__C.DEVICE = 'cuda' # Number of GPUs to use (applies to both training and testing) __C.NUM_GPUS = 1 # Pixel mean values (BGR order) as a list __C.PIXEL_MEANS = np.array([[[102.9801, 115.9465, 122.7717]]]) # Pixel std values (BGR order) as a list __C.PIXEL_STDS = np.array([[[1.0, 1.0, 1.0]]]) # Clean up the generated files during model testing __C.CLEAN_UP = True # Directory for saving checkpoints and loggers __C.CKPT = 'ckpts/rcnn/mscoco/e2e_faster_rcnn_R-50-FPN_1x' # Display the log per iteration __C.DISPLAY_ITER = 20 # Root directory of project __C.ROOT_DIR = osp.abspath(osp.join(osp.dirname(__file__), '..', '..')) # Data directory __C.DATA_DIR = osp.abspath(osp.join(__C.ROOT_DIR, 'data')) # A very small number that's used many times __C.EPS = 1e-14 # Convert image to BGR format (for Caffe2 models), in range 0-255 __C.TO_BGR255 = True
# ---------------------------------------------------------------------------- # # Device for training or testing # E.g., 'cuda' for using GPU, 'cpu' for using CPU __C.DEVICE = 'cuda' # Number of GPUs to use (applies to both training and testing) __C.NUM_GPUS = 1 # Pixel mean values (BGR order) as a list __C.PIXEL_MEANS = np.array([[[102.9801, 115.9465, 122.7717]]]) # Pixel std values (BGR order) as a list __C.PIXEL_STDS = np.array([[[1.0, 1.0, 1.0]]]) # Directory for saving checkpoints and loggers __C.CKPT = 'ckpts/mscoco_humanparts/e2e_hier_rcnn_R-50-FPN_1x/' # Display the log per iteration __C.DISPLAY_ITER = 20 # Root directory of project __C.ROOT_DIR = osp.abspath(osp.join(osp.dirname(__file__), '..', '..')) # Data directory __C.DATA_DIR = osp.abspath(osp.join(__C.ROOT_DIR, 'data')) # A very small number that's used many times __C.EPS = 1e-14 # Convert image to BGR format (for Caffe2 models), in range 0-255 __C.TO_BGR255 = True
