def main(): #if len(sys.argv) < 2: # print("usage: python %prog <torrent> [options]") # return opts = optparse.OptionParser(\ usage = "usage: python console.py <torrent> [options]") opts.add_option('-s', '--store', action = 'store_true', \ dest = 'store', help = 'files are written to disk') opts.add_option('-p', '--path', default = 'downloads', dest = 'path',\ metavar = 'DIR', help = 'in which dir to store/retrieve the files') opts.add_option('-a', '--accept', action = 'store_true', \ dest = 'accept', help = 'starts listening for incoming connections') options, args = opts.parse_args() if len(args) != 1: opts.error('incorrect number of arguments') peer_id = b'a0b1c2d3e4f5g6h7i8j9' env = environment.initialize(args[0], peer_id, \ options.store, options.path) console = code.InteractiveConsole(env) console.interact( "Python interactive console %s.\nBitTorrent chat client using bitless." % sys.version) environment.shutdown() print("Finished...")
def main(): #if len(sys.argv) < 2: # print("usage: python %prog <torrent> [options]") # return opts = optparse.OptionParser(\ usage = "usage: python console.py <torrent> [options]") opts.add_option('-s', '--store', action = 'store_true', \ dest = 'store', help = 'files are written to disk') opts.add_option('-p', '--path', default = 'downloads', dest = 'path',\ metavar = 'DIR', help = 'in which dir to store/retrieve the files') opts.add_option('-a', '--accept', action = 'store_true', \ dest = 'accept', help = 'starts listening for incoming connections') options, args = opts.parse_args() if len(args) != 1: opts.error('incorrect number of arguments') peer_id = b'a0b1c2d3e4f5g6h7i8j9' env = environment.initialize(args[0], peer_id, \ options.store, options.path) console = code.InteractiveConsole(env) console.interact("Python interactive console %s.\nBitTorrent chat client using bitless." % sys.version) environment.shutdown() print("Finished...")
//= Sola丶小克 //===== Current Version: ===================================== //= 1.0 //===== Description: ========================================= //= 此脚本用于编译复兴前和复兴后 Release 版本的 Pandas 模拟器 //= 符号文件的储存和上传工作, 将由其他脚本来实现 //===== Additional Comments: ================================= //= 1.0 首个版本. [Sola丶小克] //============================================================ ''' # -*- coding: utf-8 -*- import environment environment.initialize() import os import platform import re import shutil import time import winreg import git from dotenv import load_dotenv from libs import Common, Inputer, Message # 切换工作目录为脚本所在目录 os.chdir(os.path.split(os.path.realpath(__file__))[0])
def cxr_predict(hmp_dims=None, dcm_file=None, cuda='0', fl_gradcam=True, Nens=3, th_gradcam=0.7, input_type='dicom'): print( "\n-------------------------------------------------------------------" ) print( "| |") print( "| |") print( "| v1.0 MGH Age, View, Gender, Vendor, Abnormal Detection |") print( "| (Copyright (c) 2021-2022, MGH LMIC. All rights reserved.) |") print( "| |") print( "| |") print( "-------------------------------------------------------------------\n" ) param_cuda = cuda param_labels = [ 'ap', 'pa', 'female', 'male', 'varian', 'agfa', 'ge', 'Foreign body>.>.', 'Hilar/mediastinum>Cardiomegaly>.', 'Lung density>Increased lung density>Atelectasis', 'Lung density>Increased lung density>Pulmonary edema', 'Lung density>Increased lung density>pneumonia', 'Pleura>Pleural effusion>.', 'abnormal', 'PatientAge' ] param_path = None param_runDir = '' param_type = 0 param_preModel = 'multitask_model' param_gradcam = fl_gradcam param_arch = None param_task = 2 param_clsGcam = param_labels[-8:-2] param_Nens = Nens fl_ensemble = False if param_Nens == 1 else True runtime_path, device = initialize(param_runDir, param_cuda) # image preprocessing d = DcmToPng( param_labels, dcm_path=runtime_path.joinpath('input_dir/DICOM').resolve(), png_path=runtime_path.joinpath('input_dir/IMAGEFILE').resolve(), ds=dcm_file, localOp=True, input_type=input_type) #d = DcmToPng(param_labels, dcm_path=runtime_path.joinpath('dicoms').resolve(), png_path=runtime_path.joinpath('pngs').resolve(), ds=dcm_file) if input_type == 'dicom': d.dcm2png() # start network inference env = DemoEnvironment(device, runtime_path, mtype=param_type, name_labels=param_labels, name_paths=param_path, name_model=param_arch, task_type=param_task) t = Demo(env, pt_runtime=runtime_path, fn_net=param_preModel, fl_gradcam=param_gradcam, cls_gradcam=param_clsGcam, th_gradcam=th_gradcam, fl_ensemble=fl_ensemble) if (fl_ensemble): result = t.demo_cxr_ensemble_evaluation(hmp_dims=hmp_dims, n_ens=param_Nens) else: result = t.demo_cxr_evaluation(hmp_dims=hmp_dims) df_prob = pd.DataFrame(result['prob'], columns=print_label_name) df_pred = pd.DataFrame(result['pred'], columns=print_label_name) df_file = pd.read_csv( runtime_path.joinpath('input_dir/IMAGEFILE/images.csv')) df_prob['file'] = df_file['PATH'] df_pred['file'] = df_file['PATH'] runtime_path.joinpath('output_dir/Classification').mkdir(parents=True, exist_ok=True) df_prob.to_csv( runtime_path.joinpath('output_dir/Classification/probability.txt'), header=True, index=True, sep=',', mode='w') df_pred.to_csv( runtime_path.joinpath('output_dir/Classification/prediction.txt'), header=True, index=True, sep=',', mode='w') #print(result) return result