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model.py
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model.py
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import argparse
import torch
import torch.nn as nn
import torch.nn.functional as F
import torch.optim as optim
class CNN(nn.Module):
def __init__(self, num_classes=2):
super(CNN, self).__init__()
self.conv1 = nn.Conv2d(1, 20, 3, 1)
self.conv2 = nn.Conv2d(20, 50, 3, 1)
self.fc1 = nn.Linear(45000, 500)
self.fc2 = nn.Linear(500, num_classes)
def forward(self, x):
x = x.unsqueeze(1)
# print(x.shape)
# print(x.shape)
# print(x)
# print('dsf')
x = F.relu(self.conv1(x))
# print(x.shape)
x = F.max_pool2d(x, 2, 2)
# print(x.shape)
x = F.relu(self.conv2(x))
# print(x.shape)
x = F.max_pool2d(x, 2, 2)
# print(x.shape)
x = x.view(-1, 45000)
# print(x.shape)
x = F.relu(self.fc1(x))
# print(x.shape)
x = self.fc2(x)
return F.log_softmax(x, dim=1)
class VGG(nn.Module):
def __init__(self, batch_norm=True, num_classes=2, init_weights=True):
super(VGG, self).__init__()
self.cfg = [64, 'M', 128, 'M', 256, 256, 'M', 512, 512, 'M', 512, 512, 'M']
self.features = self.make_layers(self.cfg, batch_norm)
self.avgpool = nn.AdaptiveAvgPool2d((7, 7))
self.classifier = nn.Sequential(
nn.Linear(512 * 7 * 7, 4096),
nn.ReLU(True),
nn.Dropout(),
nn.Linear(4096, 4096),
nn.ReLU(True),
nn.Dropout(),
nn.Linear(4096, num_classes),
)
if init_weights:
self._initialize_weights()
def make_layers(self, cfg, batch_norm):
layers = []
in_channels = 1
for v in cfg:
if v == 'M':
layers += [nn.MaxPool2d(kernel_size=2, stride=2)]
else:
conv2d = nn.Conv2d(in_channels, v, kernel_size=3, padding=1)
if batch_norm:
layers += [conv2d, nn.BatchNorm2d(v), nn.ReLU(inplace=True)]
else:
layers += [conv2d, nn.ReLU(inplace=True)]
in_channels = v
return nn.Sequential(*layers)
def forward(self, x):
x = x.unsqueeze(1)
x = self.features(x)
x = self.avgpool(x)
x = x.view(x.size(0), -1)
x = self.classifier(x)
return x
def _initialize_weights(self):
for m in self.modules():
if isinstance(m, nn.Conv2d):
nn.init.kaiming_normal_(m.weight, mode='fan_out', nonlinearity='relu')
if m.bias is not None:
nn.init.constant_(m.bias, 0)
elif isinstance(m, nn.BatchNorm2d):
nn.init.constant_(m.weight, 1)
nn.init.constant_(m.bias, 0)
elif isinstance(m, nn.Linear):
nn.init.normal_(m.weight, 0, 0.01)
nn.init.constant_(m.bias, 0)