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) #