def test_loop_fn(loader):
total_samples = 0
correct = 0
model.eval()
for x, (data, target) in loader:
output = model(data)
pred = output.max(1, keepdim=True)[1]
correct += pred.eq(target.view_as(pred)).sum().item()
total_samples += data.size()[0]
accuracy = 100.0 * correct / total_samples
test_utils.print_test_update(device, accuracy)
return accuracy
def test_loop_fn(model, loader, device, context):
total_samples = 0
correct = 0
model.eval()
for data, target in loader:
data = target[0].permute(0,3,1,2)
target = target[1]
output = model(data)
#pred = output.max(1, keepdim=True)[1].float()
_, preds = torch.max(output, 1)
preds = preds.float()
correct += preds.eq(target.view_as(preds)).sum().item()
total_samples += target.shape[1]**2
print('device: {}, Running Accuracy: {}'.format(device, correct/total_samples))
accuracy = 100.0 * correct / total_samples
test_utils.print_test_update(device, accuracy)
logger.info('TEST: device: {}, accuracy: {}'.format(device, accuracy))
return accuracy
