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e94384e4d8
* 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>
108 lines
4.0 KiB
Python
108 lines
4.0 KiB
Python
# Copyright 2021 The HuggingFace Team. All rights reserved.
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#
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# Licensed under the Apache License, Version 2.0 (the "License");
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# you may not use this file except in compliance with the License.
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# You may obtain a copy of the License at
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#
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# http://www.apache.org/licenses/LICENSE-2.0
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#
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# Unless required by applicable law or agreed to in writing, software
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# distributed under the License is distributed on an "AS IS" BASIS,
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# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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# See the License for the specific language governing permissions and
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# limitations under the License.
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import hashlib
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import unittest
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from transformers import MODEL_FOR_DEPTH_ESTIMATION_MAPPING, is_torch_available, is_vision_available
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from transformers.pipelines import DepthEstimationPipeline, pipeline
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from transformers.testing_utils import nested_simplify, require_tf, require_timm, require_torch, require_vision, slow
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from .test_pipelines_common import ANY, PipelineTestCaseMeta
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if is_torch_available():
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import torch
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if is_vision_available():
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from PIL import Image
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else:
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class Image:
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@staticmethod
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def open(*args, **kwargs):
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pass
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def hashimage(image: Image) -> str:
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m = hashlib.md5(image.tobytes())
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return m.hexdigest()
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@require_vision
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@require_timm
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@require_torch
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class DepthEstimationPipelineTests(unittest.TestCase, metaclass=PipelineTestCaseMeta):
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model_mapping = MODEL_FOR_DEPTH_ESTIMATION_MAPPING
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def get_test_pipeline(self, model, tokenizer, feature_extractor):
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depth_estimator = DepthEstimationPipeline(model=model, feature_extractor=feature_extractor)
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return depth_estimator, [
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"./tests/fixtures/tests_samples/COCO/000000039769.png",
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"./tests/fixtures/tests_samples/COCO/000000039769.png",
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]
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def run_pipeline_test(self, depth_estimator, examples):
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outputs = depth_estimator("./tests/fixtures/tests_samples/COCO/000000039769.png")
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self.assertEqual({"predicted_depth": ANY(torch.Tensor), "depth": ANY(Image.Image)}, outputs)
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import datasets
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dataset = datasets.load_dataset("hf-internal-testing/fixtures_image_utils", "image", split="test")
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outputs = depth_estimator(
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[
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Image.open("./tests/fixtures/tests_samples/COCO/000000039769.png"),
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"http://images.cocodataset.org/val2017/000000039769.jpg",
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# RGBA
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dataset[0]["file"],
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# LA
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dataset[1]["file"],
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# L
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dataset[2]["file"],
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]
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)
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self.assertEqual(
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[
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{"predicted_depth": ANY(torch.Tensor), "depth": ANY(Image.Image)},
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{"predicted_depth": ANY(torch.Tensor), "depth": ANY(Image.Image)},
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{"predicted_depth": ANY(torch.Tensor), "depth": ANY(Image.Image)},
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{"predicted_depth": ANY(torch.Tensor), "depth": ANY(Image.Image)},
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{"predicted_depth": ANY(torch.Tensor), "depth": ANY(Image.Image)},
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],
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outputs,
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)
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@require_tf
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@unittest.skip("Depth estimation is not implemented in TF")
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def test_small_model_tf(self):
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pass
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@slow
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@require_torch
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def test_large_model_pt(self):
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model_id = "Intel/dpt-large"
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depth_estimator = pipeline("depth-estimation", model=model_id)
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outputs = depth_estimator("http://images.cocodataset.org/val2017/000000039769.jpg")
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outputs["depth"] = hashimage(outputs["depth"])
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# This seems flaky.
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# self.assertEqual(outputs["depth"], "1a39394e282e9f3b0741a90b9f108977")
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self.assertEqual(nested_simplify(outputs["predicted_depth"].max().item()), 29.304)
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self.assertEqual(nested_simplify(outputs["predicted_depth"].min().item()), 2.662)
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@require_torch
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def test_small_model_pt(self):
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# This is highly irregular to have no small tests.
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self.skipTest("There is not hf-internal-testing tiny model for either GLPN nor DPT")
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