W&B: Update Tables API and comply with new dataset_check (ultralytics…

…#3772)

* Update tables API and windows path fix

* update dataset check

utils/wandb_logging/wandb_utils.py

@@ -136,7 +136,6 @@ def __init__(self, opt, name, run_id, data_dict, job_type='Training'):
 
     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(check_file(opt.data),
                                                 opt.single_cls,
                                                 'YOLOv5' if opt.project == 'runs/train' else Path(opt.project).stem)
@@ -171,17 +170,19 @@ def setup_training(self, opt, data_dict):
                 data_dict['val'] = str(val_path)
                 self.val_table = self.val_artifact.get("val")
                 self.map_val_table_path()
+                wandb.log({"validation dataset": self.val_table})
+
         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"])
+            self.result_table = wandb.Table(["epoch", "id", "ground truth", "prediction", "avg_confidence"])
         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 isinstance(path, str) and path.startswith(WANDB_ARTIFACT_PREFIX):
             artifact_path = Path(remove_prefix(path, WANDB_ARTIFACT_PREFIX) + ":" + alias)
-            dataset_artifact = wandb.use_artifact(artifact_path.as_posix())
+            dataset_artifact = wandb.use_artifact(artifact_path.as_posix().replace("\\","/"))
             assert dataset_artifact is not None, "'Error: W&B dataset artifact doesn\'t exist'"
             datadir = dataset_artifact.download()
             return datadir, dataset_artifact
@@ -216,6 +217,7 @@ def log_model(self, path, opt, epoch, fitness_score, best_model=False):
     def log_dataset_artifact(self, data_file, single_cls, project, overwrite_config=False):
         with open(data_file) as f:
             data = yaml.safe_load(f)  # data dict
+        check_dataset(data)
         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(
@@ -228,6 +230,7 @@ def log_dataset_artifact(self, data_file, single_cls, project, overwrite_config=
             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)
+        data.pop('path', None)
         with open(path, 'w') as f:
             yaml.safe_dump(data, f)
 
@@ -297,6 +300,7 @@ def log_training_progress(self, predn, path, names):
             id = self.val_table_map[Path(path).name]
             self.result_table.add_data(self.current_epoch,
                                        id,
+                                       self.val_table.data[id][1],
                                        wandb.Image(self.val_table.data[id][1], boxes=boxes, classes=class_set),
                                        total_conf / max(1, len(box_data))
                                        )
@@ -312,11 +316,12 @@ def end_epoch(self, best_result=False):
                 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')
+                self.result_artifact.add(self.result_table, 'result')
                 wandb.log_artifact(self.result_artifact, aliases=['latest', 'last', 'epoch ' + str(self.current_epoch),
                                                                   ('best' if best_result else '')])
-                self.result_table = wandb.Table(["epoch", "id", "prediction", "avg_confidence"])
+
+                wandb.log({"evaluation": self.result_table})
+                self.result_table = wandb.Table(["epoch", "id", "ground truth", "prediction", "avg_confidence"])
                 self.result_artifact = wandb.Artifact("run_" + wandb.run.id + "_progress", "evaluation")
 
     def finish_run(self):