def test_running_mean_metric_factory(self, build_mock):
metric = metrics.MetricFactory
build_mock.return_value=metrics.Metric('test')
out = metrics.running_mean(metric)().build()
self.assertTrue(isinstance(out, metrics.MetricTree))
self.assertTrue(isinstance(out.children[0], metrics.ToDict))
self.assertTrue(isinstance(out.children[0].metric, metrics.RunningMean))
self.assertTrue(out.children[0].metric.name == 'running_test')
self.assertTrue(out.root.name == 'test')
def test_running_mean_metric_class(self):
metric = metrics.Metric
out = metrics.running_mean(batch_size=40, step_size=20)(metric)('test')
self.assertTrue(isinstance(out, metrics.MetricTree))
self.assertTrue(isinstance(out.children[0], metrics.ToDict))
self.assertTrue(isinstance(out.children[0].metric, metrics.RunningMean))
self.assertTrue(out.children[0].metric._batch_size == 40)
self.assertTrue(out.children[0].metric._step_size == 20)
self.assertTrue(out.children[0].metric.name == 'running_test')
self.assertTrue(out.root.name == 'test')
def test_running_mean_metric(self):
metric = metrics.Metric
out = metrics.running_mean(batch_size=40, step_size=20)(metric)('test').build()
self.assertTrue(isinstance(out, metrics.MetricTree))
self.assertTrue(isinstance(out.children[0], metrics.ToDict))
self.assertTrue(isinstance(out.children[0].metric, metrics.RunningMean))
self.assertTrue(out.children[0].metric._batch_size == 40)
self.assertTrue(out.children[0].metric._step_size == 20)
self.assertTrue(out.children[0].metric.name == 'running_test')
self.assertTrue(out.root.name == 'test')
closure_gen = base_closure(tb.X, tb.MODEL, tb.Y_PRED, tb.Y_TRUE, tb.CRITERION,
tb.LOSS, GEN_OPT)
closure_disc = base_closure(tb.Y_PRED, DISC_MODEL, None, DISC_IMGS, DISC_CRIT,
tb.LOSS, DISC_OPT)
def closure(state):
closure_gen(state)
state[GEN_OPT].step()
closure_disc(state)
state[DISC_OPT].step()
from torchbearer.metrics import mean, running_mean
metrics = ['loss', mean(running_mean(D_LOSS)), mean(running_mean(G_LOSS))]
trial = tb.Trial(generator,
None,
criterion=gen_crit,
metrics=metrics,
callbacks=[saver_callback])
trial.with_train_generator(dataloader, steps=200000)
trial.to(device)
new_keys = {
DISC_MODEL: discriminator.to(device),
DISC_OPT: optimizer_D,
GEN_OPT: optimizer_G,
DISC_CRIT: disc_crit
}