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import torch
import torch.nn as nn
import torch.optim as optim
import torch.nn.functional as F
import timm
# ResNet50 Model
class ResNet(nn.Module):
def __init__(self, num_classes, is_freeze=True):
super(ResNet, self).__init__()
self.num_classes = num_classes
self.is_freeze = is_freeze
self.base_model = timm.create_model('resnet50', pretrained=True)
if self.is_freeze:
for param in self.base_model.parameters():
param.requires_grad = False
self.base_model.fc = nn.Linear(2048, self.num_classes)
def forward(self, x):
x = self.base_model(x)
return x
# EfficientNet Model
class EfficientNet(nn.Module):
def __init__(self, num_classes):
super(EfficientNet, self).__init__()
self.num_classes = num_classes
self.base_model = timm.create_model('efficientnet_b0', pretrained=True)
self.base_model.classifier = nn.Linear(1280, self.num_classes)
def forward(self, x):
x = self.base_model(x)
return x
# BaseLine Model
class BaseLine(nn.Module):
def __init__(self, num_classes):
super(BaseLine, self).__init__()
self.Conv1 = nn.Conv2d(3, 96, kernel_size=11, stride=4, padding=1)
self.Conv2 = nn.Conv2d(96, 256, kernel_size=5, padding=2)
self.Conv3 = nn.Conv2d(256, 384, kernel_size=3, padding=1)
self.Conv4 = nn.Conv2d(384, 256, kernel_size=3, padding=1)
self.Linear1 = nn.Linear(2304, 512)
self.Linear3 = nn.Linear(512, num_classes)
self.relu = nn.ReLU()
self.dropout = nn.Dropout(p=0.5)
self.maxpool = nn.MaxPool2d(kernel_size=3, stride=2)
self.flatten = nn.Flatten()
def forward(self, x):
x = self.Conv1(x)
x = self.relu(x)
x = self.maxpool(x)
x = self.Conv2(x)
x = self.maxpool(x)
x = self.Conv3(x)
x = self.Conv4(x)
x = self.maxpool(x)
x = self.flatten(x)
x = self.Linear1(x)
x = self.relu(x)
x = self.dropout(x)
x = self.Linear3(x)
return x |