code: # --------------------------------------------------------
  # Fast R-CNN
  # Copyright (c) 2015 Microsoft
  # Licensed under The MIT License [see LICENSE for details]
  # Written by Ross Girshick
  # --------------------------------------------------------
  
  """Fast R-CNN config system.
  
  This file specifies default config options for Fast R-CNN. You should not
  change values in this file. Instead, you should write a config file (in yaml)
  and use cfg_from_file(yaml_file) to load it and override the default options.
  
  Most tools in $ROOT/tools take a --cfg option to specify an override file.
      - See tools/{train,test}_net.py for example code that uses cfg_from_file()
      - See experiments/cfgs/*.yml for example YAML config override files
  """
  
  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
  
  #
  # Training options
  #
  
  __C.TRAIN = edict()
  
  # Scales to use during training (can list multiple scales)
  # Each 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 = 2
  
  # Minibatch size (number of regions of interest [ROIs])
  __C.TRAIN.BATCH_SIZE = 128
  
  # 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 = 10000
  
  # solver.prototxt specifies the snapshot path prefix, this adds an optional
  # infix to yield the path: <prefix>[_<infix>]_iters_XYZ.caffemodel
  __C.TRAIN.SNAPSHOT_INFIX = ''
  
  # Use a prefetch thread in roi_data_layer.layer
  # So far I haven't found this useful; likely more engineering work is required
  __C.TRAIN.USE_PREFETCH = False
  
  # Normalize the targets (subtract empirical mean, divide by empirical stddev)
  __C.TRAIN.BBOX_NORMALIZE_TARGETS = True
  # Deprecated (inside weights)
  __C.TRAIN.BBOX_INSIDE_WEIGHTS = (1.0, 1.0, 1.0, 1.0)
  
  # curve
  __C.TRAIN.INFO_INSIDE_WEIGHTS = (0.0, 0.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 0.0, 0.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0) # 32
  
  
  # 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)
  
  __C.TRAIN.INFO_NORMALIZE_MEANS = (0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0) # 32
  __C.TRAIN.INFO_NORMALIZE_STDS = (1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1) # 32
  
  __C.TRAIN.RPN_NORMALIZE_TARGETS = False
  __C.TRAIN.RPN_NORMALIZE_MEANS = None
  __C.TRAIN.RPN_NORMALIZE_STDS = None
  
  # Train using these proposals
  __C.TRAIN.PROPOSAL_METHOD = 'selective_search'
  
  # 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.
  __C.TRAIN.ASPECT_GROUPING = True
  
  # Use RPN to detect objects
  __C.TRAIN.HAS_RPN = False
  # 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 statisfied 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 = 0.5
  # 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
  # Proposal height and width both need to be greater than RPN_MIN_SIZE (at orig image scale)
  __C.TRAIN.RPN_MIN_SIZE = 16
  # 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 use class aware box or not
  __C.TRAIN.AGNOSTIC = False
  
  #
  # Testing options
  #
  
  __C.TEST = edict()
  
  # Scales to use during testing (can list multiple scales)
  # Each 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
  __C.TEST.PNMS = 0.3
  __C.TEST.USE_PNMS = True
  
  # 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 = 'selective_search'
  
  ## 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
  
  # whether use class aware box or not
  __C.TEST.AGNOSTIC = False
  
  
  #
  # MISC
  #
  
  # The mapping from image coordinates to feature map coordinates might cause
  # some boxes that are distinct in image space to become identical in feature
  # coordinates. If DEDUP_BOXES > 0, then DEDUP_BOXES is used as the scale factor
  # for identifying duplicate boxes.
  # 1/16 is correct for {Alex,Caffe}Net, VGG_CNN_M_1024, and VGG16
  __C.DEDUP_BOXES = 1./16.
  
  # 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
  
  # A small number that's used many times
  __C.EPS = 1e-14
  
  # 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'))
  
  # Model directory
  __C.MODELS_DIR = osp.abspath(osp.join(__C.ROOT_DIR, 'models', 'pascal_voc'))
  
  # 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
  
  # Default GPU device id
  __C.GPU_ID = 0
  
  
  def get_output_dir(imdb, net=None):
      """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 net is not None:
          outdir = osp.join(outdir, net.name)
      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.iteritems():
          # a must specify keys that are in b
          if not b.has_key(k):
              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 d.has_key(subkey)
              d = d[subkey]
          subkey = key_list[-1]
          assert d.has_key(subkey)
          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