def test_classification_task_predict_folder_path(tmpdir):
train_dir = Path(tmpdir / "train")
train_dir.mkdir()
_rand_image().save(train_dir / "1.png")
_rand_image().save(train_dir / "2.png")
task = ImageClassifier(num_classes=10)
predictions = task.predict(str(train_dir))
assert len(predictions) == 2
def test_classification_task_predict_folder_path(tmpdir):
train_dir = Path(tmpdir / "train")
train_dir.mkdir()
def _rand_image():
return Image.fromarray(np.random.randint(0, 255, (256, 256, 3), dtype="uint8"))
_rand_image().save(train_dir / "1.png")
_rand_image().save(train_dir / "2.png")
datamodule = ImageClassificationData.from_folders(predict_folder=train_dir)
task = ImageClassifier(num_classes=10)
predictions = task.predict(str(train_dir), data_pipeline=datamodule.data_pipeline)
assert len(predictions) == 2
def test_multilabel(tmpdir):
num_classes = 4
ds = DummyMultiLabelDataset(num_classes)
model = ImageClassifier(num_classes,
multi_label=True,
serializer=Probabilities(multi_label=True))
train_dl = torch.utils.data.DataLoader(ds, batch_size=2)
trainer = Trainer(default_root_dir=tmpdir, fast_dev_run=True)
trainer.finetune(model, train_dl, strategy="freeze_unfreeze")
image, label = ds[0]
predictions = model.predict(image.unsqueeze(0))
assert (torch.tensor(predictions) > 1).sum() == 0
assert (torch.tensor(predictions) < 0).sum() == 0
assert len(predictions[0]) == num_classes == len(label)
assert len(torch.unique(label)) <= 2
def test_available_backbones():
backbones = ImageClassifier.available_backbones()
assert "resnet152" in backbones
class Foo(ImageClassifier):
backbones = None
assert Foo.available_backbones() == []
def test_classification(tmpdir):
data = ImageClassificationData.from_filepaths(
train_filepaths=["a", "b"],
train_labels=[0, 1],
train_transform=lambda x: x,
loader=_dummy_image_loader,
num_workers=0,
batch_size=2,
)
model = ImageClassifier(2, backbone="resnet18")
trainer = Trainer(default_root_dir=tmpdir, fast_dev_run=True)
trainer.finetune(model, datamodule=data, strategy="freeze")
def test_classification(tmpdir):
tmpdir = Path(tmpdir)
(tmpdir / "a").mkdir()
(tmpdir / "b").mkdir()
_rand_image().save(tmpdir / "a" / "a_1.png")
_rand_image().save(tmpdir / "b" / "a_1.png")
data = ImageClassificationData.from_filepaths(
train_filepaths=[tmpdir / "a", tmpdir / "b"],
train_labels=[0, 1],
train_transform={"per_batch_transform": lambda x: x},
num_workers=0,
batch_size=2,
)
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_classification(tmpdir):
tmpdir = Path(tmpdir)
(tmpdir / "a").mkdir()
(tmpdir / "b").mkdir()
image_a = str(tmpdir / "a" / "a_1.png")
image_b = str(tmpdir / "b" / "b_1.png")
_rand_image().save(image_a)
_rand_image().save(image_b)
data = ImageClassificationData.from_files(
train_files=[image_a, image_b],
train_targets=[0, 1],
num_workers=0,
batch_size=2,
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")
#
# 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.
from flash import Trainer
from flash.data.utils import download_data
from flash.vision import ImageClassificationData, ImageClassifier
# 1. Download the data
download_data("https://pl-flash-data.s3.amazonaws.com/hymenoptera_data.zip",
"data/")
# 2. Load the model from a checkpoint
model = ImageClassifier.load_from_checkpoint(
"https://flash-weights.s3.amazonaws.com/image_classification_model.pt")
# 3a. Predict what's on a few images! ants or bees?
predictions = model.predict([
"data/hymenoptera_data/val/bees/65038344_52a45d090d.jpg",
"data/hymenoptera_data/val/bees/590318879_68cf112861.jpg",
"data/hymenoptera_data/val/ants/540543309_ddbb193ee5.jpg",
])
print(predictions)
# 3b. Or generate predictions with a whole folder!
datamodule = ImageClassificationData.from_folders(
predict_folder="data/hymenoptera_data/predict/")
predictions = Trainer().predict(model, datamodule=datamodule)
print(predictions)
if __name__ == "__main__":
# 1. Download the data
download_data(
"https://pl-flash-data.s3.amazonaws.com/hymenoptera_data.zip", 'data/')
# 2. Load the data
datamodule = ImageClassificationData.from_folders(
train_folder="data/hymenoptera_data/train/",
valid_folder="data/hymenoptera_data/val/",
test_folder="data/hymenoptera_data/test/",
)
# 3. Build the model
model = ImageClassifier(num_classes=datamodule.num_classes)
# 4. Create the trainer. Run twice on data
trainer = flash.Trainer(max_epochs=2)
# 5. Train the model
trainer.finetune(model,
datamodule=datamodule,
strategy=FreezeUnfreeze(unfreeze_epoch=1))
# 6. Test the model
trainer.test()
# 7. Save it!
trainer.save_checkpoint("image_classification_model.pt")
# 3.a Optional: Register a custom backbone
# This is useful to create new backbone and make them accessible from `ImageClassifier`
@ImageClassifier.backbones(name="resnet18")
def fn_resnet(pretrained: bool = True):
model = torchvision.models.resnet18(pretrained)
# remove the last two layers & turn it into a Sequential model
backbone = nn.Sequential(*list(model.children())[:-2])
num_features = model.fc.in_features
# backbones need to return the num_features to build the head
return backbone, num_features
# 3.b Optional: List available backbones
print(ImageClassifier.available_backbones())
# 4. Build the model
model = ImageClassifier(backbone="resnet18",
num_classes=datamodule.num_classes)
# 5. Create the trainer.
trainer = flash.Trainer(max_epochs=1,
limit_train_batches=1,
limit_val_batches=1)
# 6. Train the model
trainer.finetune(model,
datamodule=datamodule,
strategy=FreezeUnfreeze(unfreeze_epoch=1))
def test_unfreeze():
model = ImageClassifier(2)
model.unfreeze()
for p in model.backbone.parameters():
assert p.requires_grad is True
def test_non_existent_backbone():
with pytest.raises(ValueError):
ImageClassifier(2, "i am never going to implement this lol")
def test_init_train(tmpdir, backbone):
model = ImageClassifier(10, backbone=backbone)
train_dl = torch.utils.data.DataLoader(DummyDataset())
trainer = Trainer(default_root_dir=tmpdir, fast_dev_run=True)
trainer.finetune(model, train_dl, strategy="freeze_unfreeze")
from flash.core.finetuning import FreezeUnfreeze
from flash.data.utils import download_data
from flash.vision import ImageClassificationData, ImageClassifier
# 1. Download the data
download_data("https://pl-flash-data.s3.amazonaws.com/hymenoptera_data.zip",
"data/")
# 2. Load the data
datamodule = ImageClassificationData.from_folders(
train_folder="data/hymenoptera_data/train/",
val_folder="data/hymenoptera_data/val/",
test_folder="data/hymenoptera_data/test/",
)
# 3. Build the model
model = ImageClassifier(num_classes=datamodule.num_classes)
# 4. Create the trainer.
trainer = flash.Trainer(max_epochs=1,
limit_train_batches=1,
limit_val_batches=1)
# 5. Train the model
trainer.finetune(model,
datamodule=datamodule,
strategy=FreezeUnfreeze(unfreeze_epoch=1))
# 3a. Predict what's on a few images! ants or bees?
predictions = model.predict([
"data/hymenoptera_data/val/bees/65038344_52a45d090d.jpg",
"data/hymenoptera_data/val/bees/590318879_68cf112861.jpg",
def test_non_existent_backbone():
with pytest.raises(MisconfigurationException):
ImageClassifier(2, "i am never going to implement this lol")
from flash import Trainer
from flash import download_data
from flash.vision import ImageClassificationData, ImageClassifier
# 1. Download the data
download_data("https://pl-flash-data.s3.amazonaws.com/hymenoptera_data.zip", 'data/')
# 2. Load the model from a checkpoint
model = ImageClassifier.load_from_checkpoint("image_classification_model.pt")
# 3a. Predict what's on a few images! ants or bees?
predictions = model.predict([
"data/hymenoptera_data/test/ants/8124241_36b290d372.jpg",
"data/hymenoptera_data/test/ants/147542264_79506478c2.jpg",
"data/hymenoptera_data/test/ants/212100470_b485e7b7b9.jpg",
])
print(predictions)
# 3b. Generate predictions with a whole folder
datamodule = ImageClassificationData.from_folder(folder="data/hymenoptera_data/test/ants/")
predictions = Trainer().predict(model, datamodule=datamodule)
print(predictions)
train_filepaths, train_labels = load_data('train')
test_filepaths, test_labels = load_data('test')
datamodule = ImageClassificationData.from_filepaths(
train_filepaths=train_filepaths,
train_labels=train_labels,
test_filepaths=test_filepaths,
test_labels=test_labels,
preprocess=ImageClassificationPreprocess(image_size=(128, 128)),
val_split=0.1, # Use 10 % of the train dataset to generate validation one.
)
# 3. Build the model
model = ImageClassifier(
backbone="resnet18",
num_classes=len(genres),
multi_label=True,
metrics=F1(num_classes=len(genres)),
)
# 4. Create the trainer. Train on 2 gpus for 10 epochs.
trainer = flash.Trainer(max_epochs=1, limit_train_batches=1, limit_val_batches=1)
# 5. Train the model
trainer.finetune(model, datamodule=datamodule, strategy="freeze")
# 6. Predict what's on a few images!
# Serialize predictions as labels, low threshold to see more predictions.
model.serializer = Labels(genres, multi_label=True, threshold=0.25)
predictions = model.predict([
import flash
from flash import download_data
from flash.vision import ImageClassificationData, ImageClassifier
# 1. Download the data
download_data("https://pl-flash-data.s3.amazonaws.com/hymenoptera_data.zip",
'data/')
# 2. Load the data
datamodule = ImageClassificationData.from_folders(
backbone="resnet34",
num_workers=8,
train_folder="data/hymenoptera_data/train/",
valid_folder="data/hymenoptera_data/val/",
test_folder="data/hymenoptera_data/test/",
)
# 3. Build the model
model = ImageClassifier(num_classes=datamodule.num_classes,
backbone="resnet18")
# 4. Create the trainer. Run once on data
trainer = flash.Trainer(max_epochs=4)
# 5. Finetune the model
trainer.finetune(model, datamodule=datamodule, strategy="freeze_unfreeze")
# 7. Save it!
trainer.save_checkpoint("image_classification_model.pt")