updates

detect.py

@@ -64,10 +64,10 @@ def detect(save_img=False):
     if webcam:
         view_img = True
         torch.backends.cudnn.benchmark = True  # set True to speed up constant image size inference
-        dataset = LoadStreams(source, img_size=img_size, half=half)
+        dataset = LoadStreams(source, img_size=img_size)
     else:
         save_img = True
-        dataset = LoadImages(source, img_size=img_size, half=half)
+        dataset = LoadImages(source, img_size=img_size)
 
     # Get names and colors
     names = load_classes(opt.names)
@@ -77,15 +77,14 @@ def detect(save_img=False):
     t0 = time.time()
     for path, img, im0s, vid_cap in dataset:
         t = time.time()
-
-        # Get detections
         img = torch.from_numpy(img).to(device)
+        img = img.half() if half else img.float()  # uint8 to fp16/32
+        img /= 255.0  # 0 - 255 to 0.0 - 1.0
         if img.ndimension() == 3:
             img = img.unsqueeze(0)
-        pred = model(img)[0]
 
-        if opt.half:
-            pred = pred.float()
+        # Inference
+        pred = model(img)[0].float() if half else model(img)[0]
 
         # Apply NMS
         pred = non_max_suppression(pred, opt.conf_thres, opt.iou_thres, classes=opt.classes, agnostic=opt.agnostic_nms)

utils/datasets.py

@@ -42,7 +42,7 @@ def exif_size(img):
 
 
 class LoadImages:  # for inference
-    def __init__(self, path, img_size=416, half=False):
+    def __init__(self, path, img_size=416):
         path = str(Path(path))  # os-agnostic
         files = []
         if os.path.isdir(path):
@@ -59,7 +59,6 @@ class LoadImages:  # for inference
         self.nF = nI + nV  # number of files
         self.video_flag = [False] * nI + [True] * nV
         self.mode = 'images'
-        self.half = half  # half precision fp16 images
         if any(videos):
             self.new_video(videos[0])  # new video
         else:
@@ -104,8 +103,7 @@ class LoadImages:  # for inference
 
         # Convert
         img = img[:, :, ::-1].transpose(2, 0, 1)  # BGR to RGB, to 3x416x416
-        img = np.ascontiguousarray(img, dtype=np.float16 if self.half else np.float32)  # uint8 to fp16/fp32
-        img /= 255.0  # 0 - 255 to 0.0 - 1.0
+        img = np.ascontiguousarray(img)
 
         # cv2.imwrite(path + '.letterbox.jpg', 255 * img.transpose((1, 2, 0))[:, :, ::-1])  # save letterbox image
         return path, img, img0, self.cap
@@ -120,9 +118,8 @@ class LoadImages:  # for inference
 
 
 class LoadWebcam:  # for inference
-    def __init__(self, pipe=0, img_size=416, half=False):
+    def __init__(self, pipe=0, img_size=416):
         self.img_size = img_size
-        self.half = half  # half precision fp16 images
 
         if pipe == '0':
             pipe = 0  # local camera
@@ -177,8 +174,7 @@ class LoadWebcam:  # for inference
 
         # Convert
         img = img[:, :, ::-1].transpose(2, 0, 1)  # BGR to RGB, to 3x416x416
-        img = np.ascontiguousarray(img, dtype=np.float16 if self.half else np.float32)  # uint8 to fp16/fp32
-        img /= 255.0  # 0 - 255 to 0.0 - 1.0
+        img = np.ascontiguousarray(img)
 
         return img_path, img, img0, None
 
@@ -187,10 +183,9 @@ class LoadWebcam:  # for inference
 
 
 class LoadStreams:  # multiple IP or RTSP cameras
-    def __init__(self, sources='streams.txt', img_size=416, half=False):
+    def __init__(self, sources='streams.txt', img_size=416):
         self.mode = 'images'
         self.img_size = img_size
-        self.half = half  # half precision fp16 images
 
         if os.path.isfile(sources):
             with open(sources, 'r') as f:
@@ -251,9 +246,8 @@ class LoadStreams:  # multiple IP or RTSP cameras
         img = np.stack(img, 0)
 
         # Convert
-        img = img[:, :, :, ::-1].transpose(0, 3, 1, 2)  # BGR to RGB, to 3x416x416, uint8 to float32
-        img = np.ascontiguousarray(img, dtype=np.float16 if self.half else np.float32)
-        img /= 255.0  # 0 - 255 to 0.0 - 1.0
+        img = img[:, :, :, ::-1].transpose(0, 3, 1, 2)  # BGR to RGB, to bsx3x416x416
+        img = np.ascontiguousarray(img)
 
         return self.sources, img, img0, None