def exp_concat_with_glove():
args = parser.parse_args()
not_specified_args = manage_required_args(
args,
parser,
required_args=[
'embed_dim',
'xform',
'glove_dim'])
exp_name = f'concat_with_glove_{args.glove_dim}' # alt. xformed_
out_base_dir = os.path.join(
os.getcwd(),
f'symlinks/exp/multi_sense_cooccur/{args.xform}_{args.embed_dim}')
exp_const = ExpConstants(exp_name,out_base_dir)
visual_embed_dir = exp_const.out_base_dir
data_const = Constants()
data_const.visual_word_to_idx = os.path.join(
visual_embed_dir,
'word_to_idx.json')
data_const.visual_embeddings_npy = os.path.join(
visual_embed_dir,
'visual_embeddings.npy') # alt. _xformed.npy
glove_const = GloveConstantsFactory.create(dim=str(args.glove_dim))
data_const.glove_idx = glove_const.word_to_idx_json
data_const.glove_h5py = glove_const.embeddings_h5py
concat_with_glove.main(exp_const,data_const)
def exp_gen_and_label_hoi_cand():
args = parser.parse_args()
not_specified_args = manage_required_args(
args,
parser,
required_args=['subset'],
optional_args=['gen_hoi_cand', 'label_hoi_cand'])
if len(not_specified_args) > 0:
return
exp_name = 'hoi_candidates'
exp_const = ExpConstants(exp_name=exp_name)
exp_const.subset = args.subset
data_const = HicoConstants()
data_const.selected_dets_hdf5 = os.path.join(
os.getcwd(),
'data_symlinks/hico_exp/select_confident_boxes_in_hico/' + \
'selected_coco_cls_dets.hdf5')
if args.gen_hoi_cand:
print('Generating HOI candidates from Faster-RCNN dets...')
hoi_candidates.generate(exp_const, data_const)
if args.label_hoi_cand:
print('Labelling HOI candidates from Faster-RCNN dets...')
data_const.hoi_cand_hdf5 = os.path.join(
exp_const.exp_dir, f'hoi_candidates_{exp_const.subset}.hdf5')
label_hoi_candidates.assign(exp_const, data_const)
def exp_vis_pca_tsne():
args = parser.parse_args()
not_specified_args = manage_required_args(
args,
parser,
required_args=[
'embed_dim',
'xform',
'glove_dim'])
exp_name = f'{args.xform}_{args.embed_dim}'
out_base_dir = os.path.join(
os.getcwd(),
f'symlinks/exp/multi_sense_cooccur')
exp_const = ExpConstants(exp_name,out_base_dir)
exp_const.vis_dir = os.path.join(exp_const.exp_dir,'vis/tsne')
exp_const.category_words_only = True
data_const = Constants()
embed_dir = os.path.join(
exp_const.exp_dir,
f'concat_with_glove_{args.glove_dim}')
data_const.word_vecs_h5py = os.path.join(
embed_dir,
'visual_word_vecs.h5py')
data_const.word_to_idx_json = os.path.join(
embed_dir,
'visual_word_vecs_idx.json')
genome_const = VisualGenomeConstants()
data_const.object_freqs_json = genome_const.object_freqs_json
data_const.attribute_freqs_json = genome_const.attribute_freqs_json
pca_tsne.main(exp_const,data_const)
def _exp_top_boxes_per_hoi(out_base_dir, data_const):
args = parser.parse_args()
not_specified_args = manage_required_args(
args,
parser,
required_args=['model_num'],
optional_args=[
'verb_given_appearance',
'verb_given_human_appearance',
'verb_given_object_appearance',
'verb_given_boxes_and_object_label',
'verb_given_human_pose',
'rcnn_det_prob'])
exp_name = 'factors'
if args.rcnn_det_prob:
exp_name += '_rcnn_det_prob'
if args.verb_given_appearance:
exp_name += '_appearance'
if args.verb_given_human_appearance:
exp_name += '_human_appearance'
if args.verb_given_object_appearance:
exp_name += '_object_appearance'
if args.verb_given_boxes_and_object_label:
exp_name += '_boxes_and_object_label'
if args.verb_given_human_pose:
exp_name += '_human_pose'
exp_const = ExpConstants(
exp_name=exp_name,
out_base_dir=out_base_dir)
exp_const.model_dir = os.path.join(exp_const.exp_dir,'models')
exp_const.num_to_vis = 10
data_const.pred_hoi_dets_h5py = os.path.join(
exp_const.exp_dir,
f'pred_hoi_dets_test_{args.model_num}.hdf5')
hoi_cand_dir = os.path.join(
os.getcwd(),
'data_symlinks/hico_exp/hoi_candidates')
data_const.human_pose_feats_hdf5 = os.path.join(
hoi_cand_dir,
'human_pose_feats_test.hdf5')
data_const.num_pose_keypoints = 18
model_const = Constants()
model_const.model_num = args.model_num
model_const.hoi_classifier = HoiClassifierConstants()
model_const.hoi_classifier.verb_given_appearance = args.verb_given_appearance
model_const.hoi_classifier.verb_given_boxes_and_object_label = args.verb_given_boxes_and_object_label
model_const.hoi_classifier.verb_given_human_pose = args.verb_given_human_pose
model_const.hoi_classifier.rcnn_det_prob = args.rcnn_det_prob
model_const.hoi_classifier.model_pth = os.path.join(
exp_const.model_dir,
f'hoi_classifier_{model_const.model_num}')
vis_top_boxes_per_hoi.main(exp_const, data_const, model_const)
def _exp_cache_box_feats(exp_const, data_const):
args = parser.parse_args()
not_specified_args = manage_required_args(args,
parser,
required_args=['subset'])
exp_const.subset = args.subset
data_const.hoi_cand_hdf5 = os.path.join(
exp_const.exp_dir, f'hoi_candidates_{exp_const.subset}.hdf5')
cache_box_features.main(exp_const, data_const)
def _exp_eval(out_base_dir, data_const):
args = parser.parse_args()
not_specified_args = manage_required_args(
args,
parser,
required_args=['model_num'],
optional_args=[
'verb_given_appearance',
'verb_given_human_appearance',
'verb_given_object_appearance',
'verb_given_boxes_and_object_label',
'verb_given_human_pose',
'rcnn_det_prob'])
exp_name = 'factors'
if args.rcnn_det_prob:
exp_name += '_rcnn_det_prob'
if args.verb_given_appearance:
exp_name += '_appearance'
if args.verb_given_human_appearance:
exp_name += '_human_appearance'
if args.verb_given_object_appearance:
exp_name += '_object_appearance'
if args.verb_given_boxes_and_object_label:
exp_name += '_boxes_and_object_label'
if args.verb_given_human_pose:
exp_name += '_human_pose'
exp_const = ExpConstants(
exp_name=exp_name,
out_base_dir=out_base_dir)
exp_const.model_dir = os.path.join(exp_const.exp_dir, 'models')
data_const.balanced_sampling = False
model_const = Constants()
model_const.model_num = args.model_num
model_const.hoi_classifier = HoiClassifierConstants()
model_const.hoi_classifier.verb_given_appearance = args.verb_given_appearance
model_const.hoi_classifier.verb_given_human_appearance = args.verb_given_human_appearance
model_const.hoi_classifier.verb_given_object_appearance = args.verb_given_object_appearance
model_const.hoi_classifier.verb_given_boxes_and_object_label = args.verb_given_boxes_and_object_label
model_const.hoi_classifier.verb_given_human_pose = args.verb_given_human_pose
model_const.hoi_classifier.rcnn_det_prob = args.rcnn_det_prob
model_const.hoi_classifier.model_pth = os.path.join(
exp_const.model_dir,
f'hoi_classifier_{model_const.model_num}')
if isinstance(data_const, FeatureConstantsVcoco):
data_sign = 'vcoco'
else:
data_sign = 'hico'
evaluate.main(exp_const, data_const, model_const, data_sign)
def _exp_assign_pose_to_human_cand(exp_const, data_const):
args = parser.parse_args()
not_specified_args = manage_required_args(args,
parser,
required_args=['subset'])
exp_const.subset = args.subset
data_const.hoi_cand_hdf5 = os.path.join(
exp_const.exp_dir, f'hoi_candidates_{exp_const.subset}.hdf5')
data_const.human_pose_dir = os.path.join(data_const.proc_dir, 'human_pose')
data_const.num_keypoints = 18
assign_pose_to_human_candidates.main(exp_const, data_const)
def exp_extract_embeddings():
args = parser.parse_args()
not_specified_args = manage_required_args(
args,
parser,
required_args=[
'embed_dim',
'xform',
'model_num',
'syn'])
exp_name = f'{args.xform}_{args.embed_dim}'
out_base_dir = os.path.join(
os.getcwd(),
'symlinks/exp/multi_sense_cooccur')
exp_const = ExpConstants(exp_name,out_base_dir)
exp_const.model_dir = os.path.join(exp_const.exp_dir,'models')
exp_const.cooccur_types = [
'syn',
'attr_attr',
'obj_attr',
'obj_hyp',
'context'
]
if args.syn==False:
exp_const.cooccur_types = exp_const.cooccur_types[1:]
data_const = MultiSenseCooccurDatasetConstants()
data_const.cooccur_csv = os.path.join(
os.getcwd(),
'symlinks/exp/multi_sense_cooccur/cooccurrences/merged_cooccur.csv')
model_const = Constants()
model_const.model_num = args.model_num
model_const.net = LogBilinearConstants()
model_const.net.num_words = 93553
model_const.net.embed_dims = args.embed_dim
model_const.net.two_embedding_layers = False
model_const.net.xform_type = args.xform
model_const.net.xform_num_layers = None
model_const.net.use_bias = True
model_const.net.use_fx = False
model_const.net.cooccur_types = copy.deepcopy(exp_const.cooccur_types)
model_const.net_path = os.path.join(
exp_const.model_dir,
f'net_{model_const.model_num}')
extract_embeddings.main(exp_const,data_const,model_const)
extract_embeddings_xformed.main(exp_const,data_const,model_const)
def _exp_train(out_base_dir,
data_const_train,
data_const_val,
data_sign='hico'):
args = parser.parse_args()
not_specified_args = manage_required_args(
args,
parser,
required_args=['imgs_per_batch', 'fp_to_tp_ratio'],
optional_args=[
'verb_given_appearance', 'verb_given_human_appearance',
'verb_given_object_appearance',
'verb_given_boxes_and_object_label', 'verb_given_human_pose',
'rcnn_det_prob'
])
exp_name = 'factors'
if args.rcnn_det_prob:
exp_name += '_rcnn_det_prob'
if args.verb_given_appearance:
exp_name += '_appearance'
if args.verb_given_human_appearance:
exp_name += '_human_appearance'
if args.verb_given_object_appearance:
exp_name += '_object_appearance'
if args.verb_given_boxes_and_object_label:
exp_name += '_boxes_and_object_label'
if args.verb_given_human_pose:
pose = '_human_pose'
exp_name += pose
exp_const = ExpConstants(exp_name=exp_name, out_base_dir=out_base_dir)
exp_const.log_dir = os.path.join(exp_const.exp_dir, 'log')
exp_const.model_dir = os.path.join(exp_const.exp_dir, 'models')
exp_const.num_epochs = 10
exp_const.imgs_per_batch = args.imgs_per_batch
exp_const.lr = 1e-3
model_const = Constants()
model_const.hoi_classifier = HoiClassifierConstants(data_sign)
model_const.hoi_classifier.verb_given_appearance = args.verb_given_appearance
model_const.hoi_classifier.verb_given_human_appearance = args.verb_given_human_appearance
model_const.hoi_classifier.verb_given_object_appearance = args.verb_given_object_appearance
model_const.hoi_classifier.verb_given_boxes_and_object_label = args.verb_given_boxes_and_object_label
model_const.hoi_classifier.verb_given_human_pose = args.verb_given_human_pose
model_const.hoi_classifier.rcnn_det_prob = args.rcnn_det_prob
train.main(exp_const, data_const_train, data_const_val, model_const,
data_sign)
def exp_cache_pose_feats():
args = parser.parse_args()
not_specified_args = manage_required_args(args,
parser,
required_args=['subset'])
exp_name = 'hoi_candidates'
exp_const = ExpConstants(exp_name=exp_name)
exp_const.subset = args.subset
data_const = HicoConstants()
data_const.hoi_cand_hdf5 = os.path.join(
exp_const.exp_dir, f'hoi_candidates_{exp_const.subset}.hdf5')
data_const.human_cands_pose_hdf5 = os.path.join(
exp_const.exp_dir, f'human_candidates_pose_{exp_const.subset}.hdf5')
data_const.num_keypoints = 18
cache_pose_features.main(exp_const, data_const)
def _exp_gen_and_label_hoi_cand(exp_const, data_const, data_sign='hico'):
args = parser.parse_args()
not_specified_args = manage_required_args(
args,
parser,
required_args=['subset'],
optional_args=['gen_hoi_cand', 'label_hoi_cand'])
if len(not_specified_args) > 0:
return
exp_const.subset = args.subset
if args.gen_hoi_cand:
print('Generating HOI candidates from Faster-RCNN dets...')
hoi_candidates.generate(exp_const, data_const, data_sign)
if args.label_hoi_cand:
print('Labelling HOI candidates from Faster-RCNN dets...')
data_const.hoi_cand_hdf5 = os.path.join(
exp_const.exp_dir, f'hoi_candidates_{exp_const.subset}.hdf5')
label_hoi_candidates.assign(exp_const, data_const)
def exp_train():
args = parser.parse_args()
not_specified_args = manage_required_args(
args,
parser,
required_args=['fappend', 'imgs_per_batch', 'fp_to_tp_ratio'],
optional_args=[
'verb_given_appearance', 'verb_given_human_appearance',
'verb_given_object_appearance',
'verb_given_boxes_and_object_label', 'rcnn_det_prob'
])
exp_name = 'factors'
exp_name += '_' + args.fappend
out_base_dir = os.path.join(os.getcwd(),
'data_symlinks/hico_exp/hoi_classifier')
exp_const = ExpConstants(exp_name=exp_name, out_base_dir=out_base_dir)
exp_const.log_dir = os.path.join(exp_const.exp_dir, 'log')
exp_const.model_dir = os.path.join(exp_const.exp_dir, 'models')
exp_const.num_epochs = 10
exp_const.imgs_per_batch = args.imgs_per_batch
exp_const.lr = 1e-3
data_const_train = FeatureConstants(subset='train')
data_const_val = FeatureConstants(subset='val')
model_const = Constants()
model_const.hoi_classifier = HoiClassifierConstants()
model_const.hoi_classifier.verb_given_appearance = args.verb_given_appearance
model_const.hoi_classifier.verb_given_human_appearance = args.verb_given_human_appearance
model_const.hoi_classifier.verb_given_object_appearance = args.verb_given_object_appearance
model_const.hoi_classifier.verb_given_boxes_and_object_label = args.verb_given_boxes_and_object_label
model_const.hoi_classifier.verb_given_human_pose = args.verb_given_human_pose
model_const.hoi_classifier.rcnn_det_prob = args.rcnn_det_prob
train.main(exp_const, data_const_train, data_const_val, model_const)
def exp_eval():
args = parser.parse_args()
not_specified_args = manage_required_args(
args,
parser,
required_args=['fappend', 'model_num'],
optional_args=[
'verb_given_appearance', 'verb_given_human_appearance',
'verb_given_object_appearance',
'verb_given_boxes_and_object_label', 'verb_given_human_pose',
'rcnn_det_prob'
])
exp_name = 'factors'
exp_name += '_' + args.fappend
out_base_dir = os.path.join(os.getcwd(),
'data_symlinks/hico_exp/hoi_classifier')
exp_const = ExpConstants(exp_name=exp_name, out_base_dir=out_base_dir)
exp_const.model_dir = os.path.join(exp_const.exp_dir, 'models')
data_const = FeatureConstants(subset='test')
data_const.balanced_sampling = False
model_const = Constants()
model_const.model_num = args.model_num
model_const.hoi_classifier = HoiClassifierConstants()
model_const.hoi_classifier.verb_given_appearance = args.verb_given_appearance
model_const.hoi_classifier.verb_given_human_appearance = args.verb_given_human_appearance
model_const.hoi_classifier.verb_given_object_appearance = args.verb_given_object_appearance
model_const.hoi_classifier.verb_given_boxes_and_object_label = args.verb_given_boxes_and_object_label
model_const.hoi_classifier.verb_given_human_pose = args.verb_given_human_pose
model_const.hoi_classifier.rcnn_det_prob = args.rcnn_det_prob
model_const.hoi_classifier.model_pth = os.path.join(
exp_const.model_dir, f'hoi_classifier_{model_const.model_num}')
evaluate.main(exp_const, data_const, model_const)
def exp_eval_concat_svm():
args = parser.parse_args()
not_specified_args = manage_required_args(args,
parser,
required_args=[
'batch_size',
'glove_dim',
'embed_linear_feat',
'embed_quadratic_feat',
'distance_linear_feat',
'distance_quadratic_feat',
'visual_only',
'visual_vocab_json',
],
optional_args=[
'exp_name', 'out_base_dir',
'embeddings_h5py',
'word_to_idx_json'
])
if args.exp_name is None:
exp_name = 'trial'
else:
exp_name = args.exp_name
if args.out_base_dir is None:
out_base_dir = os.path.join(os.getcwd(),
'symlinks/exp/semeval_2018_10/concat_svm')
else:
out_base_dir = args.out_base_dir
exp_const = ExpConstants(exp_name=exp_name, out_base_dir=out_base_dir)
exp_const.model_dir = os.path.join(exp_const.exp_dir, 'models')
exp_const.batch_size = 2560
data_const = SemEval201810DatasetConstants()
data_const.subset = 'test'
if args.embeddings_h5py is None:
glove_const = GloveConstantsFactory.create()
data_const.embeddings_h5py = glove_const.embeddings_h5py
data_const.word_to_idx_json = glove_const.word_to_idx_json
data_const.visual_vocab_json = data_const.word_to_idx_json
else:
data_const.embeddings_h5py = args.embeddings_h5py
data_const.word_to_idx_json = args.word_to_idx_json
data_const.visual_vocab_json = args.visual_vocab_json
# data_const.vocab_json = os.path.join(
# os.getcwd(),
# 'symlinks/data/visualgenome/proc/all_word_freqs.json')
# data_const.visual_vocab_json = os.path.join(
# os.getcwd(),
# 'symlinks/exp/combine_glove_visual_reps/concat_glove_visual_avg_reps/visual_words.json')
embed_dim = h5py.File(data_const.embeddings_h5py,
'r')['embeddings'].shape[1]
model_const = Constants()
model_const.concat_svm = ConcatSVMConstants()
model_const.concat_svm.embedding_dim = embed_dim
model_const.concat_svm.glove_dim = args.glove_dim
model_const.concat_svm.layer_units = []
model_const.concat_svm.use_embedding_linear_feats = args.embed_linear_feat
model_const.concat_svm.use_embedding_quadratic_feats = \
args.embed_quadratic_feat
model_const.concat_svm.use_distance_linear_feats = args.distance_linear_feat
model_const.concat_svm.use_distance_quadratic_feats = \
args.distance_quadratic_feat
model_const.concat_svm.visual_only = args.visual_only
eval_concat_svm.main(exp_const, data_const, model_const)
def exp_train():
args = parser.parse_args()
not_specified_args = manage_required_args(args,
parser,
required_args=[
'held_classes', 'embed_type',
'glove_dim', 'vico_dim',
'run'
],
optional_args=[])
exp_name = \
args.embed_type + '_' + \
str(args.glove_dim) + '_' + \
str(args.vico_dim) + '_' + \
'held_classes_' + str(args.held_classes)
out_base_dir = os.path.join(os.getcwd(),
f'symlinks/exp/cifar100/zero_shot_{args.run}')
exp_const = ExpConstants(exp_name, out_base_dir)
exp_const.model_dir = os.path.join(exp_const.exp_dir, 'models')
exp_const.log_dir = os.path.join(exp_const.exp_dir, 'log')
exp_const.vis_dir = os.path.join(exp_const.exp_dir, 'vis')
exp_const.log_step = 200
exp_const.model_save_step = 1000
exp_const.val_step = 1000
exp_const.batch_size = 128
exp_const.num_epochs = 50 #100
exp_const.lr = 0.01
exp_const.momentum = 0.9
exp_const.num_workers = 5
exp_const.optimizer = 'Adam'
exp_const.feedforward = False
exp_const.subset = {'training': 'train', 'test': 'test'}
data_const = Cifar100DatasetConstants()
data_const.num_held_out_classes = args.held_classes
model_const = Constants()
model_const.model_num = None
model_const.net = ResnetConstants()
model_const.net.num_layers = 32
model_const.net.num_classes = 100
model_const.net.pretrained = False
model_const.net_path = os.path.join(exp_const.model_dir,
f'net_{model_const.model_num}')
model_const.embed2class = Embed2ClassConstants()
model_const.embed2class.linear = True
model_const.embed2class_path = os.path.join(
exp_const.model_dir, f'embed2class_{model_const.model_num}')
model_const.embed2class.glove_dim = args.glove_dim
# Dimensions
if args.embed_type == 'glove':
model_const.embed2class.embed_dims = args.glove_dim
model_const.embed2class.embed_h5py = os.path.join(
os.getcwd(),
f'symlinks/data/glove/proc/glove_6B_{args.glove_dim}d.h5py')
model_const.embed2class.embed_word_to_idx_json = os.path.join(
os.getcwd(),
f'symlinks/data/glove/proc/glove_6B_{args.glove_dim}d_word_to_idx.json'
)
elif args.embed_type == 'glove_vico_linear':
model_const.embed2class.embed_dims = args.glove_dim + args.vico_dim
embed_dir = os.path.join(
os.getcwd(),
'symlinks/exp/multi_sense_cooccur/' + \
f'linear_100/concat_with_glove_{args.glove_dim}')
model_const.embed2class.embed_h5py = os.path.join(
embed_dir, 'visual_word_vecs.h5py')
model_const.embed2class.embed_word_to_idx_json = os.path.join(
embed_dir, 'visual_word_vecs_idx.json')
elif args.embed_type == 'vico_linear':
model_const.embed2class.no_glove = True # Zero out the glove component
model_const.embed2class.embed_dims = args.glove_dim + args.vico_dim
embed_dir = os.path.join(
os.getcwd(),
'symlinks/exp/multi_sense_cooccur/' + \
f'linear_100/concat_with_glove_{args.glove_dim}')
model_const.embed2class.embed_h5py = os.path.join(
embed_dir, 'visual_word_vecs.h5py')
model_const.embed2class.embed_word_to_idx_json = os.path.join(
embed_dir, 'visual_word_vecs_idx.json')
elif args.embed_type == 'glove_vico_select':
model_const.embed2class.embed_dims = args.glove_dim + args.vico_dim
embed_dir = os.path.join(
os.getcwd(),
'symlinks/exp/multi_sense_cooccur/' + \
f'select_200/concat_with_glove_{args.glove_dim}')
model_const.embed2class.embed_h5py = os.path.join(
embed_dir, 'visual_word_vecs.h5py')
model_const.embed2class.embed_word_to_idx_json = os.path.join(
embed_dir, 'visual_word_vecs_idx.json')
else:
err_str = f'{args.embed_type} is currently not implemented in the runner'
assert (False), err_str
train.main(exp_const, data_const, model_const)
def exp_train():
args = parser.parse_args()
not_specified_args = manage_required_args(
args,
parser,
required_args=[
'embed_dim',
'xform',
'model_num',
'syn'])
exp_name = f'{args.xform}_{args.embed_dim}'
out_base_dir = os.path.join(
os.getcwd(),
'symlinks/exp/multi_sense_cooccur')
exp_const = ExpConstants(exp_name,out_base_dir)
exp_const.model_dir = os.path.join(exp_const.exp_dir,'models')
exp_const.log_dir = os.path.join(exp_const.exp_dir,'log')
exp_const.vis_dir = os.path.join(exp_const.exp_dir,'vis')
exp_const.log_step = 100
exp_const.model_save_step = 10000
exp_const.batch_size = 1000
exp_const.num_epochs = 10
exp_const.lr = 0.01
exp_const.momentum = 0.9 # used only when optimizer is set to 'SGD'
exp_const.num_workers = 5
# First train with Adam then finetune with Adagrad
if args.model_num==-1:
exp_const.optimizer = 'Adam'
else:
exp_const.optimizer = 'Adagrad'
exp_const.weight_decay = 0
exp_const.cooccur_weights = {
'syn': 1,
'attr_attr': 1,
'obj_attr': 1,
'obj_hyp': 1,
'context': 1,
}
if args.syn==False:
del exp_const.cooccur_weights['syn']
exp_const.use_neg = True
data_const = MultiSenseCooccurDatasetConstants()
data_const.cooccur_csv = os.path.join(
os.getcwd(),
'symlinks/exp/multi_sense_cooccur/cooccurrences/merged_cooccur.csv')
data_const.use_self_count = True
model_const = Constants()
if args.model_num==-1:
model_const.model_num = None
else:
model_const.model_num = args.model_num
model_const.net = LogBilinearConstants()
model_const.net.num_words = 93553
model_const.net.embed_dims = args.embed_dim
model_const.net.two_embedding_layers = False
model_const.net.xform_type = args.xform
model_const.net.xform_num_layers = None
model_const.net.use_bias = True
model_const.net.use_fx = False
model_const.net.cooccur_types = [
'syn',
'attr_attr',
'obj_attr',
'obj_hyp',
'context'
]
if args.syn==False:
model_const.net.cooccur_types = model_const.net.cooccur_types[1:]
model_const.net_path = os.path.join(
exp_const.model_dir,
f'net_{model_const.model_num}')
train.main(exp_const,data_const,model_const)
def exp_train_concat_svm():
args = parser.parse_args()
not_specified_args = manage_required_args(args,
parser,
required_args=[
'lr',
'l2_weight',
'batch_size',
'glove_dim',
'embed_linear_feat',
'embed_quadratic_feat',
'distance_linear_feat',
'distance_quadratic_feat',
'visual_only',
],
optional_args=[
'exp_name', 'out_base_dir',
'embeddings_h5py',
'word_to_idx_json'
])
if args.exp_name is None:
exp_name = 'trial'
else:
exp_name = args.exp_name
if args.out_base_dir is None:
out_base_dir = os.path.join(os.getcwd(),
'symlinks/exp/semeval_2018_10/concat_svm')
else:
out_base_dir = args.out_base_dir
exp_const = ExpConstants(exp_name=exp_name, out_base_dir=out_base_dir)
exp_const.log_dir = os.path.join(exp_const.exp_dir, 'log')
exp_const.model_dir = os.path.join(exp_const.exp_dir, 'models')
exp_const.num_epochs = 20
exp_const.batch_size = args.batch_size
exp_const.lr = args.lr
data_const = SemEval201810DatasetConstants()
if args.embeddings_h5py is None:
glove_const = GloveConstantsFactory.create()
data_const.embeddings_h5py = glove_const.embeddings_h5py
data_const.word_to_idx_json = glove_const.word_to_idx_json
else:
data_const.embeddings_h5py = args.embeddings_h5py
data_const.word_to_idx_json = args.word_to_idx_json
embed_dim = h5py.File(data_const.embeddings_h5py,
'r')['embeddings'].shape[1]
model_const = Constants()
model_const.concat_svm = ConcatSVMConstants()
model_const.concat_svm.l2_weight = args.l2_weight
model_const.concat_svm.embedding_dim = embed_dim
model_const.concat_svm.glove_dim = args.glove_dim
model_const.concat_svm.layer_units = []
model_const.concat_svm.use_embedding_linear_feats = args.embed_linear_feat
model_const.concat_svm.use_embedding_quadratic_feats = \
args.embed_quadratic_feat
model_const.concat_svm.use_distance_linear_feats = args.distance_linear_feat
model_const.concat_svm.use_distance_quadratic_feats = \
args.distance_quadratic_feat
model_const.concat_svm.visual_only = args.visual_only
train_concat_svm.main(exp_const, data_const, model_const)
