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- #!/usr/bin/python
- #-*- coding: utf-8 -*-
- import sys, time, os, argparse, socket
- import yaml
- import pdb
- import glob
- import datetime
- from utils import *
- from EmbedNet import *
- from DatasetLoader import get_data_loader
- import torchvision.transforms as transforms
- # ## ===== ===== ===== ===== ===== ===== ===== =====
- # ## Parse arguments
- # ## ===== ===== ===== ===== ===== ===== ===== =====
- parser = argparse.ArgumentParser(description = "FaceNet");
- parser.add_argument('--config', type=str, default=None, help='Config YAML file');
- ## Data loader
- parser.add_argument('--batch_size', type=int, default=200, help='Batch size, number of classes per batch');
- parser.add_argument('--max_img_per_cls', type=int, default=500, help='Maximum number of images per class per epoch');
- parser.add_argument('--nDataLoaderThread', type=int, default=5, help='Number of loader threads');
- ## Training details
- parser.add_argument('--test_interval', type=int, default=5, help='Test and save every [test_interval] epochs');
- parser.add_argument('--max_epoch', type=int, default=100, help='Maximum number of epochs');
- parser.add_argument('--trainfunc', type=str, default="softmax", help='Loss function');
- ## Optimizer
- parser.add_argument('--optimizer', type=str, default="adam", help='sgd or adam');
- parser.add_argument('--scheduler', type=str, default="steplr", help='Learning rate scheduler');
- parser.add_argument('--lr', type=float, default=0.001, help='Learning rate');
- parser.add_argument("--lr_decay", type=float, default=0.90, help='Learning rate decay every [test_interval] epochs');
- parser.add_argument('--weight_decay', type=float, default=0, help='Weight decay in the optimizer');
- ## Loss functions
- parser.add_argument("--hard_prob", type=float, default=0.5, help='Hard negative mining probability, otherwise random, only for some loss functions');
- parser.add_argument("--hard_rank", type=int, default=10, help='Hard negative mining rank in the batch, only for some loss functions');
- parser.add_argument('--margin', type=float, default=0.1, help='Loss margin, only for some loss functions');
- parser.add_argument('--scale', type=float, default=30, help='Loss scale, only for some loss functions');
- parser.add_argument('--nPerClass', type=int, default=1, help='Number of images per class per batch, only for metric learning based losses');
- parser.add_argument('--nClasses', type=int, default=8700, help='Number of classes in the softmax layer, only for softmax-based losses');
- ## Load and save
- parser.add_argument('--initial_model', type=str, default="./models/amsoft_model.model", help='Initial model weights');
- parser.add_argument('--save_path', type=str, default="exps/exp1", help='Path for model and logs');
- ## Training and test data
- parser.add_argument('--train_path', type=str, default="data/vggface2", help='Absolute path to the train set');
- parser.add_argument('--train_ext', type=str, default="jpg", help='Training files extension');
- parser.add_argument('--test_path', type=str, default="data/test", help='Absolute path to the test set');
- parser.add_argument('--test_list', type=str, default="data/test_list.csv", help='Evaluation list');
- ## Model definition
- parser.add_argument('--model', type=str, default="ResNet18", help='Name of model definition');
- parser.add_argument('--nOut', type=int, default=512, help='Embedding size in the last FC layer');
- ## For test only
- parser.add_argument('--eval', dest='eval', action='store_true', help='Eval only')
- ## Distributed and mixed precision training
- parser.add_argument('--mixedprec', dest='mixedprec', action='store_true', help='Enable mixed precision training')
- args = parser.parse_args();
- ## Parse YAML
- def find_option_type(key, parser):
- for opt in parser._get_optional_actions():
- if ('--' + key) in opt.option_strings:
- return opt.type
- raise ValueError
- if args.config is not None:
- with open(args.config, "r") as f:
- yml_config = yaml.load(f, Loader=yaml.FullLoader)
- for k, v in yml_config.items():
- if k in args.__dict__:
- typ = find_option_type(k, parser)
- args.__dict__[k] = typ(v)
- else:
- sys.stderr.write("Ignored unknown parameter {} in yaml.\n".format(k))
- # ## ===== ===== ===== ===== ===== ===== ===== =====
- # ## Trainer script
- # ## ===== ===== ===== ===== ===== ===== ===== =====
- def main_worker(args):
- ## Load models
- s = EmbedNet(**vars(args)).cuda();
- it = 1
- ## Write args to scorefile
- scorefile = open(args.result_save_path+"/scores.txt", "a+");
- strtime = datetime.datetime.now().strftime("%Y-%m-%d %H:%M:%S")
- scorefile.write('%s\n%s\n'%(strtime,args))
- scorefile.flush()
- ## Input transformations for training
- train_transform = transforms.Compose(
- [transforms.ToTensor(),
- transforms.Resize(256),
- transforms.RandomCrop([224,224]),
- transforms.Normalize(mean=[0.485, 0.456, 0.406], std=[0.229, 0.224, 0.225])])
- ## Input transformations for evaluation
- test_transform = transforms.Compose(
- [transforms.ToTensor(),
- transforms.Resize(256),
- transforms.CenterCrop([224,224]),
- transforms.Normalize(mean=[0.485, 0.456, 0.406], std=[0.229, 0.224, 0.225])])
- ## Initialise trainer and data loader
- trainLoader = get_data_loader(transform=train_transform, **vars(args));
- trainer = ModelTrainer(s, **vars(args))
- ## Load model weights
- modelfiles = glob.glob('%s/model0*.model'%args.model_save_path)
- modelfiles.sort()
- ## If the target directory already exists, start from the existing file
- if len(modelfiles) >= 1:
- trainer.loadParameters(modelfiles[-1]);
- print("Model %s loaded from previous state!"%modelfiles[-1]);
- it = int(os.path.splitext(os.path.basename(modelfiles[-1]))[0][5:]) + 1
- elif(args.initial_model != ""):
- trainer.loadParameters(args.initial_model);
- print("Model %s loaded!"%args.initial_model);
- ## If the current iteration is not 1, update the scheduler
- for ii in range(1,it):
- trainer.__scheduler__.step()
-
- ## Evaluation code
- if args.eval == True:
- sc, lab = trainer.evaluateFromList(transform=test_transform, **vars(args))
- result = tuneThresholdfromScore(sc, lab, [1, 0.1]);
- print('EER %2.4f'%(result[1]))
- quit();
- ## Core training script
- for it in range(it,args.max_epoch+1):
- clr = [x['lr'] for x in trainer.__optimizer__.param_groups]
- print(time.strftime("%Y-%m-%d %H:%M:%S"), it, "Training epoch %d with LR %f "%(it,max(clr)));
- loss, traineer = trainer.train_network(trainLoader, verbose=True);
- if it % args.test_interval == 0:
-
- sc, lab = trainer.evaluateFromList(transform=test_transform, **vars(args))
- result = tuneThresholdfromScore(sc, lab, [1, 0.1]);
- print("IT %d, VEER %2.4f"%(it, result[1]));
- scorefile.write("IT %d, VEER %2.4f\n"%(it, result[1]));
- trainer.saveParameters(args.model_save_path+"/model%09d.model"%it);
- print(time.strftime("%Y-%m-%d %H:%M:%S"), "TEER/TAcc %2.2f, TLOSS %f"%( traineer, loss));
- scorefile.write("IT %d, TEER/TAcc %2.2f, TLOSS %f\n"%(it, traineer, loss));
- scorefile.flush()
- scorefile.close();
- # ## ===== ===== ===== ===== ===== ===== ===== =====
- # ## Main function
- # ## ===== ===== ===== ===== ===== ===== ===== =====
- def main():
- args.model_save_path = args.save_path+"/model"
- args.result_save_path = args.save_path+"/result"
- args.feat_save_path = ""
- if not(os.path.exists(args.model_save_path)):
- os.makedirs(args.model_save_path)
-
- if not(os.path.exists(args.result_save_path)):
- os.makedirs(args.result_save_path)
- main_worker(args)
- if __name__ == '__main__':
- main()
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