import numpy as np
import mnist
from model.network import Net
print('Loadind data......')
num_classes = 10
train_images = mnist.train_images() #[60000, 28, 28]
train_labels = mnist.train_labels()
test_images = mnist.test_images()
test_labels = mnist.test_labels()
print('Preparing data......')
train_images -= int(np.mean(train_images))
train_images /= int(np.std(train_images))
test_images -= int(np.mean(test_images))
test_images /= int(np.std(test_images))
training_data = train_images.reshape(60000, 1, 28, 28)
training_labels = np.eye(num_classes)[train_labels]
testing_data = test_images.reshape(10000, 1, 28, 28)
testing_labels = np.eye(num_classes)[test_labels]
net = Net()
print('Training Lenet......')
net.train(training_data, training_labels, 32, 1, 'weights.pkl')
print('Testing Lenet......')
net.test(testing_data, testing_labels, 100)
print('Testing with pretrained weights......')
net.test_with_pretrained_weights(testing_data, testing_labels, 100,
'pretrained_weights.pkl')
def train(args):
# CONFIGS = yaml.load(open(args.config)) # deprecated, please set the configs in parse_args()
# Set device
if torch.cuda.is_available():
os.environ["CUDA_VISIBLE_DEVICES"] = args.device.strip()
device = torch.device("cuda")
else:
device = torch.device("cpu") # Not suggested
# Set save folder & logging config
subfolder = time.strftime("%Y-%m-%d-%H-%M-%S", time.localtime(time.time()))
if not args.save_folder or (not os.path.isdir(args.save_folder)):
print(
"Warning: Not invalid value of 'save_folder', set as default value: './save_folder'.."
)
save_folder = "./save_folder"
else:
save_folder = args.save_folder
if not os.path.exists(save_folder):
os.mkdir(save_folder)
save_folder = os.path.join(save_folder, subfolder)
os.mkdir(save_folder)
#TODO:logging
# Load Dataset
trainloader = get_loader(args.train_gtfile,
batch_size=args.batch_size,
num_thread=args.num_workers)
valloader = get_loader(args.val_gtfile,
batch_size=args.batch_size,
num_thread=args.num_workers)
# Init Net
model = Net(numAngle=args.num_angle,
numRho=args.num_rho,
backbone=args.backbone)
if args.resume:
model.load_state_dict(torch.load(args.resume))
model = torch.nn.DataParallel(model).to(device)
# Optimizer
optimizer = optim.Adam(model.parameters())
# Loss
criterion = torch.nn.CrossEntropyLoss()
losses = AverageMeter()
# Start Training
model.train()
iter = 0 # iter id start from 1
for epoch in range(args.max_epoch):
for batch in trainloader:
start = time.time()
iter += 1
img_tensor, gt_tensor = batch
optimizer.zero_grad()
# Forwarding
preds = model(img_tensor)
# Calculate Loss
loss = criterion(preds, gt_tensor)
loss.backward()
optimizer.step()
losses.update(loss.item(), args.batch_size)
if iter % args.show_interval == 0:
logging.info(
f"Training [{epoch}/{args.max_epoch}][{iter}] Loss:{losses.avg} Time:{time.time()-start:.1f}s"
)
if iter % args.val_interval == 0:
pass
test_images = mnist.test_images()
test_labels = mnist.test_labels()
train_images -= int(np.mean(train_images))
train_images = train_images.astype('float64')
train_images /= int(np.std(train_images))
test_images -= int(np.mean(test_images))
test_images = test_images.astype('float64')
test_images /= int(np.std(test_images))
train_data = train_images.reshape(60000, 1, 28, 28)
train_set_labels = np.eye(num_classes)[train_labels]
testing_data = test_images.reshape(10000, 1, 28, 28)
testing_labels = np.eye(num_classes)[test_labels]
net = Net()
print('Training Lenet......')
net.train(train_data, train_set_labels, 32, 1, 'weights.pkl')
print('Testing Lenet......')
net.test(testing_data, testing_labels, 100)
print('Testing with pretrained weights......')
net.test_with_pretrained_weights(testing_data, testing_labels, 100,
'pretrained_weights.pkl')
#camera part
pygame.camera.init()
RANGE = 300
def capture_image():
cam.start()
image = cam.get_image()
cam.stop()
print("Let's use", torch.cuda.device_count(), "GPUs!")
model = Net(num_classes=n_classes)
# pretrained model in my pc. now i will train on all images for 2 epochs
# model.load_state_dict(torch.load('./epoch_5_val_loss_7.03_auc_0.844.pth'))
model = nn.DataParallel(model).to(device)
optimizer = torch.optim.AdamW(model.parameters(), lr=1e-4)
criterion = torch.nn.CrossEntropyLoss()
train_loss, val_loss = [], []
for epoch in range(epochs):
print('Epoch {}/{}'.format(epoch, epochs - 1))
print('-' * 10)
model.train()
running_loss = 0
tk0 = tqdm(train_loader, total=int(len(train_loader)))
for im, labels in tk0:
inputs = im["image"].to(device, dtype=torch.float)
labels = labels.to(device, dtype=torch.long)
optimizer.zero_grad()
outputs = model(inputs)
loss = criterion(outputs, labels)
loss.backward()
optimizer.step()
running_loss += loss.item()
tk0.set_postfix(loss=(loss.item()))
epoch_loss = running_loss / (len(train_loader) / batch_size)
train_loss.append(epoch_loss)
