def main():
# Training settings
from config import args
from config import train_dataset
from config import test_dataset
from config import fun_params
args = args
use_cuda = not args.no_cuda and torch.cuda.is_available()
torch.manual_seed(args.seed)
device = torch.device("cuda" if use_cuda else "cpu")
kwargs = {'num_workers': 1, 'pin_memory': True} if use_cuda else {}
train_dataset = train_dataset
test_dataset = test_dataset
train_loader = torch.utils.data.DataLoader(train_dataset, batch_size=args.batch_size, shuffle=True, **kwargs)
test_loader = torch.utils.data.DataLoader(test_dataset, batch_size=args.test_batch_size, shuffle=True, **kwargs)
target_model = CNN(input_channel=1)
target_model = torch.nn.DataParallel(target_model).cuda()
target_optimizer = optim.SGD(target_model.parameters(), lr=args.lr, momentum=args.momentum)
for epoch in range(args.target_epoch):
train(args, target_model, device, train_loader, target_optimizer, epoch)
target_model_path = os.path.join(fun_params['logdir'], 'target_model.pth.tar')
torch.save({'state_dict': target_model.state_dict()}, target_model_path)
test_loss, correct = test(target_model, test_loader)
print('Target model completed. Test loss %f, test accuracy %f' % (test_loss, correct))
trainer = OuterTrainer(target_model, device, train_dataset, test_dataset, fun_params)
trainer.train(fun_params['n_process'])
class Classifier:
def __init__(
self,
model_path='/Users/archiegertsman/Desktop/CS126/Final Project/finalproject-ArchieGertsman/src/classification/res/model.pt'
):
from network import CNN
# load a model
self.model = CNN()
self.model.load_state_dict(torch.load(model_path))
self.model.eval()
def classify(self, img_path):
from character_dataset import CharacterDataset
import image_utils as iu
# load image
img = iu.read_image_as_tensor(img_path)
# make prediction using model
output = self.model(img)
prediction_idx = torch.argmax(output).item()
confidence = torch.max(output).item()
# returns ascii value of predicted character
return (ord(CharacterDataset.CHARSET[prediction_idx]), confidence)
def __init__(
self,
model_path='/Users/archiegertsman/Desktop/CS126/Final Project/finalproject-ArchieGertsman/src/classification/res/model.pt'
):
from network import CNN
# load a model
self.model = CNN()
self.model.load_state_dict(torch.load(model_path))
self.model.eval()
def __init__(self, num_epochs=5, batch_size=4, lr=0.001):
self.num_epochs = num_epochs
trainset = CharacterDataset()
self.trainloader = torch.utils.data.DataLoader(trainset,
batch_size=batch_size,
shuffle=True,
num_workers=2)
self.net = CNN()
self.criterion = nn.CrossEntropyLoss()
self.optimizer = optim.SGD(self.net.parameters(), lr=lr, momentum=0.9)
def main(model_name, model_version, moves_path, data_path):
# Grab the testing data as a DataLoader
test_set = PeakDataset(moves_path + '/test_moves.pt',
data_path + '/test/%s/test_%d.pt')
# Load the saved network
hparams = torch.load('../networks/' + str(model_version) + '/hparams')
model = torch.load('../networks/' + str(model_version) + '/model_9')
net = LinearSkip(num_layers=hparams['layers'], num_units=hparams['units'])
net.load_state_dict(model['model_state_dict'])
if model_name == 'linear':
net = Linear(num_layers=hparams['layers'], num_units=hparams['units'])
net.load_state_dict(model['model_state_dict'])
elif model_name == 'linearskip':
net = LinearSkip(num_layers=hparams['layers'],
num_units=hparams['units'],
bottleneck=hparams['bottleneck'])
net.load_state_dict(model['model_state_dict'])
elif model_name == 'conv':
net = CNN(num_layers=hparams['layers'],
num_filters=hparams['filters'],
filter_size=hparams['size'],
stride=hparams['stride'],
pad=hparams['padding'])
net.load_state_dict(model['model_state_dict'])
# Test the network
test_performance(net, test_set, model_version)
def main():
model = L.Classifier(CNN())
optimizer = chainer.optimizers.Adam()
optimizer.setup(model)
train = load_data('./nene_data/train')
test = load_data('./nene_data/test')
train_iter = chainer.iterators.SerialIterator(train, batch_size=100)
test_iter = chainer.iterators.SerialIterator(test,
batch_size=100,
repeat=False,
shuffle=False)
updater = training.StandardUpdater(train_iter, optimizer, device=None)
trainer = training.Trainer(updater, (100, 'epoch'), out='result')
trainer.extend(extensions.Evaluator(test_iter, model, device=None))
trainer.extend(extensions.LogReport())
trainer.extend(
extensions.PrintReport([
'epoch', 'main/loss', 'validation/main/loss', 'main/accuracy',
'validation/main/accuracy'
]))
trainer.extend(extensions.ProgressBar())
trainer.run()
modeldir = './model'
if not os.path.isdir(modeldir):
os.mkdir(modeldir)
serializers.save_npz(os.path.join(modeldir, 'model.npz'), model)
serializers.save_npz(os.path.join(modeldir, 'optimizer.npz'), optimizer)
class Trainer:
def __init__(self, num_epochs=5, batch_size=4, lr=0.001):
self.num_epochs = num_epochs
trainset = CharacterDataset()
self.trainloader = torch.utils.data.DataLoader(trainset,
batch_size=batch_size,
shuffle=True,
num_workers=2)
self.net = CNN()
self.criterion = nn.CrossEntropyLoss()
self.optimizer = optim.SGD(self.net.parameters(), lr=lr, momentum=0.9)
def train(self):
for epoch in range(self.num_epochs):
for i, data in enumerate(self.trainloader, 0):
# get the inputs
inputs, labels = data
# zero the parameter gradients
self.optimizer.zero_grad()
# forward + backward + optimize
outputs = self.net(inputs)
loss = self.criterion(outputs, labels)
loss.backward()
self.optimizer.step()
def test_model_against_trainset(self):
correct = 0
total = 0
with torch.no_grad():
for data in self.trainloader:
images, labels = data
outputs = self.net(images)
_, predicted = torch.max(outputs.data, 1)
total += labels.size(0)
correct += (predicted == labels).sum().item()
return correct / total
def save_model(self, filepath='res/model.pt'):
torch.save(self.net.state_dict(), filepath)
def __init__(self):
# 制作模型，采用adam优化器、交叉熵作为损失函数，并以准确度作为模型训练结果
self.network = CNN()
self.network.model.compile(optimizer='adam',
loss='sparse_categorical_crossentropy',
metrics=['accuracy'])
# 读入数据
self.data = DataSource()
print('【网络建立及数据读入已完成】')
def entropy_single_input(im_path,
norm_size,
model_path,
n_bins,
ignore_lowest,
reduction,
device='cpu'):
"""
Calculate entropy of a single image and its prediction
:param im_path: path to an image file
:param norm_size: image normalization size, (width, height)
:param model_path: path of the saved model
:param n_bins: the bins to be divided
:param ignore_lowest: whether to ignore the lowest value when calculating the entropy
:param reduction: 'mean' or 'sum', the way to reduce results of c channels
:param device: 'cpu' or 'cuda'
:return: image entropy and predicted probability entropy
"""
# read image and calculate image entropy
im = cv2.imread(im_path)
im = cv2.cvtColor(im, cv2.COLOR_BGR2GRAY)
im = cv2.resize(im, norm_size)
ent_im = im_entropy(im)
# preprocess
im = (torch.from_numpy(im).float() - 127.5) / 127.5
im = im.view(1, 1, norm_size[1], norm_size[0])
im = im.to(device)
# initialize the model
print('[Info] loading checkpoints from %s ...' % model_path)
checkpoint = torch.load(model_path)
configs = checkpoint['configs']
model = CNN(configs['in_channels'],
configs['num_class'],
batch_norm=configs['batch_norm'],
p=configs['dropout'])
# load model parameters (checkpoint['model_state']) we saved in model_path using model.load_state_dict()
model.load_state_dict(checkpoint['model_state'])
model = model.to(device)
# calculate prediction entropy
model.eval()
with torch.no_grad():
f1, f2, f3, f4, f5, out = model(im, return_features=True)
ent_f1 = feature_entropy(f1[0], n_bins, ignore_lowest, reduction)
ent_f2 = feature_entropy(f2[0], n_bins, ignore_lowest, reduction)
ent_f3 = feature_entropy(f3[0], n_bins, ignore_lowest, reduction)
ent_f4 = feature_entropy(f4[0], n_bins, ignore_lowest, reduction)
ent_f5 = feature_entropy(f5[0], n_bins, ignore_lowest, reduction)
pred = out[0].argmax().item()
pred = chr(pred + ord('A'))
prob = out[0].softmax(0).cpu().numpy()
confidence = prob.max()
prob = prob[prob > 0]
ent_pred = np.sum(-prob * np.log(prob))
return pred, confidence, ent_im, ent_f1, ent_f2, ent_f3, ent_f4, ent_f5, ent_pred, f1[
0], f2[0], f3[0], f4[0], f5[0]
def main(permute):
batch_size = 100
train_data, val_data, test_data = create_train_val_test_split(batch_size)
val_data = make_batch(len(val_data), 0, val_data, use_cuda = True, volatile = True)
test_data = make_batch(len(test_data), 0, test_data, use_cuda = True, volatile = True)
cnn = CNN().cuda()
fcc = FCC().cuda()
cnn_test_loss = Logger("cnn_test_losses.txt")
cnn_test_acc = Logger("cnn_test_acc.txt")
fcc_test_loss = Logger("fcc_test_losses.txt")
fcc_test_acc = Logger("fcc_test_acc.txt")
for i in range(0, 100001, 1000):
print(i)
cnn.load_state_dict(torch.load("savedir/cnn_it"+str(i//1000)+"k.pth"))
evaluate_acc(batch_size, cnn, test_data, i, cnn_test_loss, cnn_test_acc, permute)
fcc.load_state_dict(torch.load("savedir/fcc_it"+str(i//1000)+"k.pth"))
evaluate_acc(batch_size, fcc, test_data, i, fcc_test_loss, fcc_test_acc, permute)
def evaluate(model_path, root, test_list, batch_size):
channel = 1
device = 'cuda'
model = CNN(channel)
model = nn.DataParallel(model)
model.module.load_state_dict(torch.load(model_path))
model.eval() # 推論モードへ切り替え（Dropoutなどの挙動に影響）
df_test = pd.read_csv(test_list)
test_loader = LoadDataset(df_test, root)
test_dataset = torch.utils.data.DataLoader(test_loader,
batch_size=batch_size,
shuffle=True,
drop_last=True)
correct = 0
for img, label in test_dataset:
img = img.float()
img = img / 255
# 3次元テンソルを4次元テンソルに変換（1チャネルの情報を追加）
batch, height, width = img.shape
img = torch.reshape(img, (batch_size, 1, height, width))
img = img.to(device)
output = model(img)
pred = output.data.max(1, keepdim=False)[1]
for i, l in enumerate(label):
if l == pred[i]:
correct += 1
data_num = len(test_dataset.dataset) # データの総数
acc = correct / data_num * 100 # 精度
print('Accuracy for test dataset: {}/{} ({:.1f}%)'.format(
correct, data_num, acc))
def test(self, model_path):
model = CNN(input_channel=1)
model = torch.nn.DataParallel(model).cuda()
optimizer = optim.SGD(model.parameters(),
lr=self.inner_args['inner_lr'],
momentum=self.inner_args['inner_momentum'])
cross_entropy = SoftCrossEntropy()
model.train()
self.target_model.eval()
eloss = loss_net(self.inner_args['nclass'])
eloss = torch.nn.DataParallel(eloss).cuda()
assert os.path.exists(model_path), 'model path is not exist.'
check_point = torch.load(model_path)
eloss.load_state_dict(check_point['state_dict'])
eloss.eval()
minloss = 10000
# print('start train model')
train_loader = torch.utils.data.DataLoader(
self.train_dataset,
batch_size=self.inner_args['inner_batch_size'],
shuffle=True)
test_loader = torch.utils.data.DataLoader(
self.test_dataset,
batch_size=self.inner_args['test_batch_size'],
shuffle=True)
for batch_idx, (data, target) in enumerate(train_loader):
data = data.cuda()
optimizer.zero_grad()
output = model(data)
target_output = self.target_model(data)
target = target.view(-1, 1)
target = torch.zeros(data.shape[0],
10).scatter_(1, target.long(), 1.0)
varInput = torch.cat((target.cuda(), target_output), 1)
# print(varInput.shape, target_output.shape)
soft_target = eloss.forward(varInput)
# print(soft_target)
loss = cross_entropy(output, soft_target)
if self.save_model:
self.log.log_tabular('epoch', batch_idx)
self.log.log_tabular('loss', loss.item())
self.log.dump_tabular()
if loss < minloss:
torch.save(
{
'epoch': batch_idx + 1,
'state_dict': model.state_dict(),
'best_loss': minloss,
'optimizer': optimizer.state_dict()
}, self.log.path_model)
loss.backward()
optimizer.step()
def feature_entropy_dataset(n_bins,
ignore_lowest,
reduction,
file_path,
norm_size,
batch_size,
model_path,
device='cpu'):
"""
Calculate entropy of features extracted by our model.
:param n_bins: the bins to be divided
:param ignore_lowest: whether to ignore the lowest value when calculating the entropy
:param reduction: 'mean' or 'sum', the way to reduce results of c channels
:param file_path: path to directory with images
:param norm_size: image normalization size, (width, height)
:param batch_size: batch size
:param model_path: path of the saved model
:param device: 'cpu' or 'cuda'
:return: the entropy of features
"""
# initialize dataloader and model
dataloader = dataLoader(file_path, norm_size, batch_size)
print('[Info] loading checkpoints from %s ...' % model_path)
checkpoint = torch.load(model_path)
configs = checkpoint['configs']
model = CNN(configs['in_channels'],
configs['num_class'],
batch_norm=configs['batch_norm'],
p=configs['dropout'])
# load model parameters (checkpoint['model_state']) we saved in model_path using model.load_state_dict()
model.load_state_dict(checkpoint['model_state'])
model = model.to(device)
# extract features and calculate entropy
ent1, ent2, ent3, ent4, ent5 = 0., 0., 0., 0., 0.
n_ims = 0
model.eval()
with torch.no_grad():
for ims, _ in dataloader:
ims = ims.to(device)
feats1, feats2, feats3, feats4, feats5, _ = model(
ims, return_features=True)
n_ims += ims.size(0)
for f1, f2, f3, f4, f5 in zip(feats1, feats2, feats3, feats4,
feats5):
ent1 += feature_entropy(f1, n_bins, ignore_lowest, reduction)
ent2 += feature_entropy(f2, n_bins, ignore_lowest, reduction)
ent3 += feature_entropy(f3, n_bins, ignore_lowest, reduction)
ent4 += feature_entropy(f4, n_bins, ignore_lowest, reduction)
ent5 += feature_entropy(f5, n_bins, ignore_lowest, reduction)
return ent1 / n_ims, ent2 / n_ims, ent3 / n_ims, ent4 / n_ims, ent5 / n_ims
def main():
batch_size = 100
mnist_train = datasets.MNIST("/hdd/Data/MNIST/",
train=True,
transform=transforms.ToTensor(),
download=True)
mnist_test = datasets.MNIST("/hdd/Data/MNIST/",
train=False,
transform=transforms.ToTensor(),
download=True)
data_feeder = DataFeeder(mnist_train,
preprocess_workers=1,
cuda_workers=1,
cpu_size=10,
cuda_size=10,
batch_size=batch_size,
use_cuda=True,
volatile=False)
data_feeder.start_queue_threads()
cnn = ModelStruct(CNN().cuda(), 0.001)
fcc = ModelStruct(FCC().cuda(), 0.001)
test_data = make_batch(len(mnist_test), 0, mnist_test, use_cuda=True)
for i in range(100001):
images, labels = data_feeder.get_batch()
train(cnn, images, labels, i)
train(fcc, images, labels, i)
if i % 100 == 0:
evaluate_acc(cnn, test_data, i)
evaluate_acc(fcc, test_data, i)
if i in [33333, 66666]:
decrease_lr(cnn)
decrease_lr(fcc)
if i % 20000 == 0:
torch.save(cnn.model, "savedir/cnn_it" + str(i // 1000) + "k.pt")
torch.save(fcc.model, "savedir/fcc_it" + str(i // 1000) + "k.pt")
print(max(cnn.acc))
print(max(fcc.acc))
graph(fcc, cnn)
cnn.losses = losses_to_ewma(cnn.losses)
cnn.val_losses = losses_to_ewma(cnn.val_losses, alpha=0.3)
cnn.acc = losses_to_ewma(cnn.acc)
fcc.losses = losses_to_ewma(fcc.losses)
fcc.val_losses = losses_to_ewma(fcc.val_losses, alpha=0.3)
fcc.acc = losses_to_ewma(fcc.acc)
graph(fcc, cnn)
data_feeder.kill_queue_threads()
def inference(img):
model = L.Classifier(CNN())
serializers.load_npz('./model/model.npz', model)
if img.shape[2] == 4:
img = skimage.color.rgba2rgb(img)
height, width = img.shape[:2]
img = rescale(img, (IMAGE_SIZE / height, IMAGE_SIZE / width),
mode='constant')
im = img.astype(np.float32).reshape(1, IMAGE_SIZE, IMAGE_SIZE, 3)
im = im.transpose(0, 3, 1, 2)
x = Variable(im)
y = model.predictor(x)
[pred] = y.data
recog = np.argmax(pred)
return recog, im.reshape(3, IMAGE_SIZE, IMAGE_SIZE).transpose(1, 2, 0)
def train(self, epoch, pool_rank, phi):
model = CNN(input_channel=1)
model = torch.nn.DataParallel(model).cuda()
optimizer = optim.SGD(model.parameters(),
lr=self.inner_lr,
momentum=self.inner_momentum)
cross_entropy = SoftCrossEntropy()
model.train()
self.target_model.eval()
eloss = loss_net(self.nclass)
eloss = torch.nn.DataParallel(eloss).cuda()
for key in eloss.state_dict().keys():
eloss.state_dict()[key] = eloss.state_dict()[key] + phi[key]
eloss.eval()
minloss = 10000
# print('start train model')
for e in range(self.inner_epoch_freq):
for batch_idx, (data, target) in enumerate(self.train_loader):
data = data.cuda()
optimizer.zero_grad()
output = model(data)
target_output = self.target_model(data)
target = target.view(-1, 1)
target = torch.zeros(data.shape[0],
10).scatter_(1, target.long(), 1.0)
varInput = torch.cat((target.cuda(), target_output), 1)
# print(varInput.shape, target_output.shape)
soft_target = eloss.forward(varInput)
# print(soft_target)
loss = cross_entropy(output, soft_target)
if self.save_model:
if epoch % 20 == 0:
self.log.log_tabular('epoch', batch_idx)
self.log.log_tabular('loss', loss.item())
if loss < minloss:
torch.save(
{
'epoch': batch_idx + 1,
'state_dict': model.state_dict(),
'best_loss': minloss,
'optimizer': optimizer.state_dict()
}, self.log.path_model)
loss.backward()
optimizer.step()
# print('[pool: %d] epoch %d, loss: %f' % (pool_rank, epoch, loss.item()))
if epoch % 20 == 0 and self.save_model:
self.log.dump_tabular()
accuracy = self.test(model)
return accuracy
def label_entropy_model(file_path,
norm_size,
batch_size,
model_path,
device='cpu'):
"""
We use the trained model for prediction.
:param file_path: path to directory with images
:param norm_size: image normalization size, (width, height)
:param batch_size: batch size
:param model_path: path of the saved model
:param device: 'cpu' or 'cuda'
:return: the entropy
"""
# initialize dataloader and model
dataloader = dataLoader(file_path, norm_size, batch_size)
print('[Info] loading checkpoints from %s ...' % model_path)
checkpoint = torch.load(model_path)
configs = checkpoint['configs']
model = CNN(configs['in_channels'],
configs['num_class'],
batch_norm=configs['batch_norm'],
p=configs['dropout'])
# load model parameters (checkpoint['model_state']) we saved in model_path using model.load_state_dict()
model.load_state_dict(checkpoint['model_state'])
model = model.to(device)
# extract features
outs = []
model.eval()
with torch.no_grad():
for ims, _ in dataloader:
ims = ims.to(device)
out = model(ims)
outs.append(out)
# calculate entropy
probs = torch.cat(outs, 0).softmax(
1) # [n_ims, 26], probabilities of predicted characters
probs = probs.cpu().numpy()
ent = 0.
for prob in probs:
prob = prob[prob > 0]
ent -= np.sum(prob * np.log(prob))
ent /= len(probs)
return ent
transform=transform),
batch_size=32,
shuffle=False,
num_workers=4)
print("Train : ", len(trains.dataset))
print("Test : ", len(tests.dataset))
dropmax_cnn = DropMaxCNN(n_classes=n_classes).to(device)
dropmax_loss = DropMax(device)
adam = optim.Adam(dropmax_cnn.parameters(),
lr=0.0005,
betas=(0.5, 0.999),
weight_decay=1e-4)
cnn = CNN(n_classes=n_classes).to(device)
ce_loss = nn.CrossEntropyLoss()
adam2 = optim.Adam(cnn.parameters(),
lr=0.0005,
betas=(0.5, 0.999),
weight_decay=1e-4)
for epoch in range(30):
dropmax_cnn.train()
print("Epoch : ", epoch)
for i, (img, target) in enumerate(trains):
img = img.to(device)
one_hot = torch.zeros(img.shape[0], n_classes)
one_hot.scatter_(1, target.unsqueeze(dim=1), 1)
one_hot = one_hot.to(device)
if __name__ == "__main__":
train_start = '2015-01-01 01:30:00'
train_end = '2019-03-01 15:00:00'
cv_start = '2019-03-02 01:30:00'
cv_end = '2019-12-01 15:00:00'
test_start = '2019-12-02 01:30:00'
test_end = '2021-04-20 15:00:00'
data_processor = DataProcessor()
train_loader = data_processor.get_data(train_start, train_end)
cv_loader = data_processor.get_data(cv_start, cv_end)
test_loader = data_processor.get_data(test_start, test_end)
model = CNN(transform='GAF')
model = model.double()
loss_fn = torch.nn.CrossEntropyLoss()
optimizer = torch.optim.Adam(model.parameters(), lr=1e-3)
max_epochs = 100
accuracy_list = []
for t in range(max_epochs):
print(f"Epoch {t+1}\n-------------------------------")
train_loop(train_loader, model, loss_fn, optimizer)
acc = test_loop(cv_loader, model, loss_fn)
accuracy_list.append(acc)
if len(accuracy_list) > 8:
print(accuracy_list[-8:])
def main():
batch_size = 100
train_data, val_data, test_data = create_train_val_test_split(batch_size)
data_feeder = DataFeeder(train_data,
preprocess_workers=1,
cuda_workers=1,
cpu_size=10,
cuda_size=10,
batch_size=batch_size,
use_cuda=True,
volatile=False)
data_feeder.start_queue_threads()
val_data = make_batch(len(val_data),
0,
val_data,
use_cuda=True,
volatile=True)
test_data = make_batch(len(test_data),
0,
test_data,
use_cuda=True,
volatile=True)
cnn = CNN().cuda()
fcc = FCC().cuda()
optimizer_cnn = optim.SGD(cnn.parameters(),
lr=0.001,
momentum=0.9,
weight_decay=0.00001)
optimizer_fcc = optim.SGD(fcc.parameters(),
lr=0.001,
momentum=0.9,
weight_decay=0.00001)
cnn_train_loss = Logger("cnn_train_losses.txt")
cnn_val_loss = Logger("cnn_val_losses.txt")
cnn_val_acc = Logger("cnn_val_acc.txt")
fcc_train_loss = Logger("fcc_train_losses.txt")
fcc_val_loss = Logger("fcc_val_losses.txt")
fcc_val_acc = Logger("fcc_val_acc.txt")
#permute = Variable(torch.from_numpy(np.random.permutation(28*28)).long().cuda(), requires_grad=False)
permute = None
for i in range(100001):
images, labels = data_feeder.get_batch()
train(cnn, optimizer_cnn, images, labels, i, cnn_train_loss, permute)
train(fcc, optimizer_fcc, images, labels, i, fcc_train_loss, permute)
if i % 100 == 0:
print(i)
evaluate_acc(batch_size, cnn, val_data, i, cnn_val_loss,
cnn_val_acc, permute)
evaluate_acc(batch_size, fcc, val_data, i, fcc_val_loss,
fcc_val_acc, permute)
if i in [70000, 90000]:
decrease_lr(optimizer_cnn)
decrease_lr(optimizer_fcc)
if i % 1000 == 0:
torch.save(cnn.state_dict(),
"savedir/cnn_it" + str(i // 1000) + "k.pth")
torch.save(fcc.state_dict(),
"savedir/fcc_it" + str(i // 1000) + "k.pth")
data_feeder.kill_queue_threads()
import evaluate
evaluate.main(permute)
(training_data, training_labels), (validation_data, validation_labels), (_, _) = \
pickle.load(gzip.open('mnist.pkl.gz', 'rb'))
training_data, training_labels = th.from_numpy(training_data), th.from_numpy(
training_labels)
training_data = training_data.view(-1, 1, 28, 28)
training_set = TensorDataset(training_data, training_labels)
training_loader = DataLoader(training_set, args.batch_size)
validation_data, validation_labels = th.from_numpy(
validation_data), th.from_numpy(validation_labels)
validation_data = validation_data.view(-1, 1, 28, 28)
validation_set = TensorDataset(validation_data, validation_labels)
validation_loader = DataLoader(validation_set, args.batch_size)
model = CNN()
model.cuda()
optimizer = SGD(model.parameters(), lr=1e-2, momentum=0.9)
for epoch in range(args.n_epochs):
for iteration, batch in enumerate(training_loader):
data, labels = batch
data, labels = data.cuda(), labels.cuda()
n_noises = int(args.noise * labels.size()[0])
noise = np.random.choice(np.arange(10), n_noises)
labels[:n_noises] = th.from_numpy(noise)
data, labels = Variable(data), Variable(labels)
data = model(data)
loss = F.nll_loss(F.log_softmax(data), labels)
optimizer.zero_grad()
from imageLoader import imageload
from network import CNN
from time import sleep
import os
import dlib
import json
import sys
# Load the Classifier Network
network = CNN.EmotionClassifier()
network.load_network_state("0.15_lr.npz")
# get our face detector
face_detector = dlib.get_frontal_face_detector()
emotions = [
"Contentness", "Happiness", "Sadness", "Surprise", "Fear", "Anger",
"Disgust", "Contempt"
]
def search_in_files(dir_="/in/"):
for root, name, files in os.walk(os.getcwd() + dir_):
files = sorted([
file
for file in files if file.endswith(".png") or file.endswith(".jpg")
],
key=lambda x: x[:-4])
fnames = len(files) * [os.getcwd() + dir_]
for j, f in enumerate(files):
fnames[j] += f
return fnames
return []
def get_network(lr=theano.shared(np.cast['float32'](0.1)), load=False):
E = CNN.EmotionClassifier(epochs=500, learning_rate=lr)
if load:
E.load_network_state()
return E
def create_agent_and_opponent(board_size, win_length, replay_maxlen):
#network and exp replay
if not os.path.exists(model_path):
torch.save(CNN(board_size).to(device).state_dict(), model_path)
if os.path.exists(experience_path):
with open(experience_path, "rb") as f:
exp_replay = pickle.load(f)
else:
exp_replay = ExperienceReplay(replay_maxlen)
#agent
agent_network = CNN(board_size).to(device)
agent_network.load_state_dict(torch.load(model_path))
agent_network.eval()
agent_mcts = MCTS(board_size, win_length, agent_network)
#opponent
opponent_network = CNN(board_size).to(device)
opponent_network.load_state_dict(torch.load(model_path))
opponent_network.eval()
opponent_mcts = MCTS(board_size, win_length, opponent_network)
return agent_mcts, opponent_mcts, exp_replay
def trainModel():
# Parse args
parser = argparse.ArgumentParser(description='Train the CNN')
parser.add_argument('--expt_dir',
default='./logs',
help='save dir for experiment logs')
parser.add_argument('--train',
default='./data',
help='path to training set')
parser.add_argument('--val',
default='./data',
help='path to validation set')
parser.add_argument('--test', default='./data', help='path to test set')
parser.add_argument('--save_dir',
default='./models',
help='path to save model')
parser.add_argument('--arch',
default='models/cnn.json',
help='path to model architecture')
parser.add_argument('--model_name',
default='model',
help='name of the model to save logs, weights')
parser.add_argument('--lr', default=0.001, help='learning rate')
parser.add_argument('--init', default='1', help='initialization')
parser.add_argument('--batch_size', default=20, help='batch_size')
args = parser.parse_args()
# Load data
train_path, valid_path, test_path = args.train, args.val, args.test
logs_path = args.expt_dir
model_path, model_arch, model_name = args.save_dir, args.arch, args.model_name
model_path = os.path.join(model_path, model_name)
if not os.path.isdir(model_path):
os.mkdir(model_path)
lr, batch_size, init = float(args.lr), int(args.batch_size), int(args.init)
data = loadData(train_path, valid_path, test_path)
train_X, train_Y, valid_X, valid_Y, test_X, test_Y = data['train']['X'], data['train']['Y'],\
data['valid']['X'], data['valid']['Y'],\
data['test']['X'], data['test']['Y'],
# Load architecture
arch = loadArch(model_arch)
# Logging
train_log_name = '{}.train.log'.format(model_name)
valid_log_name = '{}.valid.log'.format(model_name)
train_log = setup_logger('train-log',
os.path.join(logs_path, train_log_name))
valid_log = setup_logger('valid-log',
os.path.join(logs_path, valid_log_name))
# GPU config
gpu_options = tf.GPUOptions(per_process_gpu_memory_fraction=1.0)
# Train
num_epochs = 100
num_batches = int(float(train_X.shape[0]) / batch_size)
steps = 0
patience = 50
early_stop = 0
with tf.Session(config=tf.ConfigProto(gpu_options=gpu_options)) as session:
model = CNN(arch, session, logs_path, init, lr)
loss_history = [np.inf]
for epoch in range(num_epochs):
print 'Epoch {}'.format(epoch)
steps = 0
indices = np.arange(train_X.shape[0])
np.random.shuffle(indices)
train_X, train_Y = train_X[indices], train_Y[indices]
for batch in range(num_batches):
start, end = batch * batch_size, (batch + 1) * batch_size
x, y = Augment(train_X[range(start,
end)]).batch, train_Y[range(
start, end)]
try:
model.step(x, y)
except MemoryError:
print 'Memory error in step'
exit()
steps += batch_size
if steps % train_X.shape[0] == 0 and steps != 0:
try:
train_loss, train_acc = testModel(
model, train_X, train_Y, batch_size)
except MemoryError:
print 'Memory error in test for train'
exit()
train_log.info(
'Epoch {}, Step {}, Loss: {}, Accuracy: {}, lr: {}'.
format(epoch, steps, train_loss, train_acc, model.lr))
try:
valid_loss, valid_acc = testModel(
model, valid_X, valid_Y, batch_size)
except MemoryError:
print 'Memory error in test for valid'
exit()
valid_log.info(
'Epoch {}, Step {}, Loss: {}, Accuracy: {}, lr: {}'.
format(epoch, steps, valid_loss, valid_acc, model.lr))
if valid_loss < min(loss_history):
save_path = os.path.join(model_path, 'model')
model.save(save_path)
early_stop = 0
early_stop += 1
if (early_stop >= patience):
print "No improvement in validation loss for " + str(
patience) + " steps - stopping training!"
print("Optimization Finished!")
return 1
loss_history.append(valid_loss)
print("Optimization Finished!")
training_data, training_labels = th.from_numpy(training_data), th.from_numpy(
training_labels)
training_data = training_data.view(-1, 1, 28, 28)
training_labels = th.unsqueeze(training_labels, 1)
training_set = TensorDataset(training_data, training_labels)
training_loader = DataLoader(training_set, args.batch_size)
validation_data, validation_labels = th.from_numpy(
validation_data), th.from_numpy(validation_labels)
validation_data = validation_data.view(-1, 1, 28, 28)
validation_labels = th.unsqueeze(validation_labels, 1)
validation_set = TensorDataset(validation_data, validation_labels)
validation_loader = DataLoader(validation_set, args.batch_size)
model = CNN()
if cuda:
model.cuda()
criterion = nn.L1Loss()
optimizer = Adam(model.parameters(), lr=1e-3)
for epoch in range(args.n_epochs):
for iteration, batch in enumerate(training_loader):
data, labels = batch
if cuda:
data, labels = data.cuda(), labels.cuda()
noisy_labels = th.zeros(labels.size())
noisy_labels.copy_(labels)
n_noises = int(args.noise * labels.size()[0])
noise = th.from_numpy(np.random.choice(np.arange(1, 10), n_noises))
def evaluation(self):
'''Evaluates current state (some is already done in __init__)
Returns evaluated state value'''
if self.terminate:
self.v = -self.prev_r
return self.v
def backup(self, v):
'''Backs up tree statistics up to root
Currently uses mean Q'''
if self.parent != None:
n_a = self.parent.children_n_visited[self.prev_a]
self.parent.children_n_visited[self.prev_a] = n_a + 1
n = torch.sum(self.parent.children_n_visited) - 1
old_Q = self.parent.Q[self.prev_a]
new_Q = (-v+old_Q*n)/(n+1)
self.parent.Q[self.prev_a] = new_Q
self.parent.backup(-v)
if __name__=="__main__":
from network import CNN
network = CNN().to(device)
network.eval()
mcts = MCTS(3, 3, network)
a = mcts.monte_carlo_tree_search(100, 0.05)
print(a)
def main(model_name, model_version, num_layers, num_units, bottleneck,
num_filters, filter_size, stride, padding, batch_size, n_epochs,
learning_rate, moves_path, data_path, continue_train):
# Grab the training and validation data as a DataLoader
train_set = PeakDataset(moves_path + '/train_moves.pt',
data_path + '/train/%s/train_%d.pt')
val_set = PeakDataset(moves_path + '/val_moves.pt',
data_path + '/val/%s/val_%d.pt')
# Check if a folder exists for this network version number, if not create it
folder_path = '../networks/' + str(model_version)
if not os.path.isdir(folder_path):
os.makedirs(folder_path)
elif continue_train:
pass
else:
raise Exception("Model version already exists")
# Initialize the network
if model_name == 'linear':
net = Linear(num_layers=num_layers, num_units=num_units)
# Save out the hyperparameters
torch.save(
{
'model': model_name,
'version': model_version,
'layers': num_layers,
'units': num_units,
'batch': batch_size,
'epoch': n_epochs,
'lr': learning_rate,
}, '../networks/' + str(model_version) + '/hparams')
elif model_name == 'linearskip':
net = LinearSkip(num_layers=num_layers,
num_units=num_units,
bottleneck=bottleneck)
# Save out the hyperparameters
torch.save(
{
'model': model_name,
'version': model_version,
'layers': num_layers,
'units': num_units,
'batch': batch_size,
'epoch': n_epochs,
'lr': learning_rate,
'bottleneck': bottleneck,
}, '../networks/' + str(model_version) + '/hparams')
elif model_name == 'conv':
net = CNN(num_layers=num_layers,
num_filters=num_filters,
filter_size=filter_size,
stride=stride,
pad=padding)
# Save out the hyperparameters
torch.save(
{
'model': model_name,
'version': model_version,
'layers': num_layers,
'filters': num_filters,
'size': filter_size,
'stride': stride,
'padding': padding,
'batch': batch_size,
'epoch': n_epochs,
'lr': learning_rate,
}, '../networks/' + str(model_version) + '/hparams')
# Continue training for a network
last_epoch = -1
if continue_train:
files = os.listdir(folder_path)
for file in files:
if file[0:5] == 'model':
curr_epoch = int(file.split('_')[1])
if curr_epoch > last_epoch:
last_epoch = curr_epoch
if last_epoch >= 0:
checkpoint = folder_path + '/model_%d' % last_epoch
net.load_state_dict(torch.load(checkpoint)['model_state_dict'])
# Train the network
train(net,
batch_size=batch_size,
n_epochs=n_epochs,
start_epoch=last_epoch,
learning_rate=learning_rate,
train_set=train_set,
val_set=val_set,
L2=0,
model_name=model_name,
model_version=model_version)
valnum = get_indexNum(config, index, "val")
testnum = get_indexNum(config, index, "test")
train_dataset = IQADataset(dataset, config, index, "train")
train_loader = torch.utils.data.DataLoader(train_dataset,
batch_size=args.batch_size,
shuffle=True,
pin_memory=True,
num_workers=0)
val_dataset = IQADataset(dataset, config, index, "val")
val_loader = torch.utils.data.DataLoader(val_dataset)
test_dataset = IQADataset(dataset, config, index, "test")
test_loader = torch.utils.data.DataLoader(test_dataset)
model = CNN().to(device)
criterion = nn.L1Loss()
optimizer = torch.optim.SGD(model.parameters(),
lr=args.lr,
weight_decay=args.weight_decay)
best_SROCC = -1
for epoch in range(args.epochs):
#train
model.train()
LOSS = 0
for i, (patches, label) in enumerate(train_loader):
patches = patches.to(device)
label = label.to(device)
help='index of images for visualizing feature')
parser.add_argument('--save_dir',
type=str,
default='visual/',
help='directory to save visualization results')
opt = parser.parse_args()
if not os.path.exists(opt.save_dir):
os.mkdir(opt.save_dir)
print('[Info] loading checkpoint from %s ...' %
os.path.join(opt.ckpt_path, 'ckpt_epoch_%d.pth' % opt.epoch))
checkpoint = torch.load(
os.path.join(opt.ckpt_path, 'ckpt_epoch_%d.pth' % opt.epoch))
configs = checkpoint['configs']
model = CNN(configs['in_channels'],
configs['num_class'],
batch_norm=configs['batch_norm'],
p=configs['dropout'])
model.load_state_dict(checkpoint['model_state'])
model.eval()
conv_net = torch.nn.ModuleList()
for name, m in model.named_children():
if name != 'fc_net':
conv_net.append(m)
conv_net = torch.nn.Sequential(*conv_net)
fc_net = model.fc_net
if opt.type == 'filter':
filter_dir = os.path.join(opt.save_dir, 'filter')
if not os.path.exists(filter_dir):
os.mkdir(filter_dir)
