Detect active learning

Author: nducthang

AL_detect.py

@@ -0,0 +1,81 @@
+import time
+import torch
+from models.experimental import attempt_load
+from utils.datasets import  LoadImages
+from utils.general import check_img_size, non_max_suppression, scale_coords, xyxy2xywh, set_logging
+from utils.torch_utils import select_device, time_synchronized
+import activelearning.config as config
+import time
+
+def AL_detect(opt):
+    # Detect ảnh 
+    # File weight model, nguồn dữ liệu detect, Kích cỡ ảnh sử dụng
+    weights, source, imgsz = opt.weights, opt.source, opt.img_size
+
+    # khởi tạo
+    device = select_device(opt.device)
+    half = device.type != 'cpu'
+
+    # Load model
+    model = attempt_load(weights=weights, map_location=device)
+    imgsz = check_img_size(imgsz, s=model.stride.max())  # check img_size
+    if half:
+        model.half()  # to FP16
+    dataset = LoadImages(source, img_size=imgsz)
+
+    # Get names and colors
+    names = model.module.names if hasattr(model, 'module') else model.names
+
+    # Run inference
+    t0 = time.time()
+    img = torch.zeros((1, 3, imgsz, imgsz), device=device)  # init img
+    _ = model(img.half() if half else img) if device.type != 'cpu' else None  # run once
+
+    result = {}
+    # duyệt tất cả các ảnh
+    for path, img, im0s, vid_cap in dataset:
+        img = torch.from_numpy(img).to(device)
+        img = img.half() if half else img.float()  # uint8 to fp16/32
+        # chuuẩn hóa hình ảnh
+        img /= 255.0  # 0 - 255 to 0.0 - 1.0
+        if img.ndimension() == 3:
+            img = img.unsqueeze(0)
+
+        # Inference
+        t1 = time_synchronized()
+        pred = model(img, augment=opt.augment)[0]
+
+        # Apply NMS
+        pred = non_max_suppression(pred, opt.conf_thres, opt.iou_thres, classes=opt.classes, agnostic=opt.agnostic_nms)
+        t2 = time_synchronized()
+
+        # Process detections
+        result[path] = []
+
+        for i, det in enumerate(pred):  # detections per image
+            p, s, im0, frame = path, '', im0s, getattr(dataset, 'frame', 0)
+            s += '%gx%g ' % img.shape[2:]  # print string
+            gn = torch.tensor(im0.shape)[[1, 0, 1, 0]]  # normalization gain whwh
+            
+            if len(det):
+                # Rescale boxes from img_size to im0 size
+                det[:, :4] = scale_coords(img.shape[2:], det[:, :4], im0.shape).round()
+                
+                # Print results
+                for c in det[:, -1].unique():
+                    n = (det[:, -1] == c).sum()  # detections per class
+                    s += f'{n} {names[int(c)]}s, '  # add to string
+                
+                # Lưu thông tin về box vào 1 file
+                for *xyxy, conf, cls in reversed(det):
+                    with open(config.info_predict_path, 'a') as f:
+                        xywh = (xyxy2xywh(torch.tensor(xyxy).view(1, 4)) / gn).view(-1).tolist()  # normalized xywh)
+                        x,y,w,h = xywh
+                        data = {"class": cls.item(), "box": [x,y,w,h], "conf": conf.item()}
+                        result[path].append(data)
+            # Print time (inference + NMS)
+            print(f'{s}Done. ({t2 - t1:.3f}s)')
+    print(f'Done. ({time.time() - t0:.3f}s)')
+    return result
+
+

AL_run.py

@@ -32,17 +32,15 @@ def __init__(self, model, select_function):
         self.model = model
         self.select_function = select_function
         self.num_select = config.num_select
+        self.type = 'sum' # 'avg' , 'max', 'sum'
 
     def run(self):
-        self.queried = 0
-        # chưa thỏa mãn điều kiện dừng thì tiếp tục lặp lại active learning
-        while self.queried < config.max_queried:
-            # Xoá file dự đoán cũ
-            if os.path.exists(config.info_predict_path):
-                os.remove(config.info_predict_path)
+        # số truy vấn
+        queried = 0
+        # nếu chưa đủ số truy vấn thì tiếp tục truy vấn tiếp
+        while queried < config.max_queried:
             # Dự đoán các ảnh trong tập unlabeled
-            self.model.detect()
-            # Kết quả sau khi dự đoán được lưu ở config.info_predict_path
+            result = self.model.detect()
             # Tổng hợp kết quả
             probas = {str(file.split('/')[-1]): 0.0  for file in glob.glob(config.source + '/*')}
             num_object = probas.copy()