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transformers/tests/pipelines/test_pipelines_depth_estimation.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 DepthEstimationOutput
+from huggingface_hub.utils import insecure_hashlib
+
+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 (
+    compare_pipeline_output_to_hub_spec,
+    is_pipeline_test,
+    nested_simplify,
+    require_timm,
+    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
+
+
+def hashimage(image: Image) -> str:
+    m = insecure_hashlib.md5(image.tobytes())
+    return m.hexdigest()
+
+
+@is_pipeline_test
+@require_vision
+@require_timm
+@require_torch
+class DepthEstimationPipelineTests(unittest.TestCase):
+    model_mapping = MODEL_FOR_DEPTH_ESTIMATION_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",
+    ):
+        depth_estimator = DepthEstimationPipeline(
+            model=model,
+            tokenizer=tokenizer,
+            feature_extractor=feature_extractor,
+            image_processor=image_processor,
+            processor=processor,
+            dtype=dtype,
+        )
+        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):
+        self._load_dataset()
+        outputs = depth_estimator("./tests/fixtures/tests_samples/COCO/000000039769.png")
+        self.assertEqual({"predicted_depth": ANY(torch.Tensor), "depth": ANY(Image.Image)}, outputs)
+
+        outputs = depth_estimator(
+            [
+                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(
+            [
+                {"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,
+        )
+
+        for single_output in outputs:
+            compare_pipeline_output_to_hub_spec(single_output, DepthEstimationOutput)
+
+    @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()), 29.306)
+        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(reason="There is not hf-internal-testing tiny model for either GLPN nor DPT")
+
+    @require_torch
+    def test_multiprocess(self):
+        depth_estimator = pipeline(
+            model="hf-internal-testing/tiny-random-DepthAnythingForDepthEstimation",
+            num_workers=2,
+        )
+        outputs = depth_estimator(
+            [
+                "./tests/fixtures/tests_samples/COCO/000000039769.png",
+                "./tests/fixtures/tests_samples/COCO/000000039769.png",
+            ]
+        )
+        self.assertEqual(
+            [
+                {"predicted_depth": ANY(torch.Tensor), "depth": ANY(Image.Image)},
+                {"predicted_depth": ANY(torch.Tensor), "depth": ANY(Image.Image)},
+            ],
+            outputs,
+        )