updates

detect.py

@@ -27,6 +27,12 @@ def detect(save_txt=False, save_img=False):
     else:  # darknet format
         _ = load_darknet_weights(model, weights)
 
+    # Second-stage classifier
+    classify = False
+    if classify:
+        modelc = torch_utils.load_classifier(name='resnet101', n=2)  # initialize
+        modelc.load_state_dict(torch.load('resnet101.pt', map_location=device)['model'])  # load weights
+
     # Fuse Conv2d + BatchNorm2d layers
     # model.fuse()
 
@@ -67,12 +73,20 @@ def detect(save_txt=False, save_img=False):
         img = torch.from_numpy(img).to(device)
         if img.ndimension() == 3:
             img = img.unsqueeze(0)
-        pred, _ = model(img)
+        pred = model(img)[0]
 
         if opt.half:
             pred = pred.float()
 
-        for i, det in enumerate(non_max_suppression(pred, opt.conf_thres, opt.nms_thres)):  # detections per image
+        # Apply NMS
+        pred = non_max_suppression(pred, opt.conf_thres, opt.nms_thres)
+
+        # Apply
+        if classify:
+            pred = apply_classifier(pred, modelc, img, im0s)
+
+        # Process detections
+        for i, det in enumerate(pred):  # detections per image
             if webcam:  # batch_size >= 1
                 p, s, im0 = path[i], '%g: ' % i, im0s[i]
             else:

utils/utils.py

@@ -720,6 +720,46 @@ def print_mutation(hyp, results, bucket=''):
         os.system('gsutil cp evolve.txt gs://%s' % bucket)  # upload evolve.txt
 
 
+def apply_classifier(x, model, img, im0):
+    # applies a second stage classifier to yolo outputs
+
+    for i, d in enumerate(x):  # per image
+        if d is not None and len(d):
+            d = d.clone()
+
+            # Reshape and pad cutouts
+            b = xyxy2xywh(d[:, :4])  # boxes
+            b[:, 2:] = b[:, 2:].max(1)[0].unsqueeze(1)  # rectangle to square
+            b[:, 2:] = b[:, 2:] * 1.0 + 0  # pad
+            d[:, :4] = xywh2xyxy(b).long()
+
+            # Rescale boxes from img_size to im0 size
+            scale_coords(img.shape[2:], d[:, :4], im0.shape)
+
+            # Classes
+            pred_cls1 = d[:, 6].long()
+            ims = []
+            j = 0
+            for a in d:  # per item
+                j += 1
+                cutout = im0[int(a[1]):int(a[3]), int(a[0]):int(a[2])]
+                im = cv2.resize(cutout, (128, 128))  # BGR
+                cv2.imwrite('test%i.jpg' % j, cutout)
+
+                im = im[:, :, ::-1].transpose(2, 0, 1)  # BGR to RGB, to 3x416x416
+                im = np.expand_dims(im, axis=0)  # add batch dim
+                im = np.ascontiguousarray(im, dtype=np.float32)  # uint8 to float32
+                im /= 255.0  # 0 - 255 to 0.0 - 1.0
+                ims.append(im)
+
+            ims = torch.Tensor(np.concatenate(ims, 0))  # to torch
+            pred_cls2 = model(ims).argmax(1)  # classifier prediction
+
+            # x[i] = x[i][pred_cls1 == pred_cls2]  # retain matching class detections
+
+    return x
+
+
 def fitness(x):
     # Returns fitness (for use with results.txt or evolve.txt)
     return x[:, 2] * 0.8 + x[:, 3] * 0.2  # weighted mAP and F1 combination