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_trainer_finetune(tmpdir):
model = DummyClassifier()
train_dl = torch.utils.data.DataLoader(DummyDataset())
val_dl = torch.utils.data.DataLoader(DummyDataset())
task = ClassificationTask(model, loss_fn=F.nll_loss)
trainer = Trainer(fast_dev_run=True, default_root_dir=tmpdir)
trainer.finetune(task, train_dl, val_dl, strategy=NoFreeze())
def test_finetuning(tmpdir: str, strategy):
train_dl = torch.utils.data.DataLoader(DummyDataset())
val_dl = torch.utils.data.DataLoader(DummyDataset())
task = ImageClassifier(10, backbone="resnet18")
trainer = Trainer(fast_dev_run=True, default_root_dir=tmpdir)
if strategy == "cls":
strategy = NoFreeze()
if strategy == 'chocolat' or strategy is None:
with pytest.raises(MisconfigurationException, match="strategy should be provided"):
trainer.finetune(task, train_dl, val_dl, strategy=strategy)
else:
trainer.finetune(task, train_dl, val_dl, strategy=strategy)
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_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][DefaultDataKeys.INPUT], ds[0][DefaultDataKeys.TARGET]
predictions = model.predict([{DefaultDataKeys.INPUT: image}])
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_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")
def test_map_labels(tmpdir):
tmp_dir = Path(tmpdir)
# create random dummy data
images = [
str(tmp_dir / "img1.png"),
str(tmp_dir / "img2.png"),
str(tmp_dir / "img3.png"),
]
targets = [
str(tmp_dir / "labels_img1.png"),
str(tmp_dir / "labels_img2.png"),
str(tmp_dir / "labels_img3.png"),
]
labels_map: Dict[int, Tuple[int, int, int]] = {
0: [0, 0, 0],
1: [255, 255, 255],
}
num_classes: int = len(labels_map.keys())
img_size: Tuple[int, int] = (128, 128)
create_random_data(images, targets, img_size, num_classes)
# instantiate the data module
dm = SemanticSegmentationData.from_files(
train_files=images,
train_targets=targets,
val_files=images,
val_targets=targets,
batch_size=2,
num_workers=0,
num_classes=num_classes,
)
assert dm is not None
assert dm.train_dataloader() is not None
# disable visualisation for testing
assert dm.data_fetcher.block_viz_window is True
dm.set_block_viz_window(False)
assert dm.data_fetcher.block_viz_window is False
dm.show_train_batch("load_sample")
dm.show_train_batch("to_tensor_transform")
# check training data
data = next(iter(dm.train_dataloader()))
imgs, labels = data[DefaultDataKeys.INPUT], data[DefaultDataKeys.TARGET]
assert imgs.shape == (2, 3, 128, 128)
assert labels.shape == (2, 128, 128)
assert labels.min().item() == 0
assert labels.max().item() == 1
assert labels.dtype == torch.int64
# now train with `fast_dev_run`
model = SemanticSegmentation(num_classes=2, backbone="resnet50", head="fpn")
trainer = Trainer(default_root_dir=tmpdir, fast_dev_run=True)
trainer.finetune(model, dm, strategy="freeze_unfreeze")
def test_init_train(tmpdir, backbone, metrics):
model = ImageClassifier(10, backbone=backbone, metrics=metrics)
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")
# 1. Create the DataModule
download_data("https://pl-flash-data.s3.amazonaws.com/xsum.zip", "./data/")
datamodule = SummarizationData.from_csv(
"input",
"target",
train_file="data/xsum/train.csv",
val_file="data/xsum/valid.csv",
)
# 2. Build the task
model = SummarizationTask()
# 3. Create the trainer and finetune the model
trainer = Trainer(max_epochs=3)
trainer.finetune(model, datamodule=datamodule)
# 4. Summarize some text!
predictions = model.predict("""
Camilla bought a box of mangoes with a Brixton £10 note, introduced last year to try to keep the money of local
people within the community.The couple were surrounded by shoppers as they walked along Electric Avenue.
They came to Brixton to see work which has started to revitalise the borough.
It was Charles' first visit to the area since 1996, when he was accompanied by the former
South African president Nelson Mandela.Greengrocer Derek Chong, who has run a stall on Electric Avenue
for 20 years, said Camilla had been ""nice and pleasant"" when she purchased the fruit.
""She asked me what was nice, what would I recommend, and I said we've got some nice mangoes.
She asked me were they ripe and I said yes - they're from the Dominican Republic.""
Mr Chong is one of 170 local retailers who accept the Brixton Pound.
Customers exchange traditional pound coins for Brixton Pounds and then spend them at the market
or in participating shops.
During the visit, Prince Charles spent time talking to youth worker Marcus West, who works with children
def test_init_train(tmpdir, backbone):
model = SemanticSegmentation(num_classes=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")
download_data("https://pl-flash-data.s3.amazonaws.com/xsum.zip", "./data/")
datamodule = SummarizationData.from_csv(
"input",
"target",
train_file="data/xsum/train.csv",
val_file="data/xsum/valid.csv",
batch_size=4,
)
# 2. Build the task
model = SummarizationTask()
# 3. Create the trainer and finetune the model
trainer = Trainer(max_epochs=3)
trainer.finetune(model, datamodule=datamodule, strategy="freeze")
# 4. Summarize some text!
datamodule = SummarizationData.from_lists(
predict_data=[
"""
Camilla bought a box of mangoes with a Brixton £10 note, introduced last year to try to keep the money of local
people within the community.The couple were surrounded by shoppers as they walked along Electric Avenue.
They came to Brixton to see work which has started to revitalise the borough.
It was Charles' first visit to the area since 1996, when he was accompanied by the former
South African president Nelson Mandela.Greengrocer Derek Chong, who has run a stall on Electric Avenue
for 20 years, said Camilla had been ""nice and pleasant"" when she purchased the fruit.
""She asked me what was nice, what would I recommend, and I said we've got some nice mangoes.
She asked me were they ripe and I said yes - they're from the Dominican Republic.""
Mr Chong is one of 170 local retailers who accept the Brixton Pound.
Customers exchange traditional pound coins for Brixton Pounds and then spend them at the market
