Add MM Grounding DINO (#37925)
* first commit Added modular implementation for MM Grounding DINO from starting point created by add-new-model-like. Added conversion script from mmdetection to huggingface. TODO: Some tests are failing so that needs to be fixed. * fixed a bug with modular definition of MMGroundingDinoForObjectDetection where box and class heads were not correctly assigned to inner model * cleaned up a hack in the conversion script * Fixed the expected values in integration tests Cross att masking and cpu-gpu consistency tests are still failing however. * changes for make style and quality * add documentation * clean up contrastive embedding * add mm grounding dino to loss mapping * add model link to config docstring * hack fix for mm grounding dino consistency tests * add special cases for unused config attr check * add all models and update docs * update model doc to the new style * Use super_kwargs for modular config * Move init to the _init_weights function * Add copied from for tests * fixup * update typehints * Fix-copies for tests * fix-copies * Fix init test * fix snippets in docs * fix consistency * fix consistency * update conversion script * fix nits in readme and remove old comments from conversion script * add license * remove unused config args * remove unnecessary if/else in model init * fix quality * Update references * fix test * fixup --------- Co-authored-by: qubvel <qubvel@gmail.com>
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rziga
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title: Mistral3
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title: Mistral3
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- local: model_doc/mllama
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- local: model_doc/mllama
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title: mllama
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title: mllama
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- local: model_doc/mm-grounding-dino
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title: MM Grounding DINO
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- local: model_doc/nougat
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- local: model_doc/nougat
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title: Nougat
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title: Nougat
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- local: model_doc/omdet-turbo
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docs/source/en/model_doc/mm-grounding-dino.md
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docs/source/en/model_doc/mm-grounding-dino.md
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<!--Copyright 2025 The HuggingFace Team. All rights reserved.
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Licensed under the Apache License, Version 2.0 (the "License"); you may not use this file except in compliance with
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the License. You may obtain a copy of the License at
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http://www.apache.org/licenses/LICENSE-2.0
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Unless required by applicable law or agreed to in writing, software distributed under the License is distributed on
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an "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. See the License for the
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specific language governing permissions and limitations under the License.
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⚠️ Note that this file is in Markdown but contain specific syntax for our doc-builder (similar to MDX) that may not be
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rendered properly in your Markdown viewer.
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-->
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<div style="float: right;">
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<div class="flex flex-wrap space-x-1">
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<img alt="PyTorch" src="https://img.shields.io/badge/PyTorch-DE3412?style=flat&logo=pytorch&logoColor=white">
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</div>
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</div>
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# MM Grounding DINO
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[MM Grounding DINO](https://arxiv.org/abs/2401.02361) model was proposed in [An Open and Comprehensive Pipeline for Unified Object Grounding and Detection](https://arxiv.org/abs/2401.02361) by Xiangyu Zhao, Yicheng Chen, Shilin Xu, Xiangtai Li, Xinjiang Wang, Yining Li, Haian Huang>.
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MM Grounding DINO improves upon the [Grounding DINO](https://huggingface.co/docs/transformers/model_doc/grounding-dino) by improving the contrastive class head and removing the parameter sharing in the decoder, improving zero-shot detection performance on both COCO (50.6(+2.2) AP) and LVIS (31.9(+11.8) val AP and 41.4(+12.6) minival AP).
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You can find all the original MM Grounding DINO checkpoints under the [MM Grounding DINO](https://huggingface.co/collections/openmmlab-community/mm-grounding-dino-688cbde05b814c4e2832f9df) collection. This model also supports LLMDet inference. You can find LLMDet checkpoints under the [LLMDet](https://huggingface.co/collections/iSEE-Laboratory/llmdet-688475906dc235d5f1dc678e) collection.
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> [!TIP]
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> Click on the MM Grounding DINO models in the right sidebar for more examples of how to apply MM Grounding DINO to different MM Grounding DINO tasks.
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The example below demonstrates how to generate text based on an image with the [`AutoModelForZeroShotObjectDetection`] class.
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<hfoptions id="usage">
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<hfoption id="AutoModel">
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```py
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import torch
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from transformers import AutoModelForZeroShotObjectDetection, AutoProcessor
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from transformers.image_utils import load_image
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# Prepare processor and model
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model_id = "openmmlab-community/mm_grounding_dino_tiny_o365v1_goldg_v3det"
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device = "cuda" if torch.cuda.is_available() else "cpu"
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processor = AutoProcessor.from_pretrained(model_id)
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model = AutoModelForZeroShotObjectDetection.from_pretrained(model_id).to(device)
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# Prepare inputs
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image_url = "http://images.cocodataset.org/val2017/000000039769.jpg"
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image = load_image(image_url)
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text_labels = [["a cat", "a remote control"]]
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inputs = processor(images=image, text=text_labels, return_tensors="pt").to(device)
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# Run inference
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with torch.no_grad():
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outputs = model(**inputs)
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# Postprocess outputs
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results = processor.post_process_grounded_object_detection(
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outputs,
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threshold=0.4,
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target_sizes=[(image.height, image.width)]
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)
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# Retrieve the first image result
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result = results[0]
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for box, score, labels in zip(result["boxes"], result["scores"], result["labels"]):
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box = [round(x, 2) for x in box.tolist()]
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print(f"Detected {labels} with confidence {round(score.item(), 3)} at location {box}")
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```
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</hfoption>
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</hfoptions>
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## Notes
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- Here's a table of models and their object detection performance results on COCO (results from [official repo](https://github.com/open-mmlab/mmdetection/blob/main/configs/mm_grounding_dino/README.md)):
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| Model | Backbone | Pre-Train Data | Style | COCO mAP |
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| ------------------------------------------------------------------------------------------------------------------------------ | -------- | ------------------------ | --------- | ---------- |
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| [mm_grounding_dino_tiny_o365v1_goldg](https://huggingface.co/openmmlab-community/mm_grounding_dino_tiny_o365v1_goldg) | Swin-T | O365,GoldG | Zero-shot | 50.4(+2.3) |
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| [mm_grounding_dino_tiny_o365v1_goldg_grit](https://huggingface.co/openmmlab-community/mm_grounding_dino_tiny_o365v1_goldg_grit) | Swin-T | O365,GoldG,GRIT | Zero-shot | 50.5(+2.1) |
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| [mm_grounding_dino_tiny_o365v1_goldg_v3det](https://huggingface.co/openmmlab-community/mm_grounding_dino_tiny_o365v1_goldg_v3det) | Swin-T | O365,GoldG,V3Det | Zero-shot | 50.6(+2.2) |
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| [mm_grounding_dino_tiny_o365v1_goldg_grit_v3det](https://huggingface.co/openmmlab-community/mm_grounding_dino_tiny_o365v1_goldg_grit_v3det) | Swin-T | O365,GoldG,GRIT,V3Det | Zero-shot | 50.4(+2.0) |
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| [mm_grounding_dino_base_o365v1_goldg_v3det](https://huggingface.co/openmmlab-community/mm_grounding_dino_base_o365v1_goldg_v3det) | Swin-B | O365,GoldG,V3Det | Zero-shot | 52.5 |
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| [mm_grounding_dino_base_all](https://huggingface.co/openmmlab-community/mm_grounding_dino_base_all) | Swin-B | O365,ALL | - | 59.5 |
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| [mm_grounding_dino_large_o365v2_oiv6_goldg](https://huggingface.co/openmmlab-community/mm_grounding_dino_large_o365v2_oiv6_goldg) | Swin-L | O365V2,OpenImageV6,GoldG | Zero-shot | 53.0 |
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| [mm_grounding_dino_large_all](https://huggingface.co/openmmlab-community/mm_grounding_dino_large_all) | Swin-L | O365V2,OpenImageV6,ALL | - | 60.3 |
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- Here's a table of MM Grounding DINO tiny models and their object detection performance on LVIS (results from [official repo](https://github.com/open-mmlab/mmdetection/blob/main/configs/mm_grounding_dino/README.md)):
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| Model | Pre-Train Data | MiniVal APr | MiniVal APc | MiniVal APf | MiniVal AP | Val1.0 APr | Val1.0 APc | Val1.0 APf | Val1.0 AP |
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| ------------------------------------------------------------------------------------------------------------------------------ | --------------------- | ----------- | ----------- | ----------- | ----------- | ---------- | ---------- | ---------- | ----------- |
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| [mm_grounding_dino_tiny_o365v1_goldg](https://huggingface.co/openmmlab-community/mm_grounding_dino_tiny_o365v1_goldg) | O365,GoldG | 28.1 | 30.2 | 42.0 | 35.7(+6.9) | 17.1 | 22.4 | 36.5 | 27.0(+6.9) |
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| [mm_grounding_dino_tiny_o365v1_goldg_grit](https://huggingface.co/openmmlab-community/mm_grounding_dino_tiny_o365v1_goldg_grit) | O365,GoldG,GRIT | 26.6 | 32.4 | 41.8 | 36.5(+7.7) | 17.3 | 22.6 | 36.4 | 27.1(+7.0) |
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| [mm_grounding_dino_tiny_o365v1_goldg_v3det](https://huggingface.co/openmmlab-community/mm_grounding_dino_tiny_o365v1_goldg_v3det) | O365,GoldG,V3Det | 33.0 | 36.0 | 45.9 | 40.5(+11.7) | 21.5 | 25.5 | 40.2 | 30.6(+10.5) |
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| [mm_grounding_dino_tiny_o365v1_goldg_grit_v3det](https://huggingface.co/openmmlab-community/mm_grounding_dino_tiny_o365v1_goldg_grit_v3det) | O365,GoldG,GRIT,V3Det | 34.2 | 37.4 | 46.2 | 41.4(+12.6) | 23.6 | 27.6 | 40.5 | 31.9(+11.8) |
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- This implementation also supports inference for [LLMDet](https://github.com/iSEE-Laboratory/LLMDet). Here's a table of LLMDet models and their performance on LVIS (results from [official repo](https://github.com/iSEE-Laboratory/LLMDet)):
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| Model | Pre-Train Data | MiniVal APr | MiniVal APc | MiniVal APf | MiniVal AP | Val1.0 APr | Val1.0 APc | Val1.0 APf | Val1.0 AP |
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| --------------------------------------------------------- | -------------------------------------------- | ------------ | ----------- | ----------- | ----------- | ---------- | ---------- | ---------- | ----------- |
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| [llmdet_tiny](https://huggingface.co/iSEE-Laboratory/llmdet_tiny) | (O365,GoldG,GRIT,V3Det) + GroundingCap-1M | 44.7 | 37.3 | 39.5 | 50.7 | 34.9 | 26.0 | 30.1 | 44.3 |
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| [llmdet_base](https://huggingface.co/iSEE-Laboratory/llmdet_base) | (O365,GoldG,V3Det) + GroundingCap-1M | 48.3 | 40.8 | 43.1 | 54.3 | 38.5 | 28.2 | 34.3 | 47.8 |
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| [llmdet_large](https://huggingface.co/iSEE-Laboratory/llmdet_large) | (O365V2,OpenImageV6,GoldG) + GroundingCap-1M | 51.1 | 45.1 | 46.1 | 56.6 | 42.0 | 31.6 | 38.8 | 50.2 |
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## MMGroundingDinoConfig
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[[autodoc]] MMGroundingDinoConfig
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## MMGroundingDinoModel
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[[autodoc]] MMGroundingDinoModel
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- forward
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## MMGroundingDinoForObjectDetection
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[[autodoc]] MMGroundingDinoForObjectDetection
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- forward
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"ConditionalDetrForObjectDetection": DeformableDetrForObjectDetectionLoss,
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"ConditionalDetrForObjectDetection": DeformableDetrForObjectDetectionLoss,
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"DabDetrForObjectDetection": DeformableDetrForObjectDetectionLoss,
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"DabDetrForObjectDetection": DeformableDetrForObjectDetectionLoss,
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"GroundingDinoForObjectDetection": GroundingDinoForObjectDetectionLoss,
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"GroundingDinoForObjectDetection": GroundingDinoForObjectDetectionLoss,
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"MMGroundingDinoForObjectDetection": GroundingDinoForObjectDetectionLoss,
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"ConditionalDetrForSegmentation": DeformableDetrForSegmentationLoss,
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"ConditionalDetrForSegmentation": DeformableDetrForSegmentationLoss,
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"RTDetrForObjectDetection": RTDetrForObjectDetectionLoss,
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"RTDetrForObjectDetection": RTDetrForObjectDetectionLoss,
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"RTDetrV2ForObjectDetection": RTDetrForObjectDetectionLoss,
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"RTDetrV2ForObjectDetection": RTDetrForObjectDetectionLoss,
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@@ -244,6 +244,7 @@ CONFIG_MAPPING_NAMES = OrderedDict[str, str](
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("mixtral", "MixtralConfig"),
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("mixtral", "MixtralConfig"),
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("mlcd", "MLCDVisionConfig"),
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("mlcd", "MLCDVisionConfig"),
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("mllama", "MllamaConfig"),
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("mllama", "MllamaConfig"),
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("mm-grounding-dino", "MMGroundingDinoConfig"),
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("mobilebert", "MobileBertConfig"),
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("mobilebert", "MobileBertConfig"),
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("mobilenet_v1", "MobileNetV1Config"),
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("mobilenet_v1", "MobileNetV1Config"),
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("mobilenet_v2", "MobileNetV2Config"),
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("mobilenet_v2", "MobileNetV2Config"),
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("mlcd", "MLCD"),
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("mlcd", "MLCD"),
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("mllama", "Mllama"),
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("mllama", "Mllama"),
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("mluke", "mLUKE"),
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("mluke", "mLUKE"),
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("mm-grounding-dino", "MM Grounding DINO"),
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("mms", "MMS"),
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("mms", "MMS"),
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("mobilebert", "MobileBERT"),
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("mobilebert", "MobileBERT"),
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("mobilenet_v1", "MobileNetV1"),
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("mobilenet_v1", "MobileNetV1"),
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("mistral3", ("PixtralImageProcessor", "PixtralImageProcessorFast")),
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("mistral3", ("PixtralImageProcessor", "PixtralImageProcessorFast")),
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("mlcd", ("CLIPImageProcessor", "CLIPImageProcessorFast")),
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("mlcd", ("CLIPImageProcessor", "CLIPImageProcessorFast")),
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("mllama", ("MllamaImageProcessor",)),
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("mllama", ("MllamaImageProcessor",)),
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("mm-grounding-dino", ("GroundingDinoImageProcessor", "GroundingDinoImageProcessorFast")),
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("mobilenet_v1", ("MobileNetV1ImageProcessor", "MobileNetV1ImageProcessorFast")),
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("mobilenet_v1", ("MobileNetV1ImageProcessor", "MobileNetV1ImageProcessorFast")),
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("mobilenet_v2", ("MobileNetV2ImageProcessor", "MobileNetV2ImageProcessorFast")),
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("mobilenet_v2", ("MobileNetV2ImageProcessor", "MobileNetV2ImageProcessorFast")),
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("mobilevit", ("MobileViTImageProcessor", "MobileViTImageProcessorFast")),
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("mobilevit", ("MobileViTImageProcessor", "MobileViTImageProcessorFast")),
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("mixtral", "MixtralModel"),
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("mixtral", "MixtralModel"),
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("mlcd", "MLCDVisionModel"),
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("mlcd", "MLCDVisionModel"),
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("mllama", "MllamaModel"),
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("mllama", "MllamaModel"),
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("mm-grounding-dino", "MMGroundingDinoModel"),
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("mobilebert", "MobileBertModel"),
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("mobilebert", "MobileBertModel"),
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("mobilenet_v1", "MobileNetV1Model"),
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("mobilenet_v1", "MobileNetV1Model"),
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("mobilenet_v2", "MobileNetV2Model"),
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("mobilenet_v2", "MobileNetV2Model"),
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@@ -1057,6 +1058,7 @@ MODEL_FOR_ZERO_SHOT_OBJECT_DETECTION_MAPPING_NAMES = OrderedDict(
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[
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[
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# Model for Zero Shot Object Detection mapping
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# Model for Zero Shot Object Detection mapping
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("grounding-dino", "GroundingDinoForObjectDetection"),
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("grounding-dino", "GroundingDinoForObjectDetection"),
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("mm-grounding-dino", "MMGroundingDinoForObjectDetection"),
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("omdet-turbo", "OmDetTurboForObjectDetection"),
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("omdet-turbo", "OmDetTurboForObjectDetection"),
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("owlv2", "Owlv2ForObjectDetection"),
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("owlv2", "Owlv2ForObjectDetection"),
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("owlvit", "OwlViTForObjectDetection"),
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("owlvit", "OwlViTForObjectDetection"),
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@@ -100,6 +100,7 @@ PROCESSOR_MAPPING_NAMES = OrderedDict(
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("mgp-str", "MgpstrProcessor"),
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("mgp-str", "MgpstrProcessor"),
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("mistral3", "PixtralProcessor"),
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("mistral3", "PixtralProcessor"),
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("mllama", "MllamaProcessor"),
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("mllama", "MllamaProcessor"),
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("mm-grounding-dino", "GroundingDinoProcessor"),
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("moonshine", "Wav2Vec2Processor"),
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("moonshine", "Wav2Vec2Processor"),
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("oneformer", "OneFormerProcessor"),
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("oneformer", "OneFormerProcessor"),
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("owlv2", "Owlv2Processor"),
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("owlv2", "Owlv2Processor"),
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@@ -430,6 +430,7 @@ TOKENIZER_MAPPING_NAMES = OrderedDict[str, tuple[Optional[str], Optional[str]]](
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),
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),
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("mllama", ("LlamaTokenizer", "LlamaTokenizerFast" if is_tokenizers_available() else None)),
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("mllama", ("LlamaTokenizer", "LlamaTokenizerFast" if is_tokenizers_available() else None)),
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("mluke", ("MLukeTokenizer" if is_sentencepiece_available() else None, None)),
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("mluke", ("MLukeTokenizer" if is_sentencepiece_available() else None, None)),
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("mm-grounding-dino", ("BertTokenizer", "BertTokenizerFast" if is_tokenizers_available() else None)),
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("mobilebert", ("MobileBertTokenizer", "MobileBertTokenizerFast" if is_tokenizers_available() else None)),
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("mobilebert", ("MobileBertTokenizer", "MobileBertTokenizerFast" if is_tokenizers_available() else None)),
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("modernbert", (None, "PreTrainedTokenizerFast" if is_tokenizers_available() else None)),
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("modernbert", (None, "PreTrainedTokenizerFast" if is_tokenizers_available() else None)),
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("moonshine", (None, "PreTrainedTokenizerFast" if is_tokenizers_available() else None)),
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("moonshine", (None, "PreTrainedTokenizerFast" if is_tokenizers_available() else None)),
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27
src/transformers/models/mm_grounding_dino/__init__.py
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# Copyright 2025 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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from typing import TYPE_CHECKING
|
||||||
|
|
||||||
|
from ...utils import _LazyModule
|
||||||
|
from ...utils.import_utils import define_import_structure
|
||||||
|
|
||||||
|
|
||||||
|
if TYPE_CHECKING:
|
||||||
|
from .configuration_mm_grounding_dino import *
|
||||||
|
from .modeling_mm_grounding_dino import *
|
||||||
|
else:
|
||||||
|
import sys
|
||||||
|
|
||||||
|
_file = globals()["__file__"]
|
||||||
|
sys.modules[__name__] = _LazyModule(__name__, _file, define_import_structure(_file), module_spec=__spec__)
|
||||||
@@ -0,0 +1,292 @@
|
|||||||
|
# 🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨
|
||||||
|
# This file was automatically generated from src/transformers/models/mm_grounding_dino/modular_mm_grounding_dino.py.
|
||||||
|
# Do NOT edit this file manually as any edits will be overwritten by the generation of
|
||||||
|
# the file from the modular. If any change should be done, please apply the change to the
|
||||||
|
# modular_mm_grounding_dino.py file directly. One of our CI enforces this.
|
||||||
|
# 🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨
|
||||||
|
# coding=utf-8
|
||||||
|
# Copyright 2025 The HuggingFace Inc. team.
|
||||||
|
#
|
||||||
|
# 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.
|
||||||
|
|
||||||
|
from ...configuration_utils import PretrainedConfig
|
||||||
|
from ...utils import logging
|
||||||
|
from ...utils.backbone_utils import verify_backbone_config_arguments
|
||||||
|
from ..auto import CONFIG_MAPPING
|
||||||
|
|
||||||
|
|
||||||
|
logger = logging.get_logger(__name__)
|
||||||
|
|
||||||
|
|
||||||
|
class MMGroundingDinoConfig(PretrainedConfig):
|
||||||
|
r"""
|
||||||
|
This is the configuration class to store the configuration of a [`MMGroundingDinoModel`]. It is used to instantiate a
|
||||||
|
MM Grounding DINO model according to the specified arguments, defining the model architecture. Instantiating a
|
||||||
|
configuration with the defaults will yield a similar configuration to that of the MM Grounding DINO tiny architecture
|
||||||
|
[openmmlab-community/mm_grounding_dino_tiny_o365v1_goldg_v3det](https://huggingface.co/openmmlab-community/mm_grounding_dino_tiny_o365v1_goldg_v3det).
|
||||||
|
|
||||||
|
Configuration objects inherit from [`PretrainedConfig`] and can be used to control the model outputs. Read the
|
||||||
|
documentation from [`PretrainedConfig`] for more information.
|
||||||
|
|
||||||
|
Args:
|
||||||
|
backbone_config (`PretrainedConfig` or `dict`, *optional*, defaults to `ResNetConfig()`):
|
||||||
|
The configuration of the backbone model.
|
||||||
|
backbone (`str`, *optional*):
|
||||||
|
Name of backbone to use when `backbone_config` is `None`. If `use_pretrained_backbone` is `True`, this
|
||||||
|
will load the corresponding pretrained weights from the timm or transformers library. If `use_pretrained_backbone`
|
||||||
|
is `False`, this loads the backbone's config and uses that to initialize the backbone with random weights.
|
||||||
|
use_pretrained_backbone (`bool`, *optional*, defaults to `False`):
|
||||||
|
Whether to use pretrained weights for the backbone.
|
||||||
|
use_timm_backbone (`bool`, *optional*, defaults to `False`):
|
||||||
|
Whether to load `backbone` from the timm library. If `False`, the backbone is loaded from the transformers
|
||||||
|
library.
|
||||||
|
backbone_kwargs (`dict`, *optional*):
|
||||||
|
Keyword arguments to be passed to AutoBackbone when loading from a checkpoint
|
||||||
|
e.g. `{'out_indices': (0, 1, 2, 3)}`. Cannot be specified if `backbone_config` is set.
|
||||||
|
text_config (`Union[AutoConfig, dict]`, *optional*, defaults to `BertConfig`):
|
||||||
|
The config object or dictionary of the text backbone.
|
||||||
|
num_queries (`int`, *optional*, defaults to 900):
|
||||||
|
Number of object queries, i.e. detection slots. This is the maximal number of objects
|
||||||
|
[`MMGroundingDinoModel`] can detect in a single image.
|
||||||
|
encoder_layers (`int`, *optional*, defaults to 6):
|
||||||
|
Number of encoder layers.
|
||||||
|
encoder_ffn_dim (`int`, *optional*, defaults to 2048):
|
||||||
|
Dimension of the "intermediate" (often named feed-forward) layer in decoder.
|
||||||
|
encoder_attention_heads (`int`, *optional*, defaults to 8):
|
||||||
|
Number of attention heads for each attention layer in the Transformer encoder.
|
||||||
|
decoder_layers (`int`, *optional*, defaults to 6):
|
||||||
|
Number of decoder layers.
|
||||||
|
decoder_ffn_dim (`int`, *optional*, defaults to 2048):
|
||||||
|
Dimension of the "intermediate" (often named feed-forward) layer in decoder.
|
||||||
|
decoder_attention_heads (`int`, *optional*, defaults to 8):
|
||||||
|
Number of attention heads for each attention layer in the Transformer decoder.
|
||||||
|
is_encoder_decoder (`bool`, *optional*, defaults to `True`):
|
||||||
|
Whether the model is used as an encoder/decoder or not.
|
||||||
|
activation_function (`str` or `function`, *optional*, defaults to `"relu"`):
|
||||||
|
The non-linear activation function (function or string) in the encoder and pooler. If string, `"gelu"`,
|
||||||
|
`"relu"`, `"silu"` and `"gelu_new"` are supported.
|
||||||
|
d_model (`int`, *optional*, defaults to 256):
|
||||||
|
Dimension of the layers.
|
||||||
|
dropout (`float`, *optional*, defaults to 0.1):
|
||||||
|
The dropout probability for all fully connected layers in the embeddings, encoder, and pooler.
|
||||||
|
attention_dropout (`float`, *optional*, defaults to 0.0):
|
||||||
|
The dropout ratio for the attention probabilities.
|
||||||
|
activation_dropout (`float`, *optional*, defaults to 0.0):
|
||||||
|
The dropout ratio for activations inside the fully connected layer.
|
||||||
|
auxiliary_loss (`bool`, *optional*, defaults to `False`):
|
||||||
|
Whether auxiliary decoding losses (loss at each decoder layer) are to be used.
|
||||||
|
position_embedding_type (`str`, *optional*, defaults to `"sine"`):
|
||||||
|
Type of position embeddings to be used on top of the image features. One of `"sine"` or `"learned"`.
|
||||||
|
num_feature_levels (`int`, *optional*, defaults to 4):
|
||||||
|
The number of input feature levels.
|
||||||
|
encoder_n_points (`int`, *optional*, defaults to 4):
|
||||||
|
The number of sampled keys in each feature level for each attention head in the encoder.
|
||||||
|
decoder_n_points (`int`, *optional*, defaults to 4):
|
||||||
|
The number of sampled keys in each feature level for each attention head in the decoder.
|
||||||
|
two_stage (`bool`, *optional*, defaults to `True`):
|
||||||
|
Whether to apply a two-stage deformable DETR, where the region proposals are also generated by a variant of
|
||||||
|
Grounding DINO, which are further fed into the decoder for iterative bounding box refinement.
|
||||||
|
class_cost (`float`, *optional*, defaults to 1.0):
|
||||||
|
Relative weight of the classification error in the Hungarian matching cost.
|
||||||
|
bbox_cost (`float`, *optional*, defaults to 5.0):
|
||||||
|
Relative weight of the L1 error of the bounding box coordinates in the Hungarian matching cost.
|
||||||
|
giou_cost (`float`, *optional*, defaults to 2.0):
|
||||||
|
Relative weight of the generalized IoU loss of the bounding box in the Hungarian matching cost.
|
||||||
|
bbox_loss_coefficient (`float`, *optional*, defaults to 5.0):
|
||||||
|
Relative weight of the L1 bounding box loss in the object detection loss.
|
||||||
|
giou_loss_coefficient (`float`, *optional*, defaults to 2.0):
|
||||||
|
Relative weight of the generalized IoU loss in the object detection loss.
|
||||||
|
focal_alpha (`float`, *optional*, defaults to 0.25):
|
||||||
|
Alpha parameter in the focal loss.
|
||||||
|
disable_custom_kernels (`bool`, *optional*, defaults to `False`):
|
||||||
|
Disable the use of custom CUDA and CPU kernels. This option is necessary for the ONNX export, as custom
|
||||||
|
kernels are not supported by PyTorch ONNX export.
|
||||||
|
max_text_len (`int`, *optional*, defaults to 256):
|
||||||
|
The maximum length of the text input.
|
||||||
|
text_enhancer_dropout (`float`, *optional*, defaults to 0.0):
|
||||||
|
The dropout ratio for the text enhancer.
|
||||||
|
fusion_droppath (`float`, *optional*, defaults to 0.1):
|
||||||
|
The droppath ratio for the fusion module.
|
||||||
|
fusion_dropout (`float`, *optional*, defaults to 0.0):
|
||||||
|
The dropout ratio for the fusion module.
|
||||||
|
embedding_init_target (`bool`, *optional*, defaults to `True`):
|
||||||
|
Whether to initialize the target with Embedding weights.
|
||||||
|
query_dim (`int`, *optional*, defaults to 4):
|
||||||
|
The dimension of the query vector.
|
||||||
|
positional_embedding_temperature (`float`, *optional*, defaults to 20):
|
||||||
|
The temperature for Sine Positional Embedding that is used together with vision backbone.
|
||||||
|
init_std (`float`, *optional*, defaults to 0.02):
|
||||||
|
The standard deviation of the truncated_normal_initializer for initializing all weight matrices.
|
||||||
|
layer_norm_eps (`float`, *optional*, defaults to 1e-05):
|
||||||
|
The epsilon used by the layer normalization layers.
|
||||||
|
|
||||||
|
Examples:
|
||||||
|
|
||||||
|
```python
|
||||||
|
>>> from transformers import MMGroundingDinoConfig, MMGroundingDinoModel
|
||||||
|
|
||||||
|
>>> # Initializing a MM Grounding DINO configuration
|
||||||
|
>>> configuration = MMGroundingDinoConfig()
|
||||||
|
|
||||||
|
>>> # Initializing a model (with random weights) from the configuration
|
||||||
|
>>> model = MMGroundingDinoModel(configuration)
|
||||||
|
|
||||||
|
>>> # Accessing the model configuration
|
||||||
|
>>> configuration = model.config
|
||||||
|
```"""
|
||||||
|
|
||||||
|
model_type = "mm-grounding-dino"
|
||||||
|
attribute_map = {
|
||||||
|
"hidden_size": "d_model",
|
||||||
|
"num_attention_heads": "encoder_attention_heads",
|
||||||
|
}
|
||||||
|
|
||||||
|
def __init__(
|
||||||
|
self,
|
||||||
|
backbone_config=None,
|
||||||
|
backbone=None,
|
||||||
|
use_pretrained_backbone=False,
|
||||||
|
use_timm_backbone=False,
|
||||||
|
backbone_kwargs=None,
|
||||||
|
text_config=None,
|
||||||
|
num_queries=900,
|
||||||
|
encoder_layers=6,
|
||||||
|
encoder_ffn_dim=2048,
|
||||||
|
encoder_attention_heads=8,
|
||||||
|
decoder_layers=6,
|
||||||
|
decoder_ffn_dim=2048,
|
||||||
|
decoder_attention_heads=8,
|
||||||
|
is_encoder_decoder=True,
|
||||||
|
activation_function="relu",
|
||||||
|
d_model=256,
|
||||||
|
dropout=0.1,
|
||||||
|
attention_dropout=0.0,
|
||||||
|
activation_dropout=0.0,
|
||||||
|
auxiliary_loss=False,
|
||||||
|
position_embedding_type="sine",
|
||||||
|
num_feature_levels=4,
|
||||||
|
encoder_n_points=4,
|
||||||
|
decoder_n_points=4,
|
||||||
|
two_stage=True,
|
||||||
|
class_cost=1.0,
|
||||||
|
bbox_cost=5.0,
|
||||||
|
giou_cost=2.0,
|
||||||
|
bbox_loss_coefficient=5.0,
|
||||||
|
giou_loss_coefficient=2.0,
|
||||||
|
focal_alpha=0.25,
|
||||||
|
disable_custom_kernels=False,
|
||||||
|
# other parameters
|
||||||
|
max_text_len=256,
|
||||||
|
text_enhancer_dropout=0.0,
|
||||||
|
fusion_droppath=0.1,
|
||||||
|
fusion_dropout=0.0,
|
||||||
|
embedding_init_target=True,
|
||||||
|
query_dim=4,
|
||||||
|
positional_embedding_temperature=20,
|
||||||
|
init_std=0.02,
|
||||||
|
layer_norm_eps=1e-5,
|
||||||
|
**kwargs,
|
||||||
|
):
|
||||||
|
super().__init__(is_encoder_decoder=is_encoder_decoder, **kwargs)
|
||||||
|
if backbone_config is None and backbone is None:
|
||||||
|
logger.info("`backbone_config` is `None`. Initializing the config with the default `Swin` backbone.")
|
||||||
|
backbone_config = CONFIG_MAPPING["swin"](
|
||||||
|
window_size=7,
|
||||||
|
image_size=224,
|
||||||
|
embed_dim=96,
|
||||||
|
depths=[2, 2, 6, 2],
|
||||||
|
num_heads=[3, 6, 12, 24],
|
||||||
|
out_indices=[2, 3, 4],
|
||||||
|
)
|
||||||
|
elif isinstance(backbone_config, dict):
|
||||||
|
backbone_model_type = backbone_config.pop("model_type")
|
||||||
|
config_class = CONFIG_MAPPING[backbone_model_type]
|
||||||
|
backbone_config = config_class.from_dict(backbone_config)
|
||||||
|
|
||||||
|
verify_backbone_config_arguments(
|
||||||
|
use_timm_backbone=use_timm_backbone,
|
||||||
|
use_pretrained_backbone=use_pretrained_backbone,
|
||||||
|
backbone=backbone,
|
||||||
|
backbone_config=backbone_config,
|
||||||
|
backbone_kwargs=backbone_kwargs,
|
||||||
|
)
|
||||||
|
|
||||||
|
if text_config is None:
|
||||||
|
text_config = {}
|
||||||
|
logger.info("text_config is None. Initializing the text config with default values (`BertConfig`).")
|
||||||
|
|
||||||
|
self.backbone_config = backbone_config
|
||||||
|
self.backbone = backbone
|
||||||
|
self.use_pretrained_backbone = use_pretrained_backbone
|
||||||
|
self.use_timm_backbone = use_timm_backbone
|
||||||
|
self.backbone_kwargs = backbone_kwargs
|
||||||
|
self.num_queries = num_queries
|
||||||
|
self.d_model = d_model
|
||||||
|
self.encoder_ffn_dim = encoder_ffn_dim
|
||||||
|
self.encoder_layers = encoder_layers
|
||||||
|
self.encoder_attention_heads = encoder_attention_heads
|
||||||
|
self.decoder_ffn_dim = decoder_ffn_dim
|
||||||
|
self.decoder_layers = decoder_layers
|
||||||
|
self.decoder_attention_heads = decoder_attention_heads
|
||||||
|
self.dropout = dropout
|
||||||
|
self.attention_dropout = attention_dropout
|
||||||
|
self.activation_dropout = activation_dropout
|
||||||
|
self.activation_function = activation_function
|
||||||
|
self.auxiliary_loss = auxiliary_loss
|
||||||
|
self.position_embedding_type = position_embedding_type
|
||||||
|
# deformable attributes
|
||||||
|
self.num_feature_levels = num_feature_levels
|
||||||
|
self.encoder_n_points = encoder_n_points
|
||||||
|
self.decoder_n_points = decoder_n_points
|
||||||
|
self.two_stage = two_stage
|
||||||
|
# Hungarian matcher
|
||||||
|
self.class_cost = class_cost
|
||||||
|
self.bbox_cost = bbox_cost
|
||||||
|
self.giou_cost = giou_cost
|
||||||
|
# Loss coefficients
|
||||||
|
self.bbox_loss_coefficient = bbox_loss_coefficient
|
||||||
|
self.giou_loss_coefficient = giou_loss_coefficient
|
||||||
|
self.focal_alpha = focal_alpha
|
||||||
|
self.disable_custom_kernels = disable_custom_kernels
|
||||||
|
# Text backbone
|
||||||
|
if isinstance(text_config, dict):
|
||||||
|
text_config["model_type"] = text_config["model_type"] if "model_type" in text_config else "bert"
|
||||||
|
text_config = CONFIG_MAPPING[text_config["model_type"]](**text_config)
|
||||||
|
elif text_config is None:
|
||||||
|
text_config = CONFIG_MAPPING["bert"]()
|
||||||
|
|
||||||
|
self.text_config = text_config
|
||||||
|
self.max_text_len = max_text_len
|
||||||
|
|
||||||
|
# Text Enhancer
|
||||||
|
self.text_enhancer_dropout = text_enhancer_dropout
|
||||||
|
# Fusion
|
||||||
|
self.fusion_droppath = fusion_droppath
|
||||||
|
self.fusion_dropout = fusion_dropout
|
||||||
|
# Others
|
||||||
|
self.embedding_init_target = embedding_init_target
|
||||||
|
self.query_dim = query_dim
|
||||||
|
self.positional_embedding_temperature = positional_embedding_temperature
|
||||||
|
self.init_std = init_std
|
||||||
|
self.layer_norm_eps = layer_norm_eps
|
||||||
|
|
||||||
|
@property
|
||||||
|
def num_attention_heads(self) -> int:
|
||||||
|
return self.encoder_attention_heads
|
||||||
|
|
||||||
|
@property
|
||||||
|
def hidden_size(self) -> int:
|
||||||
|
return self.d_model
|
||||||
|
|
||||||
|
|
||||||
|
__all__ = ["MMGroundingDinoConfig"]
|
||||||
@@ -0,0 +1,504 @@
|
|||||||
|
# coding=utf-8
|
||||||
|
# Copyright 2025 The HuggingFace Inc. team.
|
||||||
|
#
|
||||||
|
# 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 argparse
|
||||||
|
import re
|
||||||
|
|
||||||
|
import requests
|
||||||
|
import torch
|
||||||
|
from PIL import Image
|
||||||
|
|
||||||
|
from transformers.models.bert.tokenization_bert import BertTokenizer
|
||||||
|
from transformers.models.grounding_dino.image_processing_grounding_dino import GroundingDinoImageProcessor
|
||||||
|
from transformers.models.grounding_dino.processing_grounding_dino import GroundingDinoProcessor
|
||||||
|
from transformers.models.mm_grounding_dino.configuration_mm_grounding_dino import MMGroundingDinoConfig
|
||||||
|
from transformers.models.mm_grounding_dino.modeling_mm_grounding_dino import MMGroundingDinoForObjectDetection
|
||||||
|
from transformers.models.swin.configuration_swin import SwinConfig
|
||||||
|
|
||||||
|
|
||||||
|
MODEL_NAME_TO_CHECKPOINT_URL_MAPPING = {
|
||||||
|
"mm_grounding_dino_tiny_o365v1_goldg": "https://download.openmmlab.com/mmdetection/v3.0/mm_grounding_dino/grounding_dino_swin-t_pretrain_obj365_goldg/grounding_dino_swin-t_pretrain_obj365_goldg_20231122_132602-4ea751ce.pth",
|
||||||
|
"mm_grounding_dino_tiny_o365v1_goldg_grit": "https://download.openmmlab.com/mmdetection/v3.0/mm_grounding_dino/grounding_dino_swin-t_pretrain_obj365_goldg_grit9m/grounding_dino_swin-t_pretrain_obj365_goldg_grit9m_20231128_200818-169cc352.pth",
|
||||||
|
"mm_grounding_dino_tiny_o365v1_goldg_v3det": "https://download.openmmlab.com/mmdetection/v3.0/mm_grounding_dino/grounding_dino_swin-t_pretrain_obj365_goldg_v3det/grounding_dino_swin-t_pretrain_obj365_goldg_v3det_20231218_095741-e316e297.pth",
|
||||||
|
"mm_grounding_dino_tiny_o365v1_goldg_grit_v3det": "https://download.openmmlab.com/mmdetection/v3.0/mm_grounding_dino/grounding_dino_swin-t_pretrain_obj365_goldg_grit9m_v3det/grounding_dino_swin-t_pretrain_obj365_goldg_grit9m_v3det_20231204_095047-b448804b.pth",
|
||||||
|
"mm_grounding_dino_base_o365v1_goldg_v3det": "https://download.openmmlab.com/mmdetection/v3.0/mm_grounding_dino/grounding_dino_swin-b_pretrain_obj365_goldg_v3det/grounding_dino_swin-b_pretrain_obj365_goldg_v3de-f83eef00.pth",
|
||||||
|
"mm_grounding_dino_base_all": "https://download.openmmlab.com/mmdetection/v3.0/mm_grounding_dino/grounding_dino_swin-b_pretrain_all/grounding_dino_swin-b_pretrain_all-f9818a7c.pth",
|
||||||
|
"mm_grounding_dino_large_o365v2_oiv6_goldg": "https://download.openmmlab.com/mmdetection/v3.0/mm_grounding_dino/grounding_dino_swin-l_pretrain_obj365_goldg/grounding_dino_swin-l_pretrain_obj365_goldg-34dcdc53.pth",
|
||||||
|
"mm_grounding_dino_large_all": "https://download.openmmlab.com/mmdetection/v3.0/mm_grounding_dino/grounding_dino_swin-l_pretrain_all/grounding_dino_swin-l_pretrain_all-56d69e78.pth",
|
||||||
|
"llmdet_tiny": "https://huggingface.co/fushh7/LLMDet/resolve/main/tiny.pth?download=true",
|
||||||
|
"llmdet_base": "https://huggingface.co/fushh7/LLMDet/resolve/main/base.pth?download=true",
|
||||||
|
"llmdet_large": "https://huggingface.co/fushh7/LLMDet/resolve/main/large.pth?download=true",
|
||||||
|
}
|
||||||
|
|
||||||
|
|
||||||
|
MODEL_NAME_TO_EXPECTED_OUTPUT_MAPPING = {
|
||||||
|
"mm_grounding_dino_tiny_o365v1_goldg": {
|
||||||
|
"scores": torch.tensor([0.7722, 0.7584, 0.7984, 0.7163]),
|
||||||
|
"boxes": torch.tensor(
|
||||||
|
[
|
||||||
|
[0.5212, 0.1594, 0.5792, 0.3895],
|
||||||
|
[0.5424, 0.0513, 0.9996, 0.7757],
|
||||||
|
[0.0629, 0.1526, 0.2746, 0.2447],
|
||||||
|
[0.0091, 0.1127, 0.4945, 0.9911],
|
||||||
|
]
|
||||||
|
),
|
||||||
|
},
|
||||||
|
"mm_grounding_dino_tiny_o365v1_goldg_grit": {
|
||||||
|
"scores": torch.tensor([0.7865, 0.7180, 0.7665, 0.8177]),
|
||||||
|
"boxes": torch.tensor(
|
||||||
|
[
|
||||||
|
[0.0084, 0.1129, 0.4940, 0.9895],
|
||||||
|
[0.5214, 0.1597, 0.5786, 0.3875],
|
||||||
|
[0.5413, 0.0507, 0.9998, 0.7768],
|
||||||
|
[0.0631, 0.1527, 0.2740, 0.2449],
|
||||||
|
]
|
||||||
|
),
|
||||||
|
},
|
||||||
|
"mm_grounding_dino_tiny_o365v1_goldg_v3det": {
|
||||||
|
"scores": torch.tensor([0.5690, 0.5553, 0.6075, 0.5775]),
|
||||||
|
"boxes": torch.tensor(
|
||||||
|
[
|
||||||
|
[0.5393, 0.0502, 0.9989, 0.7763],
|
||||||
|
[0.0090, 0.1125, 0.4950, 0.9895],
|
||||||
|
[0.5207, 0.1589, 0.5794, 0.3889],
|
||||||
|
[0.0625, 0.1519, 0.2750, 0.2446],
|
||||||
|
]
|
||||||
|
),
|
||||||
|
},
|
||||||
|
"mm_grounding_dino_tiny_o365v1_goldg_grit_v3det": {
|
||||||
|
"scores": torch.tensor([0.8381, 0.8204, 0.7970, 0.7175]),
|
||||||
|
"boxes": torch.tensor(
|
||||||
|
[
|
||||||
|
[0.0099, 0.1129, 0.4942, 0.9903],
|
||||||
|
[0.5413, 0.0506, 0.9998, 0.7753],
|
||||||
|
[0.0626, 0.1527, 0.2744, 0.2443],
|
||||||
|
[0.5211, 0.1596, 0.5790, 0.3890],
|
||||||
|
]
|
||||||
|
),
|
||||||
|
},
|
||||||
|
"mm_grounding_dino_base_o365v1_goldg_v3det": {
|
||||||
|
"scores": torch.tensor([0.8418, 0.8364, 0.8342, 0.7885]),
|
||||||
|
"boxes": torch.tensor(
|
||||||
|
[
|
||||||
|
[0.5427, 0.0502, 0.9996, 0.7770],
|
||||||
|
[0.0628, 0.1529, 0.2747, 0.2448],
|
||||||
|
[0.0085, 0.1132, 0.4947, 0.9898],
|
||||||
|
[0.5208, 0.1597, 0.5787, 0.3910],
|
||||||
|
]
|
||||||
|
),
|
||||||
|
},
|
||||||
|
"mm_grounding_dino_base_all": {
|
||||||
|
"scores": torch.tensor([0.4713]),
|
||||||
|
"boxes": torch.tensor([[0.5423, 0.0507, 0.9998, 0.7761]]),
|
||||||
|
},
|
||||||
|
"mm_grounding_dino_large_o365v2_oiv6_goldg": {
|
||||||
|
"scores": torch.tensor([0.7824, 0.8275, 0.7715, 0.8211]),
|
||||||
|
"boxes": torch.tensor(
|
||||||
|
[
|
||||||
|
[0.0082, 0.1133, 0.4945, 0.9889],
|
||||||
|
[0.5410, 0.0508, 0.9998, 0.7771],
|
||||||
|
[0.0632, 0.1526, 0.2740, 0.2439],
|
||||||
|
[0.5205, 0.1599, 0.5787, 0.3906],
|
||||||
|
]
|
||||||
|
),
|
||||||
|
},
|
||||||
|
"mm_grounding_dino_large_all": {
|
||||||
|
"scores": torch.tensor([0.7373, 0.6208, 0.6913, 0.4523]),
|
||||||
|
"boxes": torch.tensor(
|
||||||
|
[
|
||||||
|
[0.5424, 0.0509, 0.9997, 0.7765],
|
||||||
|
[0.0632, 0.1529, 0.2744, 0.2447],
|
||||||
|
[0.0121, 0.1125, 0.4947, 0.9884],
|
||||||
|
[0.5206, 0.1597, 0.5789, 0.3933],
|
||||||
|
]
|
||||||
|
),
|
||||||
|
},
|
||||||
|
"llmdet_tiny": {
|
||||||
|
"scores": torch.tensor([0.7262, 0.7552, 0.7656, 0.8207]),
|
||||||
|
"boxes": torch.tensor(
|
||||||
|
[
|
||||||
|
[0.0114, 0.1132, 0.4947, 0.9854],
|
||||||
|
[0.5387, 0.0513, 0.9992, 0.7765],
|
||||||
|
[0.5212, 0.1605, 0.5788, 0.3890],
|
||||||
|
[0.0634, 0.1536, 0.2743, 0.2440],
|
||||||
|
]
|
||||||
|
),
|
||||||
|
},
|
||||||
|
"llmdet_base": {
|
||||||
|
"scores": torch.tensor([0.8646, 0.7567, 0.6978, 0.8084]),
|
||||||
|
"boxes": torch.tensor(
|
||||||
|
[
|
||||||
|
[0.0632, 0.1529, 0.2745, 0.2438],
|
||||||
|
[0.5420, 0.0512, 0.9989, 0.7774],
|
||||||
|
[0.0110, 0.1134, 0.4950, 0.9875],
|
||||||
|
[0.5209, 0.1602, 0.5789, 0.3908],
|
||||||
|
]
|
||||||
|
),
|
||||||
|
},
|
||||||
|
"llmdet_large": {
|
||||||
|
"scores": torch.tensor([0.7107, 0.8626, 0.7458, 0.8166]),
|
||||||
|
"boxes": torch.tensor(
|
||||||
|
[
|
||||||
|
[0.0147, 0.1128, 0.4957, 0.9858],
|
||||||
|
[0.0634, 0.1528, 0.2744, 0.2447],
|
||||||
|
[0.5414, 0.0511, 0.9997, 0.7776],
|
||||||
|
[0.5209, 0.1602, 0.5792, 0.3916],
|
||||||
|
]
|
||||||
|
),
|
||||||
|
},
|
||||||
|
}
|
||||||
|
|
||||||
|
# fmt: off
|
||||||
|
ORIGINAL_TO_CONVERTED_KEY_MAPPING = {
|
||||||
|
# vision backbone
|
||||||
|
r"backbone.patch_embed.projection.(weight|bias)": r"model.backbone.conv_encoder.model.embeddings.patch_embeddings.projection.\1",
|
||||||
|
r"backbone.patch_embed.norm.(weight|bias)": r"model.backbone.conv_encoder.model.embeddings.norm.\1",
|
||||||
|
r"backbone.stages.(\d+).blocks.(\d+).attn.w_msa.(relative_position_bias_table|relative_position_index)": r"model.backbone.conv_encoder.model.encoder.layers.\1.blocks.\2.attention.self.\3",
|
||||||
|
r"backbone.stages.(\d+).blocks.(\d+).norm1.(weight|bias)": r"model.backbone.conv_encoder.model.encoder.layers.\1.blocks.\2.layernorm_before.\3",
|
||||||
|
r"backbone.stages.(\d+).blocks.(\d+).attn.w_msa.(query|key|value).(weight|bias)": r"model.backbone.conv_encoder.model.encoder.layers.\1.blocks.\2.attention.self.\3.\4",
|
||||||
|
r"backbone.stages.(\d+).blocks.(\d+).attn.w_msa.proj.(weight|bias)": r"model.backbone.conv_encoder.model.encoder.layers.\1.blocks.\2.attention.output.dense.\3",
|
||||||
|
r"backbone.stages.(\d+).blocks.(\d+).norm2.(weight|bias)": r"model.backbone.conv_encoder.model.encoder.layers.\1.blocks.\2.layernorm_after.\3",
|
||||||
|
r"backbone.stages.(\d+).blocks.(\d+).ffn.layers.0.0.(weight|bias)": r"model.backbone.conv_encoder.model.encoder.layers.\1.blocks.\2.intermediate.dense.\3",
|
||||||
|
r"backbone.stages.(\d+).blocks.(\d+).ffn.layers.1.(weight|bias)": r"model.backbone.conv_encoder.model.encoder.layers.\1.blocks.\2.output.dense.\3",
|
||||||
|
r"backbone.stages.(\d+).downsample.reduction.weight": r"model.backbone.conv_encoder.model.encoder.layers.\1.downsample.reduction.weight",
|
||||||
|
r"backbone.stages.(\d+).downsample.norm.(weight|bias)": r"model.backbone.conv_encoder.model.encoder.layers.\1.downsample.norm.\2",
|
||||||
|
r"backbone.norms.(\d+).(weight|bias)": r"model.backbone.conv_encoder.model.hidden_states_norms.stage\1.\2",
|
||||||
|
r"neck.convs.(\d+).conv.(weight|bias)": r"model.input_proj_vision.\1.0.\2",
|
||||||
|
r"neck.convs.(\d+).gn.(weight|bias)": r"model.input_proj_vision.\1.1.\2",
|
||||||
|
r"neck.extra_convs.(\d+).conv.(weight|bias)": r"model.input_proj_vision.\1.0.\2",
|
||||||
|
r"neck.extra_convs.(\d+).gn.(weight|bias)": r"model.input_proj_vision.\1.1.\2",
|
||||||
|
# text backbone
|
||||||
|
r"language_model.language_backbone.body.model.(.*)": r"model.text_backbone.\1",
|
||||||
|
r"text_feat_map.(weight|bias)": r"model.text_projection.\1",
|
||||||
|
# encoder
|
||||||
|
r"encoder.fusion_layers.(\d+).gamma_v": r"model.encoder.layers.\1.fusion_layer.vision_param",
|
||||||
|
r"encoder.fusion_layers.(\d+).gamma_l": r"model.encoder.layers.\1.fusion_layer.text_param",
|
||||||
|
r"encoder.fusion_layers.(\d+).layer_norm_v.(weight|bias)": r"model.encoder.layers.\1.fusion_layer.layer_norm_vision.\2",
|
||||||
|
r"encoder.fusion_layers.(\d+).attn.v_proj.(weight|bias)": r"model.encoder.layers.\1.fusion_layer.attn.vision_proj.\2",
|
||||||
|
r"encoder.fusion_layers.(\d+).attn.values_v_proj.(weight|bias)": r"model.encoder.layers.\1.fusion_layer.attn.values_vision_proj.\2",
|
||||||
|
r"encoder.fusion_layers.(\d+).attn.out_v_proj.(weight|bias)": r"model.encoder.layers.\1.fusion_layer.attn.out_vision_proj.\2",
|
||||||
|
r"encoder.fusion_layers.(\d+).layer_norm_l.(weight|bias)": r"model.encoder.layers.\1.fusion_layer.layer_norm_text.\2",
|
||||||
|
r"encoder.fusion_layers.(\d+).attn.l_proj.(weight|bias)": r"model.encoder.layers.\1.fusion_layer.attn.text_proj.\2",
|
||||||
|
r"encoder.fusion_layers.(\d+).attn.values_l_proj.(weight|bias)": r"model.encoder.layers.\1.fusion_layer.attn.values_text_proj.\2",
|
||||||
|
r"encoder.fusion_layers.(\d+).attn.out_l_proj.(weight|bias)": r"model.encoder.layers.\1.fusion_layer.attn.out_text_proj.\2",
|
||||||
|
r"encoder.layers.(\d+).self_attn.(sampling_offsets|attention_weights|value_proj|output_proj).(weight|bias)": r"model.encoder.layers.\1.deformable_layer.self_attn.\2.\3",
|
||||||
|
r"encoder.layers.(\d+).norms.0.(weight|bias)": r"model.encoder.layers.\1.deformable_layer.self_attn_layer_norm.\2",
|
||||||
|
r"encoder.layers.(\d+).ffn.layers.0.0.(weight|bias)": r"model.encoder.layers.\1.deformable_layer.fc1.\2",
|
||||||
|
r"encoder.layers.(\d+).ffn.layers.1.(weight|bias)": r"model.encoder.layers.\1.deformable_layer.fc2.\2",
|
||||||
|
r"encoder.layers.(\d+).norms.1.(weight|bias)": r"model.encoder.layers.\1.deformable_layer.final_layer_norm.\2",
|
||||||
|
r"encoder.text_layers.(\d+).self_attn.attn.(query|key|value)_proj_(weight|bias)": r"model.encoder.layers.\1.text_enhancer_layer.self_attn.\2.\3",
|
||||||
|
r"encoder.text_layers.(\d+).self_attn.attn.out_proj.(weight|bias)": r"model.encoder.layers.\1.text_enhancer_layer.self_attn.out_proj.\2",
|
||||||
|
r"encoder.text_layers.(\d+).norms.0.(weight|bias)": r"model.encoder.layers.\1.text_enhancer_layer.layer_norm_before.\2",
|
||||||
|
r"encoder.text_layers.(\d+).ffn.layers.0.0.(weight|bias)": r"model.encoder.layers.\1.text_enhancer_layer.fc1.\2",
|
||||||
|
r"encoder.text_layers.(\d+).ffn.layers.1.(weight|bias)": r"model.encoder.layers.\1.text_enhancer_layer.fc2.\2",
|
||||||
|
r"encoder.text_layers.(\d+).norms.1.(weight|bias)": r"model.encoder.layers.\1.text_enhancer_layer.layer_norm_after.\2",
|
||||||
|
r"encoder.bbox_head.cls_branch.bias": r"model.encoder_output_class_embed.bias",
|
||||||
|
r"encoder.bbox_head.reg_branch.0.(weight|bias)": r"model.encoder_output_bbox_embed.layers.0.\1",
|
||||||
|
r"encoder.bbox_head.reg_branch.2.(weight|bias)": r"model.encoder_output_bbox_embed.layers.1.\1",
|
||||||
|
r"encoder.bbox_head.reg_branch.4.(weight|bias)": r"model.encoder_output_bbox_embed.layers.2.\1",
|
||||||
|
# decoder
|
||||||
|
r"decoder.norm.(weight|bias)": r"model.decoder.layer_norm.\1",
|
||||||
|
r"decoder.ref_point_head.layers.(\d+).(weight|bias)": r"model.decoder.reference_points_head.layers.\1.\2",
|
||||||
|
r"decoder.layers.(\d+).self_attn.attn.(query|key|value)_proj_(weight|bias)": r"model.decoder.layers.\1.self_attn.\2.\3",
|
||||||
|
r"decoder.layers.(\d+).self_attn.attn.out_proj.(weight|bias)": r"model.decoder.layers.\1.self_attn.out_proj.\2",
|
||||||
|
r"decoder.layers.(\d+).norms.0.(weight|bias)": r"model.decoder.layers.\1.self_attn_layer_norm.\2",
|
||||||
|
r"decoder.layers.(\d+).cross_attn_text.attn.(query|key|value)_proj_(weight|bias)": r"model.decoder.layers.\1.encoder_attn_text.\2.\3",
|
||||||
|
r"decoder.layers.(\d+).cross_attn_text.attn.out_proj.(weight|bias)": r"model.decoder.layers.\1.encoder_attn_text.out_proj.\2",
|
||||||
|
r"decoder.layers.(\d+).norms.1.(weight|bias)": r"model.decoder.layers.\1.encoder_attn_text_layer_norm.\2",
|
||||||
|
r"decoder.layers.(\d+).cross_attn.(sampling_offsets|attention_weights|value_proj|output_proj).(weight|bias)": r"model.decoder.layers.\1.encoder_attn.\2.\3",
|
||||||
|
r"decoder.layers.(\d+).norms.2.(weight|bias)": r"model.decoder.layers.\1.encoder_attn_layer_norm.\2",
|
||||||
|
r"decoder.layers.(\d+).ffn.layers.0.0.(weight|bias)": r"model.decoder.layers.\1.fc1.\2",
|
||||||
|
r"decoder.layers.(\d+).ffn.layers.1.(weight|bias)": r"model.decoder.layers.\1.fc2.\2",
|
||||||
|
r"decoder.layers.(\d+).norms.3.(weight|bias)": r"model.decoder.layers.\1.final_layer_norm.\2",
|
||||||
|
r"decoder.bbox_head.cls_branches.(\d+).bias": r"model.decoder.class_embed.\1.bias",
|
||||||
|
r"decoder.bbox_head.reg_branches.(\d+).0.(weight|bias)": r"model.decoder.bbox_embed.\1.layers.0.\2",
|
||||||
|
r"decoder.bbox_head.reg_branches.(\d+).2.(weight|bias)": r"model.decoder.bbox_embed.\1.layers.1.\2",
|
||||||
|
r"decoder.bbox_head.reg_branches.(\d+).4.(weight|bias)": r"model.decoder.bbox_embed.\1.layers.2.\2",
|
||||||
|
# other
|
||||||
|
r"level_embed": r"model.level_embed",
|
||||||
|
r"query_embedding.weight": r"model.query_position_embeddings.weight",
|
||||||
|
r"memory_trans_fc.(weight|bias)": r"model.enc_output.\1",
|
||||||
|
r"memory_trans_norm.(weight|bias)": r"model.enc_output_norm.\1",
|
||||||
|
r"bbox_head.cls_branches.(\d+).bias": r"class_embed.\1.bias",
|
||||||
|
r"bbox_head.reg_branches.(\d+).0.(weight|bias)": r"bbox_embed.\1.layers.0.\2",
|
||||||
|
r"bbox_head.reg_branches.(\d+).2.(weight|bias)": r"bbox_embed.\1.layers.1.\2",
|
||||||
|
r"bbox_head.reg_branches.(\d+).4.(weight|bias)": r"bbox_embed.\1.layers.2.\2",
|
||||||
|
}
|
||||||
|
# fmt: on
|
||||||
|
|
||||||
|
|
||||||
|
def get_mm_grounding_dino_config(model_name: str) -> MMGroundingDinoConfig:
|
||||||
|
if "tiny" in model_name:
|
||||||
|
swin_image_size = 224
|
||||||
|
swin_window_size = 7
|
||||||
|
swin_embed_dim = 96
|
||||||
|
swin_depths = (2, 2, 6, 2)
|
||||||
|
swin_num_heads = (3, 6, 12, 24)
|
||||||
|
swin_out_features = ["stage2", "stage3", "stage4"]
|
||||||
|
num_feature_levels = 4
|
||||||
|
elif "base" in model_name:
|
||||||
|
swin_image_size = 384
|
||||||
|
swin_window_size = 12
|
||||||
|
swin_embed_dim = 128
|
||||||
|
swin_depths = (2, 2, 18, 2)
|
||||||
|
swin_num_heads = (4, 8, 16, 32)
|
||||||
|
swin_out_features = ["stage2", "stage3", "stage4"]
|
||||||
|
num_feature_levels = 4
|
||||||
|
elif "large" in model_name:
|
||||||
|
swin_image_size = 384
|
||||||
|
swin_window_size = 12
|
||||||
|
swin_embed_dim = 192
|
||||||
|
swin_depths = (2, 2, 18, 2)
|
||||||
|
swin_num_heads = (6, 12, 24, 48)
|
||||||
|
swin_out_features = ["stage1", "stage2", "stage3", "stage4"]
|
||||||
|
num_feature_levels = 5
|
||||||
|
else:
|
||||||
|
raise ValueError(
|
||||||
|
f"Model name: {model_name} is not supported. Only `tiny`, `base` and `large` models are currently supported."
|
||||||
|
)
|
||||||
|
|
||||||
|
backbone_config = SwinConfig(
|
||||||
|
image_size=swin_image_size,
|
||||||
|
window_size=swin_window_size,
|
||||||
|
embed_dim=swin_embed_dim,
|
||||||
|
depths=swin_depths,
|
||||||
|
num_heads=swin_num_heads,
|
||||||
|
out_features=swin_out_features,
|
||||||
|
)
|
||||||
|
|
||||||
|
model_config = MMGroundingDinoConfig(
|
||||||
|
backbone_config=backbone_config,
|
||||||
|
num_feature_levels=num_feature_levels,
|
||||||
|
)
|
||||||
|
|
||||||
|
return model_config
|
||||||
|
|
||||||
|
|
||||||
|
def get_mm_grounding_dino_processor() -> GroundingDinoProcessor:
|
||||||
|
img_processor = GroundingDinoImageProcessor()
|
||||||
|
txt_processor = BertTokenizer.from_pretrained("bert-base-uncased")
|
||||||
|
processor = GroundingDinoProcessor(img_processor, txt_processor)
|
||||||
|
return processor
|
||||||
|
|
||||||
|
|
||||||
|
# Copied from: https://github.com/iSEE-Laboratory/LLMDet/blob/96ec8c82a9d97b170db759e043afd5b81445d0f1/hf_model/mmdet2groundingdino_swint.py#L8C1-L13C13
|
||||||
|
def correct_unfold_reduction_order(x: torch.Tensor) -> torch.Tensor:
|
||||||
|
out_channel, in_channel = x.shape
|
||||||
|
x = x.reshape(out_channel, in_channel // 4, 4).transpose(1, 2)
|
||||||
|
x = x[:, [0, 2, 1, 3], :]
|
||||||
|
x = x.reshape(out_channel, in_channel)
|
||||||
|
return x
|
||||||
|
|
||||||
|
|
||||||
|
# Copied from: https://github.com/iSEE-Laboratory/LLMDet/blob/96ec8c82a9d97b170db759e043afd5b81445d0f1/hf_model/mmdet2groundingdino_swint.py#L15C1-L20C13
|
||||||
|
def correct_unfold_norm_order(x: torch.Tensor) -> torch.Tensor:
|
||||||
|
in_channel = x.shape[0]
|
||||||
|
x = x.reshape(in_channel // 4, 4).transpose(0, 1)
|
||||||
|
x = x[[0, 2, 1, 3], :]
|
||||||
|
x = x.reshape(in_channel)
|
||||||
|
return x
|
||||||
|
|
||||||
|
|
||||||
|
def preprocess_old_state(state_dict: dict, config: MMGroundingDinoConfig) -> dict:
|
||||||
|
"""
|
||||||
|
Preprocesses old state dict to enable 1-1 mapping:
|
||||||
|
- split qkv projections in Swin backbone
|
||||||
|
- reorder reduction and norm parameters in Swin backbone
|
||||||
|
- shift output norm indices in Swin backbone
|
||||||
|
- shift output proj indices in neck
|
||||||
|
- split q,k,v projections in text self and cross attentions in encoder and decoder
|
||||||
|
- duplicate detection head parameters for decoder and encoder
|
||||||
|
"""
|
||||||
|
new_state_dict = state_dict.copy()
|
||||||
|
for k in state_dict:
|
||||||
|
if k.startswith("backbone"):
|
||||||
|
if "downsample.reduction" in k:
|
||||||
|
new_state_dict[k] = correct_unfold_reduction_order(new_state_dict.pop(k))
|
||||||
|
elif "downsample.norm" in k:
|
||||||
|
new_state_dict[k] = correct_unfold_norm_order(new_state_dict.pop(k))
|
||||||
|
elif "w_msa.qkv" in k:
|
||||||
|
q_param, k_param, v_param = new_state_dict.pop(k).chunk(3)
|
||||||
|
new_state_dict[k.replace("qkv", "query")] = q_param
|
||||||
|
new_state_dict[k.replace("qkv", "key")] = k_param
|
||||||
|
new_state_dict[k.replace("qkv", "value")] = v_param
|
||||||
|
elif "backbone.norm" in k:
|
||||||
|
match = re.match(r"backbone.norm(\d+).(weight|bias)", k)
|
||||||
|
new_state_dict[f"backbone.norms.{int(match.group(1)) + 1}.{match.group(2)}"] = new_state_dict.pop(k)
|
||||||
|
elif k.startswith("neck.extra_convs"):
|
||||||
|
num_normal_convs = len(config.backbone_config.out_indices)
|
||||||
|
if "gn" in k:
|
||||||
|
match = re.match(r"neck.extra_convs.(\d+).gn.(weight|bias)", k)
|
||||||
|
new_state_dict[f"neck.extra_convs.{num_normal_convs + int(match.group(1))}.gn.{match.group(2)}"] = (
|
||||||
|
new_state_dict.pop(k)
|
||||||
|
)
|
||||||
|
elif "conv" in k:
|
||||||
|
match = re.match(r"neck.extra_convs.(\d+).conv.(weight|bias)", k)
|
||||||
|
new_state_dict[f"neck.extra_convs.{num_normal_convs + int(match.group(1))}.conv.{match.group(2)}"] = (
|
||||||
|
new_state_dict.pop(k)
|
||||||
|
)
|
||||||
|
elif k.startswith("encoder"):
|
||||||
|
if "self_attn.attn.in_proj" in k:
|
||||||
|
q_param, k_param, v_param = new_state_dict.pop(k).chunk(3)
|
||||||
|
new_state_dict[k.replace("in", "query")] = q_param
|
||||||
|
new_state_dict[k.replace("in", "key")] = k_param
|
||||||
|
new_state_dict[k.replace("in", "value")] = v_param
|
||||||
|
elif k.startswith("decoder"):
|
||||||
|
if "self_attn.attn.in_proj" in k or "cross_attn_text.attn.in_proj" in k:
|
||||||
|
q_param, k_param, v_param = new_state_dict.pop(k).chunk(3)
|
||||||
|
new_state_dict[k.replace("in", "query")] = q_param
|
||||||
|
new_state_dict[k.replace("in", "key")] = k_param
|
||||||
|
new_state_dict[k.replace("in", "value")] = v_param
|
||||||
|
elif k.startswith("bbox_head"):
|
||||||
|
num_decoder_layers = config.decoder_layers
|
||||||
|
match = re.match(r"bbox_head.(cls|reg)_branches.(\d+).(.*)", k)
|
||||||
|
cls_or_reg = match.group(1)
|
||||||
|
layer_idx = int(match.group(2))
|
||||||
|
suffix = match.group(3)
|
||||||
|
if layer_idx < num_decoder_layers:
|
||||||
|
new_key = f"decoder.bbox_head.{cls_or_reg}_branches.{layer_idx}.{suffix}"
|
||||||
|
new_state_dict[new_key] = new_state_dict[k] # copy
|
||||||
|
else:
|
||||||
|
new_key = f"encoder.bbox_head.{cls_or_reg}_branch.{suffix}"
|
||||||
|
new_state_dict[new_key] = new_state_dict.pop(k) # move
|
||||||
|
|
||||||
|
# remove unused params
|
||||||
|
if (
|
||||||
|
k == "dn_query_generator.label_embedding.weight"
|
||||||
|
or k == "language_model.language_backbone.body.model.embeddings.position_ids"
|
||||||
|
or k == "image_seperate.weight"
|
||||||
|
or k.startswith("lmm")
|
||||||
|
or k.startswith("connector")
|
||||||
|
or k.startswith("region_connector")
|
||||||
|
or k.startswith("ref_point_head")
|
||||||
|
):
|
||||||
|
new_state_dict.pop(k)
|
||||||
|
|
||||||
|
return new_state_dict
|
||||||
|
|
||||||
|
|
||||||
|
# Copied from transformers/models/siglip2/convert_siglip2_to_hf.py
|
||||||
|
def convert_old_keys_to_new_keys(state_dict_keys: list) -> dict:
|
||||||
|
"""
|
||||||
|
This function should be applied only once, on the concatenated keys to efficiently rename using
|
||||||
|
the key mappings.
|
||||||
|
"""
|
||||||
|
output_dict = {}
|
||||||
|
if state_dict_keys is not None:
|
||||||
|
old_text = "\n".join(state_dict_keys)
|
||||||
|
new_text = old_text
|
||||||
|
for pattern, replacement in ORIGINAL_TO_CONVERTED_KEY_MAPPING.items():
|
||||||
|
if replacement is None:
|
||||||
|
new_text = re.sub(pattern, "", new_text) # an empty line
|
||||||
|
continue
|
||||||
|
new_text = re.sub(pattern, replacement, new_text)
|
||||||
|
output_dict = dict(zip(old_text.split("\n"), new_text.split("\n")))
|
||||||
|
return output_dict
|
||||||
|
|
||||||
|
|
||||||
|
def convert_mm_to_hf_state(original_state: dict, hf_cfg: MMGroundingDinoConfig) -> dict:
|
||||||
|
original_state = preprocess_old_state(original_state, hf_cfg)
|
||||||
|
original_state_keys = list(original_state.keys())
|
||||||
|
original_to_hf_key_map = convert_old_keys_to_new_keys(original_state_keys)
|
||||||
|
|
||||||
|
hf_state = {}
|
||||||
|
for original_key in original_state_keys:
|
||||||
|
hf_key = original_to_hf_key_map[original_key]
|
||||||
|
hf_state[hf_key] = original_state.pop(original_key)
|
||||||
|
|
||||||
|
return hf_state
|
||||||
|
|
||||||
|
|
||||||
|
def prepare_test_inputs():
|
||||||
|
image_url = "http://images.cocodataset.org/val2017/000000039769.jpg"
|
||||||
|
image = Image.open(requests.get(image_url, stream=True).raw)
|
||||||
|
text = [["cat", "remote"]]
|
||||||
|
return image, text
|
||||||
|
|
||||||
|
|
||||||
|
@torch.no_grad()
|
||||||
|
def convert_mm_grounding_dino_checkpoint(
|
||||||
|
model_name: str,
|
||||||
|
verify_outputs: bool,
|
||||||
|
push_to_hub: bool,
|
||||||
|
hub_user_name: str,
|
||||||
|
) -> tuple[MMGroundingDinoConfig, dict]:
|
||||||
|
# Load original state
|
||||||
|
checkpoint_url = MODEL_NAME_TO_CHECKPOINT_URL_MAPPING[model_name]
|
||||||
|
print(f"Loading checkpoint from: {checkpoint_url}")
|
||||||
|
ckpt = torch.hub.load_state_dict_from_url(checkpoint_url, map_location="cpu")
|
||||||
|
mm_state = ckpt["state_dict"]
|
||||||
|
|
||||||
|
# Create hf model and processor
|
||||||
|
print("Creating model...")
|
||||||
|
hf_cfg = get_mm_grounding_dino_config(model_name)
|
||||||
|
hf_state = convert_mm_to_hf_state(mm_state, hf_cfg)
|
||||||
|
hf_model = MMGroundingDinoForObjectDetection(hf_cfg).eval()
|
||||||
|
hf_model.load_state_dict(hf_state)
|
||||||
|
hf_processor = get_mm_grounding_dino_processor()
|
||||||
|
|
||||||
|
# Verify outputs if needed
|
||||||
|
if verify_outputs:
|
||||||
|
print("Running inference to verify outputs...")
|
||||||
|
image, text = prepare_test_inputs()
|
||||||
|
model_inputs = hf_processor(images=image, text=text, return_tensors="pt")
|
||||||
|
model_outputs = hf_model(**model_inputs)
|
||||||
|
results = hf_processor.post_process_grounded_object_detection(
|
||||||
|
model_outputs,
|
||||||
|
model_inputs.input_ids,
|
||||||
|
box_threshold=0.4,
|
||||||
|
text_threshold=0.3,
|
||||||
|
)
|
||||||
|
result = results[0]
|
||||||
|
print(result)
|
||||||
|
expected = MODEL_NAME_TO_EXPECTED_OUTPUT_MAPPING[model_name]
|
||||||
|
for key in expected:
|
||||||
|
torch.testing.assert_close(result[key], expected[key], atol=1e-3, rtol=1e-3)
|
||||||
|
print("Outputs match.")
|
||||||
|
|
||||||
|
# Push to hub if needed
|
||||||
|
if push_to_hub:
|
||||||
|
print("Pushing to hub...")
|
||||||
|
hub_url = f"{hub_user_name}/{model_name}"
|
||||||
|
hf_model.push_to_hub(hub_url)
|
||||||
|
hf_processor.push_to_hub(hub_url)
|
||||||
|
print(f"Pushed to huggingface hub at: {hub_url}.")
|
||||||
|
|
||||||
|
return hf_cfg, hf_state
|
||||||
|
|
||||||
|
|
||||||
|
def parse_args():
|
||||||
|
parser = argparse.ArgumentParser()
|
||||||
|
parser.add_argument(
|
||||||
|
"--model-name",
|
||||||
|
required=True,
|
||||||
|
type=str,
|
||||||
|
choices=list(MODEL_NAME_TO_CHECKPOINT_URL_MAPPING.keys()),
|
||||||
|
help="URL to the original mm grounding dino checkpoint.",
|
||||||
|
)
|
||||||
|
parser.add_argument("--hub-user-name", type=str, help="User name on the huggingface hub.")
|
||||||
|
parser.add_argument("--push-to-hub", action="store_true", help="Whether to push model to hub or not.")
|
||||||
|
parser.add_argument(
|
||||||
|
"--verify-outputs", action="store_true", help="Whether to verify that model output is correct or not."
|
||||||
|
)
|
||||||
|
return parser.parse_args()
|
||||||
|
|
||||||
|
|
||||||
|
if __name__ == "__main__":
|
||||||
|
args = parse_args()
|
||||||
|
convert_mm_grounding_dino_checkpoint(
|
||||||
|
args.model_name,
|
||||||
|
args.verify_outputs,
|
||||||
|
args.push_to_hub,
|
||||||
|
args.hub_user_name,
|
||||||
|
)
|
||||||
File diff suppressed because it is too large
Load Diff
@@ -0,0 +1,434 @@
|
|||||||
|
# coding=utf-8
|
||||||
|
# Copyright 2025 The HuggingFace Inc. team.
|
||||||
|
#
|
||||||
|
# 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 math
|
||||||
|
|
||||||
|
import torch
|
||||||
|
from torch import nn
|
||||||
|
|
||||||
|
from ...configuration_utils import PretrainedConfig
|
||||||
|
from ...utils import logging
|
||||||
|
from ...utils.backbone_utils import verify_backbone_config_arguments
|
||||||
|
from ..auto import CONFIG_MAPPING
|
||||||
|
from ..auto.modeling_auto import AutoModel
|
||||||
|
from ..grounding_dino.configuration_grounding_dino import GroundingDinoConfig
|
||||||
|
from ..grounding_dino.modeling_grounding_dino import (
|
||||||
|
GroundingDinoContrastiveEmbedding,
|
||||||
|
GroundingDinoConvEncoder,
|
||||||
|
GroundingDinoConvModel,
|
||||||
|
GroundingDinoDecoder,
|
||||||
|
GroundingDinoEncoder,
|
||||||
|
GroundingDinoForObjectDetection,
|
||||||
|
GroundingDinoMLPPredictionHead,
|
||||||
|
GroundingDinoModel,
|
||||||
|
GroundingDinoPreTrainedModel,
|
||||||
|
build_position_encoding,
|
||||||
|
)
|
||||||
|
|
||||||
|
|
||||||
|
logger = logging.get_logger(__name__)
|
||||||
|
|
||||||
|
|
||||||
|
class MMGroundingDinoConfig(GroundingDinoConfig, PretrainedConfig):
|
||||||
|
r"""
|
||||||
|
This is the configuration class to store the configuration of a [`MMGroundingDinoModel`]. It is used to instantiate a
|
||||||
|
MM Grounding DINO model according to the specified arguments, defining the model architecture. Instantiating a
|
||||||
|
configuration with the defaults will yield a similar configuration to that of the MM Grounding DINO tiny architecture
|
||||||
|
[openmmlab-community/mm_grounding_dino_tiny_o365v1_goldg_v3det](https://huggingface.co/openmmlab-community/mm_grounding_dino_tiny_o365v1_goldg_v3det).
|
||||||
|
|
||||||
|
Configuration objects inherit from [`PretrainedConfig`] and can be used to control the model outputs. Read the
|
||||||
|
documentation from [`PretrainedConfig`] for more information.
|
||||||
|
|
||||||
|
Args:
|
||||||
|
backbone_config (`PretrainedConfig` or `dict`, *optional*, defaults to `ResNetConfig()`):
|
||||||
|
The configuration of the backbone model.
|
||||||
|
backbone (`str`, *optional*):
|
||||||
|
Name of backbone to use when `backbone_config` is `None`. If `use_pretrained_backbone` is `True`, this
|
||||||
|
will load the corresponding pretrained weights from the timm or transformers library. If `use_pretrained_backbone`
|
||||||
|
is `False`, this loads the backbone's config and uses that to initialize the backbone with random weights.
|
||||||
|
use_pretrained_backbone (`bool`, *optional*, defaults to `False`):
|
||||||
|
Whether to use pretrained weights for the backbone.
|
||||||
|
use_timm_backbone (`bool`, *optional*, defaults to `False`):
|
||||||
|
Whether to load `backbone` from the timm library. If `False`, the backbone is loaded from the transformers
|
||||||
|
library.
|
||||||
|
backbone_kwargs (`dict`, *optional*):
|
||||||
|
Keyword arguments to be passed to AutoBackbone when loading from a checkpoint
|
||||||
|
e.g. `{'out_indices': (0, 1, 2, 3)}`. Cannot be specified if `backbone_config` is set.
|
||||||
|
text_config (`Union[AutoConfig, dict]`, *optional*, defaults to `BertConfig`):
|
||||||
|
The config object or dictionary of the text backbone.
|
||||||
|
num_queries (`int`, *optional*, defaults to 900):
|
||||||
|
Number of object queries, i.e. detection slots. This is the maximal number of objects
|
||||||
|
[`MMGroundingDinoModel`] can detect in a single image.
|
||||||
|
encoder_layers (`int`, *optional*, defaults to 6):
|
||||||
|
Number of encoder layers.
|
||||||
|
encoder_ffn_dim (`int`, *optional*, defaults to 2048):
|
||||||
|
Dimension of the "intermediate" (often named feed-forward) layer in decoder.
|
||||||
|
encoder_attention_heads (`int`, *optional*, defaults to 8):
|
||||||
|
Number of attention heads for each attention layer in the Transformer encoder.
|
||||||
|
decoder_layers (`int`, *optional*, defaults to 6):
|
||||||
|
Number of decoder layers.
|
||||||
|
decoder_ffn_dim (`int`, *optional*, defaults to 2048):
|
||||||
|
Dimension of the "intermediate" (often named feed-forward) layer in decoder.
|
||||||
|
decoder_attention_heads (`int`, *optional*, defaults to 8):
|
||||||
|
Number of attention heads for each attention layer in the Transformer decoder.
|
||||||
|
is_encoder_decoder (`bool`, *optional*, defaults to `True`):
|
||||||
|
Whether the model is used as an encoder/decoder or not.
|
||||||
|
activation_function (`str` or `function`, *optional*, defaults to `"relu"`):
|
||||||
|
The non-linear activation function (function or string) in the encoder and pooler. If string, `"gelu"`,
|
||||||
|
`"relu"`, `"silu"` and `"gelu_new"` are supported.
|
||||||
|
d_model (`int`, *optional*, defaults to 256):
|
||||||
|
Dimension of the layers.
|
||||||
|
dropout (`float`, *optional*, defaults to 0.1):
|
||||||
|
The dropout probability for all fully connected layers in the embeddings, encoder, and pooler.
|
||||||
|
attention_dropout (`float`, *optional*, defaults to 0.0):
|
||||||
|
The dropout ratio for the attention probabilities.
|
||||||
|
activation_dropout (`float`, *optional*, defaults to 0.0):
|
||||||
|
The dropout ratio for activations inside the fully connected layer.
|
||||||
|
auxiliary_loss (`bool`, *optional*, defaults to `False`):
|
||||||
|
Whether auxiliary decoding losses (loss at each decoder layer) are to be used.
|
||||||
|
position_embedding_type (`str`, *optional*, defaults to `"sine"`):
|
||||||
|
Type of position embeddings to be used on top of the image features. One of `"sine"` or `"learned"`.
|
||||||
|
num_feature_levels (`int`, *optional*, defaults to 4):
|
||||||
|
The number of input feature levels.
|
||||||
|
encoder_n_points (`int`, *optional*, defaults to 4):
|
||||||
|
The number of sampled keys in each feature level for each attention head in the encoder.
|
||||||
|
decoder_n_points (`int`, *optional*, defaults to 4):
|
||||||
|
The number of sampled keys in each feature level for each attention head in the decoder.
|
||||||
|
two_stage (`bool`, *optional*, defaults to `True`):
|
||||||
|
Whether to apply a two-stage deformable DETR, where the region proposals are also generated by a variant of
|
||||||
|
Grounding DINO, which are further fed into the decoder for iterative bounding box refinement.
|
||||||
|
class_cost (`float`, *optional*, defaults to 1.0):
|
||||||
|
Relative weight of the classification error in the Hungarian matching cost.
|
||||||
|
bbox_cost (`float`, *optional*, defaults to 5.0):
|
||||||
|
Relative weight of the L1 error of the bounding box coordinates in the Hungarian matching cost.
|
||||||
|
giou_cost (`float`, *optional*, defaults to 2.0):
|
||||||
|
Relative weight of the generalized IoU loss of the bounding box in the Hungarian matching cost.
|
||||||
|
bbox_loss_coefficient (`float`, *optional*, defaults to 5.0):
|
||||||
|
Relative weight of the L1 bounding box loss in the object detection loss.
|
||||||
|
giou_loss_coefficient (`float`, *optional*, defaults to 2.0):
|
||||||
|
Relative weight of the generalized IoU loss in the object detection loss.
|
||||||
|
focal_alpha (`float`, *optional*, defaults to 0.25):
|
||||||
|
Alpha parameter in the focal loss.
|
||||||
|
disable_custom_kernels (`bool`, *optional*, defaults to `False`):
|
||||||
|
Disable the use of custom CUDA and CPU kernels. This option is necessary for the ONNX export, as custom
|
||||||
|
kernels are not supported by PyTorch ONNX export.
|
||||||
|
max_text_len (`int`, *optional*, defaults to 256):
|
||||||
|
The maximum length of the text input.
|
||||||
|
text_enhancer_dropout (`float`, *optional*, defaults to 0.0):
|
||||||
|
The dropout ratio for the text enhancer.
|
||||||
|
fusion_droppath (`float`, *optional*, defaults to 0.1):
|
||||||
|
The droppath ratio for the fusion module.
|
||||||
|
fusion_dropout (`float`, *optional*, defaults to 0.0):
|
||||||
|
The dropout ratio for the fusion module.
|
||||||
|
embedding_init_target (`bool`, *optional*, defaults to `True`):
|
||||||
|
Whether to initialize the target with Embedding weights.
|
||||||
|
query_dim (`int`, *optional*, defaults to 4):
|
||||||
|
The dimension of the query vector.
|
||||||
|
positional_embedding_temperature (`float`, *optional*, defaults to 20):
|
||||||
|
The temperature for Sine Positional Embedding that is used together with vision backbone.
|
||||||
|
init_std (`float`, *optional*, defaults to 0.02):
|
||||||
|
The standard deviation of the truncated_normal_initializer for initializing all weight matrices.
|
||||||
|
layer_norm_eps (`float`, *optional*, defaults to 1e-05):
|
||||||
|
The epsilon used by the layer normalization layers.
|
||||||
|
|
||||||
|
Examples:
|
||||||
|
|
||||||
|
```python
|
||||||
|
>>> from transformers import MMGroundingDinoConfig, MMGroundingDinoModel
|
||||||
|
|
||||||
|
>>> # Initializing a MM Grounding DINO configuration
|
||||||
|
>>> configuration = MMGroundingDinoConfig()
|
||||||
|
|
||||||
|
>>> # Initializing a model (with random weights) from the configuration
|
||||||
|
>>> model = MMGroundingDinoModel(configuration)
|
||||||
|
|
||||||
|
>>> # Accessing the model configuration
|
||||||
|
>>> configuration = model.config
|
||||||
|
```"""
|
||||||
|
|
||||||
|
model_type = "mm-grounding-dino"
|
||||||
|
|
||||||
|
def __init__(
|
||||||
|
self,
|
||||||
|
backbone_config=None,
|
||||||
|
backbone=None,
|
||||||
|
use_pretrained_backbone=False,
|
||||||
|
use_timm_backbone=False,
|
||||||
|
backbone_kwargs=None,
|
||||||
|
text_config=None,
|
||||||
|
num_queries=900,
|
||||||
|
encoder_layers=6,
|
||||||
|
encoder_ffn_dim=2048,
|
||||||
|
encoder_attention_heads=8,
|
||||||
|
decoder_layers=6,
|
||||||
|
decoder_ffn_dim=2048,
|
||||||
|
decoder_attention_heads=8,
|
||||||
|
is_encoder_decoder=True,
|
||||||
|
activation_function="relu",
|
||||||
|
d_model=256,
|
||||||
|
dropout=0.1,
|
||||||
|
attention_dropout=0.0,
|
||||||
|
activation_dropout=0.0,
|
||||||
|
auxiliary_loss=False,
|
||||||
|
position_embedding_type="sine",
|
||||||
|
num_feature_levels=4,
|
||||||
|
encoder_n_points=4,
|
||||||
|
decoder_n_points=4,
|
||||||
|
two_stage=True,
|
||||||
|
class_cost=1.0,
|
||||||
|
bbox_cost=5.0,
|
||||||
|
giou_cost=2.0,
|
||||||
|
bbox_loss_coefficient=5.0,
|
||||||
|
giou_loss_coefficient=2.0,
|
||||||
|
focal_alpha=0.25,
|
||||||
|
disable_custom_kernels=False,
|
||||||
|
# other parameters
|
||||||
|
max_text_len=256,
|
||||||
|
text_enhancer_dropout=0.0,
|
||||||
|
fusion_droppath=0.1,
|
||||||
|
fusion_dropout=0.0,
|
||||||
|
embedding_init_target=True,
|
||||||
|
query_dim=4,
|
||||||
|
positional_embedding_temperature=20,
|
||||||
|
init_std=0.02,
|
||||||
|
layer_norm_eps=1e-5,
|
||||||
|
**kwargs,
|
||||||
|
):
|
||||||
|
PretrainedConfig.__init__(is_encoder_decoder=is_encoder_decoder, **kwargs)
|
||||||
|
if backbone_config is None and backbone is None:
|
||||||
|
logger.info("`backbone_config` is `None`. Initializing the config with the default `Swin` backbone.")
|
||||||
|
backbone_config = CONFIG_MAPPING["swin"](
|
||||||
|
window_size=7,
|
||||||
|
image_size=224,
|
||||||
|
embed_dim=96,
|
||||||
|
depths=[2, 2, 6, 2],
|
||||||
|
num_heads=[3, 6, 12, 24],
|
||||||
|
out_indices=[2, 3, 4],
|
||||||
|
)
|
||||||
|
elif isinstance(backbone_config, dict):
|
||||||
|
backbone_model_type = backbone_config.pop("model_type")
|
||||||
|
config_class = CONFIG_MAPPING[backbone_model_type]
|
||||||
|
backbone_config = config_class.from_dict(backbone_config)
|
||||||
|
|
||||||
|
verify_backbone_config_arguments(
|
||||||
|
use_timm_backbone=use_timm_backbone,
|
||||||
|
use_pretrained_backbone=use_pretrained_backbone,
|
||||||
|
backbone=backbone,
|
||||||
|
backbone_config=backbone_config,
|
||||||
|
backbone_kwargs=backbone_kwargs,
|
||||||
|
)
|
||||||
|
|
||||||
|
if text_config is None:
|
||||||
|
text_config = {}
|
||||||
|
logger.info("text_config is None. Initializing the text config with default values (`BertConfig`).")
|
||||||
|
|
||||||
|
self.backbone_config = backbone_config
|
||||||
|
self.backbone = backbone
|
||||||
|
self.use_pretrained_backbone = use_pretrained_backbone
|
||||||
|
self.use_timm_backbone = use_timm_backbone
|
||||||
|
self.backbone_kwargs = backbone_kwargs
|
||||||
|
self.num_queries = num_queries
|
||||||
|
self.d_model = d_model
|
||||||
|
self.encoder_ffn_dim = encoder_ffn_dim
|
||||||
|
self.encoder_layers = encoder_layers
|
||||||
|
self.encoder_attention_heads = encoder_attention_heads
|
||||||
|
self.decoder_ffn_dim = decoder_ffn_dim
|
||||||
|
self.decoder_layers = decoder_layers
|
||||||
|
self.decoder_attention_heads = decoder_attention_heads
|
||||||
|
self.dropout = dropout
|
||||||
|
self.attention_dropout = attention_dropout
|
||||||
|
self.activation_dropout = activation_dropout
|
||||||
|
self.activation_function = activation_function
|
||||||
|
self.auxiliary_loss = auxiliary_loss
|
||||||
|
self.position_embedding_type = position_embedding_type
|
||||||
|
# deformable attributes
|
||||||
|
self.num_feature_levels = num_feature_levels
|
||||||
|
self.encoder_n_points = encoder_n_points
|
||||||
|
self.decoder_n_points = decoder_n_points
|
||||||
|
self.two_stage = two_stage
|
||||||
|
# Hungarian matcher
|
||||||
|
self.class_cost = class_cost
|
||||||
|
self.bbox_cost = bbox_cost
|
||||||
|
self.giou_cost = giou_cost
|
||||||
|
# Loss coefficients
|
||||||
|
self.bbox_loss_coefficient = bbox_loss_coefficient
|
||||||
|
self.giou_loss_coefficient = giou_loss_coefficient
|
||||||
|
self.focal_alpha = focal_alpha
|
||||||
|
self.disable_custom_kernels = disable_custom_kernels
|
||||||
|
# Text backbone
|
||||||
|
if isinstance(text_config, dict):
|
||||||
|
text_config["model_type"] = text_config["model_type"] if "model_type" in text_config else "bert"
|
||||||
|
text_config = CONFIG_MAPPING[text_config["model_type"]](**text_config)
|
||||||
|
elif text_config is None:
|
||||||
|
text_config = CONFIG_MAPPING["bert"]()
|
||||||
|
|
||||||
|
self.text_config = text_config
|
||||||
|
self.max_text_len = max_text_len
|
||||||
|
|
||||||
|
# Text Enhancer
|
||||||
|
self.text_enhancer_dropout = text_enhancer_dropout
|
||||||
|
# Fusion
|
||||||
|
self.fusion_droppath = fusion_droppath
|
||||||
|
self.fusion_dropout = fusion_dropout
|
||||||
|
# Others
|
||||||
|
self.embedding_init_target = embedding_init_target
|
||||||
|
self.query_dim = query_dim
|
||||||
|
self.positional_embedding_temperature = positional_embedding_temperature
|
||||||
|
self.init_std = init_std
|
||||||
|
self.layer_norm_eps = layer_norm_eps
|
||||||
|
|
||||||
|
|
||||||
|
class MMGroundingDinoContrastiveEmbedding(GroundingDinoContrastiveEmbedding):
|
||||||
|
def __init__(self, config):
|
||||||
|
super().__init__(config)
|
||||||
|
self.bias = nn.Parameter(torch.tensor(0.0))
|
||||||
|
|
||||||
|
def forward(
|
||||||
|
self,
|
||||||
|
vision_hidden_state: torch.FloatTensor,
|
||||||
|
text_hidden_state: torch.FloatTensor,
|
||||||
|
text_token_mask: torch.BoolTensor,
|
||||||
|
) -> torch.FloatTensor:
|
||||||
|
res = vision_hidden_state @ text_hidden_state.transpose(-1, -2)
|
||||||
|
res = res / math.sqrt(vision_hidden_state.shape[-1])
|
||||||
|
res = res + self.bias
|
||||||
|
res.masked_fill_(~text_token_mask[:, None, :], float("-inf"))
|
||||||
|
|
||||||
|
# padding to max_text_len
|
||||||
|
new_res = torch.full((*res.shape[:-1], self.max_text_len), float("-inf"), device=res.device)
|
||||||
|
new_res[..., : res.shape[-1]] = res
|
||||||
|
|
||||||
|
return new_res
|
||||||
|
|
||||||
|
|
||||||
|
class MMGroundingDinoPreTrainedModel(GroundingDinoPreTrainedModel):
|
||||||
|
def _init_weights(self, module):
|
||||||
|
super()._init_weights(module)
|
||||||
|
if isinstance(module, MMGroundingDinoContrastiveEmbedding):
|
||||||
|
nn.init.constant_(module.bias, -math.log((1 - 0.01) / 0.01))
|
||||||
|
|
||||||
|
|
||||||
|
class MMGroundingDinoConvEncoder(GroundingDinoConvEncoder):
|
||||||
|
pass
|
||||||
|
|
||||||
|
|
||||||
|
class MMGroundingDinoConvModel(GroundingDinoConvModel):
|
||||||
|
pass
|
||||||
|
|
||||||
|
|
||||||
|
class MMGroundingDinoEncoder(GroundingDinoEncoder):
|
||||||
|
pass
|
||||||
|
|
||||||
|
|
||||||
|
class MMGroundingDinoDecoder(GroundingDinoDecoder):
|
||||||
|
pass
|
||||||
|
|
||||||
|
|
||||||
|
class MMGroundingDinoModel(GroundingDinoModel, MMGroundingDinoPreTrainedModel):
|
||||||
|
def __init__(self, config: MMGroundingDinoConfig):
|
||||||
|
MMGroundingDinoPreTrainedModel.__init__(config)
|
||||||
|
|
||||||
|
# Create backbone + positional encoding
|
||||||
|
backbone = MMGroundingDinoConvEncoder(config)
|
||||||
|
position_embeddings = build_position_encoding(config)
|
||||||
|
self.backbone = MMGroundingDinoConvModel(backbone, position_embeddings)
|
||||||
|
|
||||||
|
# Create input projection layers
|
||||||
|
num_backbone_outs = len(backbone.intermediate_channel_sizes)
|
||||||
|
input_proj_list = []
|
||||||
|
for i in range(num_backbone_outs):
|
||||||
|
in_channels = backbone.intermediate_channel_sizes[i]
|
||||||
|
input_proj_list.append(
|
||||||
|
nn.Sequential(
|
||||||
|
nn.Conv2d(in_channels, config.d_model, kernel_size=1),
|
||||||
|
nn.GroupNorm(32, config.d_model),
|
||||||
|
)
|
||||||
|
)
|
||||||
|
for _ in range(config.num_feature_levels - num_backbone_outs):
|
||||||
|
input_proj_list.append(
|
||||||
|
nn.Sequential(
|
||||||
|
nn.Conv2d(in_channels, config.d_model, kernel_size=3, stride=2, padding=1),
|
||||||
|
nn.GroupNorm(32, config.d_model),
|
||||||
|
)
|
||||||
|
)
|
||||||
|
in_channels = config.d_model
|
||||||
|
self.input_proj_vision = nn.ModuleList(input_proj_list)
|
||||||
|
|
||||||
|
# Create text backbone
|
||||||
|
self.text_backbone = AutoModel.from_config(config.text_config, add_pooling_layer=False)
|
||||||
|
self.text_projection = nn.Linear(config.text_config.hidden_size, config.d_model)
|
||||||
|
|
||||||
|
if config.embedding_init_target or not config.two_stage:
|
||||||
|
self.query_position_embeddings = nn.Embedding(config.num_queries, config.d_model)
|
||||||
|
|
||||||
|
self.encoder = MMGroundingDinoEncoder(config)
|
||||||
|
self.decoder = MMGroundingDinoDecoder(config)
|
||||||
|
|
||||||
|
self.level_embed = nn.Parameter(torch.Tensor(config.num_feature_levels, config.d_model))
|
||||||
|
|
||||||
|
self.enc_output = nn.Linear(config.d_model, config.d_model)
|
||||||
|
self.enc_output_norm = nn.LayerNorm(config.d_model, config.layer_norm_eps)
|
||||||
|
self.encoder_output_bbox_embed = MMGroundingDinoMLPPredictionHead(
|
||||||
|
input_dim=config.d_model, hidden_dim=config.d_model, output_dim=4, num_layers=3
|
||||||
|
)
|
||||||
|
self.encoder_output_class_embed = MMGroundingDinoContrastiveEmbedding(config)
|
||||||
|
|
||||||
|
self.post_init()
|
||||||
|
|
||||||
|
|
||||||
|
class MMGroundingDinoMLPPredictionHead(GroundingDinoMLPPredictionHead):
|
||||||
|
pass
|
||||||
|
|
||||||
|
|
||||||
|
class MMGroundingDinoForObjectDetection(GroundingDinoForObjectDetection, MMGroundingDinoPreTrainedModel):
|
||||||
|
_tied_weights_keys = [
|
||||||
|
r"bbox_embed\.[1-9]\d*",
|
||||||
|
r"model\.decoder\.bbox_embed\.[0-9]\d*",
|
||||||
|
r"class_embed\.[1-9]\d*",
|
||||||
|
r"model\.decoder\.class_embed\.[0-9]\d*",
|
||||||
|
]
|
||||||
|
|
||||||
|
def __init__(self, config: MMGroundingDinoConfig):
|
||||||
|
MMGroundingDinoPreTrainedModel.__init__(config)
|
||||||
|
|
||||||
|
self.model = MMGroundingDinoModel(config)
|
||||||
|
|
||||||
|
self.class_embed = nn.ModuleList(
|
||||||
|
[MMGroundingDinoContrastiveEmbedding(config) for _ in range(config.decoder_layers)]
|
||||||
|
)
|
||||||
|
|
||||||
|
self.bbox_embed = nn.ModuleList(
|
||||||
|
[
|
||||||
|
MMGroundingDinoMLPPredictionHead(
|
||||||
|
input_dim=config.d_model, hidden_dim=config.d_model, output_dim=4, num_layers=3
|
||||||
|
)
|
||||||
|
for _ in range(config.decoder_layers)
|
||||||
|
]
|
||||||
|
)
|
||||||
|
|
||||||
|
# hack for box-refinement
|
||||||
|
self.model.decoder.bbox_embed = self.bbox_embed
|
||||||
|
# hack implementation for two-stage
|
||||||
|
self.model.decoder.class_embed = self.class_embed
|
||||||
|
|
||||||
|
# Initialize weights and apply final processing
|
||||||
|
self.post_init()
|
||||||
|
|
||||||
|
|
||||||
|
__all__ = [
|
||||||
|
"MMGroundingDinoConfig",
|
||||||
|
"MMGroundingDinoForObjectDetection",
|
||||||
|
"MMGroundingDinoModel",
|
||||||
|
"MMGroundingDinoPreTrainedModel",
|
||||||
|
]
|
||||||
@@ -818,7 +818,9 @@ class GroundingDinoModelIntegrationTests(unittest.TestCase):
|
|||||||
prompt = ". ".join(id2label.values()) + "."
|
prompt = ". ".join(id2label.values()) + "."
|
||||||
|
|
||||||
text_inputs = tokenizer([prompt, prompt], return_tensors="pt")
|
text_inputs = tokenizer([prompt, prompt], return_tensors="pt")
|
||||||
image_inputs = image_processor(images=ds["image"], annotations=ds["annotations"], return_tensors="pt")
|
image_inputs = image_processor(
|
||||||
|
images=list(ds["image"]), annotations=list(ds["annotations"]), return_tensors="pt"
|
||||||
|
)
|
||||||
|
|
||||||
# Passing auxiliary_loss=True to compare with the expected loss
|
# Passing auxiliary_loss=True to compare with the expected loss
|
||||||
model = GroundingDinoForObjectDetection.from_pretrained(
|
model = GroundingDinoForObjectDetection.from_pretrained(
|
||||||
|
|||||||
0
tests/models/mm_grounding_dino/__init__.py
Normal file
0
tests/models/mm_grounding_dino/__init__.py
Normal file
@@ -0,0 +1,871 @@
|
|||||||
|
# Copyright 2025 The HuggingFace Inc. 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.
|
||||||
|
"""Testing suite for the PyTorch MM Grounding DINO model."""
|
||||||
|
|
||||||
|
import collections
|
||||||
|
import inspect
|
||||||
|
import math
|
||||||
|
import re
|
||||||
|
import unittest
|
||||||
|
|
||||||
|
from datasets import load_dataset
|
||||||
|
|
||||||
|
from transformers import (
|
||||||
|
MMGroundingDinoConfig,
|
||||||
|
SwinConfig,
|
||||||
|
is_torch_available,
|
||||||
|
is_vision_available,
|
||||||
|
)
|
||||||
|
from transformers.file_utils import cached_property
|
||||||
|
from transformers.testing_utils import (
|
||||||
|
is_flaky,
|
||||||
|
require_timm,
|
||||||
|
require_torch,
|
||||||
|
require_torch_accelerator,
|
||||||
|
require_vision,
|
||||||
|
slow,
|
||||||
|
torch_device,
|
||||||
|
)
|
||||||
|
|
||||||
|
from ...test_configuration_common import ConfigTester
|
||||||
|
from ...test_modeling_common import ModelTesterMixin, _config_zero_init, floats_tensor
|
||||||
|
from ...test_pipeline_mixin import PipelineTesterMixin
|
||||||
|
|
||||||
|
|
||||||
|
if is_torch_available():
|
||||||
|
import torch
|
||||||
|
|
||||||
|
from transformers import MMGroundingDinoConfig, MMGroundingDinoForObjectDetection, MMGroundingDinoModel
|
||||||
|
from transformers.pytorch_utils import id_tensor_storage
|
||||||
|
|
||||||
|
|
||||||
|
if is_vision_available():
|
||||||
|
from PIL import Image
|
||||||
|
|
||||||
|
from transformers import AutoProcessor
|
||||||
|
|
||||||
|
|
||||||
|
# Copied from tests.models.grounding_dino.test_modeling_grounding_dino.generate_fake_bounding_boxes
|
||||||
|
def generate_fake_bounding_boxes(n_boxes):
|
||||||
|
"""Generate bounding boxes in the format (center_x, center_y, width, height)"""
|
||||||
|
# Validate the input
|
||||||
|
if not isinstance(n_boxes, int):
|
||||||
|
raise TypeError("n_boxes must be an integer")
|
||||||
|
if n_boxes <= 0:
|
||||||
|
raise ValueError("n_boxes must be a positive integer")
|
||||||
|
|
||||||
|
# Generate random bounding boxes in the format (center_x, center_y, width, height)
|
||||||
|
bounding_boxes = torch.rand((n_boxes, 4))
|
||||||
|
|
||||||
|
# Extract the components
|
||||||
|
center_x = bounding_boxes[:, 0]
|
||||||
|
center_y = bounding_boxes[:, 1]
|
||||||
|
width = bounding_boxes[:, 2]
|
||||||
|
height = bounding_boxes[:, 3]
|
||||||
|
|
||||||
|
# Ensure width and height do not exceed bounds
|
||||||
|
width = torch.min(width, torch.tensor(1.0))
|
||||||
|
height = torch.min(height, torch.tensor(1.0))
|
||||||
|
|
||||||
|
# Ensure the bounding box stays within the normalized space
|
||||||
|
center_x = torch.where(center_x - width / 2 < 0, width / 2, center_x)
|
||||||
|
center_x = torch.where(center_x + width / 2 > 1, 1 - width / 2, center_x)
|
||||||
|
center_y = torch.where(center_y - height / 2 < 0, height / 2, center_y)
|
||||||
|
center_y = torch.where(center_y + height / 2 > 1, 1 - height / 2, center_y)
|
||||||
|
|
||||||
|
# Combine back into bounding boxes
|
||||||
|
bounding_boxes = torch.stack([center_x, center_y, width, height], dim=1)
|
||||||
|
|
||||||
|
return bounding_boxes
|
||||||
|
|
||||||
|
|
||||||
|
# Copied from tests.models.grounding_dino.test_modeling_grounding_dino.GroundingDinoModelTester with GroundingDino->MMGroundingDino
|
||||||
|
class MMGroundingDinoModelTester:
|
||||||
|
def __init__(
|
||||||
|
self,
|
||||||
|
parent,
|
||||||
|
batch_size=4,
|
||||||
|
is_training=True,
|
||||||
|
use_labels=True,
|
||||||
|
hidden_size=32,
|
||||||
|
num_hidden_layers=2,
|
||||||
|
num_attention_heads=4,
|
||||||
|
intermediate_size=4,
|
||||||
|
hidden_act="gelu",
|
||||||
|
hidden_dropout_prob=0.1,
|
||||||
|
attention_probs_dropout_prob=0.1,
|
||||||
|
num_queries=2,
|
||||||
|
num_channels=3,
|
||||||
|
image_size=98,
|
||||||
|
n_targets=8,
|
||||||
|
num_labels=2,
|
||||||
|
num_feature_levels=4,
|
||||||
|
encoder_n_points=2,
|
||||||
|
decoder_n_points=6,
|
||||||
|
max_text_len=7,
|
||||||
|
):
|
||||||
|
self.parent = parent
|
||||||
|
self.batch_size = batch_size
|
||||||
|
self.is_training = is_training
|
||||||
|
self.use_labels = use_labels
|
||||||
|
self.hidden_size = hidden_size
|
||||||
|
self.num_hidden_layers = num_hidden_layers
|
||||||
|
self.num_attention_heads = num_attention_heads
|
||||||
|
self.intermediate_size = intermediate_size
|
||||||
|
self.hidden_act = hidden_act
|
||||||
|
self.hidden_dropout_prob = hidden_dropout_prob
|
||||||
|
self.attention_probs_dropout_prob = attention_probs_dropout_prob
|
||||||
|
self.num_queries = num_queries
|
||||||
|
self.num_channels = num_channels
|
||||||
|
self.image_size = image_size
|
||||||
|
self.n_targets = n_targets
|
||||||
|
self.num_labels = num_labels
|
||||||
|
self.num_feature_levels = num_feature_levels
|
||||||
|
self.encoder_n_points = encoder_n_points
|
||||||
|
self.decoder_n_points = decoder_n_points
|
||||||
|
self.max_text_len = max_text_len
|
||||||
|
|
||||||
|
# we also set the expected seq length for both encoder and decoder
|
||||||
|
self.encoder_seq_length_vision = (
|
||||||
|
math.ceil(self.image_size / 8) ** 2
|
||||||
|
+ math.ceil(self.image_size / 16) ** 2
|
||||||
|
+ math.ceil(self.image_size / 32) ** 2
|
||||||
|
+ math.ceil(self.image_size / 64) ** 2
|
||||||
|
)
|
||||||
|
|
||||||
|
self.encoder_seq_length_text = self.max_text_len
|
||||||
|
|
||||||
|
self.decoder_seq_length = self.num_queries
|
||||||
|
|
||||||
|
def prepare_config_and_inputs(self):
|
||||||
|
pixel_values = floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size])
|
||||||
|
pixel_mask = torch.ones([self.batch_size, self.image_size, self.image_size], device=torch_device)
|
||||||
|
|
||||||
|
# When using `MMGroundingDino` the text input template is '{label1}. {label2}. {label3. ... {labelN}.'
|
||||||
|
# Therefore to avoid errors when running tests with `labels` `input_ids` have to follow this structure.
|
||||||
|
# Otherwise when running `build_label_maps` it will throw an error when trying to split the input_ids into segments.
|
||||||
|
input_ids = torch.tensor([101, 3869, 1012, 11420, 3869, 1012, 102], device=torch_device)
|
||||||
|
input_ids = input_ids.unsqueeze(0).expand(self.batch_size, -1)
|
||||||
|
|
||||||
|
labels = None
|
||||||
|
if self.use_labels:
|
||||||
|
# labels is a list of Dict (each Dict being the labels for a given example in the batch)
|
||||||
|
labels = []
|
||||||
|
for i in range(self.batch_size):
|
||||||
|
target = {}
|
||||||
|
target["class_labels"] = torch.randint(
|
||||||
|
high=self.num_labels, size=(self.n_targets,), device=torch_device
|
||||||
|
)
|
||||||
|
target["boxes"] = generate_fake_bounding_boxes(self.n_targets).to(torch_device)
|
||||||
|
target["masks"] = torch.rand(self.n_targets, self.image_size, self.image_size, device=torch_device)
|
||||||
|
labels.append(target)
|
||||||
|
|
||||||
|
config = self.get_config()
|
||||||
|
return config, pixel_values, pixel_mask, input_ids, labels
|
||||||
|
|
||||||
|
def get_config(self):
|
||||||
|
swin_config = SwinConfig(
|
||||||
|
window_size=7,
|
||||||
|
embed_dim=8,
|
||||||
|
depths=[1, 1, 1, 1],
|
||||||
|
num_heads=[1, 1, 1, 1],
|
||||||
|
image_size=self.image_size,
|
||||||
|
out_features=["stage2", "stage3", "stage4"],
|
||||||
|
out_indices=[2, 3, 4],
|
||||||
|
)
|
||||||
|
text_backbone = {
|
||||||
|
"hidden_size": 8,
|
||||||
|
"num_hidden_layers": 2,
|
||||||
|
"num_attention_heads": 2,
|
||||||
|
"intermediate_size": 8,
|
||||||
|
"max_position_embeddings": 8,
|
||||||
|
"model_type": "bert",
|
||||||
|
}
|
||||||
|
return MMGroundingDinoConfig(
|
||||||
|
d_model=self.hidden_size,
|
||||||
|
encoder_layers=self.num_hidden_layers,
|
||||||
|
decoder_layers=self.num_hidden_layers,
|
||||||
|
encoder_attention_heads=self.num_attention_heads,
|
||||||
|
decoder_attention_heads=self.num_attention_heads,
|
||||||
|
encoder_ffn_dim=self.intermediate_size,
|
||||||
|
decoder_ffn_dim=self.intermediate_size,
|
||||||
|
dropout=self.hidden_dropout_prob,
|
||||||
|
attention_dropout=self.attention_probs_dropout_prob,
|
||||||
|
num_queries=self.num_queries,
|
||||||
|
num_labels=self.num_labels,
|
||||||
|
num_feature_levels=self.num_feature_levels,
|
||||||
|
encoder_n_points=self.encoder_n_points,
|
||||||
|
decoder_n_points=self.decoder_n_points,
|
||||||
|
use_timm_backbone=False,
|
||||||
|
backbone_config=swin_config,
|
||||||
|
max_text_len=self.max_text_len,
|
||||||
|
text_config=text_backbone,
|
||||||
|
)
|
||||||
|
|
||||||
|
def prepare_config_and_inputs_for_common(self):
|
||||||
|
config, pixel_values, pixel_mask, input_ids, labels = self.prepare_config_and_inputs()
|
||||||
|
inputs_dict = {"pixel_values": pixel_values, "pixel_mask": pixel_mask, "input_ids": input_ids}
|
||||||
|
return config, inputs_dict
|
||||||
|
|
||||||
|
def create_and_check_model(self, config, pixel_values, pixel_mask, input_ids, labels):
|
||||||
|
model = MMGroundingDinoModel(config=config)
|
||||||
|
model.to(torch_device)
|
||||||
|
model.eval()
|
||||||
|
|
||||||
|
result = model(pixel_values=pixel_values, pixel_mask=pixel_mask, input_ids=input_ids)
|
||||||
|
|
||||||
|
self.parent.assertEqual(result.last_hidden_state.shape, (self.batch_size, self.num_queries, self.hidden_size))
|
||||||
|
|
||||||
|
def create_and_check_object_detection_head_model(self, config, pixel_values, pixel_mask, input_ids, labels):
|
||||||
|
model = MMGroundingDinoForObjectDetection(config=config)
|
||||||
|
model.to(torch_device)
|
||||||
|
model.eval()
|
||||||
|
|
||||||
|
result = model(pixel_values=pixel_values, pixel_mask=pixel_mask, input_ids=input_ids)
|
||||||
|
|
||||||
|
self.parent.assertEqual(result.logits.shape, (self.batch_size, self.num_queries, config.max_text_len))
|
||||||
|
self.parent.assertEqual(result.pred_boxes.shape, (self.batch_size, self.num_queries, 4))
|
||||||
|
|
||||||
|
result = model(pixel_values=pixel_values, pixel_mask=pixel_mask, input_ids=input_ids, labels=labels)
|
||||||
|
|
||||||
|
self.parent.assertEqual(result.loss.shape, ())
|
||||||
|
self.parent.assertEqual(result.logits.shape, (self.batch_size, self.num_queries, config.max_text_len))
|
||||||
|
self.parent.assertEqual(result.pred_boxes.shape, (self.batch_size, self.num_queries, 4))
|
||||||
|
|
||||||
|
|
||||||
|
@require_torch
|
||||||
|
# Copied from tests.models.grounding_dino.test_modeling_grounding_dino.GroundingDinoModelTest with Grounding->MMGrounding
|
||||||
|
class MMGroundingDinoModelTest(ModelTesterMixin, PipelineTesterMixin, unittest.TestCase):
|
||||||
|
all_model_classes = (MMGroundingDinoModel, MMGroundingDinoForObjectDetection) if is_torch_available() else ()
|
||||||
|
is_encoder_decoder = True
|
||||||
|
test_torchscript = False
|
||||||
|
test_pruning = False
|
||||||
|
test_head_masking = False
|
||||||
|
test_missing_keys = False
|
||||||
|
pipeline_model_mapping = (
|
||||||
|
{
|
||||||
|
"image-feature-extraction": MMGroundingDinoModel,
|
||||||
|
"zero-shot-object-detection": MMGroundingDinoForObjectDetection,
|
||||||
|
}
|
||||||
|
if is_torch_available()
|
||||||
|
else {}
|
||||||
|
)
|
||||||
|
|
||||||
|
# special case for head models
|
||||||
|
def _prepare_for_class(self, inputs_dict, model_class, return_labels=False):
|
||||||
|
inputs_dict = super()._prepare_for_class(inputs_dict, model_class, return_labels=return_labels)
|
||||||
|
|
||||||
|
if return_labels:
|
||||||
|
if model_class.__name__ == "MMGroundingDinoForObjectDetection":
|
||||||
|
labels = []
|
||||||
|
for i in range(self.model_tester.batch_size):
|
||||||
|
target = {}
|
||||||
|
target["class_labels"] = torch.ones(
|
||||||
|
size=(self.model_tester.n_targets,), device=torch_device, dtype=torch.long
|
||||||
|
)
|
||||||
|
target["boxes"] = torch.ones(
|
||||||
|
self.model_tester.n_targets, 4, device=torch_device, dtype=torch.float
|
||||||
|
)
|
||||||
|
target["masks"] = torch.ones(
|
||||||
|
self.model_tester.n_targets,
|
||||||
|
self.model_tester.image_size,
|
||||||
|
self.model_tester.image_size,
|
||||||
|
device=torch_device,
|
||||||
|
dtype=torch.float,
|
||||||
|
)
|
||||||
|
labels.append(target)
|
||||||
|
inputs_dict["labels"] = labels
|
||||||
|
|
||||||
|
return inputs_dict
|
||||||
|
|
||||||
|
def setUp(self):
|
||||||
|
self.model_tester = MMGroundingDinoModelTester(self)
|
||||||
|
self.config_tester = ConfigTester(
|
||||||
|
self,
|
||||||
|
config_class=MMGroundingDinoConfig,
|
||||||
|
has_text_modality=False,
|
||||||
|
common_properties=["d_model", "encoder_attention_heads", "decoder_attention_heads"],
|
||||||
|
)
|
||||||
|
|
||||||
|
def test_config(self):
|
||||||
|
self.config_tester.run_common_tests()
|
||||||
|
|
||||||
|
def test_model(self):
|
||||||
|
config_and_inputs = self.model_tester.prepare_config_and_inputs()
|
||||||
|
self.model_tester.create_and_check_model(*config_and_inputs)
|
||||||
|
|
||||||
|
def test_object_detection_head_model(self):
|
||||||
|
config_and_inputs = self.model_tester.prepare_config_and_inputs()
|
||||||
|
self.model_tester.create_and_check_object_detection_head_model(*config_and_inputs)
|
||||||
|
|
||||||
|
@unittest.skip(reason="MMGrounding DINO does not use inputs_embeds")
|
||||||
|
def test_inputs_embeds(self):
|
||||||
|
pass
|
||||||
|
|
||||||
|
@unittest.skip(reason="MMGrounding DINO does not have a get_input_embeddings method")
|
||||||
|
def test_model_get_set_embeddings(self):
|
||||||
|
pass
|
||||||
|
|
||||||
|
@unittest.skip(reason="MMGrounding DINO does not use token embeddings")
|
||||||
|
def test_resize_tokens_embeddings(self):
|
||||||
|
pass
|
||||||
|
|
||||||
|
@unittest.skip(reason="Feed forward chunking is not implemented")
|
||||||
|
def test_feed_forward_chunking(self):
|
||||||
|
pass
|
||||||
|
|
||||||
|
def test_attention_outputs(self):
|
||||||
|
config, inputs_dict = self.model_tester.prepare_config_and_inputs_for_common()
|
||||||
|
config.return_dict = True
|
||||||
|
|
||||||
|
for model_class in self.all_model_classes:
|
||||||
|
inputs_dict["output_attentions"] = True
|
||||||
|
inputs_dict["output_hidden_states"] = False
|
||||||
|
config.return_dict = True
|
||||||
|
model = model_class._from_config(config, attn_implementation="eager")
|
||||||
|
config = model.config
|
||||||
|
model.to(torch_device)
|
||||||
|
model.eval()
|
||||||
|
with torch.no_grad():
|
||||||
|
outputs = model(**self._prepare_for_class(inputs_dict, model_class))
|
||||||
|
attentions = outputs.encoder_attentions[-1]
|
||||||
|
self.assertEqual(len(attentions), self.model_tester.num_hidden_layers)
|
||||||
|
|
||||||
|
# check that output_attentions also work using config
|
||||||
|
del inputs_dict["output_attentions"]
|
||||||
|
config.output_attentions = True
|
||||||
|
model = model_class(config)
|
||||||
|
model.to(torch_device)
|
||||||
|
model.eval()
|
||||||
|
with torch.no_grad():
|
||||||
|
outputs = model(**self._prepare_for_class(inputs_dict, model_class))
|
||||||
|
attentions = outputs.encoder_attentions[-1]
|
||||||
|
self.assertEqual(len(attentions), self.model_tester.num_hidden_layers)
|
||||||
|
|
||||||
|
self.assertListEqual(
|
||||||
|
list(attentions[0].shape[-3:]),
|
||||||
|
[
|
||||||
|
self.model_tester.num_attention_heads,
|
||||||
|
self.model_tester.num_feature_levels,
|
||||||
|
self.model_tester.encoder_n_points,
|
||||||
|
],
|
||||||
|
)
|
||||||
|
out_len = len(outputs)
|
||||||
|
|
||||||
|
correct_outlen = 12
|
||||||
|
|
||||||
|
# loss is at first position
|
||||||
|
if "labels" in inputs_dict:
|
||||||
|
correct_outlen += 1 # loss is added to beginning
|
||||||
|
# Object Detection model returns pred_logits and pred_boxes and input_ids
|
||||||
|
if model_class.__name__ == "MMGroundingDinoForObjectDetection":
|
||||||
|
correct_outlen += 3
|
||||||
|
|
||||||
|
self.assertEqual(out_len, correct_outlen)
|
||||||
|
|
||||||
|
# decoder attentions
|
||||||
|
decoder_attentions = outputs.decoder_attentions[0]
|
||||||
|
self.assertIsInstance(decoder_attentions, (list, tuple))
|
||||||
|
self.assertEqual(len(decoder_attentions), self.model_tester.num_hidden_layers)
|
||||||
|
self.assertListEqual(
|
||||||
|
list(decoder_attentions[0].shape[-3:]),
|
||||||
|
[self.model_tester.num_attention_heads, self.model_tester.num_queries, self.model_tester.num_queries],
|
||||||
|
)
|
||||||
|
|
||||||
|
# cross attentions
|
||||||
|
cross_attentions = outputs.decoder_attentions[-1]
|
||||||
|
self.assertIsInstance(cross_attentions, (list, tuple))
|
||||||
|
self.assertEqual(len(cross_attentions), self.model_tester.num_hidden_layers)
|
||||||
|
self.assertListEqual(
|
||||||
|
list(cross_attentions[0].shape[-3:]),
|
||||||
|
[
|
||||||
|
self.model_tester.num_attention_heads,
|
||||||
|
self.model_tester.num_feature_levels,
|
||||||
|
self.model_tester.decoder_n_points,
|
||||||
|
],
|
||||||
|
)
|
||||||
|
|
||||||
|
# Check attention is always last and order is fine
|
||||||
|
inputs_dict["output_attentions"] = True
|
||||||
|
inputs_dict["output_hidden_states"] = True
|
||||||
|
model = model_class(config)
|
||||||
|
model.to(torch_device)
|
||||||
|
model.eval()
|
||||||
|
with torch.no_grad():
|
||||||
|
outputs = model(**self._prepare_for_class(inputs_dict, model_class))
|
||||||
|
|
||||||
|
self.assertEqual(out_len + 3, len(outputs))
|
||||||
|
|
||||||
|
self_attentions = outputs.encoder_attentions[-1]
|
||||||
|
|
||||||
|
self.assertEqual(len(self_attentions), self.model_tester.num_hidden_layers)
|
||||||
|
self.assertListEqual(
|
||||||
|
list(self_attentions[0].shape[-3:]),
|
||||||
|
[
|
||||||
|
self.model_tester.num_attention_heads,
|
||||||
|
self.model_tester.num_feature_levels,
|
||||||
|
self.model_tester.encoder_n_points,
|
||||||
|
],
|
||||||
|
)
|
||||||
|
|
||||||
|
# overwrite since hidden_states are called encoder_text_hidden_states
|
||||||
|
def test_hidden_states_output(self):
|
||||||
|
def check_hidden_states_output(inputs_dict, config, model_class):
|
||||||
|
model = model_class(config)
|
||||||
|
model.to(torch_device)
|
||||||
|
model.eval()
|
||||||
|
|
||||||
|
with torch.no_grad():
|
||||||
|
outputs = model(**self._prepare_for_class(inputs_dict, model_class))
|
||||||
|
|
||||||
|
hidden_states = outputs.encoder_vision_hidden_states
|
||||||
|
|
||||||
|
expected_num_layers = getattr(
|
||||||
|
self.model_tester, "expected_num_hidden_layers", self.model_tester.num_hidden_layers + 1
|
||||||
|
)
|
||||||
|
self.assertEqual(len(hidden_states), expected_num_layers)
|
||||||
|
|
||||||
|
seq_len = self.model_tester.encoder_seq_length_vision
|
||||||
|
|
||||||
|
self.assertListEqual(
|
||||||
|
list(hidden_states[0].shape[-2:]),
|
||||||
|
[seq_len, self.model_tester.hidden_size],
|
||||||
|
)
|
||||||
|
|
||||||
|
hidden_states = outputs.encoder_text_hidden_states
|
||||||
|
|
||||||
|
self.assertEqual(len(hidden_states), expected_num_layers)
|
||||||
|
|
||||||
|
seq_len = self.model_tester.encoder_seq_length_text
|
||||||
|
|
||||||
|
self.assertListEqual(
|
||||||
|
list(hidden_states[0].shape[-2:]),
|
||||||
|
[seq_len, self.model_tester.hidden_size],
|
||||||
|
)
|
||||||
|
|
||||||
|
hidden_states = outputs.decoder_hidden_states
|
||||||
|
|
||||||
|
self.assertIsInstance(hidden_states, (list, tuple))
|
||||||
|
self.assertEqual(len(hidden_states), expected_num_layers)
|
||||||
|
seq_len = getattr(self.model_tester, "seq_length", None)
|
||||||
|
decoder_seq_length = getattr(self.model_tester, "decoder_seq_length", seq_len)
|
||||||
|
|
||||||
|
self.assertListEqual(
|
||||||
|
list(hidden_states[0].shape[-2:]),
|
||||||
|
[decoder_seq_length, self.model_tester.hidden_size],
|
||||||
|
)
|
||||||
|
|
||||||
|
config, inputs_dict = self.model_tester.prepare_config_and_inputs_for_common()
|
||||||
|
|
||||||
|
for model_class in self.all_model_classes:
|
||||||
|
inputs_dict["output_hidden_states"] = True
|
||||||
|
check_hidden_states_output(inputs_dict, config, model_class)
|
||||||
|
|
||||||
|
# check that output_hidden_states also work using config
|
||||||
|
del inputs_dict["output_hidden_states"]
|
||||||
|
config.output_hidden_states = True
|
||||||
|
|
||||||
|
check_hidden_states_output(inputs_dict, config, model_class)
|
||||||
|
|
||||||
|
# removed retain_grad and grad on decoder_hidden_states, as queries don't require grad
|
||||||
|
def test_retain_grad_hidden_states_attentions(self):
|
||||||
|
config, inputs_dict = self.model_tester.prepare_config_and_inputs_for_common()
|
||||||
|
config.output_hidden_states = True
|
||||||
|
config.output_attentions = True
|
||||||
|
|
||||||
|
# no need to test all models as different heads yield the same functionality
|
||||||
|
model_class = self.all_model_classes[0]
|
||||||
|
model = model_class(config)
|
||||||
|
model.to(torch_device)
|
||||||
|
|
||||||
|
inputs = self._prepare_for_class(inputs_dict, model_class)
|
||||||
|
|
||||||
|
outputs = model(**inputs)
|
||||||
|
|
||||||
|
output = outputs[0]
|
||||||
|
|
||||||
|
encoder_hidden_states = outputs.encoder_vision_hidden_states[0]
|
||||||
|
encoder_attentions = outputs.encoder_attentions[0][0]
|
||||||
|
encoder_hidden_states.retain_grad()
|
||||||
|
encoder_attentions.retain_grad()
|
||||||
|
|
||||||
|
cross_attentions = outputs.decoder_attentions[-1][0]
|
||||||
|
cross_attentions.retain_grad()
|
||||||
|
|
||||||
|
output.flatten()[0].backward(retain_graph=True)
|
||||||
|
|
||||||
|
self.assertIsNotNone(encoder_hidden_states.grad)
|
||||||
|
self.assertIsNotNone(encoder_attentions.grad)
|
||||||
|
self.assertIsNotNone(cross_attentions.grad)
|
||||||
|
|
||||||
|
def test_forward_signature(self):
|
||||||
|
config, _ = self.model_tester.prepare_config_and_inputs_for_common()
|
||||||
|
|
||||||
|
for model_class in self.all_model_classes:
|
||||||
|
model = model_class(config)
|
||||||
|
signature = inspect.signature(model.forward)
|
||||||
|
# signature.parameters is an OrderedDict => so arg_names order is deterministic
|
||||||
|
arg_names = [*signature.parameters.keys()]
|
||||||
|
|
||||||
|
expected_arg_names = ["pixel_values", "input_ids"]
|
||||||
|
self.assertListEqual(arg_names[: len(expected_arg_names)], expected_arg_names)
|
||||||
|
|
||||||
|
def test_different_timm_backbone(self):
|
||||||
|
config, inputs_dict = self.model_tester.prepare_config_and_inputs_for_common()
|
||||||
|
|
||||||
|
# let's pick a random timm backbone
|
||||||
|
config.backbone = "tf_mobilenetv3_small_075"
|
||||||
|
config.use_timm_backbone = True
|
||||||
|
config.backbone_config = None
|
||||||
|
config.backbone_kwargs = {"in_chans": 3, "out_indices": (2, 3, 4)}
|
||||||
|
|
||||||
|
for model_class in self.all_model_classes:
|
||||||
|
model = model_class(config)
|
||||||
|
model.to(torch_device)
|
||||||
|
model.eval()
|
||||||
|
with torch.no_grad():
|
||||||
|
outputs = model(**self._prepare_for_class(inputs_dict, model_class))
|
||||||
|
|
||||||
|
if model_class.__name__ == "MMGroundingDinoForObjectDetection":
|
||||||
|
expected_shape = (
|
||||||
|
self.model_tester.batch_size,
|
||||||
|
self.model_tester.num_queries,
|
||||||
|
config.max_text_len,
|
||||||
|
)
|
||||||
|
self.assertEqual(outputs.logits.shape, expected_shape)
|
||||||
|
|
||||||
|
self.assertTrue(outputs)
|
||||||
|
|
||||||
|
@require_timm
|
||||||
|
def test_hf_backbone(self):
|
||||||
|
config, inputs_dict = self.model_tester.prepare_config_and_inputs_for_common()
|
||||||
|
|
||||||
|
# Load a pretrained HF checkpoint as backbone
|
||||||
|
config.backbone = "microsoft/resnet-18"
|
||||||
|
config.backbone_config = None
|
||||||
|
config.use_timm_backbone = False
|
||||||
|
config.use_pretrained_backbone = True
|
||||||
|
config.backbone_kwargs = {"out_indices": [2, 3, 4]}
|
||||||
|
|
||||||
|
for model_class in self.all_model_classes:
|
||||||
|
model = model_class(config)
|
||||||
|
model.to(torch_device)
|
||||||
|
model.eval()
|
||||||
|
with torch.no_grad():
|
||||||
|
outputs = model(**self._prepare_for_class(inputs_dict, model_class))
|
||||||
|
|
||||||
|
if model_class.__name__ == "MMGroundingDinoForObjectDetection":
|
||||||
|
expected_shape = (
|
||||||
|
self.model_tester.batch_size,
|
||||||
|
self.model_tester.num_queries,
|
||||||
|
config.max_text_len,
|
||||||
|
)
|
||||||
|
self.assertEqual(outputs.logits.shape, expected_shape)
|
||||||
|
|
||||||
|
self.assertTrue(outputs)
|
||||||
|
|
||||||
|
# Ignore copy
|
||||||
|
def test_initialization(self):
|
||||||
|
config, inputs_dict = self.model_tester.prepare_config_and_inputs_for_common()
|
||||||
|
|
||||||
|
configs_no_init = _config_zero_init(config)
|
||||||
|
for model_class in self.all_model_classes:
|
||||||
|
model = model_class(config=configs_no_init)
|
||||||
|
for name, param in model.named_parameters():
|
||||||
|
if param.requires_grad:
|
||||||
|
if (
|
||||||
|
"level_embed" in name
|
||||||
|
or "sampling_offsets.bias" in name
|
||||||
|
or "text_param" in name
|
||||||
|
or "vision_param" in name
|
||||||
|
or "value_proj" in name
|
||||||
|
or "output_proj" in name
|
||||||
|
or "reference_points" in name
|
||||||
|
or "vision_proj" in name
|
||||||
|
or "text_proj" in name
|
||||||
|
or ("class_embed" in name and "bias" in name)
|
||||||
|
):
|
||||||
|
continue
|
||||||
|
self.assertIn(
|
||||||
|
((param.data.mean() * 1e9).round() / 1e9).item(),
|
||||||
|
[0.0, 1.0],
|
||||||
|
msg=f"Parameter {name} of model {model_class} seems not properly initialized",
|
||||||
|
)
|
||||||
|
|
||||||
|
# Copied from tests.models.deformable_detr.test_modeling_deformable_detr.DeformableDetrModelTest.test_two_stage_training with DeformableDetr->MMGroundingDino
|
||||||
|
def test_two_stage_training(self):
|
||||||
|
model_class = MMGroundingDinoForObjectDetection
|
||||||
|
config, inputs_dict = self.model_tester.prepare_config_and_inputs_for_common()
|
||||||
|
config.return_dict = True
|
||||||
|
config.two_stage = True
|
||||||
|
config.auxiliary_loss = True
|
||||||
|
config.with_box_refine = True
|
||||||
|
|
||||||
|
model = model_class(config)
|
||||||
|
model.to(torch_device)
|
||||||
|
model.train()
|
||||||
|
inputs = self._prepare_for_class(inputs_dict, model_class, return_labels=True)
|
||||||
|
loss = model(**inputs).loss
|
||||||
|
loss.backward()
|
||||||
|
|
||||||
|
def test_tied_weights_keys(self):
|
||||||
|
config, _ = self.model_tester.prepare_config_and_inputs_for_common()
|
||||||
|
config.tie_word_embeddings = True
|
||||||
|
for model_class in self.all_model_classes:
|
||||||
|
model_tied = model_class(config)
|
||||||
|
|
||||||
|
ptrs = collections.defaultdict(list)
|
||||||
|
for name, tensor in model_tied.state_dict().items():
|
||||||
|
ptrs[id_tensor_storage(tensor)].append(name)
|
||||||
|
|
||||||
|
# These are all the pointers of shared tensors.
|
||||||
|
tied_params = [names for _, names in ptrs.items() if len(names) > 1]
|
||||||
|
|
||||||
|
tied_weight_keys = model_tied._tied_weights_keys if model_tied._tied_weights_keys is not None else []
|
||||||
|
# Detect we get a hit for each key
|
||||||
|
for key in tied_weight_keys:
|
||||||
|
if not any(re.search(key, p) for group in tied_params for p in group):
|
||||||
|
raise ValueError(f"{key} is not a tied weight key for {model_class}.")
|
||||||
|
|
||||||
|
# Removed tied weights found from tied params -> there should only be one left after
|
||||||
|
for key in tied_weight_keys:
|
||||||
|
for i in range(len(tied_params)):
|
||||||
|
tied_params[i] = [p for p in tied_params[i] if re.search(key, p) is None]
|
||||||
|
|
||||||
|
# MMGroundingDino when sharing weights also uses the shared ones in MMGroundingDinoDecoder
|
||||||
|
# Therefore, differently from DeformableDetr, we expect the group lens to be 2
|
||||||
|
# one for self.bbox_embed in MMGroundingDinoForObejectDetection and another one
|
||||||
|
# in the decoder
|
||||||
|
tied_params = [group for group in tied_params if len(group) > 2]
|
||||||
|
self.assertListEqual(
|
||||||
|
tied_params,
|
||||||
|
[],
|
||||||
|
f"Missing `_tied_weights_keys` for {model_class}: add all of {tied_params} except one.",
|
||||||
|
)
|
||||||
|
|
||||||
|
|
||||||
|
# We will verify our results on an image of cute cats
|
||||||
|
def prepare_img():
|
||||||
|
image = Image.open("./tests/fixtures/tests_samples/COCO/000000039769.png")
|
||||||
|
return image
|
||||||
|
|
||||||
|
|
||||||
|
def prepare_text():
|
||||||
|
text = "a cat."
|
||||||
|
return text
|
||||||
|
|
||||||
|
|
||||||
|
@require_timm
|
||||||
|
@require_vision
|
||||||
|
@slow
|
||||||
|
class MMGroundingDinoModelIntegrationTests(unittest.TestCase):
|
||||||
|
@cached_property
|
||||||
|
def default_processor(self):
|
||||||
|
return (
|
||||||
|
AutoProcessor.from_pretrained("openmmlab-community/mm_grounding_dino_tiny_o365v1_goldg_v3det")
|
||||||
|
if is_vision_available()
|
||||||
|
else None
|
||||||
|
)
|
||||||
|
|
||||||
|
def test_inference_object_detection_head(self):
|
||||||
|
model = MMGroundingDinoForObjectDetection.from_pretrained(
|
||||||
|
"openmmlab-community/mm_grounding_dino_tiny_o365v1_goldg_v3det"
|
||||||
|
).to(torch_device)
|
||||||
|
|
||||||
|
processor = self.default_processor
|
||||||
|
image = prepare_img()
|
||||||
|
text = prepare_text()
|
||||||
|
encoding = processor(images=image, text=text, return_tensors="pt").to(torch_device)
|
||||||
|
|
||||||
|
with torch.no_grad():
|
||||||
|
outputs = model(**encoding)
|
||||||
|
|
||||||
|
expected_shape_logits = torch.Size((1, model.config.num_queries, model.config.d_model))
|
||||||
|
self.assertEqual(outputs.logits.shape, expected_shape_logits)
|
||||||
|
|
||||||
|
expected_boxes = torch.tensor(
|
||||||
|
[[0.7666, 0.4142, 0.4590], [0.2557, 0.5480, 0.4812], [0.5049, 0.5133, 0.9767]]
|
||||||
|
).to(torch_device)
|
||||||
|
expected_logits = torch.tensor(
|
||||||
|
[[-5.1160, -0.2143, -0.2089], [-5.0592, -0.4269, -0.4169], [-4.9087, -1.7608, -1.7372]]
|
||||||
|
).to(torch_device)
|
||||||
|
|
||||||
|
torch.testing.assert_close(outputs.logits[0, :3, :3], expected_logits, rtol=1e-3, atol=1e-3)
|
||||||
|
|
||||||
|
expected_shape_boxes = torch.Size((1, model.config.num_queries, 4))
|
||||||
|
self.assertEqual(outputs.pred_boxes.shape, expected_shape_boxes)
|
||||||
|
torch.testing.assert_close(outputs.pred_boxes[0, :3, :3], expected_boxes, rtol=1e-4, atol=1e-4)
|
||||||
|
|
||||||
|
# verify postprocessing
|
||||||
|
results = processor.image_processor.post_process_object_detection(
|
||||||
|
outputs, threshold=0.35, target_sizes=[(image.height, image.width)]
|
||||||
|
)[0]
|
||||||
|
expected_scores = torch.tensor([0.4480, 0.3973]).to(torch_device)
|
||||||
|
expected_slice_boxes = torch.tensor([343.7321, 23.8182, 637.5044, 373.8593]).to(torch_device)
|
||||||
|
|
||||||
|
self.assertEqual(len(results["scores"]), 2)
|
||||||
|
torch.testing.assert_close(results["scores"], expected_scores, rtol=1e-3, atol=1e-3)
|
||||||
|
torch.testing.assert_close(results["boxes"][0, :], expected_slice_boxes, rtol=1e-2, atol=1e-2)
|
||||||
|
|
||||||
|
# verify grounded postprocessing
|
||||||
|
expected_labels = ["a cat", "a cat"]
|
||||||
|
results = processor.post_process_grounded_object_detection(
|
||||||
|
outputs=outputs,
|
||||||
|
input_ids=encoding.input_ids,
|
||||||
|
threshold=0.35,
|
||||||
|
text_threshold=0.3,
|
||||||
|
target_sizes=[(image.height, image.width)],
|
||||||
|
)[0]
|
||||||
|
|
||||||
|
torch.testing.assert_close(results["scores"], expected_scores, rtol=1e-3, atol=1e-3)
|
||||||
|
torch.testing.assert_close(results["boxes"][0, :], expected_slice_boxes, rtol=1e-2, atol=1e-2)
|
||||||
|
self.assertListEqual(results["text_labels"], expected_labels)
|
||||||
|
|
||||||
|
@require_torch_accelerator
|
||||||
|
@is_flaky()
|
||||||
|
def test_inference_object_detection_head_equivalence_cpu_gpu(self):
|
||||||
|
processor = self.default_processor
|
||||||
|
image = prepare_img()
|
||||||
|
text = prepare_text()
|
||||||
|
encoding = processor(images=image, text=text, return_tensors="pt")
|
||||||
|
|
||||||
|
# 1. run model on CPU
|
||||||
|
model = MMGroundingDinoForObjectDetection.from_pretrained(
|
||||||
|
"openmmlab-community/mm_grounding_dino_tiny_o365v1_goldg_v3det"
|
||||||
|
)
|
||||||
|
# HACK: the issue happens during top-k (k=900) after the encoder
|
||||||
|
# there are some flips between cpu and gpu query ordering (idxs 195<->196 and 267<->268 on my machine)
|
||||||
|
# which causes different query position embedding assingments
|
||||||
|
# which in turn significantly changes the decoder pass due to self attention
|
||||||
|
model.config.num_queries = 100
|
||||||
|
model.model.query_position_embeddings.weight.data = model.model.query_position_embeddings.weight.data[:100]
|
||||||
|
|
||||||
|
with torch.no_grad():
|
||||||
|
cpu_outputs = model(**encoding)
|
||||||
|
|
||||||
|
# 2. run model on GPU
|
||||||
|
model.to(torch_device)
|
||||||
|
encoding = encoding.to(torch_device)
|
||||||
|
with torch.no_grad():
|
||||||
|
gpu_outputs = model(**encoding)
|
||||||
|
|
||||||
|
# 3. assert equivalence
|
||||||
|
for key in cpu_outputs.keys():
|
||||||
|
torch.testing.assert_close(cpu_outputs[key], gpu_outputs[key].cpu(), rtol=1e-3, atol=1e-3)
|
||||||
|
|
||||||
|
expected_logits = torch.tensor(
|
||||||
|
[[-5.0188, -1.0069, -1.0005], [-5.1177, -1.0537, -1.0444], [-5.3986, -2.4935, -2.4716]]
|
||||||
|
)
|
||||||
|
torch.testing.assert_close(cpu_outputs.logits[0, :3, :3], expected_logits, rtol=1e-3, atol=1e-3)
|
||||||
|
|
||||||
|
# assert postprocessing
|
||||||
|
results_cpu = processor.image_processor.post_process_object_detection(
|
||||||
|
cpu_outputs, threshold=0.35, target_sizes=[(image.height, image.width)]
|
||||||
|
)[0]
|
||||||
|
|
||||||
|
result_gpu = processor.image_processor.post_process_object_detection(
|
||||||
|
gpu_outputs, threshold=0.35, target_sizes=[(image.height, image.width)]
|
||||||
|
)[0]
|
||||||
|
|
||||||
|
torch.testing.assert_close(results_cpu["scores"], result_gpu["scores"].cpu(), rtol=1e-3, atol=1e-3)
|
||||||
|
torch.testing.assert_close(results_cpu["boxes"], result_gpu["boxes"].cpu(), rtol=1e-3, atol=1e-3)
|
||||||
|
|
||||||
|
@is_flaky()
|
||||||
|
def test_cross_attention_mask(self):
|
||||||
|
model = MMGroundingDinoForObjectDetection.from_pretrained(
|
||||||
|
"openmmlab-community/mm_grounding_dino_tiny_o365v1_goldg_v3det"
|
||||||
|
).to(torch_device)
|
||||||
|
# HACK: the issue happens during top-k (k=900) after the encoder
|
||||||
|
# there are some flips between cpu and gpu query ordering
|
||||||
|
# which causes different query position embedding assingments
|
||||||
|
# which in turn significantly changes the decoder pass due to self attention
|
||||||
|
model.config.num_queries = 100
|
||||||
|
model.model.query_position_embeddings.weight.data = model.model.query_position_embeddings.weight.data[:100]
|
||||||
|
|
||||||
|
processor = self.default_processor
|
||||||
|
image = prepare_img()
|
||||||
|
text1 = "a cat."
|
||||||
|
text2 = "a remote control."
|
||||||
|
text_batched = [text1, text2]
|
||||||
|
|
||||||
|
encoding1 = processor(images=image, text=text1, return_tensors="pt").to(torch_device)
|
||||||
|
encoding2 = processor(images=image, text=text2, return_tensors="pt").to(torch_device)
|
||||||
|
# If we batch the text and cross attention masking is working the batched result should be equal to
|
||||||
|
# The singe text result
|
||||||
|
encoding_batched = processor(
|
||||||
|
images=[image] * len(text_batched), text=text_batched, padding="longest", return_tensors="pt"
|
||||||
|
).to(torch_device)
|
||||||
|
|
||||||
|
with torch.no_grad():
|
||||||
|
outputs1 = model(**encoding1)
|
||||||
|
outputs2 = model(**encoding2)
|
||||||
|
outputs_batched = model(**encoding_batched)
|
||||||
|
|
||||||
|
torch.testing.assert_close(outputs1.logits, outputs_batched.logits[:1], rtol=1e-3, atol=1e-3)
|
||||||
|
# For some reason 12 elements are > 1e-3, but the rest are fine
|
||||||
|
self.assertTrue(torch.allclose(outputs2.logits, outputs_batched.logits[1:], atol=1.8e-3))
|
||||||
|
|
||||||
|
def test_mm_grounding_dino_loss(self):
|
||||||
|
ds = load_dataset("EduardoPacheco/aquarium-sample", split="train")
|
||||||
|
image_processor = self.default_processor.image_processor
|
||||||
|
tokenizer = self.default_processor.tokenizer
|
||||||
|
id2label = {0: "fish", 1: "jellyfish", 2: "penguins", 3: "sharks", 4: "puffins", 5: "stingrays", 6: "starfish"}
|
||||||
|
prompt = ". ".join(id2label.values()) + "."
|
||||||
|
|
||||||
|
text_inputs = tokenizer([prompt, prompt], return_tensors="pt")
|
||||||
|
image_inputs = image_processor(
|
||||||
|
images=list(ds["image"]), annotations=list(ds["annotations"]), return_tensors="pt"
|
||||||
|
)
|
||||||
|
|
||||||
|
# Passing auxiliary_loss=True to compare with the expected loss
|
||||||
|
model = MMGroundingDinoForObjectDetection.from_pretrained(
|
||||||
|
"openmmlab-community/mm_grounding_dino_tiny_o365v1_goldg_v3det",
|
||||||
|
auxiliary_loss=True,
|
||||||
|
)
|
||||||
|
# Interested in the loss only
|
||||||
|
model.eval()
|
||||||
|
with torch.no_grad():
|
||||||
|
outputs = model(**text_inputs, **image_inputs)
|
||||||
|
|
||||||
|
# Loss differs by CPU and GPU, also this can be changed in future.
|
||||||
|
expected_loss_dict = {
|
||||||
|
"loss_ce": torch.tensor(1.1799),
|
||||||
|
"loss_bbox": torch.tensor(0.2348),
|
||||||
|
"loss_giou": torch.tensor(0.5834),
|
||||||
|
"loss_ce_0": torch.tensor(1.1199),
|
||||||
|
"loss_bbox_0": torch.tensor(0.3083),
|
||||||
|
"loss_giou_0": torch.tensor(0.6555),
|
||||||
|
"loss_ce_1": torch.tensor(1.2075),
|
||||||
|
"loss_bbox_1": torch.tensor(0.2641),
|
||||||
|
"loss_giou_1": torch.tensor(0.6073),
|
||||||
|
"loss_ce_2": torch.tensor(1.2915),
|
||||||
|
"loss_bbox_2": torch.tensor(0.2616),
|
||||||
|
"loss_giou_2": torch.tensor(0.5730),
|
||||||
|
"loss_ce_3": torch.tensor(1.0243),
|
||||||
|
"loss_bbox_3": torch.tensor(0.2799),
|
||||||
|
"loss_giou_3": torch.tensor(0.6326),
|
||||||
|
"loss_ce_4": torch.tensor(1.2019),
|
||||||
|
"loss_bbox_4": torch.tensor(0.2430),
|
||||||
|
"loss_giou_4": torch.tensor(0.5679),
|
||||||
|
"loss_ce_enc": torch.tensor(10.2381),
|
||||||
|
"loss_bbox_enc": torch.tensor(0.2886),
|
||||||
|
"loss_giou_enc": torch.tensor(0.6335),
|
||||||
|
}
|
||||||
|
|
||||||
|
expected_loss = torch.tensor(52.4340)
|
||||||
|
|
||||||
|
for key in expected_loss_dict:
|
||||||
|
self.assertTrue(torch.allclose(outputs.loss_dict[key], expected_loss_dict[key], atol=1e-3))
|
||||||
|
|
||||||
|
self.assertTrue(torch.allclose(outputs.loss, expected_loss, atol=1e-3))
|
||||||
@@ -221,6 +221,14 @@ SPECIAL_CASES_TO_ALLOW = {
|
|||||||
"giou_cost",
|
"giou_cost",
|
||||||
"giou_loss_coefficient",
|
"giou_loss_coefficient",
|
||||||
],
|
],
|
||||||
|
"MMGroundingDinoConfig": [
|
||||||
|
"bbox_cost",
|
||||||
|
"bbox_loss_coefficient",
|
||||||
|
"class_cost",
|
||||||
|
"focal_alpha",
|
||||||
|
"giou_cost",
|
||||||
|
"giou_loss_coefficient",
|
||||||
|
],
|
||||||
"RTDetrConfig": [
|
"RTDetrConfig": [
|
||||||
"eos_coefficient",
|
"eos_coefficient",
|
||||||
"focal_loss_alpha",
|
"focal_loss_alpha",
|
||||||
|
|||||||
Reference in New Issue
Block a user