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Q2_model_e.py
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import torch
import torch.nn as nn
from torchvision import datasets, models, transforms
class Classifier(nn.Module):
def __init__(self):
super(Classifier, self).__init__()
self.features = nn.Sequential(
nn.Conv2d(in_channels=3, out_channels=64, kernel_size=(3, 3), padding=1),
nn.BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True),
nn.ReLU(),
nn.Conv2d(in_channels=64, out_channels=64, kernel_size=(3, 3), padding=1),
nn.BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True),
nn.ReLU(),
nn.MaxPool2d(kernel_size=(2, 2), stride=(2, 2), dilation=(1, 1), ceil_mode=False),
nn.Dropout(p=0.5),
nn.Conv2d(in_channels=64, out_channels=128, kernel_size=(3, 3), padding=1),
nn.BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True),
nn.ReLU(),
nn.Conv2d(in_channels=128, out_channels=128, kernel_size=(3, 3), padding=1),
nn.BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True),
nn.ReLU(),
nn.MaxPool2d(kernel_size=(2, 2), stride=(2, 2), dilation=(1, 1), ceil_mode=False),
nn.Dropout(p=0.5),
nn.Conv2d(in_channels=128, out_channels=256, kernel_size=(3, 3), padding=1),
nn.BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True),
nn.ReLU(),
nn.Conv2d(in_channels=256, out_channels=256, kernel_size=(3, 3), padding=1),
nn.BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True),
nn.ReLU(),
nn.Conv2d(in_channels=256, out_channels=256, kernel_size=(3, 3), padding=1),
nn.BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True),
nn.ReLU(),
nn.MaxPool2d(kernel_size=(2, 2), stride=(2, 2), dilation=(1, 1), ceil_mode=False),
nn.Dropout(p=0.5),
nn.Conv2d(in_channels=256, out_channels=512, kernel_size=(3, 3), padding=1),
nn.BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True),
nn.ReLU(),
nn.Conv2d(in_channels=512, out_channels=512, kernel_size=(3, 3), padding=1),
nn.BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True),
nn.ReLU(),
nn.Conv2d(in_channels=512, out_channels=512, kernel_size=(3, 3), padding=1),
nn.BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True),
nn.ReLU(),
nn.Conv2d(in_channels=512, out_channels=512, kernel_size=(3, 3), padding=1),
nn.BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True),
nn.ReLU(),
nn.MaxPool2d(kernel_size=(2, 2), stride=(2, 2), dilation=(1, 1), ceil_mode=False),
nn.Dropout(p=0.5),
nn.Conv2d(in_channels=512, out_channels=512, kernel_size=(3, 3), padding=1),
nn.BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True),
nn.ReLU(),
nn.Conv2d(in_channels=512, out_channels=512, kernel_size=(3, 3), padding=1),
nn.BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True),
nn.ReLU(),
nn.Conv2d(in_channels=512, out_channels=512, kernel_size=(3, 3), padding=1),
nn.BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True),
nn.ReLU(),
nn.Conv2d(in_channels=512, out_channels=512, kernel_size=(3, 3), padding=1),
nn.BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True),
nn.ReLU(),
nn.MaxPool2d(kernel_size=(2, 2), stride=(2, 2), dilation=(1, 1), ceil_mode=False),
nn.Dropout(p=0.5))
self.classifier = nn.Sequential(
nn.Linear(2048, 512),
nn.BatchNorm1d(512),
nn.Dropout(p=0.5),
nn.ReLU(),
nn.Linear(512, 64),
nn.BatchNorm1d(64),
nn.Dropout(p=0.5),
nn.ReLU(),
nn.Linear(64, 2))
def select_layer(self):
conv_all = [self.features[0], self.features[3],
self.features[8], self.features[11],
self.features[16], self.features[19], self.features[22],
self.features[27], self.features[30], self.features[33], self.features[36],
self.features[41], self.features[44], self.features[47], self.features[50]]
return conv_all
def forward(self, x):
out = self.features(x)
out = self.classifier(out.view(out.size(0),-1))
return out
class Classifier_pretrained(nn.Module):
def __init__(self):
super(Classifier_pretrained, self).__init__()
vgg19_bn = models.vgg19_bn(pretrained=True)
self.features = nn.Sequential(*list(vgg19_bn.children())[0])
self.classifier = nn.Sequential(
nn.Linear(2048, 512),
nn.BatchNorm1d(512),
nn.Dropout(p=0.5),
nn.ReLU(),
nn.Linear(512, 64),
nn.BatchNorm1d(64),
nn.Dropout(p=0.5),
nn.ReLU(),
nn.Linear(64, 2))
def forward(self, x):
out = self.features(x)
out = self.classifier(out.view(out.size(0),-1))
return out