| import argparse |
|
|
|
|
| def get_parser(): |
| |
| |
| parser = argparse.ArgumentParser( |
| description='The pytorch implementation for Visual Alignment Constraint ' |
| 'for Continuous Sign Language Recognition.') |
| parser.add_argument( |
| '--work-dir', |
| default='./work_dir/temp', |
| help='the work folder for storing results') |
| parser.add_argument( |
| '--config', |
| default='./configs/baseline.yaml', |
| help='path to the configuration file') |
| parser.add_argument( |
| '--random_fix', |
| type=str2bool, |
| default=True, |
| help='fix random seed or not') |
| parser.add_argument( |
| '--device', |
| type=str, |
| default=0, |
| help='the indexes of GPUs for training or testing') |
|
|
| parser.add_argument( |
| '--num-feature-aug', |
| type=int, |
| default=-1, |
| help='number of feature duplicates, by default -1 no duplication.') |
|
|
| |
| |
| parser.add_argument( |
| '--phase', default='train', help='can be train, test and features') |
|
|
| |
| |
| parser.add_argument( |
| '--save-interval', |
| type=int, |
| default=200, |
| help='the interval for storing models (#epochs)') |
| parser.add_argument( |
| '--random-seed', |
| type=int, |
| default=0, |
| help='the default value for random seed.') |
| parser.add_argument( |
| '--eval-interval', |
| type=int, |
| default=100, |
| help='the interval for evaluating models (#epochs)') |
| parser.add_argument( |
| '--print-log', |
| type=str2bool, |
| default=True, |
| help='print logging or not') |
| parser.add_argument( |
| '--log-interval', |
| type=int, |
| default=20, |
| help='the interval for printing messages (#iteration)') |
| parser.add_argument( |
| '--evaluate-tool', default="python", help='sclite or python') |
|
|
| |
| |
| parser.add_argument( |
| '--feeder', default='dataloader_video.BaseFeeder', help='data loader will be used') |
| parser.add_argument( |
| '--dataset', |
| default=None, |
| help='data loader will be used' |
| ) |
| parser.add_argument( |
| '--dataset-info', |
| default=dict(), |
| help='data loader will be used' |
| ) |
| parser.add_argument( |
| '--preprocess-sample-ratio', |
| type=float, |
| default=1.0, |
| help='preprocess-only: fraction of manifest to sample (unused by training)') |
| parser.add_argument( |
| '--label-column', |
| default='TEXT', |
| help='preprocess-only: manifest label column (unused by training)') |
| parser.add_argument( |
| '--scale-update-cycle', |
| type=str2bool, |
| default=False, |
| help='slt-only: scale update_cycle by GPU count (unused by SMKD)') |
| parser.add_argument( |
| '--dev-from-train-count', |
| type=int, |
| default=0, |
| help='preprocess-only: override dev split with N samples from train (unused by training)') |
| parser.add_argument( |
| '--dev-from-train-keep', |
| type=str2bool, |
| default=False, |
| help='preprocess-only: keep dev samples in train (unused by training)') |
| parser.add_argument( |
| '--eval-use-conv', |
| type=str2bool, |
| default=False, |
| help='evaluate using conv head outputs instead of sequence head') |
| parser.add_argument( |
| '--debug-seq-topk', |
| type=int, |
| default=0, |
| help='print top-k sequence head average probs on first eval batch (0 disables)') |
| parser.add_argument( |
| '--debug-conv-topk', |
| type=int, |
| default=0, |
| help='print top-k conv head average probs on first eval batch (0 disables)') |
| parser.add_argument( |
| '--num-worker', |
| type=int, |
| default=4, |
| help='the number of worker for data loader') |
| parser.add_argument( |
| '--feeder-args', |
| default=dict(), |
| help='the arguments of data loader') |
|
|
| |
| |
| parser.add_argument('--model', default=None, help='the model will be used') |
| parser.add_argument( |
| '--model-args', |
| type=dict, |
| default=dict(), |
| help='the arguments of model') |
| parser.add_argument( |
| '--load-weights', |
| default=None, |
| help='load weights for network initialization') |
| parser.add_argument( |
| '--load-checkpoints', |
| default=None, |
| help='load checkpoints for continue training') |
| parser.add_argument( |
| '--decode-mode', |
| default="max", |
| help='search mode for decode, max or beam') |
| parser.add_argument( |
| '--ignore-weights', |
| type=str, |
| default=[], |
| nargs='+', |
| help='the name of weights which will be ignored in the initialization') |
| parser.add_argument( |
| '--skip-sample-file', |
| default=None, |
| help='path to a newline separated list of sample IDs to ignore') |
| parser.add_argument( |
| '--disable-bad-sample-filter', |
| type=str2bool, |
| default=False, |
| help='set true to skip recording/removing samples with non-finite loss') |
| parser.add_argument( |
| '--debug-save-batch', |
| type=str2bool, |
| default=True, |
| help='save first batch frames for debugging') |
| parser.add_argument( |
| '--debug-stats', |
| type=str2bool, |
| default=True, |
| help='log first batch stats for debugging') |
| parser.add_argument( |
| '--debug-save-dir', |
| default=None, |
| help='override debug frame output directory') |
| parser.add_argument( |
| '--debug-stats-path', |
| default=None, |
| help='override debug stats output file path') |
| parser.add_argument( |
| '--debug-save-frames', |
| type=int, |
| default=8, |
| help='max frames to save from first batch') |
| parser.add_argument( |
| '--debug-save-samples', |
| type=int, |
| default=1, |
| help='max samples to save from first batch') |
|
|
| |
| |
| parser.add_argument( |
| '--batch-size', type=int, default=16, help='training batch size') |
| parser.add_argument( |
| '--test-batch-size', type=int, default=8, help='test batch size') |
|
|
| default_optimizer_dict = { |
| "base_lr": 1e-2, |
| "optimizer": "SGD", |
| "nesterov": False, |
| "step": [5, 10], |
| "weight_decay": 0.00005, |
| "start_epoch": 1, |
| } |
| default_loss_dict = { |
| "SeqCTC": 1.0, |
| } |
|
|
| parser.add_argument( |
| '--loss-weights', |
| default=default_loss_dict, |
| help='loss selection' |
| ) |
|
|
| parser.add_argument( |
| '--optimizer-args', |
| default=default_optimizer_dict, |
| help='the arguments of optimizer') |
|
|
| parser.add_argument( |
| '--num-epoch', |
| type=int, |
| default=80, |
| help='stop training in which epoch') |
| return parser |
|
|
|
|
| def str2bool(v): |
| if v.lower() in ('yes', 'true', 't', 'y', '1'): |
| return True |
| elif v.lower() in ('no', 'false', 'f', 'n', '0'): |
| return False |
| else: |
| raise argparse.ArgumentTypeError('Boolean value expected.') |
|
|
