File size: 8,652 Bytes
9e14838 | 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 | import os
import time
import torch
import importlib
from loguru import logger
from utils import seed_torch, evaluate
from dataSets import TrainDataset
import warnings
warnings.filterwarnings("ignore")
class Trainer:
def __init__(self,
mode: str,
device: str,
branch: str,
train_dataset: str,
label_smooth: bool = False,
ckpt: str = "ckpt",
epochs: int = 20,
batch_size: int = 32):
self.device = device
self.mode = mode
self.branch = branch
self.train_dataset = train_dataset
self.label_smooth = label_smooth
self.ckpt = ckpt
self.epochs = epochs
self.batch_size = batch_size
# Dynamically import the detector module from either "progan" or "sd-v1_4"
detector_module = importlib.import_module(f"detectors.{train_dataset}")
# Get the detector based on mode
if self.mode == "branch":
ArtifactDetector = getattr(detector_module, "ArtifactDetector")
SemanticDetector = getattr(detector_module, "SemanticDetector")
if self.branch == "artifact":
self.model = ArtifactDetector()
elif self.branch == "semantic":
self.model = SemanticDetector()
else:
raise ValueError(f"Unknown detector: {self.branch}")
elif self.mode == "fusion":
semantic_weights_path = os.path.join(self.ckpt, self.train_dataset, "semantic", "best_model.pth")
artifact_weights_path = os.path.join(self.ckpt, self.train_dataset, "artifact", "best_model.pth")
if not os.path.exists(semantic_weights_path) or not os.path.exists(artifact_weights_path):
raise ValueError("Semantic or Artifact weights path does not exist for fusion mode")
CoSpyFusionDetector = getattr(detector_module, "CoSpyFusionDetector")
self.model = CoSpyFusionDetector(
semantic_weights_path=semantic_weights_path,
artifact_weights_path=artifact_weights_path)
elif self.mode == "end2end":
End2EndDetector = getattr(detector_module, "End2EndDetector")
self.model = End2EndDetector()
else:
raise ValueError(f"Unknown mode: {self.mode}")
self.model.to(self.device)
# Initialize the fc layer
torch.nn.init.normal_(self.model.fc.weight.data, 0.0, 0.02)
if self.mode == "end2end":
torch.nn.init.normal_(self.model.sem.fc.weight.data, 0.0, 0.02)
torch.nn.init.normal_(self.model.art.fc.weight.data, 0.0, 0.02)
# Optimizer
_beta1 = 0.9
_weight_decay = 0.0
params = [p for p in self.model.parameters() if p.requires_grad]
logger.info(f"Trainable parameters: {len(params)}")
self._lr = 1e-4 if self.mode != "fusion" else 1e-1
self.optimizer = torch.optim.AdamW(params, lr=self._lr, betas=(_beta1, 0.999), weight_decay=_weight_decay)
# Loss function
if self.label_smooth:
self.criterion = LabelSmoothingBCEWithLogits(smoothing=0.1)
else:
self.criterion = torch.nn.BCEWithLogitsLoss()
# Scheduler
self.delr_freq = 10
def train_step(self, batch_data):
inputs, labels = batch_data
inputs, labels = inputs.to(self.device), labels.to(self.device)
self.optimizer.zero_grad()
outputs = self.model(inputs)
loss = self.criterion(outputs, labels.unsqueeze(1).float())
loss.backward()
self.optimizer.step()
eval_loss = loss.item()
y_pred = outputs.sigmoid().flatten().tolist()
y_true = labels.tolist()
return eval_loss, y_pred, y_true
def scheduler(self, status_dict):
epoch = status_dict["epoch"]
if epoch % self.delr_freq == 0 and epoch != 0:
for param_group in self.optimizer.param_groups:
param_group["lr"] *= 0.9
self._lr = param_group["lr"]
def train(self):
# Determine data split and transform based on mode
if self.mode == "fusion":
train_split, val_split = "val", "val"
train_transform = self.model.test_transform
test_transform = self.model.test_transform
else: # branch or end2end mode
train_split, val_split = "train", "val"
train_transform = self.model.train_transform
test_transform = self.model.test_transform
# Determine save directory
if self.mode == "branch":
subdir = self.branch
else:
subdir = self.mode
# Set the saving directory
model_dir = os.path.join(self.ckpt, self.train_dataset, subdir)
if not os.path.exists(model_dir):
os.makedirs(model_dir)
# Setup logger
log_path = f"{model_dir}/training.log"
if os.path.exists(log_path):
os.remove(log_path)
logger_id = logger.add(
log_path,
format="{time:MM-DD at HH:mm:ss} | {level} | {module}:{line} | {message}",
level="DEBUG",
)
# Add JPEG compression for sd-v1_4 dataset
self.add_jpeg = True if self.train_dataset == "sd-v1_4" else False
# Load the training and validation dataset
train_dataset = TrainDataset(train_dataset=self.train_dataset,
split=train_split,
add_jpeg=self.add_jpeg,
transform=train_transform)
train_loader = torch.utils.data.DataLoader(train_dataset,
batch_size=self.batch_size,
shuffle=True,
num_workers=4,
pin_memory=True)
val_dataset = TrainDataset(train_dataset=self.train_dataset,
split=val_split,
add_jpeg=self.add_jpeg,
transform=test_transform)
val_loader = torch.utils.data.DataLoader(val_dataset,
batch_size=self.batch_size,
shuffle=False,
num_workers=4,
pin_memory=True)
logger.info(f"Train size {len(train_dataset)} | Val size {len(val_dataset)}")
# Train the detector
best_acc = 0
for epoch in range(self.epochs):
self.model.train()
time_start = time.time()
for step_id, batch_data in enumerate(train_loader):
eval_loss, y_pred, y_true = self.train_step(batch_data)
ap, accuracy = evaluate(y_pred, y_true)
if (step_id + 1) % 100 == 0:
time_end = time.time()
logger.info(f"Epoch {epoch} | Batch {step_id + 1}/{len(train_loader)} | Loss {eval_loss:.4f} | AP {ap*100:.2f}% | Accuracy {accuracy*100:.2f}% | Time {time_end-time_start:.2f}s")
time_start = time.time()
# Evaluate the model
self.model.eval()
y_pred, y_true = [], []
for (images, labels) in val_loader:
y_pred.extend(self.model.predict(images))
y_true.extend(labels.tolist())
ap, accuracy = evaluate(y_pred, y_true)
eval_type = "Test" if self.mode == "branch" else "Total"
logger.info(f"Epoch {epoch} | {eval_type} AP {ap*100:.2f}% | {eval_type} Accuracy {accuracy*100:.2f}%")
# Schedule the training
status_dict = {"epoch": epoch, "AP": ap, "Accuracy": accuracy}
self.scheduler(status_dict)
# Save the model
if accuracy >= best_acc:
best_acc = accuracy
self.model.save_weights(f"{model_dir}/best_model.pth")
logger.info(f"Best model saved with accuracy {best_acc*100:.2f}%")
if epoch % 5 == 0:
self.model.save_weights(f"{model_dir}/epoch_{epoch}.pth")
logger.info(f"Model saved at epoch {epoch}")
# Save the final model
self.model.save_weights(f"{model_dir}/final_model.pth")
logger.info("Final model saved")
# Remove the logger
logger.remove(logger_id)
|