Improved W&B integration (#2125)

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
+    if isinstance(data, str):
+        is_coco = data.endswith('coco.yaml')
         with open(data) as f:
-        data = yaml.load(f, Loader=yaml.SafeLoader)  # model dict
+            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
-
+    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:
+            # 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.Image(img[si], boxes=boxes, caption=path.name))
+                    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

+
 import argparse
 import logging
 import math
@@ -33,11 +34,12 @@ from utils.google_utils import attempt_download
 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 ----------------------------------------------------------------------------------------------------
@@ -343,8 +346,9 @@ 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,
@@ -352,8 +356,9 @@ def train(hyp, opt, device, tb_writer=None, wandb=None):
                                                  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
+                wandb_logger.log({"Results": [wandb_logger.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)
-
         # 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
 
 
@@ -464,8 +473,6 @@ if __name__ == '__main__':
     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')
@@ -473,6 +480,10 @@ if __name__ == '__main__':
     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 @@ if __name__ == '__main__':
         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 @@ if __name__ == '__main__':
 
     # 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 @@ if __name__ == '__main__':
                 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

@@ -12,20 +12,7 @@ WANDB_ARTIFACT_PREFIX = 'wandb-artifact://'
 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__':
@@ -33,7 +20,6 @@ if __name__ == '__main__':
     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)

utils/wandb_logging/wandb_utils.py

+import argparse
 import json
+import os
 import shutil
 import sys
+import torch
+import yaml
 from datetime import datetime
 from pathlib import Path
-
-import torch
+from tqdm import tqdm
 
 sys.path.append(str(Path(__file__).parent.parent.parent))  # add utils/ to path
-from utils.general import colorstr, xywh2xyxy
+from utils.datasets import LoadImagesAndLabels
+from utils.datasets import img2label_paths
+from utils.general import colorstr, xywh2xyxy, check_dataset
 
 try:
     import wandb
@@ -22,87 +27,183 @@ def remove_prefix(from_string, prefix):
     return from_string[len(prefix):]
 
 
+def check_wandb_config_file(data_config_file):
+    wandb_config = '_wandb.'.join(data_config_file.rsplit('.', 1))  # updated data.yaml path
+    if Path(wandb_config).is_file():
+        return wandb_config
+    return data_config_file
+
+
+def resume_and_get_id(opt):
+    # It's more elegant to stick to 1 wandb.init call, but as useful config data is overwritten in the WandbLogger's wandb.init call
+    if isinstance(opt.resume, str):
+        if opt.resume.startswith(WANDB_ARTIFACT_PREFIX):
+            run_path = Path(remove_prefix(opt.resume, WANDB_ARTIFACT_PREFIX))
+            run_id = run_path.stem
+            project = run_path.parent.stem
+            model_artifact_name = WANDB_ARTIFACT_PREFIX + 'run_' + run_id + '_model'
+            assert wandb, 'install wandb to resume wandb runs'
+            # Resume wandb-artifact:// runs here| workaround for not overwriting wandb.config
+            run = wandb.init(id=run_id, project=project, resume='allow')
+            opt.resume = model_artifact_name
+            return run
+    return None
+
+
 class WandbLogger():
     def __init__(self, opt, name, run_id, data_dict, job_type='Training'):
-        self.wandb = wandb
-        self.wandb_run = wandb.init(config=opt, resume="allow",
+        # Pre-training routine --
+        self.job_type = job_type
+        self.wandb, self.wandb_run, self.data_dict = wandb, None if not wandb else wandb.run, data_dict
+        if self.wandb:
+            self.wandb_run = wandb.init(config=opt,
+                                        resume="allow",
                                         project='YOLOv5' if opt.project == 'runs/train' else Path(opt.project).stem,
                                         name=name,
                                         job_type=job_type,
-                                    id=run_id) if self.wandb else None
+                                        id=run_id) if not wandb.run else wandb.run
+            if self.job_type == 'Training':
+                if not opt.resume:
+                    wandb_data_dict = self.check_and_upload_dataset(opt) if opt.upload_dataset else data_dict
+                    # Info useful for resuming from artifacts
+                    self.wandb_run.config.opt = vars(opt)
+                    self.wandb_run.config.data_dict = wandb_data_dict
+                self.data_dict = self.setup_training(opt, data_dict)
+            if self.job_type == 'Dataset Creation':
+                self.data_dict = self.check_and_upload_dataset(opt)
 
-        if job_type == 'Training':
-            self.setup_training(opt, data_dict)
-            if opt.bbox_interval == -1:
-                opt.bbox_interval = (opt.epochs // 10) if opt.epochs > 10 else opt.epochs
-            if opt.save_period == -1:
-                opt.save_period = (opt.epochs // 10) if opt.epochs > 10 else opt.epochs
+    def check_and_upload_dataset(self, opt):
+        assert wandb, 'Install wandb to upload dataset'
+        check_dataset(self.data_dict)
+        config_path = self.log_dataset_artifact(opt.data,
+                                                   opt.single_cls,
+                                                   'YOLOv5' if opt.project == 'runs/train' else Path(opt.project).stem)
+        print("Created dataset config file ", config_path)
+        with open(config_path) as f:
+            wandb_data_dict = yaml.load(f, Loader=yaml.SafeLoader)
+        return wandb_data_dict
 
     def setup_training(self, opt, data_dict):
-        self.log_dict = {}
-        self.train_artifact_path, self.trainset_artifact = \
-            self.download_dataset_artifact(data_dict['train'], opt.artifact_alias)
-        self.test_artifact_path, self.testset_artifact = \
-            self.download_dataset_artifact(data_dict['val'], opt.artifact_alias)
-        self.result_artifact, self.result_table, self.weights = None, None, None
+        self.log_dict, self.current_epoch, self.log_imgs = {}, 0, 16  # Logging Constants
+        self.bbox_interval = opt.bbox_interval
+        if isinstance(opt.resume, str):
+            modeldir, _ = self.download_model_artifact(opt)
+            if modeldir:
+                self.weights = Path(modeldir) / "last.pt"
+                config = self.wandb_run.config
+                opt.weights, opt.save_period, opt.batch_size, opt.bbox_interval, opt.epochs, opt.hyp = str(
+                    self.weights), config.save_period, config.total_batch_size, config.bbox_interval, config.epochs, \
+                                                                                                       config.opt['hyp']
+            data_dict = dict(self.wandb_run.config.data_dict)  # eliminates the need for config file to resume
+        if 'val_artifact' not in self.__dict__:  # If --upload_dataset is set, use the existing artifact, don't download
+            self.train_artifact_path, self.train_artifact = self.download_dataset_artifact(data_dict.get('train'),
+                                                                                           opt.artifact_alias)
+            self.val_artifact_path, self.val_artifact = self.download_dataset_artifact(data_dict.get('val'),
+                                                                                       opt.artifact_alias)
+            self.result_artifact, self.result_table, self.val_table, self.weights = None, None, None, None
             if self.train_artifact_path is not None:
                 train_path = Path(self.train_artifact_path) / 'data/images/'
                 data_dict['train'] = str(train_path)
-        if self.test_artifact_path is not None:
-            test_path = Path(self.test_artifact_path) / 'data/images/'
-            data_dict['val'] = str(test_path)
+            if self.val_artifact_path is not None:
+                val_path = Path(self.val_artifact_path) / 'data/images/'
+                data_dict['val'] = str(val_path)
+                self.val_table = self.val_artifact.get("val")
+                self.map_val_table_path()
+        if self.val_artifact is not None:
             self.result_artifact = wandb.Artifact("run_" + wandb.run.id + "_progress", "evaluation")
             self.result_table = wandb.Table(["epoch", "id", "prediction", "avg_confidence"])
-        if opt.resume_from_artifact:
-            modeldir, _ = self.download_model_artifact(opt.resume_from_artifact)
-            if modeldir:
-                self.weights = Path(modeldir) / "best.pt"
-                opt.weights = self.weights
+        if opt.bbox_interval == -1:
+            self.bbox_interval = opt.bbox_interval = (opt.epochs // 10) if opt.epochs > 10 else 1
+        return data_dict
 
     def download_dataset_artifact(self, path, alias):
         if path.startswith(WANDB_ARTIFACT_PREFIX):
             dataset_artifact = wandb.use_artifact(remove_prefix(path, WANDB_ARTIFACT_PREFIX) + ":" + alias)
             assert dataset_artifact is not None, "'Error: W&B dataset artifact doesn\'t exist'"
             datadir = dataset_artifact.download()
-            labels_zip = Path(datadir) / "data/labels.zip"
-            shutil.unpack_archive(labels_zip, Path(datadir) / 'data/labels', 'zip')
-            print("Downloaded dataset to : ", datadir)
             return datadir, dataset_artifact
         return None, None
 
-    def download_model_artifact(self, name):
-        model_artifact = wandb.use_artifact(name + ":latest")
+    def download_model_artifact(self, opt):
+        if opt.resume.startswith(WANDB_ARTIFACT_PREFIX):
+            model_artifact = wandb.use_artifact(remove_prefix(opt.resume, WANDB_ARTIFACT_PREFIX) + ":latest")
             assert model_artifact is not None, 'Error: W&B model artifact doesn\'t exist'
             modeldir = model_artifact.download()
-        print("Downloaded model to : ", modeldir)
+            epochs_trained = model_artifact.metadata.get('epochs_trained')
+            total_epochs = model_artifact.metadata.get('total_epochs')
+            assert epochs_trained < total_epochs, 'training to %g epochs is finished, nothing to resume.' % (
+                total_epochs)
             return modeldir, model_artifact
+        return None, None
 
-    def log_model(self, path, opt, epoch):
-        datetime_suffix = datetime.today().strftime('%Y-%m-%d-%H-%M-%S')
+    def log_model(self, path, opt, epoch, fitness_score, best_model=False):
         model_artifact = wandb.Artifact('run_' + wandb.run.id + '_model', type='model', metadata={
             'original_url': str(path),
-            'epoch': epoch + 1,
+            'epochs_trained': epoch + 1,
             'save period': opt.save_period,
             'project': opt.project,
-            'datetime': datetime_suffix
+            'total_epochs': opt.epochs,
+            'fitness_score': fitness_score
         })
         model_artifact.add_file(str(path / 'last.pt'), name='last.pt')
-        model_artifact.add_file(str(path / 'best.pt'), name='best.pt')
-        wandb.log_artifact(model_artifact)
+        wandb.log_artifact(model_artifact,
+                           aliases=['latest', 'epoch ' + str(self.current_epoch), 'best' if best_model else ''])
         print("Saving model artifact on epoch ", epoch + 1)
 
-    def log_dataset_artifact(self, dataset, class_to_id, name='dataset'):
+    def log_dataset_artifact(self, data_file, single_cls, project, overwrite_config=False):
+        with open(data_file) as f:
+            data = yaml.load(f, Loader=yaml.SafeLoader)  # data dict
+        nc, names = (1, ['item']) if single_cls else (int(data['nc']), data['names'])
+        names = {k: v for k, v in enumerate(names)}  # to index dictionary
+        self.train_artifact = self.create_dataset_table(LoadImagesAndLabels(
+            data['train']), names, name='train') if data.get('train') else None
+        self.val_artifact = self.create_dataset_table(LoadImagesAndLabels(
+            data['val']), names, name='val') if data.get('val') else None
+        if data.get('train'):
+            data['train'] = WANDB_ARTIFACT_PREFIX + str(Path(project) / 'train')
+        if data.get('val'):
+            data['val'] = WANDB_ARTIFACT_PREFIX + str(Path(project) / 'val')
+        path = data_file if overwrite_config else '_wandb.'.join(data_file.rsplit('.', 1))  # updated data.yaml path
+        data.pop('download', None)
+        with open(path, 'w') as f:
+            yaml.dump(data, f)
+
+        if self.job_type == 'Training':  # builds correct artifact pipeline graph
+            self.wandb_run.use_artifact(self.val_artifact)
+            self.wandb_run.use_artifact(self.train_artifact)
+            self.val_artifact.wait()
+            self.val_table = self.val_artifact.get('val')
+            self.map_val_table_path()
+        else:
+            self.wandb_run.log_artifact(self.train_artifact)
+            self.wandb_run.log_artifact(self.val_artifact)
+        return path
+
+    def map_val_table_path(self):
+        self.val_table_map = {}
+        print("Mapping dataset")
+        for i, data in enumerate(tqdm(self.val_table.data)):
+            self.val_table_map[data[3]] = data[0]
+
+    def create_dataset_table(self, dataset, class_to_id, name='dataset'):
+        # TODO: Explore multiprocessing to slpit this loop parallely| This is essential for speeding up the the logging
         artifact = wandb.Artifact(name=name, type="dataset")
-        image_path = dataset.path
-        artifact.add_dir(image_path, name='data/images')
-        table = wandb.Table(columns=["id", "train_image", "Classes"])
+        for img_file in tqdm([dataset.path]) if Path(dataset.path).is_dir() else tqdm(dataset.img_files):
+            if Path(img_file).is_dir():
+                artifact.add_dir(img_file, name='data/images')
+                labels_path = 'labels'.join(dataset.path.rsplit('images', 1))
+                artifact.add_dir(labels_path, name='data/labels')
+            else:
+                artifact.add_file(img_file, name='data/images/' + Path(img_file).name)
+                label_file = Path(img2label_paths([img_file])[0])
+                artifact.add_file(str(label_file),
+                                  name='data/labels/' + label_file.name) if label_file.exists() else None
+        table = wandb.Table(columns=["id", "train_image", "Classes", "name"])
         class_set = wandb.Classes([{'id': id, 'name': name} for id, name in class_to_id.items()])
-        for si, (img, labels, paths, shapes) in enumerate(dataset):
+        for si, (img, labels, paths, shapes) in enumerate(tqdm(dataset)):
             height, width = shapes[0]
-            labels[:, 2:] = (xywh2xyxy(labels[:, 2:].view(-1, 4)))
-            labels[:, 2:] *= torch.Tensor([width, height, width, height])
-            box_data = []
-            img_classes = {}
+            labels[:, 2:] = (xywh2xyxy(labels[:, 2:].view(-1, 4))) * torch.Tensor([width, height, width, height])
+            box_data, img_classes = [], {}
             for cls, *xyxy in labels[:, 1:].tolist():
                 cls = int(cls)
                 box_data.append({"position": {"minX": xyxy[0], "minY": xyxy[1], "maxX": xyxy[2], "maxY": xyxy[3]},
@@ -112,34 +213,52 @@ class WandbLogger():
                                  "domain": "pixel"})
                 img_classes[cls] = class_to_id[cls]
             boxes = {"ground_truth": {"box_data": box_data, "class_labels": class_to_id}}  # inference-space
-            table.add_data(si, wandb.Image(paths, classes=class_set, boxes=boxes), json.dumps(img_classes))
+            table.add_data(si, wandb.Image(paths, classes=class_set, boxes=boxes), json.dumps(img_classes),
+                           Path(paths).name)
         artifact.add(table, name)
-        labels_path = 'labels'.join(image_path.rsplit('images', 1))
-        zip_path = Path(labels_path).parent / (name + '_labels.zip')
-        if not zip_path.is_file():  # make_archive won't check if file exists
-            shutil.make_archive(zip_path.with_suffix(''), 'zip', labels_path)
-        artifact.add_file(str(zip_path), name='data/labels.zip')
-        wandb.log_artifact(artifact)
-        print("Saving data to W&B...")
+        return artifact
+
+    def log_training_progress(self, predn, path, names):
+        if self.val_table and self.result_table:
+            class_set = wandb.Classes([{'id': id, 'name': name} for id, name in names.items()])
+            box_data = []
+            total_conf = 0
+            for *xyxy, conf, cls in predn.tolist():
+                if conf >= 0.25:
+                    box_data.append(
+                        {"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"})
+                    total_conf = total_conf + conf
+            boxes = {"predictions": {"box_data": box_data, "class_labels": names}}  # inference-space
+            id = self.val_table_map[Path(path).name]
+            self.result_table.add_data(self.current_epoch,
+                                       id,
+                                       wandb.Image(self.val_table.data[id][1], boxes=boxes, classes=class_set),
+                                       total_conf / max(1, len(box_data))
+                                       )
 
     def log(self, log_dict):
         if self.wandb_run:
             for key, value in log_dict.items():
                 self.log_dict[key] = value
 
-    def end_epoch(self):
-        if self.wandb_run and self.log_dict:
+    def end_epoch(self, best_result=False):
+        if self.wandb_run:
             wandb.log(self.log_dict)
             self.log_dict = {}
+            if self.result_artifact:
+                train_results = wandb.JoinedTable(self.val_table, self.result_table, "id")
+                self.result_artifact.add(train_results, 'result')
+                wandb.log_artifact(self.result_artifact, aliases=['latest', 'epoch ' + str(self.current_epoch),
+                                                                  ('best' if best_result else '')])
+                self.result_table = wandb.Table(["epoch", "id", "prediction", "avg_confidence"])
+                self.result_artifact = wandb.Artifact("run_" + wandb.run.id + "_progress", "evaluation")
 
     def finish_run(self):
         if self.wandb_run:
-            if self.result_artifact:
-                print("Add Training Progress Artifact")
-                self.result_artifact.add(self.result_table, 'result')
-                train_results = wandb.JoinedTable(self.testset_artifact.get("val"), self.result_table, "id")
-                self.result_artifact.add(train_results, 'joined_result')
-                wandb.log_artifact(self.result_artifact)
             if self.log_dict:
                 wandb.log(self.log_dict)
             wandb.run.finish()