class ModelDictOutputTest(ModelFittingTestCase):
def setUp(self):
super().setUp()
torch.manual_seed(42)
self.pytorch_network = DictOutputModel()
self.loss_function = nn.MSELoss()
self.optimizer = torch.optim.SGD(self.pytorch_network.parameters(), lr=1e-3)
self.model = Model(
self.pytorch_network,
self.optimizer,
lambda y_p, y_t: self.loss_function(y_p['out1'], y_t[0]) + self.loss_function(y_p['out2'], y_t[1]),
batch_metrics=self.batch_metrics,
epoch_metrics=self.epoch_metrics)
def test_fitting_with_tensor_multi_output_dict(self):
train_real_steps_per_epoch = 30
train_batch_size = ModelDictOutputTest.batch_size
train_final_batch_missing_samples = 7
train_size = train_real_steps_per_epoch * train_batch_size - \
train_final_batch_missing_samples
train_x = torch.rand(train_size, 1)
train_y = (torch.rand(train_size, 1), torch.rand(train_size, 1))
valid_real_steps_per_epoch = 10
# valid_batch_size will be the same as train_batch_size in the fit method.
valid_batch_size = train_batch_size
valid_final_batch_missing_samples = 3
valid_size = valid_real_steps_per_epoch * valid_batch_size - \
valid_final_batch_missing_samples
valid_x = torch.rand(valid_size, 1)
valid_y = (torch.rand(valid_size, 1), torch.rand(valid_size, 1))
logs = self.model.fit(train_x,
train_y,
validation_data=(valid_x, valid_y),
epochs=ModelDictOutputTest.epochs,
batch_size=train_batch_size,
steps_per_epoch=None,
validation_steps=None,
callbacks=[self.mock_callback])
params = {'epochs': ModelDictOutputTest.epochs, 'steps': train_real_steps_per_epoch}
self._test_callbacks_train(params, logs)
def test_ndarray_train_on_batch_dict_output(self):
x = np.random.rand(ModelDictOutputTest.batch_size, 1).astype(np.float32)
y1 = np.random.rand(ModelDictOutputTest.batch_size, 1).astype(np.float32)
y2 = np.random.rand(ModelDictOutputTest.batch_size, 1).astype(np.float32)
loss = self.model.train_on_batch(x, (y1, y2))
self.assertEqual(type(loss), float)
def test_evaluate_with_pred_dict_output(self):
y = (torch.rand(ModelDictOutputTest.evaluate_dataset_len,
1), torch.rand(ModelDictOutputTest.evaluate_dataset_len, 1))
x = torch.rand(ModelDictOutputTest.evaluate_dataset_len, 1)
# We also test the unpacking.
_, pred_y = self.model.evaluate(x, y, batch_size=ModelDictOutputTest.batch_size, return_pred=True)
for pred in pred_y.values():
self.assertEqual(pred.shape, (ModelDictOutputTest.evaluate_dataset_len, 1))
def test_evaluate_with_only_one_metric(self):
model = Model(self.pytorch_network,
self.optimizer,
self.loss_function,
batch_metrics=self.batch_metrics[:1])
x = torch.rand(ModelTest.evaluate_dataset_len, 1)
y = torch.rand(ModelTest.evaluate_dataset_len, 1)
loss, first_metric = model.evaluate(x,
y,
batch_size=ModelTest.batch_size)
self.assertEqual(type(loss), float)
self.assertEqual(type(first_metric), float)
self.assertEqual(first_metric, some_metric_1_value)
class ModelMultiIOTest(ModelFittingTestCase):
def setUp(self):
super().setUp()
torch.manual_seed(42)
self.pytorch_network = MultiIOModel(num_input=2, num_output=2)
self.loss_function = nn.MSELoss()
self.optimizer = torch.optim.SGD(self.pytorch_network.parameters(), lr=1e-3)
self.model = Model(
self.pytorch_network,
self.optimizer,
lambda y_pred, y_true: self.loss_function(y_pred[0], y_true[0]) + self.loss_function(y_pred[1], y_true[1]),
batch_metrics=self.batch_metrics,
epoch_metrics=self.epoch_metrics)
def test_fitting_tensor_generator_multi_io(self):
train_generator = some_data_tensor_generator_multi_io(ModelMultiIOTest.batch_size)
valid_generator = some_data_tensor_generator_multi_io(ModelMultiIOTest.batch_size)
logs = self.model.fit_generator(train_generator,
valid_generator,
epochs=ModelMultiIOTest.epochs,
steps_per_epoch=ModelMultiIOTest.steps_per_epoch,
validation_steps=ModelMultiIOTest.steps_per_epoch,
callbacks=[self.mock_callback])
params = {'epochs': ModelMultiIOTest.epochs, 'steps': ModelMultiIOTest.steps_per_epoch}
self._test_callbacks_train(params, logs)
def test_fitting_with_tensor_multi_io(self):
train_real_steps_per_epoch = 30
train_batch_size = ModelMultiIOTest.batch_size
train_final_batch_missing_samples = 7
train_size = train_real_steps_per_epoch * train_batch_size - \
train_final_batch_missing_samples
train_x = (torch.rand(train_size, 1), torch.rand(train_size, 1))
train_y = (torch.rand(train_size, 1), torch.rand(train_size, 1))
valid_real_steps_per_epoch = 10
# valid_batch_size will be the same as train_batch_size in the fit method.
valid_batch_size = train_batch_size
valid_final_batch_missing_samples = 3
valid_size = valid_real_steps_per_epoch * valid_batch_size - \
valid_final_batch_missing_samples
valid_x = (torch.rand(valid_size, 1), torch.rand(valid_size, 1))
valid_y = (torch.rand(valid_size, 1), torch.rand(valid_size, 1))
logs = self.model.fit(train_x,
train_y,
validation_data=(valid_x, valid_y),
epochs=ModelMultiIOTest.epochs,
batch_size=train_batch_size,
steps_per_epoch=None,
validation_steps=None,
callbacks=[self.mock_callback])
params = {'epochs': ModelMultiIOTest.epochs, 'steps': train_real_steps_per_epoch}
self._test_callbacks_train(params, logs)
def test_tensor_train_on_batch_multi_io(self):
x1 = torch.rand(ModelMultiIOTest.batch_size, 1)
x2 = torch.rand(ModelMultiIOTest.batch_size, 1)
y1 = torch.rand(ModelMultiIOTest.batch_size, 1)
y2 = torch.rand(ModelMultiIOTest.batch_size, 1)
loss = self.model.train_on_batch((x1, x2), (y1, y2))
self.assertEqual(type(loss), float)
def test_ndarray_train_on_batch_multi_io(self):
x1 = np.random.rand(ModelMultiIOTest.batch_size, 1).astype(np.float32)
x2 = np.random.rand(ModelMultiIOTest.batch_size, 1).astype(np.float32)
y1 = np.random.rand(ModelMultiIOTest.batch_size, 1).astype(np.float32)
y2 = np.random.rand(ModelMultiIOTest.batch_size, 1).astype(np.float32)
loss = self.model.train_on_batch((x1, x2), (y1, y2))
self.assertEqual(type(loss), float)
def test_evaluate_with_pred_multi_io(self):
x = (torch.rand(ModelMultiIOTest.evaluate_dataset_len, 1), torch.rand(ModelMultiIOTest.evaluate_dataset_len, 1))
y = (torch.rand(ModelMultiIOTest.evaluate_dataset_len, 1), torch.rand(ModelMultiIOTest.evaluate_dataset_len, 1))
# We also test the unpacking.
_, pred_y = self.model.evaluate(x, y, batch_size=ModelMultiIOTest.batch_size, return_pred=True)
for pred in pred_y:
self.assertEqual(pred.shape, (ModelMultiIOTest.evaluate_dataset_len, 1))
def test_tensor_evaluate_on_batch_multi_io(self):
y = (torch.rand(ModelMultiIOTest.batch_size, 1), torch.rand(ModelMultiIOTest.batch_size, 1))
x = (torch.rand(ModelMultiIOTest.batch_size, 1), torch.rand(ModelMultiIOTest.batch_size, 1))
loss = self.model.evaluate_on_batch(x, y)
self.assertEqual(type(loss), float)
def test_predict_with_np_array_multi_io(self):
x1 = np.random.rand(ModelMultiIOTest.evaluate_dataset_len, 1).astype(np.float32)
x2 = np.random.rand(ModelMultiIOTest.evaluate_dataset_len, 1).astype(np.float32)
x = (x1, x2)
pred_y = self.model.predict(x, batch_size=ModelMultiIOTest.batch_size)
for pred in pred_y:
self.assertEqual(pred.shape, (ModelMultiIOTest.evaluate_dataset_len, 1))
def test_predict_generator_multi_io(self):
num_steps = 10
generator = some_data_tensor_generator_multi_io(ModelMultiIOTest.batch_size)
generator = (x for x, _ in generator)
pred_y = self.model.predict_generator(generator, steps=num_steps)
for pred in pred_y:
self.assertEqual(pred.shape, (num_steps * ModelMultiIOTest.batch_size, 1))
def test_tensor_predict_on_batch_multi_io(self):
x = (torch.rand(ModelMultiIOTest.batch_size, 1), torch.rand(ModelMultiIOTest.batch_size, 1))
pred_y = self.model.predict_on_batch(x)
self._test_size_and_type_for_generator(pred_y, (ModelMultiIOTest.batch_size, 1))
import torch.nn as nn
import torch.optim as optim
from poutyne import Model
net = nn.Sequential(
nn.Linear(100, 64),
nn.ReLU(),
nn.Linear(64, 10)
)
num_features = 100
num_classes = 10
# Our training dataset with 8000 samples.
num_train_samples = 80000
x_train = torch.rand(num_train_samples, num_features)
y_train = torch.randint(num_classes, (num_train_samples,), dtype=torch.long)
# Our test dataset with 200 samples.
num_test_samples = 200
x_test = torch.rand(num_test_samples, num_features)
y_test = torch.randint(num_classes, (num_test_samples,), dtype=torch.long)
criterion = nn.CrossEntropyLoss()
optimizer = optim.SGD(net.parameters(), lr=0.001, momentum=0.9)
model = Model(net, optimizer, criterion, batch_metrics=['accuracy'])
model.fit(x_train, y_train, epochs=5, batch_size=32)
loss, accuracy = model.evaluate(x_test, y_test, batch_size=32)
class ModelFittingTestCaseProgress(ModelFittingTestCase):
# pylint: disable=too-many-public-methods
num_steps = 5
def setUp(self):
super().setUp()
self.train_generator = some_data_tensor_generator(ModelFittingTestCase.batch_size)
self.valid_generator = some_data_tensor_generator(ModelFittingTestCase.batch_size)
self.test_generator = some_data_tensor_generator(ModelFittingTestCase.batch_size)
torch.manual_seed(42)
self.pytorch_network = nn.Linear(1, 1)
self.loss_function = nn.MSELoss()
self.optimizer = torch.optim.SGD(self.pytorch_network.parameters(), lr=1e-3)
self.batch_metrics = [
some_batch_metric_1, ('custom_name', some_batch_metric_2), repeat_batch_metric, repeat_batch_metric
]
self.batch_metrics_names = [
'some_batch_metric_1', 'custom_name', 'repeat_batch_metric1', 'repeat_batch_metric2'
]
self.batch_metrics_values = [
some_metric_1_value, some_metric_2_value, repeat_batch_metric_value, repeat_batch_metric_value
]
self.epoch_metrics = [SomeConstantEpochMetric()]
self.epoch_metrics_names = ['some_constant_epoch_metric']
self.epoch_metrics_values = [some_constant_epoch_metric_value]
self.model = Model(self.pytorch_network,
self.optimizer,
self.loss_function,
batch_metrics=self.batch_metrics,
epoch_metrics=self.epoch_metrics)
self._capture_output()
def assertStdoutContains(self, values):
for value in values:
self.assertIn(value, self.test_out.getvalue().strip())
def assertStdoutNotContains(self, values):
for value in values:
self.assertNotIn(value, self.test_out.getvalue().strip())
@skipIf(color is None, "Unable to import colorama")
def test_fitting_with_default_coloring(self):
_ = self.model.fit_generator(self.train_generator,
self.valid_generator,
epochs=ModelFittingTestCase.epochs,
steps_per_epoch=ModelFittingTestCase.steps_per_epoch,
validation_steps=ModelFittingTestCase.steps_per_epoch,
callbacks=[self.mock_callback])
self.assertStdoutContains(["[32m", "[35m", "[36m", "[94m"])
def test_fitting_with_progress_bar_show_epoch(self):
_ = self.model.fit_generator(self.train_generator,
self.valid_generator,
epochs=ModelFittingTestCase.epochs,
steps_per_epoch=ModelFittingTestCase.steps_per_epoch,
validation_steps=ModelFittingTestCase.steps_per_epoch,
callbacks=[self.mock_callback])
self.assertStdoutContains(["Epoch", "1/5", "2/5"])
def test_fitting_with_progress_bar_show_steps(self):
_ = self.model.fit_generator(self.train_generator,
self.valid_generator,
epochs=ModelFittingTestCase.epochs,
steps_per_epoch=ModelFittingTestCase.steps_per_epoch,
validation_steps=ModelFittingTestCase.steps_per_epoch,
callbacks=[self.mock_callback])
self.assertStdoutContains(["steps", f"{ModelFittingTestCase.steps_per_epoch}"])
def test_fitting_with_progress_bar_show_train_val_final_steps(self):
_ = self.model.fit_generator(self.train_generator,
self.valid_generator,
epochs=ModelFittingTestCase.epochs,
steps_per_epoch=ModelFittingTestCase.steps_per_epoch,
validation_steps=ModelFittingTestCase.steps_per_epoch,
callbacks=[self.mock_callback])
self.assertStdoutContains(["Val steps", "Train steps"])
def test_fitting_with_no_progress_bar_dont_show_epoch(self):
_ = self.model.fit_generator(self.train_generator,
self.valid_generator,
epochs=ModelFittingTestCase.epochs,
steps_per_epoch=ModelFittingTestCase.steps_per_epoch,
validation_steps=ModelFittingTestCase.steps_per_epoch,
callbacks=[self.mock_callback],
verbose=False)
self.assertStdoutNotContains(["Epoch", "1/5", "2/5"])
@skipIf(color is None, "Unable to import colorama")
def test_fitting_with_user_coloring(self):
coloring = {
"text_color": 'BLACK',
"ratio_color": "BLACK",
"metric_value_color": "BLACK",
"time_color": "BLACK",
"progress_bar_color": "BLACK"
}
_ = self.model.fit_generator(self.train_generator,
self.valid_generator,
epochs=ModelFittingTestCase.epochs,
steps_per_epoch=ModelFittingTestCase.steps_per_epoch,
validation_steps=ModelFittingTestCase.steps_per_epoch,
callbacks=[self.mock_callback],
progress_options=dict(coloring=coloring))
self.assertStdoutContains(["[30m"])
@skipIf(color is None, "Unable to import colorama")
def test_fitting_with_user_partial_coloring(self):
_ = self.model.fit_generator(self.train_generator,
self.valid_generator,
epochs=ModelFittingTestCase.epochs,
steps_per_epoch=ModelFittingTestCase.steps_per_epoch,
validation_steps=ModelFittingTestCase.steps_per_epoch,
callbacks=[self.mock_callback],
progress_options=dict(coloring={
"text_color": 'BLACK',
"ratio_color": "BLACK"
}))
self.assertStdoutContains(["[30m", "[32m", "[35m", "[94m"])
def test_fitting_with_user_coloring_invalid(self):
with self.assertRaises(KeyError):
_ = self.model.fit_generator(self.train_generator,
self.valid_generator,
epochs=ModelFittingTestCase.epochs,
steps_per_epoch=ModelFittingTestCase.steps_per_epoch,
validation_steps=ModelFittingTestCase.steps_per_epoch,
callbacks=[self.mock_callback],
progress_options=dict(coloring={"invalid_name": 'A COLOR'}))
def test_fitting_with_no_coloring(self):
_ = self.model.fit_generator(self.train_generator,
self.valid_generator,
epochs=ModelFittingTestCase.epochs,
steps_per_epoch=ModelFittingTestCase.steps_per_epoch,
validation_steps=ModelFittingTestCase.steps_per_epoch,
callbacks=[self.mock_callback],
progress_options=dict(coloring=False))
self.assertStdoutNotContains(["[32m", "[35m", "[36m", "[94m"])
@skipIf(color is None, "Unable to import colorama")
def test_fitting_with_progress_bar_default_color(self):
_ = self.model.fit_generator(self.train_generator,
self.valid_generator,
epochs=ModelFittingTestCase.epochs,
steps_per_epoch=ModelFittingTestCase.steps_per_epoch,
validation_steps=ModelFittingTestCase.steps_per_epoch,
callbacks=[self.mock_callback],
progress_options=dict(coloring=True, progress_bar=True))
self.assertStdoutContains(["%", "[32m", "[35m", "[36m", "[94m", "\u2588"])
@skipIf(color is None, "Unable to import colorama")
def test_fitting_with_progress_bar_user_color(self):
coloring = {
"text_color": 'BLACK',
"ratio_color": "BLACK",
"metric_value_color": "BLACK",
"time_color": "BLACK",
"progress_bar_color": "BLACK"
}
_ = self.model.fit_generator(self.train_generator,
self.valid_generator,
epochs=ModelFittingTestCase.epochs,
steps_per_epoch=ModelFittingTestCase.steps_per_epoch,
validation_steps=ModelFittingTestCase.steps_per_epoch,
callbacks=[self.mock_callback],
progress_options=dict(coloring=coloring, progress_bar=True))
self.assertStdoutContains(["%", "[30m", "\u2588"])
def test_fitting_with_progress_bar_no_color(self):
_ = self.model.fit_generator(self.train_generator,
self.valid_generator,
epochs=ModelFittingTestCase.epochs,
steps_per_epoch=ModelFittingTestCase.steps_per_epoch,
validation_steps=ModelFittingTestCase.steps_per_epoch,
callbacks=[self.mock_callback],
progress_options=dict(coloring=False, progress_bar=True))
self.assertStdoutContains(["%", "\u2588"])
self.assertStdoutNotContains(["[32m", "[35m", "[36m", "[94m"])
def test_fitting_with_no_progress_bar(self):
_ = self.model.fit_generator(self.train_generator,
self.valid_generator,
epochs=ModelFittingTestCase.epochs,
steps_per_epoch=ModelFittingTestCase.steps_per_epoch,
validation_steps=ModelFittingTestCase.steps_per_epoch,
callbacks=[self.mock_callback],
progress_options=dict(coloring=False, progress_bar=False))
self.assertStdoutNotContains(["%", "\u2588"])
self.assertStdoutNotContains(["[32m", "[35m", "[36m", "[94m"])
def test_progress_bar_with_step_is_none(self):
train_generator = SomeDataGeneratorUsingStopIteration(ModelFittingTestCase.batch_size, 10)
valid_generator = SomeDataGeneratorUsingStopIteration(ModelFittingTestCase.batch_size, 10)
_ = self.model.fit_generator(train_generator,
valid_generator,
epochs=ModelFittingTestCase.epochs,
progress_options=dict(coloring=False, progress_bar=True))
self.assertStdoutContains(["s/step"])
self.assertStdoutNotContains(["[32m", "[35m", "[36m", "[94m", "\u2588", "%"])
def test_evaluate_without_progress_output(self):
x = torch.rand(ModelFittingTestCase.evaluate_dataset_len, 1)
y = torch.rand(ModelFittingTestCase.evaluate_dataset_len, 1)
_, _ = self.model.evaluate(x, y, batch_size=ModelFittingTestCase.batch_size, verbose=False)
self.assertStdoutNotContains(["[32m", "[35m", "[36m", "[94m"])
@skipIf(color is None, "Unable to import colorama")
def test_evaluate_with_default_coloring(self):
x = torch.rand(ModelFittingTestCase.evaluate_dataset_len, 1)
y = torch.rand(ModelFittingTestCase.evaluate_dataset_len, 1)
_, _ = self.model.evaluate(x, y, batch_size=ModelFittingTestCase.batch_size)
self.assertStdoutContains(["[32m", "[35m", "[36m", "[94m"])
@skipIf(color is None, "Unable to import colorama")
def test_evaluate_with_user_coloring(self):
x = torch.rand(ModelFittingTestCase.evaluate_dataset_len, 1)
y = torch.rand(ModelFittingTestCase.evaluate_dataset_len, 1)
coloring = {
"text_color": 'BLACK',
"ratio_color": "BLACK",
"metric_value_color": "BLACK",
"time_color": "BLACK",
"progress_bar_color": "BLACK"
}
_, _ = self.model.evaluate(x,
y,
batch_size=ModelFittingTestCase.batch_size,
progress_options=dict(coloring=coloring))
self.assertStdoutContains(["[30m"])
@skipIf(color is None, "Unable to import colorama")
def test_evaluate_with_user_partial_coloring(self):
x = torch.rand(ModelFittingTestCase.evaluate_dataset_len, 1)
y = torch.rand(ModelFittingTestCase.evaluate_dataset_len, 1)
_, _ = self.model.evaluate(x,
y,
batch_size=ModelFittingTestCase.batch_size,
progress_options=dict(coloring={
"text_color": 'BLACK',
"ratio_color": "BLACK"
}))
self.assertStdoutContains(["[30m", "[32m", "[35m", "[94m"])
def test_evaluate_with_user_coloring_invalid(self):
x = torch.rand(ModelFittingTestCase.evaluate_dataset_len, 1)
y = torch.rand(ModelFittingTestCase.evaluate_dataset_len, 1)
with self.assertRaises(KeyError):
_, _ = self.model.evaluate(x,
y,
batch_size=ModelFittingTestCase.batch_size,
progress_options=dict(coloring={"invalid_name": 'A COLOR'}))
def test_evaluate_with_no_coloring(self):
x = torch.rand(ModelFittingTestCase.evaluate_dataset_len, 1)
y = torch.rand(ModelFittingTestCase.evaluate_dataset_len, 1)
_, _ = self.model.evaluate(x,
y,
batch_size=ModelFittingTestCase.batch_size,
progress_options=dict(coloring=False))
self.assertStdoutNotContains(["[32m", "[35m", "[36m", "[94m"])
@skipIf(color is None, "Unable to import colorama")
def test_evaluate_with_progress_bar_default_coloring(self):
x = torch.rand(ModelFittingTestCase.evaluate_dataset_len, 1)
y = torch.rand(ModelFittingTestCase.evaluate_dataset_len, 1)
_, _ = self.model.evaluate(x,
y,
batch_size=ModelFittingTestCase.batch_size,
progress_options=dict(coloring=True, progress_bar=True))
self.assertStdoutContains(["%", "[32m", "[35m", "[36m", "[94m", "\u2588"])
@skipIf(color is None, "Unable to import colorama")
def test_evaluate_with_progress_bar_user_coloring(self):
x = torch.rand(ModelFittingTestCase.evaluate_dataset_len, 1)
y = torch.rand(ModelFittingTestCase.evaluate_dataset_len, 1)
coloring = {
"text_color": 'BLACK',
"ratio_color": "BLACK",
"metric_value_color": "BLACK",
"time_color": "BLACK",
"progress_bar_color": "BLACK"
}
_, _ = self.model.evaluate(x,
y,
batch_size=ModelFittingTestCase.batch_size,
progress_options=dict(coloring=coloring, progress_bar=True))
self.assertStdoutContains(["%", "[30m", "\u2588"])
@skipIf(color is None, "Unable to import colorama")
def test_evaluate_with_progress_bar_user_no_color(self):
x = torch.rand(ModelFittingTestCase.evaluate_dataset_len, 1)
y = torch.rand(ModelFittingTestCase.evaluate_dataset_len, 1)
_, _ = self.model.evaluate(x,
y,
batch_size=ModelFittingTestCase.batch_size,
progress_options=dict(coloring=False, progress_bar=True))
self.assertStdoutContains(["%", "\u2588"])
self.assertStdoutNotContains(["[32m", "[35m", "[36m", "[94m"])
def test_evaluate_with_no_progress_bar(self):
x = torch.rand(ModelFittingTestCase.evaluate_dataset_len, 1)
y = torch.rand(ModelFittingTestCase.evaluate_dataset_len, 1)
_, _ = self.model.evaluate(x,
y,
batch_size=ModelFittingTestCase.batch_size,
progress_options=dict(coloring=False, progress_bar=False))
self.assertStdoutNotContains(["%", "\u2588"])
self.assertStdoutNotContains(["[32m", "[35m", "[36m", "[94m"])
def test_evaluate_data_loader_with_progress_bar_coloring(self):
x = torch.rand(ModelFittingTestCase.evaluate_dataset_len, 1)
y = torch.rand(ModelFittingTestCase.evaluate_dataset_len, 1)
dataset = TensorDataset(x, y)
generator = DataLoader(dataset, ModelFittingTestCase.batch_size)
_, _ = self.model.evaluate_generator(generator, verbose=True)
self.assertStdoutContains(["%", "[32m", "[35m", "[36m", "[94m", "\u2588"])
def test_evaluate_generator_with_progress_bar_coloring(self):
generator = some_data_tensor_generator(ModelFittingTestCase.batch_size)
_, _ = self.model.evaluate_generator(generator, steps=ModelFittingTestCaseProgress.num_steps, verbose=True)
self.assertStdoutContains(["%", "[32m", "[35m", "[36m", "[94m", "\u2588"])
def test_evaluate_generator_with_callback_and_progress_bar_coloring(self):
generator = some_data_tensor_generator(ModelFittingTestCase.batch_size)
_, _ = self.model.evaluate_generator(generator,
steps=ModelFittingTestCaseProgress.num_steps,
callbacks=[self.mock_callback],
verbose=True)
self.assertStdoutContains(["%", "[32m", "[35m", "[36m", "[94m", "\u2588"])
def test_fitting_complete_display_test_with_progress_bar_coloring(self):
# we use the same color for all components for simplicity
coloring = {
"text_color": 'WHITE',
"ratio_color": "WHITE",
"metric_value_color": "WHITE",
"time_color": "WHITE",
"progress_bar_color": "WHITE"
}
_ = self.model.fit_generator(self.train_generator,
self.valid_generator,
epochs=1,
steps_per_epoch=ModelFittingTestCaseProgress.num_steps,
validation_steps=ModelFittingTestCaseProgress.num_steps,
callbacks=[self.mock_callback],
progress_options=dict(coloring=coloring, progress_bar=False))
# We split per step update
steps_update = self.test_out.getvalue().strip().split("\r")
# we don't validate the templating of metrics since tested before
template_format = r".*Epoch:.*{}\/1.*\[37mStep:.*{}\/5.*{:6.2f}\%.*|{}|.*ETA:"
epoch = 1
# the 5 train steps
for step, step_update in enumerate(steps_update[:ModelFittingTestCaseProgress.num_steps]):
step += 1
progress_Bar = "\u2588" * step * 2 + " " * (20 - step * 2)
regex_filled = template_format.format(epoch, step, step / ModelFittingTestCaseProgress.num_steps * 100,
progress_Bar)
self.assertRegex(step_update, regex_filled)
# The 5 val steps
for step, step_update in enumerate(steps_update[ModelFittingTestCaseProgress.num_steps:-1]):
step += 1
progress_Bar = "\u2588" * step * 2 + " " * (20 - step * 2)
regex_filled = template_format.format(epoch, step, step / ModelFittingTestCaseProgress.num_steps * 100,
progress_Bar)
self.assertRegex(step_update, regex_filled)
# last print update templating different
last_print_regex = r".*\[37mTrain steps:.*5.*Val steps:.*5.*[0-9]*\.[0-9][0-9]s"
self.assertRegex(steps_update[-1], last_print_regex)
def test_fitting_complete_display_test_with_progress_bar_no_coloring(self):
_ = self.model.fit_generator(self.train_generator,
self.valid_generator,
epochs=1,
steps_per_epoch=ModelFittingTestCaseProgress.num_steps,
validation_steps=ModelFittingTestCaseProgress.num_steps,
callbacks=[self.mock_callback],
progress_options=dict(coloring=False, progress_bar=True))
# We split per step update
steps_update = self.test_out.getvalue().strip().split("\r")
# we don't validate the templating of metrics since tested before
template_format = r".*Epoch:.*{}\/1.*Step:.*{}\/5.*{:6.2f}\%.*|{}|.*ETA:"
epoch = 1
# the 5 train steps
for step, step_update in enumerate(steps_update[:ModelFittingTestCaseProgress.num_steps]):
step += 1
progress_Bar = "\u2588" * step * 2 + " " * (20 - step * 2)
regex_filled = template_format.format(epoch, step, step / ModelFittingTestCaseProgress.num_steps * 100,
progress_Bar)
self.assertRegex(step_update, regex_filled)
# The 5 val steps
for step, step_update in enumerate(steps_update[ModelFittingTestCaseProgress.num_steps:-1]):
step += 1
progress_Bar = "\u2588" * step * 2 + " " * (20 - step * 2)
regex_filled = template_format.format(epoch, step, step / ModelFittingTestCaseProgress.num_steps * 100,
progress_Bar)
self.assertRegex(step_update, regex_filled)
# last print update templating different
last_print_regex = r".*Train steps:.*5.*Val steps:.*5.*[0-9]*\.[0-9][0-9]s"
self.assertRegex(steps_update[-1], last_print_regex)
def test_fitting_complete_display_test_with_no_progress_bar_no_coloring(self):
_ = self.model.fit_generator(self.train_generator,
self.valid_generator,
epochs=1,
steps_per_epoch=ModelFittingTestCaseProgress.num_steps,
validation_steps=ModelFittingTestCaseProgress.num_steps,
callbacks=[self.mock_callback],
progress_options=dict(coloring=False, progress_bar=False))
# We split per step update
steps_update = self.test_out.getvalue().strip().split("\r")
# we don't validate the templating of metrics since tested before
template_format = r".*Epoch:.*{}\/1.*Step:.*{}\/5.*ETA:"
epoch = 1
# the 5 train steps
for step, step_update in enumerate(steps_update[:ModelFittingTestCaseProgress.num_steps]):
step += 1
regex_filled = template_format.format(epoch, step, step / ModelFittingTestCaseProgress.num_steps * 100)
self.assertRegex(step_update, regex_filled)
# The 5 val steps
for step, step_update in enumerate(steps_update[ModelFittingTestCaseProgress.num_steps:-1]):
step += 1
regex_filled = template_format.format(epoch, step, step / ModelFittingTestCaseProgress.num_steps * 100)
self.assertRegex(step_update, regex_filled)
# last print update templating different
last_print_regex = r".*Train steps:.*5.*Val steps:.*5.*[0-9]*\.[0-9][0-9]s"
self.assertRegex(steps_update[-1], last_print_regex)
def test_evaluate_complete_display_test_with_progress_bar_coloring(self):
# we use the same color for all components for simplicity
coloring = {
"text_color": 'WHITE',
"ratio_color": "WHITE",
"metric_value_color": "WHITE",
"time_color": "WHITE",
"progress_bar_color": "WHITE"
}
_, _ = self.model.evaluate_generator(self.test_generator,
steps=ModelFittingTestCaseProgress.num_steps,
callbacks=[self.mock_callback],
verbose=True,
progress_options=dict(coloring=coloring, progress_bar=True))
# We split per step update
steps_update = self.test_out.getvalue().strip().split("\r")
# we don't validate the templating of metrics since tested before
template_format = r".*\[37mStep:.*{}\/5.*{:6.2f}\%.*|{}|.*ETA:"
for step, step_update in enumerate(steps_update[:-1]):
step += 1
progress_Bar = "\u2588" * step * 2 + " " * (20 - step * 2)
regex_filled = template_format.format(step, step / ModelFittingTestCaseProgress.num_steps * 100,
progress_Bar)
self.assertRegex(step_update, regex_filled)
# last print update templating different
last_print_regex = r".*\[37mTest steps:.*5.*[0-9]*\.[0-9][0-9]s"
self.assertRegex(steps_update[-1], last_print_regex)
def test_evaluate_complete_display_test_with_progress_bar_no_coloring(self):
_, _ = self.model.evaluate_generator(self.test_generator,
steps=ModelFittingTestCaseProgress.num_steps,
callbacks=[self.mock_callback],
verbose=True,
progress_options=dict(coloring=False, progress_bar=True))
# We split per step update
steps_update = self.test_out.getvalue().strip().split("\r")
# we don't validate the templating of metrics since tested before
template_format = r".*Step:.*{}\/5.*{:6.2f}\%.*|{}|.*ETA:"
for step, step_update in enumerate(steps_update[:-1]):
step += 1
progress_Bar = "\u2588" * step * 2 + " " * (20 - step * 2)
regex_filled = template_format.format(step, step / ModelFittingTestCaseProgress.num_steps * 100,
progress_Bar)
self.assertRegex(step_update, regex_filled)
# last print update templating different
last_print_regex = r".*Test steps:.*5.*[0-9]*\.[0-9][0-9]s"
self.assertRegex(steps_update[-1], last_print_regex)
def test_evaluate_complete_display_test_with_no_progress_bar_no_coloring(self):
_, _ = self.model.evaluate_generator(self.test_generator,
steps=ModelFittingTestCaseProgress.num_steps,
callbacks=[self.mock_callback],
verbose=True,
progress_options=dict(coloring=False, progress_bar=False))
# We split per step update
steps_update = self.test_out.getvalue().strip().split("\r")
# we don't validate the templating of metrics since tested before
template_format = r".*Step:.*{}\/5.*ETA:"
for step, step_update in enumerate(steps_update[:-1]):
step += 1
regex_filled = template_format.format(step, step / ModelFittingTestCaseProgress.num_steps * 100)
self.assertRegex(step_update, regex_filled)
# last print update templating different
last_print_regex = r".*Test steps:.*5.*[0-9]*\.[0-9][0-9]s"
self.assertRegex(steps_update[-1], last_print_regex)
def test_evaluate_with_no_metric(self):
model = Model(self.pytorch_network, self.optimizer, self.loss_function)
x = torch.rand(ModelTest.evaluate_dataset_len, 1)
y = torch.rand(ModelTest.evaluate_dataset_len, 1)
loss = model.evaluate(x, y, batch_size=ModelTest.batch_size)
self.assertEqual(type(loss), float)
class ModelTest(ModelFittingTestCase):
# pylint: disable=too-many-public-methods
def setUp(self):
super().setUp()
torch.manual_seed(42)
self.pytorch_network = nn.Linear(1, 1)
self.loss_function = nn.MSELoss()
self.optimizer = torch.optim.Adam(self.pytorch_network.parameters(),
lr=1e-3)
self.batch_metrics = [
some_batch_metric_1, ('custom_name', some_batch_metric_2),
repeat_batch_metric, repeat_batch_metric
]
self.batch_metrics_names = [
'some_batch_metric_1', 'custom_name', 'repeat_batch_metric1',
'repeat_batch_metric2'
]
self.batch_metrics_values = [
some_metric_1_value, some_metric_2_value,
repeat_batch_metric_value, repeat_batch_metric_value
]
self.epoch_metrics = [SomeConstantEpochMetric()]
self.epoch_metrics_names = ['some_constant_epoch_metric']
self.epoch_metrics_values = [some_constant_epoch_metric_value]
self.model = Model(self.pytorch_network,
self.optimizer,
self.loss_function,
batch_metrics=self.batch_metrics,
epoch_metrics=self.epoch_metrics)
def test_fitting_tensor_generator(self):
train_generator = some_data_tensor_generator(ModelTest.batch_size)
valid_generator = some_data_tensor_generator(ModelTest.batch_size)
logs = self.model.fit_generator(
train_generator,
valid_generator,
epochs=ModelTest.epochs,
steps_per_epoch=ModelTest.steps_per_epoch,
validation_steps=ModelTest.steps_per_epoch,
callbacks=[self.mock_callback])
params = {
'epochs': ModelTest.epochs,
'steps': ModelTest.steps_per_epoch,
'valid_steps': ModelTest.steps_per_epoch
}
self._test_callbacks_train(params,
logs,
valid_steps=ModelTest.steps_per_epoch)
def test_fitting_without_valid_generator(self):
train_generator = some_data_tensor_generator(ModelTest.batch_size)
logs = self.model.fit_generator(
train_generator,
None,
epochs=ModelTest.epochs,
steps_per_epoch=ModelTest.steps_per_epoch,
callbacks=[self.mock_callback])
params = {
'epochs': ModelTest.epochs,
'steps': ModelTest.steps_per_epoch
}
self._test_callbacks_train(params, logs, has_valid=False)
def test_correct_optim_calls_1_batch_per_step(self):
train_generator = some_data_tensor_generator(ModelTest.batch_size)
mocked_optimizer = some_mocked_optimizer()
mocked_optim_model = Model(self.pytorch_network,
mocked_optimizer,
self.loss_function,
batch_metrics=self.batch_metrics,
epoch_metrics=self.epoch_metrics)
mocked_optim_model.fit_generator(train_generator,
None,
epochs=1,
steps_per_epoch=1,
batches_per_step=1)
self.assertEqual(1, mocked_optimizer.step.call_count)
self.assertEqual(1, mocked_optimizer.zero_grad.call_count)
def test_correct_optim_calls__valid_n_batches_per_step(self):
n_batches = 5
items_per_batch = int(ModelTest.batch_size / n_batches)
x = torch.rand(n_batches, items_per_batch, 1)
y = torch.rand(n_batches, items_per_batch, 1)
mocked_optimizer = some_mocked_optimizer()
mocked_optim_model = Model(self.pytorch_network,
mocked_optimizer,
self.loss_function,
batch_metrics=self.batch_metrics,
epoch_metrics=self.epoch_metrics)
mocked_optim_model.fit_generator(list(zip(x, y)),
None,
epochs=1,
batches_per_step=n_batches)
self.assertEqual(1, mocked_optimizer.step.call_count)
self.assertEqual(1, mocked_optimizer.zero_grad.call_count)
def test_fitting_generator_n_batches_per_step(self):
total_batch_size = 6
x = torch.rand(1, total_batch_size, 1)
y = torch.rand(1, total_batch_size, 1)
initial_params = self.model.get_weight_copies()
self.model.fit_generator(list(zip(x, y)),
None,
epochs=1,
batches_per_step=1)
expected_params = self.model.get_weight_copies()
for mini_batch_size in [1, 2, 5]:
self.model.set_weights(initial_params)
n_batches_per_step = int(total_batch_size / mini_batch_size)
x.resize_((n_batches_per_step, mini_batch_size, 1))
y.resize_((n_batches_per_step, mini_batch_size, 1))
self.model.fit_generator(list(zip(x, y)),
None,
epochs=1,
batches_per_step=n_batches_per_step)
returned_params = self.model.get_weight_copies()
self.assertEqual(returned_params.keys(), expected_params.keys())
for k in expected_params.keys():
np.testing.assert_almost_equal(returned_params[k].numpy(),
expected_params[k].numpy(),
decimal=4)
def test_fitting_generator_n_batches_per_step_higher_than_num_batches(
self):
total_batch_size = 6
x = torch.rand(1, total_batch_size, 1)
y = torch.rand(1, total_batch_size, 1)
initial_params = self.model.get_weight_copies()
self.model.fit_generator(list(zip(x, y)),
None,
epochs=1,
batches_per_step=1)
expected_params = self.model.get_weight_copies()
self.model.set_weights(initial_params)
self.model.fit_generator(list(zip(x, y)),
None,
epochs=1,
batches_per_step=2)
returned_params = self.model.get_weight_copies()
self.assertEqual(returned_params.keys(), expected_params.keys())
for k in expected_params.keys():
np.testing.assert_almost_equal(returned_params[k].numpy(),
expected_params[k].numpy(),
decimal=4)
def test_fitting_generator_n_batches_per_step_uneven_batches(self):
total_batch_size = 6
x = torch.rand(1, total_batch_size, 1)
y = torch.rand(1, total_batch_size, 1)
initial_params = self.model.get_weight_copies()
self.model.fit_generator(list(zip(x, y)),
None,
epochs=1,
batches_per_step=1)
expected_params = self.model.get_weight_copies()
x.squeeze_(dim=0)
y.squeeze_(dim=0)
uneven_chunk_sizes = [4, 5]
for chunk_size in uneven_chunk_sizes:
self.model.set_weights(initial_params)
splitted_x = x.split(chunk_size)
splitted_y = y.split(chunk_size)
n_batches_per_step = ceil(total_batch_size / chunk_size)
self.model.fit_generator(list(zip(splitted_x, splitted_y)),
None,
epochs=1,
batches_per_step=n_batches_per_step)
returned_params = self.model.get_weight_copies()
self.assertEqual(returned_params.keys(), expected_params.keys())
for k in expected_params.keys():
np.testing.assert_almost_equal(returned_params[k].numpy(),
expected_params[k].numpy(),
decimal=4)
def test_fitting_ndarray_generator(self):
train_generator = some_ndarray_generator(ModelTest.batch_size)
valid_generator = some_ndarray_generator(ModelTest.batch_size)
logs = self.model.fit_generator(
train_generator,
valid_generator,
epochs=ModelTest.epochs,
steps_per_epoch=ModelTest.steps_per_epoch,
validation_steps=ModelTest.steps_per_epoch,
callbacks=[self.mock_callback])
params = {
'epochs': ModelTest.epochs,
'steps': ModelTest.steps_per_epoch,
'valid_steps': ModelTest.steps_per_epoch
}
self._test_callbacks_train(params,
logs,
valid_steps=ModelTest.steps_per_epoch)
def test_fitting_with_data_loader(self):
train_real_steps_per_epoch = 30
train_batch_size = ModelTest.batch_size
train_final_batch_missing_samples = 7
train_size = train_real_steps_per_epoch * train_batch_size - \
train_final_batch_missing_samples
train_x = torch.rand(train_size, 1)
train_y = torch.rand(train_size, 1)
train_dataset = TensorDataset(train_x, train_y)
train_generator = DataLoader(train_dataset, train_batch_size)
valid_real_steps_per_epoch = 10
valid_batch_size = 15
valid_final_batch_missing_samples = 3
valid_size = valid_real_steps_per_epoch * valid_batch_size - \
valid_final_batch_missing_samples
valid_x = torch.rand(valid_size, 1)
valid_y = torch.rand(valid_size, 1)
valid_dataset = TensorDataset(valid_x, valid_y)
valid_generator = DataLoader(valid_dataset, valid_batch_size)
logs = self.model.fit_generator(train_generator,
valid_generator,
epochs=ModelTest.epochs,
steps_per_epoch=None,
validation_steps=None,
callbacks=[self.mock_callback])
params = {
'epochs': ModelTest.epochs,
'steps': train_real_steps_per_epoch,
'valid_steps': valid_real_steps_per_epoch
}
self._test_callbacks_train(params, logs)
def test_fitting_generator_calls(self):
train_real_steps_per_epoch = 30
train_batch_size = ModelTest.batch_size
train_final_batch_missing_samples = 7
train_size = train_real_steps_per_epoch * train_batch_size - \
train_final_batch_missing_samples
train_x = torch.rand(train_size, 1)
train_y = torch.rand(train_size, 1)
train_dataset = TensorDataset(train_x, train_y)
train_generator = DataLoader(train_dataset, train_batch_size)
valid_real_steps_per_epoch = 10
valid_batch_size = 15
valid_final_batch_missing_samples = 3
valid_size = valid_real_steps_per_epoch * valid_batch_size - \
valid_final_batch_missing_samples
valid_x = torch.rand(valid_size, 1)
valid_y = torch.rand(valid_size, 1)
valid_dataset = TensorDataset(valid_x, valid_y)
valid_generator = DataLoader(valid_dataset, valid_batch_size)
mock_train_generator = IterableMock(train_generator)
mock_valid_generator = IterableMock(valid_generator)
self.model.fit_generator(mock_train_generator,
mock_valid_generator,
epochs=ModelTest.epochs)
expected_train_calls = ['__len__'] + \
(['__iter__'] + ['__next__'] * train_real_steps_per_epoch) * ModelTest.epochs
expected_valid_calls = ['__len__'] + \
(['__iter__'] + ['__next__'] * valid_real_steps_per_epoch) * ModelTest.epochs
self.assertEqual(mock_train_generator.calls, expected_train_calls)
self.assertEqual(mock_valid_generator.calls, expected_valid_calls)
def test_fitting_generator_calls_with_longer_validation_set(self):
train_real_steps_per_epoch = 30
train_batch_size = ModelTest.batch_size
train_final_batch_missing_samples = 7
train_size = train_real_steps_per_epoch * train_batch_size - \
train_final_batch_missing_samples
train_x = torch.rand(train_size, 1)
train_y = torch.rand(train_size, 1)
train_dataset = TensorDataset(train_x, train_y)
train_generator = DataLoader(train_dataset, train_batch_size)
valid_real_steps_per_epoch = 40
valid_batch_size = 15
valid_final_batch_missing_samples = 3
valid_size = valid_real_steps_per_epoch * valid_batch_size - \
valid_final_batch_missing_samples
valid_x = torch.rand(valid_size, 1)
valid_y = torch.rand(valid_size, 1)
valid_dataset = TensorDataset(valid_x, valid_y)
valid_generator = DataLoader(valid_dataset, valid_batch_size)
mock_train_generator = IterableMock(train_generator)
mock_valid_generator = IterableMock(valid_generator)
self.model.fit_generator(mock_train_generator,
mock_valid_generator,
epochs=ModelTest.epochs)
expected_train_calls = ['__len__'] + \
(['__iter__'] + ['__next__'] * train_real_steps_per_epoch) * ModelTest.epochs
expected_valid_calls = ['__len__'] + \
(['__iter__'] + ['__next__'] * valid_real_steps_per_epoch) * ModelTest.epochs
self.assertEqual(mock_train_generator.calls, expected_train_calls)
self.assertEqual(mock_valid_generator.calls, expected_valid_calls)
def test_fitting_with_tensor(self):
train_real_steps_per_epoch = 30
train_batch_size = ModelTest.batch_size
train_final_batch_missing_samples = 7
train_size = train_real_steps_per_epoch * train_batch_size - \
train_final_batch_missing_samples
train_x = torch.rand(train_size, 1)
train_y = torch.rand(train_size, 1)
valid_real_steps_per_epoch = 10
# valid_batch_size will be the same as train_batch_size in the fit method.
valid_batch_size = train_batch_size
valid_final_batch_missing_samples = 3
valid_size = valid_real_steps_per_epoch * valid_batch_size - \
valid_final_batch_missing_samples
valid_x = torch.rand(valid_size, 1)
valid_y = torch.rand(valid_size, 1)
logs = self.model.fit(train_x,
train_y,
validation_data=(valid_x, valid_y),
epochs=ModelTest.epochs,
batch_size=train_batch_size,
steps_per_epoch=None,
validation_steps=None,
callbacks=[self.mock_callback])
params = {
'epochs': ModelTest.epochs,
'steps': train_real_steps_per_epoch,
'valid_steps': valid_real_steps_per_epoch
}
self._test_callbacks_train(params, logs)
def test_fitting_with_np_array(self):
train_real_steps_per_epoch = 30
train_batch_size = ModelTest.batch_size
train_final_batch_missing_samples = 7
train_size = train_real_steps_per_epoch * train_batch_size - \
train_final_batch_missing_samples
train_x = np.random.rand(train_size, 1).astype(np.float32)
train_y = np.random.rand(train_size, 1).astype(np.float32)
valid_real_steps_per_epoch = 10
# valid_batch_size will be the same as train_batch_size in the fit method.
valid_batch_size = train_batch_size
valid_final_batch_missing_samples = 3
valid_size = valid_real_steps_per_epoch * valid_batch_size - \
valid_final_batch_missing_samples
valid_x = np.random.rand(valid_size, 1).astype(np.float32)
valid_y = np.random.rand(valid_size, 1).astype(np.float32)
logs = self.model.fit(train_x,
train_y,
validation_data=(valid_x, valid_y),
epochs=ModelTest.epochs,
batch_size=train_batch_size,
steps_per_epoch=None,
validation_steps=None,
callbacks=[self.mock_callback])
params = {
'epochs': ModelTest.epochs,
'steps': train_real_steps_per_epoch,
'valid_steps': valid_real_steps_per_epoch
}
self._test_callbacks_train(params, logs)
def test_fitting_with_generator_with_len(self):
train_real_steps_per_epoch = 30
train_generator = SomeDataGeneratorWithLen(
batch_size=ModelTest.batch_size,
length=train_real_steps_per_epoch,
num_missing_samples=7)
valid_real_steps_per_epoch = 10
valid_generator = SomeDataGeneratorWithLen(
batch_size=15,
length=valid_real_steps_per_epoch,
num_missing_samples=3)
logs = self.model.fit_generator(train_generator,
valid_generator,
epochs=ModelTest.epochs,
steps_per_epoch=None,
validation_steps=None,
callbacks=[self.mock_callback])
params = {
'epochs': ModelTest.epochs,
'steps': train_real_steps_per_epoch,
'valid_steps': valid_real_steps_per_epoch
}
self._test_callbacks_train(params, logs)
def test_fitting_with_generator_with_stop_iteration(self):
train_real_steps_per_epoch = 30
train_generator = SomeDataGeneratorUsingStopIteration(
batch_size=ModelTest.batch_size, length=train_real_steps_per_epoch)
valid_generator = SomeDataGeneratorUsingStopIteration(batch_size=15,
length=10)
logs = self.model.fit_generator(train_generator,
valid_generator,
epochs=ModelTest.epochs,
steps_per_epoch=None,
validation_steps=None,
callbacks=[self.mock_callback])
params = {'epochs': ModelTest.epochs, 'steps': None}
self._test_callbacks_train(params,
logs,
steps=train_real_steps_per_epoch)
def test_tensor_train_on_batch(self):
x = torch.rand(ModelTest.batch_size, 1)
y = torch.rand(ModelTest.batch_size, 1)
loss, metrics = self.model.train_on_batch(x, y)
self.assertEqual(type(loss), float)
self.assertEqual(type(metrics), np.ndarray)
self.assertEqual(metrics.tolist(), self.batch_metrics_values)
def test_train_on_batch_with_pred(self):
x = torch.rand(ModelTest.batch_size, 1)
y = torch.rand(ModelTest.batch_size, 1)
loss, metrics, pred_y = self.model.train_on_batch(x,
y,
return_pred=True)
self.assertEqual(type(loss), float)
self.assertEqual(type(metrics), np.ndarray)
self.assertEqual(metrics.tolist(), self.batch_metrics_values)
self.assertEqual(pred_y.shape, (ModelTest.batch_size, 1))
def test_ndarray_train_on_batch(self):
x = np.random.rand(ModelTest.batch_size, 1).astype(np.float32)
y = np.random.rand(ModelTest.batch_size, 1).astype(np.float32)
loss, metrics = self.model.train_on_batch(x, y)
self.assertEqual(type(loss), float)
self.assertEqual(type(metrics), np.ndarray)
self.assertEqual(metrics.tolist(), self.batch_metrics_values)
def test_evaluate(self):
x = torch.rand(ModelTest.evaluate_dataset_len, 1)
y = torch.rand(ModelTest.evaluate_dataset_len, 1)
loss, metrics = self.model.evaluate(x,
y,
batch_size=ModelTest.batch_size)
self.assertEqual(type(loss), float)
self.assertEqual(type(metrics), np.ndarray)
self.assertEqual(metrics.tolist(),
self.batch_metrics_values + self.epoch_metrics_values)
def test_evaluate_with_pred(self):
x = torch.rand(ModelTest.evaluate_dataset_len, 1)
y = torch.rand(ModelTest.evaluate_dataset_len, 1)
# We also test the unpacking.
_, _, pred_y = self.model.evaluate(x,
y,
batch_size=ModelTest.batch_size,
return_pred=True)
self.assertEqual(pred_y.shape, (ModelTest.evaluate_dataset_len, 1))
def test_evaluate_with_callback(self):
x = torch.rand(ModelTest.evaluate_dataset_len, 1)
y = torch.rand(ModelTest.evaluate_dataset_len, 1)
# We also test the unpacking.
_, _, pred_y = self.model.evaluate(x,
y,
batch_size=ModelTest.batch_size,
return_pred=True,
callbacks=[self.mock_callback])
self.assertEqual(pred_y.shape, (ModelTest.evaluate_dataset_len, 1))
def test_evaluate_with_return_dict(self):
x = torch.rand(ModelTest.evaluate_dataset_len, 1)
y = torch.rand(ModelTest.evaluate_dataset_len, 1)
logs = self.model.evaluate(x,
y,
batch_size=ModelTest.batch_size,
return_dict_format=True)
self._test_return_dict_logs(logs)
def test_evaluate_with_np_array(self):
x = np.random.rand(ModelTest.evaluate_dataset_len,
1).astype(np.float32)
y = np.random.rand(ModelTest.evaluate_dataset_len,
1).astype(np.float32)
loss, metrics, pred_y = self.model.evaluate(
x, y, batch_size=ModelTest.batch_size, return_pred=True)
self.assertEqual(type(loss), float)
self.assertEqual(type(metrics), np.ndarray)
self.assertEqual(metrics.tolist(),
self.batch_metrics_values + self.epoch_metrics_values)
self.assertEqual(pred_y.shape, (ModelTest.evaluate_dataset_len, 1))
def test_evaluate_data_loader(self):
x = torch.rand(ModelTest.evaluate_dataset_len, 1)
y = torch.rand(ModelTest.evaluate_dataset_len, 1)
dataset = TensorDataset(x, y)
generator = DataLoader(dataset, ModelTest.batch_size)
loss, metrics, pred_y = self.model.evaluate_generator(generator,
return_pred=True)
self.assertEqual(type(loss), float)
self.assertEqual(type(metrics), np.ndarray)
self.assertEqual(metrics.tolist(),
self.batch_metrics_values + self.epoch_metrics_values)
self.assertEqual(pred_y.shape, (ModelTest.evaluate_dataset_len, 1))
def test_evaluate_generator(self):
num_steps = 10
generator = some_data_tensor_generator(ModelTest.batch_size)
loss, metrics, pred_y = self.model.evaluate_generator(generator,
steps=num_steps,
return_pred=True)
self.assertEqual(type(loss), float)
self.assertEqual(type(metrics), np.ndarray)
self.assertEqual(metrics.tolist(),
self.batch_metrics_values + self.epoch_metrics_values)
self.assertEqual(type(pred_y), np.ndarray)
self.assertEqual(pred_y.shape, (num_steps * ModelTest.batch_size, 1))
def test_evaluate_generator_with_stop_iteration(self):
test_generator = SomeDataGeneratorUsingStopIteration(
ModelTest.batch_size, 10)
loss, _ = self.model.evaluate_generator(test_generator)
self.assertEqual(type(loss), float)
def test_evaluate_generator_with_callback(self):
num_steps = 10
generator = some_data_tensor_generator(ModelTest.batch_size)
self.model.evaluate_generator(generator,
steps=num_steps,
callbacks=[self.mock_callback])
params = {'steps': ModelTest.epochs}
self._test_callbacks_test(params)
def test_evaluate_generator_with_return_dict(self):
num_steps = 10
generator = some_data_tensor_generator(ModelTest.batch_size)
logs = self.model.evaluate_generator(generator,
steps=num_steps,
return_dict_format=True)
self._test_return_dict_logs(logs)
def test_evaluate_generator_with_ground_truth(self):
num_steps = 10
generator = some_data_tensor_generator(ModelTest.batch_size)
loss, metrics, pred_y, true_y = self.model.evaluate_generator(
generator,
steps=num_steps,
return_pred=True,
return_ground_truth=True)
self.assertEqual(type(loss), float)
self.assertEqual(type(metrics), np.ndarray)
self.assertEqual(metrics.tolist(),
self.batch_metrics_values + self.epoch_metrics_values)
self.assertEqual(type(pred_y), np.ndarray)
self.assertEqual(type(true_y), np.ndarray)
self.assertEqual(pred_y.shape, (num_steps * ModelTest.batch_size, 1))
self.assertEqual(true_y.shape, (num_steps * ModelTest.batch_size, 1))
def test_evaluate_generator_with_no_concatenation(self):
num_steps = 10
generator = some_data_tensor_generator(ModelTest.batch_size)
loss, metrics, pred_y, true_y = self.model.evaluate_generator(
generator,
steps=num_steps,
return_pred=True,
return_ground_truth=True,
concatenate_returns=False)
self.assertEqual(type(loss), float)
self.assertEqual(type(metrics), np.ndarray)
self.assertEqual(metrics.tolist(),
self.batch_metrics_values + self.epoch_metrics_values)
self.assertEqual(type(pred_y), list)
for pred in pred_y:
self.assertEqual(type(pred), np.ndarray)
self.assertEqual(pred.shape, (ModelTest.batch_size, 1))
self.assertEqual(type(true_y), list)
for true in true_y:
self.assertEqual(type(true), np.ndarray)
self.assertEqual(true.shape, (ModelTest.batch_size, 1))
def test_evaluate_with_only_one_metric(self):
model = Model(self.pytorch_network,
self.optimizer,
self.loss_function,
batch_metrics=self.batch_metrics[:1])
x = torch.rand(ModelTest.evaluate_dataset_len, 1)
y = torch.rand(ModelTest.evaluate_dataset_len, 1)
loss, first_metric = model.evaluate(x,
y,
batch_size=ModelTest.batch_size)
self.assertEqual(type(loss), float)
self.assertEqual(type(first_metric), float)
self.assertEqual(first_metric, some_metric_1_value)
def test_metrics_integration(self):
num_steps = 10
model = Model(self.pytorch_network,
self.optimizer,
self.loss_function,
batch_metrics=[F.mse_loss])
train_generator = some_data_tensor_generator(ModelTest.batch_size)
valid_generator = some_data_tensor_generator(ModelTest.batch_size)
model.fit_generator(train_generator,
valid_generator,
epochs=ModelTest.epochs,
steps_per_epoch=ModelTest.steps_per_epoch,
validation_steps=ModelTest.steps_per_epoch,
callbacks=[self.mock_callback])
generator = some_data_tensor_generator(ModelTest.batch_size)
loss, mse = model.evaluate_generator(generator, steps=num_steps)
self.assertEqual(type(loss), float)
self.assertEqual(type(mse), float)
def test_epoch_metrics_integration(self):
model = Model(self.pytorch_network,
self.optimizer,
self.loss_function,
epoch_metrics=[SomeEpochMetric()])
train_generator = some_data_tensor_generator(ModelTest.batch_size)
valid_generator = some_data_tensor_generator(ModelTest.batch_size)
logs = model.fit_generator(train_generator,
valid_generator,
epochs=1,
steps_per_epoch=ModelTest.steps_per_epoch,
validation_steps=ModelTest.steps_per_epoch)
actual_value = logs[-1]['some_epoch_metric']
val_actual_value = logs[-1]['val_some_epoch_metric']
expected_value = 5
self.assertEqual(val_actual_value, expected_value)
self.assertEqual(actual_value, expected_value)
def test_evaluate_with_no_metric(self):
model = Model(self.pytorch_network, self.optimizer, self.loss_function)
x = torch.rand(ModelTest.evaluate_dataset_len, 1)
y = torch.rand(ModelTest.evaluate_dataset_len, 1)
loss = model.evaluate(x, y, batch_size=ModelTest.batch_size)
self.assertEqual(type(loss), float)
def test_tensor_evaluate_on_batch(self):
x = torch.rand(ModelTest.batch_size, 1)
y = torch.rand(ModelTest.batch_size, 1)
loss, metrics = self.model.evaluate_on_batch(x, y)
self.assertEqual(type(loss), float)
self.assertEqual(type(metrics), np.ndarray)
self.assertEqual(metrics.tolist(), self.batch_metrics_values)
def test_evaluate_on_batch_with_pred(self):
x = torch.rand(ModelTest.batch_size, 1)
y = torch.rand(ModelTest.batch_size, 1)
loss, metrics, pred_y = self.model.evaluate_on_batch(x,
y,
return_pred=True)
self.assertEqual(type(loss), float)
self.assertEqual(type(metrics), np.ndarray)
self.assertEqual(metrics.tolist(), self.batch_metrics_values)
self.assertEqual(pred_y.shape, (ModelTest.batch_size, 1))
def test_ndarray_evaluate_on_batch(self):
x = np.random.rand(ModelTest.batch_size, 1).astype(np.float32)
y = np.random.rand(ModelTest.batch_size, 1).astype(np.float32)
loss, metrics = self.model.evaluate_on_batch(x, y)
self.assertEqual(type(loss), float)
self.assertEqual(type(metrics), np.ndarray)
self.assertEqual(metrics.tolist(), self.batch_metrics_values)
def test_predict(self):
x = torch.rand(ModelTest.evaluate_dataset_len, 1)
pred_y = self.model.predict(x, batch_size=ModelTest.batch_size)
self.assertEqual(pred_y.shape, (ModelTest.evaluate_dataset_len, 1))
def test_predict_with_np_array(self):
x = np.random.rand(ModelTest.evaluate_dataset_len,
1).astype(np.float32)
pred_y = self.model.predict(x, batch_size=ModelTest.batch_size)
self.assertEqual(type(pred_y), np.ndarray)
self.assertEqual(pred_y.shape, (ModelTest.evaluate_dataset_len, 1))
def test_predict_data_loader(self):
x = torch.rand(ModelTest.evaluate_dataset_len, 1)
generator = DataLoader(x, ModelTest.batch_size)
pred_y = self.model.predict_generator(generator)
self.assertEqual(type(pred_y), np.ndarray)
self.assertEqual(pred_y.shape, (ModelTest.evaluate_dataset_len, 1))
def test_predict_generator(self):
num_steps = 10
generator = some_data_tensor_generator(ModelTest.batch_size)
generator = (x for x, _ in generator)
pred_y = self.model.predict_generator(generator, steps=num_steps)
self.assertEqual(type(pred_y), np.ndarray)
self.assertEqual(pred_y.shape, (num_steps * ModelTest.batch_size, 1))
def test_predict_generator_with_no_concatenation(self):
num_steps = 10
generator = some_data_tensor_generator(ModelTest.batch_size)
generator = (x for x, _ in generator)
pred_y = self.model.predict_generator(generator,
steps=num_steps,
concatenate_returns=False)
self.assertEqual(type(pred_y), list)
for pred in pred_y:
self.assertEqual(type(pred), np.ndarray)
self.assertEqual(pred.shape, (ModelTest.batch_size, 1))
def test_tensor_predict_on_batch(self):
x = torch.rand(ModelTest.batch_size, 1)
pred_y = self.model.predict_on_batch(x)
self.assertEqual(pred_y.shape, (ModelTest.batch_size, 1))
def test_ndarray_predict_on_batch(self):
x = np.random.rand(ModelTest.batch_size, 1).astype(np.float32)
pred_y = self.model.predict_on_batch(x)
self.assertEqual(pred_y.shape, (ModelTest.batch_size, 1))
@skipIf(not torch.cuda.is_available(), "no gpu available")
def test_cpu_cuda(self):
train_generator = some_data_tensor_generator(ModelTest.batch_size)
valid_generator = some_data_tensor_generator(ModelTest.batch_size)
self._capture_output()
with torch.cuda.device(ModelTest.cuda_device):
self.model.cuda()
self.model.fit_generator(
train_generator,
valid_generator,
epochs=ModelTest.epochs,
steps_per_epoch=ModelTest.steps_per_epoch,
validation_steps=ModelTest.steps_per_epoch,
callbacks=[self.mock_callback])
# The context manager is also used here because of this bug:
# https://github.com/pytorch/pytorch/issues/7320
with torch.cuda.device(ModelTest.cuda_device):
self.model.cuda(ModelTest.cuda_device)
self._test_device(
torch.device('cuda:' + str(ModelTest.cuda_device)))
self.model.fit_generator(
train_generator,
valid_generator,
epochs=ModelTest.epochs,
steps_per_epoch=ModelTest.steps_per_epoch,
validation_steps=ModelTest.steps_per_epoch,
callbacks=[self.mock_callback])
self.model.cpu()
self._test_device(torch.device('cpu'))
self.model.fit_generator(
train_generator,
valid_generator,
epochs=ModelTest.epochs,
steps_per_epoch=ModelTest.steps_per_epoch,
validation_steps=ModelTest.steps_per_epoch,
callbacks=[self.mock_callback])
self.model.to(torch.device('cuda:' + str(ModelTest.cuda_device)))
self._test_device(
torch.device('cuda:' + str(ModelTest.cuda_device)))
self.model.fit_generator(
train_generator,
valid_generator,
epochs=ModelTest.epochs,
steps_per_epoch=ModelTest.steps_per_epoch,
validation_steps=ModelTest.steps_per_epoch,
callbacks=[self.mock_callback])
self.model.to(torch.device('cpu'))
self._test_device(torch.device('cpu'))
self.model.fit_generator(
train_generator,
valid_generator,
epochs=ModelTest.epochs,
steps_per_epoch=ModelTest.steps_per_epoch,
validation_steps=ModelTest.steps_per_epoch,
callbacks=[self.mock_callback])
def test_get_batch_size(self):
batch_size = ModelTest.batch_size
x = np.random.rand(batch_size, 1).astype(np.float32)
y = np.random.rand(batch_size, 1).astype(np.float32)
batch_size2 = ModelTest.batch_size + 1
x2 = np.random.rand(batch_size2, 1).astype(np.float32)
y2 = np.random.rand(batch_size2, 1).astype(np.float32)
other_batch_size = batch_size2 + 1
inf_batch_size = self.model.get_batch_size(x, y)
self.assertEqual(inf_batch_size, batch_size)
inf_batch_size = self.model.get_batch_size(x2, y2)
self.assertEqual(inf_batch_size, batch_size2)
inf_batch_size = self.model.get_batch_size(x, y2)
self.assertEqual(inf_batch_size, batch_size)
inf_batch_size = self.model.get_batch_size(x2, y)
self.assertEqual(inf_batch_size, batch_size2)
inf_batch_size = self.model.get_batch_size((x, x2), y)
self.assertEqual(inf_batch_size, batch_size)
inf_batch_size = self.model.get_batch_size((x2, x), y)
self.assertEqual(inf_batch_size, batch_size)
inf_batch_size = self.model.get_batch_size((x, x2), (y, y2))
self.assertEqual(inf_batch_size, batch_size)
inf_batch_size = self.model.get_batch_size((x2, x), (y, y2))
self.assertEqual(inf_batch_size, batch_size2)
inf_batch_size = self.model.get_batch_size([x, x2], y)
self.assertEqual(inf_batch_size, batch_size)
inf_batch_size = self.model.get_batch_size([x2, x], y)
self.assertEqual(inf_batch_size, batch_size)
inf_batch_size = self.model.get_batch_size([x, x2], [y, y2])
self.assertEqual(inf_batch_size, batch_size)
inf_batch_size = self.model.get_batch_size([x2, x], [y, y2])
self.assertEqual(inf_batch_size, batch_size2)
inf_batch_size = self.model.get_batch_size(
{
'batch_size': other_batch_size,
'x': x
}, {'y': y})
self.assertEqual(inf_batch_size, other_batch_size)
inf_batch_size = self.model.get_batch_size({'x': x}, {
'batch_size': other_batch_size,
'y': y
})
self.assertEqual(inf_batch_size, other_batch_size)
inf_batch_size = self.model.get_batch_size({'x': x}, {'y': y})
self.assertEqual(inf_batch_size, batch_size)
inf_batch_size = self.model.get_batch_size(
OrderedDict([('x1', x), ('x2', x2)]), {'y': y})
self.assertEqual(inf_batch_size, batch_size)
inf_batch_size = self.model.get_batch_size(
OrderedDict([('x1', x2), ('x2', x)]), {'y': y})
self.assertEqual(inf_batch_size, batch_size2)
inf_batch_size = self.model.get_batch_size([1, 2, 3], {'y': y})
self.assertEqual(inf_batch_size, batch_size)
def test_get_batch_size_warning(self):
with warnings.catch_warnings(record=True) as w:
warnings.simplefilter("always")
inf_batch_size = self.model.get_batch_size([1, 2, 3], [4, 5, 6])
self.assertEqual(inf_batch_size, 1)
self.assertEqual(len(w), 1)
with warnings.catch_warnings(record=True) as w:
warnings.simplefilter("always")
warning_settings['batch_size'] = 'ignore'
inf_batch_size = self.model.get_batch_size([1, 2, 3], [4, 5, 6])
self.assertEqual(inf_batch_size, 1)
self.assertEqual(len(w), 0)
class ModelMultiInputTest(ModelFittingTestCase):
def setUp(self):
super().setUp()
torch.manual_seed(42)
self.pytorch_network = MultiIOModel(num_input=1, num_output=1)
self.loss_function = nn.MSELoss()
self.optimizer = torch.optim.SGD(self.pytorch_network.parameters(),
lr=1e-3)
self.model = Model(
self.pytorch_network,
self.optimizer,
self.loss_function,
batch_metrics=self.batch_metrics,
epoch_metrics=self.epoch_metrics,
)
def test_fitting_tensor_generator_multi_input(self):
train_generator = some_data_tensor_generator_multi_input(
ModelMultiInputTest.batch_size)
valid_generator = some_data_tensor_generator_multi_input(
ModelMultiInputTest.batch_size)
logs = self.model.fit_generator(
train_generator,
valid_generator,
epochs=ModelMultiInputTest.epochs,
steps_per_epoch=ModelMultiInputTest.steps_per_epoch,
validation_steps=ModelMultiInputTest.steps_per_epoch,
callbacks=[self.mock_callback],
)
params = {
'epochs': ModelMultiInputTest.epochs,
'steps': ModelMultiInputTest.steps_per_epoch,
'valid_steps': ModelMultiInputTest.steps_per_epoch,
}
self._test_callbacks_train(
params, logs, valid_steps=ModelMultiInputTest.steps_per_epoch)
def test_fitting_with_tensor_multi_input(self):
train_real_steps_per_epoch = 30
train_batch_size = ModelMultiInputTest.batch_size
train_final_batch_missing_samples = 7
train_size = train_real_steps_per_epoch * train_batch_size - train_final_batch_missing_samples
train_x = (torch.rand(train_size, 1), torch.rand(train_size, 1))
train_y = torch.rand(train_size, 1)
valid_real_steps_per_epoch = 10
# valid_batch_size will be the same as train_batch_size in the fit method.
valid_batch_size = train_batch_size
valid_final_batch_missing_samples = 3
valid_size = valid_real_steps_per_epoch * valid_batch_size - valid_final_batch_missing_samples
valid_x = (torch.rand(valid_size, 1), torch.rand(valid_size, 1))
valid_y = torch.rand(valid_size, 1)
logs = self.model.fit(
train_x,
train_y,
validation_data=(valid_x, valid_y),
epochs=ModelMultiInputTest.epochs,
batch_size=train_batch_size,
steps_per_epoch=None,
validation_steps=None,
callbacks=[self.mock_callback],
)
params = {
'epochs': ModelMultiInputTest.epochs,
'steps': train_real_steps_per_epoch,
'valid_steps': valid_real_steps_per_epoch,
}
self._test_callbacks_train(params, logs)
def test_tensor_train_on_batch_multi_input(self):
x1 = torch.rand(ModelMultiInputTest.batch_size, 1)
x2 = torch.rand(ModelMultiInputTest.batch_size, 1)
y = torch.rand(ModelMultiInputTest.batch_size, 1)
loss = self.model.train_on_batch((x1, x2), y)
self.assertEqual(type(loss), float)
def test_train_on_batch_with_pred_multi_input(self):
x1 = torch.rand(ModelMultiInputTest.batch_size, 1)
x2 = torch.rand(ModelMultiInputTest.batch_size, 1)
y = torch.rand(ModelMultiInputTest.batch_size, 1)
loss, pred_y = self.model.train_on_batch((x1, x2), y, return_pred=True)
self.assertEqual(type(loss), float)
self.assertEqual(pred_y.shape, (ModelMultiInputTest.batch_size, 1))
def test_ndarray_train_on_batch_multi_input(self):
x1 = np.random.rand(ModelMultiInputTest.batch_size,
1).astype(np.float32)
x2 = np.random.rand(ModelMultiInputTest.batch_size,
1).astype(np.float32)
y = np.random.rand(ModelMultiInputTest.batch_size,
1).astype(np.float32)
loss = self.model.train_on_batch((x1, x2), y)
self.assertEqual(type(loss), float)
def test_evaluate_multi_input(self):
x = (
torch.rand(ModelMultiInputTest.evaluate_dataset_len, 1),
torch.rand(ModelMultiInputTest.evaluate_dataset_len, 1),
)
y = torch.rand(ModelMultiInputTest.evaluate_dataset_len, 1)
loss = self.model.evaluate(x,
y,
batch_size=ModelMultiInputTest.batch_size)
self.assertEqual(type(loss), float)
def test_evaluate_with_pred_multi_input(self):
x = (
torch.rand(ModelMultiInputTest.evaluate_dataset_len, 1),
torch.rand(ModelMultiInputTest.evaluate_dataset_len, 1),
)
y = torch.rand(ModelMultiInputTest.evaluate_dataset_len, 1)
# We also test the unpacking.
_, pred_y = self.model.evaluate(
x, y, batch_size=ModelMultiInputTest.batch_size, return_pred=True)
self.assertEqual(pred_y.shape,
(ModelMultiInputTest.evaluate_dataset_len, 1))
def test_evaluate_with_np_array_multi_input(self):
x1 = np.random.rand(ModelMultiInputTest.evaluate_dataset_len,
1).astype(np.float32)
x2 = np.random.rand(ModelMultiInputTest.evaluate_dataset_len,
1).astype(np.float32)
x = (x1, x2)
y = np.random.rand(ModelMultiInputTest.evaluate_dataset_len,
1).astype(np.float32)
loss, pred_y = self.model.evaluate(
x, y, batch_size=ModelMultiInputTest.batch_size, return_pred=True)
self.assertEqual(type(loss), float)
self.assertEqual(pred_y.shape,
(ModelMultiInputTest.evaluate_dataset_len, 1))
def test_evaluate_data_loader_multi_input(self):
x1 = torch.rand(ModelMultiInputTest.evaluate_dataset_len, 1)
x2 = torch.rand(ModelMultiInputTest.evaluate_dataset_len, 1)
y = torch.rand(ModelMultiInputTest.evaluate_dataset_len, 1)
dataset = TensorDataset((x1, x2), y)
generator = DataLoader(dataset, ModelMultiInputTest.batch_size)
loss, pred_y = self.model.evaluate_generator(generator,
return_pred=True)
self.assertEqual(type(loss), float)
self.assertEqual(pred_y.shape,
(ModelMultiInputTest.evaluate_dataset_len, 1))
def test_evaluate_generator_multi_input(self):
num_steps = 10
generator = some_data_tensor_generator_multi_input(
ModelMultiInputTest.batch_size)
loss, pred_y = self.model.evaluate_generator(generator,
steps=num_steps,
return_pred=True)
self.assertEqual(type(loss), float)
self.assertEqual(pred_y.shape,
(num_steps * ModelMultiInputTest.batch_size, 1))
def test_tensor_evaluate_on_batch_multi_input(self):
x1 = torch.rand(ModelMultiInputTest.batch_size, 1)
x2 = torch.rand(ModelMultiInputTest.batch_size, 1)
y = torch.rand(ModelMultiInputTest.batch_size, 1)
loss = self.model.evaluate_on_batch((x1, x2), y)
self.assertEqual(type(loss), float)
def test_predict_multi_input(self):
x = (
torch.rand(ModelMultiInputTest.evaluate_dataset_len, 1),
torch.rand(ModelMultiInputTest.evaluate_dataset_len, 1),
)
pred_y = self.model.predict(x,
batch_size=ModelMultiInputTest.batch_size)
self.assertEqual(pred_y.shape,
(ModelMultiInputTest.evaluate_dataset_len, 1))
def test_predict_with_np_array_multi_input(self):
x1 = np.random.rand(ModelMultiInputTest.evaluate_dataset_len,
1).astype(np.float32)
x2 = np.random.rand(ModelMultiInputTest.evaluate_dataset_len,
1).astype(np.float32)
x = (x1, x2)
pred_y = self.model.predict(x,
batch_size=ModelMultiInputTest.batch_size)
self.assertEqual(pred_y.shape,
(ModelMultiInputTest.evaluate_dataset_len, 1))
def test_predict_generator_multi_input(self):
num_steps = 10
generator = some_data_tensor_generator_multi_input(
ModelMultiInputTest.batch_size)
generator = (x for x, _ in generator)
pred_y = self.model.predict_generator(generator, steps=num_steps)
self.assertEqual(type(pred_y), np.ndarray)
self.assertEqual(pred_y.shape,
(num_steps * ModelMultiInputTest.batch_size, 1))
def test_tensor_predict_on_batch_multi_input(self):
x1 = torch.rand(ModelMultiInputTest.batch_size, 1)
x2 = torch.rand(ModelMultiInputTest.batch_size, 1)
pred_y = self.model.predict_on_batch((x1, x2))
self.assertEqual(pred_y.shape, (ModelMultiInputTest.batch_size, 1))
