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# Copyright 2021 The HuggingFace Team. All rights reserved. | |
# | |
# 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. | |
import unittest | |
from transformers import is_vision_available | |
from transformers.pipelines import pipeline | |
from transformers.testing_utils import ( | |
is_pipeline_test, | |
nested_simplify, | |
require_tf, | |
require_torch, | |
require_vision, | |
slow, | |
) | |
from .test_pipelines_common import ANY | |
if is_vision_available(): | |
from PIL import Image | |
else: | |
class Image: | |
def open(*args, **kwargs): | |
pass | |
class ZeroShotImageClassificationPipelineTests(unittest.TestCase): | |
# Deactivating auto tests since we don't have a good MODEL_FOR_XX mapping, | |
# and only CLIP would be there for now. | |
# model_mapping = {CLIPConfig: CLIPModel} | |
# def get_test_pipeline(self, model, tokenizer, processor): | |
# if tokenizer is None: | |
# # Side effect of no Fast Tokenizer class for these model, so skipping | |
# # But the slow tokenizer test should still run as they're quite small | |
# self.skipTest("No tokenizer available") | |
# return | |
# # return None, None | |
# image_classifier = ZeroShotImageClassificationPipeline( | |
# model=model, tokenizer=tokenizer, feature_extractor=processor | |
# ) | |
# # test with a raw waveform | |
# image = Image.open("./tests/fixtures/tests_samples/COCO/000000039769.png") | |
# image2 = Image.open("./tests/fixtures/tests_samples/COCO/000000039769.png") | |
# return image_classifier, [image, image2] | |
# def run_pipeline_test(self, pipe, examples): | |
# image = Image.open("./tests/fixtures/tests_samples/COCO/000000039769.png") | |
# outputs = pipe(image, candidate_labels=["A", "B"]) | |
# self.assertEqual(outputs, {"text": ANY(str)}) | |
# # Batching | |
# outputs = pipe([image] * 3, batch_size=2, candidate_labels=["A", "B"]) | |
def test_small_model_pt(self): | |
image_classifier = pipeline( | |
model="hf-internal-testing/tiny-random-clip-zero-shot-image-classification", | |
) | |
image = Image.open("./tests/fixtures/tests_samples/COCO/000000039769.png") | |
output = image_classifier(image, candidate_labels=["a", "b", "c"]) | |
# The floating scores are so close, we enter floating error approximation and the order is not guaranteed across | |
# python and torch versions. | |
self.assertIn( | |
nested_simplify(output), | |
[ | |
[{"score": 0.333, "label": "a"}, {"score": 0.333, "label": "b"}, {"score": 0.333, "label": "c"}], | |
[{"score": 0.333, "label": "a"}, {"score": 0.333, "label": "c"}, {"score": 0.333, "label": "b"}], | |
[{"score": 0.333, "label": "b"}, {"score": 0.333, "label": "a"}, {"score": 0.333, "label": "c"}], | |
], | |
) | |
output = image_classifier([image] * 5, candidate_labels=["A", "B", "C"], batch_size=2) | |
self.assertEqual( | |
nested_simplify(output), | |
# Pipeline outputs are supposed to be deterministic and | |
# So we could in theory have real values "A", "B", "C" instead | |
# of ANY(str). | |
# However it seems that in this particular case, the floating | |
# scores are so close, we enter floating error approximation | |
# and the order is not guaranteed anymore with batching. | |
[ | |
[ | |
{"score": 0.333, "label": ANY(str)}, | |
{"score": 0.333, "label": ANY(str)}, | |
{"score": 0.333, "label": ANY(str)}, | |
], | |
[ | |
{"score": 0.333, "label": ANY(str)}, | |
{"score": 0.333, "label": ANY(str)}, | |
{"score": 0.333, "label": ANY(str)}, | |
], | |
[ | |
{"score": 0.333, "label": ANY(str)}, | |
{"score": 0.333, "label": ANY(str)}, | |
{"score": 0.333, "label": ANY(str)}, | |
], | |
[ | |
{"score": 0.333, "label": ANY(str)}, | |
{"score": 0.333, "label": ANY(str)}, | |
{"score": 0.333, "label": ANY(str)}, | |
], | |
[ | |
{"score": 0.333, "label": ANY(str)}, | |
{"score": 0.333, "label": ANY(str)}, | |
{"score": 0.333, "label": ANY(str)}, | |
], | |
], | |
) | |
def test_small_model_tf(self): | |
image_classifier = pipeline( | |
model="hf-internal-testing/tiny-random-clip-zero-shot-image-classification", framework="tf" | |
) | |
image = Image.open("./tests/fixtures/tests_samples/COCO/000000039769.png") | |
output = image_classifier(image, candidate_labels=["a", "b", "c"]) | |
self.assertEqual( | |
nested_simplify(output), | |
[{"score": 0.333, "label": "a"}, {"score": 0.333, "label": "b"}, {"score": 0.333, "label": "c"}], | |
) | |
output = image_classifier([image] * 5, candidate_labels=["A", "B", "C"], batch_size=2) | |
self.assertEqual( | |
nested_simplify(output), | |
# Pipeline outputs are supposed to be deterministic and | |
# So we could in theory have real values "A", "B", "C" instead | |
# of ANY(str). | |
# However it seems that in this particular case, the floating | |
# scores are so close, we enter floating error approximation | |
# and the order is not guaranteed anymore with batching. | |
[ | |
[ | |
{"score": 0.333, "label": ANY(str)}, | |
{"score": 0.333, "label": ANY(str)}, | |
{"score": 0.333, "label": ANY(str)}, | |
], | |
[ | |
{"score": 0.333, "label": ANY(str)}, | |
{"score": 0.333, "label": ANY(str)}, | |
{"score": 0.333, "label": ANY(str)}, | |
], | |
[ | |
{"score": 0.333, "label": ANY(str)}, | |
{"score": 0.333, "label": ANY(str)}, | |
{"score": 0.333, "label": ANY(str)}, | |
], | |
[ | |
{"score": 0.333, "label": ANY(str)}, | |
{"score": 0.333, "label": ANY(str)}, | |
{"score": 0.333, "label": ANY(str)}, | |
], | |
[ | |
{"score": 0.333, "label": ANY(str)}, | |
{"score": 0.333, "label": ANY(str)}, | |
{"score": 0.333, "label": ANY(str)}, | |
], | |
], | |
) | |
def test_large_model_pt(self): | |
image_classifier = pipeline( | |
task="zero-shot-image-classification", | |
model="openai/clip-vit-base-patch32", | |
) | |
# This is an image of 2 cats with remotes and no planes | |
image = Image.open("./tests/fixtures/tests_samples/COCO/000000039769.png") | |
output = image_classifier(image, candidate_labels=["cat", "plane", "remote"]) | |
self.assertEqual( | |
nested_simplify(output), | |
[ | |
{"score": 0.511, "label": "remote"}, | |
{"score": 0.485, "label": "cat"}, | |
{"score": 0.004, "label": "plane"}, | |
], | |
) | |
output = image_classifier([image] * 5, candidate_labels=["cat", "plane", "remote"], batch_size=2) | |
self.assertEqual( | |
nested_simplify(output), | |
[ | |
[ | |
{"score": 0.511, "label": "remote"}, | |
{"score": 0.485, "label": "cat"}, | |
{"score": 0.004, "label": "plane"}, | |
], | |
] | |
* 5, | |
) | |
def test_large_model_tf(self): | |
image_classifier = pipeline( | |
task="zero-shot-image-classification", model="openai/clip-vit-base-patch32", framework="tf" | |
) | |
# This is an image of 2 cats with remotes and no planes | |
image = Image.open("./tests/fixtures/tests_samples/COCO/000000039769.png") | |
output = image_classifier(image, candidate_labels=["cat", "plane", "remote"]) | |
self.assertEqual( | |
nested_simplify(output), | |
[ | |
{"score": 0.511, "label": "remote"}, | |
{"score": 0.485, "label": "cat"}, | |
{"score": 0.004, "label": "plane"}, | |
], | |
) | |
output = image_classifier([image] * 5, candidate_labels=["cat", "plane", "remote"], batch_size=2) | |
self.assertEqual( | |
nested_simplify(output), | |
[ | |
[ | |
{"score": 0.511, "label": "remote"}, | |
{"score": 0.485, "label": "cat"}, | |
{"score": 0.004, "label": "plane"}, | |
], | |
] | |
* 5, | |
) | |