def main(): args = parser.parse_args() if args.directory.endswith(".zip"): directory = os.path.abspath(args.directory[0:-len(".zip")]) if not os.path.exists(directory): subprocess.check_call([ "unzip", args.directory, "-d", os.path.dirname(args.directory) ]) else: directory = args.directory with open(os.path.join(directory, "manifest.json")) as fh: manifest = json.load(fh) if manifest["letters"] and not args.skip_letters: lfiles, ljobs, lpage = collate_letters(directory, manifest["letters"], 1) print "Found", len(ljobs), "letter jobs" if ljobs: run_batch(args, lfiles, ljobs) if manifest["postcards"] and not args.skip_postcards: pfiles, pjobs, ppage = collate_postcards(manifest["postcards"], 1) print "Found", len(pjobs), "postcard jobs" if pjobs: run_batch(args, pfiles, pjobs)
def main(): args = parse_args() cfg = load_config(args) launch_job(cfg=cfg, init_method=args.init_method, func=benchmark_data_loading)
def main(): """ Main function to spawn the train and test process. """ args = parse_args() cfg = load_config(args) # Perform training. if cfg.TRAIN.ENABLE: launch_job(cfg=cfg, init_method=args.init_method, func=train) # Perform multi-clip testing. if cfg.TEST.ENABLE: launch_job(cfg=cfg, init_method=args.init_method, func=test)
def main(): args = parser.parse_args() with open(os.path.join(args.directory, "manifest.json")) as fh: manifest = json.load(fh) if manifest["letters"]: lfiles, ljobs, lpage = collate_letters(args.directory, manifest["letters"], 1) print "Found", len(ljobs), "letter jobs" if ljobs: run_batch(args, lfiles, ljobs) if manifest["postcards"]: pfiles, pjobs, ppage = collate_postcards(manifest["postcards"], 1) print "Found", len(pjobs), "postcard jobs" if pjobs: run_batch(args, pfiles, pjobs)
def main(argv): """ Main entry of our list manager Parameters: - `argv`: array of string Return: - `int`: 0 is success otherwise failure """ ret = 0 parser = utils.parser.createParser() opt = parser.parse_args(argv) try: # Stop execution if help flag is on if opt.help: raise ShowHelpException() # Read materials we need for later processing cfg = auxiliary.readConfig(opt.config) rawJson = auxiliary.readJson(opt.input[0]) # List we are going to manipulate :) commands = rawJson['commands'] # Pump commands and instruction for filtering, sorting etc rawJson['commands'] = pump(commands, cfg) # Write final result auxiliary.writeJSONFile(rawJson, opt.output[0]) except ShowHelpException: parser.print_help() ret = 0 except FilterException, e: ret = 1 print('ERROR') print(e)
def parse_args(): parser = argparse.ArgumentParser() parser.add_argument( "launch_info", help="Path to the launch file to analyze and optional arguments", nargs="+") parser.add_argument("-v", "--verbose", help="increase output verbosity", action="store_true") parser.add_argument("-q", "--quiet", help="decrease output verbosity", action="store_true") parser.add_argument("-np", "--noplot", help="don't plot the generated html", action="store_true") args = parser.parse_args() return args
- 1 train过程中的生成batch data 2 计算test集的表现 - 2021/5/13 ''' import utils.metrics as metrics from utils.parser import parse_args from utils.load_data import * import multiprocessing import heapq # 导入parser # 需要参数 评价指标的K值集合;数据集信息来构造load_data cores = multiprocessing.cpu_count() // 2 args = parse_args() Ks = eval(args.Ks) data_generator = Data(path=args.data_path + args.dataset, batch_size=args.batch_size) USR_NUM, ITEM_NUM = data_generator.n_users, data_generator.n_items N_TRAIN, N_TEST = data_generator.n_train, data_generator.n_test BATCH_SIZE = args.batch_size def ranklist_by_heapq(user_pos_test, test_items, rating, Ks): item_score = {} for i in test_items: item_score[i] = rating[i] K_max = max(Ks) K_max_item_score = heapq.nlargest(K_max, item_score, key=item_score.get)
def main(): global args args = parse_args() train_net(args)
def test_get_repo_object(): (options, args) = parse_args([]) repo = get_repo_object("utapi", "master", options) # The current working directory is the root of this project. assert os.path.isdir("./utapi"), "Repository not cloned to project root."
import utils as H from Patient import * from Waiting_Place import * from Serve_Place import * from utils.package import * from utils import parser args = parser.parse_args() p_showup = args.p_showup walk_in_rate = args.walk_in_rate arrival_rate_blood = args.arrival_rate_blood arrival_rate_scan = args.arrival_rate_scan num_node = args.num_node trans_prob = args.trans_prob walk_time = H.walk_time class Simulation(object): ''' This class, Simulation, is to define the relations or processes and to run the whole simulator. ''' def __init__(self, num_node=num_node, trans_prob=trans_prob, walk_time=walk_time): # For statistics self.all_patient = [] self.Patient_arrive_blood = [] self.Patient_served_blood = [] self.Walk_in_arrive_blood = [] self.Walk_in_served_blood = []
def main(): global args args = parse_args() predict(args)
from utils.parser import parse_args def run(args): from run_nn import run as run_nn from run_svm import run as run_svm from run_pam import run as run_pam run_nn(args) run_svm(args) run_pam(args) if __name__ == '__main__': run(parse_args().__dict__)
import sys import git from utils.parser import parse_args from utils.repo_handler import get_repo_object (options, args) = parse_args(sys.argv[1:]) # Ensure that all positional arguments are given. if len(args) != 3: parser.print_help() repo_name = args[0] source_branch = args[1] target_branch = args[2] try: repo = get_repo_object(repo_name, target_branch, options) except git.exc.GitCommandError as e: # Get the git error message from stderr and output the message without # extraneous characters. message = e.stderr.find("fatal:") sys.exit(e.stderr[message:-2]) forward_branch = "forward/" + source_branch git = repo.git # We do not want to use any pre-existing branch. try: git.branch('-D', forward_branch) except: pass
def main(): #time.sleep(3600*6.5) global args args = parse_args() train_net(args)
def main(): args = parse_args() data_path = '{}experiment_data/{}/{}_{}/'.format(args.data_path, args.dataset, args.prepro, args.test_method) # 加载数据类 生成batch_data data_generator = Data(data_path, args.batch_size) data_config = dict() data_config['n_users'] = data_generator.n_users data_config['n_items'] = data_generator.n_items # 构造pretrain_data if args.pretrain in [-1]: pretrain_data = load_pretrain_data(args) else: pretrain_data = None # 构造模型 if (args.model_type == 'bprmf'): model = BPRMF(data_config, pretrain_data, args) # 加载预训练模型参数(tf保存的整个模型参数) if args.pretrain == 1: # TODO a = 1 """ ********************************************** 初始化sess """ config = tf.ConfigProto() config.gpu_options.allow_growth = True sess = tf.Session(config=config) sess.run(tf.global_variables_initializer()) """ ********************************************** 训练 """ loss_log, pre_log, rec_log, ndcg_log, hit_log = [], [], [], [], [] stopping_step = 0 should_stop = False # 训练epoch次数 遍历每个epoch for epoch in range(args.epoch): t1 = time() loss, mf_loss, reg_loss = 0., 0., 0. n_batch = data_generator.n_train // args.batch_size + 1 for idx in range(n_batch): # 获取batch数据 batch_data = data_generator.generate_train_cf_batch(idx) # 构造feed_fict feed_dict = data_generator.generate_train_feed_dict( model, batch_data) # run _, batch_loss, batch_mf_loss, batch_reg_loss = model.train( sess, feed_dict) loss += batch_loss mf_loss += batch_mf_loss reg_loss += batch_reg_loss loss /= n_batch mf_loss /= n_batch reg_loss /= n_batch loss_log.append(loss) if (np.isnan(loss)): print('ERROR:loss is nan') sys.exit() # 每隔show_step的epoch 进行test计算评价指标 show_step = 100 if (epoch + 1) % show_step != 0: # 每隔verbose的epoch 输出当前epoch的loss信息 if (args.verbose > 0 and epoch % args.verbose == 0): print_str='Epoch {} [{:.1f}s]: train loss==[{:.5f}={:.5f}+{:.5f}]'\ .format(epoch,time()-t1,loss,mf_loss,reg_loss) print(print_str) continue """ ********************************************** 测试 计算评价指标 """ print('TODO:test')