Created
June 18, 2019 10:23
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""" | |
This is the implementation of AlexNet which is modified from [Jeicaoyu's AlexNet]. | |
Note: | |
- The number of Conv2d filters now matches with the original paper. | |
- Use PyTorch's Local Response Normalization layer which is implemented in Jan 2018. [PR #4667] | |
- This is for educational purpose only. We don't have pretrained weights for this model. | |
References: | |
- Jeicaoyu's AlexNet Model: [jiecaoyu](https://github.com/jiecaoyu/pytorch_imagenet/blob/984a2a988ba17b37e1173dd2518fa0f4dc4a1879/networks/model_list/alexnet.py) | |
- PR #4667: https://github.com/pytorch/pytorch/pull/4667 | |
""" | |
import torch.nn as nn | |
class AlexNet(nn.Module): | |
def __init__(self, num_classes=1000): | |
super(AlexNet, self).__init__() | |
self.features = nn.Sequential( | |
nn.Conv2d(3, 96, kernel_size=11, stride=4, padding=0), | |
nn.ReLU(inplace=True), | |
nn.LocalResponseNorm(size=5, alpha=0.0001, beta=0.75), | |
nn.MaxPool2d(kernel_size=3, stride=2), | |
nn.Conv2d(96, 256, kernel_size=5, padding=2, groups=2), | |
nn.ReLU(inplace=True), | |
nn.LocalResponseNorm(size=5, alpha=0.0001, beta=0.75), | |
nn.MaxPool2d(kernel_size=3, stride=2), | |
nn.Conv2d(256, 384, kernel_size=3, padding=1), | |
nn.ReLU(inplace=True), | |
nn.Conv2d(384, 384, kernel_size=3, padding=1, groups=2), | |
nn.ReLU(inplace=True), | |
nn.Conv2d(384, 256, kernel_size=3, padding=1, groups=2), | |
nn.ReLU(inplace=True), | |
nn.MaxPool2d(kernel_size=3, stride=2), | |
) | |
self.classifier = nn.Sequential( | |
nn.Linear(256 * 6 * 6, 4096), | |
nn.ReLU(inplace=True), | |
nn.Dropout(), | |
nn.Linear(4096, 4096), | |
nn.ReLU(inplace=True), | |
nn.Dropout(), | |
nn.Linear(4096, num_classes), | |
) | |
def forward(self, x): | |
x = self.features(x) | |
x = x.view(x.size(0), 256 * 6 * 6) | |
x = self.classifier(x) | |
return x |
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