Replace `nosave` and `noval` with `save` and `val` (#127)

Co-authored-by: pre-commit-ci[bot] <66853113+pre-commit-ci[bot]@users.noreply.github.com>

ultralytics/yolo/configs/default.yaml

@@ -1,71 +1,69 @@
 # YOLO 🚀 by Ultralytics, GPL-3.0 license
 # Default training settings and hyperparameters for medium-augmentation COCO training
 
-# Task and Mode
-task: "classify"  # choices=['detect', 'segment', 'classify', 'init'] # init is a special case
-mode: "train"  # choice=['train', 'val', 'predict']
+task: "classify" # choices=['detect', 'segment', 'classify', 'init'] # init is a special case. Specify task to run.
+mode: "train" # choices=['train', 'val', 'predict'] # mode to run task in.
 
 # Train settings -------------------------------------------------------------------------------------------------------
-model: null  # i.e. yolov5s.pt, yolo.yaml
-data: null  # i.e. coco128.yaml
-epochs: 100
-batch_size: 16
-imgsz: 640
-nosave: False
-cache: False  # True/ram, disk or False
-device: ''  # cuda device, i.e. 0 or 0,1,2,3 or cpu
-workers: 8
-project: null
-name: null
-exist_ok: False
-pretrained: False
-optimizer: 'SGD'  # choices=['SGD', 'Adam', 'AdamW', 'RMSProp']
-verbose: False
-seed: 0
-deterministic: True
-local_rank: -1
-single_cls: False  # train multi-class data as single-class
-image_weights: False  # use weighted image selection for training
-rect: False  # support rectangular training
-cos_lr: False  # use cosine LR scheduler
-close_mosaic: 10  # disable mosaic for final 10 epochs
-resume: False
+model: null # i.e. yolov5s.pt, yolo.yaml. Path to model file
+data: null # i.e. coco128.yaml. Path to data file
+epochs: 100 # number of epochs to train for
+batch_size: 16 # number of images per batch
+imgsz: 640 # size of input images
+save: True # save checkpoints
+cache: False # True/ram, disk or False. Use cache for data loading
+device: '' # cuda device, i.e. 0 or 0,1,2,3 or cpu. Device to run on
+workers: 8 # number of worker threads for data loading
+project: null # project name
+name: null # experiment name
+exist_ok: False # whether to overwrite existing experiment
+pretrained: False # whether to use a pretrained model
+optimizer: 'SGD' # optimizer to use, choices=['SGD', 'Adam', 'AdamW', 'RMSProp']
+verbose: False # whether to print verbose output
+seed: 0 # random seed for reproducibility
+deterministic: True # whether to enable deterministic mode
+local_rank: -1 # local rank for distributed training
+single_cls: False # train multi-class data as single-class
+image_weights: False # use weighted image selection for training
+rect: False # support rectangular training
+cos_lr: False # use cosine learning rate scheduler
+close_mosaic: 10 # disable mosaic augmentation for final 10 epochs
+resume: False # resume training from last checkpoint
 # Segmentation
-overlap_mask: True  # masks overlap
-mask_ratio: 4  # mask downsample ratio
+overlap_mask: True # masks should overlap during training
+mask_ratio: 4 # mask downsample ratio
 # Classification
-dropout: False  # use dropout
-
+dropout: False # use dropout regularization
 
 # Val/Test settings ----------------------------------------------------------------------------------------------------
-noval: False
-save_json: False
-save_hybrid: False
-conf_thres: 0.001
-iou_thres: 0.7
-max_det: 300
-half: False
-dnn: False  # use OpenCV DNN for ONNX inference
-plots: True
+val: True # validate/test during training
+save_json: False # save results to JSON file
+save_hybrid: False # save hybrid version of labels (labels + additional predictions)
+conf_thres: 0.001 # object confidence threshold for detection
+iou_thres: 0.7 # intersection over union threshold for NMS
+max_det: 300 # maximum number of detections per image
+half: False # use half precision (FP16)
+dnn: False # use OpenCV DNN for ONNX inference
+plots: True # show plots during training
 
 # Prediction settings --------------------------------------------------------------------------------------------------
-source: "ultralytics/assets/"
-view_img: False
-save_txt: False
-save_conf: False
-save_crop: False
-hide_labels: False  # hide labels
-hide_conf: False
-vid_stride: 1  # video frame-rate stride
-line_thickness: 3  # bounding box thickness (pixels)
-update: False  # Update all models
-visualize: False
-augment: False
-agnostic_nms: False  # class-agnostic NMS
-retina_masks: False
+source: "ultralytics/assets" # source directory for images or videos
+show: False # show results if possible
+save_txt: False # save results as .txt file
+save_conf: False # save results with confidence scores
+save_crop: False # save cropped images with results
+hide_labels: False # hide labels
+hide_conf: False # hide confidence scores
+vid_stride: 1 # video frame-rate stride
+line_thickness: 3 # bounding box thickness (pixels)
+update: False # Update all models
+visualize: False # visualize results
+augment: False # apply data augmentation to images
+agnostic_nms: False # class-agnostic NMS
+retina_masks: False # use retina masks for object detection
 
 # Export settings ------------------------------------------------------------------------------------------------------
-format: torchscript
+format: torchscript # format to export to
 keras: False  # use Keras
 optimize: False  # TorchScript: optimize for mobile
 int8: False  # CoreML/TF INT8 quantization
@@ -103,11 +101,11 @@ mosaic: 1.0  # image mosaic (probability)
 mixup: 0.0  # image mixup (probability)
 copy_paste: 0.0  # segment copy-paste (probability)
 
-# For debugging. Don't change
-v5loader: False
-
 # Hydra configs --------------------------------------------------------------------------------------------------------
 hydra:
   output_subdir: null  # disable hydra directory creation
   run:
     dir: .
+
+# Debug, do not modify -------------------------------------------------------------------------------------------------
+v5loader: False  # use legacy YOLOv5 dataloader

ultralytics/yolo/data/build.py

@@ -67,7 +67,7 @@ def build_dataloader(cfg, batch_size, img_path, stride=32, label_path=None, rank
             augment=mode == "train",  # augmentation
             hyp=cfg,  # TODO: probably add a get_hyps_from_cfg function
             rect=cfg.rect if mode == "train" else True,  # rectangular batches
-            cache=None if cfg.noval else cfg.get("cache", None),
+            cache=cfg.get("cache", None),
             single_cls=cfg.get("single_cls", False),
             stride=int(stride),
             pad=0.0 if mode == "train" else 0.5,

ultralytics/yolo/engine/predictor.py

@@ -104,7 +104,6 @@ class BasePredictor:
     def setup(self, source=None, model=None):
         # source
         source = str(source or self.args.source)
-        self.save_img = not self.args.nosave and not source.endswith('.txt')
         is_file = Path(source).suffix[1:] in (IMG_FORMATS + VID_FORMATS)
         is_url = source.lower().startswith(('rtsp://', 'rtmp://', 'http://', 'https://'))
         webcam = source.isnumeric() or source.endswith('.streams') or (is_url and not is_file)
@@ -168,10 +167,10 @@ class BasePredictor:
                 p = Path(path)
                 s += self.write_results(i, preds, (p, im, im0s))
 
-                if self.args.view_img:
+                if self.args.show:
                     self.show(p)
 
-                if self.save_img:
+                if self.args.save:
                     self.save_preds(vid_cap, i, str(self.save_dir / p.name))
 
             # Print time (inference-only)
@@ -182,7 +181,7 @@ class BasePredictor:
         LOGGER.info(
             f'Speed: %.1fms pre-process, %.1fms inference, %.1fms postprocess per image at shape {(1, 3, *self.imgsz)}'
             % t)
-        if self.args.save_txt or self.save_img:
+        if self.args.save_txt or self.args.save:
             s = f"\n{len(list(self.save_dir.glob('labels/*.txt')))} labels saved to {self.save_dir / 'labels'}" if self.args.save_txt else ''
             LOGGER.info(f"Results saved to {colorstr('bold', self.save_dir)}{s}")
 

ultralytics/yolo/engine/trainer.py

@@ -244,12 +244,12 @@ class BaseTrainer:
             for i, batch in pbar:
                 self.trigger_callbacks("on_train_batch_start")
 
-                # update dataloader attributes (optional)
+                # Update dataloader attributes (optional)
                 if epoch == (self.epochs - self.args.close_mosaic) and hasattr(self.train_loader.dataset, 'mosaic'):
                     LOGGER.info("Closing dataloader mosaic")
                     self.train_loader.dataset.mosaic = False
 
-                # warmup
+                # Warmup
                 ni = i + nb * epoch
                 if ni <= nw:
                     xi = [0, nw]  # x interp
@@ -261,7 +261,7 @@ class BaseTrainer:
                         if 'momentum' in x:
                             x['momentum'] = np.interp(ni, xi, [self.args.warmup_momentum, self.args.momentum])
 
-                # forward
+                # Forward
                 with torch.cuda.amp.autocast(self.amp):
                     batch = self.preprocess_batch(batch)
                     preds = self.model(batch["img"])
@@ -271,15 +271,15 @@ class BaseTrainer:
                     self.tloss = (self.tloss * i + self.loss_items) / (i + 1) if self.tloss is not None \
                         else self.loss_items
 
-                # backward
+                # Backward
                 self.scaler.scale(self.loss).backward()
 
-                # optimize - https://pytorch.org/docs/master/notes/amp_examples.html
+                # Optimize - https://pytorch.org/docs/master/notes/amp_examples.html
                 if ni - last_opt_step >= self.accumulate:
                     self.optimizer_step()
                     last_opt_step = ni
 
-                # log
+                # Log
                 mem = f'{torch.cuda.memory_reserved() / 1E9 if torch.cuda.is_available() else 0:.3g}G'  # (GB)
                 loss_len = self.tloss.shape[0] if len(self.tloss.size()) else 1
                 losses = self.tloss if loss_len > 1 else torch.unsqueeze(self.tloss, 0)
@@ -298,17 +298,17 @@ class BaseTrainer:
             self.trigger_callbacks("on_train_epoch_end")
 
             if rank in {-1, 0}:
-                # validation
+                # Validation
                 self.trigger_callbacks('on_val_start')
                 self.ema.update_attr(self.model, include=['yaml', 'nc', 'args', 'names', 'stride', 'class_weights'])
                 final_epoch = (epoch + 1 == self.epochs)
-                if not self.args.noval or final_epoch:
+                if self.args.val or final_epoch:
                     self.metrics, self.fitness = self.validate()
                 self.trigger_callbacks('on_val_end')
                 self.save_metrics(metrics={**self.label_loss_items(self.tloss), **self.metrics, **lr})
 
-                # save model
-                if (not self.args.nosave) or (epoch + 1 == self.epochs):
+                # Save model
+                if self.args.save or (epoch + 1 == self.epochs):
                     self.save_model()
                     self.trigger_callbacks('on_model_save')
 
@@ -319,7 +319,7 @@ class BaseTrainer:
             # TODO: termination condition
 
         if rank in {-1, 0}:
-            # do the last evaluation with best.pt
+            # Do final val with best.pt
             self.log(f'\n{epoch - self.start_epoch + 1} epochs completed in '
                      f'{(time.time() - self.train_time_start) / 3600:.3f} hours.')
             self.final_eval()

ultralytics/yolo/v8/classify/predict.py

@@ -43,7 +43,7 @@ class ClassificationPredictor(BasePredictor):
 
         # write
         text = '\n'.join(f'{prob[j]:.2f} {self.model.names[j]}' for j in top5i)
-        if self.save_img or self.args.view_img:  # Add bbox to image
+        if self.args.save or self.args.show:  # Add bbox to image
             self.annotator.text((32, 32), text, txt_color=(255, 255, 255))
         if self.args.save_txt:  # Write to file
             with open(f'{self.txt_path}.txt', 'a') as f:

ultralytics/yolo/v8/detect/predict.py

@@ -66,7 +66,7 @@ class DetectionPredictor(BasePredictor):
                 with open(f'{self.txt_path}.txt', 'a') as f:
                     f.write(('%g ' * len(line)).rstrip() % line + '\n')
 
-            if self.save_img or self.args.save_crop or self.args.view_img:  # Add bbox to image
+            if self.args.save or self.args.save_crop or self.args.show:  # Add bbox to image
                 c = int(cls)  # integer class
                 label = None if self.args.hide_labels else (
                     self.model.names[c] if self.args.hide_conf else f'{self.model.names[c]} {conf:.2f}')

ultralytics/yolo/v8/segment/predict.py

@@ -81,7 +81,7 @@ class SegmentationPredictor(DetectionPredictor):
                 with open(f'{self.txt_path}.txt', 'a') as f:
                     f.write(('%g ' * len(line)).rstrip() % line + '\n')
 
-            if self.save_img or self.args.save_crop or self.args.view_img:
+            if self.args.save or self.args.save_crop or self.args.show:
                 c = int(cls)  # integer class
                 label = None if self.args.hide_labels else (
                     self.model.names[c] if self.args.hide_conf else f'{self.model.names[c]} {conf:.2f}')