yolov5/utils/loggers/__init__.py

# 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 utils.general import colorstr, cv2, threaded
from utils.loggers.clearml.clearml_utils import ClearmlLogger
from utils.loggers.wandb.wandb_utils import WandbLogger
from utils.plots import plot_images, plot_labels, plot_results
from 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

    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')

    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 = opt.save_dir
        self.include = include
        self.console_logger = console_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="YOLOv5-Classifier" if opt.project == "runs/train" else opt.project,
                                    name=None if opt.name == "exp" else opt.name,
                                    config=opt)
        else:
            self.wandb = None

    def log_metrics(self, metrics_dict, epoch):
        # Log metrics dictionary to all loggers
        if self.tb:
            for k, v in metrics_dict.items():
                self.tb.add_scalar(k, v, epoch)

        if self.wandb:
            self.wandb.log(metrics_dict, 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)


@threaded
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}')