container-batch-inference/resources/FairMOT/opts.py [9:91]: - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - class opts(object): def __init__(self): self.parser = argparse.ArgumentParser() # basic experiment setting self.parser.add_argument('task', default='mot', help='mot') self.parser.add_argument('--dataset', default='jde', help='jde') self.parser.add_argument('--exp_id', default='default') self.parser.add_argument('--test', action='store_true') #self.parser.add_argument('--load_model', default='../models/ctdet_coco_dla_2x.pth', #help='path to pretrained model') self.parser.add_argument('--load_model', default='', help='path to pretrained model') self.parser.add_argument('--resume', action='store_true', help='resume an experiment. ' 'Reloaded the optimizer parameter and ' 'set load_model to model_last.pth ' 'in the exp dir if load_model is empty.') # system self.parser.add_argument('--gpus', default='0', help='-1 for CPU, use comma for multiple gpus') self.parser.add_argument('--num_workers', type=int, default=8, help='dataloader threads. 0 for single-thread.') self.parser.add_argument('--not_cuda_benchmark', action='store_true', help='disable when the input size is not fixed.') self.parser.add_argument('--seed', type=int, default=317, help='random seed') # from CornerNet # log self.parser.add_argument('--print_iter', type=int, default=0, help='disable progress bar and print to screen.') self.parser.add_argument('--hide_data_time', action='store_true', help='not display time during training.') self.parser.add_argument('--save_all', action='store_true', help='save model to disk every 5 epochs.') self.parser.add_argument('--metric', default='loss', help='main metric to save best model') self.parser.add_argument('--vis_thresh', type=float, default=0.5, help='visualization threshold.') self.parser.add_argument('--save_dir', type=str, default='/opt/ml/model', help='directory model saved') self.parser.add_argument('--checkpoint_format', type=str, default='/opt/ml/model/model-{epoch}.pth', help='checkpoint file format') # model self.parser.add_argument('--arch', default='dla_34', help='model architecture. Currently tested' 'resdcn_34 | resdcn_50 | resfpndcn_34 |' 'dla_34 | hrnet_18') self.parser.add_argument('--head_conv', type=int, default=-1, help='conv layer channels for output head' '0 for no conv layer' '-1 for default setting: ' '256 for resnets and 256 for dla.') self.parser.add_argument('--down_ratio', type=int, default=4, help='output stride. Currently only supports 4.') # input self.parser.add_argument('--input_res', type=int, default=-1, help='input height and width. -1 for default from ' 'dataset. Will be overriden by input_h | input_w') self.parser.add_argument('--input_h', type=int, default=-1, help='input height. -1 for default from dataset.') self.parser.add_argument('--input_w', type=int, default=-1, help='input width. -1 for default from dataset.') # train self.parser.add_argument('--lr', type=float, default=1e-4, help='learning rate for batch size 12.') self.parser.add_argument('--lr_step', type=str, default='20', help='drop learning rate by 10.') self.parser.add_argument('--num_epochs', type=int, default=30, help='total training epochs.') self.parser.add_argument('--batch_size', type=int, default=12, help='batch size') self.parser.add_argument('--master_batch_size', type=int, default=-1, help='batch size on the master gpu.') self.parser.add_argument('--num_iters', type=int, default=-1, help='default: #samples / batch_size.') self.parser.add_argument('--val_intervals', type=int, default=1, help='number of epochs to run validation.') self.parser.add_argument('--trainval', action='store_true', help='include validation in training and ' 'test on test set') - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - container-dp/resources/FairMOT/opts.py [9:89]: - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - class opts(object): def __init__(self): self.parser = argparse.ArgumentParser() # basic experiment setting self.parser.add_argument('task', default='mot', help='mot') self.parser.add_argument('--dataset', default='jde', help='jde') self.parser.add_argument('--exp_id', default='default') self.parser.add_argument('--test', action='store_true') self.parser.add_argument('--load_model', default='', help='path to pretrained model') self.parser.add_argument('--resume', action='store_true', help='resume an experiment. ' 'Reloaded the optimizer parameter and ' 'set load_model to model_last.pth ' 'in the exp dir if load_model is empty.') # system self.parser.add_argument('--gpus', default='0', help='-1 for CPU, use comma for multiple gpus') self.parser.add_argument('--num_workers', type=int, default=8, help='dataloader threads. 0 for single-thread.') self.parser.add_argument('--not_cuda_benchmark', action='store_true', help='disable when the input size is not fixed.') self.parser.add_argument('--seed', type=int, default=317, help='random seed') # from CornerNet # log self.parser.add_argument('--print_iter', type=int, default=0, help='disable progress bar and print to screen.') self.parser.add_argument('--hide_data_time', action='store_true', help='not display time during training.') self.parser.add_argument('--save_all', action='store_true', help='save model to disk every 5 epochs.') self.parser.add_argument('--metric', default='loss', help='main metric to save best model') self.parser.add_argument('--vis_thresh', type=float, default=0.5, help='visualization threshold.') self.parser.add_argument('--save_dir', type=str, default='/opt/ml/model', help='directory model saved') self.parser.add_argument('--checkpoint_format', type=str, default='/opt/ml/model/model-{epoch}.pth', help='checkpoint file format') # model self.parser.add_argument('--arch', default='dla_34', help='model architecture. Currently tested' 'resdcn_34 | resdcn_50 | resfpndcn_34 |' 'dla_34 | hrnet_18') self.parser.add_argument('--head_conv', type=int, default=-1, help='conv layer channels for output head' '0 for no conv layer' '-1 for default setting: ' '256 for resnets and 256 for dla.') self.parser.add_argument('--down_ratio', type=int, default=4, help='output stride. Currently only supports 4.') # input self.parser.add_argument('--input_res', type=int, default=-1, help='input height and width. -1 for default from ' 'dataset. Will be overriden by input_h | input_w') self.parser.add_argument('--input_h', type=int, default=-1, help='input height. -1 for default from dataset.') self.parser.add_argument('--input_w', type=int, default=-1, help='input width. -1 for default from dataset.') # train self.parser.add_argument('--lr', type=float, default=1e-4, help='learning rate for batch size 12.') self.parser.add_argument('--lr_step', type=str, default='20', help='drop learning rate by 10.') self.parser.add_argument('--num_epochs', type=int, default=30, help='total training epochs.') self.parser.add_argument('--batch_size', type=int, default=12, help='batch size') self.parser.add_argument('--master_batch_size', type=int, default=-1, help='batch size on the master gpu.') self.parser.add_argument('--num_iters', type=int, default=-1, help='default: #samples / batch_size.') self.parser.add_argument('--val_intervals', type=int, default=1, help='number of epochs to run validation.') self.parser.add_argument('--trainval', action='store_true', help='include validation in training and ' 'test on test set') - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -