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b20701a | 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 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 185 186 187 188 189 190 191 192 193 194 195 196 197 198 199 200 201 202 203 204 205 206 207 208 209 210 211 212 213 214 215 216 217 218 219 220 221 222 223 224 225 226 227 228 229 230 231 232 233 234 235 236 237 238 239 240 241 242 243 244 245 246 247 248 | from pathlib import Path
import logging
from datetime import datetime
import time
import pandas as pd
import numpy as np
import random
from sklearn.model_selection import train_test_split
from sklearn.metrics import precision_recall_fscore_support
import torch
import torch.nn as nn
from torch.utils.data import WeightedRandomSampler
from torch.optim import AdamW
from torch.optim.lr_scheduler import ReduceLROnPlateau, CosineAnnealingWarmRestarts
from tqdm.auto import tqdm
from safetensors.torch import save_file
from src.plot_utils import plot_confusion_matrix
def set_seed(seed):
random.seed(seed)
np.random.seed(seed)
torch.manual_seed(seed)
torch.cuda.manual_seed_all(seed)
def split_train_val_paths(train_root, metadata_path, random_state=None):
train_root = Path(train_root)
metadata_df = pd.read_csv(metadata_path)
X = metadata_df[["label", "video_name"]]
y = metadata_df["label"]
X_train, X_val, _, _ = train_test_split(
X, y, test_size=0.2, stratify=y, random_state=random_state
)
train_paths = (train_root / X_train["label"] / X_train["video_name"]).to_list()
val_paths = (train_root / X_val["label"] / X_val["video_name"]).to_list()
return train_paths, val_paths
def create_balanced_sampler(dataset):
'''Create balanced sampler for imbalanced dataset'''
all_labels = dataset.labels
class_counts = np.bincount(all_labels)
class_weights = 1.0 / class_counts
sample_weights = [class_weights[label] for label in all_labels]
sample_weights = torch.FloatTensor(sample_weights)
sampler = WeightedRandomSampler(
weights=sample_weights,
num_samples=len(sample_weights),
replacement=True
)
return sampler
def validate(model, dataloader, criterion, device):
model.eval()
total_loss, preds, labels_all = 0, [], []
with torch.no_grad():
for batch in tqdm(dataloader, desc="Validation"):
frames, labels = batch["frames"].to(device), batch["labels"].to(device)
outputs = model(frames)
loss = criterion(outputs, labels)
total_loss += loss.item()
_, predicted = outputs.max(1)
preds.extend(predicted.cpu().numpy())
labels_all.extend(labels.cpu().numpy())
precision, recall, f1, _ = precision_recall_fscore_support(
labels_all, preds, average="macro", zero_division=0
)
return (
total_loss / len(dataloader),
{"precision": precision*100, "recall": recall*100, "f1": f1*100},
preds,
labels_all
)
def train_epoch(model, dataloader, criterion, optimizer, device):
total_loss = 0
progress = tqdm(dataloader, desc="Training")
model.train()
for batch in progress:
frames, labels = batch["frames"].to(device), batch["labels"].to(device)
optimizer.zero_grad()
outputs = model(frames)
loss = criterion(outputs, labels)
loss.backward()
optimizer.step()
total_loss += loss.item()
lr = optimizer.param_groups[0]["lr"]
progress.set_postfix({"loss": f"{total_loss / (len(progress)+1e-9):.4f}"})
return total_loss / len(dataloader), lr
def train_model(
model, train_loader, val_loader, logger,
num_epochs=10, lr=5e-4, device="cuda",
early_stopping_patience=3,
save_path="best_model.safetensors",
validation_cm_path="validation_cm.png"
):
model = model.to(device)
criterion = nn.CrossEntropyLoss(label_smoothing=0.1)
optimizer = AdamW(
params=filter(lambda p: p.requires_grad, model.parameters()),
lr=lr,
weight_decay=1e-4
)
scheduler = CosineAnnealingWarmRestarts(
optimizer,
T_0=10,
T_mult=2,
eta_min=1e-6
)
train_losses = []
val_losses = []
precision_scores = []
recall_scores = []
f1_scores = []
learning_rates = []
best_f1 = 0.0
best_f1_epoch = 1
early_stopping_cnt = 0
start_time = time.time()
for epoch in range(num_epochs):
epoch_start = time.time()
logger.info(f"===== Epoch {epoch+1}/{num_epochs} =====")
train_loss, lr = train_epoch(
model, train_loader, criterion, optimizer, device
)
val_loss, val_metrics, preds, labels_all = validate(
model, val_loader, criterion, device
)
scheduler.step()
epoch_time = time.time() - epoch_start
train_losses.append(train_loss)
val_losses.append(val_loss)
precision_scores.append(val_metrics["precision"])
recall_scores.append(val_metrics["recall"])
f1_scores.append(val_metrics["f1"])
learning_rates.append(lr)
logger.info(
f"Train Loss: {train_loss:.4f} | "
f"Val Loss: {val_loss:.4f} | "
f"Val Precision: {val_metrics['precision']:.2f}% | "
f"Val Recall: {val_metrics['recall']:.2f}% | "
f"Val F1: {val_metrics['f1']:.2f}% | "
f"LR: {lr:.6f} | "
f"Time: {epoch_time:.2f}s"
)
if val_metrics["f1"] > best_f1:
label_mapping = train_loader.dataset.label2id
best_f1 = val_metrics["f1"]
best_f1_epoch = epoch + 1
early_stopping_cnt = 0
save_file(model.state_dict(), save_path)
plot_confusion_matrix(
labels_all, preds,
labels=[v for k, v in sorted(label_mapping.items(), key=lambda x: x[1])],
display_labels=[k for k, v in sorted(label_mapping.items(), key=lambda x: x[1])],
top_k=10,
figsize=(20, 24),
normalize="true",
save_path=validation_cm_path
)
logger.info(f"✓ Best model saved with F1: {best_f1:.2f}%")
logger.info(f"✓ Best validation results saved at: {validation_cm_path}")
else:
early_stopping_cnt += 1
if early_stopping_cnt == early_stopping_patience:
logger.info(
f"Early stopping triggered. Best macro F1: {best_f1:.2f}, "
f"achieved on epoch {best_f1_epoch}"
)
break
total_time = time.time() - start_time
logger.info("========== TRAINING END ==========")
logger.info(f"Best F1: {best_f1:.2f}%")
logger.info(f"Total Time: {total_time/60:.2f} minutes")
return (
train_losses, val_losses, precision_scores,
recall_scores, f1_scores, learning_rates
)
def setup_logger(log_dir="logs"):
Path(log_dir).mkdir(exist_ok=True)
log_file = Path(log_dir) / f"train_{datetime.now().strftime('%Y%m%d_%H%M%S')}.log"
logger = logging.getLogger("train_logger")
logger.setLevel(logging.INFO)
formatter = logging.Formatter(
"%(asctime)s | %(levelname)s | %(message)s",
"%Y-%m-%d %H:%M:%S"
)
file_handler = logging.FileHandler(log_file)
file_handler.setFormatter(formatter)
console_handler = logging.StreamHandler()
console_handler.setFormatter(formatter)
logger.addHandler(file_handler)
logger.addHandler(console_handler)
return logger, log_file
def seed_worker(worker_id):
worker_seed = torch.initial_seed() % 2**32
np.random.seed(worker_seed)
random.seed(worker_seed) |