def main(FLAG):
Model = SimpleModel(FLAG.input_dim,
FLAG.hidden_dim,
FLAG.output_dim,
optimizer=tf.train.RMSPropOptimizer(
FLAG.learning_rate))
image, label = load_dataset()
image, label = image_augmentation(image,
label,
horizon_flip=True,
control_brightness=True)
label = label / 96.
(train_X, train_y), (valid_X,
valid_y), (test_X, test_y) = split_data(image, label)
if FLAG.Mode == "validation":
lr_list = 10**np.random.uniform(-6, -2, 20)
Model.validation(train_X, train_y, valid_X, valid_y, lr_list)
elif FLAG.Mode == "train":
Model.train(train_X, train_y, valid_X, valid_y, FLAG.batch_size,
FLAG.Epoch, FLAG.save_graph, FLAG.save_model)
pred_Y = Model.predict(test_X[123])
print(pred_Y)
print(test_y[123])
print(np.mean(np.square(pred_Y - test_y[123])))
model = SimpleModel().to(device)
# loss function
criterion = nn.CrossEntropyLoss()
#optimizer
optimizer = optim.SGD(model.parameters(), lr=learning_rate)
#training
num_steps = len(train_loader)
for epoch in range(num_epochs):
#----training----
#set model to training
model.train()
total_loss = 0
for i, (images, labels) in enumerate(train_loader):
images, labels = images.to(device), labels.to(device)
#zero gradients
optimizer.zero_grad()
#forward
outputs = model(images)
#compute loss
loss = criterion(outputs, labels)
class MultiTaskSingleObjectiveAgent(SingleTaskSingleObjectiveAgent):
def __init__(self, architecture, search_space, task_info):
self.device = torch.device(
'cuda' if torch.cuda.is_available() else 'cpu')
self.search_size = len(search_space)
self.num_tasks = task_info.num_tasks
self.model = SimpleModel(architecture=architecture,
search_space=search_space,
in_channels=task_info.num_channels,
num_classes=task_info.num_classes)
self.compute_model_size = SimpleModelSize(architecture,
search_space,
task_info.num_channels,
sum(task_info.num_classes),
batchnorm=True)
self._init()
def _pretrain(self,
train_data,
test_data,
configs,
save_model=False,
save_history=False,
path='saved_models/default/pretrain/',
verbose=False):
self.model.train()
dataloader = train_data.get_loader()
criterion = nn.CrossEntropyLoss()
optimizer = optim.SGD(self.model.parameters(),
lr=configs.lr,
momentum=configs.momentum,
weight_decay=configs.weight_decay)
scheduler = optim.lr_scheduler.MultiStepLR(
optimizer,
milestones=configs.lr_decay_epoch,
gamma=configs.lr_decay)
for epoch in range(self.epoch['pretrain']):
scheduler.step()
for epoch in range(self.epoch['pretrain'], configs.num_epochs):
scheduler.step()
dropout = configs.dropout * epoch / configs.num_epochs
for inputs, labels, task in dataloader:
inputs, labels = inputs.to(self.device), labels.to(self.device)
masks = self.mask_sampler.rand(dropout=dropout)
outputs = self.model(inputs,
self.mask_sampler.make_batch(masks), task)
loss = criterion(outputs, labels)
optimizer.zero_grad()
loss.backward()
optimizer.step()
if verbose or save_history:
masks = self.mask_sampler.ones()
self.accuracy['pretrain'].append(
self._eval_model(test_data, masks))
if verbose:
print('[Pretrain][Epoch {}] Accuracy: {}'.format(
epoch + 1, self.accuracy['pretrain'][-1]))
if epoch % configs.save_epoch == 0 and save_model:
self._save_pretrain(path)
self.epoch['pretrain'] = epoch + 1
self._save_epoch('pretrain', path)
if save_model:
self._save_pretrain(path)
self.epoch['pretrain'] = configs.num_epochs
self._save_epoch('pretrain', path)
def _finaltrain(self,
train_data,
test_data,
configs,
save_model=False,
save_history=False,
path='saved_models/default/final/',
verbose=False):
if self.finalmodel is None:
self.finalmodel_mask = self.queue[0]
self.finalmodel = [
self.submodel(self.finalmodel_mask, task)
for task in range(self.num_tasks)
]
self.finalmodel = [
nn.DataParallel(m).to(self.device) for m in self.finalmodel
]
for model in self.finalmodel:
model.train()
dataloader = train_data.get_loader()
criterion = nn.CrossEntropyLoss()
optimizers = [
optim.SGD(model.parameters(),
lr=configs.lr,
momentum=configs.momentum,
weight_decay=configs.weight_decay)
for model in self.finalmodel
]
schedulers = [
optim.lr_scheduler.MultiStepLR(optimizer,
milestones=configs.lr_decay_epoch,
gamma=configs.lr_decay)
for optimizer in optimizers
]
for epoch in range(self.epoch['final']):
for scheduler in schedulers:
scheduler.step()
for epoch in range(self.epoch['final'], configs.num_epochs):
for scheduler in schedulers:
scheduler.step()
for inputs, labels, task in dataloader:
model = self.finalmodel[task]
optimizer = optimizers[task]
inputs, labels = inputs.to(self.device), labels.to(self.device)
outputs = model(inputs)
loss = criterion(outputs, labels)
optimizer.zero_grad()
loss.backward()
optimizer.step()
if verbose or save_history:
self.accuracy['final'].append(self._eval_final(test_data))
if verbose:
print('[Final][Epoch {}] Accuracy: {}'.format(
epoch + 1, self.accuracy['final'][-1]))
if epoch % configs.save_epoch == 0:
if save_model:
self._save_final(path)
self.epoch['final'] = epoch + 1
self._save_epoch('final', path)
if save_history:
self._save_accuracy('final', path)
if save_model:
self._save_final(path)
self.epoch['final'] = configs.num_epochs
self._save_epoch('final', path)
if save_history:
self._save_accuracy('final', path)
def _eval_model(self, data, masks):
masks = self.mask_sampler.make_batch(masks)
model = lambda x, t: self.model(x, masks, t)
accuracy = self._eval(data, model)
return accuracy
def _eval_final(self, data):
for model in self.finalmodel:
model.eval()
model = lambda x, t: self.finalmodel[t](x)
accuracy = self._eval(data, model)
for model in self.finalmodel:
model.train()
return accuracy
def _eval(self, data, model):
correct = [0 for _ in range(self.num_tasks)]
total = [0 for _ in range(self.num_tasks)]
with torch.no_grad():
for t in range(self.num_tasks):
for inputs, labels in data.get_loader(t):
inputs, labels = inputs.to(self.device), labels.to(
self.device)
outputs = model(inputs, t)
_, predict_labels = torch.max(outputs.detach(), 1)
total[t] += labels.size(0)
correct[t] += (predict_labels == labels).sum().item()
return np.mean([c / t for c, t in zip(correct, total)])
def _save_final(self, path='saved_models/default/final/'):
if not os.path.isdir(path):
os.makedirs(path)
with open(os.path.join(path, 'masks.json'), 'w') as f:
json.dump(self.finalmodel_mask.tolist(), f)
for t, model in enumerate(self.finalmodel):
torch.save(model.state_dict(),
os.path.join(path, 'model{}'.format(t)))
def _load_final(self, path='saved_models/default/final/'):
try:
with open(os.path.join(path, 'masks.json'), 'r') as f:
self.finalmodel_mask = json.load(f)
self.finalmodel_mask = torch.tensor(self.finalmodel_mask,
dtype=torch.uint8)
self.finalmodel = [
self.submodel(self.finalmodel_mask, task)
for task in range(self.num_tasks)
]
self.finalmodel = [
nn.DataParallel(m).to(self.device) for m in self.finalmodel
]
for t, model in enumerate(self.finalmodel):
filename = os.path.join(path, 'model{}'.format(t))
model.load_state_dict(torch.load(filename))
except FileNotFoundError:
pass
def train(args):
"""
Terminology: k-way n-shot, k classes, n shots per class
"""
device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
print(device)
renew_path(args.save)
shots = args.shot + args.query
train_set = IkeaSet(TRAIN_MAT_PATH)
train_sampler = Sampler(train_set.label,
args.batch_num_train,
args.train_way,
shots,
limit_class=args.limit_class)
train_loader = DataLoader(train_set,
batch_sampler=train_sampler,
num_workers=4,
pin_memory=True)
test_set = IkeaSet(TEST_MAT_PATH)
test_sampler = Sampler(test_set.label,
args.batch_num_test,
args.test_way,
shots,
limit_class=args.limit_class)
test_loader = DataLoader(test_set,
batch_sampler=test_sampler,
num_workers=4,
pin_memory=True)
model = SimpleModel().to(device)
model = load_model(model, 'model', args.save)
optimizer = torch.optim.Adam(model.parameters(), lr=0.001)
# learing rate scheduler
lr_scheduler = torch.optim.lr_scheduler.StepLR(optimizer,
step_size=20,
gamma=0.5)
loss_fn = F.cross_entropy
# training log
training_log = {}
training_log['args'] = []
training_log['train_loss'] = []
training_log['val_loss'] = []
training_log['train_acc'] = []
training_log['val_acc'] = []
training_log['max_acc'] = 0.0
for epoch in range(1, args.epoch + 1):
time_a = datetime.datetime.now()
model.train()
average_loss = 0
average_accuracy = 0
print("Start epoch: ", epoch)
for i, batch in enumerate(train_loader, 1):
num = args.shot * args.train_way
support_x, query_x = batch[0][:num].to(device), batch[0][num:].to(
device)
#support_y, query_y = batch[1][:num], batch[1][num:]
#print(support_x.shape)
embedding = model(support_x.float())
# Get the mean of all the embeddings to get the prototype for a class
embedding = embedding.reshape(args.shot, args.train_way,
-1).mean(dim=0)
#print(batch[0].shape)
# Tough it seems strange here to just use labels in range but instead of real lables
# , but that is beacause of the way the data was sampled (see sampled.py for structure
# of a batch). The real label of the data does not correspond to the index of the closest
# cluster center since the samples in the batch are shuffled, so instead we transform the data
# label into the relative index in the range of classes, in this way the closest cluster
# center index matches the relative index.
label = torch.arange(args.train_way).repeat(args.query)
#label = query_y.type(torch.cuda.LongTensor if torch.cuda.is_available() else torch.LongTensor)
label = label.type(torch.cuda.LongTensor if torch.cuda.
is_available() else torch.LongTensor)
distance = euclidean(model(query_x), embedding)
prob = F.softmax(distance, dim=1)
loss = loss_fn(prob, label)
acc = get_accuracy(label, prob)
if i % 30 == 0:
print(label.shape, distance.shape)
print('epoch{}, {}/{}, lost={:.4f} acc={:.4f}'.format(
epoch, i, len(train_loader), loss.item(), acc))
average_loss = update_avg(i + 1, average_loss, loss.item())
average_accuracy = update_avg(i + 1, average_accuracy, acc)
#optimizer.zero_grad()
loss.backward()
optimizer.step()
lr_scheduler.step()
embedding = None
loss = None
distanece = None
model.eval()
average_loss_val = 0
average_accuracy_val = 0
# evaluate after epoch.
with torch.no_grad():
for i, batch in enumerate(test_loader, 1):
num = args.shot * args.test_way
support_x, query_x = batch[0][:num].to(
device), batch[0][num:].to(device)
#support_y, query_y = batch[1][:num], batch[1][num:]
embedding = model(support_x)
embedding = embedding.reshape(args.shot, args.test_way,
-1).mean(dim=0)
label = torch.arange(args.train_way).repeat(args.query)
#label = query_y.type(torch.cuda.LongTensor if torch.cuda.is_available() else torch.LongTensor)
label = label.type(torch.cuda.LongTensor if torch.cuda.
is_available() else torch.LongTensor)
distance = euclidean(model(query_x), embedding)
prob = F.softmax(distance, dim=1)
loss = loss_fn(prob, label)
acc = get_accuracy(label, prob)
average_loss_val = update_avg(i + 1, average_loss_val,
loss.item())
average_accuracy_val = update_avg(i + 1, average_accuracy_val,
acc)
embedding = None
loss = None
distanece = None
print("epoch {} validation: loss={:4f} acc={:4f}".format(
epoch, average_loss, average_accuracy))
if average_accuracy > training_log['max_acc']:
training_log['max_acc'] = acc
save_model(model, 'max-acc', args.save)
training_log['train_loss'].append(average_loss)
training_log['train_acc'].append(average_accuracy)
training_log['val_loss'].append(average_loss_val)
training_log['val_acc'].append(average_accuracy_val)
torch.save(training_log, os.path.join(args.save, 'training_log'))
save_model(model, 'model', args.save)
if epoch % 1 == 0:
save_model(model, 'model', args.save)
time_b = datetime.datetime.now()
print('ETA:{}s/{}s'.format(
(time_b - time_a).seconds,
(time_b - time_a).seconds * (args.epoch - epoch)))
def train(args):
device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
print(device)
renew_path(args.save)
train_set = IkeaSet(TRAIN_MAT_PATH)
train_loader = DataLoader(train_set, num_workers=4, pin_memory=True)
test_set = IkeaSet(TEST_MAT_PATH)
test_loader = DataLoader(test_set, num_workers=4, pin_memory=True)
model = SimpleModel(n_class=len(train_set.classes)).to(device)
model = load_model(model, 'model', args.save)
optimizer = torch.optim.Adam(model.parameters(), lr=0.001)
# learing rate scheduler
lr_scheduler = torch.optim.lr_scheduler.StepLR(optimizer, step_size=20, gamma=0.5)
loss_fn = F.cross_entropy
# training log
training_log = {}
training_log['args'] = []
training_log['train_loss'] = []
training_log['val_loss'] = []
training_log['train_acc'] = []
training_log['val_acc'] = []
training_log['max_acc'] = 0.0
for epoch in range(1, args.epoch + 1):
time_a = datetime.datetime.now()
model.train()
average_loss = 0
average_accuracy = 0
for i, batch in enumerate(train_loader, 1):
batch[0].to(device), batch[1].to(device)
label = batch[1]
label = label.type(torch.cuda.LongTensor if torch.cuda.is_available() else torch.LongTensor)
pred = model(batch[0])
loss = loss_fn(pred, label)
print(pred)
print(torch.argmax(pred, dim=1), label)
acc = get_accuracy(label, pred)
if i % 20 == 0:
print('epoch{}, {}/{}, lost={:.4f} acc={:.4f}'.format(epoch, i, len(train_loader), loss.item(), acc))
average_loss = update_avg(i + 1, average_loss, loss.item())
average_accuracy = update_avg(i + 1, average_accuracy, acc)
#optimizer.zero_grad()
loss.backward()
optimizer.step()
lr_scheduler.step()
embedding = None
loss = None
distanece = None
model.eval()
average_loss_val = 0
average_accuracy_val = 0
# evaluate after epoch.
with torch.no_grad():
for i, batch in enumerate(test_loader, 1):
batch[0].to(device), batch[1].to(device)
label = batch[1]
#label = query_y.type(torch.cuda.LongTensor if torch.cuda.is_available() else torch.LongTensor)
label = label.type(torch.cuda.LongTensor if torch.cuda.is_available() else torch.LongTensor)
pred = model(batch[0])
loss = loss_fn(pred, label)
acc = get_accuracy(label, pred)
average_loss_val = update_avg(i + 1, average_loss_val, loss.item())
average_accuracy_val = update_avg(i + 1, average_accuracy_val, acc)
embedding = None
loss = None
distanece = None
print("epoch {} validation: loss={:4f} acc={:4f}".format(epoch, average_loss, average_accuracy))
if average_accuracy > training_log['max_acc']:
training_log['max_acc'] = acc
save_model(model, 'max-acc', args.save)
training_log['train_loss'].append(average_loss)
training_log['train_acc'].append(average_accuracy)
training_log['val_loss'].append(average_loss_val)
training_log['val_acc'].append(average_accuracy_val)
torch.save(training_log, os.path.join(args.save, 'training_log'))
save_model(model, 'model', args.save)
if epoch % 1 == 0:
save_model(model, 'model', args.save)
time_b = datetime.datetime.now()
print('ETA:{}s/{}s'.format((time_b - time_a).seconds, (time_b - time_a).seconds * (args.epoch - epoch)))
model_pytorch = SimpleModel(input_size=input_size, hidden_sizes=hidden_sizes,
output_size=output_size)
model_pytorch = model_pytorch.to(device)
# Set loss and optimizer
# Set binary cross entropy loss since 2 classes only
criterion = nn.BCELoss()
optimizer = optim.Adam(model_pytorch.parameters(), lr=1e-3)
num_epochs = 20
# Train model
time_start = time.time()
for epoch in range(num_epochs):
model_pytorch.train()
train_loss_total = 0
for data, target in train_loader:
data, target = data.to(device), target.float().to(device)
optimizer.zero_grad()
output = model_pytorch(data)
train_loss = criterion(output, target)
train_loss.backward()
optimizer.step()
train_loss_total += train_loss.item() * data.size(0)
print('Epoch {} completed. Train loss is {:.3f}'.format(epoch + 1, train_loss_total / train_size))
print('Time taken to completed {} epochs: {:.2f} minutes'.format(num_epochs, (time.time() - time_start) / 60))
class SingleTaskSingleObjectiveAgent(BaseAgent):
def __init__(self, architecture, search_space, task_info):
self.device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')
self.search_size = len(search_space)
self.model = SimpleModel(architecture=architecture,
search_space=search_space,
in_channels=task_info.num_channels,
num_classes=[task_info.num_classes]
)
self.compute_model_size = SimpleModelSize(architecture, search_space, task_info.num_channels, task_info.num_classes, batchnorm=True)
self._init()
def _init(self):
self.submodel = self.model.submodel
self.mask_sampler = MaskSampler(mask_size=self.model.mask_size)
self.model = nn.DataParallel(self.model).to(self.device)
# Record
self.epoch = {'pretrain': 0, 'search': 0, 'final': 0}
self.accuracy = {'pretrain': [], 'search': [], 'final': []}
# Search
self.accuracy_dict_valid = {}
self.accuracy_dict_test = {}
self.queue = []
self.queue_acc = []
# Final
self.finalmodel_mask = None
self.finalmodel = None
def train(self, train_data, valid_data, test_data, configs, save_model, save_history, path, verbose):
# Pretrain
if self.epoch['pretrain'] < configs.pretrain.num_epochs:
self._pretrain(train_data=train_data,
test_data=test_data,
configs=configs.pretrain,
save_model=save_model,
save_history=save_history,
path=os.path.join(path, 'pretrain'),
verbose=verbose
)
# Select final model
if self.epoch['search'] < configs.search.num_epochs:
self._search(valid_data=valid_data,
test_data=test_data,
configs=configs.search,
save_model=save_model,
save_history=save_history,
path=os.path.join(path, 'search'),
verbose=verbose
)
# Train final model
if self.epoch['final'] < configs.final.num_epochs:
self._finaltrain(train_data=train_data,
test_data=test_data,
configs=configs.final,
save_model=save_model,
save_history=save_history,
path=os.path.join(path, 'final'),
verbose=verbose
)
def _pretrain(self,
train_data,
test_data,
configs,
save_model=False,
save_history=False,
path='saved_models/default/pretrain/',
verbose=False
):
self.model.train()
criterion = nn.CrossEntropyLoss()
optimizer = optim.SGD(self.model.parameters(), lr=configs.lr, momentum=configs.momentum, weight_decay=configs.weight_decay)
scheduler = optim.lr_scheduler.MultiStepLR(optimizer, milestones=configs.lr_decay_epoch, gamma=configs.lr_decay)
for epoch in range(self.epoch['pretrain']):
scheduler.step()
for epoch in range(self.epoch['pretrain'], configs.num_epochs):
scheduler.step()
dropout = configs.dropout * epoch / configs.num_epochs
for inputs, labels in train_data:
inputs, labels = inputs.to(self.device), labels.to(self.device)
masks = self.mask_sampler.rand(dropout=dropout)
outputs = self.model(inputs, self.mask_sampler.make_batch(masks))
loss = criterion(outputs, labels)
optimizer.zero_grad()
loss.backward()
optimizer.step()
if verbose or save_history:
masks = self.mask_sampler.ones()
self.accuracy['pretrain'].append(self._eval_model(test_data, masks))
if verbose:
print('[Pretrain][Epoch {}] Accuracy: {}'.format(epoch + 1, self.accuracy['pretrain'][-1]))
if epoch % configs.save_epoch == 0 and save_model:
self._save_pretrain(path)
self.epoch['pretrain'] = epoch + 1
self._save_epoch('pretrain', path)
if save_model:
self._save_pretrain(path)
self.epoch['pretrain'] = configs.num_epochs
self._save_epoch('pretrain', path)
def _search(self,
valid_data,
test_data,
configs,
save_model=False,
save_history=False,
path='saved_models/default/search/',
verbose=False
):
# Initalization
if self.epoch['search'] == 0:
self.queue = [self.mask_sampler.rand(dropout=i/(configs.num_samples-1)) for i in range(configs.num_samples)]
self.queue_acc = []
for masks in self.queue:
masks_str = masks2str(masks)
accuracy = self._eval_model(valid_data, masks)
self.accuracy_dict_valid[masks_str] = accuracy
self.queue_acc.append(accuracy)
# Search
for epoch in range(self.epoch['search'], configs.num_epochs):
generated = []
generated_acc = []
for old_masks in self.queue:
new_masks = self.mask_sampler.mutate(old_masks, configs.mutate_prob)
new_masks_str = masks2str(new_masks)
if new_masks_str not in self.accuracy_dict_valid:
self.accuracy_dict_valid[new_masks_str] = self._eval_model(valid_data, new_masks)
generated.append(new_masks)
generated_acc.append(self.accuracy_dict_valid[new_masks_str])
candidates = self.queue + generated
candidates_acc = self.queue_acc + generated_acc
order = np.argsort(candidates_acc)[::-1][:configs.num_samples]
self.queue = [candidates[i] for i in order]
self.queue_acc = [candidates_acc[i] for i in order]
best_masks = self.queue[0]
best_masks_str = masks2str(best_masks)
if verbose or save_history:
if best_masks_str not in self.accuracy_dict_test:
self.accuracy_dict_test[best_masks_str] = self._eval_model(test_data, best_masks)
self.accuracy['search'].append(self.accuracy_dict_test[best_masks_str])
if verbose:
print('[Search][Epoch {}] Accuracy: {}'.format(epoch + 1, self.accuracy['search'][-1]))
if epoch % configs.save_epoch == 0:
if save_model:
self._save_search(path)
self.epoch['search'] = epoch + 1
self._save_epoch('search', path)
if save_history:
self._save_accuracy('search', path)
if save_model:
self._save_search(path)
self.epoch['search'] = configs.num_epochs
self._save_epoch('search', path)
if save_history:
self._save_accuracy('search', path)
def _finaltrain(self,
train_data,
test_data,
configs,
save_model=False,
save_history=False,
path='saved_models/default/final/',
verbose=False
):
if self.finalmodel is None:
self.finalmodel_mask = self.queue[0]
self.finalmodel = self.submodel(self.finalmodel_mask)
self.finalmodel = nn.DataParallel(self.finalmodel).to(self.device)
self.finalmodel.train()
criterion = nn.CrossEntropyLoss()
optimizer = optim.SGD(self.finalmodel.parameters(), lr=configs.lr, momentum=configs.momentum, weight_decay=configs.weight_decay)
scheduler = optim.lr_scheduler.MultiStepLR(optimizer, milestones=configs.lr_decay_epoch, gamma=configs.lr_decay)
for epoch in range(self.epoch['final']):
scheduler.step()
for epoch in range(self.epoch['final'], configs.num_epochs):
scheduler.step()
for inputs, labels in train_data:
inputs, labels = inputs.to(self.device), labels.to(self.device)
outputs = self.finalmodel(inputs)
loss = criterion(outputs, labels)
optimizer.zero_grad()
loss.backward()
optimizer.step()
if verbose or save_history:
self.accuracy['final'].append(self._eval_final(test_data))
if verbose:
print('[Final][Epoch {}] Accuracy: {}'.format(epoch + 1, self.accuracy['final'][-1]))
if epoch % configs.save_epoch == 0:
if save_model:
self._save_final(path)
self.epoch['final'] = epoch + 1
self._save_epoch('final', path)
if save_history:
self._save_accuracy('final', path)
if save_model:
self._save_final(path)
self.epoch['final'] = configs.num_epochs
self._save_epoch('final', path)
if save_history:
self._save_accuracy('final', path)
def eval(self, data):
accuracy = self._eval_final(data)
model_size = self.compute_model_size.compute(self.finalmodel_mask)
return accuracy, model_size
def _eval_model(self, data, masks):
masks = self.mask_sampler.make_batch(masks)
model = lambda x: self.model(x, masks)
accuracy = self._eval(data, model)
return accuracy
def _eval_final(self, data):
self.finalmodel.eval()
accuracy = self._eval(data, self.finalmodel)
self.finalmodel.train()
return accuracy
def _eval(self, data, model):
correct = 0
total = 0
with torch.no_grad():
for inputs, labels in data:
inputs, labels = inputs.to(self.device), labels.to(self.device)
outputs = model(inputs)
_, predict_labels = torch.max(outputs.detach(), 1)
total += labels.size(0)
correct += (predict_labels == labels).sum().item()
return correct / total
def _save_pretrain(self, path='saved_models/default/pretrain/'):
if not os.path.isdir(path):
os.makedirs(path)
torch.save(self.model.state_dict(), os.path.join(path, 'model'))
def _save_search(self, path='saved_models/default/search/'):
if not os.path.isdir(path):
os.makedirs(path)
with open(os.path.join(path, 'accuracy_dict_valid.json'), 'w') as f:
json.dump(self.accuracy_dict_valid, f)
with open(os.path.join(path, 'accuracy_dict_test.json'), 'w') as f:
json.dump(self.accuracy_dict_test, f)
with open(os.path.join(path, 'queue.json'), 'w') as f:
json.dump([masks.tolist() for masks in self.queue], f)
with open(os.path.join(path, 'queue_acc.json'), 'w') as f:
json.dump(self.queue_acc, f)
def _save_final(self, path='saved_models/default/final/'):
if not os.path.isdir(path):
os.makedirs(path)
with open(os.path.join(path, 'masks.json'), 'w') as f:
json.dump(self.finalmodel_mask.tolist(), f)
torch.save(self.finalmodel.state_dict(), os.path.join(path, 'model'))
def _save_epoch(self, key, path='saved_models/default/'):
if not os.path.isdir(path):
os.makedirs(path)
with open(os.path.join(path, 'last_epoch.json'), 'w') as f:
json.dump(self.epoch[key], f)
def _save_accuracy(self, key, path='saved_models/default/'):
if not os.path.isdir(path):
os.makedirs(path)
filename = os.path.join(path, 'history.json')
with open(filename, 'w') as f:
json.dump(self.accuracy[key], f)
def load(self, path='saved_models/default/'):
self._load_pretrain(os.path.join(path, 'pretrain'))
self._load_search(os.path.join(path, 'search'))
self._load_final(os.path.join(path, 'final'))
for key in ['pretrain', 'search', 'final']:
self._load_epoch(key, os.path.join(path, key))
self._load_accuracy(key, os.path.join(path, key))
def _load_pretrain(self, path='saved_models/default/pretrain/'):
try:
filename = os.path.join(path, 'model')
self.model.load_state_dict(torch.load(filename))
except FileNotFoundError:
pass
def _load_search(self, path='saved_models/default/search/'):
try:
with open(os.path.join(path, 'accuracy_dict_valid.json')) as f:
self.accuracy_dict_valid = json.load(f)
with open(os.path.join(path, 'accuracy_dict_test.json')) as f:
self.accuracy_dict_test = json.load(f)
with open(os.path.join(path, 'queue.json')) as f:
self.queue = json.load(f)
self.queue = [torch.tensor(masks, dtype=torch.uint8) for masks in self.queue]
with open(os.path.join(path, 'queue_acc.json')) as f:
self.queue_acc = json.load(f)
except FileNotFoundError:
self.queue = []
self.queue_acc = []
def _load_final(self, path='saved_models/default/final/'):
try:
with open(os.path.join(path, 'masks.json'), 'r') as f:
self.finalmodel_mask = json.load(f)
self.finalmodel_mask = torch.tensor(self.finalmodel_mask, dtype=torch.uint8)
self.finalmodel = self.submodel(self.finalmodel_mask)
self.finalmodel = nn.DataParallel(self.finalmodel).to(self.device)
filename = os.path.join(path, 'model')
self.finalmodel.load_state_dict(torch.load(filename))
except FileNotFoundError:
pass
def _load_epoch(self, key, path='saved_models/default/'):
try:
filename = os.path.join(path, 'last_epoch.json')
with open(filename, 'r') as f:
self.epoch[key] = json.load(f)
except FileNotFoundError:
self.epoch[key] = 0
def _load_accuracy(self, key, path='saved_models/default/'):
try:
with open(os.path.join(path, 'history.json'), 'r') as f:
self.accuracy[key] = json.load(f)
except FileNotFoundError:
pass