transform = transforms.Compose([
transforms.Resize((224, 224)),
transforms.ToTensor(),
transforms.Normalize((0.5, 0.5, 0.5), (0.5, 0.5, 0.5))
])
test_dataset = torchvision.datasets.ImageFolder(root="./flower_data/val",
transform=transform)
test_loader = torch.utils.data.DataLoader(dataset=test_dataset,
shuffle=False,
batch_size=test_batch_size,
num_workers=12,
drop_last=True)
# print((len(test_dataset)))
# print(len(test_loader))
# len(test_dataset)==len(test_loader)*test_batch_size
model = net.AlexNet()
model.load_state_dict(torch.load("./weights.pth"))
model.to(device)
model.eval()
criterion = nn.CrossEntropyLoss()
eval_loss = 0
eval_correct_rate = 0
eval_num_correct = 0
with tqdm(total=len(test_loader)) as pbar:
for img, label in test_loader:
img = img.to(device)
label = label.to(device)
output = model(img)
loss = criterion(output, label)
eval_loss += loss.item()
import util
import net
if __name__ == '__main__':
parser = util.default_parser('MLP Example')
args = parser.parse_args()
# get the dataset (default is MNIST)
train, test = util.get_dataset(args.dataset)
# initialize model
model = net.AlexNet(n_out=10)
# train model
util.train_model(model, train, test, args)
# get test accuracy
acc = util.accuracy(model, test, gpu=args.gpu)
print('Model accuracy: ', acc)
# generate and save C model as a header file
model.generate_c('alex.h', train._datasets[0].shape[1:])
import numpy as np
import util
import net
if __name__ == '__main__':
parser = util.default_parser('MLP Example')
args = parser.parse_args()
# get the dataset (default is MNIST)
train, test = util.get_dataset(args.dataset)
n_in = train._datasets[0].shape[1]
n_out = len(np.unique(train._datasets[1]))
# initialize model
model = net.AlexNet(n_in, n_out)
# train model
util.train_model(model, train, test, args)
# get test accuracy
acc = util.accuracy(model, test, gpu=args.gpu)
print('Model accuracy: ', acc)
# generate and save C model as a header file
model.generate_c('alex.h', train._datasets[0].shape[1:])
x_test = caltech['x_test'].astype('float32')
x_test /= 255
y_train = np.asarray(caltech['y_train'], dtype='int32')
y_test = np.asarray(caltech['y_test'], dtype='int32')
N = data.num_train
N_test = data.num_test
print('- number of training data: %d' % N)
print('- number of test data: %d' % N_test)
print('- number of labels: %d' % len(caltech['label_names']))
print('done.')
# prepare network
model = net.Classifier(net.AlexNet())
# initialize optimizer
if args.optimizer == 'adam':
optimizer = optimizers.Adam(args.alpha)
if args.optimizer == 'momentumsgd':
optimizer = optimizers.MomentumSGD(args.learningrate)
optimizer.setup(model)
# training loop
print()
print('start learning')
if args.gpu >= 0:
model.to_gpu()
for epoch in range(0, n_epoch):
print('epoch', epoch + 1)