forked from mindspore-Ecosystem/mindspore
171 lines
6.6 KiB
Python
171 lines
6.6 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.functional import vmap
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from mindspore.common import dtype as ms_type
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class LrnNet(nn.Cell):
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def __init__(self, depth_radius=5, bias=1.0, alpha=1.0, beta=0.5, norm_region="ACROSS_CHANNELS"):
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super(LrnNet, self).__init__()
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self.depth_radius = depth_radius
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self.bias = bias
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self.alpha = alpha
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self.beta = beta
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self.norm_region = norm_region
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self.lrn = P.LRN(depth_radius, bias, alpha, beta, norm_region)
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def construct(self, input_x):
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output = self.lrn(input_x)
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return output
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class LrnVMapNet(nn.Cell):
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def __init__(self, forward_net, in_axes, out_axes):
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super(LrnVMapNet, 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, input_x):
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return vmap(self.net, self.in_axes, self.out_axes)(input_x)
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def lrn_np_bencmark(data_type):
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"""
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Feature: generate a lrn numpy benchmark.
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Description: The input shape need to match to output shape.
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Expectation: match to np mindspore LRN.
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"""
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y_exp = 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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return y_exp
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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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def test_lrn(data_type):
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"""
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Feature: Test LRN.
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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=context.GRAPH_MODE)
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input_data = 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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loss = 1e-6
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if data_type == np.float16:
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loss = 1e-3
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benchmark_output = lrn_np_bencmark(data_type)
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lrn = LrnNet(depth_radius=2, bias=1.0, alpha=0.0001, beta=0.75)
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output = lrn(Tensor(input_data))
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np.testing.assert_allclose(output.asnumpy(), benchmark_output, rtol=loss, atol=loss)
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context.set_context(mode=context.PYNATIVE_MODE)
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output = lrn(Tensor(input_data))
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np.testing.assert_allclose(output.asnumpy(), benchmark_output, rtol=loss, atol=loss)
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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_vmap():
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"""
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Feature: Test LRN 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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input_x = 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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benchmark_output = 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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lrn = LrnNet(depth_radius=2, bias=1.0, alpha=0.0001, beta=0.75)
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in_axes = 0
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out_axes = 0
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output = LrnVMapNet(lrn, in_axes, out_axes)(Tensor(input_x))
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np.testing.assert_allclose(output.asnumpy(), benchmark_output, rtol=loss, atol=loss)
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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_dy_shape():
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"""
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Feature: Test LRN Dynamic Shape.
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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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ms_data_type = ms_type.float32
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data_type = np.float32
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# The shape of x is (1, 3, 2, 2)
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x = 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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loss = 1e-6
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benchmark_output = lrn_np_bencmark(data_type)
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lrn = LrnNet(depth_radius=2, bias=1.0, alpha=0.0001, beta=0.75)
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input_dyn = Tensor(shape=[1, 3, 2, None], dtype=ms_data_type)
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lrn.set_inputs(input_dyn)
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output = lrn(Tensor(x))
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np.testing.assert_allclose(output.asnumpy(), benchmark_output, rtol=loss, atol=loss)
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context.set_context(mode=context.PYNATIVE_MODE)
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input_dyn = Tensor(shape=[1, 3, 2, None], dtype=ms_data_type)
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lrn.set_inputs(input_dyn)
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output = lrn(Tensor(x))
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np.testing.assert_allclose(output.asnumpy(), benchmark_output, rtol=loss, atol=loss)
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