Fixed PGT by including it in the loss function

run_pgt_train.py

@@ -6,42 +6,42 @@ import argparse
 
 
 def main(args):
-  # model = YOLOv10()
+    # model = YOLOv10()
 
-  # If you want to finetune the model with pretrained weights, you could load the 
+    # If you want to finetune the model with pretrained weights, you could load the 
-  # pretrained weights like below
+    # pretrained weights like below
-  # model = YOLOv10.from_pretrained('jameslahm/yolov10{n/s/m/b/l/x}')
+    # model = YOLOv10.from_pretrained('jameslahm/yolov10{n/s/m/b/l/x}')
-  # or
+    # or
-  # wget https://github.com/THU-MIG/yolov10/releases/download/v1.1/yolov10{n/s/m/b/l/x}.pt
+    # wget https://github.com/THU-MIG/yolov10/releases/download/v1.1/yolov10{n/s/m/b/l/x}.pt
-  model = YOLOv10('yolov10n.pt', task='segment')
+    model = YOLOv10('yolov10n.pt', task='segment')
 
-  args = dict(model='yolov10n.pt', data='coco.yaml', 
+    args = dict(model='yolov10n.pt', data='coco128-seg.yaml', 
-              epochs=args.epochs, batch=args.batch_size,
+                epochs=args.epochs, batch=args.batch_size,
-              # cfg = 'pgt_train.yaml', # This can be edited for full control of the training process
+                # cfg = 'pgt_train.yaml', # This can be edited for full control of the training process
-              )
+                )
-  trainer = PGTSegmentationTrainer(overrides=args)
+    trainer = PGTSegmentationTrainer(overrides=args)
-  trainer.train(
+    trainer.train(
-        # debug=True, 
+                # debug=True, 
-        #   args = dict(pgt_coeff=0.1), # Should add later to config
+                # args = dict(pgt_coeff=0.1), # Should add later to config
-          )
+                )
 
-  # Save the trained model
+    # Save the trained model
-  model.save('yolov10_coco_trained.pt')
+    model.save('yolov10_coco_trained.pt')
 
-  # Evaluate the model on the validation set
+    # Evaluate the model on the validation set
-  results = model.val(data='coco.yaml')
+    results = model.val(data='coco.yaml')
 
-  # Print the evaluation results
+    # Print the evaluation results
-  print(results)
+    print(results)
 
 if __name__ == "__main__":
-  parser = argparse.ArgumentParser(description='Train YOLOv10 model with PGT segmentation.')
+    parser = argparse.ArgumentParser(description='Train YOLOv10 model with PGT segmentation.')
-  parser.add_argument('--device', type=str, default='0', help='CUDA device number')
+    parser.add_argument('--device', type=str, default='0', help='CUDA device number')
-  parser.add_argument('--batch_size', type=int, default=128, help='Batch size for training')
+    parser.add_argument('--batch_size', type=int, default=64, help='Batch size for training')
-  parser.add_argument('--epochs', type=int, default=100, help='Number of epochs for training')
+    parser.add_argument('--epochs', type=int, default=100, help='Number of epochs for training')
-  args = parser.parse_args()
+    args = parser.parse_args()
 
-  # Set CUDA device (only needed for multi-gpu machines)
+    # Set CUDA device (only needed for multi-gpu machines)
-  os.environ["CUDA_DEVICE_ORDER"] = "PCI_BUS_ID"
+    os.environ["CUDA_DEVICE_ORDER"] = "PCI_BUS_ID"
-  os.environ["CUDA_VISIBLE_DEVICES"] = args.device
+    os.environ["CUDA_VISIBLE_DEVICES"] = args.device
-  main(args)
+    main(args)

ultralytics/cfg/pgt_train.yaml

@@ -0,0 +1,127 @@
+# Ultralytics YOLO 🚀, AGPL-3.0 license
+# Default training settings and hyperparameters for medium-augmentation COCO training
+
+task: detect # (str) YOLO task, i.e. detect, segment, classify, pose
+mode: train # (str) YOLO mode, i.e. train, val, predict, export, track, benchmark
+
+# Train settings -------------------------------------------------------------------------------------------------------
+model: # (str, optional) path to model file, i.e. yolov8n.pt, yolov8n.yaml
+data: # (str, optional) path to data file, i.e. coco128.yaml
+epochs: 100 # (int) number of epochs to train for
+time: # (float, optional) number of hours to train for, overrides epochs if supplied
+patience: 100 # (int) epochs to wait for no observable improvement for early stopping of training
+batch: 16 # (int) number of images per batch (-1 for AutoBatch)
+imgsz: 640 # (int | list) input images size as int for train and val modes, or list[w,h] for predict and export modes
+save: True # (bool) save train checkpoints and predict results
+save_period: -1 # (int) Save checkpoint every x epochs (disabled if < 1)
+val_period: 1 # (int) Validation every x epochs
+cache: False # (bool) True/ram, disk or False. Use cache for data loading
+device: # (int | str | list, optional) device to run on, i.e. cuda device=0 or device=0,1,2,3 or device=cpu
+workers: 8 # (int) number of worker threads for data loading (per RANK if DDP)
+project: # (str, optional) project name
+name: # (str, optional) experiment name, results saved to 'project/name' directory
+exist_ok: False # (bool) whether to overwrite existing experiment
+pretrained: True # (bool | str) whether to use a pretrained model (bool) or a model to load weights from (str)
+optimizer: auto # (str) optimizer to use, choices=[SGD, Adam, Adamax, AdamW, NAdam, RAdam, RMSProp, auto]
+verbose: True # (bool) whether to print verbose output
+seed: 0 # (int) random seed for reproducibility
+deterministic: True # (bool) whether to enable deterministic mode
+single_cls: False # (bool) train multi-class data as single-class
+rect: False # (bool) rectangular training if mode='train' or rectangular validation if mode='val'
+cos_lr: False # (bool) use cosine learning rate scheduler
+close_mosaic: 10 # (int) disable mosaic augmentation for final epochs (0 to disable)
+resume: False # (bool) resume training from last checkpoint
+amp: True # (bool) Automatic Mixed Precision (AMP) training, choices=[True, False], True runs AMP check
+fraction: 1.0 # (float) dataset fraction to train on (default is 1.0, all images in train set)
+profile: False # (bool) profile ONNX and TensorRT speeds during training for loggers
+freeze: None # (int | list, optional) freeze first n layers, or freeze list of layer indices during training
+multi_scale: False # (bool) Whether to use multiscale during training
+# Segmentation
+overlap_mask: True # (bool) masks should overlap during training (segment train only)
+mask_ratio: 4 # (int) mask downsample ratio (segment train only)
+# Classification
+dropout: 0.0 # (float) use dropout regularization (classify train only)
+
+# Val/Test settings ----------------------------------------------------------------------------------------------------
+val: True # (bool) validate/test during training
+split: val # (str) dataset split to use for validation, i.e. 'val', 'test' or 'train'
+save_json: False # (bool) save results to JSON file
+save_hybrid: False # (bool) save hybrid version of labels (labels + additional predictions)
+conf: # (float, optional) object confidence threshold for detection (default 0.25 predict, 0.001 val)
+iou: 0.7 # (float) intersection over union (IoU) threshold for NMS
+max_det: 300 # (int) maximum number of detections per image
+half: False # (bool) use half precision (FP16)
+dnn: False # (bool) use OpenCV DNN for ONNX inference
+plots: True # (bool) save plots and images during train/val
+
+# Predict settings -----------------------------------------------------------------------------------------------------
+source: # (str, optional) source directory for images or videos
+vid_stride: 1 # (int) video frame-rate stride
+stream_buffer: False # (bool) buffer all streaming frames (True) or return the most recent frame (False)
+visualize: False # (bool) visualize model features
+augment: False # (bool) apply image augmentation to prediction sources
+agnostic_nms: False # (bool) class-agnostic NMS
+classes: # (int | list[int], optional) filter results by class, i.e. classes=0, or classes=[0,2,3]
+retina_masks: False # (bool) use high-resolution segmentation masks
+embed: # (list[int], optional) return feature vectors/embeddings from given layers
+
+# Visualize settings ---------------------------------------------------------------------------------------------------
+show: False # (bool) show predicted images and videos if environment allows
+save_frames: False # (bool) save predicted individual video frames
+save_txt: False # (bool) save results as .txt file
+save_conf: False # (bool) save results with confidence scores
+save_crop: False # (bool) save cropped images with results
+show_labels: True # (bool) show prediction labels, i.e. 'person'
+show_conf: True # (bool) show prediction confidence, i.e. '0.99'
+show_boxes: True # (bool) show prediction boxes
+line_width: # (int, optional) line width of the bounding boxes. Scaled to image size if None.
+
+# Export settings ------------------------------------------------------------------------------------------------------
+format: torchscript # (str) format to export to, choices at https://docs.ultralytics.com/modes/export/#export-formats
+keras: False # (bool) use Kera=s
+optimize: False # (bool) TorchScript: optimize for mobile
+int8: False # (bool) CoreML/TF INT8 quantization
+dynamic: False # (bool) ONNX/TF/TensorRT: dynamic axes
+simplify: False # (bool) ONNX: simplify model using `onnxslim`
+opset: # (int, optional) ONNX: opset version
+workspace: 4 # (int) TensorRT: workspace size (GB)
+nms: False # (bool) CoreML: add NMS
+
+# Hyperparameters ------------------------------------------------------------------------------------------------------
+lr0: 0.01 # (float) initial learning rate (i.e. SGD=1E-2, Adam=1E-3)
+lrf: 0.01 # (float) final learning rate (lr0 * lrf)
+momentum: 0.937 # (float) SGD momentum/Adam beta1
+weight_decay: 0.0005 # (float) optimizer weight decay 5e-4
+warmup_epochs: 3.0 # (float) warmup epochs (fractions ok)
+warmup_momentum: 0.8 # (float) warmup initial momentum
+warmup_bias_lr: 0.1 # (float) warmup initial bias lr
+box: 7.5 # (float) box loss gain
+cls: 0.5 # (float) cls loss gain (scale with pixels)
+dfl: 1.5 # (float) dfl loss gain
+pose: 12.0 # (float) pose loss gain
+kobj: 1.0 # (float) keypoint obj loss gain
+label_smoothing: 0.0 # (float) label smoothing (fraction)
+nbs: 64 # (int) nominal batch size
+hsv_h: 0.015 # (float) image HSV-Hue augmentation (fraction)
+hsv_s: 0.7 # (float) image HSV-Saturation augmentation (fraction)
+hsv_v: 0.4 # (float) image HSV-Value augmentation (fraction)
+degrees: 0.0 # (float) image rotation (+/- deg)
+translate: 0.1 # (float) image translation (+/- fraction)
+scale: 0.5 # (float) image scale (+/- gain)
+shear: 0.0 # (float) image shear (+/- deg)
+perspective: 0.0 # (float) image perspective (+/- fraction), range 0-0.001
+flipud: 0.0 # (float) image flip up-down (probability)
+fliplr: 0.5 # (float) image flip left-right (probability)
+bgr: 0.0 # (float) image channel BGR (probability)
+mosaic: 1.0 # (float) image mosaic (probability)
+mixup: 0.0 # (float) image mixup (probability)
+copy_paste: 0.0 # (float) segment copy-paste (probability)
+auto_augment: randaugment # (str) auto augmentation policy for classification (randaugment, autoaugment, augmix)
+erasing: 0.4 # (float) probability of random erasing during classification training (0-1)
+crop_fraction: 1.0 # (float) image crop fraction for classification evaluation/inference (0-1)
+
+# Custom config.yaml ---------------------------------------------------------------------------------------------------
+cfg: # (str, optional) for overriding defaults.yaml
+
+# Tracker settings ------------------------------------------------------------------------------------------------------
+tracker: botsort.yaml # (str) tracker type, choices=[botsort.yaml, bytetrack.yaml]

ultralytics/engine/pgt_trainer.py

@@ -384,7 +384,9 @@ class PGTTrainer:
                 # Forward
                 with torch.cuda.amp.autocast(self.amp):
                     batch = self.preprocess_batch(batch)
-                    (self.loss, self.loss_items), images = self.model(batch, return_images=True)
+                    batch['img'] = batch['img'].requires_grad_(True)
+                    self.loss, self.loss_items = self.model(batch)
+                    # (self.loss, self.loss_items), images = self.model(batch, return_images=True)
 
                 # smask = get_dist_reg(images, batch['masks'])
 

ultralytics/engine/pgt_validator.py

@@ -175,12 +175,14 @@ class PGTValidator:
 
             # Inference
             with dt[1]:
+                model.zero_grad()
                 preds = model(batch["img"].requires_grad_(True), augment=augment)
 
             # Loss
             with dt[2]:
                 if self.training:
                     self.loss += model.loss(batch, preds)[1]
+                    model.zero_grad()
 
             # Postprocess
             with dt[3]:

ultralytics/utils/loss.py

@@ -731,7 +731,7 @@ class v10PGTDetectLoss:
         self.one2many = v8DetectionLoss(model, tal_topk=10)
         self.one2one = v8DetectionLoss(model, tal_topk=1)
     
-    def __call__(self, preds, batch):
+    def __call__(self, preds, batch, return_plaus=True):
         batch['img'] = batch['img'].requires_grad_(True)
         one2many = preds["one2many"]
         loss_one2many = self.one2many(one2many, batch)
@@ -739,16 +739,18 @@ class v10PGTDetectLoss:
         loss_one2one = self.one2one(one2one, batch)
 
         loss = loss_one2many[0] + loss_one2one[0]
+        if return_plaus:
+            smask = get_dist_reg(batch['img'], batch['masks'])
 
-        smask = get_dist_reg(batch['img'], batch['masks'])
+            grad = torch.autograd.grad(loss, batch['img'], retain_graph=True)[0]
+            grad = torch.abs(grad)
 
-        grad = torch.autograd.grad(loss, batch['img'], retain_graph=True)[0]
+            pgt_coeff = 3.0
-        grad = torch.abs(grad)
+            plaus_loss = plaus_loss_fn(grad, smask, pgt_coeff)
+            # self.loss_items = torch.cat((self.loss_items, plaus_loss.unsqueeze(0)))
+            loss += plaus_loss
             
-        pgt_coeff = 3.0
+            return loss, torch.cat((loss_one2many[1], loss_one2one[1], plaus_loss.unsqueeze(0)))
-        plaus_loss = plaus_loss_fn(grad, smask, pgt_coeff)
+        else:
-        # self.loss_items = torch.cat((self.loss_items, plaus_loss.unsqueeze(0)))
+            return loss, torch.cat((loss_one2many[1], loss_one2one[1]))
-        loss += plaus_loss
-        
-        return loss, torch.cat((loss_one2many[1], loss_one2one[1], plaus_loss.unsqueeze(0)))