'Loss {loss.val:.4f} ({loss.avg:.4f})\t'
'Acc@1 {top1.val:.2f} ({top1.avg:.2f})\t'.format(
step, len(val_loader), batch_time=batch_time, loss=losses, top1=top1))
# vis.log('Test: [{0}/{1}]\t'
# 'Time {batch_time.val:.3f} ({batch_time.avg:.3f})\t'
# 'Loss {loss.val:.4f} ({loss.avg:.4f})\t'
# 'Acc@1 {top1.val:.2f} ({top1.avg:.2f})\t'.format(
# step, len(val_loader), batch_time=batch_time, loss=losses, top1=top1))
test_loss = losses.avg
test_acc = top1.avg
test_losses.append(test_loss)
test_accs.append(test_acc)
print(' * Acc@1 {top1.avg:.2f}%'.format(top1=top1))
# vis.log(' * Acc@1 {top1.avg:.2f}%'.format(top1=top1))
# vis.plot_many({'test loss': test_loss, 'test acc': test_acc})
is_best = test_acc > best_acc
best_acc = max(test_acc, best_acc)
state = {'args': args, 'model': net.state_dict(), 'optimizer': optimizer.state_dict(), 'epoch': epoch + 1,
'train_losses': train_losses, 'train_accs': train_accs, 'test_losses': test_losses, 'test_accs': test_accs,
'best_acc': best_acc}
save_checkpoint(state, is_best)
with open(log_name, 'a') as log_file:
log_writer = csv.writer(log_file, delimiter=',')
log_writer.writerow([epoch + 1, train_loss, train_acc, test_loss, test_acc, best_acc])
print('\nBest accuracy: {:.2f}%'.format(best_acc))
# vis.log('\nBest accuracy: {:.2f}%'.format(best_acc))
def train_val(im_dir,
train_file_path,
val_file_path,
hidden_size,
n_layers,
act_type,
norm_size,
n_epochs,
batch_size,
n_letters,
lr,
optim_type,
momentum,
weight_decay,
valInterval,
device='cpu'):
'''
The main training procedure
----------------------------
:param im_dir: path to directory with images
:param train_file_path: file list of training image paths and labels
:param val_file_path: file list of validation image paths and labels
:param hidden_size: a list of hidden size for each hidden layer
:param n_layers: number of layers in the MLP
:param act_type: type of activation function, can be none, sigmoid, tanh, or relu
:param norm_size: image normalization size, (height, width)
:param n_epochs: number of training epochs
:param batch_size: batch size of training and validation
:param n_letters: number of classes, in this task it is 26 English letters
:param lr: learning rate
:param optim_type: optimizer, can be 'sgd', 'adagrad', 'rmsprop', 'adam', or 'adadelta'
:param momentum: only used if optim_type == 'sgd'
:param weight_decay: the factor of L2 penalty on network weights
:param valInterval: the frequency of validation, e.g., if valInterval = 5, then do validation after each 5 training epochs
:param device: 'cpu' or 'cuda', we can use 'cpu' for our homework if GPU with cuda support is not available
'''
# training and validation data loader
trainloader = dataLoader(im_dir, train_file_path, norm_size, batch_size)
valloader = dataLoader(im_dir, val_file_path, norm_size, batch_size)
# TODO 1: initialize the MLP model and loss function
# what is the input size of the MLP?
# hint 1: we convert an image to a vector as the input of the MLP,
# each image has shape [norm_size[0], norm_size[1]]
# hint 2: Input parameters for MLP: input_size, output_size, hidden_size, n_layers, act_type
model = MLP(norm_size[0] * norm_size[1], n_letters, hidden_size, n_layers,
act_type)
# loss function
cal_loss = CrossEntropyLoss.apply
# End TODO 1
# put the model on CPU or GPU
model = model.to(device)
# optimizer
if optim_type == 'sgd':
optimizer = optim.SGD(model.parameters(),
lr,
momentum=momentum,
weight_decay=weight_decay)
elif optim_type == 'adagrad':
optimizer = optim.Adagrad(model.parameters(),
lr,
weight_decay=weight_decay)
elif optim_type == 'rmsprop':
optimizer = optim.RMSprop(model.parameters(),
lr,
weight_decay=weight_decay)
elif optim_type == 'adam':
optimizer = optim.Adam(model.parameters(),
lr,
weight_decay=weight_decay)
elif optim_type == 'adadelta':
optimizer = optim.Adadelta(model.parameters(),
lr,
weight_decay=weight_decay)
else:
print(
'[Error] optim_type should be one of sgd, adagrad, rmsprop, adam, or adadelta'
)
raise NotImplementedError
# training
# to save loss of each training epoch in a python "list" data structure
losses = []
for epoch in range(n_epochs):
# set the model in training mode
model.train()
# to save total loss in one epoch
total_loss = 0.
#TODO 2: calculate losses and train the network using the optimizer
for step, (ims,
labels) in enumerate(trainloader): # get a batch of data
# step 1: set data type and device
ims = ims.to(device)
labels = labels.to(device)
# step 2: convert an image to a vector as the input of the MLP
ims = ims.view(batch_size, norm_size[0] * norm_size[1])
# hint: clear gradients in the optimizer
optimizer.zero_grad()
# step 3: run the model which is the forward process
pred = model(ims)
# step 4: compute the loss, and call backward propagation function
loss = cal_loss(pred, labels)
loss.backward()
# step 5: sum up of total loss, loss.item() return the value of the tensor as a standard python number
# this operation is not differentiable
total_loss += loss.item()
# step 6: call a function, optimizer.step(), to update the parameters of the model
optimizer.step()
# End TODO 2
# average of the total loss for iterations
avg_loss = total_loss / len(trainloader)
losses.append(avg_loss)
print('Epoch {:02d}: loss = {:.3f}'.format(epoch + 1, avg_loss))
# validation
if (epoch + 1) % valInterval == 0:
# set the model in evaluation mode
model.eval()
n_correct = 0. # number of images that are correctly classified
n_ims = 0. # number of total images
with torch.no_grad(
): # we do not need to compute gradients during validation
# calculate losses for validation data and do not need train the network
for ims, labels in valloader:
# set data type and device
ims, labels = ims.to(device), labels.type(
torch.float).to(device)
# convert an image to a vector as the input of the MLP
input = ims.view(ims.size(0), -1)
# run the model which is the forward process
out = model(input)
# get the predicted value by the output using out.argmax(1)
predictions = out.argmax(1)
# sum up the number of images correctly recognized and the total image number
n_correct += torch.sum(predictions == labels)
n_ims += ims.size(0)
# show prediction accuracy
print('Epoch {:02d}: validation accuracy = {:.1f}%'.format(
epoch + 1, 100 * n_correct / n_ims))
# save model parameters in a file
model_save_path = 'saved_models/recognition.pth'.format(epoch + 1)
torch.save(
{
'state_dict': model.state_dict(),
'configs': {
'norm_size': norm_size,
'output_size': n_letters,
'hidden_size': hidden_size,
'n_layers': n_layers,
'act_type': act_type
}
}, model_save_path)
print('Model saved in {}\n'.format(model_save_path))
# draw the loss curve
plot_loss(losses)
# 'Acc@1 {top1.val:.2f} ({top1.avg:.2f})\t'.format(
# step, len(val_loader), batch_time=batch_time, loss=losses, top1=top1))
test_loss = losses.avg
test_acc = top1.avg
test_losses.append(test_loss)
test_accs.append(test_acc)
print(' * Acc@1 {top1.avg:.2f}%'.format(top1=top1))
# vis.log(' * Acc@1 {top1.avg:.2f}%'.format(top1=top1))
# vis.plot_many({'test loss': test_loss, 'test acc': test_acc})
is_best = test_acc > best_acc
best_acc = max(test_acc, best_acc)
state = {
'args': args,
'model': net.state_dict(),
'optimizer': optimizer.state_dict(),
'epoch': epoch + 1,
'train_losses': train_losses,
'train_accs': train_accs,
'test_losses': test_losses,
'test_accs': test_accs,
'best_acc': best_acc
}
save_checkpoint(state, is_best)
with open(log_name, 'a') as log_file:
log_writer = csv.writer(log_file, delimiter=',')
log_writer.writerow(
[epoch + 1, train_loss, train_acc, test_loss, test_acc, best_acc])
acc_val[epoch], loss_val[epoch] = evaluate(net, valloader, device, classes)
acc_train[epoch], _ = evaluate(net,
trainloader,
device,
classes,
loss_compute=False)
# save epoch
if epoch % 10 == 9:
path_checkpoint = os.path.join(proj_path, "checkpoint",
"MLPTwoStep{}".format(option),
'cp_{:03d}.pth'.format(epoch))
torch.save(
{
'epoch': epoch,
'model_state_dict': net.state_dict(),
'optimizer_state_dict': optimizer.state_dict()
}, path_checkpoint)
# save the loss and accuracy of train and validation into numpy array npz file.
np.savez(os.path.join(proj_path, "checkpoint",
"MLPTwoStep{}".format(option),
"training_result_{:03d}.npz".format(epoch_num - 1)),
acc_val=acc_val,
acc_train=acc_train,
loss_val=loss_val,
loss_train=loss_train)
end_t = time.time()
print("Time for training {:.03f} hrs.".format((end_t - start_t) / 3600))
def main():
parser = argparse.ArgumentParser(description='Pytorch example: MNIST')
parser.add_argument('--batchsize', '-b', type=int, default=100,
help='Number of images in each mini-batch')
parser.add_argument('--epoch', '-e', type=int, default=20,
help='Number of sweeps over the training data')
parser.add_argument('--frequency', '-f', type=int, default=-1,
help='Frequency of taking a snapshot')
parser.add_argument('--gpu', '-g', type=int, default=-1,
help='GPU ID (negative value indicates CPU)')
parser.add_argument('--out', '-o', default='result',
help='Directory to output the result')
parser.add_argument('--resume', '-r', default='',
help='Resume the training from snapshot')
parser.add_argument('--unit', '-u', type=int, default=1000,
help='Number of units')
args = parser.parse_args()
print('GPU: {}'.format(args.gpu))
print('# unit: {}'.format(args.unit))
print('# Minibatch-size: {}'.format(args.batchsize))
print('# epoch: {}'.format(args.epoch))
print('')
# Set up a neural network to train
net = MLP(args.unit, 28*28, 10)
# Load designated network weight
if args.resume:
net.load_state_dict(torch.load(args.resume))
# Set model to GPU
if args.gpu >= 0:
# Make a specified GPU current
device = 'cuda:' + str(args.gpu)
net = net.to(device)
# Setup a loss and an optimizer
criterion = nn.CrossEntropyLoss()
optimizer = optim.SGD(net.parameters(), lr=0.001, momentum=0.9)
# Load the MNIST
transform = transforms.Compose( [transforms.ToTensor()] )
trainvalset = datasets.MNIST(root='./data', train=True,
download=True, transform=transform)
# Split train/val
n_samples = len(trainvalset)
trainsize = int(n_samples * 0.9)
valsize = n_samples - trainsize
trainset, valset = torch.utils.data.random_split(trainvalset, [trainsize, valsize])
trainloader = torch.utils.data.DataLoader(trainset, batch_size=args.batchsize,
shuffle=True, num_workers=2)
valloader = torch.utils.data.DataLoader(valset, batch_size=args.batchsize,
shuffle=True, num_workers=2)
# Setup result holder
x = []
ac_train = []
ac_val = []
# Train
for ep in range(args.epoch): # Loop over the dataset multiple times
running_loss = 0.0
correct_train = 0
total_train = 0
correct_val = 0
total_val = 0
for i, data in enumerate(trainloader, 0):
# Get the inputs; data is a list of [inputs, labels]
inputs, labels = data
if args.gpu >= 0:
inputs = inputs.to(device)
labels = labels.to(device)
# Reshape the input
inputs = inputs.view(-1, 28*28)
# Reset the parameter gradients
optimizer.zero_grad()
# Forward
outputs = net(inputs)
# Predict the label
_, predicted = torch.max(outputs, 1)
# Check whether estimation is right
c = (predicted == labels).squeeze()
for i in range(len(predicted)):
correct_train += c[i].item()
total_train += 1
# Backward + Optimize
loss = criterion(outputs, labels)
loss.backward()
optimizer.step()
# Add loss
running_loss += loss.item()
# Report loss of the epoch
print('[epoch %d] loss: %.3f' % (ep + 1, running_loss))
# Save the model
if (ep + 1) % args.frequency == 0:
path = args.out + "/model_" + str(ep + 1)
torch.save(net.state_dict(), path)
# Validation
with torch.no_grad():
for data in valloader:
images, labels = data
if args.gpu >= 0:
images = images.to(device)
labels = labels.to(device)
# Reshape the input
images = images.view(-1, 28*28)
# Forward
outputs = net(images)
# Predict the label
_, predicted = torch.max(outputs, 1)
# Check whether estimation is right
c = (predicted == labels).squeeze()
for i in range(len(predicted)):
correct_val += c[i].item()
total_val += 1
# Record result
x.append(ep+1)
ac_train.append(100 * correct_train / total_train)
ac_val.append(100 * correct_val / total_val)
print('Finished Training')
path = args.out + "/model_final"
torch.save(net.state_dict(), path)
# Draw graph
fig = plt.figure()
ax = fig.add_subplot(1, 1, 1)
ax.plot(x, ac_train, label='Training')
ax.plot(x, ac_val, label='Validation')
ax.legend()
ax.set_xlabel("Epoch")
ax.set_ylabel("Accuracy [%]")
ax.set_ylim(80, 100)
plt.savefig(args.out + '/accuracy_mnist_mlp.png')
