Improved W&B integration (#2125)
* Init Commit * new wandb integration * Update * Use data_dict in test * Updates * Update: scope of log_img * Update: scope of log_img * Update * Update: Fix logging conditions * Add tqdm bar, support for .txt dataset format * Improve Result table Logger * Init Commit * new wandb integration * Update * Use data_dict in test * Updates * Update: scope of log_img * Update: scope of log_img * Update * Update: Fix logging conditions * Add tqdm bar, support for .txt dataset format * Improve Result table Logger * Add dataset creation in training script * Change scope: self.wandb_run * Add wandb-artifact:// natively you can now use --resume with wandb run links * Add suuport for logging dataset while training * Cleanup * Fix: Merge conflict * Fix: CI tests * Automatically use wandb config * Fix: Resume * Fix: CI * Enhance: Using val_table * More resume enhancement * FIX : CI * Add alias * Get useful opt config data * train.py cleanup * Cleanup train.py * more cleanup * Cleanup| CI fix * Reformat using PEP8 * FIX:CI * rebase * remove uneccesary changes * remove uneccesary changes * remove uneccesary changes * remove unecessary chage from test.py * FIX: resume from local checkpoint * FIX:resume * FIX:resume * Reformat * Performance improvement * Fix local resume * Fix local resume * FIX:CI * Fix: CI * Imporve image logging * (:(:Redo CI tests:):) * Remember epochs when resuming * Remember epochs when resuming * Update DDP location Potential fix for #2405 * PEP8 reformat * 0.25 confidence threshold * reset train.py plots syntax to previous * reset epochs completed syntax to previous * reset space to previous * remove brackets * reset comment to previous * Update: is_coco check, remove unused code * Remove redundant print statement * Remove wandb imports * remove dsviz logger from test.py * Remove redundant change from test.py * remove redundant changes from train.py * reformat and improvements * Fix typo * Add tqdm tqdm progress when scanning files, naming improvements Co-authored-by: Glenn Jocher <glenn.jocher@ultralytics.com>
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Ayush ChaurasiaandGlenn Jocher authored and GitHub committed
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@@ -35,8 +35,9 @@ def test(data,
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save_hybrid=False, # for hybrid auto-labelling
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save_conf=False, # save auto-label confidences
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plots=True,
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log_imgs=0, # number of logged images
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compute_loss=None):
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wandb_logger=None,
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compute_loss=None,
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is_coco=False):
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# Initialize/load model and set device
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training = model is not None
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if training: # called by train.py
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@@ -66,21 +67,19 @@ def test(data,
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# Configure
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model.eval()
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is_coco = data.endswith('coco.yaml') # is COCO dataset
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with open(data) as f:
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data = yaml.load(f, Loader=yaml.SafeLoader) # model dict
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if isinstance(data, str):
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is_coco = data.endswith('coco.yaml')
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with open(data) as f:
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data = yaml.load(f, Loader=yaml.SafeLoader)
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check_dataset(data) # check
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nc = 1 if single_cls else int(data['nc']) # number of classes
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iouv = torch.linspace(0.5, 0.95, 10).to(device) # iou vector for mAP@0.5:0.95
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niou = iouv.numel()
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# Logging
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log_imgs, wandb = min(log_imgs, 100), None # ceil
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try:
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import wandb # Weights & Biases
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except ImportError:
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log_imgs = 0
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log_imgs = 0
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if wandb_logger and wandb_logger.wandb:
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log_imgs = min(wandb_logger.log_imgs, 100)
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# Dataloader
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if not training:
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if device.type != 'cpu':
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@@ -147,15 +146,17 @@ def test(data,
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with open(save_dir / 'labels' / (path.stem + '.txt'), 'a') as f:
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f.write(('%g ' * len(line)).rstrip() % line + '\n')
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# W&B logging
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if plots and len(wandb_images) < log_imgs:
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box_data = [{"position": {"minX": xyxy[0], "minY": xyxy[1], "maxX": xyxy[2], "maxY": xyxy[3]},
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"class_id": int(cls),
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"box_caption": "%s %.3f" % (names[cls], conf),
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"scores": {"class_score": conf},
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"domain": "pixel"} for *xyxy, conf, cls in pred.tolist()]
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boxes = {"predictions": {"box_data": box_data, "class_labels": names}} # inference-space
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wandb_images.append(wandb.Image(img[si], boxes=boxes, caption=path.name))
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# W&B logging - Media Panel Plots
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if len(wandb_images) < log_imgs and wandb_logger.current_epoch > 0: # Check for test operation
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if wandb_logger.current_epoch % wandb_logger.bbox_interval == 0:
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box_data = [{"position": {"minX": xyxy[0], "minY": xyxy[1], "maxX": xyxy[2], "maxY": xyxy[3]},
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"class_id": int(cls),
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"box_caption": "%s %.3f" % (names[cls], conf),
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"scores": {"class_score": conf},
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"domain": "pixel"} for *xyxy, conf, cls in pred.tolist()]
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boxes = {"predictions": {"box_data": box_data, "class_labels": names}} # inference-space
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wandb_images.append(wandb_logger.wandb.Image(img[si], boxes=boxes, caption=path.name))
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wandb_logger.log_training_progress(predn, path, names) # logs dsviz tables
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# Append to pycocotools JSON dictionary
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if save_json:
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@@ -239,9 +240,11 @@ def test(data,
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# Plots
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if plots:
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confusion_matrix.plot(save_dir=save_dir, names=list(names.values()))
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if wandb and wandb.run:
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val_batches = [wandb.Image(str(f), caption=f.name) for f in sorted(save_dir.glob('test*.jpg'))]
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wandb.log({"Images": wandb_images, "Validation": val_batches}, commit=False)
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if wandb_logger and wandb_logger.wandb:
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val_batches = [wandb_logger.wandb.Image(str(f), caption=f.name) for f in sorted(save_dir.glob('test*.jpg'))]
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wandb_logger.log({"Validation": val_batches})
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if wandb_images:
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wandb_logger.log({"Bounding Box Debugger/Images": wandb_images})
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# Save JSON
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if save_json and len(jdict):
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