Merge remote-tracking branch 'origin/master'

# Conflicts:
#	.github/ISSUE_TEMPLATE/--bug-report.md
#	.github/workflows/greetings.yml
#	README.md
#	requirements.txt
#	train.py

cfg/yolov4-tiny.cfg

@@ -0,0 +1,281 @@
+[net]
+# Testing
+#batch=1
+#subdivisions=1
+# Training
+batch=64
+subdivisions=1
+width=416
+height=416
+channels=3
+momentum=0.9
+decay=0.0005
+angle=0
+saturation = 1.5
+exposure = 1.5
+hue=.1
+
+learning_rate=0.00261
+burn_in=1000
+max_batches = 500200
+policy=steps
+steps=400000,450000
+scales=.1,.1
+
+[convolutional]
+batch_normalize=1
+filters=32
+size=3
+stride=2
+pad=1
+activation=leaky
+
+[convolutional]
+batch_normalize=1
+filters=64
+size=3
+stride=2
+pad=1
+activation=leaky
+
+[convolutional]
+batch_normalize=1
+filters=64
+size=3
+stride=1
+pad=1
+activation=leaky
+
+[route]
+layers=-1
+groups=2
+group_id=1
+
+[convolutional]
+batch_normalize=1
+filters=32
+size=3
+stride=1
+pad=1
+activation=leaky
+
+[convolutional]
+batch_normalize=1
+filters=32
+size=3
+stride=1
+pad=1
+activation=leaky
+
+[route]
+layers = -1,-2
+
+[convolutional]
+batch_normalize=1
+filters=64
+size=1
+stride=1
+pad=1
+activation=leaky
+
+[route]
+layers = -6,-1
+
+[maxpool]
+size=2
+stride=2
+
+[convolutional]
+batch_normalize=1
+filters=128
+size=3
+stride=1
+pad=1
+activation=leaky
+
+[route]
+layers=-1
+groups=2
+group_id=1
+
+[convolutional]
+batch_normalize=1
+filters=64
+size=3
+stride=1
+pad=1
+activation=leaky
+
+[convolutional]
+batch_normalize=1
+filters=64
+size=3
+stride=1
+pad=1
+activation=leaky
+
+[route]
+layers = -1,-2
+
+[convolutional]
+batch_normalize=1
+filters=128
+size=1
+stride=1
+pad=1
+activation=leaky
+
+[route]
+layers = -6,-1
+
+[maxpool]
+size=2
+stride=2
+
+[convolutional]
+batch_normalize=1
+filters=256
+size=3
+stride=1
+pad=1
+activation=leaky
+
+[route]
+layers=-1
+groups=2
+group_id=1
+
+[convolutional]
+batch_normalize=1
+filters=128
+size=3
+stride=1
+pad=1
+activation=leaky
+
+[convolutional]
+batch_normalize=1
+filters=128
+size=3
+stride=1
+pad=1
+activation=leaky
+
+[route]
+layers = -1,-2
+
+[convolutional]
+batch_normalize=1
+filters=256
+size=1
+stride=1
+pad=1
+activation=leaky
+
+[route]
+layers = -6,-1
+
+[maxpool]
+size=2
+stride=2
+
+[convolutional]
+batch_normalize=1
+filters=512
+size=3
+stride=1
+pad=1
+activation=leaky
+
+##################################
+
+[convolutional]
+batch_normalize=1
+filters=256
+size=1
+stride=1
+pad=1
+activation=leaky
+
+[convolutional]
+batch_normalize=1
+filters=512
+size=3
+stride=1
+pad=1
+activation=leaky
+
+[convolutional]
+size=1
+stride=1
+pad=1
+filters=255
+activation=linear
+
+
+
+[yolo]
+mask = 3,4,5
+anchors = 10,14,  23,27,  37,58,  81,82,  135,169,  344,319
+classes=80
+num=6
+jitter=.3
+scale_x_y = 1.05
+cls_normalizer=1.0
+iou_normalizer=0.07
+iou_loss=ciou
+ignore_thresh = .7
+truth_thresh = 1
+random=0
+resize=1.5
+nms_kind=greedynms
+beta_nms=0.6
+
+[route]
+layers = -4
+
+[convolutional]
+batch_normalize=1
+filters=128
+size=1
+stride=1
+pad=1
+activation=leaky
+
+[upsample]
+stride=2
+
+[route]
+layers = -1, 23
+
+[convolutional]
+batch_normalize=1
+filters=256
+size=3
+stride=1
+pad=1
+activation=leaky
+
+[convolutional]
+size=1
+stride=1
+pad=1
+filters=255
+activation=linear
+
+[yolo]
+mask = 1,2,3
+anchors = 10,14,  23,27,  37,58,  81,82,  135,169,  344,319
+classes=80
+num=6
+jitter=.3
+scale_x_y = 1.05
+cls_normalizer=1.0
+iou_normalizer=0.07
+iou_loss=ciou
+ignore_thresh = .7
+truth_thresh = 1
+random=0
+resize=1.5
+nms_kind=greedynms
+beta_nms=0.6

models.py

@@ -438,7 +438,7 @@ def convert(cfg='cfg/yolov3-spp.cfg', weights='weights/yolov3-spp.weights'):
 
         target = weights.rsplit('.', 1)[0] + '.pt'
         torch.save(chkpt, target)
-        print("Success: converted '%s' to 's%'" % (weights, target))
+        print("Success: converted '%s' to '%s'" % (weights, target))
 
     else:
         print('Error: extension not supported.')

test.py

@@ -23,6 +23,7 @@ def test(cfg,
          multi_label=True):
     # Initialize/load model and set device
     if model is None:
+        is_training = False
         device = torch_utils.select_device(opt.device, batch_size=batch_size)
         verbose = opt.task == 'test'
 
@@ -47,6 +48,7 @@ def test(cfg,
         if device.type != 'cpu' and torch.cuda.device_count() > 1:
             model = nn.DataParallel(model)
     else:  # called by train.py
+        is_training = True
         device = next(model.parameters()).device  # get model device
         verbose = False
 
@@ -61,7 +63,7 @@ def test(cfg,
 
     # Dataloader
     if dataloader is None:
-        dataset = LoadImagesAndLabels(path, imgsz, batch_size, rect=True, single_cls=opt.single_cls)
+        dataset = LoadImagesAndLabels(path, imgsz, batch_size, rect=True, single_cls=opt.single_cls, pad=0.5)
         batch_size = min(batch_size, len(dataset))
         dataloader = DataLoader(dataset,
                                 batch_size=batch_size,
@@ -91,7 +93,7 @@ def test(cfg,
             t0 += torch_utils.time_synchronized() - t
 
             # Compute loss
-            if hasattr(model, 'hyp'):  # if model has loss hyperparameters
+            if is_training:  # if model has loss hyperparameters
                 loss += compute_loss(train_out, targets, model)[1][:3]  # GIoU, obj, cls
 
             # Run NMS

train.py

@@ -66,7 +66,7 @@ def train(hyp):
     imgsz_min, imgsz_max, imgsz_test = opt.img_size  # img sizes (min, max, test)
 
     # Image Sizes
-    gs = 64  # (pixels) grid size
+    gs = 32  # (pixels) grid size
     assert math.fmod(imgsz_min, gs) == 0, '--img-size %g must be a %g-multiple' % (imgsz_min, gs)
     opt.multi_scale |= imgsz_min != imgsz_max  # multi if different (min, max)
     if opt.multi_scale:
@@ -119,34 +119,49 @@ def train(hyp):
     if weights.endswith('.pt'):  # pytorch format
         print("LOADIN MODEL")
         # possible weights are '*.pt', 'yolov3-spp.pt', 'yolov3-tiny.pt' etc.
-        chkpt = torch.load(weights, map_location=device)
+        ckpt = torch.load(weights, map_location=device)
 
         # load model
         try:
-            chkpt['model'] = {k: v for k, v in chkpt['model'].items() if model.state_dict()[k].numel() == v.numel()}
+            ckpt['model'] = {k: v for k, v in ckpt['model'].items() if model.state_dict()[k].numel() == v.numel()}
-            model.load_state_dict(chkpt['model'], strict=False)
+            model.load_state_dict(ckpt['model'], strict=False)
         except KeyError as e:
             s = "%s is not compatible with %s. Specify --weights '' or specify a --cfg compatible with %s. " \
                 "See https://github.com/ultralytics/yolov3/issues/657" % (opt.weights, opt.cfg, opt.weights)
             raise KeyError(s) from e
 
         # load optimizer
-        if chkpt['optimizer'] is not None:
+        if ckpt['optimizer'] is not None:
-            optimizer.load_state_dict(chkpt['optimizer'])
+            optimizer.load_state_dict(ckpt['optimizer'])
-            best_fitness = chkpt['best_fitness']
+            best_fitness = ckpt['best_fitness']
 
         # load results
-        if chkpt.get('training_results') is not None:
+        if ckpt.get('training_results') is not None:
             with open(results_file, 'w') as file:
-                file.write(chkpt['training_results'])  # write results.txt
+                file.write(ckpt['training_results'])  # write results.txt
 
-        start_epoch = chkpt['epoch'] + 1
+        # epochs
-        del chkpt
+        start_epoch = ckpt['epoch'] + 1
+        if epochs < start_epoch:
+            print('%s has been trained for %g epochs. Fine-tuning for %g additional epochs.' %
+                  (opt.weights, ckpt['epoch'], epochs))
+            epochs += ckpt['epoch']  # finetune additional epochs
+
+        del ckpt
 
     elif len(weights) > 0:  # darknet format
         # possible weights are '*.weights', 'yolov3-tiny.conv.15',  'darknet53.conv.74' etc.
         load_darknet_weights(model, weights)
 
+    if opt.freeze_layers:
+        output_layer_indices = [idx - 1 for idx, module in enumerate(model.module_list) if isinstance(module, YOLOLayer)]
+        freeze_layer_indices = [x for x in range(len(model.module_list)) if
+                                (x not in output_layer_indices) and
+                                (x - 1 not in output_layer_indices)]
+        for idx in freeze_layer_indices:
+            for parameter in model.module_list[idx].parameters():
+                parameter.requires_grad_(False)
+
     # Mixed precision training https://github.com/NVIDIA/apex
     if mixed_precision:
         model, optimizer = amp.initialize(model, optimizer, opt_level='O1', verbosity=0)
@@ -343,7 +358,7 @@ def train(hyp):
         save = (not opt.nosave) or (final_epoch and not opt.evolve)
         if save:
             with open(results_file, 'r') as f:  # create checkpoint
-                chkpt = {'epoch': epoch,
+                ckpt = {'epoch': epoch,
                          'best_fitness': best_fitness,
                          'training_results': f.read(),
                          'model': ema.ema.module.state_dict() if hasattr(model, 'module') else ema.ema.state_dict(),
@@ -352,10 +367,10 @@ def train(hyp):
             if epoch % opt.save_every_nth_epoch == 0:
                 torch.save(chkpt, f'yolo_{epoch}.pt')
             # Save last, best and delete
-            torch.save(chkpt, last)
+            torch.save(ckpt, last)
             if (best_fitness == fi) and not final_epoch:
-                torch.save(chkpt, best)
+                torch.save(ckpt, best)
-            del chkpt
+            del ckpt
 
 
         # end epoch ----------------------------------------------------------------------------------------------------
@@ -400,9 +415,10 @@ if __name__ == '__main__':
     parser.add_argument('--device', default='', help='device id (i.e. 0 or 0,1 or cpu)')
     parser.add_argument('--adam', action='store_true', help='use adam optimizer')
     parser.add_argument('--single-cls', action='store_true', help='train as single-class dataset')
+    parser.add_argument('--freeze-layers', action='store_true', help='Freeze non-output layers')
     parser.add_argument('--save-every-nth-epoch', type=int, help='Saving every n-th epoth')
     opt = parser.parse_args()
-    #opt.weights = last if opt.resume else opt.weights
+    #opt.weights = last if opt.resume and not opt.weights else opt.weights
     #check_git_status()
     opt.cfg = check_file(opt.cfg)  # check file
     opt.data = check_file(opt.data)  # check file

utils/datasets.py

@@ -18,7 +18,7 @@ from utils.utils import xyxy2xywh, xywh2xyxy
 
 help_url = 'https://github.com/ultralytics/yolov3/wiki/Train-Custom-Data'
 img_formats = ['.bmp', '.jpg', '.jpeg', '.png', '.tif', '.dng']
-vid_formats = ['.mov', '.avi', '.mp4']
+vid_formats = ['.mov', '.avi', '.mp4', '.mpg', '.mpeg', '.m4v', '.wmv', '.mkv']
 
 # Get orientation exif tag
 for orientation in ExifTags.TAGS.keys():
@@ -62,7 +62,8 @@ class LoadImages:  # for inference
             self.new_video(videos[0])  # new video
         else:
             self.cap = None
-        assert self.nF > 0, 'No images or videos found in ' + path
+        assert self.nF > 0, 'No images or videos found in %s. Supported formats are:\nimages: %s\nvideos: %s' % \
+                            (path, img_formats, vid_formats)
 
     def __iter__(self):
         self.count = 0
@@ -256,7 +257,7 @@ class LoadStreams:  # multiple IP or RTSP cameras
 
 class LoadImagesAndLabels(Dataset):  # for training/testing
     def __init__(self, path, img_size=416, batch_size=16, augment=False, hyp=None, rect=False, image_weights=False,
-                 cache_images=False, single_cls=False):
+                 cache_images=False, single_cls=False, pad=0.0):
         try:
             path = str(Path(path))  # os-agnostic
             parent = str(Path(path).parent) + os.sep
@@ -290,20 +291,22 @@ class LoadImagesAndLabels(Dataset):  # for training/testing
         self.label_files = [x.replace('images', 'labels').replace(os.path.splitext(x)[-1], '.txt')
                             for x in self.img_files]
 
+        # Read image shapes (wh)
+        sp = path.replace('.txt', '') + '.shapes'  # shapefile path
+        try:
+            with open(sp, 'r') as f:  # read existing shapefile
+                s = [x.split() for x in f.read().splitlines()]
+                assert len(s) == n, 'Shapefile out of sync'
+        except:
+            s = [exif_size(Image.open(f)) for f in tqdm(self.img_files, desc='Reading image shapes')]
+            np.savetxt(sp, s, fmt='%g')  # overwrites existing (if any)
+
+        self.shapes = np.array(s, dtype=np.float64)
+
         # Rectangular Training  https://github.com/ultralytics/yolov3/issues/232
         if self.rect:
-            # Read image shapes (wh)
-            sp = path.replace('.txt', '') + '.shapes'  # shapefile path
-            try:
-                with open(sp, 'r') as f:  # read existing shapefile
-                    s = [x.split() for x in f.read().splitlines()]
-                    assert len(s) == n, 'Shapefile out of sync'
-            except:
-                s = [exif_size(Image.open(f)) for f in tqdm(self.img_files, desc='Reading image shapes')]
-                np.savetxt(sp, s, fmt='%g')  # overwrites existing (if any)
-
             # Sort by aspect ratio
-            s = np.array(s, dtype=np.float64)
+            s = self.shapes  # wh
             ar = s[:, 1] / s[:, 0]  # aspect ratio
             irect = ar.argsort()
             self.img_files = [self.img_files[i] for i in irect]
@@ -321,7 +324,7 @@ class LoadImagesAndLabels(Dataset):  # for training/testing
                 elif mini > 1:
                     shapes[i] = [1, 1 / mini]
 
-            self.batch_shapes = np.ceil(np.array(shapes) * img_size / 64.).astype(np.int) * 64
+            self.batch_shapes = np.ceil(np.array(shapes) * img_size / 32. + pad).astype(np.int) * 32
 
         # Cache labels
         self.imgs = [None] * n
@@ -529,7 +532,7 @@ def load_image(self, index):
         assert img is not None, 'Image Not Found ' + path
         h0, w0 = img.shape[:2]  # orig hw
         r = self.img_size / max(h0, w0)  # resize image to img_size
-        if r < 1 or (self.augment and r != 1):  # always resize down, only resize up if training with augmentation
+        if r != 1:  # always resize down, only resize up if training with augmentation
             interp = cv2.INTER_AREA if r < 1 and not self.augment else cv2.INTER_LINEAR
             img = cv2.resize(img, (int(w0 * r), int(h0 * r)), interpolation=interp)
         return img, (h0, w0), img.shape[:2]  # img, hw_original, hw_resized