2025 changes over the summer as needed

.gitignore

@@ -1,2 +1,11 @@
 runs
-wandb
+wandb
+yolov7/torpedoes_2025_a-1/test/labels/
+
+yolov7/torpedoes_2025_a-1/train/labels/
+
+yolov7/torpedoes_2025_a-1/valid/labels/
+
+yolov7/torpedoes_2025_a-1/
+
+*.url

yolov7/train.py

@@ -38,7 +38,7 @@
 logger = logging.getLogger(__name__)
 
 
-def train(hyp, opt, device, tb_writer=None):
+def train(hyp, opt, device, tb_writer=None, log=False):
     logger.info(colorstr('hyperparameters: ') + ', '.join(f'{k}={v}' for k, v in hyp.items()))
     save_dir, epochs, batch_size, total_batch_size, weights, rank, freeze = \
         Path(opt.save_dir), opt.epochs, opt.batch_size, opt.total_batch_size, opt.weights, opt.global_rank, opt.freeze
@@ -65,15 +65,16 @@ def train(hyp, opt, device, tb_writer=None):
     is_coco = opt.data.endswith('coco.yaml')
 
     # Logging- Doing this before checking the dataset. Might update data_dict
-    loggers = {'wandb': None}  # loggers dict
-    if rank in [-1, 0]:
-        opt.hyp = hyp  # add hyperparameters
-        run_id = torch.load(weights, map_location=device).get('wandb_id') if weights.endswith('.pt') and os.path.isfile(weights) else None
-        wandb_logger = WandbLogger(opt, Path(opt.save_dir).stem, run_id, data_dict)
-        loggers['wandb'] = wandb_logger.wandb
-        data_dict = wandb_logger.data_dict
-        if wandb_logger.wandb:
-            weights, epochs, hyp = opt.weights, opt.epochs, opt.hyp  # WandbLogger might update weights, epochs if resuming
+    if log:
+        loggers = {'wandb': None}  # loggers dict
+        if rank in [-1, 0]:
+            opt.hyp = hyp  # add hyperparameters
+            run_id = torch.load(weights, map_location=device, weights_only=False).get('wandb_id') if weights.endswith('.pt') and os.path.isfile(weights) else None
+            wandb_logger = WandbLogger(opt, Path(opt.save_dir).stem, run_id, data_dict)
+            loggers['wandb'] = wandb_logger.wandb
+            data_dict = wandb_logger.data_dict
+            if wandb_logger.wandb:
+                weights, epochs, hyp = opt.weights, opt.epochs, opt.hyp  # WandbLogger might update weights, epochs if resuming
 
     nc = 1 if opt.single_cls else int(data_dict['nc'])  # number of classes
     names = ['item'] if opt.single_cls and len(data_dict['names']) != 1 else data_dict['names']  # class names
@@ -84,7 +85,7 @@ def train(hyp, opt, device, tb_writer=None):
     if pretrained:
         with torch_distributed_zero_first(rank):
             attempt_download(weights)  # download if not found locally
-        ckpt = torch.load(weights, map_location=device)  # load checkpoint
+        ckpt = torch.load(weights, map_location=device, weights_only=False)  # load checkpoint
         model = Model(opt.cfg or ckpt['model'].yaml, ch=3, nc=nc, anchors=hyp.get('anchors')).to(device)  # create
         exclude = ['anchor'] if (opt.cfg or hyp.get('anchors')) and not opt.resume else []  # exclude keys
         state_dict = ckpt['model'].float().state_dict()  # to FP32
@@ -394,7 +395,7 @@ def train(hyp, opt, device, tb_writer=None):
                     # if tb_writer:
                     #     tb_writer.add_image(f, result, dataformats='HWC', global_step=epoch)
                     #     tb_writer.add_graph(torch.jit.trace(model, imgs, strict=False), [])  # add model graph
-                elif plots and ni == 10 and wandb_logger.wandb:
+                elif plots and ni == 10 and log and wandb_logger.wandb:
                     wandb_logger.log({"Mosaics": [wandb_logger.wandb.Image(str(x), caption=x.name) for x in
                                                   save_dir.glob('train*.jpg') if x.exists()]})
 
@@ -411,7 +412,8 @@ def train(hyp, opt, device, tb_writer=None):
             ema.update_attr(model, include=['yaml', 'nc', 'hyp', 'gr', 'names', 'stride', 'class_weights'])
             final_epoch = epoch + 1 == epochs
             if not opt.notest or final_epoch:  # Calculate mAP
-                wandb_logger.current_epoch = epoch + 1
+                if log:
+                    wandb_logger.current_epoch = epoch + 1
                 results, maps, times = test.test(data_dict,
                                                  batch_size=batch_size * 2,
                                                  imgsz=imgsz_test,
@@ -421,7 +423,7 @@ def train(hyp, opt, device, tb_writer=None):
                                                  save_dir=save_dir,
                                                  verbose=nc < 50 and final_epoch,
                                                  plots=plots and final_epoch,
-                                                 wandb_logger=wandb_logger,
+                                                 wandb_logger=wandb_logger if log else None,
                                                  compute_loss=compute_loss,
                                                  is_coco=is_coco,
                                                  v5_metric=opt.v5_metric)
@@ -440,14 +442,15 @@ def train(hyp, opt, device, tb_writer=None):
             for x, tag in zip(list(mloss[:-1]) + list(results) + lr, tags):
                 if tb_writer:
                     tb_writer.add_scalar(tag, x, epoch)  # tensorboard
-                if wandb_logger.wandb:
+                if log and wandb_logger.wandb:
                     wandb_logger.log({tag: x})  # W&B
 
             # Update best mAP
             fi = fitness(np.array(results).reshape(1, -1))  # weighted combination of [P, R, [email protected], [email protected]]
             if fi > best_fitness:
                 best_fitness = fi
-            wandb_logger.end_epoch(best_result=best_fitness == fi)
+            if log:
+                wandb_logger.end_epoch(best_result=best_fitness == fi)
 
             # Save model
             if (not opt.nosave) or (final_epoch and not opt.evolve):  # if save
@@ -458,7 +461,8 @@ def train(hyp, opt, device, tb_writer=None):
                         'ema': deepcopy(ema.ema).half(),
                         'updates': ema.updates,
                         'optimizer': optimizer.state_dict(),
-                        'wandb_id': wandb_logger.wandb_run.id if wandb_logger.wandb else None}
+                        'wandb_id': wandb_logger.wandb_run.id if (wandb_logger.wandb and log) else None
+                        }
 
                 # Save last, best and delete
                 torch.save(ckpt, last)
@@ -472,7 +476,7 @@ def train(hyp, opt, device, tb_writer=None):
                     torch.save(ckpt, wdir / 'epoch_{:03d}.pt'.format(epoch))
                 elif epoch >= (epochs-5):
                     torch.save(ckpt, wdir / 'epoch_{:03d}.pt'.format(epoch))
-                if wandb_logger.wandb:
+                if log and wandb_logger.wandb:
                     if ((epoch + 1) % opt.save_period == 0 and not final_epoch) and opt.save_period != -1:
                         wandb_logger.log_model(
                             last.parent, opt, epoch, fi, best_model=best_fitness == fi)
@@ -484,7 +488,7 @@ def train(hyp, opt, device, tb_writer=None):
         # Plots
         if plots:
             plot_results(save_dir=save_dir)  # save as results.png
-            if wandb_logger.wandb:
+            if log and wandb_logger.wandb:
                 files = ['results.png', 'confusion_matrix.png', *[f'{x}_curve.png' for x in ('F1', 'PR', 'P', 'R')]]
                 wandb_logger.log({"Results": [wandb_logger.wandb.Image(str(save_dir / f), caption=f) for f in files
                                               if (save_dir / f).exists()]})
@@ -513,7 +517,7 @@ def train(hyp, opt, device, tb_writer=None):
                 strip_optimizer(f)  # strip optimizers
         if opt.bucket:
             os.system(f'gsutil cp {final} gs://{opt.bucket}/weights')  # upload
-        if wandb_logger.wandb and not opt.evolve:  # Log the stripped model
+        if wandb_logger.wandb and log and not opt.evolve:  # Log the stripped model
             wandb_logger.wandb.log_artifact(str(final), type='model',
                                             name='run_' + wandb_logger.wandb_run.id + '_model',
                                             aliases=['last', 'best', 'stripped'])
@@ -573,8 +577,8 @@ def train(hyp, opt, device, tb_writer=None):
     #    check_requirements()
 
     # Resume
-    wandb_run = check_wandb_resume(opt)
-    if opt.resume and not wandb_run:  # resume an interrupted run
+    # wandb_run = check_wandb_resume(opt)
+    if opt.resume:  # resume an interrupted run
         ckpt = opt.resume if isinstance(opt.resume, str) else get_latest_run()  # specified or most recent path
         assert os.path.isfile(ckpt), 'ERROR: --resume checkpoint does not exist'
         apriori = opt.global_rank, opt.local_rank

yolov7/utils/datasets.py

@@ -361,7 +361,7 @@ def __init__(self, path, img_size=640, batch_size=16, augment=False, hyp=None, r
         self.mosaic = self.augment and not self.rect  # load 4 images at a time into a mosaic (only during training)
         self.mosaic_border = [-img_size // 2, -img_size // 2]
         self.stride = stride
-        self.path = path        
+        self.path = 'gate_lyps_2025-1/images'        
         #self.albumentations = Albumentations() if augment else None
 
         try:

yolov7/utils/general.py

@@ -240,7 +240,7 @@ def labels_to_image_weights(labels, nc=80, class_weights=np.ones(80)):
     return image_weights
 
 
-def coco80_to_coco91_class():  # converts 80-index (val2014) to 91-index (paper)
+def coco80_to_coco91_class():  # converts 80-index (#2014) to 91-index (paper)
     # https://tech.amikelive.com/node-718/what-object-categories-labels-are-in-coco-dataset/
     # a = np.loadtxt('data/coco.names', dtype='str', delimiter='\n')
     # b = np.loadtxt('data/coco_paper.names', dtype='str', delimiter='\n')