def proxyless_base(num_classes,
loss,
pretrained=True,
net_config=None,
net_weight=None):
assert net_config is not None, "Please input a network config"
net_config_path = download_url(net_config)
net_config_json = json.load(open(net_config_path, 'r'))
net = ProxylessNASNets.build_from_config(net_config_json, num_classes,
loss)
if 'bn' in net_config_json:
net.set_bn_param(bn_momentum=net_config_json['bn']['momentum'],
bn_eps=net_config_json['bn']['eps'])
else:
net.set_bn_param(bn_momentum=0.1, bn_eps=1e-3)
if pretrained:
assert net_weight is not None, "Please specify network weights"
init_path = download_url(net_weight)
load_weights(net, init_path)
return net
def mnasnet(num_classes, loss, pretrained='imagenet', **kwargs):
model = MnasNet(num_classes, loss, **kwargs)
if pretrained == 'imagenet':
model_url = model_urls['imagenet']
load_weights(model, model_url)
return model
def main():
global args
torch.manual_seed(args.seed)
if not args.use_avai_gpus:
os.environ['CUDA_VISIBLE_DEVICES'] = args.gpu_devices
use_gpu = torch.cuda.is_available()
if args.use_cpu: use_gpu = False
log_name = 'log_train_{}.txt'.format(time.strftime("%Y-%m-%d-%H-%M-%S"))
if args.evaluate:
log_name.replace('train', 'test')
sys.stdout = Logger(osp.join(args.save_dir, log_name))
print(' '.join(sys.argv))
print("==========\nArgs:{}\n==========".format(args))
if use_gpu:
print("Currently using GPU {}".format(args.gpu_devices))
cudnn.benchmark = True
torch.cuda.manual_seed_all(args.seed)
else:
print("Currently using CPU, however, GPU is highly recommended")
print("Initializing image data manager")
dm = ImageDataManager(use_gpu, **image_dataset_kwargs(args))
if hasattr(dm, 'lfw_dataset'):
lfw = dm.lfw_dataset
print('LFW dataset is used!')
else:
lfw = None
trainloader, trainloader_dict, testloader_dict = dm.return_dataloaders()
num_train_pids = dm.num_train_pids
print("Initializing model: {}".format(args.arch))
model = models.init_model(
name=args.arch,
num_classes=num_train_pids,
loss={'xent', 'htri'},
pretrained=False if args.load_weights else 'imagenet',
grayscale=args.grayscale,
normalize_embeddings=args.normalize_embeddings,
normalize_fc=args.normalize_fc,
convbn=args.convbn)
print("Model size: {:.3f} M".format(count_num_param(model)))
count_flops(model, args.height, args.width, args.grayscale)
if args.load_weights and check_isfile(args.load_weights):
# load pretrained weights but ignore layers that don't match in size
load_weights(model, args.load_weights)
print("Loaded pretrained weights from '{}'".format(args.load_weights))
if args.resume and check_isfile(args.resume):
checkpoint = torch.load(args.resume)
model.load_state_dict(checkpoint['state_dict'])
args.start_epoch = checkpoint['epoch'] + 1
print("Loaded checkpoint from '{}'".format(args.resume))
print("- start_epoch: {}\n- rank1: {}".format(args.start_epoch,
checkpoint['rank1']))
if use_gpu:
model = nn.DataParallel(model).cuda()
model = model.cuda()
if args.evaluate:
print("Evaluate only")
for name in args.target_names:
if not 'lfw' in name.lower():
print("Evaluating {} ...".format(name))
queryloader = testloader_dict[name]['query']
galleryloader = testloader_dict[name]['gallery']
distmat = test(args,
model,
queryloader,
galleryloader,
use_gpu,
return_distmat=True)
if args.visualize_ranks:
visualize_ranked_results(
distmat,
dm.return_testdataset_by_name(name),
save_dir=osp.join(args.save_dir, 'ranked_results',
name),
topk=20)
else:
model.eval()
same_acc, diff_acc, all_acc, auc, thresh = evaluate(
args,
dm.lfw_dataset,
model,
compute_embeddings_lfw,
args.test_batch_size,
verbose=False,
show_failed=args.show_failed)
log.info('Validation accuracy: {0:.4f}, {1:.4f}'.format(
same_acc, diff_acc))
log.info('Validation accuracy mean: {0:.4f}'.format(all_acc))
log.info('Validation AUC: {0:.4f}'.format(auc))
log.info('Estimated threshold: {0:.4f}'.format(thresh))
return
criterions = choose_losses(args, dm, model, use_gpu)
if not args.evaluate and len(criterions) == 0:
raise AssertionError('No loss functions were chosen!')
optimizer = init_optimizer(model.parameters(), **optimizer_kwargs(args))
if args.load_optim:
checkpoint = torch.load(args.load_weights)
optimizer.load_state_dict(checkpoint['optim'])
print("Loaded optimizer from '{}'".format(args.load_weights))
for param_group in optimizer.param_groups:
param_group['lr'] = args.lr
param_group['weight_decay'] = args.weight_decay
scheduler = lr_scheduler.MultiStepLR(optimizer,
milestones=args.stepsize,
gamma=args.gamma)
start_time = time.time()
ranklogger = RankLogger(args.source_names, args.target_names)
train_time = 0
train_writer = SummaryWriter(osp.join(args.save_dir, 'train_log'))
test_writer = SummaryWriter(osp.join(args.save_dir, 'test_log'))
print("=> Start training")
if args.fixbase_epoch > 0:
print(
"Train {} for {} epochs while keeping other layers frozen".format(
args.open_layers, args.fixbase_epoch))
initial_optim_state = optimizer.state_dict()
for epoch in range(args.fixbase_epoch):
start_train_time = time.time()
train(epoch,
model,
criterions,
optimizer,
trainloader,
use_gpu,
train_writer,
fixbase=True,
lfw=lfw)
train_time += round(time.time() - start_train_time)
for name in args.target_names:
if not 'lfw' in name.lower():
print("Evaluating {} ...".format(name))
queryloader = testloader_dict[name]['query']
galleryloader = testloader_dict[name]['gallery']
testloader = testloader_dict[name]['test']
criteria = None
rank1 = test(args,
model,
queryloader,
galleryloader,
use_gpu,
testloader=testloader,
criterions=criteria)
else:
model.eval()
same_acc, diff_acc, all_acc, auc, thresh = evaluate(
args,
dm.lfw_dataset,
model,
compute_embeddings_lfw,
args.test_batch_size,
verbose=False,
show_failed=args.show_failed)
print('Validation accuracy: {0:.4f}, {1:.4f}'.format(
same_acc, diff_acc))
print('Validation accuracy mean: {0:.4f}'.format(all_acc))
print('Validation AUC: {0:.4f}'.format(auc))
print('Estimated threshold: {0:.4f}'.format(thresh))
rank1 = all_acc
print("Done. All layers are open to train for {} epochs".format(
args.max_epoch))
optimizer.load_state_dict(initial_optim_state)
for epoch in range(args.start_epoch, args.max_epoch):
for criterion in criterions:
criterion.train_stats.reset()
start_train_time = time.time()
train(epoch,
model,
criterions,
optimizer,
trainloader,
use_gpu,
train_writer,
lfw=lfw)
train_time += round(time.time() - start_train_time)
scheduler.step()
if (epoch + 1) > args.start_eval and args.eval_freq > 0 and (
epoch + 1) % args.eval_freq == 0 or (epoch +
1) == args.max_epoch:
num_iter = (epoch + 1) * len(trainloader)
if not args.no_train_quality:
for name in args.source_names:
print(
"Measure quality on the {} train set...".format(name))
queryloader = trainloader_dict[name]['query']
galleryloader = trainloader_dict[name]['gallery']
rank1 = test(args, model, queryloader, galleryloader,
use_gpu)
train_writer.add_scalar('rank1/{}'.format(name), rank1,
num_iter)
print("=> Test")
for name in args.target_names:
if not 'lfw' in name.lower():
print("Evaluating {} ...".format(name))
queryloader = testloader_dict[name]['query']
galleryloader = testloader_dict[name]['gallery']
testloader = testloader_dict[name]['test']
criteria = criterions
if args.no_loss_on_val:
criteria = None
rank1 = test(args,
model,
queryloader,
galleryloader,
use_gpu,
testloader=testloader,
criterions=criteria)
test_writer.add_scalar('rank1/{}'.format(name), rank1,
num_iter)
if not args.no_loss_on_val:
for criterion in criterions:
test_writer.add_scalar(
'loss/{}'.format(criterion.name),
criterion.test_stats.avg, num_iter)
criterion.test_stats.reset()
ranklogger.write(name, epoch + 1, rank1)
else:
model.eval()
same_acc, diff_acc, all_acc, auc, thresh = evaluate(
args,
dm.lfw_dataset,
model,
compute_embeddings_lfw,
args.test_batch_size,
verbose=False,
show_failed=args.show_failed)
print('Validation accuracy: {0:.4f}, {1:.4f}'.format(
same_acc, diff_acc))
print('Validation accuracy mean: {0:.4f}'.format(all_acc))
print('Validation AUC: {0:.4f}'.format(auc))
print('Estimated threshold: {0:.4f}'.format(thresh))
test_writer.add_scalar('Accuracy/Val_same_accuracy',
same_acc, num_iter)
test_writer.add_scalar('Accuracy/Val_diff_accuracy',
diff_acc, num_iter)
test_writer.add_scalar('Accuracy/Val_accuracy', all_acc,
num_iter)
test_writer.add_scalar('Accuracy/AUC', auc, num_iter)
rank1 = all_acc
if use_gpu:
state_dict = model.module.state_dict()
else:
state_dict = model.state_dict()
save_dict = {
'state_dict': state_dict,
'epoch': epoch,
'optim': optimizer.state_dict()
}
if len(args.target_names):
save_dict['rank1'] = rank1
save_checkpoint(
save_dict, False,
osp.join(args.save_dir,
'checkpoint_ep' + str(epoch + 1) + '.pth.tar'))
elapsed = round(time.time() - start_time)
elapsed = str(datetime.timedelta(seconds=elapsed))
train_time = str(datetime.timedelta(seconds=train_time))
print(
"Finished. Total elapsed time (h:m:s): {}. Training time (h:m:s): {}.".
format(elapsed, train_time))
ranklogger.show_summary()
def shufflenet(num_classes, loss, pretrained='imagenet', **kwargs):
model = ShuffleNet(num_classes, loss, **kwargs)
if pretrained == 'imagenet':
load_weights(model, model_urls['imagenet'])
return model
def main():
global args
torch.manual_seed(args.seed)
if not args.use_avai_gpus:
os.environ['CUDA_VISIBLE_DEVICES'] = args.gpu_devices
use_gpu = torch.cuda.is_available()
if args.use_cpu: use_gpu = False
log_name = 'log_test.txt'
sys.stdout = Logger(osp.join(args.save_dir, log_name))
print("==========\nArgs:{}\n==========".format(args))
if use_gpu:
print("Currently using GPU {}".format(args.gpu_devices))
cudnn.benchmark = True
torch.cuda.manual_seed_all(args.seed)
else:
print("Currently using CPU, however, GPU is highly recommended")
print("Initializing image data manager")
if not args.convert_to_onnx: # and not args.infer:
dm = ImageDataManager(use_gpu, **image_dataset_kwargs(args))
trainloader, trainloader_dict, testloader_dict = dm.return_dataloaders(
)
num_train_pids = 100
print("Initializing model: {}".format(args.arch))
model = models.init_model(
name=args.arch,
num_classes=num_train_pids,
loss={'xent', 'htri'},
pretrained=False if args.load_weights else 'imagenet',
grayscale=args.grayscale,
ceil_mode=not args.convert_to_onnx,
infer=True,
bits=args.bits,
normalize_embeddings=args.normalize_embeddings,
normalize_fc=args.normalize_fc,
convbn=args.convbn)
print("Model size: {:.3f} M".format(count_num_param(model)))
if args.load_weights and check_isfile(args.load_weights):
# load pretrained weights but ignore layers that don't match in size
load_weights(model, args.load_weights)
print("Loaded pretrained weights from '{}'".format(args.load_weights))
if args.absorb_bn:
search_absorbed_bn(model)
if args.quantization or args.save_quantized_model:
from gap_quantization.quantization import ModelQuantizer
from gap_quantization.dump_utils import dump_quant_params, remove_extra_dump, remove_cat_files
if args.quant_data_dir is None:
raise AttributeError('quant-data-dir argument is required.')
num_channels = 1 if args.grayscale else 3
cfg = {
"bits":
args.bits, # number of bits to store weights and activations
"accum_bits":
32, # number of bits to store intermediate convolution result
"signed": True, # use signed numbers
"save_folder": args.save_dir, # folder to save results
"data_source": args.
quant_data_dir, # folder with images to collect dataset statistics
"use_gpu": False, # use GPU for inference
"batch_size": 1,
"num_workers": 0, # number of workers for PyTorch dataloader
"verbose": True,
"save_params": args.
save_quantized_model, # save quantization parameters to the file
"quantize_forward":
True, # replace usual convs, poolings, ... with GAP-like ones
"num_input_channels": num_channels,
"raw_input": args.no_normalize,
"double_precision":
args.double_precision # use double precision convolutions
}
model = model.cpu()
quantizer = ModelQuantizer(
model, cfg, dm.transform_test
) # transform test is OK if we use args.no_normalize
quantizer.quantize_model(
) # otherwise we need to add QuantizeInput operation
if args.infer:
if args.image_path == '':
raise AttributeError('Image for inference is required')
quantizer.dump_activations(args.image_path,
dm.transform_test,
save_dir=os.path.join(
args.save_dir, 'activations_dump'))
dump_quant_params(args.save_dir, args.convbn)
if args.convbn:
remove_extra_dump(
os.path.join(args.save_dir, 'activations_dump'))
remove_cat_files(args.save_dir)
if use_gpu:
model = nn.DataParallel(model).cuda()
if args.evaluate:
print("Evaluate only")
for name in args.target_names:
if not 'lfw' in name.lower():
print("Evaluating {} ...".format(name))
queryloader = testloader_dict[name]['query']
galleryloader = testloader_dict[name]['gallery']
distmat = test(args,
model,
queryloader,
galleryloader,
use_gpu,
return_distmat=True)
if args.visualize_ranks:
visualize_ranked_results(
distmat,
dm.return_testdataset_by_name(name),
save_dir=osp.join(args.save_dir, 'ranked_results',
name),
topk=20)
else:
model.eval()
same_acc, diff_acc, all_acc, auc, thresh = evaluate(
args,
dm.lfw_dataset,
model,
compute_embeddings_lfw,
args.test_batch_size,
verbose=False,
show_failed=args.show_failed,
load_embeddings=args.load_embeddings)
log.info('Validation accuracy: {0:.4f}, {1:.4f}'.format(
same_acc, diff_acc))
log.info('Validation accuracy mean: {0:.4f}'.format(all_acc))
log.info('Validation AUC: {0:.4f}'.format(auc))
log.info('Estimated threshold: {0:.4f}'.format(thresh))
#roc_auc(model, '/home/maxim/data/lfw/pairsTest.txt', '/media/slow_drive/cropped_lfw', args, use_gpu)
return
def espnet(num_classes, loss, pretrained='imagenet', **kwargs):
model = ESPNet_Encoder(num_classes, loss)
if pretrained == 'imagenet':
load_weights(model, model_urls['imagenet'])
return model
def mobilenetv2(num_classes, loss, pretrained='imagenet', **kwargs):
model = MobileNetV2(num_classes, loss, **kwargs)
if pretrained == 'imagenet':
model_url = model_urls['imagenet']
load_weights(model, model_url)
return model
def choose_losses(args, data_manager, model, use_gpu):
criteria = []
if args.xent_loss == 'am':
criteria.append(Criterion('am', AMSoftmaxLoss(), args.weight_xent))
elif args.xent_loss is not None:
criteria.append(
Criterion(
'xent',
CrossEntropyLoss(num_classes=data_manager.num_train_pids,
use_gpu=use_gpu,
label_smooth=args.label_smooth),
args.weight_xent))
if args.euclid_loss:
if args.euclid_loss == 'triplet':
loss = TripletLoss(margin=args.margin)
elif args.euclid_loss == 'lifted':
loss = LiftedLoss(margin=args.margin)
else:
raise KeyError('Unknown euclidean loss: {}'.format(
args.euclid_loss))
criteria.append(Criterion('euclid', loss, args.weight_euclid))
if args.face_loss:
input_channels = 3
if args.grayscale:
input_channels = 1
features = model(
torch.zeros(1, input_channels, args.height, args.width))
feature_vector_size = features.shape[1]
if args.face_loss == 'arc':
loss = ArcFace(feature_vector_size, data_manager.num_train_pids)
elif args.face_loss == 'cos':
loss = CosFace(feature_vector_size, data_manager.num_train_pids)
elif args.face_loss == 'sphere':
loss = SphereFace(feature_vector_size, data_manager.num_train_pids)
elif args.face_loss == 'am':
loss = AMSoftmaxLoss()
else:
raise KeyError('Unknown face loss: {}'.format(args.face_loss))
if use_gpu:
loss = loss.cuda()
criteria.append(Criterion('face', loss, args.weight_face))
if args.teacher_arch:
print("Initializing teacher model: {}".format(args.teacher_arch))
teacher_model = models.init_model(
name=args.teacher_arch,
num_classes=data_manager.num_train_pids,
loss={'xent', 'htri'},
pretrained=False)
if not args.load_teacher_weights or not check_isfile(
args.load_teacher_weights):
print('Teacher model checkpoint wasn\'t provided!')
return
load_weights(teacher_model, args.load_teacher_weights)
print("Loaded pretrained weights from '{}' for teacher model".format(
args.load_teacher_weights))
if use_gpu:
teacher_model = nn.DataParallel(teacher_model).cuda()
criteria.append(
Criterion('distill', DistillLoss(teacher_model),
args.weight_distill))
return criteria
def dilated_thin_mobilenet(num_classes, loss, pretrained='imagenet', **kwargs):
model = DilatedThinMobileNet(num_classes, loss, **kwargs)
if pretrained == 'imagenet':
model_url = pretrained_settings['mobilenet']['imagenet']['url']
load_weights(model, model_url)
return model
def shufflenetv2small(num_classes, loss, pretrained='imagenet', **kwargs):
model = ShuffleNetV2(num_classes, loss, width_mult=0.5)
if pretrained == 'imagenet':
model_url = model_urls['imagenet_small']
load_weights(model, model_url)
return model
