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opts.lua
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opts.lua
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local DATASETS_PATH = 'data/datasets'
local DATA_CNN_MODELS = 'data/models'
local RESULT_SAVE = 'output/' .. SETTINGS.RESULT_SAVE_FOLDER
local temp = io.open(RESULT_SAVE)
if not temp then
os.execute('mkdir ' .. RESULT_SAVE)
end
PATHS = {
EXTERNAL = {
PRETRAINED_MODEL_VGGF = {
PROTOTXT = paths.concat(DATA_CNN_MODELS, 'VGG_CNN_F_deploy.prototxt'),
CAFFEMODEL = paths.concat(DATA_CNN_MODELS, 'VGG_CNN_F.caffemodel')
},
PRETRAINED_MODEL_VGG16 = {
PROTOTXT = paths.concat('../py-faster-rcnn/data/imagenet_models/VGG16_deploy.prototxt'),
CAFFEMODEL = paths.concat('../py-faster-rcnn/data/imagenet_models/VGG16.v2.caffemodel')
},
PRETRAINED_MODEL_AlexNet = {
PROTOTXT = paths.concat('../py-faster-rcnn/data/imagenet_models/AlexNet_deploy.prototxt'),
CAFFEMODEL = paths.concat('../py-faster-rcnn/data/imagenet_models/AlexNet.v2.caffemodel')
},
SSW_VOC2007 = {
trainval = paths.concat(DATASETS_PATH, 'SelectiveSearchVOC2007trainval.mat'),
test = paths.concat(DATASETS_PATH, 'SelectiveSearchVOC2007test.mat')
},
EB_VOC2007 = {
trainval = paths.concat(DATASETS_PATH, 'EdgeBoxesVOC2007trainval.mat'),
test = paths.concat(DATASETS_PATH, 'EdgeBoxesVOC2007test.mat')
},
SSW_VOC2012 = {
train = paths.concat(DATASETS_PATH, 'voc_2012_train.mat'),
val = paths.concat(DATASETS_PATH, 'voc_2012_val.mat'),
trainval = paths.concat(DATASETS_PATH, 'voc_2012_trainval.mat'),
test = paths.concat(DATASETS_PATH, 'voc_2012_test.mat')
},
VOC_DEVKIT_VOCYEAR = {
VOC2007 = paths.concat(DATASETS_PATH, 'VOCdevkit_2007/VOC2007'),
VOC2010 = paths.concat(DATASETS_PATH, 'VOCdevkit_2010/VOC2010'),
VOC2012 = paths.concat(DATASETS_PATH, 'VOCdevkit_2012/VOC2012')
}
},
BASE_MODEL_CACHED = {
VGGF = paths.concat(DATA_CNN_MODELS, 'VGG_CNN_F.t7'),
GoogleNet = paths.concat(DATA_CNN_MODELS, 'GoogleNet.t7'),
VGG16 = paths.concat(DATA_CNN_MODELS, 'VGG16.t7'),
AlexNet = paths.concat(DATA_CNN_MODELS, 'AlexNet.t7')
},
BASE_MODEL_CACHED_ZB = {
VGGF = paths.concat(DATA_CNN_MODELS, 'VGG_CNN_F_zb.t7'),
GoogleNet = paths.concat(DATA_CNN_MODELS, 'GoogleNet_zb.t7')
},
DATASET_CACHED_PATTERN = paths.concat(DATASETS_PATH, '%s_%s.t7'),
CHECKPOINT_PATTERN = paths.concat(RESULT_SAVE, 'model_epoch%02d.h5'),
CHECKPOINT_PATTERN_ROOT_FOLDER = paths.concat(RESULT_SAVE, ''),
LOG = paths.concat(RESULT_SAVE, 'log.json'),
SCORES_PATTERN = paths.concat(RESULT_SAVE, 'scores_%s_epoch%02d.h5'),
CORLOC = paths.concat(RESULT_SAVE, 'corloc.json'),
DETECTION_MAP = paths.concat(RESULT_SAVE, 'detection_mAP_epoch%02d%s.json')
}
local BASE_MODEL_CACHED_ = PATHS.BASE_MODEL_CACHED[SETTINGS.BASE_MODEL]
local BASE_MODEL_CACHEDZB = PATHS.BASE_MODEL_CACHED_ZB[SETTINGS.BASE_MODEL]
opts = {
ROI_FACTOR = 1.8,
SEED = 666,
NMS_OVERLAP_THRESHOLD = 0.3,
NMS_SCORE_THRESHOLD = 5e-3,
--IMAGE_SCALES = {{608, 800}, {368, 480}, {432, 576}, {528, 688}, {656, 864}, {912, 1200}}
IMAGE_SCALES = {{608, 800}, {496, 656}, {400, 544}, {720, 960}, {864, 1152}},
NUM_SCALES = 5,
OUTPUT_FIELDS = {'output_prod_cls', 'output_prod_det', 'output_prod_det2'},
NUM_EPOCHS = SETTINGS.NUM_EPOCHS,
DATASET = SETTINGS.DATASET,
BASE_MODEL = SETTINGS.BASE_MODEL,
SUBSET = SETTINGS.SUBSET,
PATHS = {
--MODEL = arg[1],
MODEL = nil,
CHECKPOINT_PATTERN = PATHS.CHECKPOINT_PATTERN,
LOG = PATHS.LOG,
SCORES_PATTERN = PATHS.SCORES_PATTERN,
PROPOSALS = PATHS.EXTERNAL[SETTINGS.PROPOSALS .. '_' .. SETTINGS.DATASET],
CORLOC = PATHS.CORLOC,
DETECTION_MAP = PATHS.DETECTION_MAP,
RUN_STATS_PATTERN = PATHS.RUN_STATS_PATTERN,
BASE_MODEL_RAW = PATHS.EXTERNAL['PRETRAINED_MODEL_' .. SETTINGS.BASE_MODEL],
VOC_DEVKIT_VOCYEAR = PATHS.EXTERNAL.VOC_DEVKIT_VOCYEAR[SETTINGS.DATASET],
DATASET_CACHED = PATHS.DATASET_CACHED_PATTERN:format(SETTINGS.DATASET,SETTINGS.PROPOSALS),
BASE_MODEL_CACHED = SETTINGS.ZB_DEBUG and BASE_MODEL_CACHEDZB or BASE_MODEL_CACHED_
}
}