GTSDB - German Traffic Sign Detection Benchmark - Kaggle
According to classes' distribution, the dataset is not balanced
We define custom dataset to handle the preprocessing of images with their bounding boxes and classes
class myDataset(torch.utils.data.Dataset):
def __init__(self, root, transforms=None):
self.root = root
self.transforms = transforms
# load all image files, sorting them to ensure that they are aligned
self.imgs = list(sorted(os.listdir(os.path.join(root, "imagesf"))))
def __getitem__(self, idx):
# Load image path
img_path = os.path.join(self.root, "imagesf", self.imgs[idx])
#Load image as PIL
img = Image.open(img_path).convert("RGB")
# Get objects in the image
objects = dic[self.imgs[idx]]
# Get bounding box coordinates for each object in image
boxes = []
labels = []
for obj in objects:
#print(idx, obj[-1], self.imgs)
name = obj[-1]
labels.append(np.int(name))
#Get bounding box coordinates
xmin = np.float(obj[0])
ymin = np.float(obj[1])
xmax = np.float(obj[2])
ymax = np.float(obj[3])
boxes.append([xmin, ymin, xmax, ymax])
boxes = torch.as_tensor(boxes, dtype=torch.float32)
labels = torch.as_tensor(labels, dtype=torch.int64)
image_id = torch.tensor([idx])
area = (boxes[:, 3] - boxes[:, 1]) * (boxes[:, 2] - boxes[:, 0])
# suppose all instances are not crowd
iscrowd = torch.zeros((len(objects),), dtype=torch.int64)
target = {}
target["boxes"] = boxes
target["labels"] = labels
target["image_id"] = image_id
target["area"] = area
target["iscrowd"] = iscrowd
if self.transforms is not None:
# Note that target (including bbox) is also transformed\enhanced here, which is different from transforms from torchvision import
# Https://github.com/pytorch/vision/tree/master/references/detectionOfTransforms.pyThere are examples of target transformations when RandomHorizontalFlip
img, target = self.transforms(img, target)
return img, target
def __len__(self):
return len(self.imgs)I used the model provided by the pytorch community
- Faster R-CNN model with a ResNet-50-FPN backbone from the Faster R-CNN
- Constructs an improved Faster R-CNN model with a ResNet-50-FPN backbone
- Constructs a high resolution Faster R-CNN model with a MobileNetV3-Large FPN backbone
- Low resolution Faster R-CNN model with a MobileNetV3-Large backbone tunned for mobile use cases
I used the 1 and 3 options
Main training loop
for epoch in range(num_epochs):
metrics = train_one_epoch(model, optimizer, data_loader, device, epoch, print_freq=50)
losses.append(float(str(metrics.meters['loss']).split(" ")[0]))
loss_box_reg.append(float(str(metrics.meters['loss_box_reg']).split(" ")[0]))
loss_rpn_box_reg.append(float(str(metrics.meters['loss_rpn_box_reg']).split(" ")[0]))
loss_classifier.append(float(str(metrics.meters['loss_classifier']).split(" ")[0]))
loss_objectness.append(float(str(metrics.meters['loss_objectness']).split(" ")[0]))
# Update the learning rate
lr_scheduler.step()
# Evaluate on the test dataset
# _ gives coco_evaL obj from coco_eval.py from CocoEvaluator()
_, metric_logger = evaluate(model, data_loader_test, device=device)
stat = _.coco_eval['bbox'].stats
def showbbox(model, dataset):
# The img entered is a tensor in the 0-1 range
plt.subplots_adjust(wspace=0, hspace=0)
fig, axes = plt.subplots(10,sharex=True,sharey=True, figsize=(60, 60))
model.eval()
indexes = np.random.permutation(10)
for i in range(10):
img, _ = dataset[indexes[i]]
with torch.no_grad():
'''
prediction Like:
[{'boxes': tensor([[1221.7869, 523.7036, 1272.7373, 575.1018],
[ 192.8189, 527.5751, 240.7135, 589.8405],
[ 197.3745, 538.7914, 235.9153, 572.1550],
[ 195.1216, 533.9565, 238.6585, 578.0548],
[ 194.0861, 517.0943, 238.0777, 582.4178]], device='cuda:0'),
'labels': tensor([7, 7, 7, 8, 5], device='cuda:0'),
'scores': tensor([0.9792, 0.9036, 0.2619, 0.2407, 0.0575], device='cuda:0')}]
'''
prediction = model([img.to(device)])
b = prediction[0]['boxes']
#print(b)
s = prediction[0]['scores']
#print(s)
#Apply Non-maximum suppression:
keep = torchvision.ops.nms(b,s,0.1)
#print(keep)
img = img.permute(1,2,0) # C,H,W_H,W,C, for drawing
img = (img * 255).byte().data.cpu() # * 255, float to 0-255
img = np.array(img) # tensor → ndarray
#Convert np array img to right format.
img = cv2.cvtColor(img, cv2.COLOR_BGR2RGB)
#Class number coressponding to Classes
classes = { 0:' Speed limit (20km/h)' ,
1:' Speed limit (30km/h)' ,
2:' Speed limit (50km/h)' ,
3:' Speed limit (60km/h)' ,
4:' Speed limit (70km/h)' ,
5:' Speed limit (80km/h)' ,
6:' End of speed limit (80km/h)' ,
7:' Speed limit (100km/h)' ,
8:' Speed limit (120km/h)' ,
9:' No passing' ,
10:' No passing veh over 3.5 tons' ,
11:' Right-of-way at intersection' ,
12:' Priority road' ,
13:' Yield' ,
14:' Stop' ,
15:' No vehicles' ,
16:' Veh > 3.5 tons prohibited' ,
17:' No entry' ,
18:' General caution' ,
19:' Dangerous curve left' ,
20:' Dangerous curve right' ,
21:' Double curve' ,
22:' Bumpy road' ,
23:' Slippery road' ,
24:' Road narrows on the right' ,
25:' Road work' ,
26:' Traffic signals' ,
27:' Pedestrians' ,
28:' Children crossing' ,
29:' Bicycles crossing' ,
30:' Beware of ice/snow' ,
31:' Wild animals crossing' ,
32:' End speed + passing limits' ,
33:' Turn right ahead' ,
34:' Turn left ahead' ,
35:' Ahead only' ,
36:' Go straight or right' ,
37:' Go straight or left' ,
38:' Keep right' ,
39:' Keep left' ,
40:' Roundabout mandatory' ,
41:' End of no passing' ,
42:' End no passing veh > 3.5 tons' }
for k in range(len(keep)):
xmin = round(prediction[0]['boxes'][k][0].item())
ymin = round(prediction[0]['boxes'][k][1].item())
xmax = round(prediction[0]['boxes'][k][2].item())
ymax = round(prediction[0]['boxes'][k][3].item())
label = prediction[0]['labels'][k].item()
#color = list(np.random.random(size=3)*256)
colors = np.random.uniform(0, 255, size=(43, 3))
if label in classes:
pt1 = (xmin, ymin)
pt2 = (xmax, ymax)
score = prediction[0]['scores'][k].item()
color = list(colors[label])
cv2.rectangle(img, pt1, pt2, color, thickness=2)
cv2.putText(img, classes[label]+"-"+str(round(score,2)), (xmin, ymin), cv2.FONT_HERSHEY_SIMPLEX, 0.7, color,
thickness=2)
axes[i].imshow(img)
axes[i].axis("off")
fig.tight_layout()
fig.show()
print("Function Loaded")
Sat Nov 19 11:17:23 2022
+-----------------------------------------------------------------------------+
| NVIDIA-SMI 470.129.06 Driver Version: 470.129.06 CUDA Version: 11.4 |
|-------------------------------+----------------------+----------------------+
| GPU Name Persistence-M| Bus-Id Disp.A | Volatile Uncorr. ECC |
| Fan Temp Perf Pwr:Usage/Cap| Memory-Usage | GPU-Util Compute M. |
| | | MIG M. |
|===============================+======================+======================|
| 0 NVIDIA RTX A5000 On | 00000000:01:00.0 Off | Off |
| 51% 79C P2 206W / 230W | 2851MiB / 24247MiB | 58% Default |
| | | N/A |
+-------------------------------+----------------------+----------------------+