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app/yolov5/utils/loggers/__init__.py

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+# YOLOv5 🚀 by Ultralytics, GPL-3.0 license
+"""
+Logging utils
+"""
+
+import os
+import warnings
+from pathlib import Path
+
+import pkg_resources as pkg
+import torch
+from torch.utils.tensorboard import SummaryWriter
+
+from app.yolov5.utils.general import colorstr, cv2
+from app.yolov5.utils.loggers.clearml.clearml_utils import ClearmlLogger
+from app.yolov5.utils.loggers.wandb.wandb_utils import WandbLogger
+from app.yolov5.utils.plots import plot_images, plot_labels, plot_results
+from app.yolov5.utils.torch_utils import de_parallel
+
+LOGGERS = ('csv', 'tb', 'wandb', 'clearml')  # *.csv, TensorBoard, Weights & Biases, ClearML
+RANK = int(os.getenv('RANK', -1))
+
+try:
+    import wandb
+
+    assert hasattr(wandb, '__version__')  # verify package import not local dir
+    if pkg.parse_version(wandb.__version__) >= pkg.parse_version('0.12.2') and RANK in {0, -1}:
+        try:
+            wandb_login_success = wandb.login(timeout=30)
+        except wandb.errors.UsageError:  # known non-TTY terminal issue
+            wandb_login_success = False
+        if not wandb_login_success:
+            wandb = None
+except (ImportError, AssertionError):
+    wandb = None
+
+try:
+    import clearml
+
+    assert hasattr(clearml, '__version__')  # verify package import not local dir
+except (ImportError, AssertionError):
+    clearml = None
+
+
+class Loggers():
+    # YOLOv5 Loggers class
+    def __init__(self, save_dir=None, weights=None, opt=None, hyp=None, logger=None, include=LOGGERS):
+        self.save_dir = save_dir
+        self.weights = weights
+        self.opt = opt
+        self.hyp = hyp
+        self.plots = not opt.noplots  # plot results
+        self.logger = logger  # for printing results to console
+        self.include = include
+        self.keys = [
+            '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',  # metrics
+            'val/box_loss',
+            'val/obj_loss',
+            'val/cls_loss',  # val loss
+            'x/lr0',
+            'x/lr1',
+            'x/lr2']  # params
+        self.best_keys = ['best/epoch', 'best/precision', 'best/recall', 'best/mAP_0.5', 'best/mAP_0.5:0.95']
+        for k in LOGGERS:
+            setattr(self, k, None)  # init empty logger dictionary
+        self.csv = True  # always log to csv
+
+        # Messages
+        if not wandb:
+            prefix = colorstr('Weights & Biases: ')
+            s = f"{prefix}run 'pip install wandb' to automatically track and visualize YOLOv5 🚀 runs in Weights & Biases"
+            self.logger.info(s)
+        if not clearml:
+            prefix = colorstr('ClearML: ')
+            s = f"{prefix}run 'pip install clearml' to automatically track, visualize and remotely train YOLOv5 🚀 in ClearML"
+            self.logger.info(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
+        if wandb and 'wandb' in self.include:
+            wandb_artifact_resume = isinstance(self.opt.resume, str) and self.opt.resume.startswith('wandb-artifact://')
+            run_id = torch.load(self.weights).get('wandb_id') if self.opt.resume and not wandb_artifact_resume else None
+            self.opt.hyp = self.hyp  # add hyperparameters
+            self.wandb = WandbLogger(self.opt, run_id)
+            # temp warn. because nested artifacts not supported after 0.12.10
+            if pkg.parse_version(wandb.__version__) >= pkg.parse_version('0.12.11'):
+                s = "YOLOv5 temporarily requires wandb version 0.12.10 or below. Some features may not work as expected."
+                self.logger.warning(s)
+        else:
+            self.wandb = None
+
+        # ClearML
+        if clearml and 'clearml' in self.include:
+            self.clearml = ClearmlLogger(self.opt, self.hyp)
+        else:
+            self.clearml = None
+
+    @property
+    def remote_dataset(self):
+        # Get data_dict if custom dataset artifact link is provided
+        data_dict = None
+        if self.clearml:
+            data_dict = self.clearml.data_dict
+        if self.wandb:
+            data_dict = self.wandb.data_dict
+
+        return data_dict
+
+    def on_train_start(self):
+        # Callback runs on train start
+        pass
+
+    def on_pretrain_routine_end(self, labels, names):
+        # Callback runs on pre-train routine end
+        if self.plots:
+            plot_labels(labels, names, self.save_dir)
+            paths = self.save_dir.glob('*labels*.jpg')  # training labels
+            if self.wandb:
+                self.wandb.log({"Labels": [wandb.Image(str(x), caption=x.name) for x in paths]})
+            # if self.clearml:
+            #    pass  # ClearML saves these images automatically using hooks
+
+    def on_train_batch_end(self, model, ni, imgs, targets, paths):
+        # Callback runs on train batch end
+        # ni: number integrated batches (since train start)
+        if self.plots:
+            if ni < 3:
+                f = self.save_dir / f'train_batch{ni}.jpg'  # filename
+                plot_images(imgs, targets, paths, f)
+                if ni == 0 and self.tb and not self.opt.sync_bn:
+                    log_tensorboard_graph(self.tb, model, imgsz=(self.opt.imgsz, self.opt.imgsz))
+            if ni == 10 and (self.wandb or self.clearml):
+                files = sorted(self.save_dir.glob('train*.jpg'))
+                if self.wandb:
+                    self.wandb.log({'Mosaics': [wandb.Image(str(f), caption=f.name) for f in files if f.exists()]})
+                if self.clearml:
+                    self.clearml.log_debug_samples(files, title='Mosaics')
+
+    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_image_end(self, pred, predn, path, names, im):
+        # Callback runs on val image end
+        if self.wandb:
+            self.wandb.val_one_image(pred, predn, path, names, im)
+        if self.clearml:
+            self.clearml.log_image_with_boxes(path, pred, names, im)
+
+    def on_val_end(self):
+        # Callback runs on val end
+        if self.wandb or self.clearml:
+            files = sorted(self.save_dir.glob('val*.jpg'))
+            if self.wandb:
+                self.wandb.log({"Validation": [wandb.Image(str(f), caption=f.name) for f in files]})
+            if self.clearml:
+                self.clearml.log_debug_samples(files, title='Validation')
+
+    def on_fit_epoch_end(self, vals, epoch, best_fitness, fi):
+        # Callback runs at the end of each fit (train+val) epoch
+        x = dict(zip(self.keys, vals))
+        if self.csv:
+            file = self.save_dir / 'results.csv'
+            n = len(x) + 1  # number of cols
+            s = '' if file.exists() else (('%20s,' * n % tuple(['epoch'] + self.keys)).rstrip(',') + '\n')  # add header
+            with open(file, 'a') as f:
+                f.write(s + ('%20.5g,' * n % tuple([epoch] + vals)).rstrip(',') + '\n')
+
+        if self.tb:
+            for k, v in x.items():
+                self.tb.add_scalar(k, v, epoch)
+        elif self.clearml:  # log to ClearML if TensorBoard not used
+            for k, v in x.items():
+                title, series = k.split('/')
+                self.clearml.task.get_logger().report_scalar(title, series, v, epoch)
+
+        if self.wandb:
+            if best_fitness == fi:
+                best_results = [epoch] + vals[3:7]
+                for i, name in enumerate(self.best_keys):
+                    self.wandb.wandb_run.summary[name] = best_results[i]  # log best results in the summary
+            self.wandb.log(x)
+            self.wandb.end_epoch(best_result=best_fitness == fi)
+
+        if self.clearml:
+            self.clearml.current_epoch_logged_images = set()  # reset epoch image limit
+            self.clearml.current_epoch += 1
+
+    def on_model_save(self, last, epoch, final_epoch, best_fitness, fi):
+        # Callback runs on model save event
+        if (epoch + 1) % self.opt.save_period == 0 and not final_epoch and self.opt.save_period != -1:
+            if self.wandb:
+                self.wandb.log_model(last.parent, self.opt, epoch, fi, best_model=best_fitness == fi)
+            if self.clearml:
+                self.clearml.task.update_output_model(model_path=str(last),
+                                                      model_name='Latest Model',
+                                                      auto_delete_file=False)
+
+    def on_train_end(self, last, best, epoch, results):
+        # Callback runs on training end, i.e. saving best model
+        if self.plots:
+            plot_results(file=self.save_dir / 'results.csv')  # save results.png
+        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
+        self.logger.info(f"Results saved to {colorstr('bold', self.save_dir)}")
+
+        if self.tb and not self.clearml:  # These images are already captured by ClearML by now, we don't want doubles
+            for f in files:
+                self.tb.add_image(f.stem, cv2.imread(str(f))[..., ::-1], epoch, dataformats='HWC')
+
+        if self.wandb:
+            self.wandb.log(dict(zip(self.keys[3:10], results)))
+            self.wandb.log({"Results": [wandb.Image(str(f), caption=f.name) for f in files]})
+            # Calling wandb.log. TODO: Refactor this into WandbLogger.log_model
+            if not self.opt.evolve:
+                wandb.log_artifact(str(best if best.exists() else last),
+                                   type='model',
+                                   name=f'run_{self.wandb.wandb_run.id}_model',
+                                   aliases=['latest', 'best', 'stripped'])
+            self.wandb.finish_run()
+
+        if self.clearml and not self.opt.evolve:
+            self.clearml.task.update_output_model(model_path=str(best if best.exists() else last),
+                                                  name='Best Model',
+                                                  auto_delete_file=False)
+
+    def on_params_update(self, params: dict):
+        # Update hyperparams or configs of the experiment
+        if self.wandb:
+            self.wandb.wandb_run.config.update(params, allow_val_change=True)
+
+
+class GenericLogger:
+    """
+    YOLOv5 General purpose logger for non-task specific logging
+    Usage: from utils.loggers import GenericLogger; logger = GenericLogger(...)
+    Arguments
+        opt:             Run arguments
+        console_logger:  Console logger
+        include:         loggers to include
+    """
+
+    def __init__(self, opt, console_logger, include=('tb', 'wandb')):
+        # init default loggers
+        self.save_dir = Path(opt.save_dir)
+        self.include = include
+        self.console_logger = console_logger
+        self.csv = self.save_dir / 'results.csv'  # CSV logger
+        if 'tb' in self.include:
+            prefix = colorstr('TensorBoard: ')
+            self.console_logger.info(
+                f"{prefix}Start with 'tensorboard --logdir {self.save_dir.parent}', view at http://localhost:6006/")
+            self.tb = SummaryWriter(str(self.save_dir))
+
+        if wandb and 'wandb' in self.include:
+            self.wandb = wandb.init(project=web_project_name(str(opt.project)),
+                                    name=None if opt.name == "exp" else opt.name,
+                                    config=opt)
+        else:
+            self.wandb = None
+
+    def log_metrics(self, metrics, epoch):
+        # Log metrics dictionary to all loggers
+        if self.csv:
+            keys, vals = list(metrics.keys()), list(metrics.values())
+            n = len(metrics) + 1  # number of cols
+            s = '' if self.csv.exists() else (('%23s,' * n % tuple(['epoch'] + keys)).rstrip(',') + '\n')  # header
+            with open(self.csv, 'a') as f:
+                f.write(s + ('%23.5g,' * n % tuple([epoch] + vals)).rstrip(',') + '\n')
+
+        if self.tb:
+            for k, v in metrics.items():
+                self.tb.add_scalar(k, v, epoch)
+
+        if self.wandb:
+            self.wandb.log(metrics, step=epoch)
+
+    def log_images(self, files, name='Images', epoch=0):
+        # Log images to all loggers
+        files = [Path(f) for f in (files if isinstance(files, (tuple, list)) else [files])]  # to Path
+        files = [f for f in files if f.exists()]  # filter by exists
+
+        if self.tb:
+            for f in files:
+                self.tb.add_image(f.stem, cv2.imread(str(f))[..., ::-1], epoch, dataformats='HWC')
+
+        if self.wandb:
+            self.wandb.log({name: [wandb.Image(str(f), caption=f.name) for f in files]}, step=epoch)
+
+    def log_graph(self, model, imgsz=(640, 640)):
+        # Log model graph to all loggers
+        if self.tb:
+            log_tensorboard_graph(self.tb, model, imgsz)
+
+    def log_model(self, model_path, epoch=0, metadata={}):
+        # Log model to all loggers
+        if self.wandb:
+            art = wandb.Artifact(name=f"run_{wandb.run.id}_model", type="model", metadata=metadata)
+            art.add_file(str(model_path))
+            wandb.log_artifact(art)
+
+    def update_params(self, params):
+        # Update the paramters logged
+        if self.wandb:
+            wandb.run.config.update(params, allow_val_change=True)
+
+
+def log_tensorboard_graph(tb, model, imgsz=(640, 640)):
+    # Log model graph to TensorBoard
+    try:
+        p = next(model.parameters())  # for device, type
+        imgsz = (imgsz, imgsz) if isinstance(imgsz, int) else imgsz  # expand
+        im = torch.zeros((1, 3, *imgsz)).to(p.device).type_as(p)  # input image (WARNING: must be zeros, not empty)
+        with warnings.catch_warnings():
+            warnings.simplefilter('ignore')  # suppress jit trace warning
+            tb.add_graph(torch.jit.trace(de_parallel(model), im, strict=False), [])
+    except Exception as e:
+        print(f'WARNING: TensorBoard graph visualization failure {e}')
+
+
+def web_project_name(project):
+    # Convert local project name to web project name
+    if not project.startswith('runs/train'):
+        return project
+    suffix = '-Classify' if project.endswith('-cls') else '-Segment' if project.endswith('-seg') else ''
+    return f'YOLOv5{suffix}'