-
Notifications
You must be signed in to change notification settings - Fork 1
/
Copy pathflags.py
92 lines (80 loc) · 7.28 KB
/
flags.py
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49
50
51
52
53
54
55
56
57
58
59
60
61
62
63
64
65
66
67
68
69
70
71
72
73
74
75
76
77
78
79
80
81
82
83
84
85
86
87
88
89
90
91
92
import argparse
DATA_FOLDER = "DATA_ROOT"
parser = argparse.ArgumentParser(formatter_class=argparse.ArgumentDefaultsHelpFormatter)
parser.add_argument('--config', default='configs/args.yml', help='path of the config file (training only)')
parser.add_argument('--dataset', default='mitstates', help='mitstates|zappos')
parser.add_argument('--data_dir', default='mit-states', help='local path to data root dir from ' + DATA_FOLDER)
parser.add_argument('--logpath', default=None, help='Path to dir where to logs are stored (test only)')
parser.add_argument('--splitname', default='compositional-split-natural', help="dataset split")
parser.add_argument('--cv_dir', default='logs/', help='dir to save checkpoints and logs to')
parser.add_argument('--name', default='temp', help='Name of exp used to name models')
parser.add_argument('--load', default=None, help='path to checkpoint to load from')
parser.add_argument('--image_extractor', default = 'resnet18', help = 'Feature extractor model')
parser.add_argument('--norm_family', default = 'imagenet', help = 'Normalization values from dataset')
parser.add_argument('--num_negs', type=int, default=1, help='Number of negatives to sample per positive (triplet loss)')
parser.add_argument('--pair_dropout', type=float, default=0.0, help='Each epoch drop this fraction of train pairs')
parser.add_argument('--test_set', default='val', help='val|test mode')
parser.add_argument('--clean_only', action='store_true', default=False, help='use only clean subset of data (mitstates)')
parser.add_argument('--subset', action='store_true', default=False, help='test on a 1000 image subset (debug purpose)')
parser.add_argument('--open_world', action='store_true', default=False, help='perform open world experiment')
parser.add_argument('--test_batch_size', type=int, default=32, help="Batch size at test/eval time")
parser.add_argument('--cpu_eval', action='store_true', help='Perform test on cpu')
# Evaluation
parser.add_argument('--closed_eval', action='store_true', help='Closed world evaluation (useful only if a method has been trained for OW-CZSL)')
parser.add_argument('--fast_eval', action='store_true', help='Fast version of evaluator (for the open world)')
# Model parameters
parser.add_argument('--model', default='graphfull', help='visprodNN|redwine|labelembed+|attributeop|tmn|compcos')
parser.add_argument('--emb_dim', type=int, default=300, help='dimension of share embedding space')
parser.add_argument('--nlayers', type=int, default=3, help='Layers in the image embedder')
parser.add_argument('--nmods', type=int, default=24, help='number of mods per layer for TMN')
parser.add_argument('--embed_rank', type=int, default=64, help='intermediate dimension in the gating model for TMN')
parser.add_argument('--bias', type=float, default=1e3, help='Bias value for unseen concepts')
parser.add_argument('--update_features', action = 'store_true', default=False, help='If specified, train feature extractor')
parser.add_argument('--freeze_features', action = 'store_true', default=False, help='If specified, put extractor in eval mode')
parser.add_argument('--emb_init', default=None, help='w2v|ft|gl|glove|word2vec|fasttext, name of embeddings to use for initializing the primitives')
parser.add_argument('--clf_init', action='store_true', default=False, help='initialize inputs with SVM weights')
parser.add_argument('--static_inp', action='store_true', default=False, help='do not optimize primitives representations')
parser.add_argument('--composition', default='mlp_add', help='add|mul|mlp|mlp_add, how to compose primitives')
parser.add_argument('--relu', action='store_true', default=False, help='Use relu in image embedder')
parser.add_argument('--dropout', action='store_true', default=False, help='Use dropout in image embedder')
parser.add_argument('--norm', action='store_true', default=False, help='Use normalization in image embedder')
parser.add_argument('--train_only', action='store_true', default=False, help='Optimize only for train pairs')
# Forward
parser.add_argument('--eval_type', default='dist', help='dist|prod|direct, function for computing the predictions')
# Primitive-based loss
parser.add_argument('--lambda_aux', type=float, default=0.0, help='weight of auxiliar losses on the primitives')
# AoP
parser.add_argument('--lambda_inv', type=float, default=0.0, help='weight of inverse consistency loss')
parser.add_argument('--lambda_comm', type=float, default=0.0, help='weight of commutative loss')
parser.add_argument('--lambda_ant', type=float, default=0.0, help='weight of antonyms loss')
# SymNet
parser.add_argument("--lambda_trip", type=float, default=0, help='weight of triplet loss')
parser.add_argument("--lambda_sym", type=float, default=0, help='weight of symmetry loss')
parser.add_argument("--lambda_axiom", type=float, default=0, help='weight of axiom loss')
parser.add_argument("--lambda_cls_attr", type=float, default=0, help='weight of classification loss on attributes')
parser.add_argument("--lambda_cls_obj", type=float, default=0, help='weight of classification loss on objects')
# CompCos (for the margin, see below)
parser.add_argument('--cosine_scale', type=float, default=20,help="Scale for cosine similarity")
parser.add_argument('--epoch_max_margin', type=float, default=15,help="Epoch of max margin")
parser.add_argument('--update_feasibility_every', type=float, default=1,help="Frequency of feasibility scores update in epochs")
parser.add_argument('--hard_masking', action='store_true', default=False, help='hard masking during test time')
parser.add_argument('--threshold', default=None,help="Apply a specific threshold at test time for the hard masking")
parser.add_argument('--threshold_trials', type=int, default=50,help="how many threshold values to try")
# Graph methods
parser.add_argument('--graph_init', default=None, help='filename, file from which initializing the nodes and adjacency matrix of the graph')
parser.add_argument('--gcn_type', default='gcn', help='GCN Version')
parser.add_argument("--gr_emb", default='d4096,d',help="graph layers config")
parser.add_argument("--cosine_classifier", action='store_true', default=False, help="use a cosine classifier (Co-CGE)")
parser.add_argument("--feasibility_adjacency", action='store_true', default=False, help="weight the graph connections by the feasibility scores (Co-CGE)")
# Hyperparameters
parser.add_argument('--topk', type=int, default=1,help="Compute topk accuracy")
parser.add_argument('--margin', type=float, default=2,help="Margin for triplet loss or feasibility scores in CompCos")
parser.add_argument('--workers', type=int, default=8,help="Number of workers")
parser.add_argument('--batch_size', type=int, default=512,help="Training batch size")
parser.add_argument('--lr', type=float, default=5e-5,help="Learning rate")
parser.add_argument('--lrg', type=float, default=1e-3,help="Learning rate feature extractor")
parser.add_argument('--wd', type=float, default=5e-5,help="Weight decay")
parser.add_argument('--save_every', type=int, default=10000,help="Frequency of snapshots in epochs")
parser.add_argument('--eval_val_every', type=int, default=1,help="Frequency of eval in epochs")
parser.add_argument('--max_epochs', type=int, default=800,help="Max number of epochs")
parser.add_argument("--fc_emb", default='768,1024,1200',help="Image embedder layer config")