for vci in val_chosen_ids:
vci = list(set(set(vci) & set(name_to_id.values())))
for cid in chosen_ids:
if cid == 0:
continue
if ((len(target_images[id_to_name[cid]]) < 1) or
(len(target_images[id_to_name[cid]][0])) < 1):
print('Missing target images for {}!'.format(id_to_name[cid]))
sys.exit()
#CREATE TRAIN/TEST splits
train_set = GetDataSet.get_fasterRCNN_AVD(data_path,
train_list,
#test_list,
max_difficulty=max_difficulty,
chosen_ids=chosen_ids,
by_box=False,
fraction_of_no_box=0.1,
bn_normalize=use_torch_vgg,
to_tensor=False)
#create train/test loaders, with CUSTOM COLLATE function
trainloader = torch.utils.data.DataLoader(train_set,
batch_size=batch_size,
shuffle=True,
collate_fn=AVD.collate)
chosen_ids = list(set(set(chosen_ids) & set(name_to_id.values())))
for vci in val_chosen_ids:
vci = list(set(set(vci) & set(name_to_id.values())))
for cid in chosen_ids:
if cid == 0:
continue
if ((len(target_images[id_to_name[cid]]) < 1)
or (len(target_images[id_to_name[cid]][0])) < 1):
print('Missing target images for {}!'.format(id_to_name[cid]))
sys.exit()
#CREATE TRAIN/TEST splits
train_set = GetDataSet.get_fasterRCNN_AVD(
data_path,
train_list,
#test_list,
max_difficulty=max_difficulty,
chosen_ids=chosen_ids,
by_box=False,
fraction_of_no_box=0.02)
#create train/test loaders, with CUSTOM COLLATE function
trainloader = torch.utils.data.DataLoader(train_set,
batch_size=1,
shuffle=True,
collate_fn=AVD.collate)
print chosen_ids
#load net definition and init parameters
net = TDID()
if load_trained_model:
#target_path = '/net/bvisionserver3/playpen/ammirato/Data/instance_detection_targets/sygen_many_bb_similar_targets/'
target_path = '/net/bvisionserver3/playpen/ammirato/Data/instance_detection_targets/AVD_BB_exact_few/'
output_dir = '/net/bvisionserver3/playpen/ammirato/Data/Detections/FasterRCNN_AVD/'
scene_list = [
'Home_003_1',
#'Home_003_2',
#'test',
#'Office_001_1'
]
chosen_ids = [1] #range(28)
#CREATE TRAIN/TEST splits
dataset = GetDataSet.get_fasterRCNN_AVD(data_path,
scene_list,
preload=False,
chosen_ids=chosen_ids,
by_box=False,
fraction_of_no_box=0)
#create train/test loaders, with CUSTOM COLLATE function
dataloader = torch.utils.data.DataLoader(dataset,
batch_size=1,
shuffle=True,
collate_fn=AVD.collate)
id_to_name = GetDataSet.get_class_id_to_name_dict(data_path)
name_to_id = {}
for cid in id_to_name.keys():
name_to_id[id_to_name[cid]] = cid
target_images = get_target_images(target_path,
'Home_014_1',
'Home_014_2',
'Office_001_1'
#'Home_101_1',
#'Home_102_1',
#'test',
]
chosen_ids = [5,10,12,14,21,28]#range(28)
#CREATE TRAIN/TEST splits
dataset = GetDataSet.get_fasterRCNN_AVD(data_path,
scene_list,
preload=False,
chosen_ids=chosen_ids,
by_box=False,
fraction_of_no_box=1,
bn_normalize=use_torch_vgg)
#CREATE TRAIN/TEST splits
# dataset = GetDataSet.get_fasterRCNN_GMU(data_path,
# scene_list,
# preload=False,
# chosen_ids=[6],#chosen_ids,
# by_box=False,
# fraction_of_no_box=0,
# bn_normalize=use_torch_vgg)
#