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classify_model.py
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classify_model.py
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# -*- codeing = utf-8 -*-
import torch
import torch.nn as nn
import torchvision
# import timm
CLASSES_NAME = ('bacterial_leaf_blight', 'bacterial_leaf_streak', 'bacterial_panicle_blight',
'blast', 'brown_spot', 'dead_heart', 'downy_mildew', 'hispa',
'normal', 'tungro')
class ResNet(nn.Module):
def __init__(self):
super().__init__()
self.classes_name = CLASSES_NAME
self.num_classes = len(self.classes_name)
# net = torchvision.models.resnet50(weights='IMAGENET1K_V2')
# self.new_fc = nn.Linear(2048, self.num_classes)
net = torchvision.models.resnet34(weights='IMAGENET1K_V1')
self.new_fc = nn.Linear(512, self.num_classes)
self.net = nn.Sequential(
net.conv1,
net.bn1,
net.relu,
net.maxpool,
net.layer1,
net.layer2,
net.layer3,
net.layer4,
net.avgpool,
)
del net
def forward(self, x):
x = self.net(x)
x = torch.flatten(x, 1)
x = self.new_fc(x)
return x
class ResNeXt(nn.Module):
def __init__(self):
super().__init__()
self.classes_name = CLASSES_NAME
self.num_classes = len(self.classes_name)
net = torchvision.models.resnext50_32x4d(weights='IMAGENET1K_V2')
self.new_fc = nn.Linear(2048, self.num_classes)
self.net = nn.Sequential(
net.conv1,
net.bn1,
net.relu,
net.maxpool,
net.layer1,
net.layer2,
net.layer3,
net.layer4,
net.avgpool,
)
del net
def forward(self, x):
x = self.net(x)
x = torch.flatten(x, 1)
x = self.new_fc(x)
return x
class ConvNext(nn.Module):
def __init__(self):
super().__init__()
self.classes_name = CLASSES_NAME
self.num_classes = len(self.classes_name)
# net = torchvision.models.convnext_base(weights='IMAGENET1K_V1')
# self.new_fc = nn.Linear(1024, self.num_classes)
# net = torchvision.models.convnext_small(weights='IMAGENET1K_V1')
# self.new_fc = nn.Linear(768, self.num_classes)
net = torchvision.models.convnext_tiny(weights='IMAGENET1K_V1')
self.new_fc = nn.Linear(768, self.num_classes)
self.net = nn.Sequential(
net.features,
net.avgpool,
net.classifier[:-1]
)
del net
def forward(self, x):
return self.new_fc(self.net(x))
class EfficientNet(nn.Module):
def __init__(self):
super().__init__()
self.classes_name = CLASSES_NAME
self.num_classes = len(self.classes_name)
# net = torchvision.models.efficientnet_v2_s(weights='IMAGENET1K_V1')
# self.new_fc = nn.Linear(1280, self.num_classes)
# net = torchvision.models.efficientnet_b4(weights='IMAGENET1K_V1')
# self.new_fc = nn.Linear(1792, self.num_classes)
net = torchvision.models.efficientnet_b3(weights='IMAGENET1K_V1')
self.new_fc = nn.Linear(1536, self.num_classes)
self.classifier = net.classifier[:-1]
self.net = nn.Sequential(
net.features,
net.avgpool,
)
del net
def forward(self, x):
x = self.net(x)
x = torch.flatten(x, 1)
x = self.classifier(x)
x = self.new_fc(x)
return x