Add the ImageClassificationPipeline (#11598)

* Add the ImageClassificationPipeline

* Code review

Co-authored-by: patrickvonplaten <patrick.v.platen@gmail.com>

* Have `load_image` at the module level

Co-authored-by: patrickvonplaten <patrick.v.platen@gmail.com>

tests/test_pipelines_image_classification.py

@@ -0,0 +1,115 @@
+# 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 (
+    AutoFeatureExtractor,
+    AutoModelForImageClassification,
+    PreTrainedTokenizer,
+    is_vision_available,
+)
+from transformers.pipelines import ImageClassificationPipeline, pipeline
+from transformers.testing_utils import require_torch, require_vision
+
+
+if is_vision_available():
+    from PIL import Image
+else:
+
+    class Image:
+        @staticmethod
+        def open(*args, **kwargs):
+            pass
+
+
+@require_vision
+@require_torch
+class ImageClassificationPipelineTests(unittest.TestCase):
+    pipeline_task = "image-classification"
+    small_models = ["lysandre/tiny-vit-random"]  # Models tested without the @slow decorator
+    valid_inputs = [
+        {"images": "http://images.cocodataset.org/val2017/000000039769.jpg"},
+        {
+            "images": [
+                "http://images.cocodataset.org/val2017/000000039769.jpg",
+                "http://images.cocodataset.org/val2017/000000039769.jpg",
+            ]
+        },
+        {"images": "tests/fixtures/coco.jpg"},
+        {"images": ["tests/fixtures/coco.jpg", "tests/fixtures/coco.jpg"]},
+        {"images": Image.open("tests/fixtures/coco.jpg")},
+        {"images": [Image.open("tests/fixtures/coco.jpg"), Image.open("tests/fixtures/coco.jpg")]},
+        {"images": [Image.open("tests/fixtures/coco.jpg"), "tests/fixtures/coco.jpg"]},
+    ]
+
+    def test_small_model_from_factory(self):
+        for small_model in self.small_models:
+
+            image_classifier = pipeline("image-classification", model=small_model)
+
+            for valid_input in self.valid_inputs:
+                output = image_classifier(**valid_input)
+                top_k = valid_input.get("top_k", 5)
+
+                def assert_valid_pipeline_output(pipeline_output):
+                    self.assertTrue(isinstance(pipeline_output, list))
+                    self.assertEqual(len(pipeline_output), top_k)
+                    for label_result in pipeline_output:
+                        self.assertTrue(isinstance(label_result, dict))
+                        self.assertIn("label", label_result)
+                        self.assertIn("score", label_result)
+
+                if isinstance(valid_input["images"], list):
+                    self.assertEqual(len(valid_input["images"]), len(output))
+                    for individual_output in output:
+                        assert_valid_pipeline_output(individual_output)
+                else:
+                    assert_valid_pipeline_output(output)
+
+    def test_small_model_from_pipeline(self):
+        for small_model in self.small_models:
+
+            model = AutoModelForImageClassification.from_pretrained(small_model)
+            feature_extractor = AutoFeatureExtractor.from_pretrained(small_model)
+            image_classifier = ImageClassificationPipeline(model=model, feature_extractor=feature_extractor)
+
+            for valid_input in self.valid_inputs:
+                output = image_classifier(**valid_input)
+                top_k = valid_input.get("top_k", 5)
+
+                def assert_valid_pipeline_output(pipeline_output):
+                    self.assertTrue(isinstance(pipeline_output, list))
+                    self.assertEqual(len(pipeline_output), top_k)
+                    for label_result in pipeline_output:
+                        self.assertTrue(isinstance(label_result, dict))
+                        self.assertIn("label", label_result)
+                        self.assertIn("score", label_result)
+
+                if isinstance(valid_input["images"], list):
+                    # When images are batched, pipeline output is a list of lists of dictionaries
+                    self.assertEqual(len(valid_input["images"]), len(output))
+                    for individual_output in output:
+                        assert_valid_pipeline_output(individual_output)
+                else:
+                    # When images are batched, pipeline output is a list of dictionaries
+                    assert_valid_pipeline_output(output)
+
+    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=self.small_models[0], tokenizer=tokenizer)
+
+        self.assertIs(image_classifier.tokenizer, tokenizer)