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87 lines
2.9 KiB
87 lines
2.9 KiB
# 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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""" tests for quant """
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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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from mindspore import Tensor
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from mindspore import nn
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from mindspore.train.quant import quant as qat
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from mobilenetv2_combined import MobileNetV2
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context.set_context(mode=context.GRAPH_MODE, device_target="GPU")
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class LeNet5(nn.Cell):
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"""
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Lenet network
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Args:
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num_class (int): Num classes. Default: 10.
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Returns:
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Tensor, output tensor
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Examples:
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>>> LeNet(num_class=10)
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"""
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def __init__(self, num_class=10):
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super(LeNet5, self).__init__()
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self.num_class = num_class
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self.conv1 = nn.Conv2dBnAct(1, 6, kernel_size=5, batchnorm=True, activation='relu6', pad_mode="valid")
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self.conv2 = nn.Conv2dBnAct(6, 16, kernel_size=5, activation='relu', pad_mode="valid")
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self.fc1 = nn.DenseBnAct(16 * 5 * 5, 120, activation='relu')
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self.fc2 = nn.DenseBnAct(120, 84, activation='relu')
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self.fc3 = nn.DenseBnAct(84, self.num_class)
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self.max_pool2d = nn.MaxPool2d(kernel_size=2, stride=2)
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self.flatten = nn.Flatten()
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def construct(self, x):
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x = self.conv1(x)
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x = self.max_pool2d(x)
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x = self.conv2(x)
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x = self.max_pool2d(x)
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x = self.flatten(x)
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x = self.fc1(x)
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x = self.fc2(x)
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x = self.fc3(x)
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return x
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@pytest.mark.skip(reason="no `te.lang.cce` in ut env")
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def test_qat_lenet():
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img = Tensor(np.ones((32, 1, 32, 32)).astype(np.float32))
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net = LeNet5()
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net = qat.convert_quant_network(
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net, quant_delay=0, bn_fold=False, freeze_bn=10000, weight_bits=8, act_bits=8)
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# should load the checkpoint. mock here
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for param in net.get_parameters():
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param.init_data()
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qat.export_geir(net, img, file_name="quant.pb")
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@pytest.mark.skip(reason="no `te.lang.cce` in ut env")
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def test_qat_mobile():
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net = MobileNetV2()
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img = Tensor(np.ones((1, 3, 224, 224)).astype(np.float32))
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net = qat.convert_quant_network(
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net, quant_delay=0, bn_fold=True, freeze_bn=10000, weight_bits=8, act_bits=8)
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# should load the checkpoint. mock here
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for param in net.get_parameters():
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param.init_data()
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qat.export_geir(net, img, file_name="quant.pb")
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