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 main(**kwargs):
exp_base_dir = coco_paths['exp_dir']
if kwargs['dataset'] == 'flickr':
exp_base_dir = flickr_paths['exp_dir']
exp_const = ExpConstants(kwargs['exp_name'], exp_base_dir)
exp_const.log_dir = os.path.join(exp_const.exp_dir, 'logs')
exp_const.model_dir = os.path.join(exp_const.exp_dir, 'models')
exp_const.vis_dir = os.path.join(exp_const.exp_dir, 'vis')
exp_const.dataset = kwargs['dataset']
exp_const.optimizer = 'Adam'
exp_const.lr = kwargs['lr']
exp_const.momentum = None
exp_const.num_epochs = 10
exp_const.log_step = 20
# Save models approx. twice every epoch
exp_const.model_save_step = 400000 // (2 * kwargs['train_batch_size']
) # 4000=400000/(2*50)
if exp_const.dataset == 'flickr':
exp_const.model_save_step = 150000 // (2 * kwargs['train_batch_size'])
val_freq_factor = 2
if kwargs['val_frequently'] is True:
val_freq_factor = 1
exp_const.val_step = val_freq_factor * exp_const.model_save_step # set to 1*model_save_step for plotting mi vs perf
exp_const.num_val_samples = None
exp_const.train_batch_size = kwargs['train_batch_size']
exp_const.val_batch_size = 20
exp_const.num_workers = 10
exp_const.seed = 0
exp_const.neg_noun_loss_wt = kwargs['neg_noun_loss_wt']
exp_const.self_sup_loss_wt = kwargs['self_sup_loss_wt']
exp_const.lang_sup_loss_wt = kwargs['lang_sup_loss_wt']
exp_const.contextualize = not kwargs['no_context']
exp_const.random_lang = kwargs['random_lang']
DatasetConstants = CocoDatasetConstants
if exp_const.dataset == 'flickr':
DatasetConstants = FlickrDatasetConstants
data_const = {
'train': DatasetConstants('train'),
'val': DatasetConstants('val'),
}
model_const = Constants()
model_const.model_num = kwargs['model_num']
model_const.object_encoder = ObjectEncoderConstants()
model_const.object_encoder.context_layer.output_attentions = True
model_const.object_encoder.object_feature_dim = 2048
model_const.cap_encoder = CapEncoderConstants()
model_const.cap_encoder.output_attentions = True
model_const.cap_info_nce_layers = kwargs['cap_info_nce_layers']
model_const.object_encoder_path = os.path.join(
exp_const.model_dir, f'object_encoder_{model_const.model_num}')
model_const.self_sup_criterion_path = os.path.join(
exp_const.model_dir, f'self_sup_criterion_{model_const.model_num}')
model_const.lang_sup_criterion_path = os.path.join(
exp_const.model_dir, f'lang_sup_criterion_{model_const.model_num}')
train(exp_const, data_const, model_const)
def main(**kwargs):
exp_const = ExpConstants(kwargs['exp_name'], kwargs['exp_base_dir'])
exp_const.log_dir = os.path.join(exp_const.exp_dir, 'logs')
exp_const.model_dir = os.path.join(exp_const.exp_dir, 'models')
exp_const.vis_dir = os.path.join(exp_const.exp_dir, 'vis')
exp_const.optimizer = 'Adam'
exp_const.lr = 1e-3
exp_const.momentum = None
exp_const.num_epochs = 100
exp_const.log_step = 100
exp_const.model_save_step = 1000
exp_const.val_step = 1000
exp_const.num_val_samples = None
data_const = {'train': Constants(), 'val': Constants()}
model_const = Constants()
model_const.model_num = kwargs['model_num']
model_const.net = Constants()
model_const.net_path = os.path.join(exp_const.model_dir,
f'net_{model_const.model_num}')
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)
