from model.loss import CrossEntropyLoss
from model.optimizer import Adadelta
from trainer.trainer import Trainer
from data_loader.data_loader import GTRSBDataLoader
import ctypes
ctypes.cdll.LoadLibrary('caffe2_nvrtc.dll')
data_dir = 'data'
batch_size = 32
num_workers = 4
if __name__ == "__main__":
model = VGG16_GTSRB()
print(model.name)
optimizer = Adadelta(model.parameters(), lr=1.0)
loss_fn = CrossEntropyLoss()
train_loader = GTRSBDataLoader(data_dir, batch_size, is_train=True)
val_loader = GTRSBDataLoader(data_dir, batch_size, is_train=False)
trainer = Trainer(model,
loss_fn,
optimizer,
train_loader,
val_loader=val_loader,
num_epoch=50)
trainer.train()
trainer.save_best_weight('VGG16_GTRSB')
from trainer.trainer import Trainer
from data_loader.data_loader import VGGFaceDataLoader
from utils.config import fix_random_seed
import ctypes
ctypes.cdll.LoadLibrary('caffe2_nvrtc.dll') # for windows
data_dir = 'data/pubfig65/'
batch_size = 32
num_workers = 4
random_seed = 1337
if __name__ == "__main__":
fix_random_seed(random_seed)
model = VGG_Face_PubFig()
optimizer = Adadelta(model.parameters(), lr=1.0)
loss_fn = CrossEntropyLoss()
train_loader = VGGFaceDataLoader(data_dir, batch_size, is_train=True)
val_loader = VGGFaceDataLoader(data_dir, batch_size, is_train=False)
trainer = Trainer(model,
loss_fn,
optimizer,
train_loader,
val_loader=val_loader,
num_epoch=200)
trainer.train()
trainer.save_best_weight('VGGFace_PubFig65_deep')
from model.models import ResNet50_VGGFlower
from model.loss import CrossEntropyLoss
from model.optimizer import SGD
from trainer.trainer import Trainer
from data_loader.data_loader import VGGFlowerDataLoader
import ctypes
ctypes.cdll.LoadLibrary('caffe2_nvrtc.dll')
data_dir = 'data/flower_data/'
batch_size = 50
num_workers = 4
if __name__=="__main__":
model = ResNet50_VGGFlower()
print(model)
optimizer = SGD(model.parameters(), lr=0.01)
loss_fn = CrossEntropyLoss()
train_loader = VGGFlowerDataLoader(data_dir, batch_size, is_train=True)
val_loader = VGGFlowerDataLoader(data_dir, batch_size, is_train=False)
print(train_loader.dataset_size, val_loader.dataset_size)
trainer = Trainer(model, loss_fn, optimizer, train_loader, val_loader=val_loader, num_epoch=150)
trainer.train()
trainer.save_best_weight('ResNet50_VGGFlower')
