Added precision to train log

models.py

@@ -156,6 +156,7 @@ def forward(self, x, targets=None):
             if x.is_cuda:
                 self.mse_loss = self.mse_loss.cuda()
                 self.bce_loss = self.bce_loss.cuda()
+                self.ce_loss = self.ce_loss.cuda()
 
             nGT, nCorrect, mask, conf_mask, tx, ty, tw, th, tconf, tcls = build_targets(
                 pred_boxes=pred_boxes.cpu().data,
@@ -170,8 +171,9 @@ def forward(self, x, targets=None):
                 img_dim=self.image_dim,
             )
 
-            nProposals = int((pred_conf > 0.25).sum().item())
+            nProposals = int((pred_conf > 0.5).sum().item())
             recall = float(nCorrect / nGT) if nGT else 1
+            precision = float(nCorrect / nProposals)
 
             # Handle masks
             mask = Variable(mask.type(ByteTensor))
@@ -210,6 +212,7 @@ def forward(self, x, targets=None):
                 loss_conf.item(),
                 loss_cls.item(),
                 recall,
+                precision,
             )
 
         else:
@@ -235,7 +238,7 @@ def __init__(self, config_path, img_size=416):
         self.img_size = img_size
         self.seen = 0
         self.header_info = np.array([0, 0, 0, self.seen, 0])
-        self.loss_names = ["x", "y", "w", "h", "conf", "cls", "recall"]
+        self.loss_names = ["x", "y", "w", "h", "conf", "cls", "recall", "precision"]
 
     def forward(self, x, targets=None):
         is_training = targets is not None
@@ -264,6 +267,7 @@ def forward(self, x, targets=None):
             layer_outputs.append(x)
 
         self.losses["recall"] /= 3
+        self.losses["precision"] /= 3
         return sum(output) if is_training else torch.cat(output, 1)
 
     def load_weights(self, weights_path):

test.py

@@ -20,26 +20,26 @@
 import torch.optim as optim
 
 parser = argparse.ArgumentParser()
-parser.add_argument('--batch_size', type=int, default=16, help='size of each image batch')
-parser.add_argument('--model_config_path', type=str, default='config/yolov3.cfg', help='path to model config file')
-parser.add_argument('--data_config_path', type=str, default='config/coco.data', help='path to data config file')
-parser.add_argument('--weights_path', type=str, default='weights/yolov3.weights', help='path to weights file')
-parser.add_argument('--class_path', type=str, default='data/coco.names', help='path to class label file')
-parser.add_argument('--iou_thres', type=float, default=0.5, help='iou threshold required to qualify as detected')
-parser.add_argument('--conf_thres', type=float, default=0.5, help='object confidence threshold')
-parser.add_argument('--nms_thres', type=float, default=0.45, help='iou thresshold for non-maximum suppression')
-parser.add_argument('--n_cpu', type=int, default=0, help='number of cpu threads to use during batch generation')
-parser.add_argument('--img_size', type=int, default=416, help='size of each image dimension')
-parser.add_argument('--use_cuda', type=bool, default=True, help='whether to use cuda if available')
+parser.add_argument("--batch_size", type=int, default=16, help="size of each image batch")
+parser.add_argument("--model_config_path", type=str, default="config/yolov3.cfg", help="path to model config file")
+parser.add_argument("--data_config_path", type=str, default="config/coco.data", help="path to data config file")
+parser.add_argument("--weights_path", type=str, default="weights/yolov3.weights", help="path to weights file")
+parser.add_argument("--class_path", type=str, default="data/coco.names", help="path to class label file")
+parser.add_argument("--iou_thres", type=float, default=0.5, help="iou threshold required to qualify as detected")
+parser.add_argument("--conf_thres", type=float, default=0.5, help="object confidence threshold")
+parser.add_argument("--nms_thres", type=float, default=0.45, help="iou thresshold for non-maximum suppression")
+parser.add_argument("--n_cpu", type=int, default=0, help="number of cpu threads to use during batch generation")
+parser.add_argument("--img_size", type=int, default=416, help="size of each image dimension")
+parser.add_argument("--use_cuda", type=bool, default=True, help="whether to use cuda if available")
 opt = parser.parse_args()
 print(opt)
 
 cuda = torch.cuda.is_available() and opt.use_cuda
 
 # Get data configuration
-data_config     = parse_data_config(opt.data_config_path)
-test_path       = data_config['valid']
-num_classes     = int(data_config['classes'])
+data_config = parse_data_config(opt.data_config_path)
+test_path = data_config["valid"]
+num_classes = int(data_config["classes"])
 
 # Initiate model
 model = Darknet(opt.model_config_path)
@@ -52,19 +52,11 @@
 
 # Get dataloader
 dataset = ListDataset(test_path)
-dataloader = torch.utils.data.DataLoader(dataset,
-    batch_size=opt.batch_size, shuffle=False, num_workers=opt.n_cpu)
+dataloader = torch.utils.data.DataLoader(dataset, batch_size=opt.batch_size, shuffle=False, num_workers=opt.n_cpu)
 
 Tensor = torch.cuda.FloatTensor if cuda else torch.FloatTensor
 
-n_gt = 0
-correct = 0
-
-print ('Compute mAP...')
-
-outputs = []
-targets = None
-APs = []
+print("Compute mAP...")
 
 all_detections = []
 all_annotations = []
@@ -102,10 +94,10 @@
 
             # Reformat to x1, y1, x2, y2 and rescale to image dimensions
             annotation_boxes = np.empty_like(_annotation_boxes)
-            annotation_boxes[:, 0] = (_annotation_boxes[:, 0] - _annotation_boxes[:, 2] / 2)
-            annotation_boxes[:, 1] = (_annotation_boxes[:, 1] - _annotation_boxes[:, 3] / 2)
-            annotation_boxes[:, 2] = (_annotation_boxes[:, 0] + _annotation_boxes[:, 2] / 2)
-            annotation_boxes[:, 3] = (_annotation_boxes[:, 1] + _annotation_boxes[:, 3] / 2)
+            annotation_boxes[:, 0] = _annotation_boxes[:, 0] - _annotation_boxes[:, 2] / 2
+            annotation_boxes[:, 1] = _annotation_boxes[:, 1] - _annotation_boxes[:, 3] / 2
+            annotation_boxes[:, 2] = _annotation_boxes[:, 0] + _annotation_boxes[:, 2] / 2
+            annotation_boxes[:, 3] = _annotation_boxes[:, 1] + _annotation_boxes[:, 3] / 2
             annotation_boxes *= opt.img_size
 
             for label in range(num_classes):