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https://github.com/THU-MIG/yolov10.git
synced 2025-11-04 08:56:11 +08:00
Fixed PGT by including it in the loss function
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nielseni6
parent
38fa59edf2
commit
3a449d5a6c
@ -6,42 +6,42 @@ import argparse
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def main(args):
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# model = YOLOv10()
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# model = YOLOv10()
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# If you want to finetune the model with pretrained weights, you could load the
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# pretrained weights like below
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# model = YOLOv10.from_pretrained('jameslahm/yolov10{n/s/m/b/l/x}')
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# or
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# wget https://github.com/THU-MIG/yolov10/releases/download/v1.1/yolov10{n/s/m/b/l/x}.pt
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model = YOLOv10('yolov10n.pt', task='segment')
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# If you want to finetune the model with pretrained weights, you could load the
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# pretrained weights like below
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# model = YOLOv10.from_pretrained('jameslahm/yolov10{n/s/m/b/l/x}')
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# or
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# wget https://github.com/THU-MIG/yolov10/releases/download/v1.1/yolov10{n/s/m/b/l/x}.pt
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model = YOLOv10('yolov10n.pt', task='segment')
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args = dict(model='yolov10n.pt', data='coco.yaml',
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epochs=args.epochs, batch=args.batch_size,
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# cfg = 'pgt_train.yaml', # This can be edited for full control of the training process
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)
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trainer = PGTSegmentationTrainer(overrides=args)
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trainer.train(
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# debug=True,
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# args = dict(pgt_coeff=0.1), # Should add later to config
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)
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args = dict(model='yolov10n.pt', data='coco128-seg.yaml',
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epochs=args.epochs, batch=args.batch_size,
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# cfg = 'pgt_train.yaml', # This can be edited for full control of the training process
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)
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trainer = PGTSegmentationTrainer(overrides=args)
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trainer.train(
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# debug=True,
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# args = dict(pgt_coeff=0.1), # Should add later to config
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)
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# Save the trained model
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model.save('yolov10_coco_trained.pt')
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# Save the trained model
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model.save('yolov10_coco_trained.pt')
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# Evaluate the model on the validation set
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results = model.val(data='coco.yaml')
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# Evaluate the model on the validation set
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results = model.val(data='coco.yaml')
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# Print the evaluation results
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print(results)
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# Print the evaluation results
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print(results)
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if __name__ == "__main__":
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parser = argparse.ArgumentParser(description='Train YOLOv10 model with PGT segmentation.')
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parser.add_argument('--device', type=str, default='0', help='CUDA device number')
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parser.add_argument('--batch_size', type=int, default=128, help='Batch size for training')
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parser.add_argument('--epochs', type=int, default=100, help='Number of epochs for training')
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args = parser.parse_args()
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parser = argparse.ArgumentParser(description='Train YOLOv10 model with PGT segmentation.')
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parser.add_argument('--device', type=str, default='0', help='CUDA device number')
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parser.add_argument('--batch_size', type=int, default=64, help='Batch size for training')
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parser.add_argument('--epochs', type=int, default=100, help='Number of epochs for training')
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args = parser.parse_args()
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# Set CUDA device (only needed for multi-gpu machines)
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os.environ["CUDA_DEVICE_ORDER"] = "PCI_BUS_ID"
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os.environ["CUDA_VISIBLE_DEVICES"] = args.device
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main(args)
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# Set CUDA device (only needed for multi-gpu machines)
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os.environ["CUDA_DEVICE_ORDER"] = "PCI_BUS_ID"
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os.environ["CUDA_VISIBLE_DEVICES"] = args.device
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main(args)
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127
ultralytics/cfg/pgt_train.yaml
Normal file
127
ultralytics/cfg/pgt_train.yaml
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@ -0,0 +1,127 @@
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# Ultralytics YOLO 🚀, AGPL-3.0 license
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# Default training settings and hyperparameters for medium-augmentation COCO training
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task: detect # (str) YOLO task, i.e. detect, segment, classify, pose
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mode: train # (str) YOLO mode, i.e. train, val, predict, export, track, benchmark
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# Train settings -------------------------------------------------------------------------------------------------------
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model: # (str, optional) path to model file, i.e. yolov8n.pt, yolov8n.yaml
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data: # (str, optional) path to data file, i.e. coco128.yaml
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epochs: 100 # (int) number of epochs to train for
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time: # (float, optional) number of hours to train for, overrides epochs if supplied
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patience: 100 # (int) epochs to wait for no observable improvement for early stopping of training
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batch: 16 # (int) number of images per batch (-1 for AutoBatch)
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imgsz: 640 # (int | list) input images size as int for train and val modes, or list[w,h] for predict and export modes
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save: True # (bool) save train checkpoints and predict results
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save_period: -1 # (int) Save checkpoint every x epochs (disabled if < 1)
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val_period: 1 # (int) Validation every x epochs
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cache: False # (bool) True/ram, disk or False. Use cache for data loading
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device: # (int | str | list, optional) device to run on, i.e. cuda device=0 or device=0,1,2,3 or device=cpu
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workers: 8 # (int) number of worker threads for data loading (per RANK if DDP)
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project: # (str, optional) project name
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name: # (str, optional) experiment name, results saved to 'project/name' directory
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exist_ok: False # (bool) whether to overwrite existing experiment
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pretrained: True # (bool | str) whether to use a pretrained model (bool) or a model to load weights from (str)
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optimizer: auto # (str) optimizer to use, choices=[SGD, Adam, Adamax, AdamW, NAdam, RAdam, RMSProp, auto]
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verbose: True # (bool) whether to print verbose output
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seed: 0 # (int) random seed for reproducibility
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deterministic: True # (bool) whether to enable deterministic mode
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single_cls: False # (bool) train multi-class data as single-class
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rect: False # (bool) rectangular training if mode='train' or rectangular validation if mode='val'
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cos_lr: False # (bool) use cosine learning rate scheduler
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close_mosaic: 10 # (int) disable mosaic augmentation for final epochs (0 to disable)
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resume: False # (bool) resume training from last checkpoint
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amp: True # (bool) Automatic Mixed Precision (AMP) training, choices=[True, False], True runs AMP check
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fraction: 1.0 # (float) dataset fraction to train on (default is 1.0, all images in train set)
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profile: False # (bool) profile ONNX and TensorRT speeds during training for loggers
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freeze: None # (int | list, optional) freeze first n layers, or freeze list of layer indices during training
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multi_scale: False # (bool) Whether to use multiscale during training
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# Segmentation
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overlap_mask: True # (bool) masks should overlap during training (segment train only)
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mask_ratio: 4 # (int) mask downsample ratio (segment train only)
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# Classification
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dropout: 0.0 # (float) use dropout regularization (classify train only)
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# Val/Test settings ----------------------------------------------------------------------------------------------------
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val: True # (bool) validate/test during training
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split: val # (str) dataset split to use for validation, i.e. 'val', 'test' or 'train'
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save_json: False # (bool) save results to JSON file
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save_hybrid: False # (bool) save hybrid version of labels (labels + additional predictions)
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conf: # (float, optional) object confidence threshold for detection (default 0.25 predict, 0.001 val)
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iou: 0.7 # (float) intersection over union (IoU) threshold for NMS
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max_det: 300 # (int) maximum number of detections per image
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half: False # (bool) use half precision (FP16)
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dnn: False # (bool) use OpenCV DNN for ONNX inference
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plots: True # (bool) save plots and images during train/val
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# Predict settings -----------------------------------------------------------------------------------------------------
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source: # (str, optional) source directory for images or videos
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vid_stride: 1 # (int) video frame-rate stride
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stream_buffer: False # (bool) buffer all streaming frames (True) or return the most recent frame (False)
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visualize: False # (bool) visualize model features
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augment: False # (bool) apply image augmentation to prediction sources
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agnostic_nms: False # (bool) class-agnostic NMS
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classes: # (int | list[int], optional) filter results by class, i.e. classes=0, or classes=[0,2,3]
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retina_masks: False # (bool) use high-resolution segmentation masks
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embed: # (list[int], optional) return feature vectors/embeddings from given layers
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# Visualize settings ---------------------------------------------------------------------------------------------------
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show: False # (bool) show predicted images and videos if environment allows
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save_frames: False # (bool) save predicted individual video frames
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save_txt: False # (bool) save results as .txt file
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save_conf: False # (bool) save results with confidence scores
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save_crop: False # (bool) save cropped images with results
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show_labels: True # (bool) show prediction labels, i.e. 'person'
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show_conf: True # (bool) show prediction confidence, i.e. '0.99'
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show_boxes: True # (bool) show prediction boxes
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line_width: # (int, optional) line width of the bounding boxes. Scaled to image size if None.
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# Export settings ------------------------------------------------------------------------------------------------------
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format: torchscript # (str) format to export to, choices at https://docs.ultralytics.com/modes/export/#export-formats
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keras: False # (bool) use Kera=s
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optimize: False # (bool) TorchScript: optimize for mobile
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int8: False # (bool) CoreML/TF INT8 quantization
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dynamic: False # (bool) ONNX/TF/TensorRT: dynamic axes
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simplify: False # (bool) ONNX: simplify model using `onnxslim`
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opset: # (int, optional) ONNX: opset version
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workspace: 4 # (int) TensorRT: workspace size (GB)
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nms: False # (bool) CoreML: add NMS
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# Hyperparameters ------------------------------------------------------------------------------------------------------
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lr0: 0.01 # (float) initial learning rate (i.e. SGD=1E-2, Adam=1E-3)
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lrf: 0.01 # (float) final learning rate (lr0 * lrf)
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momentum: 0.937 # (float) SGD momentum/Adam beta1
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weight_decay: 0.0005 # (float) optimizer weight decay 5e-4
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warmup_epochs: 3.0 # (float) warmup epochs (fractions ok)
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warmup_momentum: 0.8 # (float) warmup initial momentum
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warmup_bias_lr: 0.1 # (float) warmup initial bias lr
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box: 7.5 # (float) box loss gain
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cls: 0.5 # (float) cls loss gain (scale with pixels)
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dfl: 1.5 # (float) dfl loss gain
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pose: 12.0 # (float) pose loss gain
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kobj: 1.0 # (float) keypoint obj loss gain
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label_smoothing: 0.0 # (float) label smoothing (fraction)
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nbs: 64 # (int) nominal batch size
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hsv_h: 0.015 # (float) image HSV-Hue augmentation (fraction)
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hsv_s: 0.7 # (float) image HSV-Saturation augmentation (fraction)
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hsv_v: 0.4 # (float) image HSV-Value augmentation (fraction)
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degrees: 0.0 # (float) image rotation (+/- deg)
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translate: 0.1 # (float) image translation (+/- fraction)
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scale: 0.5 # (float) image scale (+/- gain)
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shear: 0.0 # (float) image shear (+/- deg)
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perspective: 0.0 # (float) image perspective (+/- fraction), range 0-0.001
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flipud: 0.0 # (float) image flip up-down (probability)
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fliplr: 0.5 # (float) image flip left-right (probability)
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bgr: 0.0 # (float) image channel BGR (probability)
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mosaic: 1.0 # (float) image mosaic (probability)
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mixup: 0.0 # (float) image mixup (probability)
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copy_paste: 0.0 # (float) segment copy-paste (probability)
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auto_augment: randaugment # (str) auto augmentation policy for classification (randaugment, autoaugment, augmix)
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erasing: 0.4 # (float) probability of random erasing during classification training (0-1)
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crop_fraction: 1.0 # (float) image crop fraction for classification evaluation/inference (0-1)
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# Custom config.yaml ---------------------------------------------------------------------------------------------------
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cfg: # (str, optional) for overriding defaults.yaml
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# Tracker settings ------------------------------------------------------------------------------------------------------
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tracker: botsort.yaml # (str) tracker type, choices=[botsort.yaml, bytetrack.yaml]
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@ -380,11 +380,13 @@ class PGTTrainer:
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)
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if "momentum" in x:
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x["momentum"] = np.interp(ni, xi, [self.args.warmup_momentum, self.args.momentum])
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# Forward
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with torch.cuda.amp.autocast(self.amp):
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batch = self.preprocess_batch(batch)
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(self.loss, self.loss_items), images = self.model(batch, return_images=True)
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batch['img'] = batch['img'].requires_grad_(True)
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self.loss, self.loss_items = self.model(batch)
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# (self.loss, self.loss_items), images = self.model(batch, return_images=True)
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# smask = get_dist_reg(images, batch['masks'])
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@ -418,7 +420,7 @@ class PGTTrainer:
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x1, y1, x2, y2 = bboxes[idx]
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x1, y1, x2, y2 = int(torch.round(x1)), int(torch.round(y1)), int(torch.round(x2)), int(torch.round(y2))
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mask[irx, :, y1:y2, x1:x2] = 1.0
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save_imgs = True
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if save_imgs:
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# Convert tensors to numpy arrays
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@ -498,7 +500,7 @@ class PGTTrainer:
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self.run_callbacks("on_batch_end")
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if self.args.plots and ni in self.plot_idx:
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self.plot_training_samples(batch, ni)
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self.run_callbacks("on_train_batch_end")
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self.lr = {f"lr/pg{ir}": x["lr"] for ir, x in enumerate(self.optimizer.param_groups)} # for loggers
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@ -175,12 +175,14 @@ class PGTValidator:
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# Inference
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with dt[1]:
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model.zero_grad()
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preds = model(batch["img"].requires_grad_(True), augment=augment)
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# Loss
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with dt[2]:
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if self.training:
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self.loss += model.loss(batch, preds)[1]
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model.zero_grad()
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# Postprocess
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with dt[3]:
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@ -731,7 +731,7 @@ class v10PGTDetectLoss:
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self.one2many = v8DetectionLoss(model, tal_topk=10)
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self.one2one = v8DetectionLoss(model, tal_topk=1)
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def __call__(self, preds, batch):
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def __call__(self, preds, batch, return_plaus=True):
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batch['img'] = batch['img'].requires_grad_(True)
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one2many = preds["one2many"]
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loss_one2many = self.one2many(one2many, batch)
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@ -739,16 +739,18 @@ class v10PGTDetectLoss:
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loss_one2one = self.one2one(one2one, batch)
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loss = loss_one2many[0] + loss_one2one[0]
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smask = get_dist_reg(batch['img'], batch['masks'])
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if return_plaus:
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smask = get_dist_reg(batch['img'], batch['masks'])
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grad = torch.autograd.grad(loss, batch['img'], retain_graph=True)[0]
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grad = torch.abs(grad)
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grad = torch.autograd.grad(loss, batch['img'], retain_graph=True)[0]
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grad = torch.abs(grad)
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pgt_coeff = 3.0
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plaus_loss = plaus_loss_fn(grad, smask, pgt_coeff)
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# self.loss_items = torch.cat((self.loss_items, plaus_loss.unsqueeze(0)))
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loss += plaus_loss
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return loss, torch.cat((loss_one2many[1], loss_one2one[1], plaus_loss.unsqueeze(0)))
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pgt_coeff = 3.0
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plaus_loss = plaus_loss_fn(grad, smask, pgt_coeff)
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# self.loss_items = torch.cat((self.loss_items, plaus_loss.unsqueeze(0)))
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loss += plaus_loss
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return loss, torch.cat((loss_one2many[1], loss_one2one[1], plaus_loss.unsqueeze(0)))
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else:
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return loss, torch.cat((loss_one2many[1], loss_one2one[1]))
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