def system_check():
import traceback
try:
import torch
from olympus.models import Model
from olympus.optimizers import Optimizer
batch = torch.randn((32, 3, 64, 64)).cuda()
model = Model('resnet18', input_size=(3, 64, 64),
output_size=(10, )).cuda()
model.init()
optimizer = Optimizer('sgd', params=model.parameters())
optimizer.init(**optimizer.defaults)
optimizer.zero_grad()
loss = model(batch).sum()
optimizer.backward(loss)
optimizer.step()
return True
except:
error(traceback.format_exc())
return False
ProgressView(speed, max_epochs=epochs,
max_steps=len(loader.train())).every(epoch=1, batch=1))
model = model.to(device=device)
loss = 0
event_handler.start_train()
for e in range(epochs):
losses = []
event_handler.new_epoch(e + 1)
for step, ((batch, ), target) in enumerate(loader.train()):
event_handler.new_batch(step)
optimizer.zero_grad()
predict = model(batch.to(device=device))
loss = F.cross_entropy(predict, target.to(device=device))
losses.append(loss.detach())
optimizer.backward(loss)
optimizer.step()
event_handler.end_batch(step)
losses = [l.item() for l in losses]
loss = sum(losses) / len(losses)
event_handler.end_epoch(e + 1)
event_handler.end_train()