Python config.cfg.DATA_DIR Examples

The following are 11 code examples of config.cfg.DATA_DIR(). You can vote up the ones you like or vote down the ones you don't like, and go to the original project or source file by following the links above each example. You may also want to check out all available functions/classes of the module config.cfg , or try the search function .
Example #1
Source File: ult.py    From iCAN with MIT License 6 votes vote down vote up 
def Get_Next_Instance_HO_Neg_HICO(trainval_GT, Trainval_Neg, iter, Pos_augment, Neg_select, Data_length):

    GT       = trainval_GT[iter%Data_length]
    image_id = GT[0]
    im_file = cfg.DATA_DIR + '/' + 'hico_20160224_det/images/train2015/HICO_train2015_' + (str(image_id)).zfill(8) + '.jpg'
    im       = cv2.imread(im_file)
    im_orig  = im.astype(np.float32, copy=True)
    im_orig -= cfg.PIXEL_MEANS
    im_shape = im_orig.shape
    im_orig  = im_orig.reshape(1, im_shape[0], im_shape[1], 3)

    Pattern, Human_augmented, Object_augmented, action_HO, num_pos = Augmented_HO_Neg_HICO(GT, Trainval_Neg, im_shape, Pos_augment, Neg_select)
    
    blobs = {}
    blobs['image']       = im_orig
    blobs['H_boxes']     = Human_augmented
    blobs['O_boxes']     = Object_augmented
    blobs['gt_class_HO'] = action_HO
    blobs['sp']          = Pattern
    blobs['H_num']       = num_pos

    return blobs
Example #2
Source File: train.py    From CapsNet-tensorflow with MIT License 6 votes vote down vote up 
def parse_arg():
    """
    parse input arguments
    """
    parser = argparse.ArgumentParser(description="Train CapsNet")

    parser.add_argument('--data_dir', dest='data_dir',
                        type=str, default=cfg.DATA_DIR,
                        help='Directory for storing input data')
    parser.add_argument('--ckpt', dest='ckpt',
                        type=str, default=None,
                        help='path to the directory of check point')
    parser.add_argument('--max_iters', dest='max_iters', type=int,
                        default=10000, help='max of training iterations')
    parser.add_argument('--batch_size', dest='batch_size', type=int,
                        default=100, help='training batch size')

    # if len(sys.argv) == 1:
    #     parser.print_help()
    #     sys.exit(1)

    args = parser.parse_args()
    return args
Example #3
Source File: pre_glove.py    From densecap-tensorflow with MIT License 5 votes vote down vote up 
def process_glove(vocab_list, save_path, size=4e5, random_init=True):
    """
    :param vocab_list: [vocab]
    :return:
    """
    if not gfile.Exists(save_path + ".npz"):
        glove_path = os.path.join(cfg.DATA_DIR, "glove.6B.{}d.txt".format(cfg.GLOVE_DIM))
        if random_init:
            glove = np.random.randn(len(vocab_list), cfg.GLOVE_DIM)
        else:
            glove = np.zeros((len(vocab_list), cfg.GLOVE_DIM))
        found = 0
        with open(glove_path, 'r') as fh:
            for line in tqdm(fh, total=size):
                array = line.lstrip().rstrip().split(" ")
                word = array[0]
                vector = list(map(float, array[1:]))
                if word in vocab_list:
                    idx = vocab_list.index(word)
                    glove[idx, :] = vector
                    found += 1
                if word.capitalize() in vocab_list:
                    idx = vocab_list.index(word.capitalize())
                    glove[idx, :] = vector
                    found += 1
                if word.upper() in vocab_list:
                    idx = vocab_list.index(word.upper())
                    glove[idx, :] = vector
                    found += 1

        print("{}/{} of word vocab have corresponding vectors in {}".format(found, len(vocab_list), glove_path))
        np.savez_compressed(save_path, glove=glove)
        print("saved trimmed glove matrix at: {}".format(save_path))
Example #4
Source File: ult.py    From iCAN with MIT License 5 votes vote down vote up 
def Get_Next_Instance_HO_Neg(trainval_GT, Trainval_Neg, iter, Pos_augment, Neg_select, Data_length):

    GT       = trainval_GT[iter%Data_length]
    image_id = GT[0]
    im_file  = cfg.DATA_DIR + '/' + 'v-coco/coco/images/train2014/COCO_train2014_' + (str(image_id)).zfill(12) + '.jpg'
    im       = cv2.imread(im_file)
    im_orig  = im.astype(np.float32, copy=True)
    im_orig -= cfg.PIXEL_MEANS
    im_shape = im_orig.shape
    im_orig  = im_orig.reshape(1, im_shape[0], im_shape[1], 3)


    Pattern, Human_augmented, Human_augmented_solo, Object_augmented, action_HO, action_H, mask_HO, mask_H = Augmented_HO_Neg(GT, Trainval_Neg, im_shape, Pos_augment, Neg_select)
    
    blobs = {}
    blobs['image']       = im_orig
    blobs['H_boxes_solo']= Human_augmented_solo
    blobs['H_boxes']     = Human_augmented
    blobs['O_boxes']     = Object_augmented
    blobs['gt_class_HO'] = action_HO
    blobs['gt_class_H']  = action_H
    blobs['Mask_HO']     = mask_HO
    blobs['Mask_H']      = mask_H
    blobs['sp']          = Pattern
    blobs['H_num']       = len(action_H)

    return blobs
Example #5
Source File: ult.py    From iCAN with MIT License 5 votes vote down vote up 
def Get_Next_Instance_HO_spNeg(trainval_GT, Trainval_Neg, iter, Pos_augment, Neg_select, Data_length):

    GT       = trainval_GT[iter%Data_length]
    image_id = GT[0]
    im_file  = cfg.DATA_DIR + '/' + 'v-coco/coco/images/train2014/COCO_train2014_' + (str(image_id)).zfill(12) + '.jpg'
    im       = cv2.imread(im_file)
    im_orig  = im.astype(np.float32, copy=True)
    im_orig -= cfg.PIXEL_MEANS
    im_shape = im_orig.shape
    im_orig  = im_orig.reshape(1, im_shape[0], im_shape[1], 3)


    Pattern, Human_augmented_sp, Human_augmented, Object_augmented, action_sp, action_HO, action_H, mask_sp, mask_HO, mask_H = Augmented_HO_spNeg(GT, Trainval_Neg, im_shape, Pos_augment, Neg_select)
    
    blobs = {}
    blobs['image']       = im_orig
    blobs['H_boxes']     = Human_augmented
    blobs['Hsp_boxes']   = Human_augmented_sp
    blobs['O_boxes']     = Object_augmented
    blobs['gt_class_sp'] = action_sp
    blobs['gt_class_HO'] = action_HO
    blobs['gt_class_H']  = action_H
    blobs['Mask_sp']     = mask_sp
    blobs['Mask_HO']     = mask_HO
    blobs['Mask_H']      = mask_H
    blobs['sp']          = Pattern
    blobs['H_num']       = len(action_H)

    return blobs
Example #6
Source File: base_utils.py    From pvnet-rendering with Apache License 2.0 5 votes vote down vote up 
def align_model(self):
        blender_model = self.load_ply_model(self.blender_model_path)
        orig_model = self.load_orig_model()
        blender_model = np.dot(blender_model, self.rotation_transform.T)
        blender_model += (np.mean(orig_model, axis=0) - np.mean(blender_model, axis=0))
        np.savetxt(os.path.join(cfg.DATA_DIR, 'blender_model.txt'), blender_model)
        np.savetxt(os.path.join(cfg.DATA_DIR, 'orig_model.txt'), orig_model)
Example #7
Source File: render_utils.py    From pvnet-rendering with Apache License 2.0 5 votes vote down vote up 
def __init__(self, class_type):
        self.class_type = class_type
        self.mask_path = os.path.join(cfg.LINEMOD,'{}/mask/*.png'.format(class_type))
        self.dir_path = os.path.join(cfg.LINEMOD_ORIG,'{}/data'.format(class_type))

        dataset_pose_dir_path = os.path.join(cfg.DATA_DIR, 'dataset_poses')
        os.system('mkdir -p {}'.format(dataset_pose_dir_path))
        self.dataset_poses_path = os.path.join(dataset_pose_dir_path, '{}_poses.npy'.format(class_type))
        blender_pose_dir_path = os.path.join(cfg.DATA_DIR, 'blender_poses')
        os.system('mkdir -p {}'.format(blender_pose_dir_path))
        self.blender_poses_path = os.path.join(blender_pose_dir_path, '{}_poses.npy'.format(class_type))
        os.system('mkdir -p {}'.format(blender_pose_dir_path))

        self.pose_transformer = PoseTransformer(class_type)
Example #8
Source File: render_utils.py    From pvnet-rendering with Apache License 2.0 5 votes vote down vote up 
def __init__(self, class_type):
        super(YCBDataStatistics, self).__init__(class_type)
        self.dir_path = os.path.join(cfg.LINEMOD_ORIG, '{}/data'.format(class_type))
        self.class_types = np.loadtxt(os.path.join(cfg.YCB, 'image_sets/classes.txt'), dtype=np.str)
        self.class_types = np.insert(self.class_types, 0, 'background')
        self.train_set = np.loadtxt(os.path.join(cfg.YCB, 'image_sets/train.txt'), dtype=np.str)
        self.meta_pattern = os.path.join(cfg.YCB, 'data/{}-meta.mat')
        self.dataset_poses_pattern = os.path.join(cfg.DATA_DIR, 'dataset_poses/{}_poses.npy')
Example #9
Source File: render_utils.py    From pvnet-rendering with Apache License 2.0 5 votes vote down vote up 
def __init__(self, class_type):
        self.class_type = class_type
        self.bg_imgs_path = os.path.join(cfg.DATA_DIR, 'bg_imgs.npy')
        self.poses_path = os.path.join(cfg.DATA_DIR, 'blender_poses', '{}_poses.npy').format(class_type)
        self.output_dir_path = os.path.join(cfg.LINEMOD,'renders/{}').format(class_type)
        self.blender_path = cfg.BLENDER_PATH
        self.blank_blend = os.path.join(cfg.DATA_DIR, 'blank.blend')
        self.py_path = os.path.join(cfg.BLENDER_DIR, 'render_backend.py')
        self.obj_path = os.path.join(cfg.LINEMOD,'{}/{}.ply').format(class_type, class_type)
        self.plane_height_path = os.path.join(cfg.DATA_DIR, 'plane_height.pkl')
Example #10
Source File: render_utils.py    From pvnet-rendering with Apache License 2.0 5 votes vote down vote up 
def __init__(self, class_type):
        super(YCBRenderer, self).__init__(class_type)
        self.output_dir_path = os.path.join(cfg.YCB, 'renders/{}').format(class_type)
        self.blank_blend = os.path.join(cfg.DATA_DIR, 'blank.blend')
        self.obj_path = os.path.join(cfg.YCB, 'models', class_type, 'textured.obj')
        self.class_types = np.loadtxt(os.path.join(cfg.YCB, 'image_sets/classes.txt'), dtype=np.str)
        self.class_types = np.insert(self.class_types, 0, 'background')
Example #11
Source File: eval.py    From CapsNet-tensorflow with MIT License 5 votes vote down vote up 
def parse_arg():
    """
    parse input arguments
    """
    parser = argparse.ArgumentParser(description="Train CapsNet")

    parser.add_argument('--data_dir', dest='data_dir',
                        type=str, default=cfg.DATA_DIR,
                        help='Directory for storing input data')
    parser.add_argument('--ckpt', dest='ckpt',
                        type=str, default=cfg.TRAIN_DIR,
                        help='path to the directory of check point')
    parser.add_argument('--mode', dest='mode',
                        type=str, default=None,
                        help='evaluation mode: reconstruct, cap_tweak, adversarial')
    parser.add_argument('--batch_size', dest='batch_size', type=int,
                        default=30, help='batch size for reconstruct evaluation')
    parser.add_argument('--max_iters', dest='max_iters', type=int,
                        default=50, help='batch size for reconstruct evaluation')
    parser.add_argument('--tweak_target', dest='tweak_target', type=int,
                        default=None, help='target number for capsule tweaking experiment')
    parser.add_argument('--fig_dir', dest='fig_dir', type=str,
                        default='../figs', help='directory to save figures')
    parser.add_argument('--lr', dest='lr', type=float,
                        default=1, help='learning rate of adversarial test')

    args = parser.parse_args()

    if len(sys.argv) == 1 or \
                    args.mode not in \
                    ('reconstruct', 'cap_tweak', 'adversarial'):
        parser.print_help()
        sys.exit(1)
    return args