Improved W&B integration

models/common.py

@@ -278,7 +278,7 @@ def display(self, pprint=False, show=False, save=False, render=False, save_dir='
     def print(self):
         self.display(pprint=True)  # print results
         print(f'Speed: %.1fms pre-process, %.1fms inference, %.1fms NMS per image at shape {tuple(self.s)}' %
-              tuple(self.t))                      
+              tuple(self.t))
 
     def show(self):
         self.display(show=True)  # show results

test.py

@@ -35,8 +35,9 @@ def test(data,
          save_hybrid=False,  # for hybrid auto-labelling
          save_conf=False,  # save auto-label confidences
          plots=True,
-         log_imgs=0,  # number of logged images
-         compute_loss=None):
+         wandb_logger=None,
+         compute_loss=None,
+         is_coco=False):
     # Initialize/load model and set device
     training = model is not None
     if training:  # called by train.py
@@ -66,21 +67,19 @@ def test(data,
 
     # Configure
     model.eval()
-    is_coco = data.endswith('coco.yaml')  # is COCO dataset
-    with open(data) as f:
-        data = yaml.load(f, Loader=yaml.SafeLoader)  # model dict
+    if isinstance(data, str):
+        is_coco = data.endswith('coco.yaml')
+        with open(data) as f:
+            data = yaml.load(f, Loader=yaml.SafeLoader)
     check_dataset(data)  # check
     nc = 1 if single_cls else int(data['nc'])  # number of classes
     iouv = torch.linspace(0.5, 0.95, 10).to(device)  # iou vector for mAP@0.5:0.95
     niou = iouv.numel()
 
     # Logging
-    log_imgs, wandb = min(log_imgs, 100), None  # ceil
-    try:
-        import wandb  # Weights & Biases
-    except ImportError:
-        log_imgs = 0
-
+    log_imgs = 0
+    if wandb_logger and wandb_logger.wandb:
+        log_imgs = min(wandb_logger.log_imgs, 100)
     # Dataloader
     if not training:
         if device.type != 'cpu':
@@ -147,15 +146,17 @@ def test(data,
                     with open(save_dir / 'labels' / (path.stem + '.txt'), 'a') as f:
                         f.write(('%g ' * len(line)).rstrip() % line + '\n')
 
-            # W&B logging
-            if plots and len(wandb_images) < log_imgs:
-                box_data = [{"position": {"minX": xyxy[0], "minY": xyxy[1], "maxX": xyxy[2], "maxY": xyxy[3]},
-                             "class_id": int(cls),
-                             "box_caption": "%s %.3f" % (names[cls], conf),
-                             "scores": {"class_score": conf},
-                             "domain": "pixel"} for *xyxy, conf, cls in pred.tolist()]
-                boxes = {"predictions": {"box_data": box_data, "class_labels": names}}  # inference-space
-                wandb_images.append(wandb.Image(img[si], boxes=boxes, caption=path.name))
+            # W&B logging - Media Panel Plots
+            if len(wandb_images) < log_imgs and wandb_logger.current_epoch > 0:  # Check for test operation
+                if wandb_logger.current_epoch % wandb_logger.bbox_interval == 0:
+                    box_data = [{"position": {"minX": xyxy[0], "minY": xyxy[1], "maxX": xyxy[2], "maxY": xyxy[3]},
+                                 "class_id": int(cls),
+                                 "box_caption": "%s %.3f" % (names[cls], conf),
+                                 "scores": {"class_score": conf},
+                                 "domain": "pixel"} for *xyxy, conf, cls in pred.tolist()]
+                    boxes = {"predictions": {"box_data": box_data, "class_labels": names}}  # inference-space
+                    wandb_images.append(wandb_logger.wandb.Image(img[si], boxes=boxes, caption=path.name))
+                wandb_logger.log_training_progress(predn, path, names)  # logs dsviz tables
 
             # Append to pycocotools JSON dictionary
             if save_json:
@@ -239,9 +240,11 @@ def test(data,
     # Plots
     if plots:
         confusion_matrix.plot(save_dir=save_dir, names=list(names.values()))
-        if wandb and wandb.run:
-            val_batches = [wandb.Image(str(f), caption=f.name) for f in sorted(save_dir.glob('test*.jpg'))]
-            wandb.log({"Images": wandb_images, "Validation": val_batches}, commit=False)
+        if wandb_logger and wandb_logger.wandb:
+            val_batches = [wandb_logger.wandb.Image(str(f), caption=f.name) for f in sorted(save_dir.glob('test*.jpg'))]
+            wandb_logger.log({"Validation": val_batches})
+    if wandb_images:
+        wandb_logger.log({"Bounding Box Debugger/Images": wandb_images})
 
     # Save JSON
     if save_json and len(jdict):

train.py

@@ -1,3 +1,4 @@
+
 import argparse
 import logging
 import math
@@ -33,11 +34,12 @@
 from utils.loss import ComputeLoss
 from utils.plots import plot_images, plot_labels, plot_results, plot_evolution
 from utils.torch_utils import ModelEMA, select_device, intersect_dicts, torch_distributed_zero_first, is_parallel
+from utils.wandb_logging.wandb_utils import WandbLogger, resume_and_get_id, check_wandb_config_file
 
 logger = logging.getLogger(__name__)
 
 
-def train(hyp, opt, device, tb_writer=None, wandb=None):
+def train(hyp, opt, device, tb_writer=None):
     logger.info(colorstr('hyperparameters: ') + ', '.join(f'{k}={v}' for k, v in hyp.items()))
     save_dir, epochs, batch_size, total_batch_size, weights, rank = \
         Path(opt.save_dir), opt.epochs, opt.batch_size, opt.total_batch_size, opt.weights, opt.global_rank
@@ -61,10 +63,17 @@ def train(hyp, opt, device, tb_writer=None, wandb=None):
     init_seeds(2 + rank)
     with open(opt.data) as f:
         data_dict = yaml.load(f, Loader=yaml.SafeLoader)  # data dict
-    with torch_distributed_zero_first(rank):
-        check_dataset(data_dict)  # check
-    train_path = data_dict['train']
-    test_path = data_dict['val']
+    is_coco = opt.data.endswith('coco.yaml')
+
+    # Logging- Doing this before checking the dataset. Might update data_dict
+    if rank in [-1, 0]:
+        opt.hyp = hyp  # add hyperparameters
+        run_id = torch.load(weights).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)
+        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
+    loggers = {'wandb': wandb_logger.wandb}  # loggers dict
     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
     assert len(names) == nc, '%g names found for nc=%g dataset in %s' % (len(names), nc, opt.data)  # check
@@ -83,6 +92,10 @@ def train(hyp, opt, device, tb_writer=None, wandb=None):
         logger.info('Transferred %g/%g items from %s' % (len(state_dict), len(model.state_dict()), weights))  # report
     else:
         model = Model(opt.cfg, ch=3, nc=nc, anchors=hyp.get('anchors')).to(device)  # create
+    with torch_distributed_zero_first(rank):
+        check_dataset(data_dict)  # check
+    train_path = data_dict['train']
+    test_path = data_dict['val']
 
     # Freeze
     freeze = []  # parameter names to freeze (full or partial)
@@ -126,16 +139,6 @@ def train(hyp, opt, device, tb_writer=None, wandb=None):
     scheduler = lr_scheduler.LambdaLR(optimizer, lr_lambda=lf)
     # plot_lr_scheduler(optimizer, scheduler, epochs)
 
-    # Logging
-    if rank in [-1, 0] and wandb and wandb.run is None:
-        opt.hyp = hyp  # add hyperparameters
-        wandb_run = wandb.init(config=opt, resume="allow",
-                               project='YOLOv5' if opt.project == 'runs/train' else Path(opt.project).stem,
-                               name=save_dir.stem,
-                               entity=opt.entity,
-                               id=ckpt.get('wandb_id') if 'ckpt' in locals() else None)
-    loggers = {'wandb': wandb}  # loggers dict
-
     # EMA
     ema = ModelEMA(model) if rank in [-1, 0] else None
 
@@ -326,9 +329,9 @@ def train(hyp, opt, device, tb_writer=None, wandb=None):
                     # if tb_writer:
                     #     tb_writer.add_image(f, result, dataformats='HWC', global_step=epoch)
                     #     tb_writer.add_graph(model, imgs)  # add model to tensorboard
-                elif plots and ni == 10 and wandb:
-                    wandb.log({"Mosaics": [wandb.Image(str(x), caption=x.name) for x in save_dir.glob('train*.jpg')
-                                           if x.exists()]}, commit=False)
+                elif plots and ni == 10 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()]})
 
             # end batch ------------------------------------------------------------------------------------------------
         # end epoch ----------------------------------------------------------------------------------------------------
@@ -343,17 +346,19 @@ def train(hyp, opt, device, tb_writer=None, wandb=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
-                results, maps, times = test.test(opt.data,
-                                                 batch_size=batch_size * 2,
+                wandb_logger.current_epoch = epoch + 1
+                results, maps, times = test.test(data_dict,
+                                                 batch_size=total_batch_size,
                                                  imgsz=imgsz_test,
                                                  model=ema.ema,
                                                  single_cls=opt.single_cls,
                                                  dataloader=testloader,
                                                  save_dir=save_dir,
                                                  verbose=nc < 50 and final_epoch,
                                                  plots=plots and final_epoch,
-                                                 log_imgs=opt.log_imgs if wandb else 0,
-                                                 compute_loss=compute_loss)
+                                                 wandb_logger=wandb_logger,
+                                                 compute_loss=compute_loss,
+                                                 is_coco=is_coco)
 
             # Write
             with open(results_file, 'a') as f:
@@ -369,8 +374,8 @@ def train(hyp, opt, device, tb_writer=None, wandb=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:
-                    wandb.log({tag: x}, step=epoch, commit=tag == tags[-1])  # W&B
+                if 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, mAP@.5, mAP@.5-.95]
@@ -386,36 +391,29 @@ def train(hyp, opt, device, tb_writer=None, wandb=None):
                         'ema': deepcopy(ema.ema).half(),
                         'updates': ema.updates,
                         'optimizer': optimizer.state_dict(),
-                        'wandb_id': wandb_run.id if wandb else None}
+                        'wandb_id': wandb_logger.wandb_run.id if wandb_logger.wandb else None}
 
                 # Save last, best and delete
                 torch.save(ckpt, last)
                 if best_fitness == fi:
                     torch.save(ckpt, best)
+                if 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)
                 del ckpt
-
+        wandb_logger.end_epoch(best_result=best_fitness == fi)
+
         # end epoch ----------------------------------------------------------------------------------------------------
     # end training
-
     if rank in [-1, 0]:
-        # Strip optimizers
-        final = best if best.exists() else last  # final model
-        for f in last, best:
-            if f.exists():
-                strip_optimizer(f)
-        if opt.bucket:
-            os.system(f'gsutil cp {final} gs://{opt.bucket}/weights')  # upload
-
         # Plots
         if plots:
             plot_results(save_dir=save_dir)  # save as results.png
-            if wandb:
+            if wandb_logger.wandb:
                 files = ['results.png', 'confusion_matrix.png', *[f'{x}_curve.png' for x in ('F1', 'PR', 'P', 'R')]]
-                wandb.log({"Results": [wandb.Image(str(save_dir / f), caption=f) for f in files
-                                       if (save_dir / f).exists()]})
-                if opt.log_artifacts:
-                    wandb.log_artifact(artifact_or_path=str(final), type='model', name=save_dir.stem)
-
+                wandb_logger.log({"Results": [wandb_logger.wandb.Image(str(save_dir / f), caption=f) for f in files
+                                              if (save_dir / f).exists()]})
         # Test best.pt
         logger.info('%g epochs completed in %.3f hours.\n' % (epoch - start_epoch + 1, (time.time() - t0) / 3600))
         if opt.data.endswith('coco.yaml') and nc == 80:  # if COCO
@@ -430,13 +428,24 @@ def train(hyp, opt, device, tb_writer=None, wandb=None):
                                           dataloader=testloader,
                                           save_dir=save_dir,
                                           save_json=True,
-                                          plots=False)
+                                          plots=False,
+                                          is_coco=is_coco)
 
+        # Strip optimizers
+        final = best if best.exists() else last  # final model
+        for f in last, best:
+            if f.exists():
+                strip_optimizer(f)  # strip optimizers
+        if opt.bucket:
+            os.system(f'gsutil cp {final} gs://{opt.bucket}/weights')  # upload
+        if wandb_logger.wandb:  # 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'])
     else:
         dist.destroy_process_group()
-
-    wandb.run.finish() if wandb and wandb.run else None
     torch.cuda.empty_cache()
+    wandb_logger.finish_run()
     return results
 
 
@@ -464,15 +473,17 @@ def train(hyp, opt, device, tb_writer=None, wandb=None):
     parser.add_argument('--adam', action='store_true', help='use torch.optim.Adam() optimizer')
     parser.add_argument('--sync-bn', action='store_true', help='use SyncBatchNorm, only available in DDP mode')
     parser.add_argument('--local_rank', type=int, default=-1, help='DDP parameter, do not modify')
-    parser.add_argument('--log-imgs', type=int, default=16, help='number of images for W&B logging, max 100')
-    parser.add_argument('--log-artifacts', action='store_true', help='log artifacts, i.e. final trained model')
     parser.add_argument('--workers', type=int, default=8, help='maximum number of dataloader workers')
     parser.add_argument('--project', default='runs/train', help='save to project/name')
     parser.add_argument('--entity', default=None, help='W&B entity')
     parser.add_argument('--name', default='exp', help='save to project/name')
     parser.add_argument('--exist-ok', action='store_true', help='existing project/name ok, do not increment')
     parser.add_argument('--quad', action='store_true', help='quad dataloader')
     parser.add_argument('--linear-lr', action='store_true', help='linear LR')
+    parser.add_argument('--upload_dataset', action='store_true', help='Upload dataset as W&B artifact table')
+    parser.add_argument('--bbox_interval', type=int, default=-1, help='Set bounding-box image logging interval for W&B')
+    parser.add_argument('--save_period', type=int, default=-1, help='Log model after every "save_period" epoch')
+    parser.add_argument('--artifact_alias', type=str, default="latest", help='version of dataset artifact to be used')
     opt = parser.parse_args()
 
     # Set DDP variables
@@ -484,7 +495,8 @@ def train(hyp, opt, device, tb_writer=None, wandb=None):
         check_requirements()
 
     # Resume
-    if opt.resume:  # resume an interrupted run
+    wandb_run = resume_and_get_id(opt)
+    if opt.resume and not wandb_run:  # 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
@@ -517,18 +529,12 @@ def train(hyp, opt, device, tb_writer=None, wandb=None):
 
     # Train
     logger.info(opt)
-    try:
-        import wandb
-    except ImportError:
-        wandb = None
-        prefix = colorstr('wandb: ')
-        logger.info(f"{prefix}Install Weights & Biases for YOLOv5 logging with 'pip install wandb' (recommended)")
     if not opt.evolve:
         tb_writer = None  # init loggers
         if opt.global_rank in [-1, 0]:
             logger.info(f'Start Tensorboard with "tensorboard --logdir {opt.project}", view at http://localhost:6006/')
             tb_writer = SummaryWriter(opt.save_dir)  # Tensorboard
-        train(hyp, opt, device, tb_writer, wandb)
+        train(hyp, opt, device, tb_writer)
 
     # Evolve hyperparameters (optional)
     else:
@@ -602,7 +608,7 @@ def train(hyp, opt, device, tb_writer=None, wandb=None):
                 hyp[k] = round(hyp[k], 5)  # significant digits
 
             # Train mutation
-            results = train(hyp.copy(), opt, device, wandb=wandb)
+            results = train(hyp.copy(), opt, device)
 
             # Write mutation results
             print_mutation(hyp.copy(), results, yaml_file, opt.bucket)

utils/wandb_logging/log_dataset.py

@@ -12,28 +12,14 @@
 def create_dataset_artifact(opt):
     with open(opt.data) as f:
         data = yaml.load(f, Loader=yaml.SafeLoader)  # data dict
-    logger = WandbLogger(opt, '', None, data, job_type='create_dataset')
-    nc, names = (1, ['item']) if opt.single_cls else (int(data['nc']), data['names'])
-    names = {k: v for k, v in enumerate(names)}  # to index dictionary
-    logger.log_dataset_artifact(LoadImagesAndLabels(data['train']), names, name='train')  # trainset
-    logger.log_dataset_artifact(LoadImagesAndLabels(data['val']), names, name='val')  # valset
-
-    # Update data.yaml with artifact links
-    data['train'] = WANDB_ARTIFACT_PREFIX + str(Path(opt.project) / 'train')
-    data['val'] = WANDB_ARTIFACT_PREFIX + str(Path(opt.project) / 'val')
-    path = opt.data if opt.overwrite_config else opt.data.replace('.', '_wandb.')  # updated data.yaml path
-    data.pop('download', None)  # download via artifact instead of predefined field 'download:'
-    with open(path, 'w') as f:
-        yaml.dump(data, f)
-    print("New Config file => ", path)
+    logger = WandbLogger(opt, '', None, data, job_type='Dataset Creation')
 
 
 if __name__ == '__main__':
     parser = argparse.ArgumentParser()
     parser.add_argument('--data', type=str, default='data/coco128.yaml', help='data.yaml path')
     parser.add_argument('--single-cls', action='store_true', help='train as single-class dataset')
     parser.add_argument('--project', type=str, default='YOLOv5', help='name of W&B Project')
-    parser.add_argument('--overwrite_config', action='store_true', help='overwrite data.yaml')
     opt = parser.parse_args()
 
     create_dataset_artifact(opt)