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be236361f1
* Tentative enabling of `batch_size` for pipelines. * Add systematic test for pipeline batching. * Enabling batch_size on almost all pipelines - Not `zero-shot` (it's already passing stuff as batched so trickier) - Not `QA` (preprocess uses squad features, we need to switch to real tensors at this boundary. * Adding `min_length_for_response` for conversational. * Making CTC, speech mappings avaiable regardless of framework. * Attempt at fixing automatic tests (ffmpeg not enabled for fast tests) * Removing ffmpeg dependency in tests. * Small fixes. * Slight cleanup. * Adding docs and adressing comments. * Quality. * Update docs/source/main_classes/pipelines.rst Co-authored-by: Sylvain Gugger <35901082+sgugger@users.noreply.github.com> * Update src/transformers/pipelines/question_answering.py Co-authored-by: Sylvain Gugger <35901082+sgugger@users.noreply.github.com> * Update src/transformers/pipelines/zero_shot_classification.py Co-authored-by: Sylvain Gugger <35901082+sgugger@users.noreply.github.com> * Improving docs. * Update docs/source/main_classes/pipelines.rst Co-authored-by: Philipp Schmid <32632186+philschmid@users.noreply.github.com> * N -> oberved_batch_size softmax trick. * Follow `padding_side`. * Supporting image pipeline batching (and padding). * Rename `unbatch` -> `loader_batch`. * unbatch_size forgot. * Custom padding for offset mappings. * Attempt to remove librosa. * Adding require_audio. * torchaudio. * Back to using datasets librosa. * Adding help to set a pad_token on the tokenizer. * Update src/transformers/pipelines/base.py Co-authored-by: Sylvain Gugger <35901082+sgugger@users.noreply.github.com> * Update src/transformers/pipelines/base.py Co-authored-by: Sylvain Gugger <35901082+sgugger@users.noreply.github.com> * Update src/transformers/pipelines/base.py Co-authored-by: Sylvain Gugger <35901082+sgugger@users.noreply.github.com> * Quality. Co-authored-by: Sylvain Gugger <35901082+sgugger@users.noreply.github.com> Co-authored-by: Philipp Schmid <32632186+philschmid@users.noreply.github.com>
96 lines
4.1 KiB
Python
96 lines
4.1 KiB
Python
# Copyright 2020 The HuggingFace Team. All rights reserved.
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#
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# Licensed under the Apache License, Version 2.0 (the "License");
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# you may not use this file except in compliance with the License.
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# You may obtain a copy of the License at
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#
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# http://www.apache.org/licenses/LICENSE-2.0
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#
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# Unless required by applicable law or agreed to in writing, software
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# distributed under the License is distributed on an "AS IS" BASIS,
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# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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# See the License for the specific language governing permissions and
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# limitations under the License.
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import unittest
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from transformers import (
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MODEL_FOR_SEQUENCE_CLASSIFICATION_MAPPING,
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TF_MODEL_FOR_SEQUENCE_CLASSIFICATION_MAPPING,
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TextClassificationPipeline,
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pipeline,
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)
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from transformers.testing_utils import is_pipeline_test, nested_simplify, require_tf, require_torch, slow
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from .test_pipelines_common import ANY, PipelineTestCaseMeta
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@is_pipeline_test
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class TextClassificationPipelineTests(unittest.TestCase, metaclass=PipelineTestCaseMeta):
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model_mapping = MODEL_FOR_SEQUENCE_CLASSIFICATION_MAPPING
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tf_model_mapping = TF_MODEL_FOR_SEQUENCE_CLASSIFICATION_MAPPING
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@require_torch
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def test_small_model_pt(self):
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text_classifier = pipeline(
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task="text-classification", model="Narsil/tiny-distilbert-sequence-classification", framework="pt"
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)
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outputs = text_classifier("This is great !")
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self.assertEqual(nested_simplify(outputs), [{"label": "LABEL_1", "score": 0.502}])
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@require_tf
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def test_small_model_tf(self):
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text_classifier = pipeline(
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task="text-classification", model="Narsil/tiny-distilbert-sequence-classification", framework="tf"
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)
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outputs = text_classifier("This is great !")
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self.assertEqual(nested_simplify(outputs), [{"label": "LABEL_1", "score": 0.502}])
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@slow
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@require_torch
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def test_pt_bert(self):
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text_classifier = pipeline("text-classification")
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outputs = text_classifier("This is great !")
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self.assertEqual(nested_simplify(outputs), [{"label": "POSITIVE", "score": 1.0}])
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outputs = text_classifier("This is bad !")
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self.assertEqual(nested_simplify(outputs), [{"label": "NEGATIVE", "score": 1.0}])
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outputs = text_classifier("Birds are a type of animal")
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self.assertEqual(nested_simplify(outputs), [{"label": "POSITIVE", "score": 0.988}])
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@slow
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@require_tf
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def test_tf_bert(self):
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text_classifier = pipeline("text-classification", framework="tf")
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outputs = text_classifier("This is great !")
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self.assertEqual(nested_simplify(outputs), [{"label": "POSITIVE", "score": 1.0}])
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outputs = text_classifier("This is bad !")
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self.assertEqual(nested_simplify(outputs), [{"label": "NEGATIVE", "score": 1.0}])
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outputs = text_classifier("Birds are a type of animal")
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self.assertEqual(nested_simplify(outputs), [{"label": "POSITIVE", "score": 0.988}])
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def get_test_pipeline(self, model, tokenizer, feature_extractor):
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text_classifier = TextClassificationPipeline(model=model, tokenizer=tokenizer)
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return text_classifier, ["HuggingFace is in", "This is another test"]
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def run_pipeline_test(self, text_classifier, _):
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model = text_classifier.model
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# Small inputs because BartTokenizer tiny has maximum position embeddings = 22
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valid_inputs = "HuggingFace is in"
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outputs = text_classifier(valid_inputs)
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self.assertEqual(nested_simplify(outputs), [{"label": ANY(str), "score": ANY(float)}])
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self.assertTrue(outputs[0]["label"] in model.config.id2label.values())
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valid_inputs = ["HuggingFace is in ", "Paris is in France"]
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outputs = text_classifier(valid_inputs)
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self.assertEqual(
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nested_simplify(outputs),
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[{"label": ANY(str), "score": ANY(float)}, {"label": ANY(str), "score": ANY(float)}],
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)
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self.assertTrue(outputs[0]["label"] in model.config.id2label.values())
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self.assertTrue(outputs[1]["label"] in model.config.id2label.values())
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