mirror of
https://github.com/huggingface/transformers.git
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59407bbeb3
* First draft * More improvements * Improve model, add custom CUDA code * Import torch before * Add script that imports custom layer * Add everything in new ops directory * Import custom layer in modeling file * Fix ARCHIVE_MAP typo * Creating the custom kernel on the fly. * Import custom layer in modeling file * More improvements * Fix CUDA loading * More improvements * Improve conversion script * Improve conversion script * Make it work until encoder_outputs * Make forward pass work * More improvements * Make logits match original implementation * Make implementation also support single_scale model * Add support for single_scale and dilation checkpoint * Add support for with_box_refine model * Support also two stage model * Improve tests * Fix more tests * Make more tests pass * Upload all models to the hub * Clean up some code * Improve decoder outputs * Rename intermediate hidden states and reference points * Improve model outputs * Move tests to dedicated folder * Improve model outputs * Fix retain_grad test * Improve docs * Clean up and make test_initialization pass * Improve variable names * Add copied from statements * Improve docs * Fix style * Improve docs * Improve docs, move tests to model folder * Fix rebase * Remove DetrForSegmentation from auto mapping * Apply suggestions from code review * Improve variable names and docstrings * Apply some more suggestions from code review * Apply suggestion from code review * better docs and variables names * hint to num_queries and two_stage confusion * remove asserts and code refactor * add exception if two_stage is True and with_box_refine is False * use f-strings * Improve docs and variable names * Fix code quality * Fix rebase * Add require_torch_gpu decorator * Add pip install ninja to CI jobs * Apply suggestion of @sgugger * Remove DeformableDetrForObjectDetection from auto mapping * Remove DeformableDetrModel from auto mapping * Add model to toctree * Add model back to mappings, skip model in pipeline tests * Apply @sgugger's suggestion * Fix imports in the init * Fix copies * Add CPU implementation * Comment out GPU function * Undo previous change * Apply more suggestions * Remove require_torch_gpu annotator * Fix quality * Add logger.info * Fix logger * Fix variable names * Fix initializaztion * Add missing initialization * Update checkpoint name * Add model to doc tests * Add CPU/GPU equivalence test * Add Deformable DETR to pipeline tests * Skip model for object detection pipeline Co-authored-by: Nicolas Patry <patry.nicolas@protonmail.com> Co-authored-by: Nouamane Tazi <nouamane98@gmail.com> Co-authored-by: Sylvain Gugger <Sylvain.gugger@gmail.com>
261 lines
11 KiB
Python
261 lines
11 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 unittest
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from transformers import (
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MODEL_FOR_OBJECT_DETECTION_MAPPING,
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AutoFeatureExtractor,
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AutoModelForObjectDetection,
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ObjectDetectionPipeline,
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is_vision_available,
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pipeline,
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)
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from transformers.testing_utils import (
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is_pipeline_test,
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nested_simplify,
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require_tf,
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require_timm,
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require_torch,
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require_vision,
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slow,
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)
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from .test_pipelines_common import ANY, PipelineTestCaseMeta
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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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@require_vision
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@require_timm
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@require_torch
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@is_pipeline_test
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class ObjectDetectionPipelineTests(unittest.TestCase, metaclass=PipelineTestCaseMeta):
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model_mapping = MODEL_FOR_OBJECT_DETECTION_MAPPING
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def get_test_pipeline(self, model, tokenizer, feature_extractor):
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if model.__class__.__name__ == "DeformableDetrForObjectDetection":
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self.skipTest(
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"""Deformable DETR requires a custom CUDA kernel.
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"""
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)
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object_detector = ObjectDetectionPipeline(model=model, feature_extractor=feature_extractor)
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return object_detector, ["./tests/fixtures/tests_samples/COCO/000000039769.png"]
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def run_pipeline_test(self, object_detector, examples):
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outputs = object_detector("./tests/fixtures/tests_samples/COCO/000000039769.png", threshold=0.0)
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self.assertGreater(len(outputs), 0)
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for detected_object in outputs:
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self.assertEqual(
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detected_object,
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{
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"score": ANY(float),
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"label": ANY(str),
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"box": {"xmin": ANY(int), "ymin": ANY(int), "xmax": ANY(int), "ymax": ANY(int)},
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},
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)
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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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batch = [
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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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batch_outputs = object_detector(batch, threshold=0.0)
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self.assertEqual(len(batch), len(batch_outputs))
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for outputs in batch_outputs:
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self.assertGreater(len(outputs), 0)
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for detected_object in outputs:
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self.assertEqual(
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detected_object,
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{
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"score": ANY(float),
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"label": ANY(str),
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"box": {"xmin": ANY(int), "ymin": ANY(int), "xmax": ANY(int), "ymax": ANY(int)},
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},
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)
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@require_tf
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@unittest.skip("Object detection not implemented in TF")
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def test_small_model_tf(self):
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pass
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@require_torch
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def test_small_model_pt(self):
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model_id = "hf-internal-testing/tiny-detr-mobilenetsv3"
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model = AutoModelForObjectDetection.from_pretrained(model_id)
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feature_extractor = AutoFeatureExtractor.from_pretrained(model_id)
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object_detector = ObjectDetectionPipeline(model=model, feature_extractor=feature_extractor)
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outputs = object_detector("http://images.cocodataset.org/val2017/000000039769.jpg", threshold=0.0)
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self.assertEqual(
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nested_simplify(outputs, decimals=4),
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[
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{"score": 0.3376, "label": "LABEL_0", "box": {"xmin": 159, "ymin": 120, "xmax": 480, "ymax": 359}},
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{"score": 0.3376, "label": "LABEL_0", "box": {"xmin": 159, "ymin": 120, "xmax": 480, "ymax": 359}},
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],
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)
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outputs = object_detector(
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[
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"http://images.cocodataset.org/val2017/000000039769.jpg",
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"http://images.cocodataset.org/val2017/000000039769.jpg",
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],
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threshold=0.0,
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)
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self.assertEqual(
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nested_simplify(outputs, decimals=4),
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[
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[
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{"score": 0.3376, "label": "LABEL_0", "box": {"xmin": 159, "ymin": 120, "xmax": 480, "ymax": 359}},
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{"score": 0.3376, "label": "LABEL_0", "box": {"xmin": 159, "ymin": 120, "xmax": 480, "ymax": 359}},
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],
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[
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{"score": 0.3376, "label": "LABEL_0", "box": {"xmin": 159, "ymin": 120, "xmax": 480, "ymax": 359}},
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{"score": 0.3376, "label": "LABEL_0", "box": {"xmin": 159, "ymin": 120, "xmax": 480, "ymax": 359}},
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],
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],
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)
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@require_torch
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@slow
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def test_large_model_pt(self):
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model_id = "facebook/detr-resnet-50"
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model = AutoModelForObjectDetection.from_pretrained(model_id)
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feature_extractor = AutoFeatureExtractor.from_pretrained(model_id)
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object_detector = ObjectDetectionPipeline(model=model, feature_extractor=feature_extractor)
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outputs = object_detector("http://images.cocodataset.org/val2017/000000039769.jpg")
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self.assertEqual(
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nested_simplify(outputs, decimals=4),
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[
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{"score": 0.9982, "label": "remote", "box": {"xmin": 40, "ymin": 70, "xmax": 175, "ymax": 117}},
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{"score": 0.9960, "label": "remote", "box": {"xmin": 333, "ymin": 72, "xmax": 368, "ymax": 187}},
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{"score": 0.9955, "label": "couch", "box": {"xmin": 0, "ymin": 1, "xmax": 639, "ymax": 473}},
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{"score": 0.9988, "label": "cat", "box": {"xmin": 13, "ymin": 52, "xmax": 314, "ymax": 470}},
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{"score": 0.9987, "label": "cat", "box": {"xmin": 345, "ymin": 23, "xmax": 640, "ymax": 368}},
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],
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)
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outputs = object_detector(
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[
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"http://images.cocodataset.org/val2017/000000039769.jpg",
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"http://images.cocodataset.org/val2017/000000039769.jpg",
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]
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)
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self.assertEqual(
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nested_simplify(outputs, decimals=4),
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[
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[
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{"score": 0.9982, "label": "remote", "box": {"xmin": 40, "ymin": 70, "xmax": 175, "ymax": 117}},
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{"score": 0.9960, "label": "remote", "box": {"xmin": 333, "ymin": 72, "xmax": 368, "ymax": 187}},
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{"score": 0.9955, "label": "couch", "box": {"xmin": 0, "ymin": 1, "xmax": 639, "ymax": 473}},
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{"score": 0.9988, "label": "cat", "box": {"xmin": 13, "ymin": 52, "xmax": 314, "ymax": 470}},
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{"score": 0.9987, "label": "cat", "box": {"xmin": 345, "ymin": 23, "xmax": 640, "ymax": 368}},
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],
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[
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{"score": 0.9982, "label": "remote", "box": {"xmin": 40, "ymin": 70, "xmax": 175, "ymax": 117}},
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{"score": 0.9960, "label": "remote", "box": {"xmin": 333, "ymin": 72, "xmax": 368, "ymax": 187}},
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{"score": 0.9955, "label": "couch", "box": {"xmin": 0, "ymin": 1, "xmax": 639, "ymax": 473}},
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{"score": 0.9988, "label": "cat", "box": {"xmin": 13, "ymin": 52, "xmax": 314, "ymax": 470}},
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{"score": 0.9987, "label": "cat", "box": {"xmin": 345, "ymin": 23, "xmax": 640, "ymax": 368}},
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],
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],
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)
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@require_torch
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@slow
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def test_integration_torch_object_detection(self):
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model_id = "facebook/detr-resnet-50"
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object_detector = pipeline("object-detection", model=model_id)
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outputs = object_detector("http://images.cocodataset.org/val2017/000000039769.jpg")
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self.assertEqual(
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nested_simplify(outputs, decimals=4),
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[
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{"score": 0.9982, "label": "remote", "box": {"xmin": 40, "ymin": 70, "xmax": 175, "ymax": 117}},
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{"score": 0.9960, "label": "remote", "box": {"xmin": 333, "ymin": 72, "xmax": 368, "ymax": 187}},
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{"score": 0.9955, "label": "couch", "box": {"xmin": 0, "ymin": 1, "xmax": 639, "ymax": 473}},
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{"score": 0.9988, "label": "cat", "box": {"xmin": 13, "ymin": 52, "xmax": 314, "ymax": 470}},
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{"score": 0.9987, "label": "cat", "box": {"xmin": 345, "ymin": 23, "xmax": 640, "ymax": 368}},
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],
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)
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outputs = object_detector(
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[
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"http://images.cocodataset.org/val2017/000000039769.jpg",
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"http://images.cocodataset.org/val2017/000000039769.jpg",
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]
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)
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self.assertEqual(
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nested_simplify(outputs, decimals=4),
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[
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[
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{"score": 0.9982, "label": "remote", "box": {"xmin": 40, "ymin": 70, "xmax": 175, "ymax": 117}},
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{"score": 0.9960, "label": "remote", "box": {"xmin": 333, "ymin": 72, "xmax": 368, "ymax": 187}},
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{"score": 0.9955, "label": "couch", "box": {"xmin": 0, "ymin": 1, "xmax": 639, "ymax": 473}},
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{"score": 0.9988, "label": "cat", "box": {"xmin": 13, "ymin": 52, "xmax": 314, "ymax": 470}},
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{"score": 0.9987, "label": "cat", "box": {"xmin": 345, "ymin": 23, "xmax": 640, "ymax": 368}},
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],
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[
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{"score": 0.9982, "label": "remote", "box": {"xmin": 40, "ymin": 70, "xmax": 175, "ymax": 117}},
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{"score": 0.9960, "label": "remote", "box": {"xmin": 333, "ymin": 72, "xmax": 368, "ymax": 187}},
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{"score": 0.9955, "label": "couch", "box": {"xmin": 0, "ymin": 1, "xmax": 639, "ymax": 473}},
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{"score": 0.9988, "label": "cat", "box": {"xmin": 13, "ymin": 52, "xmax": 314, "ymax": 470}},
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{"score": 0.9987, "label": "cat", "box": {"xmin": 345, "ymin": 23, "xmax": 640, "ymax": 368}},
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],
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],
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)
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@require_torch
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@slow
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def test_threshold(self):
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threshold = 0.9985
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model_id = "facebook/detr-resnet-50"
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object_detector = pipeline("object-detection", model=model_id)
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outputs = object_detector("http://images.cocodataset.org/val2017/000000039769.jpg", threshold=threshold)
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self.assertEqual(
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nested_simplify(outputs, decimals=4),
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[
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{"score": 0.9988, "label": "cat", "box": {"xmin": 13, "ymin": 52, "xmax": 314, "ymax": 470}},
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{"score": 0.9987, "label": "cat", "box": {"xmin": 345, "ymin": 23, "xmax": 640, "ymax": 368}},
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],
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)
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