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import torch |
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import torch.nn as nn |
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import torch.nn.init as init |
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from classes.fc4.squeezenet.Fire import Fire |
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""" |
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This is the standard SqueezeNet implementation included in PyTorch at: |
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https://github.com/pytorch/vision/blob/072d8b2280569a2d13b91d3ed51546d201a57366/torchvision/models/squeezenet.py |
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SqueezeNet 1.0 |
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-> Architecture from https://arxiv.org/abs/1602.07360, 'SqueezeNet: AlexNet-level accuracy with 50x fewer parameters and <0.5MB model size'. |
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SqueezeNet 1.1 (has 2.4x less computation and slightly fewer parameters than 1.0, without sacrificing accuracy) |
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-> Architecture from: <https://github.com/DeepScale/SqueezeNet/tree/master/SqueezeNet_v1.1>`. |
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""" |
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class SqueezeNet(nn.Module): |
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def __init__(self, version: float = 1.0, num_classes: int = 1000): |
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super().__init__() |
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self.num_classes = num_classes |
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if version == 1.0: |
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self.features = nn.Sequential( |
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nn.Conv2d(3, 96, kernel_size=7, stride=2), |
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nn.ReLU(inplace=True), |
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nn.MaxPool2d(kernel_size=3, stride=2, ceil_mode=True), |
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Fire(96, 16, 64, 64), |
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Fire(128, 16, 64, 64), |
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Fire(128, 32, 128, 128), |
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nn.MaxPool2d(kernel_size=3, stride=2, ceil_mode=True), |
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Fire(256, 32, 128, 128), |
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Fire(256, 48, 192, 192), |
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Fire(384, 48, 192, 192), |
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Fire(384, 64, 256, 256), |
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nn.MaxPool2d(kernel_size=3, stride=2, ceil_mode=True), |
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Fire(512, 64, 256, 256), |
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) |
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elif version == 1.1: |
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self.features = nn.Sequential( |
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nn.Conv2d(3, 64, kernel_size=3, stride=2), |
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nn.ReLU(inplace=True), |
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nn.MaxPool2d(kernel_size=3, stride=2, ceil_mode=True), |
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Fire(64, 16, 64, 64), |
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Fire(128, 16, 64, 64), |
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nn.MaxPool2d(kernel_size=3, stride=2, ceil_mode=True), |
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Fire(128, 32, 128, 128), |
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Fire(256, 32, 128, 128), |
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nn.MaxPool2d(kernel_size=3, stride=2, ceil_mode=True), |
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Fire(256, 48, 192, 192), |
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Fire(384, 48, 192, 192), |
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Fire(384, 64, 256, 256), |
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Fire(512, 64, 256, 256), |
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) |
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else: |
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raise ValueError("Unsupported SqueezeNet version {version}: 1.0 or 1.1 expected".format(version=version)) |
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final_conv = nn.Conv2d(512, self.num_classes, kernel_size=1) |
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self.classifier = nn.Sequential( |
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nn.Dropout(p=0.5), |
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final_conv, |
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nn.ReLU(inplace=True), |
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nn.AdaptiveAvgPool2d((1, 1)) |
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) |
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for m in self.modules(): |
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if isinstance(m, nn.Conv2d): |
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if m is final_conv: |
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init.normal_(m.weight, mean=0.0, std=0.01) |
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else: |
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init.kaiming_uniform_(m.weight) |
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if m.bias is not None: |
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init.constant_(m.bias, 0) |
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def forward(self, x: torch): |
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x = self.features(x) |
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x = self.classifier(x) |
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return x.view(x.size(0), self.num_classes) |
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