from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
from __future__ import unicode_literals
import os
import six
import yaml
import copy
import numpy as np
from lib.utils.collections import AttrDict
from lib.utils.misc import get_run_name
from ast import literal_eval
__C = AttrDict()
# Consumers can get config by:
cfg = __C
# Root directory of project
__C.ROOT_DIR = os.path.dirname(
os.path.dirname(os.path.dirname(os.path.abspath(__file__))))
# ---------------------------------------------------------------------------- #
# Data configurations
# ---------------------------------------------------------------------------- #
__C.DATASET = AttrDict()
__C.DATASET.NAME = 'nyu'
__C.DATASET.RGB_PIXEL_MEANS = (0.485, 0.456, 0.406
) # (102.9801, 115.9465, 122.7717)
__C.DATASET.RGB_PIXEL_VARS = (0.229, 0.224, 0.225) # (1, 1, 1)
# Scale the depth map
from __future__ import division from __future__ import print_function from __future__ import unicode_literals import six import os import os.path as osp import copy from ast import literal_eval import numpy as np import yaml from lib.utils.collections import AttrDict __C = AttrDict() # Consumers can get config by: # from fast_rcnn_config import cfg cfg = __C __C.ROOT_DIR = osp.abspath(osp.join(osp.dirname(__file__), '..', '..')) # Random note: avoid using '.ON' as a config key since yaml converts it to True; # prefer 'ENABLED' instead # ---------------------------------------------------------------------------- # # DATASET Options # ---------------------------------------------------------------------------- # __C.DATASET = AttrDict() __C.DATASET.SELF_SPLIT_DATASET = False
def merge_cfg_from_file(cfg_filename):
"""Load a yaml config file and merge it into the global config."""
with open(cfg_filename, 'r') as f:
yaml_cfg = AttrDict(yaml.load(f))
_merge_a_into_b(yaml_cfg, __C)
import os import os.path as osp import copy from ast import literal_eval import numpy as np from packaging import version import torch import torch.nn as nn from torch.nn import init import yaml #import nn as mynn from lib.utils.collections import AttrDict __C = AttrDict() # Consumers can get config by: # from fast_rcnn_config import cfg cfg = __C # Random note: avoid using '.ON' as a config key since yaml converts it to True; # prefer 'ENABLED' instead # ---------------------------------------------------------------------------- # # Training options # ---------------------------------------------------------------------------- # __C.TRAIN = AttrDict() # Datasets to train on # Available dataset list: datasets.dataset_catalog.DATASETS.keys()