def test_classification_fiftyone(tmpdir):
tmpdir = Path(tmpdir)
(tmpdir / "a").mkdir()
(tmpdir / "b").mkdir()
_rand_image().save(tmpdir / "a_1.png")
_rand_image().save(tmpdir / "b_1.png")
train_images = [
str(tmpdir / "a_1.png"),
str(tmpdir / "b_1.png"),
]
train_dataset = fo.Dataset.from_dir(str(tmpdir),
dataset_type=fo.types.ImageDirectory)
s1 = train_dataset[train_images[0]]
s2 = train_dataset[train_images[1]]
s1["test"] = fo.Classification(label="1")
s2["test"] = fo.Classification(label="2")
s1.save()
s2.save()
data = ImageClassificationData.from_fiftyone(
train_dataset=train_dataset,
label_field="test",
batch_size=2,
num_workers=0,
image_size=(64, 64),
)
model = ImageClassifier(num_classes=2, backbone="resnet18")
trainer = Trainer(default_root_dir=tmpdir, fast_dev_run=True)
trainer.finetune(model, datamodule=data, strategy="freeze")
def test_from_fiftyone(tmpdir):
tmpdir = Path(tmpdir)
(tmpdir / "a").mkdir()
(tmpdir / "b").mkdir()
_rand_image().save(tmpdir / "a_1.png")
_rand_image().save(tmpdir / "b_1.png")
train_images = [
str(tmpdir / "a_1.png"),
str(tmpdir / "b_1.png"),
]
dataset = fo.Dataset.from_dir(str(tmpdir),
dataset_type=fo.types.ImageDirectory)
s1 = dataset[train_images[0]]
s2 = dataset[train_images[1]]
s1["test"] = fo.Classification(label="1")
s2["test"] = fo.Classification(label="2")
s1.save()
s2.save()
img_data = ImageClassificationData.from_fiftyone(
train_dataset=dataset,
test_dataset=dataset,
val_dataset=dataset,
label_field="test",
batch_size=2,
num_workers=0,
)
assert img_data.train_dataloader() is not None
assert img_data.val_dataloader() is not None
assert img_data.test_dataloader() is not None
# check train data
data = next(iter(img_data.train_dataloader()))
imgs, labels = data['input'], data['target']
assert imgs.shape == (2, 3, 196, 196)
assert labels.shape == (2, )
assert sorted(list(labels.numpy())) == [0, 1]
# check val data
data = next(iter(img_data.val_dataloader()))
imgs, labels = data['input'], data['target']
assert imgs.shape == (2, 3, 196, 196)
assert labels.shape == (2, )
assert sorted(list(labels.numpy())) == [0, 1]
# check test data
data = next(iter(img_data.test_dataloader()))
imgs, labels = data['input'], data['target']
assert imgs.shape == (2, 3, 196, 196)
assert labels.shape == (2, )
assert sorted(list(labels.numpy())) == [0, 1]
dataset_dir="data/hymenoptera_data/train/",
dataset_type=fo.types.ImageClassificationDirectoryTree,
)
val_dataset = fo.Dataset.from_dir(
dataset_dir="data/hymenoptera_data/val/",
dataset_type=fo.types.ImageClassificationDirectoryTree,
)
test_dataset = fo.Dataset.from_dir(
dataset_dir="data/hymenoptera_data/test/",
dataset_type=fo.types.ImageClassificationDirectoryTree,
)
# 3 Load FiftyOne datasets
datamodule = ImageClassificationData.from_fiftyone(
train_dataset=train_dataset,
val_dataset=val_dataset,
test_dataset=test_dataset,
)
# 4 Fine tune a model
model = ImageClassifier(
backbone="resnet18",
num_classes=datamodule.num_classes,
serializer=Labels(),
)
trainer = flash.Trainer(
max_epochs=1,
limit_train_batches=1,
limit_val_batches=1,
)
trainer.finetune(