149 lines
5.1 KiB
Python
149 lines
5.1 KiB
Python
# Copyright 2022 Dakewe Biotech Corporation. All Rights Reserved.
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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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from typing import Callable, Any, Optional
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import torch
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from torch import Tensor
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from torch import nn
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from torchvision.ops.misc import Conv2dNormActivation
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from config import config as conf
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__all__ = [
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"MobileNetV1",
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"DepthWiseSeparableConv2d",
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"mobilenet_v1",
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]
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class MobileNetV1(nn.Module):
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def __init__(
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self,
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num_classes: int = conf.embedding_size,
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) -> None:
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super(MobileNetV1, self).__init__()
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self.features = nn.Sequential(
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Conv2dNormActivation(3,
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32,
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kernel_size=3,
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stride=2,
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padding=1,
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norm_layer=nn.BatchNorm2d,
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activation_layer=nn.ReLU,
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inplace=True,
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bias=False,
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),
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DepthWiseSeparableConv2d(32, 64, 1),
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DepthWiseSeparableConv2d(64, 128, 2),
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DepthWiseSeparableConv2d(128, 128, 1),
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DepthWiseSeparableConv2d(128, 256, 2),
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DepthWiseSeparableConv2d(256, 256, 1),
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DepthWiseSeparableConv2d(256, 512, 2),
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DepthWiseSeparableConv2d(512, 512, 1),
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DepthWiseSeparableConv2d(512, 512, 1),
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DepthWiseSeparableConv2d(512, 512, 1),
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DepthWiseSeparableConv2d(512, 512, 1),
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DepthWiseSeparableConv2d(512, 512, 1),
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DepthWiseSeparableConv2d(512, 1024, 2),
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DepthWiseSeparableConv2d(1024, 1024, 1),
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)
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self.avgpool = nn.AvgPool2d((7, 7))
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self.classifier = nn.Linear(1024, num_classes)
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# Initialize neural network weights
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self._initialize_weights()
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def forward(self, x: Tensor) -> Tensor:
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out = self._forward_impl(x)
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return out
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# Support torch.script function
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def _forward_impl(self, x: Tensor) -> Tensor:
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out = self.features(x)
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out = self.avgpool(out)
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out = torch.flatten(out, 1)
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out = self.classifier(out)
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return out
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def _initialize_weights(self) -> None:
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for module in self.modules():
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if isinstance(module, nn.Conv2d):
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nn.init.kaiming_normal_(module.weight, mode="fan_out", nonlinearity="relu")
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if module.bias is not None:
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nn.init.zeros_(module.bias)
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elif isinstance(module, (nn.BatchNorm2d, nn.GroupNorm)):
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nn.init.ones_(module.weight)
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nn.init.zeros_(module.bias)
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elif isinstance(module, nn.Linear):
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nn.init.normal_(module.weight, 0, 0.01)
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nn.init.zeros_(module.bias)
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class DepthWiseSeparableConv2d(nn.Module):
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def __init__(
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self,
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in_channels: int,
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out_channels: int,
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stride: int,
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norm_layer: Optional[Callable[..., nn.Module]] = None
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) -> None:
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super(DepthWiseSeparableConv2d, self).__init__()
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self.stride = stride
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if stride not in [1, 2]:
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raise ValueError(f"stride should be 1 or 2 instead of {stride}")
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if norm_layer is None:
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norm_layer = nn.BatchNorm2d
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self.conv = nn.Sequential(
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Conv2dNormActivation(in_channels,
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in_channels,
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kernel_size=3,
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stride=stride,
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padding=1,
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groups=in_channels,
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norm_layer=norm_layer,
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activation_layer=nn.ReLU,
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inplace=True,
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bias=False,
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),
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Conv2dNormActivation(in_channels,
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out_channels,
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kernel_size=1,
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stride=1,
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padding=0,
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norm_layer=norm_layer,
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activation_layer=nn.ReLU,
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inplace=True,
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bias=False,
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),
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)
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def forward(self, x: Tensor) -> Tensor:
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out = self.conv(x)
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return out
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def mobilenet_v1(**kwargs: Any) -> MobileNetV1:
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model = MobileNetV1(**kwargs)
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return model
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