File size: 10,575 Bytes
9869d0d | 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 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 185 186 187 188 189 190 191 192 193 194 195 196 197 198 199 200 201 202 203 204 205 206 207 208 209 210 211 212 213 214 215 216 217 218 219 220 221 222 223 224 225 226 227 228 229 230 231 232 233 234 235 236 237 238 239 240 241 242 243 244 245 246 247 248 249 250 251 252 253 254 255 256 257 258 259 260 261 262 263 264 265 266 267 268 269 270 271 272 273 274 275 276 277 278 279 280 281 282 283 284 285 286 287 288 289 290 291 292 293 294 | """
Training script for 2D Semantic Segmentation using only RGB and Semantic masks.
This script uses a pre-trained DeepLabV3 model for a more direct 2D approach.
"""
import os
import torch
import torch.nn as nn
from torch.utils.data import Dataset, DataLoader
import torchvision.transforms as T
import torchvision.transforms.functional as TF
import numpy as np
import cv2
import glob
import segmentation_models_pytorch as smp
from tqdm import tqdm
import matplotlib.pyplot as plt
# --- CONFIGURATION ---
CONFIG = {
'data_dir': 'data/dataset/soybean',
'num_classes': 5, # ground, leaf, branch, weed, obstacle
'class_names': {0: "ground", 1: "leaf", 2: "branch", 3: "weed", 4: "obstacle"},
'batch_size': 4,
'epochs': 50,
'learning_rate': 1e-4,
'device': torch.device('cuda' if torch.cuda.is_available() else 'cpu'),
'ignore_index': -1, # Value in the semantic mask to be ignored during loss calculation
'data_augmentation': True, # Enable/disable data augmentation
'model_save_path': 'unet_efficientnetb3_semantic.pth',
}
# --- DATASET ---
class ImageSemanticDataset(Dataset):
"""
A simplified dataset that loads only RGB images and their corresponding
2D semantic segmentation masks.
"""
def __init__(self, root_dir, num_classes=5, transform=None, data_augmentation=False):
self.root_dir = root_dir
self.transform = transform
self.num_classes = num_classes
self.data_augmentation = data_augmentation
self.rgb_files = sorted(glob.glob(os.path.join(root_dir, 'rgb', '*.png')))
if not self.rgb_files:
self.rgb_files = sorted(glob.glob(os.path.join(root_dir, 'rgb', '*.jpg')))
if not self.rgb_files:
raise ValueError(f"No images found in {os.path.join(root_dir, 'rgb')}")
def __len__(self):
return len(self.rgb_files)
def __getitem__(self, idx):
rgb_path = self.rgb_files[idx]
base_name = os.path.splitext(os.path.basename(rgb_path))[0]
# Construct path to semantic mask
# The folder name in the original dataset is 'camera_semantic'
semantic_path = os.path.join(self.root_dir, 'camera_semantic', f"{base_name}.png")
# Load RGB Image
rgb_img = cv2.imread(rgb_path)
rgb_img = cv2.cvtColor(rgb_img, cv2.COLOR_BGR2RGB)
rgb_tensor = TF.to_tensor(rgb_img)
# Load Semantic Mask
semantic_mask = cv2.imread(semantic_path, cv2.IMREAD_UNCHANGED)
if semantic_mask is None:
raise FileNotFoundError(f"Semantic mask not found: {semantic_path}")
# Ensure mask is single channel
if semantic_mask.ndim == 3:
semantic_mask = semantic_mask[:, :, 0]
semantic_tensor = torch.from_numpy(semantic_mask.astype(np.int64))
# Apply transformations if any
if self.transform:
rgb_tensor, semantic_tensor = self.transform(rgb_tensor, semantic_tensor)
# Apply data augmentation if enabled
if self.data_augmentation:
# Random horizontal flip
if torch.rand(1) > 0.5:
rgb_tensor = TF.hflip(rgb_tensor)
semantic_tensor = TF.hflip(semantic_tensor)
# Color Jitter (only on RGB)
if torch.rand(1) > 0.5:
color_jitter = T.ColorJitter(brightness=0.3, contrast=0.3, saturation=0.3, hue=0.1)
rgb_tensor = color_jitter(rgb_tensor)
# Noise
if torch.rand(1) > 0.5:
noise = torch.randn_like(rgb_tensor) * 0.1
rgb_tensor = torch.clamp(rgb_tensor + noise, 0, 1)
# Blur
if torch.rand(1) > 0.5:
rgb_tensor = TF.gaussian_blur(rgb_tensor, kernel_size=5, sigma=(0.1, 2.0))
return {'rgb': rgb_tensor, 'semantic': semantic_tensor, 'frame_id': base_name}
# --- METRICS ---
def compute_metrics(pred, target, num_classes):
"""Computes Pixel Accuracy and Mean IoU, ignoring -1 labels."""
pred = pred.cpu().numpy()
target = target.cpu().numpy()
# Create a mask to ignore pixels with the ignore_index value
valid_mask = (target != CONFIG['ignore_index'])
# Pixel Accuracy
correct = np.sum((pred == target) & valid_mask)
total = np.sum(valid_mask)
pixel_acc = correct / total
# Mean IoU
iou_list = []
for c in range(num_classes):
tp = np.sum((pred == c) & (target == c))
fp = np.sum((pred == c) & (target != c))
fn = np.sum((pred != c) & (target == c) & valid_mask) # Only count valid target pixels as false negatives
union = tp + fp + fn
if union == 0:
iou = 0.0 # Or float('nan')
else:
iou = tp / union
iou_list.append(iou)
miou = np.nanmean(iou_list)
return pixel_acc, miou
# --- TRAINING AND VALIDATION ---
def train_one_epoch(model, dataloader, criterion, optimizer, device, num_classes):
model.train()
total_loss = 0.0
total_acc = 0.0
total_miou = 0.0
for batch in tqdm(dataloader, desc="Training"):
rgb = batch['rgb'].to(device)
semantic = batch['semantic'].to(device)
optimizer.zero_grad()
# DeepLabV3 returns a dictionary
output = model(rgb)
loss = criterion(output, semantic)
loss.backward()
optimizer.step()
total_loss += loss.item()
preds = torch.argmax(output, dim=1)
acc, miou = compute_metrics(preds, semantic, num_classes)
total_acc += acc
total_miou += miou
return total_loss / len(dataloader), total_acc / len(dataloader), total_miou / len(dataloader)
def validate_one_epoch(model, dataloader, criterion, device, num_classes):
model.eval()
total_loss = 0.0
total_acc = 0.0
total_miou = 0.0
with torch.no_grad():
for batch in tqdm(dataloader, desc="Validating"):
rgb = batch['rgb'].to(device)
semantic = batch['semantic'].to(device)
output = model(rgb)
loss = criterion(output, semantic)
total_loss += loss.item()
preds = torch.argmax(output, dim=1)
acc, miou = compute_metrics(preds, semantic, num_classes)
total_acc += acc
total_miou += miou
return total_loss / len(dataloader), total_acc / len(dataloader), total_miou / len(dataloader)
def main():
print(f"Using device: {CONFIG['device']}")
# --- DATA ---
# Create separate datasets for training and validation
# Augmentation should only be applied to the training set.
train_dataset = ImageSemanticDataset(
CONFIG['data_dir'],
num_classes=CONFIG['num_classes'],
data_augmentation=CONFIG['data_augmentation']
)
val_dataset = ImageSemanticDataset(
CONFIG['data_dir'],
num_classes=CONFIG['num_classes'],
data_augmentation=False # No augmentation for validation
)
# Create a random split of indices, then create subsets
indices = torch.randperm(len(train_dataset)).tolist()
train_indices = indices[:int(0.8 * len(train_dataset))]
val_indices = indices[int(0.8 * len(train_dataset)):]
train_dataset = torch.utils.data.Subset(train_dataset, train_indices)
val_dataset = torch.utils.data.Subset(val_dataset, val_indices)
train_loader = DataLoader(train_dataset, batch_size=CONFIG['batch_size'], shuffle=True, num_workers=2)
val_loader = DataLoader(val_dataset, batch_size=CONFIG['batch_size'], shuffle=False, num_workers=2)
print(f"Training on {len(train_dataset)} samples, validating on {len(val_dataset)} samples.")
# --- MODEL ---
# Load a U-Net model with a pre-trained EfficientNet encoder
print("Loading U-Net with EfficientNet encoder...")
model = smp.Unet(
encoder_name="efficientnet-b3",
encoder_weights="imagenet",
in_channels=3,
classes=CONFIG['num_classes'],
)
model = model.to(CONFIG['device'])
# --- LOSS & OPTIMIZER ---
# CrossEntropyLoss is standard for multi-class segmentation
criterion = nn.CrossEntropyLoss(ignore_index=CONFIG['ignore_index'])
optimizer = torch.optim.Adam(model.parameters(), lr=CONFIG['learning_rate'])
# --- TRAINING LOOP ---
best_val_miou = 0.0
for epoch in range(CONFIG['epochs']):
print(f"\n--- Epoch {epoch+1}/{CONFIG['epochs']} ---")
train_loss, train_acc, train_miou = train_one_epoch(
model, train_loader, criterion, optimizer, CONFIG['device'], CONFIG['num_classes']
)
print(f"Train -> Loss: {train_loss:.4f}, Accuracy: {train_acc:.4f}, mIoU: {train_miou:.4f}")
val_loss, val_acc, val_miou = validate_one_epoch(
model, val_loader, criterion, CONFIG['device'], CONFIG['num_classes']
)
print(f"Validation -> Loss: {val_loss:.4f}, Accuracy: {val_acc:.4f}, mIoU: {val_miou:.4f}")
# Save the best model based on validation mIoU
if val_miou > best_val_miou:
best_val_miou = val_miou
torch.save(model.state_dict(), CONFIG['model_save_path'])
print(f"✨ New best model saved to {CONFIG['model_save_path']} (mIoU: {best_val_miou:.4f})")
print("\n--- Training Complete ---")
print(f"Best validation mIoU: {best_val_miou:.4f}")
print(f"Model saved to {CONFIG['model_save_path']}")
# --- VISUALIZE A PREDICTION ---
model.load_state_dict(torch.load(CONFIG['model_save_path']))
model.eval()
sample = val_dataset[np.random.randint(len(val_dataset))]
rgb_tensor = sample['rgb'].unsqueeze(0).to(CONFIG['device'])
with torch.no_grad():
pred_mask = model(rgb_tensor)
pred_mask = torch.argmax(pred_mask, dim=1).squeeze(0).cpu().numpy()
rgb_img = sample['rgb'].permute(1, 2, 0).numpy()
true_mask = sample['semantic'].numpy()
fig, axes = plt.subplots(1, 3, figsize=(18, 6))
axes[0].imshow(rgb_img)
axes[0].set_title("Input RGB")
axes[0].axis('off')
axes[1].imshow(true_mask, cmap='tab10', vmin=0, vmax=CONFIG['num_classes']-1)
axes[1].set_title("Ground Truth Mask")
axes[1].axis('off')
axes[2].imshow(pred_mask, cmap='tab10', vmin=0, vmax=CONFIG['num_classes']-1)
axes[2].set_title("Predicted Mask")
axes[2].axis('off')
plt.tight_layout()
plt.savefig('2d_semantic_prediction_example.png')
print("\nSaved a sample prediction to '2d_semantic_prediction_example.png'")
if __name__ == '__main__':
main() |