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tests/pipelines/test_pipelines_image_classification.py

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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
+
+import datasets
+from huggingface_hub import ImageClassificationOutputElement
+
+from transformers import (
+    MODEL_FOR_IMAGE_CLASSIFICATION_MAPPING,
+    PreTrainedTokenizerBase,
+    is_torch_available,
+    is_vision_available,
+)
+from transformers.pipelines import ImageClassificationPipeline, pipeline
+from transformers.testing_utils import (
+    compare_pipeline_output_to_hub_spec,
+    is_pipeline_test,
+    nested_simplify,
+    require_torch,
+    require_vision,
+    slow,
+)
+
+from .test_pipelines_common import ANY
+
+
+if is_torch_available():
+    import torch
+
+if is_vision_available():
+    from PIL import Image
+else:
+
+    class Image:
+        @staticmethod
+        def open(*args, **kwargs):
+            pass
+
+
+@is_pipeline_test
+@require_torch
+@require_vision
+class ImageClassificationPipelineTests(unittest.TestCase):
+    model_mapping = MODEL_FOR_IMAGE_CLASSIFICATION_MAPPING
+    _dataset = None
+
+    @classmethod
+    def _load_dataset(cls):
+        # Lazy loading of the dataset. Because it is a class method, it will only be loaded once per pytest process.
+        if cls._dataset is None:
+            # we use revision="refs/pr/1" until the PR is merged
+            # https://hf.co/datasets/hf-internal-testing/fixtures_image_utils/discussions/1
+            cls._dataset = datasets.load_dataset(
+                "hf-internal-testing/fixtures_image_utils", split="test", revision="refs/pr/1"
+            )
+
+    def get_test_pipeline(
+        self,
+        model,
+        tokenizer=None,
+        image_processor=None,
+        feature_extractor=None,
+        processor=None,
+        dtype="float32",
+    ):
+        image_classifier = ImageClassificationPipeline(
+            model=model,
+            tokenizer=tokenizer,
+            feature_extractor=feature_extractor,
+            image_processor=image_processor,
+            processor=processor,
+            dtype=dtype,
+            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):
+        self._load_dataset()
+        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)},
+            ],
+        )
+
+        # 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
+                self._dataset[0]["image"],
+                # LA
+                self._dataset[1]["image"],
+                # L
+                self._dataset[2]["image"],
+            ]
+        )
+        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)},
+                ],
+            ],
+        )
+
+        for single_output in outputs:
+            for output_element in single_output:
+                compare_pipeline_output_to_hub_spec(output_element, ImageClassificationOutputElement)
+
+    @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}],
+            ],
+        )
+
+    def test_custom_tokenizer(self):
+        tokenizer = PreTrainedTokenizerBase()
+
+        # 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)
+
+    @require_torch
+    def test_torch_float16_pipeline(self):
+        image_classifier = pipeline(
+            "image-classification", model="hf-internal-testing/tiny-random-vit", dtype=torch.float16
+        )
+        outputs = image_classifier("http://images.cocodataset.org/val2017/000000039769.jpg")
+
+        self.assertEqual(
+            nested_simplify(outputs, decimals=3),
+            [{"label": "LABEL_1", "score": 0.574}, {"label": "LABEL_0", "score": 0.426}],
+        )
+
+    @require_torch
+    def test_torch_bfloat16_pipeline(self):
+        image_classifier = pipeline(
+            "image-classification", model="hf-internal-testing/tiny-random-vit", dtype=torch.bfloat16
+        )
+        outputs = image_classifier("http://images.cocodataset.org/val2017/000000039769.jpg")
+
+        self.assertEqual(
+            nested_simplify(outputs, decimals=3),
+            [{"label": "LABEL_1", "score": 0.574}, {"label": "LABEL_0", "score": 0.426}],
+        )
+
+    @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"},
+            ],
+        )
+
+    @slow
+    @require_torch
+    def test_multilabel_classification(self):
+        small_model = "hf-internal-testing/tiny-random-vit"
+
+        # Sigmoid is applied for multi-label classification
+        image_classifier = pipeline("image-classification", model=small_model)
+        image_classifier.model.config.problem_type = "multi_label_classification"
+
+        outputs = image_classifier("http://images.cocodataset.org/val2017/000000039769.jpg")
+        self.assertEqual(
+            nested_simplify(outputs, decimals=4),
+            [{"label": "LABEL_1", "score": 0.5356}, {"label": "LABEL_0", "score": 0.4612}],
+        )
+
+        outputs = image_classifier(
+            [
+                "http://images.cocodataset.org/val2017/000000039769.jpg",
+                "http://images.cocodataset.org/val2017/000000039769.jpg",
+            ]
+        )
+        self.assertEqual(
+            nested_simplify(outputs, decimals=4),
+            [
+                [{"label": "LABEL_1", "score": 0.5356}, {"label": "LABEL_0", "score": 0.4612}],
+                [{"label": "LABEL_1", "score": 0.5356}, {"label": "LABEL_0", "score": 0.4612}],
+            ],
+        )
+
+    @slow
+    @require_torch
+    def test_function_to_apply(self):
+        small_model = "hf-internal-testing/tiny-random-vit"
+
+        # Sigmoid is applied for multi-label classification
+        image_classifier = pipeline("image-classification", model=small_model)
+
+        outputs = image_classifier(
+            "http://images.cocodataset.org/val2017/000000039769.jpg",
+            function_to_apply="sigmoid",
+        )
+        self.assertEqual(
+            nested_simplify(outputs, decimals=4),
+            [{"label": "LABEL_1", "score": 0.5356}, {"label": "LABEL_0", "score": 0.4612}],
+        )