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Fix indentation in `log_training_progress()` (#4126)

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imyhxy 2021-07-23 20:55:00 +08:00 committed by GitHub
parent 4bad914761
commit 4495e00016
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1 changed files with 20 additions and 20 deletions
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40
utils/wandb_logging/wandb_utils.py
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@ -293,26 +293,26 @@ class WandbLogger():
return artifact return artifact
def log_training_progress(self, predn, path, names): def log_training_progress(self, predn, path, names):
class_set = wandb.Classes([{'id': id, 'name': name} for id, name in names.items()]) class_set = wandb.Classes([{'id': id, 'name': name} for id, name in names.items()])
box_data = [] box_data = []
total_conf = 0 total_conf = 0
for *xyxy, conf, cls in predn.tolist(): for *xyxy, conf, cls in predn.tolist():
if conf >= 0.25: if conf >= 0.25:
box_data.append( box_data.append(
{"position": {"minX": xyxy[0], "minY": xyxy[1], "maxX": xyxy[2], "maxY": xyxy[3]}, {"position": {"minX": xyxy[0], "minY": xyxy[1], "maxX": xyxy[2], "maxY": xyxy[3]},
"class_id": int(cls), "class_id": int(cls),
"box_caption": "%s %.3f" % (names[cls], conf), "box_caption": "%s %.3f" % (names[cls], conf),
"scores": {"class_score": conf}, "scores": {"class_score": conf},
"domain": "pixel"}) "domain": "pixel"})
total_conf = total_conf + conf total_conf = total_conf + conf
boxes = {"predictions": {"box_data": box_data, "class_labels": names}} # inference-space boxes = {"predictions": {"box_data": box_data, "class_labels": names}} # inference-space
id = self.val_table_path_map[Path(path).name] id = self.val_table_path_map[Path(path).name]
self.result_table.add_data(self.current_epoch, self.result_table.add_data(self.current_epoch,
id, id,
self.val_table.data[id][1], self.val_table.data[id][1],
wandb.Image(self.val_table.data[id][1], boxes=boxes, classes=class_set), wandb.Image(self.val_table.data[id][1], boxes=boxes, classes=class_set),
total_conf / max(1, len(box_data)) total_conf / max(1, len(box_data))
) )
def val_one_image(self, pred, predn, path, names, im): def val_one_image(self, pred, predn, path, names, im):
if self.val_table and self.result_table: # Log Table if Val dataset is uploaded as artifact if self.val_table and self.result_table: # Log Table if Val dataset is uploaded as artifact