def get_configs():
configs = []
if os.path.isfile("configs.cfg"):
options = parse_configs("configs.cfg")
if "server" in options and "port" in options and "user" in options:
configs.append(options["server"])
configs.append(int(options["port"]))
configs.append(options["user"])
configs.append(getpass())
else:
print "Configuration file missing options."
exit(1)
else:
configs.append(raw_input("Server: "))
configs.append(int(raw_input("Port: ")))
configs.append(raw_input("User: "))
configs.append(getpass())
return tuple(configs)
import sys
import tensorflow as tf
import cv2
import time
sys.path.append("../../")
from net import ordinal_3_2
from utils.dataread_utils import ordinal_3_1_reader as ordinal_reader
from utils.preprocess_utils import ordinal_3_2 as preprocessor
from utils.visualize_utils import display_utils
##################### Setting for training ######################
import configs
# t means gt(0) or ord(1)
configs.parse_configs(1)
configs.print_configs()
train_log_dir = os.path.join(configs.log_dir, "train")
valid_log_dir = os.path.join(configs.log_dir, "valid")
if not os.path.exists(configs.model_dir):
os.makedirs(configs.model_dir)
restore_model_iteration = None
#################################################################
if __name__ == "__main__":
################### Initialize the data reader ###################
train_range = np.load(configs.train_range_file)
import time
sys.path.append("../../")
from net import ordinal_3_1
from utils.preprocess_utils import ordinal_3_1 as preprocessor
from utils.visualize_utils import display_utils
from utils.common_utils import my_utils
from utils.evaluate_utils import evaluators
from utils.postprocess_utils import volume_utils
##################### Evaluation Configs ######################
import configs
# t means gt(0) or ord(1)
# d means validset(0) or trainset(1)
configs.parse_configs(1, 0)
configs.print_configs()
evaluation_models = [300000, 250000, 200000, 150000, 100000, 50000]
###############################################################
if __name__ == "__main__":
################### Initialize the data reader ###################
#### Used for valid
range_arr = np.load(configs.range_file)
data_from = 0
data_to = len(range_arr)
img_list = [configs.img_path_fn(i) for i in range_arr]
lbl_list = [configs.lbl_path_fn(i) for i in range_arr]
from net import ordinal_F
from utils.preprocess_utils import ordinal_3_3 as preprocessor
from utils.visualize_utils import display_utils
from utils.common_utils import my_utils
from utils.evaluate_utils import evaluators
from utils.postprocess_utils import volume_utils
from utils.postprocess_utils.skeleton17 import skeleton_opt
##################### Evaluation Configs ######################
import configs
# t means gt(0) or ord(1)
# ver means experiment version
# d means validset(0) or trainset(1)
configs.parse_configs(t=0, ver=1, d=0)
configs.print_configs()
evaluation_models = [140000]
###############################################################
if __name__ == "__main__":
network_batch_size = 2 * configs.batch_size
################### Initialize the data reader ###################
range_arr = np.load(configs.range_file)
data_from = 0
data_to = len(range_arr)
img_list = [configs.img_path_fn(i) for i in range_arr]
lbl_list = [configs.lbl_path_fn(i) for i in range_arr]
import time
sys.path.append("../../")
from net import ordinal_3_3
from utils.preprocess_utils import ordinal_3_3 as preprocessor
from utils.visualize_utils import display_utils
from utils.common_utils import my_utils
from utils.evaluate_utils import evaluators
from utils.postprocess_utils import volume_utils
##################### Evaluation Configs ######################
import configs
# t means gt(0) or ord(1)
# d means validset(0) or trainset(1)
configs.parse_configs(0, 0)
configs.print_configs()
# evaluation_models = [275000, 325000, 425000, 475000, 500000, 600000, 625000]
evaluation_models = [
275000, 300000, 325000, 350000, 375000, 400000, 425000, 450000, 475000,
500000
]
###############################################################
if __name__ == "__main__":
################### Initialize the data reader ###################
# another half batchs are the flipped batchs
network_batch_size = configs.batch_size * 2
import tensorflow as tf
import cv2
import time
sys.path.append("../../")
from net import ordinal_F
from utils.dataread_utils import ordinal_3_1_reader as ordinal_reader
from utils.preprocess_utils import ordinal_3_3 as preprocessor
from utils.visualize_utils import display_utils
##################### Setting for training ######################
import configs
# t means gt(0) or ord(1)
# ver means version
configs.parse_configs(t=0, ver=1)
configs.print_configs()
train_log_dir = os.path.join(configs.log_dir, "train")
valid_log_dir = os.path.join(configs.log_dir, "valid")
if not os.path.exists(configs.model_dir):
os.makedirs(configs.model_dir)
restore_model_iteration = None
#################################################################
if __name__ == "__main__":
################### Initialize the data reader ####################
train_range = np.load(configs.train_range_file)
import argparse
from PIL import Image
from torch.utils.data import DataLoader, Dataset
from mec.data_manip.metrics import Accuracy
from mec.training.sync_trainer import startWorkers, trainAndVal, trainAndValLocal
# 演示数据
from demo_dataset import train_set, valid_set
# 预训练公开模型
from torchvision.models.resnet import resnet50, resnet18
# 运行参数
from configs import parse_configs
parse_configs()
from configs import *
#print( [(k,eval(k)) for k in dir()] )
# 多机运行时需指定本地使用哪个网卡，否则可能因网络连接速度太慢拖累训练速度
# 单机训练时不需要此参数，默认指定本地地址127.0.0.1
# os.environ['NCCL_SOCKET_IFNAME'] = 'eno2'
# os.environ['NCCL_SOCKET_IFNAME'] = 'eno1np0'
# -------------------------------------------------------------------------
def main():
# model
class_to_idx = train_set.class_to_idx
import numpy as np
import sys
import tensorflow as tf
import cv2
import time
sys.path.append("../")
from utils.dataread_utils import ordinal_3_1_reader as ordinal_reader
from utils.preprocess_utils import ordinal_3_1 as preprocessor
from utils.visualize_utils import display_utils
from utils.postprocess_utils import volume_utils
import configs
# t means gt(0) or ord(1)
# sec is 0:"3_1" or 1:"3_2" or 2:"3_3"
configs.parse_configs(1, 2)
configs.print_configs()
if __name__ == "__main__":
############################ Train and valid data list ##########################
train_range = np.load(configs.train_range_file)
np.random.shuffle(train_range)
valid_range = np.load(configs.valid_range_file)
train_img_list = [configs.train_img_path_fn(i) for i in train_range]
train_lbl_list = [configs.train_lbl_path_fn(i) for i in train_range]
valid_img_list = [configs.valid_img_path_fn(i) for i in valid_range]
valid_lbl_list = [configs.valid_lbl_path_fn(i) for i in valid_range]
###################################################################