def parseArgs(args): # Handle all command-line options p = argParser() arg_data = p.parse_known_args(args) args = arg_data[0] args.script_args = arg_data[1] args.disk_cache = False if args.disk_cache == 'no' else True args.ignore_ssl_errors = False if args.ignore_ssl_errors == 'no' else True args.load_images = True if args.load_images == 'yes' else False args.load_plugins = False if args.load_plugins == 'no' else True if args.proxy: item = args.proxy.split(':') if len(item) < 2 or not len(item[1]): p.print_help() sys.exit(1) args.proxy = item do_action('ParseArgs', Bunch(locals())) if not args.script: p.print_help() sys.exit(1) try: with codecs.open(args.script, encoding='utf-8') as script: args.script_name = script.name args.script = script.read() except IOError as (errno, stderr): sys.exit('%s: \'%s\'' % (stderr, args.script))
def parseArgs(args): # Handle all command-line options p = argParser() arg_data = p.parse_known_args(args) args = arg_data[0] args.script_args = arg_data[1] args.disk_cache = False if args.disk_cache == 'no' else True args.ignore_ssl_errors = False if args.ignore_ssl_errors == 'no' else True args.load_images = True if args.load_images == 'yes' else False args.load_plugins = False if args.load_plugins == 'no' else True args.local_access_remote = False if args.local_access_remote == 'no' else True if args.proxy: item = args.proxy.split(':') if len(item) < 2 or not len(item[1]): p.print_help() sys.exit(1) args.proxy = item if args.cookies and not os.path.exists(args.cookies): sys.exit("No such file or directory: '%s'" % args.cookies) do_action('ParseArgs') if not args.script: p.print_help() sys.exit(1) if not os.path.exists(args.script): sys.exit("No such file or directory: '%s'" % args.script) return args
def main(): args = argParser() cifarLoader = CifarLoader(args) if not os.path.exists(args.logdir): os.makedirs(args.logdir) device = torch.device("cuda" if args.cuda else "cpu") net = args.model(args.logdir, device).to(device) print('The log is recorded in ') print(net.logFile.name) criterion = net.criterion().to(device) optimizer = net.optimizer() for epoch in trange(args.epochs): # loop over the dataset multiple times net.adjust_learning_rate(optimizer, epoch, args) train(net, cifarLoader, optimizer, criterion, epoch, device) if epoch % 10 == 0: # Comment out this part if you want a faster training test(net, cifarLoader, device, 'Train') test(net, cifarLoader, device, 'Test') print('The log is recorded in ') print(net.logFile.name)
def main(): args = argParser() cifarLoader = CifarLoader(args) net = args.model() print('The log is recorded in ') print(net.logFile.name) if os.path.isfile('model.pth'): net.load_state_dict(torch.load('model.pth')) criterion = net.criterion() optimizer = net.optimizer() for epoch in range(args.epochs): # loop over the dataset multiple times net.adjust_learning_rate(optimizer, epoch, args) train(net, cifarLoader, optimizer, criterion, epoch) if epoch % 10 == 0: # Comment out this part if you want a faster training test(net, cifarLoader, 'Train') test(net, cifarLoader, 'Test') torch.save(net.state_dict(), 'model.pth') print('The log is recorded in ') print(net.logFile.name)
def parseArgs(args): # Handle all command-line options p = argParser() arg_data = p.parse_known_args(args) args = arg_data[0] args.script_args = arg_data[1] args.disk_cache = False if args.disk_cache == 'no' else True args.ignore_ssl_errors = False if args.ignore_ssl_errors == 'no' else True args.load_images = True if args.load_images == 'yes' else False args.load_plugins = False if args.load_plugins == 'no' else True args.local_access_remote = False if args.local_access_remote == 'no' else True if args.proxy: item = args.proxy.split(':') if len(item) < 2 or not len(item[1]): p.print_help() sys.exit(1) args.proxy = item do_action('ParseArgs') if not args.script: p.print_help() sys.exit(1) if not os.path.exists(args.script): sys.exit('No such file or directory: \'%s\'' % args.script) return args
def parseArgs(app, args): # Handle all command-line options p = argParser() arg_data = p.parse_known_args(args) args = arg_data[0] args.script_args = arg_data[1] args.disk_cache = False if args.disk_cache == 'no' else True args.ignore_ssl_errors = False if args.ignore_ssl_errors == 'no' else True args.load_images = True if args.load_images == 'yes' else False args.load_plugins = False if args.load_plugins == 'no' else True args.local_access_remote = False if args.local_access_remote == 'no' else True # register an alternative Message Handler messageHandler = MessageHandler(args.verbose) qInstallMsgHandler(messageHandler.process) file_check = (args.cookies, args.config) for file_ in file_check: if file_ is not None and not os.path.exists(file_): sys.exit("No such file or directory: '%s'" % file_) if args.config: config = Config(app, args.config) # apply settings for setting in config.settings: setattr(args, config.settings[setting]['mapping'], config.property(setting)) # special case for verbose arg, which will need to be re-applied if setting == 'verbose': messageHandler.verbose = args.verbose split_check = ( (args.proxy, 'proxy'), (args.auth, 'auth') ) for arg, name in split_check: if arg: item = arg.split(':') if len(item) < 2 or not len(item[1]): p.print_help() sys.exit(1) setattr(args, name, item) do_action('ParseArgs') if args.script is None: p.print_help() sys.exit(1) if not os.path.exists(args.script): sys.exit("No such file or directory: '%s'" % args.script) return args
def parseArgs(app, args): # Handle all command-line options p = argParser() arg_data = p.parse_known_args(args) args = arg_data[0] args.script_args = arg_data[1] # register an alternative Message Handler messageHandler = MessageHandler(args.verbose) qInstallMsgHandler(messageHandler.process) file_check = (args.cookies_file, args.config) for file_ in file_check: if file_ is not None and not os.path.exists(file_): sys.exit("No such file or directory: '%s'" % file_) if args.config: config = Config(app, args.config) # apply settings for setting in config.settings: setattr(args, config.settings[setting]['mapping'], config.property(setting)) split_check = ( (args.proxy, 'proxy'), ) for arg, name in split_check: if arg: item = arg.split(':') if len(item) < 2 or not len(item[1]): p.print_help() sys.exit(1) setattr(args, name, item) do_action('ParseArgs', args) if args.debug: debug(args.debug) # verbose flag got changed on us, so we reload the flag if messageHandler.verbose != args.verbose: messageHandler.verbose = args.verbose if args.script is None: p.print_help() sys.exit(1) if not os.path.exists(args.script): sys.exit("No such file or directory: '%s'" % args.script) return args
def main(): args = argParser() cifarLoader = YohoLoader(args) net = args.model() criterion = net.criterion() optimizer = net.optimizer() for epoch in range(args.epochs): # loop over the dataset multiple times net.adjust_learning_rate(optimizer, epoch, args) train(net, cifarLoader, optimizer, criterion, epoch) if epoch % 4 == 0: # Comment out this part if you want a faster training test(net, cifarLoader, 'Train') test(net, cifarLoader, 'Test') torch.save(net, "weights/epoch" + str(epoch) + ".yoho")
def main(): args = argParser() loader = FaceLoader(args) net = args.model() print('The log is recorded in ') print(net.logFile.name) criterion = net.criterion() optimizer = net.optimizer() for epoch in range(args.epochs): # loop over the dataset multiple times net.adjust_learning_rate(optimizer, epoch, args) train(net, loader, optimizer, criterion, epoch) if epoch % 1 == 0: # Comment out this part if you want a faster training test(net, loader, 'Train') test(net, loader, 'Test') print('The log is recorded in ') print(net.logFile.name)
def main(): args = argParser() cifarLoader = CifarLoader(args) net = args.model() print('The log is recorded in ') print(net.logFile.name) criterion = net.criterion() optimizer = net.optimizer() for epoch in range(args.epochs): # loop over the dataset multiple times net.adjust_learning_rate(optimizer, epoch, args) train(net, cifarLoader, optimizer, criterion, epoch) if epoch % 1 == 0: # Comment out this part if you want a faster training test(net, cifarLoader, 'Train') test(net, cifarLoader, 'Test') print('The log is recorded in ') print(net.logFile.name)
def main(): # cuda = torch.device('cuda') # torch.set_default_tensor_type('torch.cuda.FloatTensor') args = argParser() cifarLoader = CifarLoader(args) net = args.model() # net.cuda() print('The log is recorded in ') print(net.logFile.name) criterion = net.criterion() optimizer = net.optimizer() for epoch in range(args.epochs): # loop over the dataset multiple times net.adjust_learning_rate(optimizer, epoch, args) train(net, cifarLoader, optimizer, criterion, epoch) if epoch % 1 == 0: # Comment out this part if you want a faster training test(net, cifarLoader, 'Train') test(net, cifarLoader, 'Test') print('The log is recorded in ') print(net.logFile.name)
def main(): args = argParser() cifarLoader = CifarLoader(args) net = args.model() print('The log is recorded in ') print(net.logFile.name) criterion = net.criterion() optimizer = net.optimizer() device = "cuda" if torch.cuda.is_available() else "cpu" net.to(device) for epoch in range(args.epochs): # loop over the dataset multiple times net.adjust_learning_rate(optimizer, epoch, args) train(net, cifarLoader, optimizer, criterion, epoch, device) if epoch % 1 == 0: # Comment out this part if you want a faster training test(net, cifarLoader, device, 'Train') test(net, cifarLoader, device, 'Test') print('The log is recorded in ') print(net.logFile.name)
def parseArgs(args): # Handle all command-line options p = argParser() arg_data = p.parse_known_args(args) args = arg_data[0] args.script_args = arg_data[1] if args.upload_file: # process the tags item_buffer = {} for i in range(len(args.upload_file)): item = args.upload_file[i].split('=') if len(item) < 2 or not len(item[1]): # if buffer is empty, or tag has no # value 'tag=', print help and exit if not len(item_buffer) or \ item[1:] and not item[1:][0]: p.print_help() sys.exit(1) # this is a bug workaround for argparse. # if you call parse_known_args, and you # have an --option script arg, the args # get jumbled up, and it's inconsistent # # we're just going to check for -- and # swap it all back to the right order if args.script_args: for i in range(len(args.upload_file)): if not args.upload_file[i].count('='): # insert the arg after --option (make sure it's not None) if args.script: args.script_args.insert(1, args.script) # insert value args before --option if args.upload_file[i+1:]: arg_buffer = args.upload_file[i+1:] arg_buffer.reverse() for val in arg_buffer: args.script_args.insert(0, val) args.script = args.upload_file[i] break else: args.script = args.upload_file[i] args.script_args = args.upload_file[i+1:] break # duplicate tag checking if item[0] in item_buffer: sys.exit('Multiple tags named \'%s\' were found' % item[0]) item_buffer[item[0]] = item[1] # make sure files exist for tag in item_buffer: if not os.path.exists(item_buffer[tag]): sys.exit('No such file or directory: \'%s\'' % item_buffer[tag]) args.upload_file = item_buffer if args.proxy: item = args.proxy.split(':') if len(item) < 2 or not len(item[1]): p.print_help() sys.exit(1) args.proxy = item do_action('ParseArgs', Bunch(locals())) if not args.script: p.print_help() sys.exit(1) try: args.script = codecs.open(args.script, encoding='utf-8') except IOError as (errno, stderr): sys.exit('%s: \'%s\'' % (stderr, args.script))
def parseArgs(args): # Handle all command-line options p = argParser() arg_data = p.parse_known_args(args) args = arg_data[0] args.script_args = arg_data[1] if args.upload_file: # process the tags item_buffer = {} for i in range(len(args.upload_file)): item = args.upload_file[i].split('=') if len(item) < 2 or not len(item[1]): # if buffer is empty, or tag has no # value 'tag=', print help and exit if not len(item_buffer) or \ item[1:] and not item[1:][0]: p.print_help() sys.exit(1) # this is a bug workaround for argparse. # if you call parse_known_args, and you # have an --option script arg, the args # get jumbled up, and it's inconsistent # # we're just going to check for -- and # swap it all back to the right order if args.script_args: for i in range(len(args.upload_file)): if not args.upload_file[i].count('='): # insert the arg after --option (make sure it's not None) if args.script: args.script_args.insert(1, args.script) # insert value args before --option if args.upload_file[i + 1:]: arg_buffer = args.upload_file[i + 1:] arg_buffer.reverse() for val in arg_buffer: args.script_args.insert(0, val) args.script = args.upload_file[i] break else: args.script = args.upload_file[i] args.script_args = args.upload_file[i + 1:] break # duplicate tag checking if item[0] in item_buffer: sys.exit('Multiple tags named \'%s\' were found' % item[0]) item_buffer[item[0]] = item[1] # make sure files exist for tag in item_buffer: if not os.path.exists(item_buffer[tag]): sys.exit('No such file or directory: \'%s\'' % item_buffer[tag]) args.upload_file = item_buffer if args.proxy: item = args.proxy.split(':') if len(item) < 2 or not len(item[1]): p.print_help() sys.exit(1) args.proxy = item do_action('ParseArgs', Bunch(locals())) if not args.script: p.print_help() sys.exit(1) try: args.script = codecs.open(args.script, encoding='utf-8') except IOError as (errno, stderr): sys.exit('%s: \'%s\'' % (stderr, args.script))
import os, sys, resources from phantom import Phantom from utils import argParser, MessageHandler, version from PyQt4.QtCore import QString, qInstallMsgHandler, qFatal from PyQt4.QtGui import * # make keyboard interrupt quit program import signal signal.signal(signal.SIGINT, signal.SIG_DFL) if __name__ == "__main__": # Handle all command-line options p = argParser() args = p.parse_args() # register an alternative Message Handler messageHandler = MessageHandler(args.verbose) qInstallMsgHandler(messageHandler.process) if args.upload_file: item_buffer = {} for i in range(len(args.upload_file)): item = args.upload_file[i].split("=") if len(item) < 2 or not len(item[1]): if len(item_buffer) == 0: p.print_help() sys.exit(1) args.script = args.upload_file[i:]
if index > 1: continue print item_dir for item in os.listdir(item_dir): emulFile = item[:item.rfind( '_')] + '_Emulator_' + item[:-5].split('_')[-1] + '.root' if not os.path.exists(list_emulDir[index] + '/' + emulFile): print list_emulDir[ index] + '/' + emulFile + "does not exist" list_data.append(item) list_emul.append(emulFile) if args.compare: pool = mp.Pool(processes=50) with tqdm(total=len(list_data)) as pbar: for _ in pool.imap_unordered(cp.compareDataEmul, [ utils.argParser(item_dir, list_emulDir[index], item, outDir, True) for item in list_data ]): pbar.update() pool.close() pool.join() # NEED TO FIX if args.efficiency: pool = mp.Pool(processes=50) with tqdm(total=len(list_emul)) as pbar: for _ in pool.imap_unordered(ef.calculateEfficiency, [ utils.argParser(list_emulDir[index], item, outDir, True) for item in list_emul ]): pbar.update() pool.close()
import os, sys, resources from phantom import Phantom from utils import argParser, MessageHandler, version from PyQt4.QtCore import QString, qInstallMsgHandler, qFatal from PyQt4.QtGui import QIcon, QApplication # make keyboard interrupt quit program import signal signal.signal(signal.SIGINT, signal.SIG_DFL) if __name__ == '__main__': # Handle all command-line options p = argParser() arg_data = p.parse_known_args(sys.argv[1:]) args = arg_data[0] args.script_args = arg_data[1] # register an alternative Message Handler messageHandler = MessageHandler(args.verbose) qInstallMsgHandler(messageHandler.process) if args.upload_file: item_buffer = {} for i in range(len(args.upload_file)): item = args.upload_file[i].split('=') if len(item) < 2 or not len(item[1]): if len(item_buffer) == 0: p.print_help()
def main(): args = argParser() #torch.cuda.set_device(1) device = torch.device("cuda:0") #device = torch.device('cpu') print(device) transform = transforms.Compose([ # TODO: Use these data augmentations later transforms.RandomHorizontalFlip(), transforms.RandomResizedCrop(224), transforms.ToTensor(), #transforms.ColorJitter(), transforms.Normalize((0.485, 0.456, 0.406), (0.229, 0.224, 0.225)) ]) #print(os.path.isdir('newtrain')) #trainset = torchvision.datasets.ImageFolder('newtrain', transform=transform) #cifarLoader = torch.utils.data.DataLoader(trainset, batch_size=4, shuffle=True, num_workers=2) cifarLoader = BirdLoader(args) #print('BirdLoader initialized') outputFile = open(args.outputfile, 'w+') logFile = open(args.logfile, 'w+') net = args.model() net = net.to(device) #print('The log is recorded in ') #print(net.logFile.name) if not args.getoutput: criterion = net.criterion().to(device) params = list(net.parameters()) for p in params: print(p.requires_grad) #optimizer = optim.Adam(params) optimizer = net.optimizer() convergeCount = 5 currentLoss = float("inf") startTime = datetime.datetime.now() log(logFile, 'Training began at ' + str(startTime)) for epoch in range( args.epochs): # loop over the dataset multiple times log(logFile, 'Epoch ' + str(epoch + 1)) net.adjust_learning_rate(optimizer, epoch, args) loss = train(net, cifarLoader.trainloader, optimizer, criterion, epoch, device, logFile) if epoch % 1 == 0: # Comment out this part if you want a faster training test(net, cifarLoader, device, logFile, 'Train') if abs(currentLoss - loss) < 0.001: convergeCount -= 1 else: convergeCount = 5 if loss < currentLoss: currentLoss = loss torch.save(net.state_dict(), args.modelfile) if convergeCount == 0: break endTime = datetime.datetime.now() log( logFile, 'Training ended at ' + str(endTime) + '. It trained for ' + str(endTime - startTime)) else: net.load_state_dict(torch.load(args.modelfile)) output( net, outputFile, transforms.Compose([ transforms.Resize(256), transforms.CenterCrop(224), transforms.ToTensor(), transforms.Normalize((0.485, 0.456, 0.406), (0.229, 0.224, 0.225)) ]), device) #test(net) #net.save_state_dict('mytraining.pt') log(logFile, 'The log is recorded in ') log(logFile, args.logfile) log(logFile, 'The output is recorded in ') log(logFile, args.outputfile) log(logFile, 'The model is recorded in ') log(logFile, args.modelfile) logFile.close() outputFile.close()