【Pytorch】RuntimeError: stack expects each tensor to be equal size, but got〜

前提・実現したいこと

pytorchで物体検出の学習を行おうとしたところ、学習部分で以下のようなエラーが発生しました。エラーとして表示されるテンソルのサイズが毎回異なることと、kaggleのnotebookを利用すると同じデータでもうまくいくことなどから、テンソルのサイズが異なっているというわけではなく、何か別の原因があるように感じています。
原因について何かお心当たりのある方がいらっしゃいましたらぜひご回答いただけますと幸いです。

発生している問題・エラーメッセージ

RuntimeError                              Traceback (most recent call last)
<ipython-input-31-5bb99a4def7d> in <module>
     95     manager.extend(save_ema_model, trigger=(flags.snapshot_freq, "epoch"))
     96 
---> 97 _ = trainer.run(train_loader, max_epochs=epoch)

~/home/user/libraries/Miniconda3/envs/pytorch/lib/python3.9/site-packages/ignite/engine/engine.py in run(self, data, max_epochs, epoch_length, seed)
    700 
    701         self.state.dataloader = data
--> 702         return self._internal_run()
    703 
    704     @staticmethod

~/home/user/libraries/Miniconda3/envs/pytorch/lib/python3.9/site-packages/ignite/engine/engine.py in _internal_run(self)
    773             self._dataloader_iter = None
    774             self.logger.error(f"Engine run is terminating due to exception: {e}")
--> 775             self._handle_exception(e)
    776 
    777         self._dataloader_iter = None

~/home/user/libraries/Miniconda3/envs/pytorch/lib/python3.9/site-packages/ignite/engine/engine.py in _handle_exception(self, e)
    467             self._fire_event(Events.EXCEPTION_RAISED, e)
    468         else:
--> 469             raise e
    470 
    471     @property

~/home/user/libraries/Miniconda3/envs/pytorch/lib/python3.9/site-packages/ignite/engine/engine.py in _internal_run(self)
    743                     self._setup_engine()
    744 
--> 745                 time_taken = self._run_once_on_dataset()
    746                 # time is available for handlers but must be update after fire
    747                 self.state.times[Events.EPOCH_COMPLETED.name] = time_taken

~/home/user/libraries/Miniconda3/envs/pytorch/lib/python3.9/site-packages/ignite/engine/engine.py in _run_once_on_dataset(self)
    848         except Exception as e:
    849             self.logger.error(f"Current run is terminating due to exception: {e}")
--> 850             self._handle_exception(e)
    851 
    852         return time.time() - start_time

~/home/user/libraries/Miniconda3/envs/pytorch/lib/python3.9/site-packages/ignite/engine/engine.py in _handle_exception(self, e)
    467             self._fire_event(Events.EXCEPTION_RAISED, e)
    468         else:
--> 469             raise e
    470 
    471     @property

~/home/user/libraries/Miniconda3/envs/pytorch/lib/python3.9/site-packages/ignite/engine/engine.py in _run_once_on_dataset(self)
    799                     if self.last_event_name != Events.DATALOADER_STOP_ITERATION:
    800                         self._fire_event(Events.GET_BATCH_STARTED)
--> 801                     self.state.batch = next(self._dataloader_iter)
    802                     self._fire_event(Events.GET_BATCH_COMPLETED)
    803                     iter_counter += 1

~/home/user/libraries/Miniconda3/envs/pytorch/lib/python3.9/site-packages/torch/utils/data/dataloader.py in __next__(self)
    433         if self._sampler_iter is None:
    434             self._reset()
--> 435         data = self._next_data()
    436         self._num_yielded += 1
    437         if self._dataset_kind == _DatasetKind.Iterable and \

~/home/user/libraries/Miniconda3/envs/pytorch/lib/python3.9/site-packages/torch/utils/data/dataloader.py in _next_data(self)
   1083             else:
   1084                 del self._task_info[idx]
-> 1085                 return self._process_data(data)
   1086 
   1087     def _try_put_index(self):

~/home/user/libraries/Miniconda3/envs/pytorch/lib/python3.9/site-packages/torch/utils/data/dataloader.py in _process_data(self, data)
   1109         self._try_put_index()
   1110         if isinstance(data, ExceptionWrapper):
-> 1111             data.reraise()
   1112         return data
   1113 

~/home/user/libraries/Miniconda3/envs/pytorch/lib/python3.9/site-packages/torch/_utils.py in reraise(self)
    426             # have message field
    427             raise self.exc_type(message=msg)
--> 428         raise self.exc_type(msg)
    429 
    430 

RuntimeError: Caught RuntimeError in DataLoader worker process 0.
Original Traceback (most recent call last):
  File "/home/user/home/user/libraries/Miniconda3/envs/pytorch/lib/python3.9/site-packages/torch/utils/data/_utils/worker.py", line 198, in _worker_loop
    data = fetcher.fetch(index)
  File "/home/user/home/user/libraries/Miniconda3/envs/pytorch/lib/python3.9/site-packages/torch/utils/data/_utils/fetch.py", line 47, in fetch
    return self.collate_fn(data)
  File "/home/user/home/user/libraries/Miniconda3/envs/pytorch/lib/python3.9/site-packages/torch/utils/data/_utils/collate.py", line 83, in default_collate
    return [default_collate(samples) for samples in transposed]
  File "/home/user/home/user/libraries/Miniconda3/envs/pytorch/lib/python3.9/site-packages/torch/utils/data/_utils/collate.py", line 83, in <listcomp>
    return [default_collate(samples) for samples in transposed]
  File "/home/user/home/user/libraries/Miniconda3/envs/pytorch/lib/python3.9/site-packages/torch/utils/data/_utils/collate.py", line 55, in default_collate
    return torch.stack(batch, 0, out=out)
RuntimeError: stack expects each tensor to be equal size, but got [3, 512, 499] at entry 0 and [3, 512, 458] at entry 1 #この部分の数字は実行のたびに異なるものが表示されます

該当のソースコード（コードが長いため、エラーが発生した部分のみ抜粋しております）

Python
1train_loader = DataLoader(
2    train_dataset,
3    batch_size=flags.batchsize,
4    num_workers=flags.num_workers,
5    shuffle=True,
6    pin_memory=True,
7)
8valid_loader = DataLoader(
9    valid_dataset,
10    batch_size=flags.valid_batchsize,
11    num_workers=flags.num_workers,
12    shuffle=False,
13    pin_memory=True,
14)
15
16device = torch.device(flags.device)
17
18predictor = build_predictor(model_name=flags.model_name, model_mode=flags.model_mode)
19classifier = Classifier(predictor)
20model = classifier
21# optimizer = optim.Adam(model.parameters(), lr=1e-3)
22optimizer = optim.Adam([param for param in model.parameters() if param.requires_grad], lr=1e-3)
23
24# Train setup
25trainer = create_trainer(model, optimizer, device)
26
27ema = EMA(predictor, decay=flags.ema_decay)
28
29def eval_func(*batch):
30    loss, metrics = model(*[elem.to(device) for elem in batch])
31    # HACKING: report ema value with prefix.
32    if flags.ema_decay > 0:
33        classifier.prefix = "ema_"
34        ema.assign()
35        loss, metrics = model(*[elem.to(device) for elem in batch])
36        ema.resume()
37        classifier.prefix = ""
38
39valid_evaluator = E.Evaluator(
40    valid_loader, model, progress_bar=False, eval_func=eval_func, device=device
41)
42
43# log_trigger = (10 if debug else 1000, "iteration")
44log_trigger = (1, "epoch")
45log_report = E.LogReport(trigger=log_trigger)
46extensions = [
47    log_report,
48    E.ProgressBarNotebook(update_interval=10 if debug else 100),  # Show progress bar during training
49    E.PrintReportNotebook(),  # Show "log" on jupyter notebook  
50    # E.ProgressBar(update_interval=10 if debug else 100),  # Show progress bar during training
51    # E.PrintReport(),  # Print "log" to terminal
52    E.FailOnNonNumber(),  # Stop training when nan is detected.
53]
54epoch = flags.epoch
55models = {"main": model}
56optimizers = {"main": optimizer}
57manager = IgniteExtensionsManager(
58    trainer, models, optimizers, epoch, extensions=extensions, out_dir=str(outdir),
59)
60# Run evaluation for valid dataset in each epoch.
61manager.extend(valid_evaluator)
62
63# Save predictor.pt every epoch
64manager.extend(
65    E.snapshot_object(predictor, "predictor.pt"), trigger=(flags.snapshot_freq, "epoch")
66)
67# Check & Save best validation predictor.pt every epoch
68# manager.extend(E.snapshot_object(predictor, "best_predictor.pt"),
69#                trigger=MinValueTrigger("validation/module/nll",
70#                trigger=(flags.snapshot_freq, "iteration")))
71
72# --- lr scheduler ---
73if flags.scheduler_type != "":
74    scheduler_type = flags.scheduler_type
75    print(f"using {scheduler_type} scheduler with kwargs {flags.scheduler_kwargs}")
76    manager.extend(
77        LRScheduler(optimizer, scheduler_type, flags.scheduler_kwargs),
78        trigger=flags.scheduler_trigger,
79    )
80
81manager.extend(E.observe_lr(optimizer=optimizer), trigger=log_trigger)
82
83if flags.ema_decay > 0:
84    # Exponential moving average
85    manager.extend(lambda manager: ema(), trigger=(1, "iteration"))
86
87    def save_ema_model(manager):
88        ema.assign()
89        #torch.save(predictor.state_dict(), outdir / "predictor_ema.pt")
90        torch.save(predictor.state_dict(), "/result/predictor_ema.pt")
91        ema.resume()
92
93    manager.extend(save_ema_model, trigger=(flags.snapshot_freq, "epoch"))
94
95_ = trainer.run(train_loader, max_epochs=epoch)