def valid_func(image, label):
model.eval()
logits = model(image)
loss = F.cross_entropy_with_softmax(logits, label, label_smooth=0.1)
acc1, acc5 = F.accuracy(logits, label, (1, 5))
if dist.is_distributed(): # all_reduce_mean
loss = dist.all_reduce_sum(loss) / dist.get_world_size()
acc1 = dist.all_reduce_sum(acc1) / dist.get_world_size()
acc5 = dist.all_reduce_sum(acc5) / dist.get_world_size()
return loss, acc1, acc5
def train_func(image, label):
model.train()
logits = model(image)
loss = F.cross_entropy_with_softmax(logits, label, label_smooth=0.1)
acc1, acc5 = F.accuracy(logits, label, (1, 5))
optimizer.backward(loss) # compute gradients
if dist.is_distributed(): # all_reduce_mean
loss = dist.all_reduce_sum(loss) / dist.get_world_size()
acc1 = dist.all_reduce_sum(acc1) / dist.get_world_size()
acc5 = dist.all_reduce_sum(acc5) / dist.get_world_size()
return loss, acc1, acc5
def calculate_scale(image, label):
model.eval()
enable_observer(model)
logits = model(image)
loss = F.cross_entropy_with_softmax(logits, label, label_smooth=0.1)
acc1, acc5 = F.accuracy(logits, label, (1, 5))
if dist.is_distributed(): # all_reduce_mean
loss = dist.all_reduce_sum(loss, "valid_loss") / dist.get_world_size()
acc1 = dist.all_reduce_sum(acc1, "valid_acc1") / dist.get_world_size()
acc5 = dist.all_reduce_sum(acc5, "valid_acc5") / dist.get_world_size()
return loss, acc1, acc5
def train_func():
loss = model.calc_loss()
optimizer.backward(loss) # compute gradients
if dist.is_distributed(): # all_reduce_mean
loss = dist.all_reduce_sum(loss,
"train_loss") / dist.get_world_size()
return loss
