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776855c752
Fixing a regression with `return_all_scores` introduced in #17606 - The legacy test actually tested `return_all_scores=False` (the actual default) instead of `return_all_scores=True` (the actual weird case). This commit adds the correct legacy test and fixes it. Tmp legacy tests. Actually fix the regression (also contains lists) Less diffed code.
188 lines
7.9 KiB
Python
188 lines
7.9 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="hf-internal-testing/tiny-random-distilbert", 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_0", "score": 0.504}])
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outputs = text_classifier("This is great !", top_k=2)
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self.assertEqual(
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nested_simplify(outputs), [{"label": "LABEL_0", "score": 0.504}, {"label": "LABEL_1", "score": 0.496}]
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)
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outputs = text_classifier(["This is great !", "This is bad"], top_k=2)
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self.assertEqual(
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nested_simplify(outputs),
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[
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[{"label": "LABEL_0", "score": 0.504}, {"label": "LABEL_1", "score": 0.496}],
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[{"label": "LABEL_0", "score": 0.504}, {"label": "LABEL_1", "score": 0.496}],
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],
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)
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outputs = text_classifier("This is great !", top_k=1)
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self.assertEqual(nested_simplify(outputs), [{"label": "LABEL_0", "score": 0.504}])
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# Legacy behavior
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outputs = text_classifier("This is great !", return_all_scores=False)
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self.assertEqual(nested_simplify(outputs), [{"label": "LABEL_0", "score": 0.504}])
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outputs = text_classifier("This is great !", return_all_scores=True)
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self.assertEqual(
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nested_simplify(outputs), [[{"label": "LABEL_0", "score": 0.504}, {"label": "LABEL_1", "score": 0.496}]]
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)
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outputs = text_classifier(["This is great !", "Something else"], return_all_scores=True)
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self.assertEqual(
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nested_simplify(outputs),
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[
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[{"label": "LABEL_0", "score": 0.504}, {"label": "LABEL_1", "score": 0.496}],
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[{"label": "LABEL_0", "score": 0.504}, {"label": "LABEL_1", "score": 0.496}],
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],
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)
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outputs = text_classifier(["This is great !", "Something else"], return_all_scores=False)
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self.assertEqual(
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nested_simplify(outputs),
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[
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{"label": "LABEL_0", "score": 0.504},
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{"label": "LABEL_0", "score": 0.504},
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],
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)
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@require_torch
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def test_accepts_torch_device(self):
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import torch
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text_classifier = pipeline(
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task="text-classification",
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model="hf-internal-testing/tiny-random-distilbert",
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framework="pt",
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device=torch.device("cpu"),
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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_0", "score": 0.504}])
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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="hf-internal-testing/tiny-random-distilbert", 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_0", "score": 0.504}])
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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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# Forcing to get all results with `top_k=None`
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# This is NOT the legacy format
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outputs = text_classifier(valid_inputs, top_k=None)
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N = len(model.config.id2label.values())
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self.assertEqual(
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nested_simplify(outputs),
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[[{"label": ANY(str), "score": ANY(float)}] * N, [{"label": ANY(str), "score": ANY(float)}] * N],
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)
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valid_inputs = {"text": "HuggingFace is in ", "text_pair": "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)},
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)
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self.assertTrue(outputs["label"] in model.config.id2label.values())
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# This might be used a text pair, but tokenizer + pipe interaction
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# makes it hard to understand that it's not using the pair properly
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# https://github.com/huggingface/transformers/issues/17305
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# We disabled this usage instead as it was outputting wrong outputs.
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invalid_input = [["HuggingFace is in ", "Paris is in France"]]
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with self.assertRaises(ValueError):
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text_classifier(invalid_input)
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# This used to be valid for doing text pairs
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# We're keeping it working because of backward compatibility
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outputs = text_classifier([[["HuggingFace is in ", "Paris is in France"]]])
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self.assertEqual(
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nested_simplify(outputs),
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[{"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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