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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 (
MODEL_FOR_IMAGE_CLASSIFICATION_MAPPING,
TF_MODEL_FOR_IMAGE_CLASSIFICATION_MAPPING,
PreTrainedTokenizer,
is_vision_available,
)
from transformers.pipelines import ImageClassificationPipeline, pipeline
from transformers.testing_utils import (
is_pipeline_test,
nested_simplify,
require_tf,
require_torch,
require_torch_or_tf,
require_vision,
slow,
)
from .test_pipelines_common import ANY
if is_vision_available():
from PIL import Image
else:
class Image:
@staticmethod
def open(*args, **kwargs):
pass
@is_pipeline_test
@require_torch_or_tf
@require_vision
class ImageClassificationPipelineTests(unittest.TestCase):
model_mapping = MODEL_FOR_IMAGE_CLASSIFICATION_MAPPING
tf_model_mapping = TF_MODEL_FOR_IMAGE_CLASSIFICATION_MAPPING
def get_test_pipeline(self, model, tokenizer, processor):
image_classifier = ImageClassificationPipeline(model=model, image_processor=processor, top_k=2)
examples = [
Image.open("./tests/fixtures/tests_samples/COCO/000000039769.png"),
"http://images.cocodataset.org/val2017/000000039769.jpg",
]
return image_classifier, examples
def run_pipeline_test(self, image_classifier, examples):
outputs = image_classifier("./tests/fixtures/tests_samples/COCO/000000039769.png")
self.assertEqual(
outputs,
[
{"score": ANY(float), "label": ANY(str)},
{"score": ANY(float), "label": ANY(str)},
],
)
import datasets
dataset = datasets.load_dataset("hf-internal-testing/fixtures_image_utils", "image", split="test")
# Accepts URL + PIL.Image + lists
outputs = image_classifier(
[
Image.open("./tests/fixtures/tests_samples/COCO/000000039769.png"),
"http://images.cocodataset.org/val2017/000000039769.jpg",
# RGBA
dataset[0]["file"],
# LA
dataset[1]["file"],
# L
dataset[2]["file"],
]
)
self.assertEqual(
outputs,
[
[
{"score": ANY(float), "label": ANY(str)},
{"score": ANY(float), "label": ANY(str)},
],
[
{"score": ANY(float), "label": ANY(str)},
{"score": ANY(float), "label": ANY(str)},
],
[
{"score": ANY(float), "label": ANY(str)},
{"score": ANY(float), "label": ANY(str)},
],
[
{"score": ANY(float), "label": ANY(str)},
{"score": ANY(float), "label": ANY(str)},
],
[
{"score": ANY(float), "label": ANY(str)},
{"score": ANY(float), "label": ANY(str)},
],
],
)
@require_torch
def test_small_model_pt(self):
small_model = "hf-internal-testing/tiny-random-vit"
image_classifier = pipeline("image-classification", model=small_model)
outputs = image_classifier("http://images.cocodataset.org/val2017/000000039769.jpg")
self.assertEqual(
nested_simplify(outputs, decimals=4),
[{"label": "LABEL_1", "score": 0.574}, {"label": "LABEL_0", "score": 0.426}],
)
outputs = image_classifier(
[
"http://images.cocodataset.org/val2017/000000039769.jpg",
"http://images.cocodataset.org/val2017/000000039769.jpg",
],
top_k=2,
)
self.assertEqual(
nested_simplify(outputs, decimals=4),
[
[{"label": "LABEL_1", "score": 0.574}, {"label": "LABEL_0", "score": 0.426}],
[{"label": "LABEL_1", "score": 0.574}, {"label": "LABEL_0", "score": 0.426}],
],
)
@require_tf
def test_small_model_tf(self):
small_model = "hf-internal-testing/tiny-random-vit"
image_classifier = pipeline("image-classification", model=small_model, framework="tf")
outputs = image_classifier("http://images.cocodataset.org/val2017/000000039769.jpg")
self.assertEqual(
nested_simplify(outputs, decimals=4),
[{"label": "LABEL_1", "score": 0.574}, {"label": "LABEL_0", "score": 0.426}],
)
outputs = image_classifier(
[
"http://images.cocodataset.org/val2017/000000039769.jpg",
"http://images.cocodataset.org/val2017/000000039769.jpg",
],
top_k=2,
)
self.assertEqual(
nested_simplify(outputs, decimals=4),
[
[{"label": "LABEL_1", "score": 0.574}, {"label": "LABEL_0", "score": 0.426}],
[{"label": "LABEL_1", "score": 0.574}, {"label": "LABEL_0", "score": 0.426}],
],
)
def test_custom_tokenizer(self):
tokenizer = PreTrainedTokenizer()
# Assert that the pipeline can be initialized with a feature extractor that is not in any mapping
image_classifier = pipeline(
"image-classification", model="hf-internal-testing/tiny-random-vit", tokenizer=tokenizer
)
self.assertIs(image_classifier.tokenizer, tokenizer)
@slow
@require_torch
def test_perceiver(self):
# Perceiver is not tested by `run_pipeline_test` properly.
# That is because the type of feature_extractor and model preprocessor need to be kept
# in sync, which is not the case in the current design
image_classifier = pipeline("image-classification", model="deepmind/vision-perceiver-conv")
outputs = image_classifier("http://images.cocodataset.org/val2017/000000039769.jpg")
self.assertEqual(
nested_simplify(outputs, decimals=4),
[
{"score": 0.4385, "label": "tabby, tabby cat"},
{"score": 0.321, "label": "tiger cat"},
{"score": 0.0502, "label": "Egyptian cat"},
{"score": 0.0137, "label": "crib, cot"},
{"score": 0.007, "label": "radiator"},
],
)
image_classifier = pipeline("image-classification", model="deepmind/vision-perceiver-fourier")
outputs = image_classifier("http://images.cocodataset.org/val2017/000000039769.jpg")
self.assertEqual(
nested_simplify(outputs, decimals=4),
[
{"score": 0.5658, "label": "tabby, tabby cat"},
{"score": 0.1309, "label": "tiger cat"},
{"score": 0.0722, "label": "Egyptian cat"},
{"score": 0.0707, "label": "remote control, remote"},
{"score": 0.0082, "label": "computer keyboard, keypad"},
],
)
image_classifier = pipeline("image-classification", model="deepmind/vision-perceiver-learned")
outputs = image_classifier("http://images.cocodataset.org/val2017/000000039769.jpg")
self.assertEqual(
nested_simplify(outputs, decimals=4),
[
{"score": 0.3022, "label": "tabby, tabby cat"},
{"score": 0.2362, "label": "Egyptian cat"},
{"score": 0.1856, "label": "tiger cat"},
{"score": 0.0324, "label": "remote control, remote"},
{"score": 0.0096, "label": "quilt, comforter, comfort, puff"},
],
)
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