]]
#pick which objects to include
#will be further refined by the name_to_id_map loaded later
#excluded_cids = [53,76,78,79,82,86,16, 1,2,18,21,25]
#excluded_cids = [53,76,78,79,82,86,16,33,32, 18,50,79,94,96] #10, 5,11,12,14]
excluded_cids = []
chosen_ids = [x for x in range(0,32) if x not in excluded_cids]
#val_chosen_ids = [[4,5,17,19,23],[18,50,79,94,96]] #, [5,10,17]]#chosen_ids #range(0,28)#for validation testing
#val_chosen_ids = [[18,50,79,94,96,5,10,12,14,21],[5,10,12,14,21]] #, [5,10,17]]#chosen_ids #range(0,28)#for validation testing
val_chosen_ids = [[1,5,9,13,17,21,25,28,29],[28,29]] #, [5,10,17]]#chosen_ids #range(0,28)#for validation testing
max_difficulty = 4
#get a map from instance name to id, and back
id_to_name = GetDataSet.get_class_id_to_name_dict(data_path, file_name=id_map_fname)
name_to_id = {}
for cid in id_to_name.keys():
name_to_id[id_to_name[cid]] = cid
##prepare target images (gather paths to the images)
#
target_images ={}
#means to subtract from each channel of target image
means = np.array([[[102.9801, 115.9465, 122.7717]]])
#path that holds dirs of all targets
#i.e. target_path/target_0/* has one type of target image for each object
# target_path/target_1/* has another type of target image
#type of target image can mean different things,
'Gen_004_2',
]
#pick which objects to include
#will be further refined by the name_to_id_map loaded later
#excluded_cids = [53,76,78,79,82,86,16, 1,2,18,21,25]
excluded_cids = [53, 76, 78, 79, 82, 86, 16, 33, 32, 10, 5, 11, 12, 14]
chosen_ids = [x for x in range(0, 111) if x not in excluded_cids]
val_chosen_ids = [[4, 5, 17, 19], [
5, 11, 12, 14
]] #, [5,10,17]]#chosen_ids #range(0,28)#for validation testing
max_difficulty = 4
#get a map from instance name to id, and back
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
##prepare target images (gather paths to the images)
#
target_images = {}
#means to subtract from each channel of target image
means = np.array([[[102.9801, 115.9465, 122.7717]]])
#path that holds dirs of all targets
#i.e. target_path/target_0/* has one type of target image for each object
# target_path/target_1/* has another type of target image
#type of target image can mean different things,
#probably different type is different view
#pick which objects to include
#will be further refined by the name_to_id_map loaded later
#excluded_cids = [53,76,78,79,82,86,16, 1,2,18,21,25]
#excluded_cids = [53,76,78,79,82,86,16,33,32, 1,2,18,21,25]
excluded_cids = []
chosen_ids = [x for x in range(0, 32) if x not in excluded_cids]
val_chosen_ids = [
chosen_ids
] # [[1,2,18,21,25]] #, [5,10,17]]#chosen_ids #range(0,28)#for validation testing
max_difficulty = 4
map_fname = 'all_instance_id_map.txt'
#get a map from instance name to id, and back
id_to_name = GetDataSet.get_class_id_to_name_dict(data_path,
file_name=map_fname)
name_to_id = {}
for cid in id_to_name.keys():
name_to_id[id_to_name[cid]] = cid
##prepare target images (gather paths to the images)
#
target_images = {}
#means to subtract from each channel of target image
means = np.array([[[102.9801, 115.9465, 122.7717]]])
#path that holds dirs of all targets
#i.e. target_path/target_0/* has one type of target image for each object
# target_path/target_1/* has another type of target image
#type of target image can mean different things,
#probably different type is different view
#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)
#