Skip to content

Trainer

All task Trainers are inherited from BaseTrainer class that contains the model training and optimization routine boilerplate. You can override any function of these Trainers to suit your needs.


BaseTrainer API Reference

BaseTrainer

A base class for creating trainers.

Attributes:

Name Type Description
args SimpleNamespace

Configuration for the trainer.

check_resume method

Method to check if training should be resumed from a saved checkpoint.

console logging.Logger

Logger instance.

validator BaseValidator

Validator instance.

model nn.Module

Model instance.

callbacks defaultdict

Dictionary of callbacks.

save_dir Path

Directory to save results.

wdir Path

Directory to save weights.

last Path

Path to last checkpoint.

best Path

Path to best checkpoint.

batch_size int

Batch size for training.

epochs int

Number of epochs to train for.

start_epoch int

Starting epoch for training.

device torch.device

Device to use for training.

amp bool

Flag to enable AMP (Automatic Mixed Precision).

scaler amp.GradScaler

Gradient scaler for AMP.

data str

Path to data.

trainset torch.utils.data.Dataset

Training dataset.

testset torch.utils.data.Dataset

Testing dataset.

ema nn.Module

EMA (Exponential Moving Average) of the model.

lf nn.Module

Loss function.

scheduler torch.optim.lr_scheduler._LRScheduler

Learning rate scheduler.

best_fitness float

The best fitness value achieved.

fitness float

Current fitness value.

loss float

Current loss value.

tloss float

Total loss value.

loss_names list

List of loss names.

csv Path

Path to results CSV file.

Source code in ultralytics/yolo/engine/trainer.py
 37
 38
 39
 40
 41
 42
 43
 44
 45
 46
 47
 48
 49
 50
 51
 52
 53
 54
 55
 56
 57
 58
 59
 60
 61
 62
 63
 64
 65
 66
 67
 68
 69
 70
 71
 72
 73
 74
 75
 76
 77
 78
 79
 80
 81
 82
 83
 84
 85
 86
 87
 88
 89
 90
 91
 92
 93
 94
 95
 96
 97
 98
 99
100
101
102
103
104
105
106
107
108
109
110
111
112
113
114
115
116
117
118
119
120
121
122
123
124
125
126
127
128
129
130
131
132
133
134
135
136
137
138
139
140
141
142
143
144
145
146
147
148
149
150
151
152
153
154
155
156
157
158
159
160
161
162
163
164
165
166
167
168
169
170
171
172
173
174
175
176
177
178
179
180
181
182
183
184
185
186
187
188
189
190
191
192
193
194
195
196
197
198
199
200
201
202
203
204
205
206
207
208
209
210
211
212
213
214
215
216
217
218
219
220
221
222
223
224
225
226
227
228
229
230
231
232
233
234
235
236
237
238
239
240
241
242
243
244
245
246
247
248
249
250
251
252
253
254
255
256
257
258
259
260
261
262
263
264
265
266
267
268
269
270
271
272
273
274
275
276
277
278
279
280
281
282
283
284
285
286
287
288
289
290
291
292
293
294
295
296
297
298
299
300
301
302
303
304
305
306
307
308
309
310
311
312
313
314
315
316
317
318
319
320
321
322
323
324
325
326
327
328
329
330
331
332
333
334
335
336
337
338
339
340
341
342
343
344
345
346
347
348
349
350
351
352
353
354
355
356
357
358
359
360
361
362
363
364
365
366
367
368
369
370
371
372
373
374
375
376
377
378
379
380
381
382
383
384
385
386
387
388
389
390
391
392
393
394
395
396
397
398
399
400
401
402
403
404
405
406
407
408
409
410
411
412
413
414
415
416
417
418
419
420
421
422
423
424
425
426
427
428
429
430
431
432
433
434
435
436
437
438
439
440
441
442
443
444
445
446
447
448
449
450
451
452
453
454
455
456
457
458
459
460
461
462
463
464
465
466
467
468
469
470
471
472
473
474
475
476
477
478
479
480
481
482
483
484
485
486
487
488
489
490
491
492
493
494
495
496
497
498
499
500
501
502
503
504
505
506
507
508
509
510
511
512
513
514
515
516
517
518
519
520
521
522
523
524
525
526
527
528
529
530
531
532
533
534
535
536
537
538
539
540
541
542
543
544
545
546
547
548
549
550
551
552
553
554
555
556
557
558
559
560
561
562
563
564
565
566
567
568
569
570
571
572
573
574
575
576
577
578
579
580
581
582
583
584
585
586
587
588
589
590
591
592
593
class BaseTrainer:
    """
    BaseTrainer

    A base class for creating trainers.

    Attributes:
        args (SimpleNamespace): Configuration for the trainer.
        check_resume (method): Method to check if training should be resumed from a saved checkpoint.
        console (logging.Logger): Logger instance.
        validator (BaseValidator): Validator instance.
        model (nn.Module): Model instance.
        callbacks (defaultdict): Dictionary of callbacks.
        save_dir (Path): Directory to save results.
        wdir (Path): Directory to save weights.
        last (Path): Path to last checkpoint.
        best (Path): Path to best checkpoint.
        batch_size (int): Batch size for training.
        epochs (int): Number of epochs to train for.
        start_epoch (int): Starting epoch for training.
        device (torch.device): Device to use for training.
        amp (bool): Flag to enable AMP (Automatic Mixed Precision).
        scaler (amp.GradScaler): Gradient scaler for AMP.
        data (str): Path to data.
        trainset (torch.utils.data.Dataset): Training dataset.
        testset (torch.utils.data.Dataset): Testing dataset.
        ema (nn.Module): EMA (Exponential Moving Average) of the model.
        lf (nn.Module): Loss function.
        scheduler (torch.optim.lr_scheduler._LRScheduler): Learning rate scheduler.
        best_fitness (float): The best fitness value achieved.
        fitness (float): Current fitness value.
        loss (float): Current loss value.
        tloss (float): Total loss value.
        loss_names (list): List of loss names.
        csv (Path): Path to results CSV file.
    """

    def __init__(self, cfg=DEFAULT_CFG, overrides=None):
        """
        Initializes the BaseTrainer class.

        Args:
            cfg (str, optional): Path to a configuration file. Defaults to DEFAULT_CFG.
            overrides (dict, optional): Configuration overrides. Defaults to None.
        """
        self.args = get_cfg(cfg, overrides)
        self.device = select_device(self.args.device, self.args.batch)
        self.check_resume()
        self.console = LOGGER
        self.validator = None
        self.model = None
        init_seeds(self.args.seed + 1 + RANK, deterministic=self.args.deterministic)

        # Dirs
        project = self.args.project or Path(SETTINGS['runs_dir']) / self.args.task
        name = self.args.name or f"{self.args.mode}"
        if hasattr(self.args, 'save_dir'):
            self.save_dir = Path(self.args.save_dir)
        else:
            self.save_dir = Path(
                increment_path(Path(project) / name, exist_ok=self.args.exist_ok if RANK in {-1, 0} else True))
        self.wdir = self.save_dir / 'weights'  # weights dir
        if RANK in {-1, 0}:
            self.wdir.mkdir(parents=True, exist_ok=True)  # make dir
            self.args.save_dir = str(self.save_dir)
            yaml_save(self.save_dir / 'args.yaml', vars(self.args))  # save run args
        self.last, self.best = self.wdir / 'last.pt', self.wdir / 'best.pt'  # checkpoint paths

        self.batch_size = self.args.batch
        self.epochs = self.args.epochs
        self.start_epoch = 0
        if RANK == -1:
            print_args(vars(self.args))

        # Device
        self.amp = self.device.type != 'cpu'
        self.scaler = amp.GradScaler(enabled=self.amp)
        if self.device.type == 'cpu':
            self.args.workers = 0  # faster CPU training as time dominated by inference, not dataloading

        # Model and Dataloaders.
        self.model = self.args.model
        self.data = self.args.data
        if self.data.endswith(".yaml"):
            self.data = check_det_dataset(self.data)
        elif self.args.task == 'classify':
            self.data = check_cls_dataset(self.data)
        else:
            raise FileNotFoundError(emojis(f"Dataset '{self.args.data}' not found ❌"))
        self.trainset, self.testset = self.get_dataset(self.data)
        self.ema = None

        # Optimization utils init
        self.lf = None
        self.scheduler = None

        # Epoch level metrics
        self.best_fitness = None
        self.fitness = None
        self.loss = None
        self.tloss = None
        self.loss_names = ['Loss']
        self.csv = self.save_dir / 'results.csv'
        self.plot_idx = [0, 1, 2]

        # Callbacks
        self.callbacks = defaultdict(list, callbacks.default_callbacks)  # add callbacks
        if RANK in {0, -1}:
            callbacks.add_integration_callbacks(self)

    def add_callback(self, event: str, callback):
        """
        Appends the given callback.
        """
        self.callbacks[event].append(callback)

    def set_callback(self, event: str, callback):
        """
        Overrides the existing callbacks with the given callback.
        """
        self.callbacks[event] = [callback]

    def run_callbacks(self, event: str):
        for callback in self.callbacks.get(event, []):
            callback(self)

    def train(self):
        # Allow device='', device=None on Multi-GPU systems to default to device=0
        if isinstance(self.args.device, int) or self.args.device:  # i.e. device=0 or device=[0,1,2,3]
            world_size = torch.cuda.device_count()
        elif torch.cuda.is_available():  # i.e. device=None or device=''
            world_size = 1  # default to device 0
        else:  # i.e. device='cpu' or 'mps'
            world_size = 0

        # Run subprocess if DDP training, else train normally
        if world_size > 1 and "LOCAL_RANK" not in os.environ:
            command = generate_ddp_command(world_size, self)
            try:
                subprocess.run(command)
            except Exception as e:
                self.console(e)
            finally:
                ddp_cleanup(command, self)
        else:
            self._do_train(int(os.getenv("RANK", -1)), world_size)

    def _setup_ddp(self, rank, world_size):
        # os.environ['MASTER_ADDR'] = 'localhost'
        # os.environ['MASTER_PORT'] = '9020'
        torch.cuda.set_device(rank)
        self.device = torch.device('cuda', rank)
        self.console.info(f"DDP settings: RANK {rank}, WORLD_SIZE {world_size}, DEVICE {self.device}")
        dist.init_process_group("nccl" if dist.is_nccl_available() else "gloo", rank=rank, world_size=world_size)

    def _setup_train(self, rank, world_size):
        """
        Builds dataloaders and optimizer on correct rank process.
        """
        # model
        self.run_callbacks("on_pretrain_routine_start")
        ckpt = self.setup_model()
        self.model = self.model.to(self.device)
        self.set_model_attributes()
        if world_size > 1:
            self.model = DDP(self.model, device_ids=[rank])
        # Check imgsz
        gs = max(int(self.model.stride.max() if hasattr(self.model, 'stride') else 32), 32)  # grid size (max stride)
        self.args.imgsz = check_imgsz(self.args.imgsz, stride=gs, floor=gs, max_dim=1)
        # Batch size
        if self.batch_size == -1:
            if RANK == -1:  # single-GPU only, estimate best batch size
                self.batch_size = check_train_batch_size(self.model, self.args.imgsz, self.amp)
            else:
                SyntaxError('batch=-1 to use AutoBatch is only available in Single-GPU training. '
                            'Please pass a valid batch size value for Multi-GPU DDP training, i.e. batch=16')

        # Optimizer
        self.accumulate = max(round(self.args.nbs / self.batch_size), 1)  # accumulate loss before optimizing
        self.args.weight_decay *= self.batch_size * self.accumulate / self.args.nbs  # scale weight_decay
        self.optimizer = self.build_optimizer(model=self.model,
                                              name=self.args.optimizer,
                                              lr=self.args.lr0,
                                              momentum=self.args.momentum,
                                              decay=self.args.weight_decay)
        # Scheduler
        if self.args.cos_lr:
            self.lf = one_cycle(1, self.args.lrf, self.epochs)  # cosine 1->hyp['lrf']
        else:
            self.lf = lambda x: (1 - x / self.epochs) * (1.0 - self.args.lrf) + self.args.lrf  # linear
        self.scheduler = lr_scheduler.LambdaLR(self.optimizer, lr_lambda=self.lf)
        self.scheduler.last_epoch = self.start_epoch - 1  # do not move
        self.stopper, self.stop = EarlyStopping(patience=self.args.patience), False

        # dataloaders
        batch_size = self.batch_size // world_size if world_size > 1 else self.batch_size
        self.train_loader = self.get_dataloader(self.trainset, batch_size=batch_size, rank=rank, mode="train")
        if rank in {0, -1}:
            self.test_loader = self.get_dataloader(self.testset, batch_size=batch_size * 2, rank=-1, mode="val")
            self.validator = self.get_validator()
            metric_keys = self.validator.metrics.keys + self.label_loss_items(prefix="val")
            self.metrics = dict(zip(metric_keys, [0] * len(metric_keys)))  # TODO: init metrics for plot_results()?
            self.ema = ModelEMA(self.model)
        self.resume_training(ckpt)
        self.run_callbacks("on_pretrain_routine_end")

    def _do_train(self, rank=-1, world_size=1):
        if world_size > 1:
            self._setup_ddp(rank, world_size)

        self._setup_train(rank, world_size)

        self.epoch_time = None
        self.epoch_time_start = time.time()
        self.train_time_start = time.time()
        nb = len(self.train_loader)  # number of batches
        nw = max(round(self.args.warmup_epochs * nb), 100)  # number of warmup iterations
        last_opt_step = -1
        self.run_callbacks("on_train_start")
        self.log(f"Image sizes {self.args.imgsz} train, {self.args.imgsz} val\n"
                 f'Using {self.train_loader.num_workers * (world_size or 1)} dataloader workers\n'
                 f"Logging results to {colorstr('bold', self.save_dir)}\n"
                 f"Starting training for {self.epochs} epochs...")
        if self.args.close_mosaic:
            base_idx = (self.epochs - self.args.close_mosaic) * nb
            self.plot_idx.extend([base_idx, base_idx + 1, base_idx + 2])
        for epoch in range(self.start_epoch, self.epochs):
            self.epoch = epoch
            self.run_callbacks("on_train_epoch_start")
            self.model.train()
            if rank != -1:
                self.train_loader.sampler.set_epoch(epoch)
            pbar = enumerate(self.train_loader)
            # Update dataloader attributes (optional)
            if epoch == (self.epochs - self.args.close_mosaic):
                self.console.info("Closing dataloader mosaic")
                if hasattr(self.train_loader.dataset, 'mosaic'):
                    self.train_loader.dataset.mosaic = False
                if hasattr(self.train_loader.dataset, 'close_mosaic'):
                    self.train_loader.dataset.close_mosaic(hyp=self.args)

            if rank in {-1, 0}:
                self.console.info(self.progress_string())
                pbar = tqdm(enumerate(self.train_loader), total=nb, bar_format=TQDM_BAR_FORMAT)
            self.tloss = None
            self.optimizer.zero_grad()
            for i, batch in pbar:
                self.run_callbacks("on_train_batch_start")
                # Warmup
                ni = i + nb * epoch
                if ni <= nw:
                    xi = [0, nw]  # x interp
                    self.accumulate = max(1, np.interp(ni, xi, [1, self.args.nbs / self.batch_size]).round())
                    for j, x in enumerate(self.optimizer.param_groups):
                        # bias lr falls from 0.1 to lr0, all other lrs rise from 0.0 to lr0
                        x['lr'] = np.interp(
                            ni, xi, [self.args.warmup_bias_lr if j == 0 else 0.0, x['initial_lr'] * self.lf(epoch)])
                        if 'momentum' in x:
                            x['momentum'] = np.interp(ni, xi, [self.args.warmup_momentum, self.args.momentum])

                # Forward
                with torch.cuda.amp.autocast(self.amp):
                    batch = self.preprocess_batch(batch)
                    preds = self.model(batch["img"])
                    self.loss, self.loss_items = self.criterion(preds, batch)
                    if rank != -1:
                        self.loss *= world_size
                    self.tloss = (self.tloss * i + self.loss_items) / (i + 1) if self.tloss is not None \
                        else self.loss_items

                # Backward
                self.scaler.scale(self.loss).backward()

                # Optimize - https://pytorch.org/docs/master/notes/amp_examples.html
                if ni - last_opt_step >= self.accumulate:
                    self.optimizer_step()
                    last_opt_step = ni

                # Log
                mem = f'{torch.cuda.memory_reserved() / 1E9 if torch.cuda.is_available() else 0:.3g}G'  # (GB)
                loss_len = self.tloss.shape[0] if len(self.tloss.size()) else 1
                losses = self.tloss if loss_len > 1 else torch.unsqueeze(self.tloss, 0)
                if rank in {-1, 0}:
                    pbar.set_description(
                        ('%11s' * 2 + '%11.4g' * (2 + loss_len)) %
                        (f'{epoch + 1}/{self.epochs}', mem, *losses, batch["cls"].shape[0], batch["img"].shape[-1]))
                    self.run_callbacks('on_batch_end')
                    if self.args.plots and ni in self.plot_idx:
                        self.plot_training_samples(batch, ni)

                self.run_callbacks("on_train_batch_end")

            self.lr = {f"lr/pg{ir}": x['lr'] for ir, x in enumerate(self.optimizer.param_groups)}  # for loggers

            self.scheduler.step()
            self.run_callbacks("on_train_epoch_end")

            if rank in {-1, 0}:

                # Validation
                self.ema.update_attr(self.model, include=['yaml', 'nc', 'args', 'names', 'stride', 'class_weights'])
                final_epoch = (epoch + 1 == self.epochs) or self.stopper.possible_stop

                if self.args.val or final_epoch:
                    self.metrics, self.fitness = self.validate()
                self.save_metrics(metrics={**self.label_loss_items(self.tloss), **self.metrics, **self.lr})
                self.stop = self.stopper(epoch + 1, self.fitness)

                # Save model
                if self.args.save or (epoch + 1 == self.epochs):
                    self.save_model()
                    self.run_callbacks('on_model_save')

            tnow = time.time()
            self.epoch_time = tnow - self.epoch_time_start
            self.epoch_time_start = tnow
            self.run_callbacks("on_fit_epoch_end")

            # Early Stopping
            if RANK != -1:  # if DDP training
                broadcast_list = [self.stop if RANK == 0 else None]
                dist.broadcast_object_list(broadcast_list, 0)  # broadcast 'stop' to all ranks
                if RANK != 0:
                    self.stop = broadcast_list[0]
            if self.stop:
                break  # must break all DDP ranks

        if rank in {-1, 0}:
            # Do final val with best.pt
            self.log(f'\n{epoch - self.start_epoch + 1} epochs completed in '
                     f'{(time.time() - self.train_time_start) / 3600:.3f} hours.')
            self.final_eval()
            if self.args.plots:
                self.plot_metrics()
            self.log(f"Results saved to {colorstr('bold', self.save_dir)}")
            self.run_callbacks('on_train_end')
        torch.cuda.empty_cache()
        self.run_callbacks('teardown')

    def save_model(self):
        ckpt = {
            'epoch': self.epoch,
            'best_fitness': self.best_fitness,
            'model': deepcopy(de_parallel(self.model)).half(),
            'ema': deepcopy(self.ema.ema).half(),
            'updates': self.ema.updates,
            'optimizer': self.optimizer.state_dict(),
            'train_args': vars(self.args),  # save as dict
            'date': datetime.now().isoformat(),
            'version': __version__}

        # Save last, best and delete
        torch.save(ckpt, self.last)
        if self.best_fitness == self.fitness:
            torch.save(ckpt, self.best)
        del ckpt

    def get_dataset(self, data):
        """
        Get train, val path from data dict if it exists. Returns None if data format is not recognized.
        """
        return data["train"], data.get("val") or data.get("test")

    def setup_model(self):
        """
        load/create/download model for any task.
        """
        if isinstance(self.model, torch.nn.Module):  # if model is loaded beforehand. No setup needed
            return

        model, weights = self.model, None
        ckpt = None
        if str(model).endswith(".pt"):
            weights, ckpt = attempt_load_one_weight(model)
            cfg = ckpt["model"].yaml
        else:
            cfg = model
        self.model = self.get_model(cfg=cfg, weights=weights)  # calls Model(cfg, weights)
        return ckpt

    def optimizer_step(self):
        self.scaler.unscale_(self.optimizer)  # unscale gradients
        torch.nn.utils.clip_grad_norm_(self.model.parameters(), max_norm=10.0)  # clip gradients
        self.scaler.step(self.optimizer)
        self.scaler.update()
        self.optimizer.zero_grad()
        if self.ema:
            self.ema.update(self.model)

    def preprocess_batch(self, batch):
        """
        Allows custom preprocessing model inputs and ground truths depending on task type.
        """
        return batch

    def validate(self):
        """
        Runs validation on test set using self.validator. The returned dict is expected to contain "fitness" key.
        """
        metrics = self.validator(self)
        fitness = metrics.pop("fitness", -self.loss.detach().cpu().numpy())  # use loss as fitness measure if not found
        if not self.best_fitness or self.best_fitness < fitness:
            self.best_fitness = fitness
        return metrics, fitness

    def log(self, text, rank=-1):
        """
        Logs the given text to given ranks process if provided, otherwise logs to all ranks.

        Args"
            text (str): text to log
            rank (List[Int]): process rank

        """
        if rank in {-1, 0}:
            self.console.info(text)

    def get_model(self, cfg=None, weights=None, verbose=True):
        raise NotImplementedError("This task trainer doesn't support loading cfg files")

    def get_validator(self):
        raise NotImplementedError("get_validator function not implemented in trainer")

    def get_dataloader(self, dataset_path, batch_size=16, rank=0):
        """
        Returns dataloader derived from torch.data.Dataloader.
        """
        raise NotImplementedError("get_dataloader function not implemented in trainer")

    def criterion(self, preds, batch):
        """
        Returns loss and individual loss items as Tensor.
        """
        raise NotImplementedError("criterion function not implemented in trainer")

    def label_loss_items(self, loss_items=None, prefix="train"):
        """
        Returns a loss dict with labelled training loss items tensor
        """
        # Not needed for classification but necessary for segmentation & detection
        return {"loss": loss_items} if loss_items is not None else ["loss"]

    def set_model_attributes(self):
        """
        To set or update model parameters before training.
        """
        self.model.names = self.data["names"]

    def build_targets(self, preds, targets):
        pass

    def progress_string(self):
        return ""

    # TODO: may need to put these following functions into callback
    def plot_training_samples(self, batch, ni):
        pass

    def save_metrics(self, metrics):
        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([self.epoch] + vals)).rstrip(',') + '\n')

    def plot_metrics(self):
        pass

    def final_eval(self):
        for f in self.last, self.best:
            if f.exists():
                strip_optimizer(f)  # strip optimizers
                if f is self.best:
                    self.console.info(f'\nValidating {f}...')
                    self.metrics = self.validator(model=f)
                    self.metrics.pop('fitness', None)
                    self.run_callbacks('on_fit_epoch_end')

    def check_resume(self):
        resume = self.args.resume
        if resume:
            try:
                last = Path(
                    check_file(resume) if isinstance(resume, (str,
                                                              Path)) and Path(resume).exists() else get_latest_run())
                self.args = get_cfg(attempt_load_weights(last).args)
                self.args.model, resume = str(last), True  # reinstate
            except Exception as e:
                raise FileNotFoundError("Resume checkpoint not found. Please pass a valid checkpoint to resume from, "
                                        "i.e. 'yolo train resume model=path/to/last.pt'") from e
        self.resume = resume

    def resume_training(self, ckpt):
        if ckpt is None:
            return
        best_fitness = 0.0
        start_epoch = ckpt['epoch'] + 1
        if ckpt['optimizer'] is not None:
            self.optimizer.load_state_dict(ckpt['optimizer'])  # optimizer
            best_fitness = ckpt['best_fitness']
        if self.ema and ckpt.get('ema'):
            self.ema.ema.load_state_dict(ckpt['ema'].float().state_dict())  # EMA
            self.ema.updates = ckpt['updates']
        if self.resume:
            assert start_epoch > 0, \
                f'{self.args.model} training to {self.epochs} epochs is finished, nothing to resume.\n' \
                f"Start a new training without --resume, i.e. 'yolo task=... mode=train model={self.args.model}'"
            LOGGER.info(
                f'Resuming training from {self.args.model} from epoch {start_epoch + 1} to {self.epochs} total epochs')
        if self.epochs < start_epoch:
            LOGGER.info(
                f"{self.model} has been trained for {ckpt['epoch']} epochs. Fine-tuning for {self.epochs} more epochs.")
            self.epochs += ckpt['epoch']  # finetune additional epochs
        self.best_fitness = best_fitness
        self.start_epoch = start_epoch

    @staticmethod
    def build_optimizer(model, name='Adam', lr=0.001, momentum=0.9, decay=1e-5):
        """
        Builds an optimizer with the specified parameters and parameter groups.

        Args:
            model (nn.Module): model to optimize
            name (str): name of the optimizer to use
            lr (float): learning rate
            momentum (float): momentum
            decay (float): weight decay

        Returns:
            optimizer (torch.optim.Optimizer): the built optimizer
        """
        g = [], [], []  # optimizer parameter groups
        bn = tuple(v for k, v in nn.__dict__.items() if 'Norm' in k)  # normalization layers, i.e. BatchNorm2d()
        for v in model.modules():
            if hasattr(v, 'bias') and isinstance(v.bias, nn.Parameter):  # bias (no decay)
                g[2].append(v.bias)
            if isinstance(v, bn):  # weight (no decay)
                g[1].append(v.weight)
            elif hasattr(v, 'weight') and isinstance(v.weight, nn.Parameter):  # weight (with decay)
                g[0].append(v.weight)

        if name == 'Adam':
            optimizer = torch.optim.Adam(g[2], lr=lr, betas=(momentum, 0.999))  # adjust beta1 to momentum
        elif name == 'AdamW':
            optimizer = torch.optim.AdamW(g[2], lr=lr, betas=(momentum, 0.999), weight_decay=0.0)
        elif name == 'RMSProp':
            optimizer = torch.optim.RMSprop(g[2], lr=lr, momentum=momentum)
        elif name == 'SGD':
            optimizer = torch.optim.SGD(g[2], lr=lr, momentum=momentum, nesterov=True)
        else:
            raise NotImplementedError(f'Optimizer {name} not implemented.')

        optimizer.add_param_group({'params': g[0], 'weight_decay': decay})  # add g0 with weight_decay
        optimizer.add_param_group({'params': g[1], 'weight_decay': 0.0})  # add g1 (BatchNorm2d weights)
        LOGGER.info(f"{colorstr('optimizer:')} {type(optimizer).__name__}(lr={lr}) with parameter groups "
                    f"{len(g[1])} weight(decay=0.0), {len(g[0])} weight(decay={decay}), {len(g[2])} bias")
        return optimizer

__init__(cfg=DEFAULT_CFG, overrides=None)

Initializes the BaseTrainer class.

Parameters:

Name Type Description Default
cfg str

Path to a configuration file. Defaults to DEFAULT_CFG.

DEFAULT_CFG
overrides dict

Configuration overrides. Defaults to None.

None
Source code in ultralytics/yolo/engine/trainer.py
def __init__(self, cfg=DEFAULT_CFG, overrides=None):
    """
    Initializes the BaseTrainer class.

    Args:
        cfg (str, optional): Path to a configuration file. Defaults to DEFAULT_CFG.
        overrides (dict, optional): Configuration overrides. Defaults to None.
    """
    self.args = get_cfg(cfg, overrides)
    self.device = select_device(self.args.device, self.args.batch)
    self.check_resume()
    self.console = LOGGER
    self.validator = None
    self.model = None
    init_seeds(self.args.seed + 1 + RANK, deterministic=self.args.deterministic)

    # Dirs
    project = self.args.project or Path(SETTINGS['runs_dir']) / self.args.task
    name = self.args.name or f"{self.args.mode}"
    if hasattr(self.args, 'save_dir'):
        self.save_dir = Path(self.args.save_dir)
    else:
        self.save_dir = Path(
            increment_path(Path(project) / name, exist_ok=self.args.exist_ok if RANK in {-1, 0} else True))
    self.wdir = self.save_dir / 'weights'  # weights dir
    if RANK in {-1, 0}:
        self.wdir.mkdir(parents=True, exist_ok=True)  # make dir
        self.args.save_dir = str(self.save_dir)
        yaml_save(self.save_dir / 'args.yaml', vars(self.args))  # save run args
    self.last, self.best = self.wdir / 'last.pt', self.wdir / 'best.pt'  # checkpoint paths

    self.batch_size = self.args.batch
    self.epochs = self.args.epochs
    self.start_epoch = 0
    if RANK == -1:
        print_args(vars(self.args))

    # Device
    self.amp = self.device.type != 'cpu'
    self.scaler = amp.GradScaler(enabled=self.amp)
    if self.device.type == 'cpu':
        self.args.workers = 0  # faster CPU training as time dominated by inference, not dataloading

    # Model and Dataloaders.
    self.model = self.args.model
    self.data = self.args.data
    if self.data.endswith(".yaml"):
        self.data = check_det_dataset(self.data)
    elif self.args.task == 'classify':
        self.data = check_cls_dataset(self.data)
    else:
        raise FileNotFoundError(emojis(f"Dataset '{self.args.data}' not found ❌"))
    self.trainset, self.testset = self.get_dataset(self.data)
    self.ema = None

    # Optimization utils init
    self.lf = None
    self.scheduler = None

    # Epoch level metrics
    self.best_fitness = None
    self.fitness = None
    self.loss = None
    self.tloss = None
    self.loss_names = ['Loss']
    self.csv = self.save_dir / 'results.csv'
    self.plot_idx = [0, 1, 2]

    # Callbacks
    self.callbacks = defaultdict(list, callbacks.default_callbacks)  # add callbacks
    if RANK in {0, -1}:
        callbacks.add_integration_callbacks(self)

add_callback(event, callback)

Appends the given callback.

Source code in ultralytics/yolo/engine/trainer.py
def add_callback(self, event: str, callback):
    """
    Appends the given callback.
    """
    self.callbacks[event].append(callback)

build_optimizer(model, name='Adam', lr=0.001, momentum=0.9, decay=1e-05) staticmethod

Builds an optimizer with the specified parameters and parameter groups.

Parameters:

Name Type Description Default
model nn.Module

model to optimize

required
name str

name of the optimizer to use

'Adam'
lr float

learning rate

0.001
momentum float

momentum

0.9
decay float

weight decay

1e-05

Returns:

Name Type Description
optimizer torch.optim.Optimizer

the built optimizer

Source code in ultralytics/yolo/engine/trainer.py
@staticmethod
def build_optimizer(model, name='Adam', lr=0.001, momentum=0.9, decay=1e-5):
    """
    Builds an optimizer with the specified parameters and parameter groups.

    Args:
        model (nn.Module): model to optimize
        name (str): name of the optimizer to use
        lr (float): learning rate
        momentum (float): momentum
        decay (float): weight decay

    Returns:
        optimizer (torch.optim.Optimizer): the built optimizer
    """
    g = [], [], []  # optimizer parameter groups
    bn = tuple(v for k, v in nn.__dict__.items() if 'Norm' in k)  # normalization layers, i.e. BatchNorm2d()
    for v in model.modules():
        if hasattr(v, 'bias') and isinstance(v.bias, nn.Parameter):  # bias (no decay)
            g[2].append(v.bias)
        if isinstance(v, bn):  # weight (no decay)
            g[1].append(v.weight)
        elif hasattr(v, 'weight') and isinstance(v.weight, nn.Parameter):  # weight (with decay)
            g[0].append(v.weight)

    if name == 'Adam':
        optimizer = torch.optim.Adam(g[2], lr=lr, betas=(momentum, 0.999))  # adjust beta1 to momentum
    elif name == 'AdamW':
        optimizer = torch.optim.AdamW(g[2], lr=lr, betas=(momentum, 0.999), weight_decay=0.0)
    elif name == 'RMSProp':
        optimizer = torch.optim.RMSprop(g[2], lr=lr, momentum=momentum)
    elif name == 'SGD':
        optimizer = torch.optim.SGD(g[2], lr=lr, momentum=momentum, nesterov=True)
    else:
        raise NotImplementedError(f'Optimizer {name} not implemented.')

    optimizer.add_param_group({'params': g[0], 'weight_decay': decay})  # add g0 with weight_decay
    optimizer.add_param_group({'params': g[1], 'weight_decay': 0.0})  # add g1 (BatchNorm2d weights)
    LOGGER.info(f"{colorstr('optimizer:')} {type(optimizer).__name__}(lr={lr}) with parameter groups "
                f"{len(g[1])} weight(decay=0.0), {len(g[0])} weight(decay={decay}), {len(g[2])} bias")
    return optimizer

criterion(preds, batch)

Returns loss and individual loss items as Tensor.

Source code in ultralytics/yolo/engine/trainer.py
def criterion(self, preds, batch):
    """
    Returns loss and individual loss items as Tensor.
    """
    raise NotImplementedError("criterion function not implemented in trainer")

get_dataloader(dataset_path, batch_size=16, rank=0)

Returns dataloader derived from torch.data.Dataloader.

Source code in ultralytics/yolo/engine/trainer.py
def get_dataloader(self, dataset_path, batch_size=16, rank=0):
    """
    Returns dataloader derived from torch.data.Dataloader.
    """
    raise NotImplementedError("get_dataloader function not implemented in trainer")

get_dataset(data)

Get train, val path from data dict if it exists. Returns None if data format is not recognized.

Source code in ultralytics/yolo/engine/trainer.py
def get_dataset(self, data):
    """
    Get train, val path from data dict if it exists. Returns None if data format is not recognized.
    """
    return data["train"], data.get("val") or data.get("test")

label_loss_items(loss_items=None, prefix='train')

Returns a loss dict with labelled training loss items tensor

Source code in ultralytics/yolo/engine/trainer.py
def label_loss_items(self, loss_items=None, prefix="train"):
    """
    Returns a loss dict with labelled training loss items tensor
    """
    # Not needed for classification but necessary for segmentation & detection
    return {"loss": loss_items} if loss_items is not None else ["loss"]

log(text, rank=-1)

Logs the given text to given ranks process if provided, otherwise logs to all ranks.

Args" text (str): text to log rank (List[Int]): process rank

Source code in ultralytics/yolo/engine/trainer.py
def log(self, text, rank=-1):
    """
    Logs the given text to given ranks process if provided, otherwise logs to all ranks.

    Args"
        text (str): text to log
        rank (List[Int]): process rank

    """
    if rank in {-1, 0}:
        self.console.info(text)

preprocess_batch(batch)

Allows custom preprocessing model inputs and ground truths depending on task type.

Source code in ultralytics/yolo/engine/trainer.py
def preprocess_batch(self, batch):
    """
    Allows custom preprocessing model inputs and ground truths depending on task type.
    """
    return batch

set_callback(event, callback)

Overrides the existing callbacks with the given callback.

Source code in ultralytics/yolo/engine/trainer.py
def set_callback(self, event: str, callback):
    """
    Overrides the existing callbacks with the given callback.
    """
    self.callbacks[event] = [callback]

set_model_attributes()

To set or update model parameters before training.

Source code in ultralytics/yolo/engine/trainer.py
def set_model_attributes(self):
    """
    To set or update model parameters before training.
    """
    self.model.names = self.data["names"]

setup_model()

load/create/download model for any task.

Source code in ultralytics/yolo/engine/trainer.py
def setup_model(self):
    """
    load/create/download model for any task.
    """
    if isinstance(self.model, torch.nn.Module):  # if model is loaded beforehand. No setup needed
        return

    model, weights = self.model, None
    ckpt = None
    if str(model).endswith(".pt"):
        weights, ckpt = attempt_load_one_weight(model)
        cfg = ckpt["model"].yaml
    else:
        cfg = model
    self.model = self.get_model(cfg=cfg, weights=weights)  # calls Model(cfg, weights)
    return ckpt

validate()

Runs validation on test set using self.validator. The returned dict is expected to contain "fitness" key.

Source code in ultralytics/yolo/engine/trainer.py
def validate(self):
    """
    Runs validation on test set using self.validator. The returned dict is expected to contain "fitness" key.
    """
    metrics = self.validator(self)
    fitness = metrics.pop("fitness", -self.loss.detach().cpu().numpy())  # use loss as fitness measure if not found
    if not self.best_fitness or self.best_fitness < fitness:
        self.best_fitness = fitness
    return metrics, fitness