Refactor train.py and val.py `loggers` (#4137)

* Update loggers

* Config

* Update val.py

* cleanup

* fix1

* fix2

* fix3 and reformat

* format sweep.py

* Logger() class

* cleanup

* cleanup2

* wandb package import fix

* wandb package import fix2

* txt fix

* fix4

* fix5

* fix6

* drop wandb into utils/loggers

* fix 7

* rename loggers/wandb_logging to loggers/wandb

* Update message

* Update message

* Update message

* cleanup

* Fix x axis bug

* fix rank 0 issue

* cleanup

train.py

 import random
 import sys
 import time
-import warnings
 from copy import deepcopy
 from pathlib import Path
 from threading import Thread
 from torch.cuda import amp
 from torch.nn.parallel import DistributedDataParallel as DDP
 from torch.optim import Adam, SGD, lr_scheduler
-from torch.utils.tensorboard import SummaryWriter
 from tqdm import tqdm
 
 FILE = Path(__file__).absolute()
 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, de_parallel
-from utils.wandb_logging.wandb_utils import WandbLogger, check_wandb_resume
+from utils.loggers.wandb.wandb_utils import check_wandb_resume
 from utils.metrics import fitness
+from utils.loggers import Loggers
 
 LOGGER = logging.getLogger(__name__)
 LOCAL_RANK = int(os.getenv('LOCAL_RANK', -1))  # https://pytorch.org/docs/stable/elastic/run.html
     with open(save_dir / 'opt.yaml', 'w') as f:
         yaml.safe_dump(vars(opt), f, sort_keys=False)
 
-    # Configure
+    # Config
     plots = not evolve  # create plots
     cuda = device.type != 'cpu'
     init_seeds(1 + RANK)
     with open(data) as f:
         data_dict = yaml.safe_load(f)  # data dict
-
-    # Loggers
-    loggers = {'wandb': None, 'tb': None}  # loggers dict
-    if RANK in [-1, 0]:
-        # TensorBoard
-        if plots:
-            prefix = colorstr('tensorboard: ')
-            LOGGER.info(f"{prefix}Start with 'tensorboard --logdir {opt.project}', view at http://localhost:6006/")
-            loggers['tb'] = SummaryWriter(str(save_dir))
-
-        # W&B
-        opt.hyp = hyp  # add hyperparameters
-        run_id = torch.load(weights).get('wandb_id') if weights.endswith('.pt') and os.path.isfile(weights) else None
-        run_id = run_id if opt.resume else None  # start fresh run if transfer learning
-        wandb_logger = WandbLogger(opt, save_dir.stem, run_id, data_dict)
-        loggers['wandb'] = wandb_logger.wandb
-        if loggers['wandb']:
-            data_dict = wandb_logger.data_dict
-            weights, epochs, hyp = opt.weights, opt.epochs, opt.hyp  # may update values if resuming
-
     nc = 1 if single_cls else int(data_dict['nc'])  # number of classes
     names = ['item'] if single_cls and len(data_dict['names']) != 1 else data_dict['names']  # class names
     assert len(names) == nc, f'{len(names)} names found for nc={nc} dataset in {data}'  # check
     is_coco = data.endswith('coco.yaml') and nc == 80  # COCO dataset
 
+    # Loggers
+    if RANK in [-1, 0]:
+        loggers = Loggers(save_dir, results_file, weights, opt, hyp, data_dict, LOGGER).start()  # loggers dict
+        if loggers.wandb and resume:
+            weights, epochs, hyp, data_dict = opt.weights, opt.epochs, opt.hyp, loggers.wandb.data_dict
+
     # Model
     pretrained = weights.endswith('.pt')
     if pretrained:
                 pbar.set_description(s)
 
                 # Plot
-                if plots and ni < 3:
-                    f = save_dir / f'train_batch{ni}.jpg'  # filename
-                    Thread(target=plot_images, args=(imgs, targets, paths, f), daemon=True).start()
-                    if loggers['tb'] and ni == 0:  # TensorBoard
-                        with warnings.catch_warnings():
-                            warnings.simplefilter('ignore')  # suppress jit trace warning
-                            loggers['tb'].add_graph(torch.jit.trace(de_parallel(model), imgs[0:1], strict=False), [])
-                elif plots and ni == 10 and loggers['wandb']:
-                    wandb_logger.log({'Mosaics': [loggers['wandb'].Image(str(x), caption=x.name) for x in
-                                                  save_dir.glob('train*.jpg') if x.exists()]})
+                if plots:
+                    if ni < 3:
+                        f = save_dir / f'train_batch{ni}.jpg'  # filename
+                        Thread(target=plot_images, args=(imgs, targets, paths, f), daemon=True).start()
+                    loggers.on_train_batch_end(ni, model, imgs)
 
             # end batch ------------------------------------------------------------------------------------------------
 
         lr = [x['lr'] for x in optimizer.param_groups]  # for loggers
         scheduler.step()
 
-        # DDP process 0 or single-GPU
         if RANK in [-1, 0]:
             # mAP
+            loggers.on_train_epoch_end(epoch)
             ema.update_attr(model, include=['yaml', 'nc', 'hyp', 'names', 'stride', 'class_weights'])
             final_epoch = epoch + 1 == epochs
             if not noval or final_epoch:  # Calculate mAP
-                wandb_logger.current_epoch = epoch + 1
                 results, maps, _ = val.run(data_dict,
                                            batch_size=batch_size // WORLD_SIZE * 2,
                                            imgsz=imgsz,
                                            save_json=is_coco and final_epoch,
                                            verbose=nc < 50 and final_epoch,
                                            plots=plots and final_epoch,
-                                           wandb_logger=wandb_logger,
+                                           loggers=loggers,
                                            compute_loss=compute_loss)
 
-            # Write
-            with open(results_file, 'a') as f:
-                f.write(s + '%10.4g' * 7 % results + '\n')  # append metrics, val_loss
-
-            # Log
-            tags = ['train/box_loss', 'train/obj_loss', 'train/cls_loss',  # train loss
-                    'metrics/precision', 'metrics/recall', 'metrics/mAP_0.5', 'metrics/mAP_0.5:0.95',
-                    'val/box_loss', 'val/obj_loss', 'val/cls_loss',  # val loss
-                    'x/lr0', 'x/lr1', 'x/lr2']  # params
-            for x, tag in zip(list(mloss[:-1]) + list(results) + lr, tags):
-                if loggers['tb']:
-                    loggers['tb'].add_scalar(tag, x, epoch)  # TensorBoard
-                if loggers['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]
             if fi > best_fitness:
                 best_fitness = fi
-            wandb_logger.end_epoch(best_result=best_fitness == fi)
+            loggers.on_train_val_end(mloss, results, lr, epoch, s, best_fitness, fi)
 
             # Save model
             if (not nosave) or (final_epoch and not evolve):  # if save
                         'ema': deepcopy(ema.ema).half(),
                         'updates': ema.updates,
                         'optimizer': optimizer.state_dict(),
-                        'wandb_id': wandb_logger.wandb_run.id if loggers['wandb'] else None}
+                        'wandb_id': loggers.wandb.wandb_run.id if loggers.wandb else None}
 
                 # Save last, best and delete
                 torch.save(ckpt, last)
                 if best_fitness == fi:
                     torch.save(ckpt, best)
-                if loggers['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
+                loggers.on_model_save(last, epoch, final_epoch, best_fitness, fi)
 
         # end epoch ----------------------------------------------------------------------------------------------------
     # end training -----------------------------------------------------------------------------------------------------
         LOGGER.info(f'{epoch - start_epoch + 1} epochs completed in {(time.time() - t0) / 3600:.3f} hours.\n')
         if plots:
             plot_results(save_dir=save_dir)  # save as results.png
-            if loggers['wandb']:
-                files = ['results.png', 'confusion_matrix.png', *[f'{x}_curve.png' for x in ('F1', 'PR', 'P', 'R')]]
-                wandb_logger.log({"Results": [loggers['wandb'].Image(str(save_dir / f), caption=f) for f in files
-                                              if (save_dir / f).exists()]})
 
         if not evolve:
             if is_coco:  # COCO dataset
             for f in last, best:
                 if f.exists():
                     strip_optimizer(f)  # strip optimizers
-            if loggers['wandb']:  # Log the stripped model
-                loggers['wandb'].log_artifact(str(best if best.exists() else last), type='model',
-                                              name='run_' + wandb_logger.wandb_run.id + '_model',
-                                              aliases=['latest', 'best', 'stripped'])
-        wandb_logger.finish_run()
+
+        loggers.on_train_end(last, best)
 
     torch.cuda.empty_cache()
     return results

utils/loggers/__init__.py

+# YOLOv5 experiment logging utils
+
+import warnings
+
+import torch
+from torch.utils.tensorboard import SummaryWriter
+
+from utils.general import colorstr, emojis
+from utils.loggers.wandb.wandb_utils import WandbLogger
+from utils.torch_utils import de_parallel
+
+LOGGERS = ('txt', 'tb', 'wandb')  # text-file, TensorBoard, Weights & Biases
+
+try:
+    import wandb
+
+    assert hasattr(wandb, '__version__')  # verify package import not local dir
+except (ImportError, AssertionError):
+    wandb = None
+
+
+class Loggers():
+    # YOLOv5 Loggers class
+    def __init__(self, save_dir=None, results_file=None, weights=None, opt=None, hyp=None,
+                 data_dict=None, logger=None, include=LOGGERS):
+        self.save_dir = save_dir
+        self.results_file = results_file
+        self.weights = weights
+        self.opt = opt
+        self.hyp = hyp
+        self.data_dict = data_dict
+        self.logger = logger  # for printing results to console
+        self.include = include
+        for k in LOGGERS:
+            setattr(self, k, None)  # init empty logger dictionary
+
+    def start(self):
+        self.txt = True  # always log to txt
+
+        # Message
+        try:
+            import wandb
+        except ImportError:
+            prefix = colorstr('Weights & Biases: ')
+            s = f"{prefix}run 'pip install wandb' to automatically track and visualize YOLOv5 🚀 runs (RECOMMENDED)"
+            print(emojis(s))
+
+        # TensorBoard
+        s = self.save_dir
+        if 'tb' in self.include and not self.opt.evolve:
+            prefix = colorstr('TensorBoard: ')
+            self.logger.info(f"{prefix}Start with 'tensorboard --logdir {s.parent}', view at http://localhost:6006/")
+            self.tb = SummaryWriter(str(s))
+
+        # W&B
+        try:
+            assert 'wandb' in self.include and wandb
+            run_id = torch.load(self.weights).get('wandb_id') if self.opt.resume else None
+            self.opt.hyp = self.hyp  # add hyperparameters
+            self.wandb = WandbLogger(self.opt, s.stem, run_id, self.data_dict)
+        except:
+            self.wandb = None
+
+        return self
+
+    def on_train_batch_end(self, ni, model, imgs):
+        # Callback runs on train batch end
+        if ni == 0:
+            with warnings.catch_warnings():
+                warnings.simplefilter('ignore')  # suppress jit trace warning
+                self.tb.add_graph(torch.jit.trace(de_parallel(model), imgs[0:1], strict=False), [])
+        if self.wandb and ni == 10:
+            files = sorted(self.save_dir.glob('train*.jpg'))
+            self.wandb.log({'Mosaics': [wandb.Image(str(f), caption=f.name) for f in files if f.exists()]})
+
+    def on_train_epoch_end(self, epoch):
+        # Callback runs on train epoch end
+        if self.wandb:
+            self.wandb.current_epoch = epoch + 1
+
+    def on_val_batch_end(self, pred, predn, path, names, im):
+        # Callback runs on train batch end
+        if self.wandb:
+            self.wandb.val_one_image(pred, predn, path, names, im)
+
+    def on_val_end(self):
+        # Callback runs on val end
+        if self.wandb:
+            files = sorted(self.save_dir.glob('val*.jpg'))
+            self.wandb.log({"Validation": [wandb.Image(str(f), caption=f.name) for f in files]})
+
+    def on_train_val_end(self, mloss, results, lr, epoch, s, best_fitness, fi):
+        # Callback runs on validation end during training
+        vals = list(mloss[:-1]) + list(results) + lr
+        tags = ['train/box_loss', 'train/obj_loss', 'train/cls_loss',  # train loss
+                'metrics/precision', 'metrics/recall', 'metrics/mAP_0.5', 'metrics/mAP_0.5:0.95',
+                'val/box_loss', 'val/obj_loss', 'val/cls_loss',  # val loss
+                'x/lr0', 'x/lr1', 'x/lr2']  # params
+        if self.txt:
+            with open(self.results_file, 'a') as f:
+                f.write(s + '%10.4g' * 7 % results + '\n')  # append metrics, val_loss
+        if self.tb:
+            for x, tag in zip(vals, tags):
+                self.tb.add_scalar(tag, x, epoch)  # TensorBoard
+        if self.wandb:
+            self.wandb.log({k: v for k, v in zip(tags, vals)})
+            self.wandb.end_epoch(best_result=best_fitness == fi)
+
+    def on_model_save(self, last, epoch, final_epoch, best_fitness, fi):
+        # Callback runs on model save event
+        if self.wandb:
+            if ((epoch + 1) % self.opt.save_period == 0 and not final_epoch) and self.opt.save_period != -1:
+                self.wandb.log_model(last.parent, self.opt, epoch, fi, best_model=best_fitness == fi)
+
+    def on_train_end(self, last, best):
+        # Callback runs on training end
+        files = ['results.png', 'confusion_matrix.png', *[f'{x}_curve.png' for x in ('F1', 'PR', 'P', 'R')]]
+        files = [(self.save_dir / f) for f in files if (self.save_dir / f).exists()]  # filter
+        if self.wandb:
+            wandb.log({"Results": [wandb.Image(str(f), caption=f.name) for f in files]})
+            wandb.log_artifact(str(best if best.exists() else last), type='model',
+                               name='run_' + self.wandb.wandb_run.id + '_model',
+                               aliases=['latest', 'best', 'stripped'])
+            self.wandb.finish_run()
+
+    def log_images(self, paths):
+        # Log images
+        if self.wandb:
+            self.wandb.log({"Labels": [wandb.Image(str(x), caption=x.name) for x in paths]})

utils/wandb_logging/sweep.py → utils/loggers/wandb/sweep.py

 import sys
 from pathlib import Path
+
 import wandb
 
 FILE = Path(__file__).absolute()
 sys.path.append(FILE.parents[2].as_posix())  # add utils/ to path
 
 from train import train, parse_opt
-import test
 from utils.general import increment_path
 from utils.torch_utils import select_device
 

utils/wandb_logging/sweep.yaml → utils/loggers/wandb/sweep.yaml

 # You can use grid, bayesian and hyperopt search strategy 
 # For more info on configuring sweeps visit - https://docs.wandb.ai/guides/sweeps/configuration
 
-program: utils/wandb_logging/sweep.py
+program: utils/loggers/wandb/sweep.py
 method: random
 metric:
   name: metrics/mAP_0.5

utils/wandb_logging/wandb_utils.py → utils/loggers/wandb/wandb_utils.py

 """Utilities and tools for tracking runs with Weights & Biases."""
+
 import logging
 import os
 import sys
 import yaml
 from tqdm import tqdm
 
-sys.path.append(str(Path(__file__).parent.parent.parent))  # add utils/ to path
+FILE = Path(__file__).absolute()
+sys.path.append(FILE.parents[3].as_posix())  # add yolov5/ to path
+
 from utils.datasets import LoadImagesAndLabels
 from utils.datasets import img2label_paths
-from utils.general import colorstr, check_dataset, check_file
+from utils.general import check_dataset, check_file
 
 try:
     import wandb
-    from wandb import init, finish
-except ImportError:
+
+    assert hasattr(wandb, '__version__')  # verify package import not local dir
+except (ImportError, AssertionError):
     wandb = None
 
 RANK = int(os.getenv('RANK', -1))
         self.data_dict = data_dict
         self.bbox_media_panel_images = []
         self.val_table_path_map = None
-        self.max_imgs_to_log = 16 
+        self.max_imgs_to_log = 16
         # It's more elegant to stick to 1 wandb.init call, but useful config data is overwritten in the WandbLogger's wandb.init call
         if isinstance(opt.resume, str):  # checks resume from artifact
             if opt.resume.startswith(WANDB_ARTIFACT_PREFIX):
                 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.update({'opt': vars(opt), 'data_dict': wandb_data_dict}, allow_val_change=True)
+                    self.wandb_run.config.update({'opt': vars(opt), 'data_dict': wandb_data_dict},
+                                                 allow_val_change=True)
                 self.data_dict = self.setup_training(opt, data_dict)
             if self.job_type == 'Dataset Creation':
                 self.data_dict = self.check_and_upload_dataset(opt)
-        else:
-            prefix = colorstr('wandb: ')
-            print(f"{prefix}Install Weights & Biases for YOLOv5 logging with 'pip install wandb' (recommended)")
 
     def check_and_upload_dataset(self, opt):
         assert wandb, 'Install wandb to upload dataset'
                                                                                            opt.artifact_alias)
             self.val_artifact_path, self.val_artifact = self.download_dataset_artifact(data_dict.get('val'),
                                                                                        opt.artifact_alias)
-            
+
         if self.train_artifact_path is not None:
             train_path = Path(self.train_artifact_path) / 'data/images/'
             data_dict['train'] = str(train_path)
             val_path = Path(self.val_artifact_path) / 'data/images/'
             data_dict['val'] = str(val_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", "ground truth", "prediction", "avg_confidence"])
                                    )
 
     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
             self.log_training_progress(predn, path, names)
-        else: # Default to bbox media panelif Val artifact not found
+        else:  # Default to bbox media panelif Val artifact not found
             if len(self.bbox_media_panel_images) < self.max_imgs_to_log and self.current_epoch > 0:
                 if self.current_epoch % self.bbox_interval == 0:
                     box_data = [{"position": {"minX": xyxy[0], "minY": xyxy[1], "maxX": xyxy[2], "maxY": xyxy[3]},
                     boxes = {"predictions": {"box_data": box_data, "class_labels": names}}  # inference-space
                     self.bbox_media_panel_images.append(wandb.Image(im, boxes=boxes, caption=path.name))
 
-        
     def log(self, log_dict):
         if self.wandb_run:
             for key, value in log_dict.items():

utils/plots.py

     plt.close()
 
     # loggers
-    for k, v in loggers.items() or {}:
-        if k == 'wandb' and v:
-            v.log({"Labels": [v.Image(str(x), caption=x.name) for x in save_dir.glob('*labels*.jpg')]}, commit=False)
+    if loggers:
+        loggers.log_images(save_dir.glob('*labels*.jpg'))
 
 
 def plot_evolution(yaml_file='data/hyp.finetune.yaml'):  # from utils.plots import *; plot_evolution()

val.py

 from utils.metrics import ap_per_class, ConfusionMatrix
 from utils.plots import plot_images, output_to_target, plot_study_txt
 from utils.torch_utils import select_device, time_sync
+from utils.loggers import Loggers
 
 
 def save_one_txt(predn, save_conf, shape, file):
         dataloader=None,
         save_dir=Path(''),
         plots=True,
-        wandb_logger=None,
+        loggers=Loggers(),
         compute_loss=None,
         ):
     # Initialize/load model and set device
                 save_one_txt(predn, save_conf, shape, file=save_dir / 'labels' / (path.stem + '.txt'))
             if save_json:
                 save_one_json(predn, jdict, path, class_map)  # append to COCO-JSON dictionary
-            if wandb_logger and wandb_logger.wandb_run:
-                wandb_logger.val_one_image(pred, predn, path, names, img[si])
+            loggers.on_val_batch_end(pred, predn, path, names, img[si])
 
         # Plot images
         if plots and batch_i < 3:
     # Plots
     if plots:
         confusion_matrix.plot(save_dir=save_dir, names=list(names.values()))
-        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('val*.jpg'))]
-            wandb_logger.log({"Validation": val_batches})
+        loggers.on_val_end()
 
     # Save JSON
     if save_json and len(jdict):