def test_loop_fn(model, loader, device, context):
print("***********************")
print("ENTERING TEST FUNCTION")
print("***********************")
print('Evaluating...')
total_samples = 0
correct = 0
model.eval()
for step, (data, target) in enumerate(loader):
if step >= FLAGS.test_max_step:
break
output = model(data)
pred = output.max(1, keepdim=True)[1]
if FLAGS.mp:
correct += pred.eq(target.view_as(pred)).sum()
else:
correct += pred.eq(target.view_as(pred)).sum().item()
total_samples += data.size()[0]
if FLAGS.mp:
this_accuracy = 100.0 * correct.item() / total_samples
print("CALLING: mesh_reduce('test_accuracy')")
this_accuracy = xm.mesh_reduce(
'test_accuracy', this_accuracy, np.mean
)
print("BACK FROM: mesh_reduce('test_accuracy')")
else:
this_accuracy = 100.0 * correct / total_samples
test_utils.print_test_update(device, this_accuracy)
print("***********************")
print("LEAVING TEST FUNCTION")
print("***********************")
return this_accuracy
def test_loop_fn(model, loader, device, context):
total_samples = 0
correct = 0
model.eval()
for 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(loader, epoch):
total_samples, correct = 0, 0
model.eval()
for step, (data, target) in enumerate(loader):
output = model(data)
pred = output.max(1, keepdim=True)[1]
correct += pred.eq(target.view_as(pred)).sum()
total_samples += data.size()[0]
if step % FLAGS.log_steps == 0:
test_utils.print_test_update(device, None, epoch, step)
# xm.add_step_closure(test_utils.print_test_update, args=(device, None, epoch, step))
accuracy = 100.0 * correct.item() / total_samples
# accuracy = xm.mesh_reduce("test_accuracy", accuracy, np.mean)
return accuracy
