def test_givenATrainWithoutStepsValidWithoutStepsLoop_whenAlternatingLoop_thenProgressBarIsProperlySet(self):
params = {'epochs': 2, 'steps': None, 'valid_steps': None}
with patch("poutyne.framework.progress.ColorProgress") as color_progress_patch:
self.progression_callback = ProgressionCallback()
self.progression_callback.set_params(params)
self.progression_callback.set_model(MagicMock())
self.progression_callback.on_train_begin(self.an_empty_log)
color_progress_calls = [call(coloring=self.coloring), call().close_progress_bar()]
# first train loop
epoch = 1
color_progress_calls.extend(self._a_training_loop(epoch, self.num_steps, with_progress_bar=False))
# first valid loop
color_progress_calls.extend(self._a_valid_loop(epoch, self.num_steps, with_progress_bar=False))
# second train loop
epoch = 2
color_progress_calls.extend(self._a_training_loop(epoch, self.num_steps, with_progress_bar=False))
# second valid loop
color_progress_calls.extend(self._a_valid_loop(epoch, self.num_steps, with_progress_bar=False))
color_progress_patch.assert_has_calls(color_progress_calls)
def test_givenATrainLoop_whenShowingEveryNSteps_thenProgressBarIsProperlySet(
self):
params = {
'epochs': 2,
'steps': self.num_steps,
'valid_steps': self.num_valid_steps
}
with patch("poutyne.framework.progress.ColorProgress"
) as color_progress_patch:
self.progression_callback = ProgressionCallback(
show_every_n_train_steps=2, show_every_n_valid_steps=3)
self.progression_callback.set_params(params)
self.progression_callback.set_model(MagicMock())
self.progression_callback.on_train_begin(self.an_empty_log)
color_progress_calls = [call(coloring=self.coloring)]
# first train loop
epoch = 1
color_progress_calls.extend(
self._a_training_loop(epoch,
self.num_steps,
with_progress_bar=True,
show_every_n_steps=2))
# first valid loop
color_progress_calls.extend(
self._a_valid_loop(epoch,
self.num_steps,
with_progress_bar=True,
show_every_n_steps=3))
# second train loop
epoch = 2
color_progress_calls.extend(
self._a_training_loop(epoch,
self.num_steps,
with_progress_bar=True,
show_every_n_steps=2))
# second valid loop
color_progress_calls.extend(
self._a_valid_loop(epoch,
self.num_steps,
with_progress_bar=True,
show_every_n_steps=3))
color_progress_patch.assert_has_calls(color_progress_calls)
class ProgressTest(TestCase):
def setUp(self) -> None:
self.epochs = 2
self.num_steps = 10
self.num_valid_steps = 10
self.step_times_rate = 1.0
self.any_log = {"time": self.step_times_rate, "loss": 1.0}
self.total_time = 5
self.final_log = {"time": self.total_time, "loss": 1.0}
self.an_empty_log = {}
self.metrics_str = ''
self.coloring = True
def test_givenATrainWithStepsValidWithoutStepsLoop_whenAlternatingLoop_thenProgressBarIsProperlySet(self):
params = {'epochs': 2, 'steps': self.num_steps, 'valid_steps': None}
with patch("poutyne.framework.progress.ColorProgress") as color_progress_patch:
self.progression_callback = ProgressionCallback()
self.progression_callback.set_params(params)
self.progression_callback.set_model(MagicMock())
self.progression_callback.on_train_begin(self.an_empty_log)
color_progress_calls = [call(coloring=self.coloring), call().set_progress_bar(self.num_steps)]
# first train loop
epoch = 1
color_progress_calls.extend(self._a_training_loop(epoch, self.num_steps, with_progress_bar=True))
# first valid loop
color_progress_calls.extend(self._a_valid_loop(epoch, self.num_steps, with_progress_bar=False))
# second train loop
epoch = 2
color_progress_calls.extend(self._a_training_loop(epoch, self.num_steps, with_progress_bar=True))
# second valid loop
color_progress_calls.extend(self._a_valid_loop(epoch, self.num_steps, with_progress_bar=False))
color_progress_patch.assert_has_calls(color_progress_calls)
def test_givenATrainWithoutStepsValidWitStepsLoop_whenAlternatingLoop_thenProgressBarIsProperlySet(self):
params = {'epochs': 2, 'steps': None, 'valid_steps': self.num_valid_steps}
with patch("poutyne.framework.progress.ColorProgress") as color_progress_patch:
self.progression_callback = ProgressionCallback()
self.progression_callback.set_params(params)
self.progression_callback.set_model(MagicMock())
self.progression_callback.on_train_begin(self.an_empty_log)
color_progress_calls = [call(coloring=self.coloring), call().close_progress_bar()]
# first train loop
epoch = 1
color_progress_calls.extend(self._a_training_loop(epoch, self.num_steps, with_progress_bar=False))
# first valid loop
color_progress_calls.extend(self._a_valid_loop(epoch, self.num_steps, with_progress_bar=True))
# second train loop
epoch = 2
color_progress_calls.extend(self._a_training_loop(epoch, self.num_steps, with_progress_bar=False))
# second valid loop
color_progress_calls.extend(self._a_valid_loop(epoch, self.num_steps, with_progress_bar=True))
color_progress_patch.assert_has_calls(color_progress_calls)
def test_givenATrainWithoutStepsValidWithoutStepsLoop_whenAlternatingLoop_thenProgressBarIsProperlySet(self):
params = {'epochs': 2, 'steps': None, 'valid_steps': None}
with patch("poutyne.framework.progress.ColorProgress") as color_progress_patch:
self.progression_callback = ProgressionCallback()
self.progression_callback.set_params(params)
self.progression_callback.set_model(MagicMock())
self.progression_callback.on_train_begin(self.an_empty_log)
color_progress_calls = [call(coloring=self.coloring), call().close_progress_bar()]
# first train loop
epoch = 1
color_progress_calls.extend(self._a_training_loop(epoch, self.num_steps, with_progress_bar=False))
# first valid loop
color_progress_calls.extend(self._a_valid_loop(epoch, self.num_steps, with_progress_bar=False))
# second train loop
epoch = 2
color_progress_calls.extend(self._a_training_loop(epoch, self.num_steps, with_progress_bar=False))
# second valid loop
color_progress_calls.extend(self._a_valid_loop(epoch, self.num_steps, with_progress_bar=False))
color_progress_patch.assert_has_calls(color_progress_calls)
def _a_training_loop(self, epoch_number, num_steps: int = 5, with_progress_bar: bool = True) -> List:
color_progress_calls = []
self.progression_callback.on_epoch_begin(epoch_number, self.an_empty_log)
if with_progress_bar:
color_progress_calls.append(call().set_progress_bar(self.num_steps))
else:
color_progress_calls.append(call().close_progress_bar())
color_progress_calls.append(call().on_epoch_begin(epoch_number=epoch_number, epochs=self.epochs))
step_times_weighted_sum = 0.0
for batch_number in range(0, num_steps):
batch_number += 1 # 1 base counting
self.progression_callback.on_train_batch_end(batch_number, self.any_log)
if with_progress_bar:
remaining_time = self.step_times_rate * (self.num_steps - batch_number)
color_progress_calls.append(call().on_train_batch_end(remaining_time=remaining_time,
batch_number=batch_number,
metrics_str=self.metrics_str,
steps=self.num_steps))
else:
step_times_weighted_sum += batch_number * self.step_times_rate
normalizing_factor = (batch_number * (batch_number + 1)) / 2
step_times_rate = step_times_weighted_sum / normalizing_factor
color_progress_calls.append(call().on_train_batch_end(remaining_time=step_times_rate,
batch_number=batch_number,
metrics_str=self.metrics_str))
return color_progress_calls
def _a_valid_loop(self, epoch_number, num_steps: int = 5, with_progress_bar: bool = True) -> List:
color_progress_calls = []
self.progression_callback.on_valid_begin(self.an_empty_log)
if with_progress_bar:
color_progress_calls.append(call().set_progress_bar(self.num_steps))
else:
color_progress_calls.append(call().close_progress_bar())
color_progress_calls.append(call().on_valid_begin())
step_times_weighted_sum = 0.0
for valid_batch_number in range(0, num_steps):
valid_batch_number += 1 # 1 base counting
self.progression_callback.on_valid_batch_end(valid_batch_number, self.any_log)
if with_progress_bar:
remaining_time = self.step_times_rate * (self.num_steps - valid_batch_number)
color_progress_calls.append(call().on_valid_batch_end(remaining_time=remaining_time,
batch_number=valid_batch_number,
metrics_str=self.metrics_str,
steps=self.num_valid_steps))
else:
step_times_weighted_sum += valid_batch_number * self.step_times_rate
normalizing_factor = (valid_batch_number * (valid_batch_number + 1)) / 2
step_times_rate = step_times_weighted_sum / normalizing_factor
color_progress_calls.append(call().on_valid_batch_end(remaining_time=step_times_rate,
batch_number=valid_batch_number,
metrics_str=self.metrics_str))
self.progression_callback.on_valid_end(self.final_log)
self.progression_callback.on_epoch_end(epoch_number, self.final_log)
color_progress_calls.append(call().on_epoch_end(total_time=self.total_time,
train_last_steps=self.num_steps,
valid_last_steps=self.num_valid_steps,
metrics_str=self.metrics_str))
return color_progress_calls