Spaces:
Running
Running
File size: 5,086 Bytes
718c4ae |
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 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 |
import torch
import torch.nn.functional as F
from torch.optim import AdamW
from torch.utils.data import DataLoader, random_split
from tqdm import tqdm
from sklearn.metrics import accuracy_score, precision_score, recall_score, f1_score
from model import AuctionAuthenticityModel
from dataset_loader import AuctionDatasetFromJSON, get_transforms
import json
def train_epoch(model, loader, optimizer, device, epoch):
model.train()
total_loss = 0
progress_bar = tqdm(loader, desc=f"Epoch {epoch} [TRAIN]")
for batch in progress_bar:
images = batch['image'].to(device)
texts = batch['text']
labels = batch['label'].to(device)
optimizer.zero_grad()
logits = model(images, texts)
loss = F.cross_entropy(logits, labels)
loss.backward()
torch.nn.utils.clip_grad_norm_(model.parameters(), 1.0)
optimizer.step()
total_loss += loss.item()
progress_bar.set_postfix(loss=f'{loss.item():.4f}')
return total_loss / len(loader)
def validate(model, loader, device, epoch):
model.eval()
all_preds = []
all_labels = []
total_loss = 0
with torch.no_grad():
progress_bar = tqdm(loader, desc=f"Epoch {epoch} [VAL]")
for batch in progress_bar:
images = batch['image'].to(device)
texts = batch['text']
labels = batch['label'].to(device)
logits = model(images, texts)
loss = F.cross_entropy(logits, labels)
total_loss += loss.item()
preds = torch.argmax(logits, dim=1).cpu().numpy()
all_preds.extend(preds)
all_labels.extend(labels.cpu().numpy())
acc = accuracy_score(all_labels, all_preds)
prec = precision_score(all_labels, all_preds, zero_division=0)
rec = recall_score(all_labels, all_preds, zero_division=0)
f1 = f1_score(all_labels, all_preds, zero_division=0)
return {
'loss': total_loss / len(loader),
'accuracy': acc,
'precision': prec,
'recall': rec,
'f1': f1
}
def main():
# Konfiguracja
BATCH_SIZE = 4
EPOCHS = 5
LEARNING_RATE = 2e-5
DEVICE = torch.device('cuda' if torch.cuda.is_available() else 'cpu')
print(f"🖥️ Device: {DEVICE}")
print(f"📦 Batch size: {BATCH_SIZE}")
print(f"📚 Epochs: {EPOCHS}")
# Załaduj dataset
print("\n📥 Ładowanie datasetu...")
dataset = AuctionDatasetFromJSON(
json_path='../dataset/dataset.json',
root_dir='../dataset/raw_data',
transform=get_transforms()
)
print(f"✓ {len(dataset)} aukcji załadowanych")
# Split: 80% train, 20% val
train_size = int(0.8 * len(dataset))
val_size = len(dataset) - train_size
train_dataset, val_dataset = random_split(dataset, [train_size, val_size])
train_loader = DataLoader(train_dataset, batch_size=BATCH_SIZE, shuffle=True, num_workers=0)
val_loader = DataLoader(val_dataset, batch_size=BATCH_SIZE, shuffle=False, num_workers=0)
print(f" - Train: {len(train_dataset)}")
print(f" - Val: {len(val_dataset)}")
# Model
print("\n🧠 Inicjalizacja modelu...")
model = AuctionAuthenticityModel(device=DEVICE).to(DEVICE)
print(f"✓ Model gotowy ({model.count_parameters():,} parametrów)")
# Optimizer
optimizer = AdamW(model.parameters(), lr=LEARNING_RATE)
# Training loop
print("\n🚀 Rozpoczynam trening...\n")
history = {
'train_loss': [],
'val_loss': [],
'val_accuracy': [],
'val_f1': []
}
for epoch in range(EPOCHS):
# Train
train_loss = train_epoch(model, train_loader, optimizer, DEVICE, epoch+1)
# Validate
val_metrics = validate(model, val_loader, DEVICE, epoch+1)
# Log
history['train_loss'].append(train_loss)
history['val_loss'].append(val_metrics['loss'])
history['val_accuracy'].append(val_metrics['accuracy'])
history['val_f1'].append(val_metrics['f1'])
print(f"\n{'='*60}")
print(f"Epoch {epoch+1}/{EPOCHS}")
print(f" Train Loss: {train_loss:.4f}")
print(f" Val Loss: {val_metrics['loss']:.4f}")
print(f" Val Acc: {val_metrics['accuracy']:.4f}")
print(f" Val Prec: {val_metrics['precision']:.4f}")
print(f" Val Rec: {val_metrics['recall']:.4f}")
print(f" Val F1: {val_metrics['f1']:.4f}")
print(f"{'='*60}\n")
# Zapis modelu
print("\n💾 Zapis modelu...")
torch.save(model.state_dict(), '../weights/auction_model.pt')
print("✓ Zapisano: weights/auction_model.pt")
# Zapis historii
with open('../weights/training_history.json', 'w') as f:
json.dump(history, f, indent=2)
print("✓ Zapisano: weights/training_history.json")
print("\n✅ Trening ukończony!")
if __name__ == '__main__':
main() |