py-faster-rcnn代碼閱讀2-config.py

 

簡介

該文件指定了用於fast rcnn訓練的默認config選項，不能隨意更改，如需更改，應當用yaml再寫一個config_file，而後使用cfg_from_file(filename)導入以覆蓋默認config。cfg_from_file(filename)定義見該文件。 tools目錄下的絕大多數文件採用--cfg 選項來指定重寫的配置文件（默認採用默認的config）。   - See tools/{train,test}_net.py for example code that uses cfg_from_file()   - See experiments/cfgs/*.yml for example YAML config override files

 

代碼解析

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: # 在其餘文件使用config要加的命令，例子見train_net.py # from fast_rcnn_config import cfg cfg = __C # # Training options # 訓練的選項 # __C.TRAIN = edict() # Scales to use during training (can list multiple scales) # Each scale is the pixel size of an image's shortest side # 最短邊Scale成600 __C.TRAIN.SCALES = (600,) # Max pixel size of the longest side of a scaled input image # 最長邊最大爲1000 __C.TRAIN.MAX_SIZE = 1000 # Images to use per minibatch # 一個minibatch包含兩張圖片 __C.TRAIN.IMS_PER_BATCH = 2 # Minibatch size (number of regions of interest [ROIs]) # Minibatch大小，即ROI的數量 __C.TRAIN.BATCH_SIZE = 128 # Fraction of minibatch that is labeled foreground (i.e. class > 0) # minibatch中前景樣本所佔的比例 __C.TRAIN.FG_FRACTION = 0.25 # Overlap threshold for a ROI to be considered foreground (if >= FG_THRESH) # 與前景的overlap大於等於0.5認爲該ROI爲前景樣本 __C.TRAIN.FG_THRESH = 0.5 # Overlap threshold for a ROI to be considered background (class = 0 if # overlap in [LO, HI)) # 與前景的overlap在0.1-0.5認爲該ROI爲背景樣本 __C.TRAIN.BG_THRESH_HI = 0.5 __C.TRAIN.BG_THRESH_LO = 0.1 # Use horizontally-flipped images during training? # 水平翻轉圖像，增長數據量 __C.TRAIN.USE_FLIPPED = True # Train bounding-box regressors # 訓練bb迴歸器 __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 # BBOX閾值，只有ROI與gt的重疊度大於閾值，這樣的ROI才能用做bb迴歸的訓練樣本 __C.TRAIN.BBOX_THRESH = 0.5 # Iterations between snapshots # 每迭代1000次產生一次snapshot __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 # 爲產生的snapshot文件名稱添加一個可選的infix. solver.prototxt指定了snapshot名稱的前綴 __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 # 在roi_data_layer.layer使用預取線程，做者認爲不太有效，所以設爲False __C.TRAIN.USE_PREFETCH = False # Normalize the targets (subtract empirical mean, divide by empirical stddev) # 歸一化目標BBOX_NORMALIZE_TARGETS，減去經驗均值，除以標準差 __C.TRAIN.BBOX_NORMALIZE_TARGETS = True # Deprecated (inside weights) # 棄用 __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) # 在BBOX_NORMALIZE_TARGETS爲True時，歸一化targets,使用經驗均值和方差 __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 # 使用'selective_search'的proposal訓練！注意該文件來自fast rcnn，下文提到RPN __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. # minibatch的兩個圖片應該有類似的寬高比，以免冗餘的zero-padding計算 __C.TRAIN.ASPECT_GROUPING = True # Use RPN to detect objects # 使用RPN檢測目標 __C.TRAIN.HAS_RPN = False # IOU >= thresh: positive example # RPN的正樣本閾值 __C.TRAIN.RPN_POSITIVE_OVERLAP = 0.7 # IOU < thresh: negative example # RPN的負樣本閾值 __C.TRAIN.RPN_NEGATIVE_OVERLAP = 0.3 # If an anchor statisfied by positive and negative conditions set to negative # 若是一個anchor同時知足正負樣本條件，設爲負樣本（應該用不到） __C.TRAIN.RPN_CLOBBER_POSITIVES = False # Max number of foreground examples # 前景樣本的比例 __C.TRAIN.RPN_FG_FRACTION = 0.5 # Total number of examples # batch size大小 __C.TRAIN.RPN_BATCHSIZE = 256 # NMS threshold used on RPN proposals # 非極大值抑制的閾值 __C.TRAIN.RPN_NMS_THRESH = 0.7 # Number of top scoring boxes to keep before apply NMS to RPN proposals # 在對RPN proposal使用NMS前，要保留的top scores的box數量 __C.TRAIN.RPN_PRE_NMS_TOP_N = 12000 # Number of top scoring boxes to keep after applying NMS to RPN proposals # 在對RPN proposal使用NMS後，要保留的top scores的box數量 __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) # proposal的高和寬都應該大於RPN_MIN_SIZE，不然，映射到conv5上不足一個像素點 __C.TRAIN.RPN_MIN_SIZE = 16 # Deprecated (outside weights) # 棄用 __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} # 給定正RPN樣本的權重 # and give negatives a weight of (1 - p) # 給定負RPN樣本的權重 # Set to -1.0 to use uniform example weighting # 這裏正負樣本使用相同權重 __C.TRAIN.RPN_POSITIVE_WEIGHT = -1.0 # # Testing options # 測試選項 # __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) # 測試時非極大值抑制的閾值 __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). # 分類再也不用SVM，設置爲False __C.TEST.SVM = False # Test using bounding-box regressors # 使用bb迴歸 __C.TEST.BBOX_REG = True # Propose boxes # 不使用RPN生成proposal __C.TEST.HAS_RPN = False # Test using these proposals # 使用selective_search生成proposal __C.TEST.PROPOSAL_METHOD = 'selective_search' ## NMS threshold used on RPN proposals # RPN proposal的NMS閾值 __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 # # MISC # # The mapping from image coordinates to feature map coordinates might cause # 從原圖到feature map的座標映射，可能會形成在原圖上不一樣的box到了feature map座標系上變得相同了 # 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 # 縮放因子 __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 # 全部network所用的像素均值設爲相同 __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 # 極小的數 __C.EPS = 1e-14 # Root directory of project # 項目根路徑 __C.ROOT_DIR = osp.abspath(osp.join(osp.dirname(__file__), '..', '..')) # Data directory # 數據路徑 __C.DATA_DIR = osp.abspath(osp.join(__C.ROOT_DIR, 'data')) # Model directory # 模型路徑 __C.MODELS_DIR = osp.abspath(osp.join(__C.ROOT_DIR, 'models', 'pascal_voc')) # Name (or path to) the matlab executable # matlab executable __C.MATLAB = 'matlab' # Place outputs under an experiments directory # 輸出在experiments路徑下 __C.EXP_DIR = 'default' # Use GPU implementation of non-maximum suppression # GPU實施非極大值抑制 __C.USE_GPU_NMS = True # Default GPU device id # 默認GPU id __C.GPU_ID = 0 def get_output_dir(imdb, net=None): #返回輸出路徑，在experiments路徑下 """Return the directory where experimental artifacts are placed. If the directory does not exist, it is created. A canonical標準 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): #兩個配置文件融合 """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 #用配置a更新配置b的對應項 else: b[k] = v def cfg_from_file(filename): """Load a config file and merge it into the default options.""" # 導入配置文件並與默認選項融合 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): # 命令行設置config """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