__init__()
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self.lrn = P.LRN(depth_radius=2, bias=1.0, alpha=0.0001, beta=0.75)
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def construct(self, x):
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out = self.lrn(x)
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return out
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class LrnGradNet(nn.Cell):
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def __init__(self):
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super(LrnGradNet, self).__init__()
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self.lrn_grad = G.LRNGrad(depth_radius=2, bias=1.0, alpha=0.0001, beta=0.75)
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def construct(self, dy, x, y):
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out = self.lrn_grad(dy, x, y)
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return out
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class LrnGradVMapNet(nn.Cell):
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def __init__(self, forward_net, in_axes, out_axes):
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super(LrnGradVMapNet, self).__init__()
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self.net = forward_net
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self.in_axes = in_axes
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self.out_axes = out_axes
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def construct(self, dy, x, y):
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return vmap(self.net, self.in_axes, self.out_axes)(dy, x, y)
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@pytest.mark.level0
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@pytest.mark.env_onecard
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@pytest.mark.platform_x86_cpu
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@pytest.mark.parametrize("data_type", [np.float32, np.float16])
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@pytest.mark.parametrize('mode', [context.GRAPH_MODE, context.PYNATIVE_MODE])
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def test_lrn_grad(mode, data_type):
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"""
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Feature: Test LrnGrad.
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Description: The input shape need to match to output shape.
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Expectation: match to np benchmark.
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"""
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context.set_context(mode=mode)
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x = Tensor(np.array([[[[1.6243454, -0.6117564],
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[-0.5281718, -1.0729686]],
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[[0.86540765, -2.3015387],
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[1.7448118, -0.7612069]],
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[[0.3190391, -0.24937038],
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[1.4621079, -2.0601406]]]]).astype(data_type))
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dy = Tensor(np.array([[[[-0.3224172, -0.38405436],
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[1.1337694, -1.0998913]],
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[[-0.1724282, -0.8778584],
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[0.04221375, 0.58281523]],
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[[-1.1006192, 1.1447237],
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[0.9015907, 0.50249434]]]]).astype(data_type))
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y = Tensor(np.array([[[[1.6239204, -0.61149347],
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[-0.5279556, -1.0724881]],
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[[0.86518127, -2.3005495],
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[1.7440975, -0.760866]],
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[[0.31895563, -0.2492632],
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[1.4615093, -2.059218]]]]).astype(data_type))
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dx_exp = np.array([[[[-0.3220835, -0.3837087],
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[1.133368, -1.0994467]],
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[[-0.17225023, -0.8768017],
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[0.04198911, 0.5825201]],
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[[-1.1002823, 1.1443052],
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[0.9010479, 0.50217706]]]]).astype(data_type)
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loss = 1e-6
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if data_type == np.float16:
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loss = 1e-3
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lrn_grad_net = LrnGradNet()
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dx = lrn_grad_net(dy, x, y)
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assert np.allclose(dx.asnumpy(), dx_exp, atol=loss, rtol=loss, equal_nan=True)
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@pytest.mark.level0
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@pytest.mark.env_onecard
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@pytest.mark.platform_x86_cpu
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def test_lrn_grad_vmap():
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"""
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Feature: Test LRN Grad Vmap on CPU.
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Description: The output shape match to input shape.
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Expectation: match to np benchmark.
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"""
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context.set_context(mode=context.GRAPH_MODE)
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data_type = np.float32
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loss = 1e-6
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x = Tensor(np.array([[[[[1.6243454, -0.6117564],
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[-0.5281718, -1.0729686]],
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[[0.86540765, -2.3015387],
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[1.7448118, -0.7612069]],
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[[0.3190391, -0.24937038],
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[1.4621079, -2.0601406]]]],
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[[[[1.6243454, -0.6117564],
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[-0.5281718, -1.0729686]],
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[[0.86540765, -2.3015387],
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[1.7448118, -0.7612069]],
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[[0.3190391, -0.24937038],
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[1.4621079, -2.0601406]]]]]).astype(data_type))
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y = Tensor(np.array([[[[[1.6239204, -0.61149347],
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[-0.5279556, -1.0724881]],
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[[0.86518127, -2.3005495],
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[1.7440975, -0.760866]],
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[[0.31895563, -0.2492632],
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[1.4615093, -2.059218]]]],
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[[[[1.6239204, -0.61149347],
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[-0.5279556, -1.0724881]],
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[[0.86518127, -2.3005495],
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[1.7440975, -0.760866]],
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[[0.31895563, -0.2492632],
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[1.4615093, -2.059218]]]]]).astype(data_type))
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dy = Tensor(np.array([[[[[-0.3224172, -0.38405436],
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[1.1337694, -1.0998913]],
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[[-0.1724282, -0.8778584],
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[0.04221375, 0.58281523]],
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[[-1.1006192, 1.1447237],
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[0.9015907, 0.50249434]]]],
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[[[[-0.3224172, -0.38405436],
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[1.1337694, -1.0998913]],
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[[-0.1724282, -0.8778584],
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[0.04221375, 0.58281523]],
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[[-1.1006192, 1.1447237],
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[0.9015907, 0.50249434]]]]]).astype(data_type))
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dx_exp = np.array([[[[[-0.3220835, -0.3837087],
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[1.133368, -1.0994467]],
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[[-0.17225023, -0.8768017],
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[0.04198911, 0.5825201]],
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[[-1.1002823, 1.1443052],
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[0.9010479, 0.50217706]]]],
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[[[[-0.3220835, -0.3837087],
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[1.133368, -1.0994467]],
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[[-0.17225023, -0.8768017],
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[0.04198911, 0.5825201]],
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[[-1.1002823, 1.1443052],
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[0.9010479, 0.50217706]]]]]).astype(data_type)
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lrn_grad_net = LrnGradNet()
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in_axes = 0
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out_axes = 0
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output = LrnGradVMapNet(lrn_grad_net, in_axes, out_axes)(dy, x, y)
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dx = output.asnumpy()
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np.testing.assert_allclose(dx, dx_exp, rtol=loss, atol=loss)
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