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test_data.py
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test_data.py
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# Copyright The PyTorch Lightning team.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
import numpy as np
import pytest
from flash.core.data.io.input import DataKeys
from flash.core.utilities.imports import _SKLEARN_AVAILABLE, _TOPIC_CORE_AVAILABLE
from flash.template.classification.data import TemplateData
if _SKLEARN_AVAILABLE:
from sklearn import datasets
@pytest.mark.skipif(not _TOPIC_CORE_AVAILABLE, reason="Not testing core.")
class TestTemplateData:
"""Tests ``TemplateData``."""
num_classes: int = 3
num_features: int = 4
@staticmethod
def test_smoke():
"""A simple test that the class can be instantiated."""
dm = TemplateData(batch_size=2)
assert dm is not None
def test_from_numpy(self):
"""Tests that ``TemplateData`` is properly created when using the ``from_numpy`` method."""
data = np.random.rand(10, self.num_features)
targets = np.random.randint(0, self.num_classes, (10,))
# instantiate the data module
dm = TemplateData.from_numpy(
train_data=data,
train_targets=targets,
val_data=data,
val_targets=targets,
test_data=data,
test_targets=targets,
batch_size=2,
num_workers=0,
)
assert dm is not None
assert dm.train_dataloader() is not None
assert dm.val_dataloader() is not None
assert dm.test_dataloader() is not None
# check training data
data = next(iter(dm.train_dataloader()))
rows, targets = data[DataKeys.INPUT], data[DataKeys.TARGET]
assert rows.shape == (2, self.num_features)
assert targets.shape == (2,)
# check val data
data = next(iter(dm.val_dataloader()))
rows, targets = data[DataKeys.INPUT], data[DataKeys.TARGET]
assert rows.shape == (2, self.num_features)
assert targets.shape == (2,)
# check test data
data = next(iter(dm.test_dataloader()))
rows, targets = data[DataKeys.INPUT], data[DataKeys.TARGET]
assert rows.shape == (2, self.num_features)
assert targets.shape == (2,)
@staticmethod
def test_from_sklearn():
"""Tests that ``TemplateData`` is properly created when using the ``from_sklearn`` method."""
data = datasets.load_iris()
# instantiate the data module
dm = TemplateData.from_sklearn(
train_bunch=data,
val_bunch=data,
test_bunch=data,
batch_size=2,
num_workers=0,
)
assert dm is not None
assert dm.train_dataloader() is not None
assert dm.val_dataloader() is not None
assert dm.test_dataloader() is not None
# check training data
data = next(iter(dm.train_dataloader()))
rows, targets = data[DataKeys.INPUT], data[DataKeys.TARGET]
assert rows.shape == (2, dm.num_features)
assert targets.shape == (2,)
# check val data
data = next(iter(dm.val_dataloader()))
rows, targets = data[DataKeys.INPUT], data[DataKeys.TARGET]
assert rows.shape == (2, dm.num_features)
assert targets.shape == (2,)
# check test data
data = next(iter(dm.test_dataloader()))
rows, targets = data[DataKeys.INPUT], data[DataKeys.TARGET]
assert rows.shape == (2, dm.num_features)
assert targets.shape == (2,)