Add depth estimation pipeline (#18618)

* Add initial files for depth estimation pipelines

* Add test file for depth estimation pipeline

* Update model mapping names

* Add updates for depth estimation output

* Add generic test

* Hopefully fixing the tests.

* Check if test passes

* Add make fixup and make fix-copies changes after rebase with main

* Rebase with main

* Fixing up depth pipeline.

* This is not used anymore.

* Fixing the test. `Image` is a module `Image.Image` is the type.

* Update docs/source/en/main_classes/pipelines.mdx

Co-authored-by: Sylvain Gugger <35901082+sgugger@users.noreply.github.com>

Co-authored-by: Nicolas Patry <patry.nicolas@protonmail.com>
Co-authored-by: Sylvain Gugger <35901082+sgugger@users.noreply.github.com>

tests/pipelines/test_pipelines_depth_estimation.py

@@ -0,0 +1,107 @@
+# 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 hashlib
+import unittest
+
+from transformers import MODEL_FOR_DEPTH_ESTIMATION_MAPPING, is_torch_available, is_vision_available
+from transformers.pipelines import DepthEstimationPipeline, pipeline
+from transformers.testing_utils import nested_simplify, require_tf, require_timm, require_torch, require_vision, slow
+
+from .test_pipelines_common import ANY, PipelineTestCaseMeta
+
+
+if is_torch_available():
+    import torch
+
+if is_vision_available():
+    from PIL import Image
+else:
+
+    class Image:
+        @staticmethod
+        def open(*args, **kwargs):
+            pass
+
+
+def hashimage(image: Image) -> str:
+    m = hashlib.md5(image.tobytes())
+    return m.hexdigest()
+
+
+@require_vision
+@require_timm
+@require_torch
+class DepthEstimationPipelineTests(unittest.TestCase, metaclass=PipelineTestCaseMeta):
+
+    model_mapping = MODEL_FOR_DEPTH_ESTIMATION_MAPPING
+
+    def get_test_pipeline(self, model, tokenizer, feature_extractor):
+        depth_estimator = DepthEstimationPipeline(model=model, feature_extractor=feature_extractor)
+        return depth_estimator, [
+            "./tests/fixtures/tests_samples/COCO/000000039769.png",
+            "./tests/fixtures/tests_samples/COCO/000000039769.png",
+        ]
+
+    def run_pipeline_test(self, depth_estimator, examples):
+        outputs = depth_estimator("./tests/fixtures/tests_samples/COCO/000000039769.png")
+        self.assertEqual({"predicted_depth": ANY(torch.Tensor), "depth": ANY(Image.Image)}, outputs)
+        import datasets
+
+        dataset = datasets.load_dataset("hf-internal-testing/fixtures_image_utils", "image", split="test")
+        outputs = depth_estimator(
+            [
+                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(
+            [
+                {"predicted_depth": ANY(torch.Tensor), "depth": ANY(Image.Image)},
+                {"predicted_depth": ANY(torch.Tensor), "depth": ANY(Image.Image)},
+                {"predicted_depth": ANY(torch.Tensor), "depth": ANY(Image.Image)},
+                {"predicted_depth": ANY(torch.Tensor), "depth": ANY(Image.Image)},
+                {"predicted_depth": ANY(torch.Tensor), "depth": ANY(Image.Image)},
+            ],
+            outputs,
+        )
+
+    @require_tf
+    @unittest.skip("Depth estimation is not implemented in TF")
+    def test_small_model_tf(self):
+        pass
+
+    @slow
+    @require_torch
+    def test_large_model_pt(self):
+        model_id = "Intel/dpt-large"
+        depth_estimator = pipeline("depth-estimation", model=model_id)
+        outputs = depth_estimator("http://images.cocodataset.org/val2017/000000039769.jpg")
+        outputs["depth"] = hashimage(outputs["depth"])
+
+        # This seems flaky.
+        # self.assertEqual(outputs["depth"], "1a39394e282e9f3b0741a90b9f108977")
+        self.assertEqual(nested_simplify(outputs["predicted_depth"].max().item()), 29.304)
+        self.assertEqual(nested_simplify(outputs["predicted_depth"].min().item()), 2.662)
+
+    @require_torch
+    def test_small_model_pt(self):
+        # This is highly irregular to have no small tests.
+        self.skipTest("There is not hf-internal-testing tiny model for either GLPN nor DPT")