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import torch
import torch.nn as nn
from torch.utils.data import Dataset, DataLoader
from torchvision import transforms
import timm
from transformers import ViTFeatureExtractor, ViTForImageClassification
from pathlib import Path
import pandas as pd
import numpy as np
from PIL import Image
from sklearn.model_selection import train_test_split
from tqdm.auto import tqdm
import wandb
class PlantDiseaseDataset(Dataset):
def __init__(self, image_paths, labels, transform=None):
self.image_paths = image_paths
self.labels = labels
self.transform = transform
def __len__(self):
return len(self.image_paths)
def __getitem__(self, idx):
image_path = self.image_paths[idx]
image = Image.open(image_path).convert('RGB')
label = self.labels[idx]
if self.transform:
image = self.transform(image)
return image, label
class PlantDiseaseClassifier:
def __init__(self, data_dir, model_name='vit_base_patch16_224', num_classes=38):
self.data_dir = Path(data_dir)
self.model_name = model_name
self.num_classes = num_classes
self.device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')
# Initialize wandb
wandb.init(project="plant-disease-classification")
def prepare_data(self):
"""Prepare dataset and create data loaders"""
# Data augmentation and normalization for training
train_transform = transforms.Compose([
transforms.RandomResizedCrop(224),
transforms.RandomHorizontalFlip(),
transforms.RandomVerticalFlip(),
transforms.RandomRotation(20),
transforms.ColorJitter(brightness=0.2, contrast=0.2),
transforms.ToTensor(),
transforms.Normalize([0.485, 0.456, 0.406], [0.229, 0.224, 0.225])
])
# Just normalization for validation/testing
val_transform = transforms.Compose([
transforms.Resize(256),
transforms.CenterCrop(224),
transforms.ToTensor(),
transforms.Normalize([0.485, 0.456, 0.406], [0.229, 0.224, 0.225])
])
# Collect all image paths and labels
image_paths = []
labels = []
self.class_to_idx = {}
for idx, class_dir in enumerate(sorted(self.data_dir.glob('*'))):
if class_dir.is_dir():
self.class_to_idx[class_dir.name] = idx
for img_path in class_dir.glob('*.jpg'):
image_paths.append(str(img_path))
labels.append(idx)
# Split data
train_paths, val_paths, train_labels, val_labels = train_test_split(
image_paths, labels, test_size=0.2, stratify=labels, random_state=42
)
# Create datasets
train_dataset = PlantDiseaseDataset(train_paths, train_labels, train_transform)
val_dataset = PlantDiseaseDataset(val_paths, val_labels, val_transform)
# Create data loaders
self.train_loader = DataLoader(train_dataset, batch_size=32, shuffle=True, num_workers=4)
self.val_loader = DataLoader(val_dataset, batch_size=32, shuffle=False, num_workers=4)
return self.train_loader, self.val_loader
def create_model(self):
"""Initialize the Vision Transformer model"""
self.model = timm.create_model(
self.model_name,
pretrained=True,
num_classes=self.num_classes
)
self.model = self.model.to(self.device)
# Loss function and optimizer
self.criterion = nn.CrossEntropyLoss()
self.optimizer = torch.optim.AdamW(
self.model.parameters(),
lr=2e-5,
weight_decay=0.01
)
self.scheduler = torch.optim.lr_scheduler.CosineAnnealingLR(
self.optimizer,
T_max=10
)
return self.model
def train_epoch(self, epoch):
"""Train for one epoch"""
self.model.train()
total_loss = 0
correct = 0
total = 0
progress_bar = tqdm(self.train_loader, desc=f'Epoch {epoch}')
for batch_idx, (inputs, targets) in enumerate(progress_bar):
inputs, targets = inputs.to(self.device), targets.to(self.device)
self.optimizer.zero_grad()
outputs = self.model(inputs)
loss = self.criterion(outputs, targets)
loss.backward()
self.optimizer.step()
total_loss += loss.item()
_, predicted = outputs.max(1)
total += targets.size(0)
correct += predicted.eq(targets).sum().item()
progress_bar.set_postfix({
'Loss': total_loss/(batch_idx+1),
'Acc': 100.*correct/total
})
# Log to wandb
wandb.log({
'train_loss': loss.item(),
'train_acc': 100.*correct/total
})
return total_loss/len(self.train_loader), 100.*correct/total
def validate(self):
"""Validate the model"""
self.model.eval()
total_loss = 0
correct = 0
total = 0
with torch.no_grad():
for inputs, targets in tqdm(self.val_loader, desc='Validating'):
inputs, targets = inputs.to(self.device), targets.to(self.device)
outputs = self.model(inputs)
loss = self.criterion(outputs, targets)
total_loss += loss.item()
_, predicted = outputs.max(1)
total += targets.size(0)
correct += predicted.eq(targets).sum().item()
accuracy = 100.*correct/total
avg_loss = total_loss/len(self.val_loader)
# Log to wandb
wandb.log({
'val_loss': avg_loss,
'val_acc': accuracy
})
return avg_loss, accuracy
def train(self, epochs=10):
"""Complete training process"""
best_acc = 0
for epoch in range(epochs):
train_loss, train_acc = self.train_epoch(epoch)
val_loss, val_acc = self.validate()
self.scheduler.step()
print(f'\nEpoch {epoch}:')
print(f'Train Loss: {train_loss:.4f} | Train Acc: {train_acc:.2f}%')
print(f'Val Loss: {val_loss:.4f} | Val Acc: {val_acc:.2f}%')
# Save best model
if val_acc > best_acc:
best_acc = val_acc
torch.save({
'model_state_dict': self.model.state_dict(),
'optimizer_state_dict': self.optimizer.state_dict(),
'class_to_idx': self.class_to_idx
}, 'best_model.pth')
wandb.finish()
def save_for_huggingface(self):
"""Save model in HF中国镜像站 format"""
# Load best model
checkpoint = torch.load('best_model.pth')
self.model.load_state_dict(checkpoint['model_state_dict'])
# Save model and config
self.model.save_pretrained('plant_disease_model')
# Save class mapping
idx_to_class = {v: k for k, v in self.class_to_idx.items()}
pd.Series(idx_to_class).to_json('class_mapping.json')
if __name__ == "__main__":
classifier = PlantDiseaseClassifier(data_dir="path/to/dataset")
classifier.prepare_data()
classifier.create_model()
classifier.train(epochs=10)
classifier.save_for_huggingface() |