in container-serving/resources/FairMOT/config.py [0:0]
def __init__(self, load_model, frame_rate):
# basic experiment setting
self.task = 'mot'
self.dataset = 'jde'
self.exp_id = 'default'
self.test = True
self.pretrained = False
# path to pretrained model
self.load_model = load_model
# resume an experiment. Reloaded the selfimizer parameter and
# set load_model to model_last.pth in the exp dir if load_model is empty.
self.resume = True
# system
# -1 for CPU, use comma for multiple gpus
self.gpus = '0'
# dataloader threads. 0 for single-thread.
self.num_workes = 0
# disable when the input size is not fixed.
self.not_cuda_benchmark = True
self.seed = 317 # random seed for CornerNet
# log
# disable progress bar and print to screen.
self.print_iter = 0
# not display time during training.
self.hide_data_time = True
# save model to disk every 5 epochs.
self.save_all = True
# main metric to save best model
self.metric = 'loss'
# visualization threshold.
self.vis_thresh = 0.5
# model
# model architecture. Currently tested resdcn_34 | resdcn_50 | resfpndcn_34 | dla_34 | hrnet_18
self.arch = 'dla_34'
# conv layer channels for output head 0 for no conv layer -1 for default setting:
# 256 for resnets and 256 for dla.
self.head_conv = -1
# output stride. Currently only supports 4.
self.down_ratio = 4
# input
# input height and width. -1 for default from
# dataset. Will be overriden by input_h | input_w
self.input_res = -1
# input height. -1 for default from dataset.
self.input_h = -1
# input width. -1 for default from dataset.
self.input_w = -1
self.frame_w = 1920
self.frame_h = 1080
self.inp_w = 1088
self.inp_h = 608
self.frame_rate = frame_rate
# train
# learning rate for batch size 12.
self.lr = 1e-4
# drop learning rate by 10.
self.lr_step = '20'
# total training epochs.
self.num_epochs = 30
# batch size
self.batch_size = 12
# batch size on the master gpu.
self.master_batch_size = -1
# default: #samples / batch_size.
self.num_iters = -1
# number of epochs to run validation.
self.val_intervals = 5
# include validation in training and test on test set
self.trainval = True
# test
# max number of output objects.
self.K = 500
# not use parallal data pre-processing.
self.not_prefetch_test = True
# fix testing resolution or keep the original resolution
self.fix_res = True
# keep the original resolution during validation.
self.keep_res = True
# confidence thresh for tracking
self.conf_thres = 0.4
# confidence thresh for detection
self.det_thres = 0.3
# iou thresh for nms
self.nms_thres = 0.4
# track_buffer
self.track_buffer = 2
# filter out tiny boxes
self.min_box_area = 300
# path to the input video
self.input_video = '../videos/MOT16-03.mp4'
# video or text
self.output_format = 'video'
# expected output root path
self.output_root = '../demos'
# mot
# load data from cfg
self.data_cfg = '../src/lib/cfg/data.json'
#
self.data_dir = '/home/ubuntu/mot/FairMOT'
# loss
# use mse loss or focal loss to train keypoint heatmaps.
self.mse_loss = True
# regression loss: sl1 | l1 | l2
self.reg_loss = 'l1'
# loss weight for keypoint heatmaps.
self.hm_weight = 1
# loss weight for keypoint local offsets.
self.off_weight = 1
# loss weight for bounding box size.
self.wh_weight = 0.1
# reid loss: ce | triplet
self.id_loss = 'ce'
# loss weight for id
self.id_weight = 1
# feature dim for reid
# self.reid_dim = 128
self.reid_dim = 32
# regress left, top, right, bottom of bbox
self.ltrb = True
# L1(\hat(y) / y, 1) or L1(\hat(y), y)
self.norm_wh = True
# apply weighted regression near center or just apply regression on center point.
self.dense_wh = True
# category specific bounding box size.
self.cat_spec_wh = True
# not regress local offset.
self.not_reg_offset = False
self.init()