def compile_model(model, loss="ce"):
if loss is not None:
loss = models.get_loss(loss, model.output_shape[1])
model.compile(optimizer="adam",
loss=[loss],
metrics=[metrics.sparse_categorical_accuracy])
else:
model.compile(optimizer="adam")
def main(config):
train_loader = get_dataloader(config['data_loader']['type'], config['data_loader']['args'])
criterion = get_loss(config).cuda()
model = get_model(config)
trainer = Trainer(config=config,
model=model,
criterion=criterion,
train_loader=train_loader)
trainer.train()
def evaluate(self, dataset, num_classes, num_shots, inner_batch_size,
inner_iters, transductive):
"""
Run a single evaluation of the model.
Samples a few-shot learning task and measures
performance.
Args:
dataset: a sequence of data classes, where each data
class has a sample(n) method.
input_ph: placeholder for a batch of samples.
label_ph: placeholder for a batch of labels.
minimize_op: TensorFlow Op to minimize a loss on the
batch specified by input_ph and label_ph.
predictions: a Tensor of integer label predictions.
num_classes: number of data classes to sample.
num_shots: number of examples per data class.
inner_batch_size: batch size for every inner-loop
training iteration.
inner_iters: number of inner-loop iterations.
Returns:
The number of correctly predicted samples.
This always ranges from 0 to num_classes.
"""
model_clone = clone_model(num_classes, self.model_state)
optimizer_clone = get_optimizer(model_clone, self.op_state)
model_clone.train()
train, test = dataset.get_random_task_split(num_classes,
train_shots=num_shots,
test_shots=1)
"""
Sampling with replacement
"""
sampler = RandomSampler(train,
replacement=True,
num_samples=inner_batch_size * inner_iters)
train_loader = DataLoader(train,
batch_size=inner_batch_size,
sampler=sampler,
pin_memory=self.pin_memory)
for _, (inputs, labels) in enumerate(train_loader):
inputs, labels = Variable_(inputs, labels, self.cuda)
prediction = model_clone(inputs)
loss = get_loss(prediction, labels)
optimizer_clone.zero_grad()
loss.backward()
optimizer_clone.step()
return self._test_predictions(model_clone, train, test, transductive,
self.cuda)
def train_step(self, dataset, num_classes, num_shots, inner_batch_size,
inner_iters, meta_step_size, meta_batch_size):
"""
Perform a Reptile training step.
Args:
dataset: object contains images to be trained.
num_classes: number of data classes to sample.
num_shots: number of examples per data class.
inner_batch_size: batch size for every inner-loop
training iteration.
inner_iters: number of inner-loop iterations.
meta_step_size: interpolation coefficient.
meta_batch_size: how many inner-loops to run.
"""
updates = []
for _ in range(meta_batch_size):
model = clone_model(num_classes, self.model_state)
optimizer = get_optimizer(model, self.op_state)
model.train()
mini_train, _ = dataset.get_random_task_split(
num_classes, train_shots=num_shots, test_shots=0)
"""
Sampling without replacement
"""
train_loader = DataLoader(mini_train,
batch_size=inner_batch_size,
drop_last=True,
shuffle=True,
pin_memory=self.pin_memory)
for _, (inputs, labels) in enumerate(train_loader):
inputs, labels = Variable_(inputs, labels, self.cuda)
last_backup = model.state_dict()
prediction = model(inputs)
loss = get_loss(prediction, labels)
optimizer.zero_grad()
loss.backward()
optimizer.step()
updates.append(subtract_vars(model.state_dict(), last_backup))
self.op_state = optimizer.state_dict()
update = average_vars(updates)
self.model_state = add_vars(self.model_state,
scale_vars(update, meta_step_size))
self.op_state = optimizer.state_dict()
def main(config):
import torch
from models import get_model, get_loss
from data_loader import get_dataloader
from trainer import Trainer
from post_processing import get_post_processing
from utils import get_metric
if torch.cuda.device_count() > 1:
torch.cuda.set_device(args.local_rank)
torch.distributed.init_process_group(
backend="nccl",
init_method="env://",
world_size=torch.cuda.device_count(),
rank=args.local_rank)
config['distributed'] = True
else:
config['distributed'] = False
config['local_rank'] = args.local_rank
train_loader = get_dataloader(config['dataset']['train'],
config['distributed'])
assert train_loader is not None
if 'validate' in config['dataset']:
validate_loader = get_dataloader(config['dataset']['validate'], False)
else:
validate_loader = None
criterion = get_loss(config['loss']).cuda()
model = get_model(config['arch'])
post_p = get_post_processing(config['post_processing'])
metric = get_metric(config['metric'])
trainer = Trainer(config=config,
model=model,
criterion=criterion,
train_loader=train_loader,
post_process=post_p,
metric_cls=metric,
validate_loader=validate_loader)
trainer.train()
def compile_model(model, loss='ce'):
loss = models.get_loss(loss, model.output_shape[1])
model.compile(optimizer='adam',
loss=[loss],
metrics=[metrics.sparse_categorical_accuracy])
def main(config):
train_loader = get_dataloader(config['data_loader']['type'],
config['data_loader']['args'])
print(train_loader)
criterion = get_loss(config).cuda()
def compile_model(model, loss='ce'):
loss = models.get_loss(loss, model.output_shape[1])
model.compile(optimizer='adam', loss=[loss], metrics=['accuracy'])
