__C.MODEL.MSCALE_OLDARCH = False
__C.MODEL.MSCALE_INIT = 0.5
__C.MODEL.ATTNSCALE_BN_HEAD = False
__C.MODEL.GRAD_CKPT = False
WEIGHTS_PATH = os.path.join(__C.ASSETS_PATH, 'seg_weights')
__C.MODEL.WRN38_CHECKPOINT = \
os.path.join(WEIGHTS_PATH, 'wider_resnet38.pth.tar')
__C.MODEL.WRN41_CHECKPOINT = \
os.path.join(WEIGHTS_PATH, 'wider_resnet41_cornflower_sunfish.pth')
__C.MODEL.X71_CHECKPOINT = \
os.path.join(WEIGHTS_PATH, 'aligned_xception71.pth')
__C.MODEL.HRNET_CHECKPOINT = \
os.path.join(WEIGHTS_PATH, 'hrnetv2_w48_imagenet_pretrained.pth')
__C.LOSS = AttrDict()
# Weight for OCR aux loss
__C.LOSS.OCR_ALPHA = 0.4
# Use RMI for the OCR aux loss
__C.LOSS.OCR_AUX_RMI = False
# Supervise the multi-scale predictions directly
__C.LOSS.SUPERVISED_MSCALE_WT = 0
__C.MODEL.OCR = AttrDict()
__C.MODEL.OCR.MID_CHANNELS = 512
__C.MODEL.OCR.KEY_CHANNELS = 256
__C.MODEL.OCR_EXTRA = AttrDict()
__C.MODEL.OCR_EXTRA.FINAL_CONV_KERNEL = 1
__C.MODEL.OCR_EXTRA.STAGE1 = AttrDict()
__C.MODEL.OCR_EXTRA.STAGE1.NUM_MODULES = 1
__C.MODEL.OCR_EXTRA.STAGE1.NUM_RANCHES = 1
