def test_serve():
model = TextClassifier(2, TEST_BACKBONE)
# TODO: Currently only servable once a preprocess and postprocess have been attached
model._preprocess = TextClassificationPreprocess(backbone=TEST_BACKBONE)
model._postprocess = TextClassificationPostprocess()
model.eval()
model.serve()
def test_jit(tmpdir):
sample_input = {"input_ids": torch.randint(1000, size=(1, 100))}
path = os.path.join(tmpdir, "test.pt")
model = TextClassifier(2, TEST_BACKBONE)
model.eval()
# Huggingface bert model only supports `torch.jit.trace` with `strict=False`
model = torch.jit.trace(model, sample_input, strict=False)
torch.jit.save(model, path)
model = torch.jit.load(path)
out = model(sample_input)["logits"]
assert isinstance(out, torch.Tensor)
assert out.shape == torch.Size([1, 2])
def test_init_train(tmpdir):
if os.name == "nt":
# TODO: huggingface stuff timing out on windows
#
return True
model = TextClassifier(2, TEST_BACKBONE)
train_dl = torch.utils.data.DataLoader(DummyDataset())
trainer = Trainer(default_root_dir=tmpdir, fast_dev_run=True)
trainer.fit(model, train_dl)
def test_classification(tmpdir):
csv_path = csv_data(tmpdir)
data = TextClassificationData.from_csv(
"sentence",
"label",
train_file=csv_path,
num_workers=0,
batch_size=2,
)
model = TextClassifier(2, TEST_BACKBONE)
trainer = Trainer(default_root_dir=tmpdir, fast_dev_run=True)
trainer.fit(model, datamodule=data)
def test_init_train_enable_ort(tmpdir):
class TestCallback(Callback):
def on_train_start(self, trainer: Trainer,
pl_module: LightningModule) -> None:
assert isinstance(pl_module.model, ORTModule)
model = TextClassifier(2, TEST_BACKBONE, enable_ort=True)
trainer = Trainer(default_root_dir=tmpdir,
fast_dev_run=True,
callbacks=TestCallback())
trainer.fit(
model,
train_dataloader=torch.utils.data.DataLoader(DummyDataset()),
val_dataloaders=torch.utils.data.DataLoader(DummyDataset()),
)
trainer.test(model,
test_dataloaders=torch.utils.data.DataLoader(DummyDataset()))
def test_classification(tmpdir):
if os.name == "nt":
# TODO: huggingface stuff timing out on windows
return True
csv_path = csv_data(tmpdir)
data = TextClassificationData.from_files(
backbone=TEST_BACKBONE,
train_file=csv_path,
input="sentence",
target="label",
num_workers=0,
batch_size=2,
)
model = TextClassifier(2, TEST_BACKBONE)
trainer = Trainer(default_root_dir=tmpdir, fast_dev_run=True)
trainer.fit(model, datamodule=data)
# 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.
from flash.text import TextClassifier
model = TextClassifier.load_from_checkpoint(
"https://flash-weights.s3.amazonaws.com/0.7.0/text_classification_model.pt"
)
model.serve()
# limitations under the License.
from flash.text import TextClassificationData, TextClassifier
# 1. Download the data
download_data("https://pl-flash-data.s3.amazonaws.com/imdb.zip", 'data/')
# 2. Load the data
datamodule = TextClassificationData.from_files(
train_file="data/imdb/train.csv",
valid_file="data/imdb/valid.csv",
test_file="data/imdb/test.csv",
input="review",
target="sentiment",
batch_size=512
)
# 3. Build the model
model = TextClassifier(num_classes=datamodule.num_classes)
# 4. Create the trainer. Run once on data
trainer = flash.Trainer(max_epochs=1)
# 5. Fine-tune the model
trainer.finetune(model, datamodule=datamodule, strategy='freeze')
# 6. Test model
trainer.test()
# 7. Save it!
trainer.save_checkpoint("text_classification_model.pt")
# 2. Load the data
datamodule = TextClassificationData.from_csv(
"comment_text",
["toxic", "severe_toxic", "obscene", "threat", "insult", "identity_hate"],
train_file="data/jigsaw_toxic_comments/train.csv",
test_file="data/jigsaw_toxic_comments/test.csv",
predict_file="data/jigsaw_toxic_comments/predict.csv",
batch_size=16,
val_split=0.1,
backbone="unitary/toxic-bert",
)
# 3. Build the model
model = TextClassifier(
num_classes=datamodule.num_classes,
multi_label=True,
metrics=F1(num_classes=datamodule.num_classes),
backbone="unitary/toxic-bert",
)
# 4. Create the trainer
trainer = flash.Trainer(fast_dev_run=True)
# 5. Fine-tune the model
trainer.finetune(model, datamodule=datamodule, strategy="freeze")
# 6. Generate predictions for a few comments!
predictions = model.predict([
"No, he is an arrogant, self serving, immature idiot. Get it right.",
"U SUCK HANNAH MONTANA",
"Would you care to vote? Thx.",
])
def test_load_from_checkpoint_dependency_error():
with pytest.raises(ModuleNotFoundError,
match=re.escape("'lightning-flash[text]'")):
TextClassifier.load_from_checkpoint("not_a_real_checkpoint.pt")
def test_init_train(tmpdir):
model = TextClassifier(2, TEST_BACKBONE)
train_dl = torch.utils.data.DataLoader(DummyDataset())
trainer = Trainer(default_root_dir=tmpdir, fast_dev_run=True)
trainer.fit(model, train_dl)
download_data(
"https://pl-flash-data.s3.amazonaws.com/jigsaw_toxic_comments.zip",
"./data")
datamodule = TextClassificationData.from_csv(
"comment_text",
["toxic", "severe_toxic", "obscene", "threat", "insult", "identity_hate"],
train_file="data/jigsaw_toxic_comments/train.csv",
val_split=0.1,
batch_size=4,
)
# 2. Build the task
model = TextClassifier(
backbone="unitary/toxic-bert",
labels=datamodule.labels,
multi_label=datamodule.multi_label,
)
# 3. Create the trainer and finetune the model
trainer = flash.Trainer(max_epochs=1, gpus=torch.cuda.device_count())
trainer.finetune(model, datamodule=datamodule, strategy="freeze")
# 4. Generate predictions for a few comments!
datamodule = TextClassificationData.from_lists(
predict_data=[
"No, he is an arrogant, self serving, immature idiot. Get it right.",
"U SUCK HANNAH MONTANA",
"Would you care to vote? Thx.",
],
batch_size=4,
def test_serve():
model = TextClassifier(2, backbone=TEST_BACKBONE)
model.eval()
model.serve()
import flash
from flash.core.data import download_data
from flash.text import TextClassificationData, TextClassifier
# 1. Download the data
download_data("https://pl-flash-data.s3.amazonaws.com/imdb.zip", "data/")
# 2. Load the data
datamodule = TextClassificationData.from_files(
train_file="data/imdb/train.csv",
valid_file="data/imdb/valid.csv",
test_file="data/imdb/test.csv",
input="review",
target="sentiment",
batch_size=512)
# 3. Build the model
model = TextClassifier(num_classes=datamodule.num_classes)
# 4. Create the trainer. Run once on data
trainer = flash.Trainer(max_epochs=1)
# 5. Fine-tune the model
trainer.finetune(model, datamodule=datamodule, strategy="freeze")
# 6. Test model
trainer.test()
# 7. Predict on new data
predictions = model.predict([
"Turgid dialogue, feeble characterization - Harvey Keitel a judge?.",
"I come from Bulgaria where it 's almost impossible to have a tornado."
"This guy has done a great job with this movie!",
])
print(predictions)
# 8. Save the model!
trainer.save_checkpoint("text_classification_model.pt")
# 1. Create the DataModule
download_data(
"https://label-studio-testdata.s3.us-east-2.amazonaws.com/lightning-flash/text_data.zip",
"./data/")
backbone = "prajjwal1/bert-medium"
datamodule = TextClassificationData.from_labelstudio(
export_json="data/project.json",
val_split=0.2,
backbone=backbone,
)
# 2. Build the task
model = TextClassifier(backbone=backbone, num_classes=datamodule.num_classes)
# 3. Create the trainer and finetune the model
trainer = flash.Trainer(max_epochs=3)
trainer.finetune(model, datamodule=datamodule, strategy="freeze")
# 4. Classify a few sentences! How was the movie?
datamodule = TextClassificationData.from_lists(predict_data=[
"Turgid dialogue, feeble characterization - Harvey Keitel a judge?.",
"The worst movie in the history of cinema.",
"I come from Bulgaria where it 's almost impossible to have a tornado.",
])
predictions = trainer.predict(model, datamodule=datamodule)
# 5. Save the model!
trainer.save_checkpoint("text_classification_model.pt")
from flash.core.data.utils import download_data
from flash.text import TextClassificationData, TextClassifier
# 1. Create the DataModule
download_data("https://pl-flash-data.s3.amazonaws.com/imdb.zip", "./data/")
datamodule = TextClassificationData.from_csv(
"review",
"sentiment",
train_file="data/imdb/train.csv",
val_file="data/imdb/valid.csv",
batch_size=4,
)
# 2. Build the task
model = TextClassifier(backbone="prajjwal1/bert-medium",
labels=datamodule.labels)
# 3. Create the trainer and finetune the model
trainer = flash.Trainer(max_epochs=3, gpus=torch.cuda.device_count())
trainer.finetune(model, datamodule=datamodule, strategy="freeze")
# 4. Classify a few sentences! How was the movie?
datamodule = TextClassificationData.from_lists(
predict_data=[
"Turgid dialogue, feeble characterization - Harvey Keitel a judge?.",
"The worst movie in the history of cinema.",
"I come from Bulgaria where it 's almost impossible to have a tornado.",
],
batch_size=4,
)
predictions = trainer.predict(model, datamodule=datamodule, output="labels")
