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import argparse |
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import os |
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def get_parser(): |
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parser = argparse.ArgumentParser() |
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parser.add_argument('--save_dir', type=str, default='path/to/save/models', |
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help='path where checkpoints will be saved') |
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parser.add_argument('--project_name', type=str, default='inversecooking', |
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help='name of the directory where models will be saved within save_dir') |
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parser.add_argument('--model_name', type=str, default='model', |
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help='save_dir/project_name/model_name will be the path where logs and checkpoints are stored') |
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parser.add_argument('--transfer_from', type=str, default='', |
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help='specify model name to transfer from') |
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parser.add_argument('--suff', type=str, default='', |
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help='the id of the dictionary to load for training') |
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parser.add_argument('--image_model', type=str, default='resnet50', choices=['resnet18', 'resnet50', 'resnet101', |
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'resnet152', 'inception_v3']) |
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parser.add_argument('--recipe1m_dir', type=str, default='path/to/recipe1m', |
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help='directory where recipe1m dataset is extracted') |
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parser.add_argument('--aux_data_dir', type=str, default='../data', |
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help='path to other necessary data files (eg. vocabularies)') |
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parser.add_argument('--crop_size', type=int, default=224, help='size for randomly or center cropping images') |
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parser.add_argument('--image_size', type=int, default=256, help='size to rescale images') |
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parser.add_argument('--log_step', type=int , default=10, help='step size for printing log info') |
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parser.add_argument('--learning_rate', type=float, default=0.001, |
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help='base learning rate') |
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parser.add_argument('--scale_learning_rate_cnn', type=float, default=0.01, |
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help='lr multiplier for cnn weights') |
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parser.add_argument('--lr_decay_rate', type=float, default=0.99, |
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help='learning rate decay factor') |
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parser.add_argument('--lr_decay_every', type=int, default=1, |
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help='frequency of learning rate decay (default is every epoch)') |
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parser.add_argument('--weight_decay', type=float, default=0.) |
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parser.add_argument('--embed_size', type=int, default=512, |
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help='hidden size for all projections') |
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parser.add_argument('--n_att', type=int, default=8, |
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help='number of attention heads in the instruction decoder') |
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parser.add_argument('--n_att_ingrs', type=int, default=4, |
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help='number of attention heads in the ingredient decoder') |
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parser.add_argument('--transf_layers', type=int, default=16, |
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help='number of transformer layers in the instruction decoder') |
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parser.add_argument('--transf_layers_ingrs', type=int, default=4, |
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help='number of transformer layers in the ingredient decoder') |
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parser.add_argument('--num_epochs', type=int, default=400, |
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help='maximum number of epochs') |
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parser.add_argument('--batch_size', type=int, default=128) |
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parser.add_argument('--num_workers', type=int, default=8) |
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parser.add_argument('--dropout_encoder', type=float, default=0.3, |
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help='dropout ratio for the image and ingredient encoders') |
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parser.add_argument('--dropout_decoder_r', type=float, default=0.3, |
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help='dropout ratio in the instruction decoder') |
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parser.add_argument('--dropout_decoder_i', type=float, default=0.3, |
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help='dropout ratio in the ingredient decoder') |
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parser.add_argument('--finetune_after', type=int, default=-1, |
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help='epoch to start training cnn. -1 is never, 0 is from the beginning') |
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parser.add_argument('--loss_weight', nargs='+', type=float, default=[1.0, 0.0, 0.0, 0.0], |
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help='training loss weights. 1) instruction, 2) ingredient, 3) eos 4) cardinality') |
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parser.add_argument('--max_eval', type=int, default=4096, |
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help='number of validation samples to evaluate during training') |
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parser.add_argument('--label_smoothing_ingr', type=float, default=0.1, |
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help='label smoothing for bce loss for ingredients') |
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parser.add_argument('--patience', type=int, default=50, |
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help='maximum number of epochs to allow before early stopping') |
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parser.add_argument('--maxseqlen', type=int, default=15, |
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help='maximum length of each instruction') |
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parser.add_argument('--maxnuminstrs', type=int, default=10, |
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help='maximum number of instructions') |
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parser.add_argument('--maxnumims', type=int, default=5, |
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help='maximum number of images per sample') |
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parser.add_argument('--maxnumlabels', type=int, default=20, |
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help='maximum number of ingredients per sample') |
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parser.add_argument('--es_metric', type=str, default='loss', choices=['loss', 'iou_sample'], |
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help='early stopping metric to track') |
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parser.add_argument('--eval_split', type=str, default='val') |
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parser.add_argument('--numgens', type=int, default=3) |
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parser.add_argument('--greedy', dest='greedy', action='store_true', |
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help='enables greedy sampling (inference only)') |
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parser.set_defaults(greedy=False) |
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parser.add_argument('--temperature', type=float, default=1.0, |
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help='sampling temperature (when greedy is False)') |
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parser.add_argument('--beam', type=int, default=-1, |
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help='beam size. -1 means no beam search (either greedy or sampling)') |
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parser.add_argument('--ingrs_only', dest='ingrs_only', action='store_true', |
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help='train or evaluate the model only for ingredient prediction') |
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parser.set_defaults(ingrs_only=False) |
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parser.add_argument('--recipe_only', dest='recipe_only', action='store_true', |
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help='train or evaluate the model only for instruction generation') |
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parser.set_defaults(recipe_only=False) |
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parser.add_argument('--log_term', dest='log_term', action='store_true', |
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help='if used, shows training log in stdout instead of saving it to a file.') |
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parser.set_defaults(log_term=False) |
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parser.add_argument('--notensorboard', dest='tensorboard', action='store_false', |
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help='if used, tensorboard logs will not be saved') |
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parser.set_defaults(tensorboard=True) |
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parser.add_argument('--resume', dest='resume', action='store_true', |
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help='resume training from the checkpoint in model_name') |
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parser.set_defaults(resume=False) |
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parser.add_argument('--nodecay_lr', dest='decay_lr', action='store_false', |
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help='disables learning rate decay') |
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parser.set_defaults(decay_lr=True) |
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parser.add_argument('--load_jpeg', dest='use_lmdb', action='store_false', |
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help='if used, images are loaded from jpg files instead of lmdb') |
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parser.set_defaults(use_lmdb=True) |
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parser.add_argument('--get_perplexity', dest='get_perplexity', action='store_true', |
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help='used to get perplexity in evaluation') |
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parser.set_defaults(get_perplexity=False) |
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parser.add_argument('--use_true_ingrs', dest='use_true_ingrs', action='store_true', |
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help='if used, true ingredients will be used as input to obtain the recipe in evaluation') |
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parser.set_defaults(use_true_ingrs=False) |
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args = parser.parse_args() |
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return args |
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