download=True,
transform=transform_test)
testloader = torch.utils.data.DataLoader(testset,
batch_size=100,
shuffle=False,
num_workers=2)
classes = ('plane', 'car', 'bird', 'cat', 'deer', 'dog', 'frog', 'horse',
'ship', 'truck')
for seed in range(3):
net = VGG('VGG16').cuda()
criterion = nn.CrossEntropyLoss()
optimizer = optim.SGD(net.parameters(), lr=1e-2)
def train(epoch):
print('\nEpoch: %d' % epoch)
net.train()
train_loss = 0
correct = 0
total = 0
for batch_idx, (inputs, targets) in enumerate(trainloader):
inputs, targets = inputs.cuda(), targets.cuda().long()
optimizer.zero_grad()
outputs = net(inputs)
loss = criterion(outputs, targets)
loss.backward()
optimizer.step()
SHOW_PIC_NUM = args.show_picture_num
DATASET_PATH = args.dataset_path
RGB = args.rgb
CSV = args.csv
SHOW_VALID_PIC = args.show_valid
DATASET_PATH, TRAIN_PATH, VALID_PATH = set_path(DEVICE)
# print(DATASET_PATH)
if torch.cuda.is_available():
net = VGG(NET).cuda()
else:
net = VGG(NET)
params = net.parameters()
if ONE_HOT:
loss_func = nn.CrossEntropyLoss()
optimizer = optim.SGD(net.parameters(), lr=LR)
else:
loss_func = nn.BCELoss()
optimizer = optim.RMSprop(net.parameters(), lr=LR, alpha=0.9)
def train(epoch=10, batch_size=10, dataset_path=None, one_hot=False):
if one_hot:
loss_func = nn.CrossEntropyLoss()
optimizer = optim.SGD(net.parameters(), lr=LR)
else:
# optimizer = optim.SGD(model.parameters(), lr=the_lr, momentum=0.9)
#
# model.load_state_dict(torch.load('{}.pth.tar'.format(icon)))
# trained_model, val_acc_history, loss, acc = train_model(model, train_loader, test_loader, optimizer, 5)
# print('{}_loss_{:2f}__acc_{:2f}.pth.tar'.format(icon, loss, acc))
# torch.save(trained_model.state_dict(), '{}.pth.tar'.format(icon))
# # #########################################################################################################
in_size = 64
icon = 'gun_butt'
dataRoot = os.path.join("dataSets", icon, "train")
out_num = len(os.listdir(dataRoot))
model = VGG(in_size, out_num)
train_loader, test_loader = load_dataset(dataRoot, in_size)
optimizer = optim.SGD(model.parameters(), lr=the_lr, momentum=0.9)
model.load_state_dict(torch.load('{}.pth.tar'.format(icon)))
trained_model, val_acc_history, loss, acc = train_model(model, train_loader, test_loader, optimizer, 10)
print('{}_loss_{:2f}__acc_{:2f}.pth.tar'.format(icon, loss, acc))
torch.save(trained_model.state_dict(), '{}.pth.tar'.format(icon))
#
# # ########################################################################################################
# in_size = 64
# icon = 'gun_name'
#
# dataRoot = os.path.join("dataSets", icon, "train")
# out_num = len(os.listdir(dataRoot))
#
# model = VGG(in_size, out_num)
# train_loader, test_loader = load_dataset(dataRoot, in_size)