MetaMOT/exps/example/metamot/yolox_x_mot17_on_mot20.py

# encoding: utf-8
import os
import random
import torch
import torch.nn as nn
import torch.distributed as dist

from yolox.exp import MetaExp as MyMetaExp
from yolox.data import get_yolox_datadir
from os import listdir
from os.path import isfile, join


class Exp(MyMetaExp):
    def __init__(self):
        super(Exp, self).__init__()
        self.num_classes = 1
        self.depth = 1.33
        self.width = 1.25
        self.exp_name = os.path.split(os.path.realpath(__file__))[1].split(".")[0]
        self.train_dir = '/home/abdollahpour.ce.sharif/ByteTrackData/MOT17/annotations'
        onlyfiles = [f for f in listdir(self.train_dir) if isfile(join(self.train_dir, f))]
        self.train_anns = [file for file in onlyfiles if file.__contains__('train') and file.__contains__('FRCNN')]
        # # TODO: remove
        # self.train_anns = self.train_anns[3:]

        self.val_dir = '/home/abdollahpour.ce.sharif/ByteTrackData/MOT20/annotations'
        onlyfiles = [f for f in listdir(self.val_dir) if isfile(join(self.val_dir, f))]
        self.val_anns = [file for file in onlyfiles if file.__contains__('train') and file.__contains__(
            'MOT20')]
        # self.val_anns = self.val_anns[-1:]
        print('train_anns', self.train_anns)
        print('val_anns', self.val_anns)
        self.input_size = (800, 1440)
        # TODO: try this
        self.test_size = (800, 1440)
        # self.test_size = (896, 1600)
        # self.test_size = (736, 1920)
        self.random_size = (20, 36)
        self.max_epoch = 80
        self.print_interval = 250
        self.eval_interval = 10
        self.test_conf = 0.001
        self.nmsthre = 0.7
        self.no_aug_epochs = 10
        # self.basic_lr_per_img = 0.001 / 64.0
        self.basic_lr_per_img = 0.0001 / 64.0
        self.warmup_epochs = 1

    def get_data_loaders(self, batch_size, is_distributed, no_aug=False):
        from yolox.data import (
            MOTDataset,
            TrainTransform,
            YoloBatchSampler,
            DataLoader,
            InfiniteSampler,
            MosaicDetection,
        )
        train_loaders = []
        for train_ann in self.train_anns:
            dataset = MOTDataset(
                data_dir=os.path.join(get_yolox_datadir(), "MOT17"),
                json_file=train_ann,
                name='train',
                img_size=self.input_size,
                preproc=TrainTransform(
                    rgb_means=(0.485, 0.456, 0.406),
                    std=(0.229, 0.224, 0.225),
                    max_labels=500,
                ),
            )
            dataset = MosaicDetection(
                dataset,
                mosaic=not no_aug,
                img_size=self.input_size,
                preproc=TrainTransform(
                    rgb_means=(0.485, 0.456, 0.406),
                    std=(0.229, 0.224, 0.225),
                    max_labels=1000,
                ),
                degrees=self.degrees,
                translate=self.translate,
                scale=self.scale,
                shear=self.shear,
                perspective=self.perspective,
                enable_mixup=self.enable_mixup,
            )

            self.dataset = dataset

            if is_distributed:
                batch_size = batch_size // dist.get_world_size()

            sampler = InfiniteSampler(
                len(self.dataset), seed=self.seed if self.seed else 0
            )

            batch_sampler = YoloBatchSampler(
                sampler=sampler,
                batch_size=batch_size,
                drop_last=False,
                input_dimension=self.input_size,
                mosaic=not no_aug,
            )

            dataloader_kwargs = {"num_workers": self.data_num_workers, "pin_memory": True}
            dataloader_kwargs["batch_sampler"] = batch_sampler
            train_loader = DataLoader(self.dataset, **dataloader_kwargs)
            train_loaders.append(train_loader)

        return train_loaders

    def get_eval_loaders(self, batch_size, is_distributed, testdev=False):
        from yolox.data import MOTDataset, ValTransform, ValTransformWithPseudo, TrainTransform
        val_loaders = []
        for val_ann in self.val_anns:
            valdataset = MOTDataset(
                data_dir=os.path.join(get_yolox_datadir(), "MOT20"),
                json_file=val_ann,
                img_size=self.test_size,
                name='train',  # change to train when running on training set
                preproc=ValTransformWithPseudo(
                    rgb_means=(0.485, 0.456, 0.406),
                    std=(0.229, 0.224, 0.225), max_labels=500,
                ),

                load_weak=False
            )

            if is_distributed:
                batch_size = batch_size // dist.get_world_size()
                sampler = torch.utils.data.distributed.DistributedSampler(
                    valdataset, shuffle=False
                )
            else:
                sampler = torch.utils.data.SequentialSampler(valdataset)

            dataloader_kwargs = {
                "num_workers": self.data_num_workers,
                "pin_memory": True,
                "sampler": sampler,
            }
            dataloader_kwargs["batch_size"] = batch_size
            val_loader = torch.utils.data.DataLoader(valdataset, **dataloader_kwargs)
            val_loaders.append(val_loader)
        return val_loaders

    def get_evaluators(self, batch_size, is_distributed, testdev=False):
        from yolox.evaluators import COCOEvaluator

        val_loaders = self.get_eval_loaders(batch_size, is_distributed, testdev=testdev)
        evaluators = []
        for val_loader in val_loaders:
            evaluator = COCOEvaluator(
                dataloader=val_loader,
                img_size=self.test_size,
                confthre=self.test_conf,
                nmsthre=self.nmsthre,
                num_classes=self.num_classes,
                testdev=testdev,
            )
            evaluators.append(evaluator)
        return evaluators