def __init__(self,
params,
lr=1e-3,
eps=1e-8,
alpha=1e-7,
beta=1e-5,
gamma=0.9,
momentum=1,
sgd_steps=5,
K=10):
if not 0.0 <= lr:
raise ValueError("Invalid learning rate: {}".format(lr))
if not 0.0 <= eps:
raise ValueError("Invalid epsilon value: {}".format(eps))
if not 1 >= momentum:
raise ValueError("Invalid momentum value: {}".format(eps))
self.iter = 0
self.sgd = SGD(params, lr=lr, momentum=0.9)
param_count = np.sum([np.prod(p.size())
for p in params]) # got from MNIST-GAN
defaults = dict(lr=lr,
eps=eps,
alpha=alpha,
beta=beta * param_count,
gamma=gamma,
sgd_steps=sgd_steps,
momentum=momentum,
K=K)
super(Neumann, self).__init__(params, defaults)
def split_optimizer(model: nn.Module, cfg: dict):
param_weight_decay, param_bias, param_other = split_params(model)
if len(param_other) != 0:
if cfg['optimizer'] == 'Adam':
optimizer = Adam(param_other, lr=cfg['lr'])
elif cfg['optimizer'] == 'SGD':
optimizer = SGD(param_other,
lr=cfg['lr'],
momentum=cfg['momentum'])
else:
raise NotImplementedError("optimizer {:s} is not support!".format(
cfg['optimizer']))
optimizer.add_param_group({
'params': param_weight_decay,
'weight_decay': cfg['weight_decay']
}) # add pg1 with weight_decay
optimizer.add_param_group({'params': param_bias})
else:
if cfg['optimizer'] == 'Adam':
optimizer = Adam(param_weight_decay,
lr=cfg['lr'],
weight_decay=cfg['weight_decay'])
elif cfg['optimizer'] == 'SGD':
optimizer = SGD(param_weight_decay,
lr=cfg['lr'],
momentum=cfg['momentum'],
weight_decay=cfg['weight_decay'])
else:
raise NotImplementedError("optimizer {:s} is not support!".format(
cfg['optimizer']))
optimizer.add_param_group({'params': param_bias})
return optimizer
def optimizer_choose(model, args, writer, block):
params = []
for key, value in model.named_parameters():
if value.requires_grad:
params += [{
'params': [value],
'lr': args.lr,
'key': key,
'weight_decay': args.wd
}]
if args.optimizer == 'adam':
optimizer = torch.optim.Adam(params)
block.log('Using Adam optimizer')
elif args.optimizer == 'sgd':
momentum = 0.9
optimizer = SGD(params, momentum=momentum)
block.log('Using SGD with momentum ' + str(momentum))
elif args.optimizer == 'sgd_nev':
momentum = 0.9
optimizer = SGD(params, momentum=momentum, nesterov=True)
block.log('Using SGD with momentum ' + str(momentum) + 'and nesterov')
else:
momentum = 0.9
optimizer = SGD(params, momentum=momentum)
block.log('Using SGD with momentum ' + str(momentum))
# shutil.copy2(inspect.getfile(optimizer), args.model_saved_name)
shutil.copy2(__file__, args.model_saved_name)
return optimizer
def step(self, optimizer: SGD, *args, **kwargs) -> Optional[float]: # type: ignore
"""
:meth:`step` carries out the following two operations:
1. Internally invokes ``unscale_(optimizer)`` (unless :meth:`unscale_` was explicitly called for ``optimizer``
earlier in the iteration). As part of the :meth:`unscale_`, gradients are checked for infs/NaNs.
2. If no inf/NaN gradients are found, invokes ``optimizer.step()`` using the unscaled
gradients. Otherwise, ``optimizer.step()`` is skipped to avoid corrupting the params.
``*args`` and ``**kwargs`` are forwarded to ``optimizer.step()``.
Returns the return value of ``optimizer.step(*args, **kwargs)``.
Args:
optimizer (torch.optim.Optimizer): Optimizer that applies the gradients.
args: Any arguments.
kwargs: Any keyword arguments.
.. warning::
Closure use is not currently supported.
Note: This is an exact copy of the step function in grad_scaler.py. If this copy is deleted then the
unittest test_cpu_offload_and_cpu_grads fails. This is because the parent class step function calls
the parent class unscale_ function which does not handle torch.distributed.all_reduce on cpu.
"""
if not self._enabled:
return optimizer.step(*args, **kwargs)
if "closure" in kwargs:
raise RuntimeError("Closure use is not currently supported if GradScaler is enabled.")
self._check_scale_growth_tracker("step") # type: ignore
optimizer_state = self._per_optimizer_states[id(optimizer)]
if optimizer_state["stage"] is OptState.STEPPED:
raise RuntimeError("step() has already been called since the last update().")
retval = None
if hasattr(optimizer, "_step_supports_amp_scaling") and optimizer._step_supports_amp_scaling:
# This optimizer has customized scale-handling logic, so we can call optimizer.step() directly.
# The contract with custom optimizers is that their step() should accept an additional,
# optional grad_scaler kwarg. We append self to the kwargs so the custom optimizer has full information:
# it can query its own state, invoke unscale_ on itself, etc
retval = optimizer.step(*args, **dict(kwargs, grad_scaler=self))
optimizer_state["stage"] = OptState.STEPPED
return retval
if optimizer_state["stage"] is OptState.READY:
self.unscale_(optimizer)
assert len(optimizer_state["found_inf_per_device"]) > 0, "No inf checks were recorded for this optimizer."
retval = self._maybe_opt_step(optimizer, optimizer_state, *args, **kwargs) # type: ignore
optimizer_state["stage"] = OptState.STEPPED
return retval
def _initOptimizer(self):
modelParallel = self.getModelParallel()
args = self.getArgs()
optimizer = SGD(modelParallel.parameters(),
args.learning_rate,
momentum=args.momentum,
weight_decay=args.weight_decay)
# load optimizer pre-trained state dict if exists
if self.optimizerStateDict:
optimizer.load_state_dict(self.optimizerStateDict)
return optimizer
def create_model(args, model=None):
# Create MVCCN model based on the given architecture.
if model is None:
model = SVCNN(nclasses=args.num_classes,
pretraining=args.pretrained,
cnn_name=args.arch,
feature_extraction=args.feature_extraction)
else:
model = MVCNN(model, num_views=args.nview)
# Multi GPUs
if torch.cuda.device_count() > 1:
model = torch.nn.DataParallel(model)
# Send model to GPU or keep it to the CPU
model = model.to(device=args.device)
if args.optimizer == "ADAM":
optimizer = Adam(filter(lambda p: p.requires_grad, model.parameters()),
args.learning_rate,
weight_decay=args.weight_decay)
elif args.optimizer == "ADAGRAD":
optimizer = Adagrad(filter(lambda p: p.requires_grad,
model.parameters()),
args.learning_rate,
weight_decay=args.weight_decay)
else:
# If we use feature extraction (features weights are frozen), we need to keep only differentiable params
optimizer = SGD(filter(lambda p: p.requires_grad, model.parameters()),
args.learning_rate,
momentum=args.momentum,
weight_decay=args.weight_decay)
return model, optimizer
def configure_optimizers(self):
# Add args to define the values
params_list = [{
'params': self.model.parameters(),
'lr': 0.01
}, {
'params': self.classifier0.parameters()
}, {
'params': self.classifier1.parameters()
}, {
'params': self.classifier2.parameters()
}, {
'params': self.classifier3.parameters()
}, {
'params': self.classifier4.parameters()
}, {
'params': self.classifier5.parameters()
}]
optim = SGD(params_list,
lr=self.learning_rate,
weight_decay=5e-4,
momentum=0.9,
nesterov=True)
scheduler = lr_scheduler.StepLR(optim, step_size=40, gamma=0.130)
return [optim], [scheduler]
def test_trainer_train_full(fake_loader, simple_neural_net):
def transform_fn(batch):
inputs, y_true = batch
return inputs, y_true.float()
metrics = [BinaryAccuracy()]
transform = transforms.Compose([
transforms.ToTensor(),
transforms.Normalize((0.5, 0.5, 0.5), (0.5, 0.5, 0.5))
])
train_loader = DataLoader(FakeData(size=100,
image_size=(3, 32, 32),
num_classes=2,
transform=transform),
batch_size=4,
shuffle=True,
num_workers=1)
val_loader = DataLoader(FakeData(size=50,
image_size=(3, 32, 32),
num_classes=2,
transform=transform),
batch_size=4,
shuffle=True,
num_workers=1)
model = Net()
loss = nn.BCELoss()
optimizer = SGD(model.parameters(), lr=0.001, momentum=0.9)
plotter = VisdomLinePlotter(env_name=f'Model {11}')
callbacks = [
ProgressBar(log_every=10),
VisdomEpoch(plotter, on_iteration_every=10),
VisdomEpoch(plotter, on_iteration_every=10, monitor='binary_acc'),
EarlyStoppingEpoch(min_delta=0.1,
monitor='val_running_loss',
patience=10),
ReduceLROnPlateauCallback(factor=0.1,
threshold=0.1,
patience=2,
verbose=True)
]
trainer = TorchTrainer(model)
trainer.prepare(optimizer,
loss,
train_loader,
val_loader,
transform_fn=transform_fn,
callbacks=callbacks,
metrics=metrics)
epochs = 10
batch_size = 10
trainer.train(epochs, batch_size)
def create_optimizer(name, parameters, lr):
if name == 'Adadelta':
return Adadelta(parameters, lr=lr)
elif name == 'Adam':
return Adam(parameters, lr=lr)
elif name == 'SGD':
return SGD(parameters, lr=lr)
else:
raise KeyError(
'Unknown optimizer type {!r}. Choose from [Adadelta | Adam | SGD]')
def train(self):
args = self.args
model = self.model
logger = self.logger
epochRange = self._getEpochRange(self.nEpochs)
# init optimizer
optimizer = SGD(model.alphas(),
args.search_learning_rate,
momentum=args.search_momentum,
weight_decay=args.search_weight_decay)
# init scheduler
scheduler = ReduceLROnPlateau(optimizer,
mode='min',
factor=0.95,
patience=args.search_patience,
min_lr=args.search_learning_rate_min)
for epoch in epochRange:
print('========== Epoch:[{}/{}] =============='.format(
epoch, self.nEpochs))
# init epoch train logger
trainLogger = HtmlLogger(self.trainFolderPath, epoch)
# set loggers dictionary
loggersDict = {self.trainLoggerKey: trainLogger}
# create epoch jobs
epochDataRows = self._createEpochJobs(epoch)
# add epoch data rows
for jobDataRow in epochDataRows:
logger.addDataRow(jobDataRow, trType='<tr bgcolor="#2CBDD6">')
# train alphas
# epochLossDict, alphasDataRow = self.trainAlphas(self._getNextSearchQueueDataLoader(), optimizer, epoch, loggersDict)
epochLossDict, alphasDataRow = self.trainAlphas(
self.valid_queue, optimizer, epoch, loggersDict)
# update scheduler
scheduler.step(epochLossDict.get(self.flopsLoss.totalKey()))
# calc model choosePathAlphasAsPartition flops ratio
model.choosePathAlphasAsPartition()
# add values to alphas data row
additionalData = {
self.epochNumKey: epoch,
self.lrKey: optimizer.param_groups[0]['lr'],
self.validFlopsRatioKey: model.flopsRatio()
}
self._applyFormats(additionalData)
# add alphas data row
alphasDataRow.update(additionalData)
logger.addDataRow(alphasDataRow)
# save checkpoint
save_checkpoint(self.trainFolderPath, model, optimizer,
epochLossDict)
def main():
args = ArgumentsTrainVal().parse_args()
print('***************************Arguments****************************')
print(args)
model, distribution = construct_model(args)
print('--------------------------Model Info----------------------------')
print(model)
if args.resume_model is None:
init_network(model)
criterion = functional.cross_entropy
optimizer = SGD(model.parameters(),
args.learning_rate,
momentum=args.momentum,
weight_decay=args.weight_decay)
train_iterator, validate_iterator = construct_train_dataloaders(args)
engine_args = [
args.gpu_ids, model, criterion, distribution, train_iterator,
validate_iterator, optimizer
]
if args.num_classes == 1000 or args.num_classes == 1001:
topk = [1, 5]
else:
topk = [1]
# learning rate points
lr_points = []
if args.num_classes == 100:
lr_points = [150, 225]
elif args.num_classes == 1000 or args.num_classes == 1001:
lr_points = [30, 60]
print('==> Set lr_points for resnet54: {}'.format(lr_points))
engine = construct_engine(*engine_args,
checkpoint_iter_freq=args.checkpoint_iter_freq,
checkpoint_epoch_freq=args.checkpoint_epoch_freq,
checkpoint_save_path=args.checkpoint_save_path,
iter_log_freq=args.iter_log_freq,
topk=topk,
num_classes=args.num_classes,
lambda_error=args.lambda_error,
environment=args.environment,
lr_points=lr_points)
if args.ada_train:
engine.ada_train(args.maxepoch)
else:
engine.resume(args.maxepoch, args.resume_epoch, args.resume_iteration)
def test_linreg(n_epochs):
print('Test: linear regression')
x_values = []
y_values = []
for i in range(5):
x_values.append(i)
y_values.append(5*i + 2 + torch.randn(1).data.item())
x_data = np.array(x_values, dtype=np.float32).reshape(-1, 1)
y_data = np.array(y_values, dtype=np.float32).reshape(-1, 1)
answer = RegressionAnswer(LinearRegression(), lambda model : LBFGS(model.parameters()), torch.nn.MSELoss(), x_data, y_data).run(60)
test = [
RegressionTest('A2Grad-uni', LinearRegression(), lambda model : A2Grad(model.parameters(), 'uni', 1e-1), torch.nn.MSELoss(), x_data, y_data),
RegressionTest('A2Grad-inc', LinearRegression(), lambda model : A2Grad(model.parameters(), 'inc', 1e-1), torch.nn.MSELoss(), x_data, y_data),
RegressionTest('A2Grad-exp', LinearRegression(), lambda model : A2Grad(model.parameters(), 'exp', 1e-1), torch.nn.MSELoss(), x_data, y_data),
RegressionTest('Adam', LinearRegression(), lambda model : Adam(model.parameters()), torch.nn.MSELoss(), x_data, y_data),
RegressionTest('SGD', LinearRegression(), lambda model : SGD(model.parameters(), lr=1e-2), torch.nn.MSELoss(), x_data, y_data),
RegressionTest('LBFGS', LinearRegression(), lambda model : LBFGS(model.parameters()), torch.nn.MSELoss(), x_data, y_data)
]
plt.figure(figsize=(14, 8))
for i in range(len(test)):
test[i].run(n_epochs)
plt.plot(np.arange(1, n_epochs + 1), np.array(test[i].errors) - answer, label=test[i].name)
plt.legend(fontsize=12, loc=1)
plt.title('Linear regression')
plt.xlabel('Epoch')
plt.ylabel('MSE')
plt.savefig('linear.png')
plt.figure(figsize=(14, 8))
for i in range(len(test)):
plt.plot(np.arange(1, n_epochs + 1), np.array(test[i].errors) - answer, label=test[i].name)
plt.legend(fontsize=12, loc=1)
plt.ylim(0, 1e-5)
plt.title('Linear regression')
plt.xlabel('Epoch')
plt.ylabel('MSE')
plt.savefig('linear2.png')
points = np.arange(10, n_epochs, 10)
header = "method "
for i in points:
header += "{} ".format(i)
print(header)
for i in range(len(test)):
test[i].output(points)
print('')
def test_trainer_train_without_plugins(fake_loader, simple_neural_net):
train_loader = fake_loader
val_loader = fake_loader
loss = nn.BCELoss()
optimizer = SGD(simple_neural_net.parameters(), lr=0.001, momentum=0.9)
trainer = TorchTrainer(simple_neural_net)
trainer.prepare(optimizer,
loss,
train_loader,
val_loader,
transform_fn=transform_fn)
trainer.train(1, 4)
def train_model(training_file: str):
oracles = read_oracles_from_file(DiscriminativeOracle, training_file)
dataset = OracleDataset(oracles)
dataset.load()
dataset_loader = DataLoader(dataset, collate_fn=lambda x: x[0])
parser = DiscriminativeRnnGrammar(action_store=dataset.action_store,
word2id=dataset.word_store,
pos2id=dataset.pos_store,
non_terminal2id=dataset.nt_store)
optimiser = SGD(parser.parameters(), 0.1)
train_early_stopping(dataset_loader, dataset_loader, parser, optimiser)
def __init__(self, hparams, ckpt_name, homedir, separate_history,
patience):
self.hparams = hparams
# batch size
self.batch_size = 256
# loader
self.loader_train, self.loader_valid, self.loader_test = mnist_data_loader(
self.batch_size, homedir)
# model
self.model = Network(hparams)
# loss function
self.loss_fn = nn.CrossEntropyLoss()
# initial learning rate
self.lr = hparams['lr']
# momentum coef
self.momentum = hparams['momentum']
# optimizer
self.optimizer = SGD(self.model.parameters(),
lr=self.lr,
momentum=self.momentum,
nesterov=True)
# epoch
self.epoch = 0
# check point
self.ckpt_dir = homedir + "ckpt"
self.ckpt_name = ckpt_name
# history
self.separate_history = separate_history
# patience
self.patience = patience
try:
ckpt = self._load_checkpoint(self.ckpt_name)
self.model.load_state_dict(ckpt['state_dict'])
self.epoch = ckpt['current_epoch']
except FileNotFoundError:
pass
def preTraining():
'''
Creates the model, data and optimizor for training
-----
Returns
-----
data - tuple with caption_ids, good_image and bad_image
optimizer - Adam optimizer from mynn
model - traiing model
'''
model = Model()
data = ct.create_triples(300000, 25).reshape(-1,3)
optim = SGD(model.parameters(), lr = 0.001, momentum = 0.9)
return data, optim, model
def get_optimizer(project_variable, model):
# project_variable = ProjectVariable()
if project_variable.optimizer == 'adam':
optimizer = Adam(model.parameters(), lr=project_variable.learning_rate)
elif project_variable.optimizer == 'sgd':
optimizer = SGD(model.parameters(),
lr=project_variable.learning_rate,
momentum=project_variable.momentum)
else:
print('Error: optimizer %s not supported' % project_variable.optimizer)
optimizer = None
return optimizer
def test_train_MEG_swap():
dataset_path = ["Z:\Desktop\sub8\\ball1_sss.fif"]
dataset = MEG_Dataset(dataset_path, duration=1.0, overlap=0.0)
train_len, valid_len, test_len = len_split(len(dataset))
train_dataset, valid_dataset, test_dataset = random_split(
dataset, [train_len, valid_len, test_len]
)
device = "cpu"
trainloader = DataLoader(
train_dataset, batch_size=10, shuffle=False, num_workers=1
)
validloader = DataLoader(
valid_dataset, batch_size=2, shuffle=False, num_workers=1
)
epochs = 1
with torch.no_grad():
x, _, _ = iter(trainloader).next()
n_times = x.shape[-1]
net = models.MNet(n_times)
optimizer = SGD(net.parameters(), lr=0.0001, weight_decay=5e-4)
loss_function = torch.nn.MSELoss()
model, _, _ = train(
net,
trainloader,
validloader,
optimizer,
loss_function,
device,
epochs,
10,
0,
"",
)
print("Test succeeded!")
def test_trainer_train_steplr(fake_loader, simple_neural_net):
def transform_fn(batch):
inputs, y_true = batch
return inputs, y_true.float()
metrics = [BinaryAccuracy()]
transform = transforms.Compose([
transforms.ToTensor(),
transforms.Normalize((0.5, 0.5, 0.5), (0.5, 0.5, 0.5))
])
train_loader = DataLoader(FakeData(size=100,
image_size=(3, 32, 32),
num_classes=2,
transform=transform),
batch_size=4,
shuffle=True,
num_workers=1)
val_loader = DataLoader(FakeData(size=50,
image_size=(3, 32, 32),
num_classes=2,
transform=transform),
batch_size=4,
shuffle=True,
num_workers=1)
model = Net()
loss = nn.BCELoss()
optimizer = SGD(model.parameters(), lr=0.001, momentum=0.9)
callbacks = [StepLREpochCallback()]
trainer = TorchTrainer(model)
trainer.prepare(optimizer,
loss,
train_loader,
val_loader,
transform_fn=transform_fn,
callbacks=callbacks,
metrics=metrics)
epochs = 10
batch_size = 10
trainer.train(epochs, batch_size)
def get_optimizer(model: nn.Module, optim: str, lr: float) -> Optimizer:
"""
Return the optimizer that corresponds to string optim. Add the parameters from model and set learning rate to lr
:param model: model to get the parameters from
:param optim: name of the optimizer
:param lr: learning rate to use in the optimizer
:return:
"""
if optim == "adagrad":
return Adagrad(model.parameters(), lr=lr)
elif optim == "sgd":
return SGD(model.parameters(), lr=lr)
elif optim == "rmsprop":
return RMSprop(model.parameters(), lr=lr)
elif optim == "adam":
return Adam(model.parameters(), lr=lr)
else:
raise ValueError("Invalid optimizer")
def learn_second(network,
lr,
model,
examples_files,
total_example,
alpha=1.0,
batch_size=20):
"""
Helper function used to optimize O2
:param network: network model to optimize
:param lr: learning rate
:param model: model containing the shared data
:param examples_files: list of files containing the examples
:param total_example: total example for training
:param alpha: trade-off param
:param batch_size: size of the batch
:return: loss value
"""
num_batch = 0
log.info("compute o2")
optimizer = SGD(network.parameters(), lr)
log.debug("read example file: {}".format("\t".join(examples_files)))
loss_val = 0
if alpha <= 0:
return loss_val
for batch in emb_utils.batch_generator(emb_utils.prepare_sentences(
model, graph_utils.combine_example_files_iter(examples_files),
network.transfer_fn(model.vocab)),
batch_size,
long_tensor=LongTensor):
input, output = batch
loss = (alpha * network.forward(
input, output, negative_sampling_fn=model.negative_sample))
loss_val += loss.data[0]
optimizer.zero_grad()
loss.backward()
optimizer.step()
num_batch += 1
if (num_batch) % 10000 == 0:
log.info("community embedding batches completed: {}".format(
num_batch / (total_example / batch_size)))
log.debug("O2 loss: {}".format(loss_val))
return loss_val
def split_optimizer(model: nn.Module, cfg: dict):
param_weight_decay, param_bias, param_other = split_params(model)
if cfg['optimizer'] == 'Adam':
optimizer = Adam(param_other,
lr=cfg['lr'],
betas=(cfg['momentum'],
0.999)) # adjust beta1 to momentum
elif cfg['optimizer'] == 'SGD':
optimizer = SGD(param_other,
lr=cfg['lr'],
momentum=cfg['momentum'],
nesterov=True)
else:
raise NotImplementedError("optimizer {:s} is not support!".format(
cfg['optimizer']))
optimizer.add_param_group({
'params': param_weight_decay,
'weight_decay': cfg['weight_decay']
}) # add pg1 with weight_decay
optimizer.add_param_group({'params': param_bias}) # add pg2 (biases)
return optimizer
def test_trainer_train_with_metric(fake_loader, simple_neural_net):
train_loader = fake_loader
val_loader = fake_loader
metrics = [BinaryAccuracy()]
loss = nn.BCELoss()
optimizer = SGD(simple_neural_net.parameters(), lr=0.001, momentum=0.9)
trainer = TorchTrainer(simple_neural_net)
trainer.prepare(optimizer,
loss,
train_loader,
val_loader,
transform_fn=transform_fn,
metrics=metrics,
validate_every=1)
final_result = trainer.train(1, 4)
assert 'binary_acc' in final_result
assert 'val_binary_acc' in final_result
def learn_community(network, lr, model, nodes, beta=1.0, batch_size=20):
"""
Helper function used to optimize O3
:param network: model to optimize
:param lr: learning rate
:param model: model containing the shared data
:param nodes: nodes on which execute the learning
:param beta: trade-off value
:param batch_size: size of the batch
:return: loss value
"""
num_batch = 0
log.info("compute o3")
optimizer = SGD(network.parameters(), lr)
loss_val = 0
if beta <= 0.:
return loss_val
for batch in emb_utils.batch_generator(emb_utils.prepare_sentences(
model, nodes, network.transfer_fn()),
batch_size,
long_tensor=LongTensor):
input, output = batch
loss = network.forward(input, model)
loss.data *= (beta / model.k)
loss_val += loss.data[0]
optimizer.zero_grad()
loss.backward()
optimizer.step()
num_batch += 1
if (num_batch) % 10000 == 0:
log.info("community embedding batches completed: {}".format(
num_batch / (total_example / batch_size)))
log.debug("O3 loss: {}".format(loss_val))
return loss_val
def learn_first(network, lr, model, edges, num_iter=1, batch_size=20):
"""
Helper function used to optimize O1
:param network: neural network to train
:param lr: learning rate
:param model: model containing the shared data
:param edges: numpy list of edges used for training
:param num_iter: iteration number over the edges
:param batch_size: size of the batch
:return: loss value
"""
log.info("computing o1")
optimizer = SGD(network.parameters(), lr)
num_batch = 0
total_batch = (edges.shape[0] * num_iter) / batch_size
loss_val = 0
for batch in emb_utils.batch_generator(emb_utils.prepare_sentences(
model, edges, network.transfer_fn(model.vocab)),
batch_size,
long_tensor=LongTensor):
input, output = batch
loss = network.forward(input,
output,
negative_sampling_fn=model.negative_sample)
loss_val += loss.data[0]
optimizer.zero_grad()
loss.backward()
optimizer.step()
num_batch += 1
if (num_batch) % 10000 == 0:
log.info("community embedding batches completed: {}".format(
num_batch / total_batch))
log.debug("O1 loss: {}".format(loss_val))
return loss_val
def learn_second(network, lr, model, examples_files, alpha=1.0):
"""
Helper function used to optimize O1 and O3
:param loss: loss to optimize
:param lr: learning rate
:param model: deprecated_model used to compute the batches and the negative sampling
:param examples_files: list of files containing the examples
:param num_iter: iteration number over the edges
:return:
"""
log.info("compute o2")
optimizer = SGD(network.parameters(), lr)
log.debug("read example file: {}".format("\t".join(examples_files)))
for batch in emb_utils.batch_generator(
emb_utils.prepare_sentences(
model, graph_utils.combine_example_files_iter(examples_files),
network.transfer_fn(model.vocab)), 20):
input, output = batch
loss = (alpha * network.forward(
input, output, negative_sampling_fn=model.negative_sample))
optimizer.zero_grad()
loss.backward()
optimizer.step()
def learn_first(network, lr, model, edges, num_iter=1):
"""
Helper function used to optimize O1 and O3
:param network: neural network to train
:param lr: learning rate
:param model: deprecated_model used to compute the batches and the negative sampling
:param edges: numpy list of edges used for training
:param num_iter: iteration number over the edges
:return:
"""
log.info("computing o1")
optimizer = SGD(network.parameters(), lr)
for batch in emb_utils.batch_generator(
emb_utils.prepare_sentences(
model, emb_utils.RepeatCorpusNTimes(edges, n=num_iter),
network.transfer_fn(model.vocab)), 20):
input, output = batch
loss = network.forward(input,
output,
negative_sampling_fn=model.negative_sample)
optimizer.zero_grad()
loss.backward()
optimizer.step()
def main(args):
data_dir = args.data_dir
figure_path = args.figure_dir
model_path = args.model_dir
# Set skip_training to False if the model has to be trained, to True if the model has to be loaded.
skip_training = False
# Set the torch device
device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
print("Device = {}".format(device))
# Initialize parameters
parameters = Params_cross(
subject_n=args.sub,
hand=args.hand,
batch_size=args.batch_size,
valid_batch_size=args.batch_size_valid,
test_batch_size=args.batch_size_test,
epochs=args.epochs,
lr=args.learning_rate,
wd=args.weight_decay,
patience=args.patience,
device=device,
desc=args.desc,
)
# Import data and generate train-, valid- and test-set
# Set if generate with RPS values or not (check network architecture used later)
print("Testing: {} ".format(parameters.desc))
mlp = False
train_dataset = MEG_Cross_Dataset(data_dir,
parameters.subject_n,
parameters.hand,
mode="train")
valid_dataset = MEG_Cross_Dataset(data_dir,
parameters.subject_n,
parameters.hand,
mode="val")
test_dataset = MEG_Cross_Dataset(data_dir,
parameters.subject_n,
parameters.hand,
mode="test")
transfer_dataset = MEG_Cross_Dataset(data_dir,
parameters.subject_n,
parameters.hand,
mode="transf")
print("Train dataset len {}, valid dataset len {}, test dataset len {}, "
"transfer dataset len {}".format(
len(train_dataset),
len(valid_dataset),
len(test_dataset),
len(transfer_dataset),
))
# Initialize the dataloaders
trainloader = DataLoader(train_dataset,
batch_size=parameters.batch_size,
shuffle=True,
num_workers=4)
validloader = DataLoader(valid_dataset,
batch_size=parameters.valid_batch_size,
shuffle=True,
num_workers=4)
testloader = DataLoader(
test_dataset,
batch_size=parameters.test_batch_size,
shuffle=False,
num_workers=4,
)
transferloader = DataLoader(transfer_dataset,
batch_size=parameters.valid_batch_size,
shuffle=True,
num_workers=4)
# Initialize network
if mlp:
net = RPS_MLP()
else:
# Get the n_times dimension
with torch.no_grad():
sample, y, _ = iter(trainloader).next()
n_times = sample.shape[-1]
net = RPS_MNet_ivan(n_times)
print(net)
if torch.cuda.device_count() > 1:
print("Let's use", torch.cuda.device_count(), "GPUs!")
# dim = 0 [30, xxx] -> [10, ...], [10, ...], [10, ...] on 3 GPUs
net = nn.DataParallel(net)
# Training loop
if not skip_training:
print("Begin training....")
# Check the optimizer before running (different from model to model)
optimizer = Adam(net.parameters(),
lr=parameters.lr,
weight_decay=parameters.wd)
# optimizer = SGD(net.parameters(), lr=parameters.lr, momentum=0.9, weight_decay=parameters.wd)
scheduler = ReduceLROnPlateau(optimizer,
mode="min",
factor=0.5,
patience=15)
print("scheduler : ", scheduler)
loss_function = torch.nn.MSELoss()
# loss_function = torch.nn.L1Loss()
start_time = timer.time()
if mlp:
net, train_loss, valid_loss = train_bp_MLP(
net,
trainloader,
validloader,
optimizer,
scheduler,
loss_function,
parameters.device,
parameters.epochs,
parameters.patience,
parameters.hand,
model_path,
)
else:
net, train_loss, valid_loss = train_bp(
net,
trainloader,
validloader,
optimizer,
scheduler,
loss_function,
parameters.device,
parameters.epochs,
parameters.patience,
parameters.hand,
model_path,
)
train_time = timer.time() - start_time
print("Training done in {:.4f}".format(train_time))
# visualize the loss as the network trained
fig = plt.figure(figsize=(10, 4))
plt.plot(range(1,
len(train_loss) + 1),
train_loss,
label="Training Loss")
plt.plot(range(1,
len(valid_loss) + 1),
valid_loss,
label="Validation Loss")
# find position of lowest validation loss
minposs = valid_loss.index(min(valid_loss)) + 1
plt.axvline(
minposs,
linestyle="--",
color="r",
label="Early Stopping Checkpoint",
)
plt.xlabel("epochs")
plt.ylabel("loss")
# plt.ylim(0, 0.5) # consistent scale
# plt.xlim(0, len(train_loss)+1) # consistent scale
plt.grid(True)
plt.legend()
plt.tight_layout()
plt.show()
image1 = fig
plt.savefig(os.path.join(figure_path, "loss_plot.pdf"))
if not skip_training:
# Save the trained model
save_pytorch_model(net, model_path, "model.pth")
else:
# Load the model (properly select the model architecture)
net = RPS_MNet()
net = load_pytorch_model(net, os.path.join(model_path, "model.pth"),
parameters.device)
# Evaluation
print("Evaluation...")
net.eval()
y_pred = []
y = []
y_pred_valid = []
y_valid = []
# if RPS integration
with torch.no_grad():
if mlp:
for _, labels, bp in testloader:
labels, bp = (
labels.to(parameters.device),
bp.to(parameters.device),
)
y.extend(list(labels[:, parameters.hand]))
y_pred.extend((list(net(bp))))
for _, labels, bp in validloader:
labels, bp = (
labels.to(parameters.device),
bp.to(parameters.device),
)
y_valid.extend(list(labels[:, parameters.hand]))
y_pred_valid.extend((list(net(bp))))
else:
for data, labels, bp in testloader:
data, labels, bp = (
data.to(parameters.device),
labels.to(parameters.device),
bp.to(parameters.device),
)
y.extend(list(labels[:, parameters.hand]))
y_pred.extend((list(net(data, bp))))
for data, labels, bp in validloader:
data, labels, bp = (
data.to(parameters.device),
labels.to(parameters.device),
bp.to(parameters.device),
)
y_valid.extend(list(labels[:, parameters.hand]))
y_pred_valid.extend((list(net(data, bp))))
# Calculate Evaluation measures
print("Evaluation measures")
mse = mean_squared_error(y, y_pred)
rmse = mean_squared_error(y, y_pred, squared=False)
mae = mean_absolute_error(y, y_pred)
r2 = r2_score(y, y_pred)
rmse_valid = mean_squared_error(y_valid, y_pred_valid, squared=False)
r2_valid = r2_score(y_valid, y_pred_valid)
valid_loss_last = min(valid_loss)
print("Test set ")
print("mean squared error {}".format(mse))
print("root mean squared error {}".format(rmse))
print("mean absolute error {}".format(mae))
print("r2 score {}".format(r2))
print("Validation set")
print("root mean squared error valid {}".format(rmse_valid))
print("r2 score valid {}".format(r2_valid))
print("last value of the validation loss: {}".format(valid_loss_last))
# plot y_new against the true value focus on 100 timepoints
fig, ax = plt.subplots(1, 1, figsize=[10, 4])
times = np.arange(200)
ax.plot(times, y_pred[0:200], color="b", label="Predicted")
ax.plot(times, y[0:200], color="r", label="True")
ax.set_xlabel("Times")
ax.set_ylabel("Target")
ax.set_title("Sub {}, hand {}, Target prediction".format(
str(parameters.subject_n), "sx" if parameters.hand == 0 else "dx"))
plt.legend()
plt.savefig(os.path.join(figure_path, "Times_prediction_focus.pdf"))
plt.show()
# plot y_new against the true value
fig, ax = plt.subplots(1, 1, figsize=[10, 4])
times = np.arange(len(y_pred))
ax.plot(times, y_pred, color="b", label="Predicted")
ax.plot(times, y, color="r", label="True")
ax.set_xlabel("Times")
ax.set_ylabel("Target")
ax.set_title("Sub {}, hand {}, target prediction".format(
str(parameters.subject_n), "sx" if parameters.hand == 0 else "dx"))
plt.legend()
plt.savefig(os.path.join(figure_path, "Times_prediction.pdf"))
plt.show()
# scatterplot y predicted against the true value
fig, ax = plt.subplots(1, 1, figsize=[10, 4])
ax.scatter(np.array(y), np.array(y_pred), color="b", label="Predicted")
ax.set_xlabel("True")
ax.set_ylabel("Predicted")
# plt.legend()
plt.savefig(os.path.join(figure_path, "Scatter.pdf"))
plt.show()
# scatterplot y predicted against the true value
fig, ax = plt.subplots(1, 1, figsize=[10, 4])
ax.scatter(np.array(y_valid),
np.array(y_pred_valid),
color="b",
label="Predicted")
ax.set_xlabel("True")
ax.set_ylabel("Predicted")
# plt.legend()
plt.savefig(os.path.join(figure_path, "Scatter_valid.pdf"))
plt.show()
# Transfer learning, feature extraction.
optimizer_trans = SGD(net.parameters(), lr=3e-4)
loss_function_trans = torch.nn.MSELoss()
# loss_function_trans = torch.nn.L1Loss()
if mlp:
net, train_loss = train_mlp_transfer(
net,
transferloader,
optimizer_trans,
loss_function_trans,
parameters.device,
50,
parameters.patience,
parameters.hand,
model_path,
)
else:
# net, train_loss = train_bp_transfer(
# net,
# transferloader,
# optimizer_trans,
# loss_function_trans,
# parameters.device,
# 50,
# parameters.patience,
# parameters.hand,
# model_path,
# )
net, train_loss = train_bp_fine_tuning(net, transferloader,
optimizer_trans,
loss_function_trans,
parameters.device, 50, 10,
parameters.hand, model_path)
# Evaluation
print("Evaluation after transfer...")
net.eval()
y_pred = []
y = []
# if RPS integration
with torch.no_grad():
if mlp:
for _, labels, bp in testloader:
labels, bp = (
labels.to(parameters.device),
bp.to(parameters.device),
)
y.extend(list(labels[:, parameters.hand]))
y_pred.extend((list(net(bp))))
else:
for data, labels, bp in testloader:
data, labels, bp = (
data.to(parameters.device),
labels.to(parameters.device),
bp.to(parameters.device),
)
y.extend(list(labels[:, parameters.hand]))
y_pred.extend((list(net(data, bp))))
print("Evaluation measures")
rmse_trans = mean_squared_error(y, y_pred, squared=False)
r2_trans = r2_score(y, y_pred)
print("root mean squared error after transfer learning {}".format(
rmse_trans))
print("r2 score after transfer learning {}".format(r2_trans))
# scatterplot y predicted against the true value
fig, ax = plt.subplots(1, 1, figsize=[10, 4])
ax.scatter(np.array(y), np.array(y_pred), color="b", label="Predicted")
ax.set_xlabel("True")
ax.set_ylabel("Predicted")
# plt.legend()
plt.savefig(os.path.join(figure_path, "Scatter_after_trans.pdf"))
plt.show()
# log the model and parameters using mlflow tracker
with mlflow.start_run(experiment_id=args.experiment) as run:
for key, value in vars(parameters).items():
mlflow.log_param(key, value)
mlflow.log_param("Time", train_time)
mlflow.log_metric("MSE", mse)
mlflow.log_metric("RMSE", rmse)
mlflow.log_metric("MAE", mae)
mlflow.log_metric("R2", r2)
mlflow.log_metric("RMSE_Valid", rmse_valid)
mlflow.log_metric("R2_Valid", r2_valid)
mlflow.log_metric("Valid_loss", valid_loss_last)
mlflow.log_metric("RMSE_T", rmse_trans)
mlflow.log_metric("R2_T", r2_trans)
mlflow.log_artifact(os.path.join(figure_path, "Times_prediction.pdf"))
mlflow.log_artifact(
os.path.join(figure_path, "Times_prediction_focus.pdf"))
mlflow.log_artifact(os.path.join(figure_path, "loss_plot.pdf"))
mlflow.log_artifact(os.path.join(figure_path, "Scatter.pdf"))
mlflow.log_artifact(os.path.join(figure_path, "Scatter_valid.pdf"))
mlflow.log_artifact(
os.path.join(figure_path, "Scatter_after_trans.pdf"))
mlflow.pytorch.log_model(net, "models")
assert False, "Invalid model name"
if args.weights_path is None:
model.load_vgg_weights()
else:
model.load_weights(args.weights_path)
if args.device == 'cuda':
model = nn.DataParallel(model)
logging.info(model)
### train info ###
if args.optimizer == 'SGD':
optimizer = SGD(model.parameters(),
lr=args.learning_rate,
momentum=args.momentum,
weight_decay=args.weight_decay)
logging.info(
'Optimizer Info:'
'\nOptimizer: {}'
'\nlearning rate: {}, Momentum: {}, Weight decay: {}\n'.format(
args.optimizer, args.learning_rate, args.momentum,
args.weight_decay))
elif args.optimizer == 'Adam':
optimizer = Adam(model.parameters(),
lr=args.learning_rate,
weight_decay=args.weight_decay)
logging.info('Optimizer Info:'
'\nOptimizer: {}'
'\nlearning rate: {}, Weight decay: {}\n'.format(
args.optimizer, args.learning_rate, args.weight_decay))
def main():
args = parser.parse_args()
# REPRODUCIBILITY
torch.manual_seed(0)
np.random.seed(0)
if args.debug:
logger.setLevel(logging.DEBUG)
else:
logger.setLevel(logging.INFO)
# Retrieve views candidates and right number of views
if args.case == '1':
args.vcand = np.load('view_candidates/vcand_case1.npy')
args.nview = 12
elif args.case == '2':
args.vcand = np.load('view_candidates/vcand_case2.npy')
args.nview = 20
elif args.case == '3':
args.vcand = np.load('view_candidates/vcand_case3.npy')
args.nview = 160
# Names for the saved checkpoints
args.fname_best = 'rotationnet{}_model_best{}.pth.tar'.format(args.nview,
datetime.now().strftime("%d_%b_%Y_%H_%M_%S"))
args.fname = 'rotationnet{}_model{}.pth.tar'.format(args.nview, datetime.now().strftime("%d_%b_%Y_%H_%M_%S"))
logger.debug("Number of view candidates: {}".format(np.shape(args.vcand)[0]))
logger.debug("Number of views: {}".format(args.nview))
if torch.cuda.is_available():
args.device = torch.device('cuda')
else:
args.device = torch.device('cpu')
logger.debug("PyTorch is using {}".format(args.device))
# Mini batch size is used to do an update of the gradient so it need to be divisible by the number of views
# otherwise one or more classification are not complete
if args.batch_size % args.nview != 0:
logger.error('Batch size should be multiplication of the number of views, {}'.format(args.nview))
exit(1)
# Get number of classes
logger.debug("Number of classes: {}".format(args.num_classes))
# Create RotationNet model based on the given architecture.
# The output size is (num_classes + wrong_view class) * the number of views
model = RotationNet(args.arch, args.pretrained, (args.num_classes + 1) * args.nview, args.feature_extraction,
args.depth)
# Multi GPUs
if torch.cuda.device_count() > 1:
logger.debug("Using multiple GPUs")
model = torch.nn.DataParallel(model)
# Send model to GPU or keep it to the CPU
model = model.to(device=args.device)
# Define loss function (criterion) and optimizer
# Sending loss to cuda is unnecessary because loss function is not stateful
# TODO test if it works without sending loss to GPU
criterion = nn.CrossEntropyLoss().to(device=args.device)
if args.optimizer == "ADAM":
optimizer = Adam(filter(lambda p: p.requires_grad, model.parameters()), args.learning_rate,
weight_decay=args.weight_decay)
elif args.optimizer == "ADAGRAD":
optimizer = Adagrad(filter(lambda p: p.requires_grad, model.parameters()), args.learning_rate,
weight_decay=args.weight_decay)
else:
# If we use feature extraction (features weights are frozen), we need to keep only differentiable params
optimizer = SGD(filter(lambda p: p.requires_grad, model.parameters()), args.learning_rate,
momentum=args.momentum, weight_decay=args.weight_decay)
# https://stackoverflow.com/questions/58961768/set-torch-backends-cudnn-benchmark-true-or-not
# some boost when the network do not change
# useless because cluster do not have cudnn
# cudnn.benchmark = True
logger.info("Model args: {}".format(args))
if args.train_type == 'k-fold':
logger.debug("K-fold training")
train_k_fold(model, criterion, optimizer, args)
elif args.train_type == 'hold-out':
logger.debug("Hold-out training")
train_hold_out(model, criterion, optimizer, args)
elif args.train_type == 'full':
logger.debug("Full training")
train_all(model, criterion, optimizer, args)
elif args.train_type == 'evaluate':
logger.debug("Start evaluation on test set")
test_model(model, criterion, args)
elif args.train_type == 'aligned':
logger.debug("Holt-out training on aligned set")
train_hold_out_aligned(model, criterion,optimizer, args)
elif args.train_type == "test":
logger.debug("Start real time test")
threshold_evaluation(model, args)
