wehub-resource-sync
70 lines
2.3 KiB
Python
70 lines
2.3 KiB
Python
#
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# SPDX-FileCopyrightText: Copyright (c) 1993-2023 NVIDIA CORPORATION & AFFILIATES. All rights reserved.
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# SPDX-License-Identifier: Apache-2.0
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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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"""Model used for tests"""
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import pytest
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import torch.nn as nn
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import torch.nn.functional as F
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from pytorch_quantization.nn import QuantConv2d, QuantLinear
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from pytorch_quantization.tensor_quant import QuantDescriptor
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class LeNet(nn.Module):
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def __init__(self, **kwargs):
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super(LeNet, self).__init__()
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self.conv1 = nn.Conv2d(1, 10, kernel_size=5, **kwargs)
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self.conv2 = nn.Conv2d(10, 20, kernel_size=5, **kwargs)
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self.fc1 = nn.Linear(320, 50, **kwargs)
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self.fc2 = nn.Linear(50, 10, **kwargs)
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def forward(self, x):
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x = F.relu(F.max_pool2d(self.conv1(x), 2))
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x = F.relu(F.max_pool2d(self.conv2(x), 2))
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x = x.view(-1, 320)
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x = F.relu(self.fc1(x))
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x = F.dropout(x, training=self.training)
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x = self.fc2(x)
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return F.log_softmax(x, dim=1)
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class QuantLeNet(nn.Module):
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def __init__(self, **kwargs):
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super(QuantLeNet, self).__init__()
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self.conv1 = QuantConv2d(1, 10, kernel_size=5, **kwargs)
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self.conv2 = QuantConv2d(10, 20, kernel_size=5, **kwargs)
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self.fc1 = QuantLinear(320, 50, **kwargs)
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self.fc2 = QuantLinear(50, 10, **kwargs)
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def forward(self, x):
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x = F.relu(F.max_pool2d(self.conv1(x), 2))
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x = F.relu(F.max_pool2d(self.conv2(x), 2))
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x = x.view(-1, 320)
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x = F.relu(self.fc1(x))
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x = F.dropout(x, training=self.training)
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x = self.fc2(x)
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return F.log_softmax(x, dim=1)
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@pytest.fixture
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def resnet18():
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import torchvision
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return torchvision.models.resnet18()
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@pytest.fixture
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def quant_lenet():
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return QuantLeNet(quant_desc_input=QuantDescriptor(), quant_desc_weight=QuantDescriptor())
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