kaylode / theseus Goto Github PK

General template for most Pytorch projects

License: MIT License

Python 99.42% Dockerfile 0.58%

template pytorch classification segmentation computer-vision natural-language-processing object-detection structured-data

theseus's Issues

[Wanted] Use Observer pattern for logging

Use Observer (or else) design pattern for logging module. Help easily adding other logging system in the future

Possible issue with trainer

Hi @kaylode, if you have time, could you update the notebooks (the config part)?
When tested, i encountered this error:

Traceback (most recent call last):
  File "/content/main/configs/classification/train.py", line 10, in <module>
    train_pipeline.fit()
  File "/content/main/theseus/base/pipeline.py", line 237, in fit
    self.trainer.fit()
  File "/content/main/theseus/base/trainer/base_trainer.py", line 71, in fit
    self.training_epoch()
  File "/content/main/theseus/base/trainer/supervised_trainer.py", line 83, in training_epoch
    self.scaler(loss, self.optimizer)
TypeError: 'bool' object is not callable

So i think it might be problem with the scaler, after changing use_fp16 to True as default in BaseTrainer. It's runnable, like this:

class BaseTrainer():
def __init__(self,
                use_fp16: bool = True, 
                ...
                ):

It doesn't work albeit i've already set the global use_fp16 variable to True.

global:
  exp_name: null
  exist_ok: false
  debug: true
  cfg_transform: configs/classification/transform.yaml
  save_dir: /content/main/runs
  device: cuda:0
  use_fp16: true
  pretrained: null
  resume: null

So i think it might be a possible issue. I notice that in SupervisedTrainer there isn't any catch for the scaler when we set False use_fp16, therefore it can trigger this error TypeError: 'bool' object is not callable

Resume error in WandB.

Forgot to create an issue in recent days.
When tested with resume argument in WandBCallbacks, i encountered this error. Here's the log:

[Errno 2] No such file or directory: 'main'
/content/main
2022-04-04 12:21:56 | DEBUG    | opt.py:override:78 - Overriding configuration...
2022-04-04 12:21:56 | INFO     | classification/pipeline.py:__init__:51 - {
    "global": {
        "exp_name": null,
        "exist_ok": false,
        "debug": true,
        "cfg_transform": "configs/classification/transform.yaml",
        "save_dir": "/content/main/runs",
        "device": "cuda:0",
        "use_fp16": true,
        "pretrained": null,
        "resume": null
    },
    "trainer": {
        "name": "SupervisedTrainer",
        "args": {
            "num_iterations": 2000,
            "clip_grad": 10.0,
            "evaluate_interval": 1,
            "print_interval": 20,
            "save_interval": 500
        }
    },
    "model": {
        "name": "BaseTimmModel",
        "args": {
            "name": "convnext_tiny",
            "from_pretrained": true,
            "num_classes": 180
        }
    },
    "loss": {
        "name": "FocalLoss"
    },
    "callbacks": [
        {
            "name": "LoggerCallbacks",
            "args": null
        },
        {
            "name": "CheckpointCallbacks",
            "args": {
                "best_key": "bl_acc"
            }
        },
        {
            "name": "VisualizerCallbacks",
            "args": null
        },
        {
            "name": "TensorboardCallbacks",
            "args": null
        },
        {
            "name": "WandbCallbacks",
            "args": {
                "username": "lannguyen",
                "project_name": "theseus_classification",
                "resume": true
            }
        }
    ],
    "metrics": [
        {
            "name": "Accuracy",
            "args": null
        },
        {
            "name": "BalancedAccuracyMetric",
            "args": null
        },
        {
            "name": "F1ScoreMetric",
            "args": {
                "average": "weighted"
            }
        },
        {
            "name": "ConfusionMatrix",
            "args": null
        },
        {
            "name": "ErrorCases",
            "args": null
        }
    ],
    "optimizer": {
        "name": "AdamW",
        "args": {
            "lr": 0.001,
            "weight_decay": 0.0005,
            "betas": [
                0.937,
                0.999
            ]
        }
    },
    "scheduler": {
        "name": "SchedulerWrapper",
        "args": {
            "scheduler_name": "cosine2",
            "t_initial": 7,
            "t_mul": 0.9,
            "eta_mul": 0.9,
            "eta_min": 1e-06
        }
    },
    "data": {
        "dataset": {
            "train": {
                "name": "ImageFolderDataset",
                "args": {
                    "image_dir": "/content/main/data/food-classification/train",
                    "txt_classnames": "configs/classification/classes.txt"
                }
            },
            "val": {
                "name": "ImageFolderDataset",
                "args": {
                    "image_dir": "/content/main/data/food-classification/val",
                    "txt_classnames": "configs/classification/classes.txt"
                }
            }
        },
        "dataloader": {
            "train": {
                "name": "DataLoaderWithCollator",
                "args": {
                    "batch_size": 32,
                    "drop_last": true,
                    "shuffle": false,
                    "collate_fn": {
                        "name": "MixupCutmixCollator",
                        "args": {
                            "mixup_alpha": 0.4,
                            "cutmix_alpha": 1.0,
                            "weight": [
                                0.2,
                                0.2
                            ]
                        }
                    },
                    "sampler": {
                        "name": "BalanceSampler",
                        "args": null
                    }
                }
            },
            "val": {
                "name": "DataLoaderWithCollator",
                "args": {
                    "batch_size": 32,
                    "drop_last": false,
                    "shuffle": true
                }
            }
        }
    }
}
2022-04-04 12:21:56 | DEBUG    | opt.py:load_yaml:36 - Loading config from configs/classification/transform.yaml...
2022-04-04 12:21:57 | DEBUG    | classification/datasets/folder_dataset.py:_calculate_classes_dist:71 - Calculating class distribution...
Downloading: "https://dl.fbaipublicfiles.com/convnext/convnext_tiny_1k_224_ema.pth" to /root/.cache/torch/hub/checkpoints/convnext_tiny_1k_224_ema.pth
Traceback (most recent call last):
  File "/content/main/configs/classification/train.py", line 9, in <module>
    train_pipeline = Pipeline(opts)
  File "/content/main/theseus/classification/pipeline.py", line 159, in __init__
    registry=CALLBACKS_REGISTRY
  File "/content/main/theseus/utilities/getter.py", line 15, in get_instance_recursively
    out = [get_instance_recursively(item, registry=registry, **kwargs) for item in config]
  File "/content/main/theseus/utilities/getter.py", line 15, in <listcomp>
    out = [get_instance_recursively(item, registry=registry, **kwargs) for item in config]
  File "/content/main/theseus/utilities/getter.py", line 26, in get_instance_recursively
    return registry.get(config['name'])(**args, **kwargs)
TypeError: type object got multiple values for keyword argument 'resume'

I guess because of the resume arg is both repeated in global and WandBCallbacks. Maybe it also happens with Tensorboard.

Happy Birthday

On this special day of the year, I just want you to know how much I appreciate your work and your passion for each project . Hopefully we will have great results for the upcoming thesis defense.

I wish you all the best today, my friend!

Add rename for download_from_wandb

Adding a rename param for theseus.base.utilities.download: download_from_wandb()

theseus/base/utilities/download.py

def download_from_wandb(filename, run_path, save_dir, rename=None, generate_id_text_file=False):
    import wandb
    import os
    from pathlib import Path

    try:
        path = wandb.restore(filename, run_path=run_path, root=save_dir)
        
        # Save run id to wandb_id.txt
        if generate_id_text_file:
            wandb_id = osp.basename(run_path)
            with open(osp.join(save_dir, "wandb_id.txt"), "w") as f:
                f.write(wandb_id)
        
        if rename:
            os.rename(path.name, str(Path(path.name).parent / rename))
            return str(Path(path.name).parent / rename)

        return path.name
    except:
        LOGGER.text("Failed to download from wandb.", level=LoggerObserver.ERROR)
        return None

An example to run the previous function:

import argparse
from theseus.base.utilities.download import download_from_wandb
from theseus.base.utilities.loggers.observer import LoggerObserver

LOGGER = LoggerObserver.getLogger("main")

if __name__ == "__main__":
    parser = argparse.ArgumentParser()
    parser.add_argument('--filename', type=str, help='most of the time is checkpoints/best.pth')
    parser.add_argument('--run_path', type=str, help='model path on WANDB server')
    parser.add_argument('--save_dir', type=str, help='the directory to save the weight', nargs='+', default=".")
    parser.add_argument('--rename',   type=str, help='the new name for the weight')
    opt = parser.parse_args()
    
    download_from_wandb(
        filename=opt.filename,
        run_path=opt.run_path,
        save_dir=opt.save_dir,
        rename=opt.rename
    )
"""
Bash script:
PYTHONPATH=. python3 tools/download_wandb_weights.py \
--filename "checkpoints/best.pth" \
--run_path "wandb_run_path" \
--rename "new_name"
"""

To be A+ rank (or highest) on CodeFactor and beyond

Current rank:

Solve these to unlock this achievement:
https://www.codefactor.io/repository/github/kaylode/theseus/issues

Better logging system

https://github.com/Delgan/loguru

About master branch and detection branch

Hello, Thanks your repo! It's very helpful for me. Again thanks for contribution.
I want to ask you about detection branch. Can you meger it into master branch or set it to master branch. I clone your repo to use, but it only master branch. And it miss alot of file. It's very difficut for me to run train.
I hope you do it soon as soon possible.
Have a nice day!

Multiprocessing

Multiprocessing for:
Callbacks
Loggers
Metrics

Hyperparamter tuning

Need a tool to automatically search hyperparameters (initial lr, bach size, image size, ensenmble weights, ...)
Sugestion:
- optuna
- pytorch-lightning

Implement independent Callbacks class

Question on Segmentation task?

Hey @kaylode, does the Theseus only support Binary Class Segmentation for now right? I try to use it but it returned an runtime error at dice loss, like this:

File "/content/main/theseus/segmentation/losses/dice_loss.py", line 37, in forward
    num = torch.sum(torch.mul(predict, target), dim=1) + self.smooth

RuntimeError: The size of tensor a (6750208) must match the size of tensor b (65536) at non-singleton dimension 1

Thanks!

Classification losses error

I encountered these errors when testing with SmoothCELoss and FocalLoss, the logs are below:

Focal loss

[Errno 2] No such file or directory: 'main'
/content/main
2022-03-27 11:07:21 | DEBUG    | stdout_logger.py:log_text:34 - Overriding configuration...
2022-03-27 11:07:21 | INFO     | stdout_logger.py:log_text:28 - {
    "global": {
        "debug": true,
        "cfg_transform": "configs/classification/transform.yaml",
        "save_dir": "/content/main/runs",
        "device": "cuda:0",
        "use_fp16": true,
        "pretrained": null,
        "resume": null
    },
    "trainer": {
        "name": "SupervisedTrainer",
        "args": {
            "num_iterations": 3000,
            "clip_grad": 10.0,
            "evaluate_interval": 1,
            "print_interval": 20,
            "save_interval": 500
        }
    },
    "model": {
        "name": "BaseTimmModel",
        "args": {
            "name": "convnext_small",
            "from_pretrained": true,
            "num_classes": 180
        }
    },
    "loss": {
        "name": "FocalLoss"
    },
    "callbacks": [
        {
            "name": "LoggerCallbacks",
            "args": null
        },
        {
            "name": "CheckpointCallbacks",
            "args": {
                "best_key": "bl_acc"
            }
        },
        {
            "name": "VisualizerCallbacks",
            "args": null
        },
        {
            "name": "TensorboardCallbacks",
            "args": null
        }
    ],
    "metrics": [
        {
            "name": "Accuracy",
            "args": null
        },
        {
            "name": "BalancedAccuracyMetric",
            "args": null
        },
        {
            "name": "F1ScoreMetric",
            "args": {
                "average": "weighted"
            }
        },
        {
            "name": "ConfusionMatrix",
            "args": null
        },
        {
            "name": "ErrorCases",
            "args": null
        }
    ],
    "optimizer": {
        "name": "AdamW",
        "args": {
            "lr": 0.001,
            "weight_decay": 0.0005,
            "betas": [
                0.937,
                0.999
            ]
        }
    },
    "scheduler": {
        "name": "SchedulerWrapper",
        "args": {
            "scheduler_name": "cosine2",
            "t_initial": 7,
            "t_mul": 0.9,
            "eta_mul": 0.9,
            "eta_min": 1e-06
        }
    },
    "data": {
        "dataset": {
            "train": {
                "name": "ImageFolderDataset",
                "args": {
                    "image_dir": "/content/main/data/food-classification/train",
                    "txt_classnames": "configs/classification/classes.txt"
                }
            },
            "val": {
                "name": "ImageFolderDataset",
                "args": {
                    "image_dir": "/content/main/data/food-classification/val",
                    "txt_classnames": "configs/classification/classes.txt"
                }
            }
        },
        "dataloader": {
            "train": {
                "name": "DataLoaderWithCollator",
                "args": {
                    "batch_size": 32,
                    "drop_last": true,
                    "shuffle": false,
                    "collate_fn": {
                        "name": "MixupCutmixCollator",
                        "args": {
                            "mixup_alpha": 0.4,
                            "cutmix_alpha": 1.0,
                            "weight": [
                                0.2,
                                0.2
                            ]
                        }
                    },
                    "sampler": {
                        "name": "BalanceSampler",
                        "args": null
                    }
                }
            },
            "val": {
                "name": "DataLoaderWithCollator",
                "args": {
                    "batch_size": 32,
                    "drop_last": false,
                    "shuffle": true
                }
            }
        }
    }
}
2022-03-27 11:07:21 | DEBUG    | stdout_logger.py:log_text:34 - Loading config from configs/classification/transform.yaml...
2022-03-27 11:07:21 | DEBUG    | stdout_logger.py:log_text:34 - Calculating class distribution...
Downloading: "https://dl.fbaipublicfiles.com/convnext/convnext_small_1k_224_ema.pth" to /root/.cache/torch/hub/checkpoints/convnext_small_1k_224_ema.pth
2022-03-27 11:07:46 | INFO     | stdout_logger.py:log_text:28 - Number of trainable parameters: 49,593,108
2022-03-27 11:07:46 | INFO     | stdout_logger.py:log_text:28 - Using CUDA:0 (Tesla T4, 15109.75MB)

2022-03-27 11:07:46 | INFO     | stdout_logger.py:log_text:28 - Number of training samples: 88814
2022-03-27 11:07:46 | INFO     | stdout_logger.py:log_text:28 - Number of validation samples: 21775
2022-03-27 11:07:46 | INFO     | stdout_logger.py:log_text:28 - Number of training iterations each epoch: 2775
2022-03-27 11:07:46 | INFO     | stdout_logger.py:log_text:28 - Number of validation iterations each epoch: 681
2022-03-27 11:07:46 | INFO     | stdout_logger.py:log_text:28 - Everything will be saved to /content/main/runs/2022-03-27_11-07-21
2022-03-27 11:07:46 | DEBUG    | stdout_logger.py:log_text:34 - Saving config to /content/main/runs/2022-03-27_11-07-21/pipeline.yaml...
2022-03-27 11:07:46 | DEBUG    | stdout_logger.py:log_text:34 - Saving config to /content/main/runs/2022-03-27_11-07-21/transform.yaml...
2022-03-27 11:07:46 | DEBUG    | stdout_logger.py:log_text:34 - Start sanity checks
2022-03-27 11:07:47 | DEBUG    | stdout_logger.py:log_text:34 - Visualizing architecture...
2022-03-27 11:07:50 | INFO     | stdout_logger.py:log_text:28 - =============================EVALUATION===================================
100% 681/681 [04:04<00:00,  2.78it/s]
2022-03-27 11:11:56 | INFO     | stdout_logger.py:log_text:28 - [0|3000] || L: 0.13242 || Time:     2.7617 (it/s)
2022-03-27 11:11:56 | INFO     | stdout_logger.py:log_text:28 - acc: 0.00455 | bl_acc: 0.00411 | weighted-f1: 0.00332 | 

2022-03-27 11:11:56 | INFO     | stdout_logger.py:log_text:28 - ==========================================================================
2022-03-27 11:11:57 | DEBUG    | stdout_logger.py:log_text:34 - Visualizing model predictions...
2022-03-27 11:11:59 | DEBUG    | stdout_logger.py:log_text:34 - Visualizing dataset...
2022-03-27 11:12:01 | DEBUG    | stdout_logger.py:log_text:34 - Analyzing datasets...
100% 88814/88814 [12:01<00:00, 123.05it/s]
100% 21775/21775 [02:12<00:00, 163.82it/s]
2022-03-27 11:26:17 | INFO     | stdout_logger.py:log_text:28 - ===========================START TRAINING=================================
Traceback (most recent call last):
  File "/content/main/configs/classification/train.py", line 10, in <module>
    train_pipeline.fit()
  File "/content/main/theseus/classification/pipeline.py", line 171, in fit
    self.trainer.fit()
  File "/content/main/theseus/base/trainer/base_trainer.py", line 65, in fit
    self.training_epoch()
  File "/content/main/theseus/base/trainer/supervised_trainer.py", line 68, in training_epoch
    outputs = self.model.training_step(batch)
  File "/content/main/theseus/classification/models/wrapper.py", line 34, in training_step
    return self.forward(batch)
  File "/content/main/theseus/classification/models/wrapper.py", line 22, in forward
    loss, loss_dict = self.criterion(outputs, batch, self.device)
  File "/usr/local/lib/python3.7/dist-packages/torch/nn/modules/module.py", line 1102, in _call_impl
    return forward_call(*input, **kwargs)
  File "/content/main/theseus/classification/losses/focal_loss.py", line 21, in forward
    targets = nn.functional.one_hot(targets, num_classes=num_classes)
RuntimeError: one_hot is only applicable to index tensor.

SmoothCELoss

[Errno 2] No such file or directory: 'main'
/content/main
2022-03-27 11:48:37 | DEBUG    | stdout_logger.py:log_text:34 - Overriding configuration...
2022-03-27 11:48:37 | INFO     | stdout_logger.py:log_text:28 - {
    "global": {
        "debug": true,
        "cfg_transform": "configs/classification/transform.yaml",
        "save_dir": "/content/main/runs",
        "device": "cuda:0",
        "use_fp16": true,
        "pretrained": null,
        "resume": null
    },
    "trainer": {
        "name": "SupervisedTrainer",
        "args": {
            "num_iterations": 3000,
            "clip_grad": 10.0,
            "evaluate_interval": 1,
            "print_interval": 20,
            "save_interval": 500
        }
    },
    "model": {
        "name": "BaseTimmModel",
        "args": {
            "name": "convnext_small",
            "from_pretrained": true,
            "num_classes": 180
        }
    },
    "loss": {
        "name": "SmoothCELoss"
    },
    "callbacks": [
        {
            "name": "LoggerCallbacks",
            "args": null
        },
        {
            "name": "CheckpointCallbacks",
            "args": {
                "best_key": "bl_acc"
            }
        },
        {
            "name": "VisualizerCallbacks",
            "args": null
        },
        {
            "name": "TensorboardCallbacks",
            "args": null
        }
    ],
    "metrics": [
        {
            "name": "Accuracy",
            "args": null
        },
        {
            "name": "BalancedAccuracyMetric",
            "args": null
        },
        {
            "name": "F1ScoreMetric",
            "args": {
                "average": "weighted"
            }
        },
        {
            "name": "ConfusionMatrix",
            "args": null
        },
        {
            "name": "ErrorCases",
            "args": null
        }
    ],
    "optimizer": {
        "name": "AdamW",
        "args": {
            "lr": 0.001,
            "weight_decay": 0.0005,
            "betas": [
                0.937,
                0.999
            ]
        }
    },
    "scheduler": {
        "name": "SchedulerWrapper",
        "args": {
            "scheduler_name": "cosine2",
            "t_initial": 7,
            "t_mul": 0.9,
            "eta_mul": 0.9,
            "eta_min": 1e-06
        }
    },
    "data": {
        "dataset": {
            "train": {
                "name": "ImageFolderDataset",
                "args": {
                    "image_dir": "/content/main/data/food-classification/train",
                    "txt_classnames": "configs/classification/classes.txt"
                }
            },
            "val": {
                "name": "ImageFolderDataset",
                "args": {
                    "image_dir": "/content/main/data/food-classification/val",
                    "txt_classnames": "configs/classification/classes.txt"
                }
            }
        },
        "dataloader": {
            "train": {
                "name": "DataLoaderWithCollator",
                "args": {
                    "batch_size": 32,
                    "drop_last": true,
                    "shuffle": false,
                    "collate_fn": {
                        "name": "MixupCutmixCollator",
                        "args": {
                            "mixup_alpha": 0.4,
                            "cutmix_alpha": 1.0,
                            "weight": [
                                0.2,
                                0.2
                            ]
                        }
                    },
                    "sampler": {
                        "name": "BalanceSampler",
                        "args": null
                    }
                }
            },
            "val": {
                "name": "DataLoaderWithCollator",
                "args": {
                    "batch_size": 32,
                    "drop_last": false,
                    "shuffle": true
                }
            }
        }
    }
}
2022-03-27 11:48:37 | DEBUG    | stdout_logger.py:log_text:34 - Loading config from configs/classification/transform.yaml...
2022-03-27 11:48:37 | DEBUG    | stdout_logger.py:log_text:34 - Calculating class distribution...
2022-03-27 11:48:43 | INFO     | stdout_logger.py:log_text:28 - Number of trainable parameters: 49,593,108
2022-03-27 11:48:43 | INFO     | stdout_logger.py:log_text:28 - Using CUDA:0 (Tesla T4, 15109.75MB)

2022-03-27 11:48:43 | INFO     | stdout_logger.py:log_text:28 - Number of training samples: 88814
2022-03-27 11:48:43 | INFO     | stdout_logger.py:log_text:28 - Number of validation samples: 21775
2022-03-27 11:48:43 | INFO     | stdout_logger.py:log_text:28 - Number of training iterations each epoch: 2775
2022-03-27 11:48:43 | INFO     | stdout_logger.py:log_text:28 - Number of validation iterations each epoch: 681
2022-03-27 11:48:43 | INFO     | stdout_logger.py:log_text:28 - Everything will be saved to /content/main/runs/2022-03-27_11-48-37
2022-03-27 11:48:43 | DEBUG    | stdout_logger.py:log_text:34 - Saving config to /content/main/runs/2022-03-27_11-48-37/pipeline.yaml...
2022-03-27 11:48:43 | DEBUG    | stdout_logger.py:log_text:34 - Saving config to /content/main/runs/2022-03-27_11-48-37/transform.yaml...
2022-03-27 11:48:43 | DEBUG    | stdout_logger.py:log_text:34 - Start sanity checks
2022-03-27 11:48:44 | DEBUG    | stdout_logger.py:log_text:34 - Visualizing architecture...
2022-03-27 11:48:47 | INFO     | stdout_logger.py:log_text:28 - =============================EVALUATION===================================
100% 681/681 [04:04<00:00,  2.78it/s]
2022-03-27 11:52:53 | INFO     | stdout_logger.py:log_text:28 - [0|3000] || CE: 5.19444 || Time:     2.7645 (it/s)
2022-03-27 11:52:53 | INFO     | stdout_logger.py:log_text:28 - acc: 0.00822 | bl_acc: 0.00766 | weighted-f1: 0.00479 | 

2022-03-27 11:52:53 | INFO     | stdout_logger.py:log_text:28 - ==========================================================================
2022-03-27 11:52:54 | DEBUG    | stdout_logger.py:log_text:34 - Visualizing model predictions...
2022-03-27 11:52:56 | DEBUG    | stdout_logger.py:log_text:34 - Visualizing dataset...
2022-03-27 11:52:58 | DEBUG    | stdout_logger.py:log_text:34 - Analyzing datasets...
100% 88814/88814 [12:02<00:00, 122.99it/s]
100% 21775/21775 [02:13<00:00, 163.64it/s]
2022-03-27 12:07:15 | INFO     | stdout_logger.py:log_text:28 - ===========================START TRAINING=================================
Traceback (most recent call last):
  File "/content/main/configs/classification/train.py", line 10, in <module>
    train_pipeline.fit()
  File "/content/main/theseus/classification/pipeline.py", line 171, in fit
    self.trainer.fit()
  File "/content/main/theseus/base/trainer/base_trainer.py", line 65, in fit
    self.training_epoch()
  File "/content/main/theseus/base/trainer/supervised_trainer.py", line 68, in training_epoch
    outputs = self.model.training_step(batch)
  File "/content/main/theseus/classification/models/wrapper.py", line 34, in training_step
    return self.forward(batch)
  File "/content/main/theseus/classification/models/wrapper.py", line 22, in forward
    loss, loss_dict = self.criterion(outputs, batch, self.device)
  File "/usr/local/lib/python3.7/dist-packages/torch/nn/modules/module.py", line 1102, in _call_impl
    return forward_call(*input, **kwargs)
  File "/content/main/theseus/classification/losses/ce_loss.py", line 37, in forward
    loss = self.criterion(pred, target.view(-1).contiguous())
  File "/usr/local/lib/python3.7/dist-packages/torch/nn/modules/module.py", line 1102, in _call_impl
    return forward_call(*input, **kwargs)
  File "/usr/local/lib/python3.7/dist-packages/timm/loss/cross_entropy.py", line 22, in forward
    nll_loss = -logprobs.gather(dim=-1, index=target.unsqueeze(1))
RuntimeError: gather(): Expected dtype int64 for index

I guess it's an error from Mixup Cutmix collator, something with torch.int64.

Here's the link to notebook that i've used for testing, if you want to have a look at: notebook

ValueError: Expected x_max/y_max for bbox to be in the range [0.0, 1.0]

If anyone has this kind of error, it is usually due to the annotations of the dataset. Please try following these steps:
Find the image that gives the error (Use python's try + except to catch the image id) then

Check whether its bounding boxes format in .JSON file are in (x_topleft ,y_topleft ,width ,height)
Check whether the image size match with the one in .JSON file
Check whether its bounding boxes are fully inside the image. (Check x + w <= image_width, y +h <= image_height)

If None of the solutions above help, feel free to contact me. :>

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