def exp_train():
exp_name = 'EXP_NAME'
out_base_dir = os.path.join(os.getcwd(), 'symlinks/exp/EXP_GROUP')
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 = 10
exp_const.model_save_step = 1000
exp_const.val_step = 1000
exp_const.num_val_samples = 1000
exp_const.batch_size = 32
exp_const.num_epochs = 1000
exp_const.lr = 0.01
exp_const.momentum = 0.9
exp_const.num_workers = 5
exp_const.optimizer = 'SGD'
exp_const.subset = {'training': 'train', 'validation': 'val'}
data_const = DATASET_CONSTANTS()
model_const = Constants()
model_const.model_num = None
model_const.net = NET_CONSTANTS()
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_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_cache_box_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')
cache_box_features.main(exp_const, data_const)
def exp_assign_pose_to_human_cand():
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_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_train():
args = parser.parse_args()
# create experiments directory and required folders
out_base_dir = os.path.join(os.getcwd(), f'exp/{args.dataset_type}')
exp_const = ExpConstants(args.run_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')
use_cuda = torch.cuda.is_available()
exp_const.device = "cuda:0" if use_cuda else "cpu"
# tranining params
exp_const.optimizer = args.optimizer
exp_const.num_epochs = args.num_epochs
exp_const.batch_size = args.batch_size
exp_const.lr = args.lr
exp_const.momentum = args.momentum
exp_const.num_workers = args.num_workers
# logging, saving
exp_const.log_step = args.log_step
exp_const.model_save_epoch = args.model_save_epoch
exp_const.val_epoch = args.val_epoch
exp_const.subset = {'training': 'train', 'test': 'test'}
# dataset
data_const = DatasetConstants(root=args.dataroot,
download=args.download_dataset,
train=True)
data_const.dataset_type = args.dataset_type
# model (resnet and attribute embeddings)
model_const = Constants()
model_const.model_num = None
model_const.sim_loss = args.sim_loss
model_const.ce_loss_warmup = args.ce_loss_warmup
model_const.net = ResnetConstants()
if args.dataset_type == 'Cifar100':
model_const.net.num_layers = "cifar100" # a custom resnet for cifar100, to adjust the dimensions of the feature maps
model_const.net.num_classes = 100
else:
model_const.net.num_layers = args.num_layers
if args.dataset_type == "Imagenet":
model_const.net.num_classes = 1000
elif args.dataset_type == "VOC":
model_const.net.num_classes = 20
elif args.dataset_type == "STL10":
model_const.net.num_layers = 'cifar100' # TODO: deeper resnets does not work on STL10.
model_const.net.num_classes = 10
model_const.net.pretrained = False
model_const.net_path = os.path.join(exp_const.model_dir,
f'net_{model_const.model_num}')
model_const.attr_embed = AttributeEmbeddingsConstants()
model_const.attr_embed_path = os.path.join(
exp_const.model_dir, f'attr_embed_{model_const.model_num}')
model_const.attr_embed.glove_dim = 300
model_const.attr_embed.num_classes = model_const.net.num_classes
# attribute embedding dimensions
if args.embed_type == 'vico_linear':
model_const.attr_embed.no_glove = True # Zero out the glove component
model_const.attr_embed.embed_dims = 300 + args.vico_dim
embed_dir = os.path.join(
os.getcwd(),
'data/pretrained-embeddings/' + \
f'glove_300_vico_linear_100/')
model_const.attr_embed.embed_h5py = os.path.join(
embed_dir, 'visual_word_vecs.h5py')
model_const.attr_embed.embed_word_to_idx_json = os.path.join(
embed_dir, 'visual_word_vecs_idx.json')
elif args.embed_type == 'vico_select':
model_const.attr_embed.no_glove = True # Zero out the glove component
model_const.attr_embed.hypernym = args.hypernym
model_const.attr_embed.embed_dims = 300 + args.vico_dim
embed_dir = os.path.join(
os.getcwd(),
'data/pretrained-embeddings/' + \
f'glove_300_vico_select_200/')
model_const.attr_embed.embed_h5py = os.path.join(
embed_dir, 'visual_word_vecs.h5py')
model_const.attr_embed.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
# pass all constants to training method
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=[
'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)
