File size: 2,834 Bytes
1a4a0d3 | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 | import os
import torch
import torch.nn as nn
import torch.optim as optim
from torch.utils.data import DataLoader
from torchvision import datasets, transforms
from src.model import get_model, get_transforms
import numpy as np
from sklearn.metrics import accuracy_score
# Assume data/ has train/ and val/ folders with subfolders for classes: normal, crazing, inclusion, etc.
# https://www.kaggle.com/datasets/kaustubhdikshit/neu-surface-defect-database
DATA_DIR = '../data/neu_surface_defect_database'
BATCH_SIZE = 32
EPOCHS = 10
LEARNING_RATE = 0.001
DEVICE = torch.device('cuda' if torch.cuda.is_available() else 'cpu')
def main():
transform = get_transforms()
train_dataset = datasets.ImageFolder(os.path.join(DATA_DIR, 'train'), transform=transform)
val_dataset = datasets.ImageFolder(os.path.join(DATA_DIR, 'val'), transform=transform)
train_loader = DataLoader(train_dataset, batch_size=BATCH_SIZE, shuffle=True)
val_loader = DataLoader(val_dataset, batch_size=BATCH_SIZE, shuffle=False)
model = get_model(pretrained=True).to(DEVICE)
criterion = nn.CrossEntropyLoss()
optimizer = optim.Adam(model.parameters(), lr=LEARNING_RATE)
for epoch in range(EPOCHS):
model.train()
running_loss = 0.0
for inputs, labels in train_loader:
inputs, labels = inputs.to(DEVICE), labels.to(DEVICE)
optimizer.zero_grad()
outputs = model(inputs)
loss = criterion(outputs, labels)
loss.backward()
optimizer.step()
running_loss += loss.item()
print(f'Epoch {epoch+1}/{EPOCHS}, Loss: {running_loss / len(train_loader):.4f}')
# Validation
model.eval()
preds, trues = [], []
with torch.no_grad():
for inputs, labels in val_loader:
inputs, labels = inputs.to(DEVICE), labels.to(DEVICE)
outputs = model(inputs)
_, predicted = torch.max(outputs, 1)
preds.extend(predicted.cpu().numpy())
trues.extend(labels.cpu().numpy())
acc = accuracy_score(trues, preds)
print(f'Validation Accuracy: {acc:.4f}')
# Save PyTorch model
torch.save(model.state_dict(), '../models/resnet18_anomaly.pth')
# Export to ONNX
model.eval()
dummy_input = torch.randn(1, 3, 224, 224).to(DEVICE)
torch.onnx.export(model, dummy_input, '../models/resnet18_anomaly.onnx',
export_params=True, opset_version=11,
do_constant_folding=True,
input_names=['input'], output_names=['output'])
print('Model trained and exported to ONNX!')
if __name__ == '__main__':
main() |