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Moving pipeline tests from Narsil to hf-internal-testing. (#14463)

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* Moving everything to `hf-internal-testing`.

* Fixing test values.

* Moving to other repo.

* Last touch?
This commit is contained in:
Nicolas Patry 2021-11-22 10:40:45 +01:00 committed by GitHub
parent 1a92bc5788
commit a4553e6c64
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6 changed files with 22 additions and 22 deletions
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20
tests/test_pipelines_common.py
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@ -258,7 +258,7 @@ class CommonPipelineTest(unittest.TestCase):
return self.data[i]
text_classifier = pipeline(
task="text-classification", model="Narsil/tiny-distilbert-sequence-classification", framework="pt"
task="text-classification", model="hf-internal-testing/tiny-random-distilbert", framework="pt"
)
dataset = MyDataset()
for output in text_classifier(dataset):
@ -266,7 +266,7 @@ class CommonPipelineTest(unittest.TestCase):
@require_torch
def test_check_task_auto_inference(self):
pipe = pipeline(model="Narsil/tiny-distilbert-sequence-classification")
pipe = pipeline(model="hf-internal-testing/tiny-random-distilbert")
self.assertIsInstance(pipe, TextClassificationPipeline)
@ -275,7 +275,7 @@ class CommonPipelineTest(unittest.TestCase):
class MyPipeline(TextClassificationPipeline):
pass
text_classifier = pipeline(model="Narsil/tiny-distilbert-sequence-classification", pipeline_class=MyPipeline)
text_classifier = pipeline(model="hf-internal-testing/tiny-random-distilbert", pipeline_class=MyPipeline)
self.assertIsInstance(text_classifier, MyPipeline)
@ -293,11 +293,11 @@ class CommonPipelineTest(unittest.TestCase):
for _ in range(n):
yield "This is a test"
pipe = pipeline(model="Narsil/tiny-distilbert-sequence-classification")
pipe = pipeline(model="hf-internal-testing/tiny-random-distilbert")
results = []
for out in pipe(data(10)):
self.assertEqual(nested_simplify(out), {"label": "LABEL_1", "score": 0.502})
self.assertEqual(nested_simplify(out), {"label": "LABEL_0", "score": 0.504})
results.append(out)
self.assertEqual(len(results), 10)
@ -305,7 +305,7 @@ class CommonPipelineTest(unittest.TestCase):
# This will force using `num_workers=1` with a warning for now.
results = []
for out in pipe(data(10), num_workers=2):
self.assertEqual(nested_simplify(out), {"label": "LABEL_1", "score": 0.502})
self.assertEqual(nested_simplify(out), {"label": "LABEL_0", "score": 0.504})
results.append(out)
self.assertEqual(len(results), 10)
@ -315,20 +315,20 @@ class CommonPipelineTest(unittest.TestCase):
for _ in range(n):
yield "This is a test"
pipe = pipeline(model="Narsil/tiny-distilbert-sequence-classification", framework="tf")
pipe = pipeline(model="hf-internal-testing/tiny-random-distilbert", framework="tf")
out = pipe("This is a test")
results = []
for out in pipe(data(10)):
self.assertEqual(nested_simplify(out), {"label": "LABEL_1", "score": 0.502})
self.assertEqual(nested_simplify(out), {"label": "LABEL_0", "score": 0.504})
results.append(out)
self.assertEqual(len(results), 10)
@require_torch
def test_unbatch_attentions_hidden_states(self):
model = DistilBertForSequenceClassification.from_pretrained(
"Narsil/tiny-distilbert-sequence-classification", output_hidden_states=True, output_attentions=True
"hf-internal-testing/tiny-random-distilbert", output_hidden_states=True, output_attentions=True
)
tokenizer = AutoTokenizer.from_pretrained("Narsil/tiny-distilbert-sequence-classification")
tokenizer = AutoTokenizer.from_pretrained("hf-internal-testing/tiny-random-distilbert")
text_classifier = TextClassificationPipeline(model=model, tokenizer=tokenizer)
# Used to throw an error because `hidden_states` are a tuple of tensors

2
tests/test_pipelines_image_classification.py
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@ -67,7 +67,7 @@ class ImageClassificationPipelineTests(unittest.TestCase, metaclass=PipelineTest
import datasets
dataset = datasets.load_dataset("Narsil/image_dummy", "image", split="test")
dataset = datasets.load_dataset("hf-internal-testing/fixtures_image_utils", "image", split="test")
# Accepts URL + PIL.Image + lists
outputs = image_classifier(

2
tests/test_pipelines_image_segmentation.py
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@ -68,7 +68,7 @@ class ImageSegmentationPipelineTests(unittest.TestCase, metaclass=PipelineTestCa
import datasets
dataset = datasets.load_dataset("Narsil/image_dummy", "image", split="test")
dataset = datasets.load_dataset("hf-internal-testing/fixtures_image_utils", "image", split="test")
batch = [
Image.open("./tests/fixtures/tests_samples/COCO/000000039769.png"),

2
tests/test_pipelines_object_detection.py
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@ -74,7 +74,7 @@ class ObjectDetectionPipelineTests(unittest.TestCase, metaclass=PipelineTestCase
import datasets
dataset = datasets.load_dataset("Narsil/image_dummy", "image", split="test")
dataset = datasets.load_dataset("hf-internal-testing/fixtures_image_utils", "image", split="test")
batch = [
Image.open("./tests/fixtures/tests_samples/COCO/000000039769.png"),

8
tests/test_pipelines_text_classification.py
View File

@ -33,20 +33,20 @@ class TextClassificationPipelineTests(unittest.TestCase, metaclass=PipelineTestC
@require_torch
def test_small_model_pt(self):
text_classifier = pipeline(
task="text-classification", model="Narsil/tiny-distilbert-sequence-classification", framework="pt"
task="text-classification", model="hf-internal-testing/tiny-random-distilbert", framework="pt"
)
outputs = text_classifier("This is great !")
self.assertEqual(nested_simplify(outputs), [{"label": "LABEL_1", "score": 0.502}])
self.assertEqual(nested_simplify(outputs), [{"label": "LABEL_0", "score": 0.504}])
@require_tf
def test_small_model_tf(self):
text_classifier = pipeline(
task="text-classification", model="Narsil/tiny-distilbert-sequence-classification", framework="tf"
task="text-classification", model="hf-internal-testing/tiny-random-distilbert", framework="tf"
)
outputs = text_classifier("This is great !")
self.assertEqual(nested_simplify(outputs), [{"label": "LABEL_1", "score": 0.502}])
self.assertEqual(nested_simplify(outputs), [{"label": "LABEL_0", "score": 0.504}])
@slow
@require_torch

10
tests/test_pipelines_token_classification.py
View File

@ -582,14 +582,14 @@ class TokenClassificationPipelineTests(unittest.TestCase, metaclass=PipelineTest
@require_tf
def test_tf_only(self):
model_name = "Narsil/small" # This model only has a TensorFlow version
model_name = "hf-internal-testing/tiny-random-bert-tf-only" # This model only has a TensorFlow version
# We test that if we don't specificy framework='tf', it gets detected automatically
token_classifier = pipeline(task="ner", model=model_name)
self.assertEqual(token_classifier.framework, "tf")
@require_tf
def test_small_model_tf(self):
model_name = "Narsil/small2"
model_name = "hf-internal-testing/tiny-bert-for-token-classification"
token_classifier = pipeline(task="token-classification", model=model_name, framework="tf")
outputs = token_classifier("This is a test !")
self.assertEqual(
@ -602,8 +602,8 @@ class TokenClassificationPipelineTests(unittest.TestCase, metaclass=PipelineTest
@require_torch
def test_no_offset_tokenizer(self):
model_name = "Narsil/small2"
tokenizer = AutoTokenizer.from_pretrained("Narsil/small2", use_fast=False)
model_name = "hf-internal-testing/tiny-bert-for-token-classification"
tokenizer = AutoTokenizer.from_pretrained(model_name, use_fast=False)
token_classifier = pipeline(task="token-classification", model=model_name, tokenizer=tokenizer, framework="pt")
outputs = token_classifier("This is a test !")
self.assertEqual(
@ -616,7 +616,7 @@ class TokenClassificationPipelineTests(unittest.TestCase, metaclass=PipelineTest
@require_torch
def test_small_model_pt(self):
model_name = "Narsil/small2"
model_name = "hf-internal-testing/tiny-bert-for-token-classification"
token_classifier = pipeline(task="token-classification", model=model_name, framework="pt")
outputs = token_classifier("This is a test !")
self.assertEqual(