File size: 23,080 Bytes
45d16e9
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
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
594
595
596
597
598
599
600
601
602
603
604
605
606
607
608
609
610
611
612
613
614
615
616
617
618
619
620
621
622
623
624
625
626
627
628
629
630
631
632
633
634
635
636
637
638
639
640
641
642
643
644
645
646
647
648
649
650
651
652
653
654
655
656
657
658
659
"""
 Copyright (c) 2022, salesforce.com, inc.
 All rights reserved.
 SPDX-License-Identifier: BSD-3-Clause
 For full license text, see the LICENSE_Lavis file in the repo root or https://opensource.org/licenses/BSD-3-Clause
"""

import datetime
import json
import logging
import os
import time
from pathlib import Path

import torch
import torch.distributed as dist
import webdataset as wds
from video_llama.common.dist_utils import (
    download_cached_file,
    get_rank,
    get_world_size,
    is_main_process,
    main_process,
)
from video_llama.common.registry import registry
from video_llama.common.utils import is_url
from video_llama.datasets.data_utils import concat_datasets, reorg_datasets_by_split, ChainDataset
from video_llama.datasets.datasets.dataloader_utils import (
    IterLoader,
    MultiIterLoader,
    PrefetchLoader,
)
from torch.nn.parallel import DistributedDataParallel as DDP
from torch.utils.data import DataLoader, DistributedSampler


@registry.register_runner("runner_base")
class RunnerBase:
    """
    A runner class to train and evaluate a model given a task and datasets.

    The runner uses pytorch distributed data parallel by default. Future release
    will support other distributed frameworks.
    """

    def __init__(self, cfg, task, model, datasets, job_id):
        self.config = cfg
        self.job_id = job_id

        self.task = task
        self.datasets = datasets

        self._model = model

        self._wrapped_model = None
        self._device = None
        self._optimizer = None
        self._scaler = None
        self._dataloaders = None
        self._lr_sched = None

        self.start_epoch = 0

        # self.setup_seeds()
        self.setup_output_dir()

    @property
    def device(self):
        if self._device is None:
            self._device = torch.device(self.config.run_cfg.device)

        return self._device

    @property
    def use_distributed(self):
        return self.config.run_cfg.distributed

    @property
    def model(self):
        """
        A property to get the DDP-wrapped model on the device.
        """
        # move model to device
        if self._model.device != self.device:
            self._model = self._model.to(self.device)

            # distributed training wrapper
            if self.use_distributed:
                if self._wrapped_model is None:
                    self._wrapped_model = DDP(
                        self._model, device_ids=[self.config.run_cfg.gpu]
                    )
            else:
                self._wrapped_model = self._model

        return self._wrapped_model

    @property
    def optimizer(self):
        # TODO make optimizer class and configurations
        if self._optimizer is None:
            num_parameters = 0
            p_wd, p_non_wd = [], []
            for n, p in self.model.named_parameters():
                if not p.requires_grad:
                    continue  # frozen weights
                print(n)
                if p.ndim < 2 or "bias" in n or "ln" in n or "bn" in n:
                    p_non_wd.append(p)
                else:
                    p_wd.append(p)
                num_parameters += p.data.nelement()
            logging.info("number of trainable parameters: %d" % num_parameters)
            optim_params = [
                {
                    "params": p_wd,
                    "weight_decay": float(self.config.run_cfg.weight_decay),
                },
                {"params": p_non_wd, "weight_decay": 0},
            ]
            beta2 = self.config.run_cfg.get("beta2", 0.999)
            self._optimizer = torch.optim.AdamW(
                optim_params,
                lr=float(self.config.run_cfg.init_lr),
                weight_decay=float(self.config.run_cfg.weight_decay),
                betas=(0.9, beta2),
            )

        return self._optimizer

    @property
    def scaler(self):
        amp = self.config.run_cfg.get("amp", False)

        if amp:
            if self._scaler is None:
                self._scaler = torch.cuda.amp.GradScaler()

        return self._scaler

    @property
    def lr_scheduler(self):
        """
        A property to get and create learning rate scheduler by split just in need.
        """
        if self._lr_sched is None:
            lr_sched_cls = registry.get_lr_scheduler_class(self.config.run_cfg.lr_sched)

            # max_epoch = self.config.run_cfg.max_epoch
            max_epoch = self.max_epoch
            # min_lr = self.config.run_cfg.min_lr
            min_lr = self.min_lr
            # init_lr = self.config.run_cfg.init_lr
            init_lr = self.init_lr

            # optional parameters
            decay_rate = self.config.run_cfg.get("lr_decay_rate", None)
            warmup_start_lr = self.config.run_cfg.get("warmup_lr", -1)
            warmup_steps = self.config.run_cfg.get("warmup_steps", 0)
            iters_per_epoch = self.config.run_cfg.get("iters_per_epoch", None)

            if iters_per_epoch is None:
                try:
                    iters_per_epoch = len(self.dataloaders['train'])
                except (AttributeError, TypeError):
                    iters_per_epoch = 10000

            self._lr_sched = lr_sched_cls(
                optimizer=self.optimizer,
                max_epoch=max_epoch,
                iters_per_epoch=iters_per_epoch,
                min_lr=min_lr,
                init_lr=init_lr,
                decay_rate=decay_rate,
                warmup_start_lr=warmup_start_lr,
                warmup_steps=warmup_steps,
            )

        return self._lr_sched

    @property
    def dataloaders(self) -> dict:
        """
        A property to get and create dataloaders by split just in need.

        If no train_dataset_ratio is provided, concatenate map-style datasets and
        chain wds.DataPipe datasets separately. Training set becomes a tuple
        (ConcatDataset, ChainDataset), both are optional but at least one of them is
        required. The resultant ConcatDataset and ChainDataset will be sampled evenly.

        If train_dataset_ratio is provided, create a MultiIterLoader to sample
        each dataset by ratios during training.

        Currently do not support multiple datasets for validation and test.

        Returns:
            dict: {split_name: (tuples of) dataloader}
        """
        if self._dataloaders is None:

            # concatenate map-style datasets and chain wds.DataPipe datasets separately
            # training set becomes a tuple (ConcatDataset, ChainDataset), both are
            # optional but at least one of them is required. The resultant ConcatDataset
            # and ChainDataset will be sampled evenly.
            logging.info(
                "dataset_ratios not specified, datasets will be concatenated (map-style datasets) or chained (webdataset.DataPipeline)."
            )

            datasets = reorg_datasets_by_split(self.datasets)
            self.datasets = datasets
            # self.datasets = concat_datasets(datasets)

            # print dataset statistics after concatenation/chaining
            for split_name in self.datasets:
                if isinstance(self.datasets[split_name], tuple) or isinstance(
                    self.datasets[split_name], list
                ):
                    # mixed wds.DataPipeline and torch.utils.data.Dataset
                    num_records = sum(
                        [
                            len(d)
                            if not type(d) in [wds.DataPipeline, ChainDataset]
                            else 0
                            for d in self.datasets[split_name]
                        ]
                    )

                else:
                    if hasattr(self.datasets[split_name], "__len__"):
                        # a single map-style dataset
                        num_records = len(self.datasets[split_name])
                    else:
                        # a single wds.DataPipeline
                        num_records = -1
                        logging.info(
                            "Only a single wds.DataPipeline dataset, no __len__ attribute."
                        )

                if num_records >= 0:
                    logging.info(
                        "Loaded {} records for {} split from the dataset.".format(
                            num_records, split_name
                        )
                    )

            # create dataloaders
            split_names = sorted(self.datasets.keys())

            datasets = [self.datasets[split] for split in split_names]
            is_trains = [split in self.train_splits for split in split_names]

            batch_sizes = [
                self.config.run_cfg.batch_size_train
                if split == "train"
                else self.config.run_cfg.batch_size_eval
                for split in split_names
            ]

            collate_fns = []
            for dataset in datasets:
                if isinstance(dataset, tuple) or isinstance(dataset, list):
                    collate_fns.append([getattr(d, "collater", None) for d in dataset])
                else:
                    collate_fns.append(getattr(dataset, "collater", None))

            dataloaders = self.create_loaders(
                datasets=datasets,
                num_workers=self.config.run_cfg.num_workers,
                batch_sizes=batch_sizes,
                is_trains=is_trains,
                collate_fns=collate_fns,
            )

            self._dataloaders = {k: v for k, v in zip(split_names, dataloaders)}

        return self._dataloaders

    @property
    def cuda_enabled(self):
        return self.device.type == "cuda"

    @property
    def max_epoch(self):
        return int(self.config.run_cfg.max_epoch)

    @property
    def log_freq(self):
        log_freq = self.config.run_cfg.get("log_freq", 50)
        return int(log_freq)

    @property
    def init_lr(self):
        return float(self.config.run_cfg.init_lr)

    @property
    def min_lr(self):
        return float(self.config.run_cfg.min_lr)

    @property
    def accum_grad_iters(self):
        return int(self.config.run_cfg.get("accum_grad_iters", 1))

    @property
    def valid_splits(self):
        valid_splits = self.config.run_cfg.get("valid_splits", [])

        if len(valid_splits) == 0:
            logging.info("No validation splits found.")

        return valid_splits

    @property
    def test_splits(self):
        test_splits = self.config.run_cfg.get("test_splits", [])

        return test_splits

    @property
    def train_splits(self):
        train_splits = self.config.run_cfg.get("train_splits", [])

        if len(train_splits) == 0:
            logging.info("Empty train splits.")

        return train_splits

    @property
    def evaluate_only(self):
        """
        Set to True to skip training.
        """
        return self.config.run_cfg.evaluate

    @property
    def use_dist_eval_sampler(self):
        return self.config.run_cfg.get("use_dist_eval_sampler", True)

    @property
    def resume_ckpt_path(self):
        return self.config.run_cfg.get("resume_ckpt_path", None)

    @property
    def train_loader(self):
        train_dataloader = self.dataloaders["train"]

        return train_dataloader

    def setup_output_dir(self):
        lib_root = Path(registry.get_path("library_root"))

        output_dir = lib_root / self.config.run_cfg.output_dir / self.job_id
        result_dir = output_dir / "result"

        output_dir.mkdir(parents=True, exist_ok=True)
        result_dir.mkdir(parents=True, exist_ok=True)

        registry.register_path("result_dir", str(result_dir))
        registry.register_path("output_dir", str(output_dir))

        self.result_dir = result_dir
        self.output_dir = output_dir

    def train(self):
        start_time = time.time()
        best_agg_metric = 0
        best_epoch = 0

        self.log_config()

        # resume from checkpoint if specified
        if not self.evaluate_only and self.resume_ckpt_path is not None:
            self._load_checkpoint(self.resume_ckpt_path)

        for cur_epoch in range(self.start_epoch, self.max_epoch):
            # training phase
            if not self.evaluate_only:
                logging.info("Start training")
                train_stats = self.train_epoch(cur_epoch)
                self.log_stats(split_name="train", stats=train_stats)

            # evaluation phase
            if len(self.valid_splits) > 0:
                for split_name in self.valid_splits:
                    logging.info("Evaluating on {}.".format(split_name))

                    val_log = self.eval_epoch(
                        split_name=split_name, cur_epoch=cur_epoch
                    )
                    if val_log is not None:
                        if is_main_process():
                            assert (
                                "agg_metrics" in val_log
                            ), "No agg_metrics found in validation log."

                            agg_metrics = val_log["agg_metrics"]
                            if agg_metrics > best_agg_metric and split_name == "val":
                                best_epoch, best_agg_metric = cur_epoch, agg_metrics

                                self._save_checkpoint(cur_epoch, is_best=True)

                            val_log.update({"best_epoch": best_epoch})
                            self.log_stats(val_log, split_name)

            else:
                # if no validation split is provided, we just save the checkpoint at the end of each epoch.
                if not self.evaluate_only:
                    self._save_checkpoint(cur_epoch, is_best=False)

            if self.evaluate_only:
                break

            if self.config.run_cfg.distributed:
                dist.barrier()

        # testing phase
        test_epoch = "best" if len(self.valid_splits) > 0 else cur_epoch
        self.evaluate(cur_epoch=test_epoch, skip_reload=self.evaluate_only)

        total_time = time.time() - start_time
        total_time_str = str(datetime.timedelta(seconds=int(total_time)))
        logging.info("Training time {}".format(total_time_str))

    def evaluate(self, cur_epoch="best", skip_reload=False):
        test_logs = dict()

        if len(self.test_splits) > 0:
            for split_name in self.test_splits:
                test_logs[split_name] = self.eval_epoch(
                    split_name=split_name, cur_epoch=cur_epoch, skip_reload=skip_reload
                )

            return test_logs

    def train_epoch(self, epoch):
        # train
        self.model.train()

        return self.task.train_epoch(
            epoch=epoch,
            model=self.model,
            data_loader=self.train_loader,
            optimizer=self.optimizer,
            scaler=self.scaler,
            lr_scheduler=self.lr_scheduler,
            cuda_enabled=self.cuda_enabled,
            log_freq=self.log_freq,
            accum_grad_iters=self.accum_grad_iters,
        )

    @torch.no_grad()
    def eval_epoch(self, split_name, cur_epoch, skip_reload=False):
        """
        Evaluate the model on a given split.

        Args:
            split_name (str): name of the split to evaluate on.
            cur_epoch (int): current epoch.
            skip_reload_best (bool): whether to skip reloading the best checkpoint.
                During training, we will reload the best checkpoint for validation.
                During testing, we will use provided weights and skip reloading the best checkpoint .
        """
        data_loader = self.dataloaders.get(split_name, None)
        assert data_loader, "data_loader for split {} is None.".format(split_name)

        # TODO In validation, you need to compute loss as well as metrics
        # TODO consider moving to model.before_evaluation()
        model = self.unwrap_dist_model(self.model)
        if not skip_reload and cur_epoch == "best":
            model = self._reload_best_model(model)
        model.eval()

        self.task.before_evaluation(
            model=model,
            dataset=self.datasets[split_name],
        )
        results = self.task.evaluation(model, data_loader)

        if results is not None:
            return self.task.after_evaluation(
                val_result=results,
                split_name=split_name,
                epoch=cur_epoch,
            )

    def unwrap_dist_model(self, model):
        if self.use_distributed:
            return model.module
        else:
            return model

    def create_loaders(
        self,
        datasets,
        num_workers,
        batch_sizes,
        is_trains,
        collate_fns,
        dataset_ratios=None,
    ):
        """
        Create dataloaders for training and validation.
        """

        def _create_loader(dataset, num_workers, bsz, is_train, collate_fn):
            # create a single dataloader for each split
            if isinstance(dataset, ChainDataset) or isinstance(
                dataset, wds.DataPipeline
            ):
                # wds.WebdDataset instance are chained together
                # webdataset.DataPipeline has its own sampler and collate_fn
                loader = iter(
                    DataLoader(
                        dataset,
                        batch_size=bsz,
                        num_workers=num_workers,
                        pin_memory=True,
                    )
                )
            else:
                # map-style dataset are concatenated together
                # setup distributed sampler
                if self.use_distributed:
                    sampler = DistributedSampler(
                        dataset,
                        shuffle=is_train,
                        num_replicas=get_world_size(),
                        rank=get_rank(),
                    )
                    if not self.use_dist_eval_sampler:
                        # e.g. retrieval evaluation
                        sampler = sampler if is_train else None
                else:
                    sampler = None

                loader = DataLoader(
                    dataset,
                    batch_size=bsz,
                    num_workers=num_workers,
                    pin_memory=True,
                    sampler=sampler,
                    shuffle=sampler is None and is_train,
                    collate_fn=collate_fn,
                    drop_last=True if is_train else False,
                )
                loader = PrefetchLoader(loader)

                if is_train:
                    loader = IterLoader(loader, use_distributed=self.use_distributed)

            return loader

        loaders = []

        for dataset, bsz, is_train, collate_fn in zip(
            datasets, batch_sizes, is_trains, collate_fns
        ):
            if isinstance(dataset, list) or isinstance(dataset, tuple):
                if hasattr(dataset[0], 'sample_ratio') and dataset_ratios is None:
                    dataset_ratios = [d.sample_ratio for d in dataset]
                loader = MultiIterLoader(
                    loaders=[
                        _create_loader(d, num_workers, bsz, is_train, collate_fn[i])
                        for i, d in enumerate(dataset)
                    ],
                    ratios=dataset_ratios,
                )
            else:
                loader = _create_loader(dataset, num_workers, bsz, is_train, collate_fn)

            loaders.append(loader)

        return loaders

    @main_process
    def _save_checkpoint(self, cur_epoch, is_best=False):
        """
        Save the checkpoint at the current epoch.
        """
        model_no_ddp = self.unwrap_dist_model(self.model)
        param_grad_dic = {
            k: v.requires_grad for (k, v) in model_no_ddp.named_parameters()
        }
        state_dict = model_no_ddp.state_dict()
        for k in list(state_dict.keys()):
            if k in param_grad_dic.keys() and not param_grad_dic[k]:
                # delete parameters that do not require gradient
                del state_dict[k]
        save_obj = {
            "model": state_dict,
            "optimizer": self.optimizer.state_dict(),
            "config": self.config.to_dict(),
            "scaler": self.scaler.state_dict() if self.scaler else None,
            "epoch": cur_epoch,
        }
        save_to = os.path.join(
            self.output_dir,
            "checkpoint_{}.pth".format("best" if is_best else cur_epoch),
        )
        logging.info("Saving checkpoint at epoch {} to {}.".format(cur_epoch, save_to))
        torch.save(save_obj, save_to)

    def _reload_best_model(self, model):
        """
        Load the best checkpoint for evaluation.
        """
        checkpoint_path = os.path.join(self.output_dir, "checkpoint_best.pth")

        logging.info("Loading checkpoint from {}.".format(checkpoint_path))
        checkpoint = torch.load(checkpoint_path, map_location="cpu")
        try:
            model.load_state_dict(checkpoint["model"])
        except RuntimeError as e:
            logging.warning(
                """
                Key mismatch when loading checkpoint. This is expected if only part of the model is saved.
                Trying to load the model with strict=False.
                """
            )
            model.load_state_dict(checkpoint["model"], strict=False)
        return model

    def _load_checkpoint(self, url_or_filename):
        """
        Resume from a checkpoint.
        """
        if is_url(url_or_filename):
            cached_file = download_cached_file(
                url_or_filename, check_hash=False, progress=True
            )
            checkpoint = torch.load(cached_file, map_location=self.device, strict=False)
        elif os.path.isfile(url_or_filename):
            checkpoint = torch.load(url_or_filename, map_location=self.device, strict=False)
        else:
            raise RuntimeError("checkpoint url or path is invalid")

        state_dict = checkpoint["model"]
        self.unwrap_dist_model(self.model).load_state_dict(state_dict)

        self.optimizer.load_state_dict(checkpoint["optimizer"])
        if self.scaler and "scaler" in checkpoint:
            self.scaler.load_state_dict(checkpoint["scaler"])

        self.start_epoch = checkpoint["epoch"] + 1
        logging.info("Resume checkpoint from {}".format(url_or_filename))

    @main_process
    def log_stats(self, stats, split_name):
        if isinstance(stats, dict):
            log_stats = {**{f"{split_name}_{k}": v for k, v in stats.items()}}
            with open(os.path.join(self.output_dir, "log.txt"), "a") as f:
                f.write(json.dumps(log_stats) + "\n")
        elif isinstance(stats, list):
            pass

    @main_process
    def log_config(self):
        with open(os.path.join(self.output_dir, "log.txt"), "a") as f:
            f.write(json.dumps(self.config.to_dict(), indent=4) + "\n")