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CNN.py
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CNN.py
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import torch
import torch.nn as nn
import torch.nn.functional as F
class AlexNet(nn.Module):
def __init__(self):
super(AlexNet, self).__init__()
self.conv_layer = nn.Sequential(
nn.Conv2d(in_channels=3, out_channels= 96, kernel_size= 5, stride=2, padding=0),
nn.ReLU(),
nn.MaxPool2d(kernel_size=2, stride=2),
nn.Conv2d(in_channels=96, out_channels=256, kernel_size=3, stride= 1, padding= 2),
nn.ReLU(),
nn.MaxPool2d(kernel_size=2, stride=2),
nn.Conv2d(in_channels=256, out_channels=384, kernel_size=3, stride= 1, padding= 1),
nn.ReLU(),
nn.Conv2d(in_channels=384, out_channels=984, kernel_size=3, stride=1, padding=1),
nn.ReLU(),
nn.Conv2d(in_channels=984, out_channels=4048, kernel_size=3, stride=1, padding=1),
nn.ReLU(),
nn.Conv2d(in_channels=4048, out_channels=2024, kernel_size=3, stride=1, padding=1),
nn.ReLU(),
nn.Conv2d(in_channels=2024, out_channels=384, kernel_size=3, stride=1, padding=1),
nn.ReLU(),
nn.Conv2d(in_channels=384, out_channels=384, kernel_size=3, stride=1, padding=1),
nn.ReLU(),
nn.Conv2d(in_channels=384, out_channels=256, kernel_size=3, stride=1, padding=1),
nn.ReLU(),
nn.MaxPool2d(kernel_size=3, stride=2)
)
self.fc_layer = nn.Sequential(
nn.Linear(in_features= 12544, out_features= 512),
nn.ReLU(),
nn.Linear(in_features= 512, out_features= 256),
nn.ReLU(),
nn.Linear(in_features=256 , out_features=5)
)
def forward(self, tensor):
tensor = self.conv_layer(tensor)
tensor = tensor.view(tensor.size(0), -1)
tensor = self.fc_layer(tensor)
return tensor
# saving the model into the folder model
def saveModelAlexNet(model, optimizer, MODEL_FILEPATH):
model_info = {
'model': AlexNet(),
'state_dict': model.state_dict(),
'optimizer': optimizer.state_dict()
}
torch.save(model_info, MODEL_FILEPATH)
# loading the model
def loadModel(MODEL_FILEPATH, DEVICE):
model_info = torch.load(MODEL_FILEPATH)
model = model_info['model'].to(DEVICE)
model.load_state_dict(model_info['state_dict'])
return model