config.py

import os
from easydict import EasyDict as edict
import time
import torch

# init
__C = edict()
cfg = __C

#------------------------------TRAIN------------------------
__C.SEED = 3035 # random seed,  for reporduction
__C.DATASET = 'SHHA' # dataset selection: SHHA, SHHB, UCF50, QNRF, WE

if __C.DATASET == 'UCF50':# only for UCF50
	from datasets.UCF50.setting import cfg_data
	__C.VAL_INDEX = cfg_data.VAL_INDEX 

if __C.DATASET == 'GCC':# only for GCC
	from datasets.GCC.setting import cfg_data
	__C.VAL_MODE = cfg_data.VAL_MODE 


__C.NET = 'MobileCount' # net selection: MobileCount, MobileCountx1_25, MobileCountx2

__C.PRE_GCC = False # use the pretrained model on GCC dataset
__C.PRE_GCC_MODEL = './exp/04-06_16-19_GCC_CSRNet_0.0001_rd/all_ep_130_mae_34.9_mse_71.9.pth' # path to model

__C.GPU_ID = [0] # sigle gpu: [0], [1] ...; multi gpus: [0,1]

# learning rate settings
__C.LR = 1e-4 # learning rate
__C.LR_DECAY = 0.995 # decay rate
__C.LR_DECAY_START = -1 # when training epoch is more than it, the learning rate will be begin to decay
__C.NUM_EPOCH_LR_DECAY = 1 # decay frequency
__C.MAX_EPOCH = 500

# print 
__C.PRINT_FREQ = 10

now = time.strftime("%m-%d_%H-%M", time.localtime())

__C.EXP_NAME = now \
			 + '_' + __C.DATASET \
             + '_' + __C.NET \
             + '_' + str(__C.LR)

if __C.DATASET == 'UCF50':
	__C.EXP_NAME += '_' + str(__C.VAL_INDEX)	

if __C.DATASET == 'GCC':
	__C.EXP_NAME += '_' + __C.VAL_MODE	

__C.EXP_PATH = './exp' # the path of logs, checkpoints, and current codes


#------------------------------VAL------------------------
__C.VAL_DENSE_START = 1
__C.VAL_FREQ = 10 # Before __C.VAL_DENSE_START epoches, the freq is set as __C.VAL_FREQ

#------------------------------VIS------------------------
__C.VISIBLE_NUM_IMGS = 1 #  must be 1 for training images with the different sizes



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