def test_external_registry():
def getter(key: str):
return key
registry = ExternalRegistry(getter, "backbones", "test_provider")
assert registry.get("testing")() == "testing"
available = registry.available_keys()
assert len(available) == 1
assert "test_provider" in available[0]
registry = ExternalRegistry(getter, "backbones", ["test_provider_1", "test_provider_2"])
assert "test_provider_1, test_provider_2" in registry.available_keys()[0]
registry = ExternalRegistry(getter, "backbones")
assert len(registry.available_keys()) == 0
# 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 functools import partial
from flash.core.registry import ExternalRegistry, FlashRegistry
from flash.core.utilities.imports import _AUDIO_AVAILABLE
from flash.core.utilities.providers import _FAIRSEQ, _HUGGINGFACE
SPEECH_RECOGNITION_BACKBONES = FlashRegistry("backbones")
if _AUDIO_AVAILABLE:
from transformers import Wav2Vec2ForCTC
WAV2VEC_MODELS = [
"facebook/wav2vec2-base-960h", "facebook/wav2vec2-large-960h-lv60"
]
for model_name in WAV2VEC_MODELS:
SPEECH_RECOGNITION_BACKBONES(
fn=partial(Wav2Vec2ForCTC.from_pretrained, model_name),
name=model_name,
providers=[_HUGGINGFACE, _FAIRSEQ],
)
HUGGINGFACE_BACKBONES = ExternalRegistry(Wav2Vec2ForCTC.from_pretrained,
"backbones",
providers=_HUGGINGFACE)
SPEECH_RECOGNITION_BACKBONES += HUGGINGFACE_BACKBONES
from flash.core.data.io.input import DataKeys
from flash.core.model import Task
from flash.core.registry import ExternalRegistry, FlashRegistry
from flash.core.utilities.imports import _TEXT_AVAILABLE, _TM_GREATER_EQUAL_0_7_0
from flash.core.utilities.providers import _HUGGINGFACE
from flash.core.utilities.types import LR_SCHEDULER_TYPE, METRICS_TYPE, OPTIMIZER_TYPE
from flash.text.ort_callback import ORTCallback
from flash.text.question_answering.collate import TextQuestionAnsweringCollate
from flash.text.question_answering.output_transform import QuestionAnsweringOutputTransform
if _TEXT_AVAILABLE:
from transformers import AutoModelForQuestionAnswering
HUGGINGFACE_BACKBONES = ExternalRegistry(
AutoModelForQuestionAnswering.from_pretrained,
"backbones",
_HUGGINGFACE,
)
else:
AutoModelForQuestionAnswering = None
HUGGINGFACE_BACKBONES = FlashRegistry("backbones")
class QuestionAnsweringTask(Task):
"""The ``QuestionAnsweringTask`` is a :class:`~flash.Task` for extractive question answering. For more details,
see `question_answering`.
You can change the backbone to any question answering model from `HuggingFace/transformers
<https://huggingface.co/transformers/model_doc/auto.html#automodelforquestionanswering>`_ using the ``backbone``
argument.
from torchmetrics import Metric
from flash.core.classification import ClassificationTask, Labels
from flash.core.data.process import Serializer
from flash.core.registry import ExternalRegistry, FlashRegistry
from flash.core.utilities.imports import _TEXT_AVAILABLE
from flash.core.utilities.providers import _HUGGINGFACE
from flash.text.ort_callback import ORTCallback
if _TEXT_AVAILABLE:
from transformers import AutoModelForSequenceClassification
from transformers.modeling_outputs import Seq2SeqSequenceClassifierOutput, SequenceClassifierOutput
HUGGINGFACE_BACKBONES = ExternalRegistry(
AutoModelForSequenceClassification.from_pretrained,
"backbones",
_HUGGINGFACE,
)
else:
HUGGINGFACE_BACKBONES = FlashRegistry("backbones")
class TextClassifier(ClassificationTask):
"""The ``TextClassifier`` is a :class:`~flash.Task` for classifying text. For more details, see
:ref:`text_classification`. The ``TextClassifier`` also supports multi-label classification with
``multi_label=True``. For more details, see :ref:`text_classification_multi_label`.
Args:
num_classes: Number of classes to classify.
backbone: A model to use to compute text features can be any BERT model from HuggingFace/transformersimage .
optimizer: Optimizer to use for training, defaults to `torch.optim.Adam`.
from torchmetrics import Metric
from flash.core.finetuning import FlashBaseFinetuning
from flash.core.model import Task
from flash.core.registry import ExternalRegistry, FlashRegistry
from flash.core.utilities.imports import _TEXT_AVAILABLE
from flash.core.utilities.providers import _HUGGINGFACE
from flash.text.ort_callback import ORTCallback
from flash.text.seq2seq.core.finetuning import Seq2SeqFreezeEmbeddings
if _TEXT_AVAILABLE:
from transformers import AutoModelForSeq2SeqLM, PreTrainedTokenizerBase
HUGGINGFACE_BACKBONES = ExternalRegistry(
AutoModelForSeq2SeqLM.from_pretrained,
"backbones",
_HUGGINGFACE,
)
else:
AutoModelForSeq2SeqLM, PreTrainedTokenizerBase = None, None
HUGGINGFACE_BACKBONES = FlashRegistry("backbones")
def _pad_tensors_to_max_len(model_cfg, tensor, max_length):
pad_token_id = model_cfg.pad_token_id if model_cfg.pad_token_id else model_cfg.eos_token_id
if pad_token_id is None:
raise ValueError(
f"Make sure that either `config.pad_token_id` or `config.eos_token_id` "
f"is defined if tensor has to be padded to `max_length`={max_length}"
)
# 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.
#
#
# ResNet encoder adapted from: https://github.com/facebookresearch/swav/blob/master/src/resnet50.py
# as the official torchvision implementation does not support wide resnet architecture
# found in self-supervised learning model weights
from flash.core.registry import ExternalRegistry, FlashRegistry
from flash.core.utilities.imports import _TRANSFORMERS_AVAILABLE
from flash.core.utilities.providers import _HUGGINGFACE
if _TRANSFORMERS_AVAILABLE:
from transformers import AutoModelForSequenceClassification
TEXT_CLASSIFIER_BACKBONES = FlashRegistry("backbones")
if _TRANSFORMERS_AVAILABLE:
HUGGINGFACE_TEXT_CLASSIFIER_BACKBONES = ExternalRegistry(
getter=AutoModelForSequenceClassification.from_pretrained,
name="backbones",
providers=_HUGGINGFACE,
)
TEXT_CLASSIFIER_BACKBONES += HUGGINGFACE_TEXT_CLASSIFIER_BACKBONES