elif params.teacher == "wrn":
teacher_model = wrn.WideResNet(depth=28,
num_classes=10,
widen_factor=10,
dropRate=0.3)
teacher_checkpoint = 'experiments/base_wrn/best.pth.tar'
teacher_model = nn.DataParallel(teacher_model).cuda()
elif params.teacher == "densenet":
teacher_model = densenet.DenseNet(depth=100, growthRate=12)
teacher_checkpoint = 'experiments/base_densenet/best.pth.tar'
teacher_model = nn.DataParallel(teacher_model).cuda()
elif params.teacher == "resnext29":
teacher_model = resnext.CifarResNeXt(cardinality=8,
depth=29,
num_classes=10)
teacher_checkpoint = 'experiments/base_resnext29/best.pth.tar'
teacher_model = nn.DataParallel(teacher_model).cuda()
elif params.teacher == "preresnet110":
teacher_model = preresnet.PreResNet(depth=110, num_classes=10)
teacher_checkpoint = 'experiments/base_preresnet110/best.pth.tar'
teacher_model = nn.DataParallel(teacher_model).cuda()
utils.load_checkpoint(teacher_checkpoint, teacher_model)
# Train the model with KD
logging.info("Experiment - model version: {}".format(
params.model_version))
logging.info("Starting training for {} epoch(s)".format(
def main():
# Load the parameters from json file
args = parser.parse_args()
json_path = os.path.join(args.model_dir, 'params.json')
assert os.path.isfile(
json_path), "No json configuration file found at {}".format(json_path)
params = utils.Params(json_path)
# Set the random seed for reproducible experiments
random.seed(230)
torch.manual_seed(230)
np.random.seed(230)
torch.cuda.manual_seed(230)
warnings.filterwarnings("ignore")
# Set the logger
utils.set_logger(os.path.join(args.model_dir, 'train.log'))
# Create the input data pipeline
logging.info("Loading the datasets...")
# fetch dataloaders, considering full-set vs. sub-set scenarios
if params.subset_percent < 1.0:
train_dl = data_loader.fetch_subset_dataloader('train', params)
else:
train_dl = data_loader.fetch_dataloader('train', params)
dev_dl = data_loader.fetch_dataloader('dev', params)
logging.info("- done.")
"""
Load student and teacher model
"""
if "distill" in params.model_version:
# Specify the student models
if params.model_version == "cnn_distill": # 5-layers Plain CNN
print("Student model: {}".format(params.model_version))
model = net.Net(params).cuda()
elif params.model_version == "shufflenet_v2_distill":
print("Student model: {}".format(params.model_version))
model = shufflenet.shufflenetv2(class_num=args.num_class).cuda()
elif params.model_version == "mobilenet_v2_distill":
print("Student model: {}".format(params.model_version))
model = mobilenet.mobilenetv2(class_num=args.num_class).cuda()
elif params.model_version == 'resnet18_distill':
print("Student model: {}".format(params.model_version))
model = resnet.ResNet18(num_classes=args.num_class).cuda()
elif params.model_version == 'resnet50_distill':
print("Student model: {}".format(params.model_version))
model = resnet.ResNet50(num_classes=args.num_class).cuda()
elif params.model_version == "alexnet_distill":
print("Student model: {}".format(params.model_version))
model = alexnet.alexnet(num_classes=args.num_class).cuda()
elif params.model_version == "vgg19_distill":
print("Student model: {}".format(params.model_version))
model = models.vgg19_bn(num_classes=args.num_class).cuda()
elif params.model_version == "googlenet_distill":
print("Student model: {}".format(params.model_version))
model = googlenet.GoogleNet(num_class=args.num_class).cuda()
elif params.model_version == "resnext29_distill":
print("Student model: {}".format(params.model_version))
model = resnext.CifarResNeXt(cardinality=8,
depth=29,
num_classes=args.num_class).cuda()
elif params.model_version == "densenet121_distill":
print("Student model: {}".format(params.model_version))
model = densenet.densenet121(num_class=args.num_class).cuda()
# optimizer
if params.model_version == "cnn_distill":
optimizer = optim.Adam(model.parameters(),
lr=params.learning_rate *
(params.batch_size / 128))
else:
optimizer = optim.SGD(model.parameters(),
lr=params.learning_rate *
(params.batch_size / 128),
momentum=0.9,
weight_decay=5e-4)
iter_per_epoch = len(train_dl)
warmup_scheduler = utils.WarmUpLR(
optimizer, iter_per_epoch *
args.warm) # warmup the learning rate in the first epoch
# specify loss function
if args.self_training:
print(
'>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>self training>>>>>>>>>>>>>>>>>>>>>>>>>>>>>'
)
loss_fn_kd = loss_kd_self
else:
loss_fn_kd = loss_kd
"""
Specify the pre-trained teacher models for knowledge distillation
Checkpoints can be obtained by regular training or downloading our pretrained models
For model which is pretrained in multi-GPU, use "nn.DaraParallel" to correctly load the model weights.
"""
if params.teacher == "resnet18":
print("Teacher model: {}".format(params.teacher))
teacher_model = resnet.ResNet18(num_classes=args.num_class)
teacher_checkpoint = 'experiments/pretrained_teacher_models/base_resnet18/best.pth.tar'
if args.pt_teacher: # poorly-trained teacher for Defective KD experiments
teacher_checkpoint = 'experiments/pretrained_teacher_models/base_resnet18/0.pth.tar'
teacher_model = teacher_model.cuda()
elif params.teacher == "alexnet":
print("Teacher model: {}".format(params.teacher))
teacher_model = alexnet.alexnet(num_classes=args.num_class)
teacher_checkpoint = 'experiments/pretrained_teacher_models/base_alexnet/best.pth.tar'
teacher_model = teacher_model.cuda()
elif params.teacher == "googlenet":
print("Teacher model: {}".format(params.teacher))
teacher_model = googlenet.GoogleNet(num_class=args.num_class)
teacher_checkpoint = 'experiments/pretrained_teacher_models/base_googlenet/best.pth.tar'
teacher_model = teacher_model.cuda()
elif params.teacher == "vgg19":
print("Teacher model: {}".format(params.teacher))
teacher_model = models.vgg19_bn(num_classes=args.num_class)
teacher_checkpoint = 'experiments/pretrained_teacher_models/base_vgg19/best.pth.tar'
teacher_model = teacher_model.cuda()
elif params.teacher == "resnet50":
print("Teacher model: {}".format(params.teacher))
teacher_model = resnet.ResNet50(num_classes=args.num_class).cuda()
teacher_checkpoint = 'experiments/pretrained_teacher_models/base_resnet50/best.pth.tar'
if args.pt_teacher: # poorly-trained teacher for Defective KD experiments
teacher_checkpoint = 'experiments/pretrained_teacher_models/base_resnet50/50.pth.tar'
elif params.teacher == "resnet101":
print("Teacher model: {}".format(params.teacher))
teacher_model = resnet.ResNet101(num_classes=args.num_class).cuda()
teacher_checkpoint = 'experiments/pretrained_teacher_models/base_resnet101/best.pth.tar'
teacher_model = teacher_model.cuda()
elif params.teacher == "densenet121":
print("Teacher model: {}".format(params.teacher))
teacher_model = densenet.densenet121(
num_class=args.num_class).cuda()
teacher_checkpoint = 'experiments/pretrained_teacher_models/base_densenet121/best.pth.tar'
# teacher_model = nn.DataParallel(teacher_model).cuda()
elif params.teacher == "resnext29":
print("Teacher model: {}".format(params.teacher))
teacher_model = resnext.CifarResNeXt(
cardinality=8, depth=29, num_classes=args.num_class).cuda()
teacher_checkpoint = 'experiments/pretrained_teacher_models/base_resnext29/best.pth.tar'
if args.pt_teacher: # poorly-trained teacher for Defective KD experiments
teacher_checkpoint = 'experiments/pretrained_teacher_models/base_resnext29/50.pth.tar'
teacher_model = nn.DataParallel(teacher_model).cuda()
elif params.teacher == "mobilenet_v2":
print("Teacher model: {}".format(params.teacher))
teacher_model = mobilenet.mobilenetv2(
class_num=args.num_class).cuda()
teacher_checkpoint = 'experiments/pretrained_teacher_models/base_mobilenet_v2/best.pth.tar'
elif params.teacher == "shufflenet_v2":
print("Teacher model: {}".format(params.teacher))
teacher_model = shufflenet.shufflenetv2(
class_num=args.num_class).cuda()
teacher_checkpoint = 'experiments/pretrained_teacher_models/base_shufflenet_v2/best.pth.tar'
utils.load_checkpoint(teacher_checkpoint, teacher_model)
# Train the model with KD
logging.info("Starting training for {} epoch(s)".format(
params.num_epochs))
train_and_evaluate_kd(model, teacher_model, train_dl, dev_dl,
optimizer, loss_fn_kd, warmup_scheduler, params,
args, args.restore_file)
# non-KD mode: regular training to obtain a baseline model
else:
print("Train base model")
if params.model_version == "cnn":
model = net.Net(params).cuda()
elif params.model_version == "mobilenet_v2":
print("model: {}".format(params.model_version))
model = mobilenet.mobilenetv2(class_num=args.num_class).cuda()
elif params.model_version == "shufflenet_v2":
print("model: {}".format(params.model_version))
model = shufflenet.shufflenetv2(class_num=args.num_class).cuda()
elif params.model_version == "alexnet":
print("model: {}".format(params.model_version))
model = alexnet.alexnet(num_classes=args.num_class).cuda()
elif params.model_version == "vgg19":
print("model: {}".format(params.model_version))
model = models.vgg19_bn(num_classes=args.num_class).cuda()
elif params.model_version == "googlenet":
print("model: {}".format(params.model_version))
model = googlenet.GoogleNet(num_class=args.num_class).cuda()
elif params.model_version == "densenet121":
print("model: {}".format(params.model_version))
model = densenet.densenet121(num_class=args.num_class).cuda()
elif params.model_version == "resnet18":
model = resnet.ResNet18(num_classes=args.num_class).cuda()
elif params.model_version == "resnet50":
model = resnet.ResNet50(num_classes=args.num_class).cuda()
elif params.model_version == "resnet101":
model = resnet.ResNet101(num_classes=args.num_class).cuda()
elif params.model_version == "resnet152":
model = resnet.ResNet152(num_classes=args.num_class).cuda()
elif params.model_version == "resnext29":
model = resnext.CifarResNeXt(cardinality=8,
depth=29,
num_classes=args.num_class).cuda()
# model = nn.DataParallel(model).cuda()
if args.regularization:
print(
">>>>>>>>>>>>>>>>>>>>>>>>Loss of Regularization>>>>>>>>>>>>>>>>>>>>>>>>"
)
loss_fn = loss_kd_regularization
elif args.label_smoothing:
print(
">>>>>>>>>>>>>>>>>>>>>>>>Label Smoothing>>>>>>>>>>>>>>>>>>>>>>>>"
)
loss_fn = loss_label_smoothing
else:
print(
">>>>>>>>>>>>>>>>>>>>>>>>Normal Training>>>>>>>>>>>>>>>>>>>>>>>>"
)
loss_fn = nn.CrossEntropyLoss()
if args.double_training: # double training, compare to self-KD
print(
">>>>>>>>>>>>>>>>>>>>>>>>Double Training>>>>>>>>>>>>>>>>>>>>>>>>"
)
checkpoint = 'experiments/pretrained_teacher_models/base_' + str(
params.model_version) + '/best.pth.tar'
utils.load_checkpoint(checkpoint, model)
if params.model_version == "cnn":
optimizer = optim.Adam(model.parameters(),
lr=params.learning_rate *
(params.batch_size / 128))
else:
optimizer = optim.SGD(model.parameters(),
lr=params.learning_rate *
(params.batch_size / 128),
momentum=0.9,
weight_decay=5e-4)
iter_per_epoch = len(train_dl)
warmup_scheduler = utils.WarmUpLR(optimizer,
iter_per_epoch * args.warm)
# Train the model
logging.info("Starting training for {} epoch(s)".format(
params.num_epochs))
train_and_evaluate(model, train_dl, dev_dl, optimizer, loss_fn, params,
args.model_dir, warmup_scheduler, args,
args.restore_file)
