code: from __future__ import absolute_import
  from __future__ import division
  from __future__ import print_function
  
  import os
  import os.path as osp
  import numpy as np
  # `pip install easydict` if you don't have it
  from easydict import EasyDict as edict
  
  __C = edict()
  # Consumers can get config by:
  #   from fast_rcnn_config import cfg
  cfg = __C
  
  #
  # Memory options
  #
  __C.MEM = edict()
  
  # Number of memory iterations
  __C.MEM.ITER = 2
  
  # Height of the memory
  __C.MEM.INIT_H = 20
  # Width of the memory
  __C.MEM.INIT_W = 20
  
  # Channel of the memory
  __C.MEM.C = 512
  
  # Basic stds in the memory
  __C.MEM.STD = 0.01
  # Base stds in the memory update function for input features
  __C.MEM.U_STD = 0.01
  # Region classification
  __C.MEM.C_STD = 0.01
  
  # Feature to memory ratio
  __C.MEM.FM_R = 1.
  # Value to gate ratio
  __C.MEM.VG_R = 1.
  # FC to Pool ratio when combing the input
  __C.MEM.FP_R = 1.
  
  # Conv kernel size for memory
  __C.MEM.CONV = 3
  
  # Canonical region size
  __C.MEM.CROP_SIZE = 7
  
  # Context aggregation
  __C.MEM.CT_L = 3
  __C.MEM.CT_CONV = 3
  __C.MEM.CT_FCONV = 3
  
  # Input feature
  __C.MEM.IN_L = 2
  __C.MEM.IN_CONV = 3
  
  # Memory final fc layer channels
  __C.MEM.FC_C = 4096
  __C.MEM.FC_L = 2
  
  # The weight for the memory based prediction
  __C.MEM.WEIGHT = 1.
  
  __C.MEM.REL_WEIGHT = 1.
  
  # Final supervision weight
  __C.MEM.WEIGHT_FINAL = 1.
  # The threshold to control the entropy of the distribution
  __C.MEM.BETA = .5
  
  # The dimension of predicted tag
  __C.MEM.TAG_D = 16
  
  
  #
  # Training options
  #
  __C.TRAIN = edict()
  
  # Initial learning rate
  __C.TRAIN.RATE = 0.0005
  
  # Momentum
  __C.TRAIN.MOMENTUM = 0.9
  
  # Weight decay, for regularization
  __C.TRAIN.WEIGHT_DECAY = 0.0001
  
  # Factor for reducing the learning rate
  __C.TRAIN.GAMMA = 0.1
  
  # Step size for reducing the learning rate, currently only support one step
  __C.TRAIN.STEPSIZE = [30000]
  
  # Iteration intervals for showing the loss during training, on command line interface
  __C.TRAIN.DISPLAY = 10
  
  # Whether to double the learning rate for bias
  __C.TRAIN.DOUBLE_BIAS = True
  
  # Whether to initialize the weights with truncated normal distribution 
  __C.TRAIN.TRUNCATED = False
  
  # Whether to have weight decay on bias as well
  __C.TRAIN.BIAS_DECAY = False
  
  # Whether to add ground truth boxes to the pool when sampling regions
  __C.TRAIN.USE_GT = False
  
  # Whether to use aspect-ratio grouping of training images, introduced merely for saving
  # GPU memory
  __C.TRAIN.ASPECT_GROUPING = False
  
  # The number of snapshots kept, older ones are deleted to save space
  __C.TRAIN.SNAPSHOT_KEPT = 3
  
  # The time interval for saving tensorflow summaries
  __C.TRAIN.SUMMARY_INTERVAL = 180
  
  # The time interval for saving tensorflow summaries
  __C.TRAIN.SUMMARY_ITERS = 500
  
  # Scale to use during training (can list multiple scales)
  # The scale is the pixel size of an image's shortest side
  __C.TRAIN.SCALES = (600,)
  
  # Max pixel size of the longest side of a scaled input image
  __C.TRAIN.MAX_SIZE = 1000
  
  # Images to use per minibatch
  __C.TRAIN.IMS_PER_BATCH = 1
  
  # Minibatch size (number of regions of interest [ROIs])
  __C.TRAIN.BATCH_SIZE = 128
  
  __C.TRAIN.REL_BATCH_SIZE = 128
  
  __C.TRAIN.POS_REL_FRACTION = 0.5
  
  # Fraction of minibatch that is labeled foreground (i.e. class > 0)
  __C.TRAIN.FG_FRACTION = 0.25
  
  # Overlap threshold for a ROI to be considered foreground (if >= FG_THRESH)
  __C.TRAIN.FG_THRESH = 0.5
  
  # Overlap threshold for a ROI to be considered background (class = 0 if
  # overlap in [LO, HI))
  __C.TRAIN.BG_THRESH_HI = 0.5
  __C.TRAIN.BG_THRESH_LO = 0.1
  
  # Use horizontally-flipped images during training?
  __C.TRAIN.USE_FLIPPED = True
  
  # Train bounding-box regressors
  __C.TRAIN.BBOX_REG = True
  
  # Overlap required between a ROI and ground-truth box in order for that ROI to
  # be used as a bounding-box regression training example
  __C.TRAIN.BBOX_THRESH = 0.5
  
  # Iterations between snapshots
  __C.TRAIN.SNAPSHOT_ITERS = 5000
  
  # solver.prototxt specifies the snapshot path prefix, this adds an optional
  # infix to yield the path: <prefix>[_<infix>]_iters_XYZ.caffemodel
  __C.TRAIN.SNAPSHOT_PREFIX = 'res101_faster_rcnn'
  
  # Normalize the targets (subtract empirical mean, divide by empirical stddev)
  __C.TRAIN.BBOX_NORMALIZE_TARGETS = True
  __C.TRAIN.BBOX_TARGET_NORMALIZATION_FILE = 'bbox_distribution.npy'
  # Deprecated (inside weights)
  __C.TRAIN.BBOX_INSIDE_WEIGHTS = (1.0, 1.0, 1.0, 1.0)
  
  # Normalize the targets using "precomputed" (or made up) means and stdevs
  # (BBOX_NORMALIZE_TARGETS must also be True)
  __C.TRAIN.BBOX_NORMALIZE_TARGETS_PRECOMPUTED = False
  
  __C.TRAIN.BBOX_NORMALIZE_MEANS = (0.0, 0.0, 0.0, 0.0)
  
  __C.TRAIN.BBOX_NORMALIZE_STDS = (0.1, 0.1, 0.2, 0.2)
  
  # Train using these proposals
  __C.TRAIN.PROPOSAL_METHOD = 'gt'
  
  # Make minibatches from images that have similar aspect ratios (i.e. both
  # tall and thin or both short and wide) in order to avoid wasting computation
  # on zero-padding.
  
  # Use RPN to detect objects
  __C.TRAIN.HAS_RPN = True
  
  # IOU >= thresh: positive example
  __C.TRAIN.RPN_POSITIVE_OVERLAP = 0.7
  
  # IOU < thresh: negative example
  __C.TRAIN.RPN_NEGATIVE_OVERLAP = 0.3
  
  # If an anchor satisfied by positive and negative conditions set to negative
  __C.TRAIN.RPN_CLOBBER_POSITIVES = False
  
  # Max number of foreground examples
  __C.TRAIN.RPN_FG_FRACTION = 0.5
  
  # Total number of examples
  __C.TRAIN.RPN_BATCHSIZE = 256
  
  # NMS threshold used on RPN proposals
  __C.TRAIN.RPN_NMS_THRESH = 0.7
  
  # Number of top scoring boxes to keep before apply NMS to RPN proposals
  __C.TRAIN.RPN_PRE_NMS_TOP_N = 12000
  
  # Number of top scoring boxes to keep after applying NMS to RPN proposals
  __C.TRAIN.RPN_POST_NMS_TOP_N = 2000
  
  # Deprecated (outside weights)
  __C.TRAIN.RPN_BBOX_INSIDE_WEIGHTS = (1.0, 1.0, 1.0, 1.0)
  
  # Give the positive RPN examples weight of p * 1 / {num positives}
  # and give negatives a weight of (1 - p)
  # Set to -1.0 to use uniform example weighting
  __C.TRAIN.RPN_POSITIVE_WEIGHT = -1.0
  
  # Whether to use all ground truth bounding boxes for training, 
  # For COCO, setting USE_ALL_GT to False will exclude boxes that are flagged as ''iscrowd''
  __C.TRAIN.USE_ALL_GT = True
  
  __C.TRAIN.USE_RPN_DB = True
  
  __C.TRAIN.NUM_NEG_RELS = 128
  
  #
  # Testing options
  #
  __C.TEST = edict()
  
  # Scale to use during testing (can NOT list multiple scales)
  # The scale is the pixel size of an image's shortest side
  __C.TEST.SCALES = (600,)
  
  # Max pixel size of the longest side of a scaled input image
  __C.TEST.MAX_SIZE = 1000
  
  # Overlap threshold used for non-maximum suppression (suppress boxes with
  # IoU >= this threshold)
  __C.TEST.NMS = 0.3
  
  # Experimental: treat the (K+1) units in the cls_score layer as linear
  # predictors (trained, eg, with one-vs-rest SVMs).
  __C.TEST.SVM = False
  
  # Test using bounding-box regressors
  __C.TEST.BBOX_REG = True
  
  # Propose boxes
  __C.TEST.HAS_RPN = False
  
  # Test using these proposals
  __C.TEST.PROPOSAL_METHOD = 'gt'
  
  ## NMS threshold used on RPN proposals
  __C.TEST.RPN_NMS_THRESH = 0.7
  
  # Number of top scoring boxes to keep before apply NMS to RPN proposals
  __C.TEST.RPN_PRE_NMS_TOP_N = 6000
  
  # Number of top scoring boxes to keep after applying NMS to RPN proposals
  __C.TEST.RPN_POST_NMS_TOP_N = 300
  
  # Proposal height and width both need to be greater than RPN_MIN_SIZE (at orig image scale)
  # __C.TEST.RPN_MIN_SIZE = 16
  
  # Testing mode, default to be 'nms', 'top' is slower but better
  # See report for details
  __C.TEST.MODE = 'nms'
  
  # Only useful when TEST.MODE is 'top', specifies the number of top proposals to select
  __C.TEST.RPN_TOP_N = 5000
  
  #
  # ResNet options
  #
  
  __C.RESNET = edict()
  
  # Option to set if max-pooling is appended after crop_and_resize. 
  # if true, the region will be resized to a square of 2xPOOLING_SIZE, 
  # then 2x2 max-pooling is applied; otherwise the region will be directly
  # resized to a square of POOLING_SIZE
  __C.RESNET.MAX_POOL = False
  
  # Number of fixed blocks during training, by default the first of all 4 blocks is fixed
  # Range: 0 (none) to 3 (all)
  __C.RESNET.FIXED_BLOCKS = 1
  
  #
  # MobileNet options
  #
  
  __C.MOBILENET = edict()
  
  # Whether to regularize the depth-wise filters during training
  __C.MOBILENET.REGU_DEPTH = False
  
  # Number of fixed layers during training, by default the bottom 5 of 14 layers is fixed
  # Range: 0 (none) to 12 (all)
  __C.MOBILENET.FIXED_LAYERS = 5
  
  # Weight decay for the mobilenet weights
  __C.MOBILENET.WEIGHT_DECAY = 0.00004
  
  # Depth multiplier
  __C.MOBILENET.DEPTH_MULTIPLIER = 1.
  
  #
  # MISC
  #
  
  # Pixel mean values (BGR order) as a (1, 1, 3) array
  # We use the same pixel mean for all networks even though it's not exactly what
  # they were trained with
  __C.PIXEL_MEANS = np.array([[[102.9801, 115.9465, 122.7717]]])
  
  # For reproducibility
  __C.RNG_SEED = 3
  
  # 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'))
  
  __C.VG_DIR = osp.abspath(osp.join(__C.DATA_DIR, 'vg'))
  
  # Name (or path to) the matlab executable
  __C.MATLAB = 'matlab'
  
  # Place outputs under an experiments directory
  __C.EXP_DIR = 'default'
  
  # Use GPU implementation of non-maximum suppression
  __C.USE_GPU_NMS = True
  
  # Use an end-to-end tensorflow model.
  # Note: models in E2E tensorflow mode have only been tested in feed-forward mode,
  #       but these models are exportable to other tensorflow instances as GraphDef files.
  __C.USE_E2E_TF = True
  
  # Default pooling mode, only 'crop' is available
  __C.POOLING_MODE = 'crop'
  
  # Size of the pooled region after RoI pooling
  __C.POOLING_SIZE = 7
  
  # Anchor scales for RPN
  __C.ANCHOR_SCALES = [8, 16, 32]
  
  # Anchor ratios for RPN
  __C.ANCHOR_RATIOS = [0.5, 1, 2]
  
  # Number of filters for the RPN layer
  __C.RPN_CHANNELS = 512
  
  __C.BOX_SCALE = 1024
  
  __C.IMG_SCALE = 1024
  
  cfg.BOTTLE_SCALE = 16.0
  
  # EPS, a small number for numerical issue
  __C.EPS = 1e-14
  
  __C.GROUP_DIST_THRESH = 20.
  
  __C.PUSH_WEIGHT = 0.1
  
  __C.PULL_WEIGHT = 0.1
  
  
  def get_output_dir(imdb, weights_filename):
      """Return the directory where experimental artifacts are placed.
      If the directory does not exist, it is created.
  
      A canonical path is built using the name from an imdb and a network
      (if not None).
      """
      outdir = osp.abspath(osp.join(__C.ROOT_DIR, 'output', __C.EXP_DIR, imdb.name))
      if weights_filename is None:
          weights_filename = 'default'
      outdir = osp.join(outdir, weights_filename)
      if not os.path.exists(outdir):
          os.makedirs(outdir)
      return outdir
  
  
  def get_output_tb_dir(imdb, weights_filename):
      """Return the directory where tensorflow summaries are placed.
      If the directory does not exist, it is created.
  
      A canonical path is built using the name from an imdb and a network
      (if not None).
      """
      outdir = osp.abspath(osp.join(__C.ROOT_DIR, 'tensorboard', __C.EXP_DIR, imdb.name))
      if weights_filename is None:
          weights_filename = 'default'
      outdir = osp.join(outdir, weights_filename)
      if not os.path.exists(outdir):
          os.makedirs(outdir)
      return outdir
  
  
  def _merge_a_into_b(a, b):
      """Merge config dictionary a into config dictionary b, clobbering the
      options in b whenever they are also specified in a.
      """
      if type(a) is not edict:
          return
  
      for k, v in a.items():
          # a must specify keys that are in b
          if k not in b:
              raise KeyError('{} is not a valid config key'.format(k))
  
          # the types must match, too
          old_type = type(b[k])
          if old_type is not type(v):
              if isinstance(b[k], np.ndarray):
                  v = np.array(v, dtype=b[k].dtype)
              else:
                  raise ValueError(('Type mismatch ({} vs. {}) '
                                    'for config key: {}').format(type(b[k]),
                                                                 type(v), k))
  
          # recursively merge dicts
          if type(v) is edict:
              try:
                  _merge_a_into_b(a[k], b[k])
              except:
                  print(('Error under config key: {}'.format(k)))
                  raise
          else:
              b[k] = v
  
  
  def cfg_from_file(filename):
      """Load a config file and merge it into the default options."""
      import yaml
      with open(filename, 'r') as f:
          yaml_cfg = edict(yaml.load(f))
  
      _merge_a_into_b(yaml_cfg, __C)
  
  
  def cfg_from_list(cfg_list):
      """Set config keys via list (e.g., from command line)."""
      from ast import literal_eval
      assert len(cfg_list) % 2 == 0
      for k, v in zip(cfg_list[0::2], cfg_list[1::2]):
          key_list = k.split('.')
          d = __C
          for subkey in key_list[:-1]:
              assert subkey in d
              d = d[subkey]
          subkey = key_list[-1]
          assert subkey in d
          try:
              value = literal_eval(v)
          except:
              # handle the case when v is a string literal
              value = v
          assert type(value) == type(d[subkey]), \
              'type {} does not match original type {}'.format(
                  type(value), type(d[subkey]))
          d[subkey] = value
  
apis: numpy.array