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main
| Author | SHA1 | Date |
|---|---|---|
Shilong Liu
|
9dac4c605b | 2 years ago |
SlongLiu
|
3bb2c86c9a | 2 years ago |
|
SlongLiu
|
d3bc35fdea | 2 years ago |
|
SlongLiu
|
15ade007a8 | 2 years ago |
|
Shilong Liu
|
22292c4b78 | 2 years ago |
rentainhe
|
4c8f9206b6 | 2 years ago |
|
rentainhe
|
97ad9935ac | 2 years ago |
George Pearse
|
e93548c805 | 2 years ago |
Piotr Skalski
|
e45c11c4c3 | 2 years ago |
|
Piotr Skalski
|
f6b1145481 | 2 years ago |
|
SlongLiu
|
3023d1a26f | 2 years ago |
|
SlongLiu
|
a02cf79301 | 2 years ago |
|
SlongLiu
|
67a3c1940d | 2 years ago |
|
SlongLiu
|
ac00bd4a36 | 2 years ago |
|
Piotr Skalski
|
c974f60d73 | 2 years ago |
|
SlongLiu
|
858efccbad | 2 years ago |
|
Piotr Skalski
|
2309f9f468 | 2 years ago |
|
SlongLiu
|
12ef464f9e | 2 years ago |
|
Shilong Liu
|
3e7a8ca2dc | 2 years ago |
49 changed files with 11169 additions and 11 deletions
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# IDE |
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.idea/ |
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.vscode/ |
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# Byte-compiled / optimized / DLL files |
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__pycache__/ |
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*.py[cod] |
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*$py.class |
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# C extensions |
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*.so |
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# Distribution / packaging |
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.Python |
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build/ |
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develop-eggs/ |
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dist/ |
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downloads/ |
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eggs/ |
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.eggs/ |
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lib/ |
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lib64/ |
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parts/ |
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sdist/ |
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var/ |
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wheels/ |
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pip-wheel-metadata/ |
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share/python-wheels/ |
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*.egg-info/ |
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.installed.cfg |
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*.egg |
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MANIFEST |
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# PyInstaller |
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# Usually these files are written by a python script from a template |
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# before PyInstaller builds the exe, so as to inject date/other infos into it. |
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*.manifest |
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*.spec |
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# Installer logs |
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pip-log.txt |
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pip-delete-this-directory.txt |
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# Unit test / coverage reports |
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htmlcov/ |
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.tox/ |
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.nox/ |
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.coverage |
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.coverage.* |
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.cache |
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nosetests.xml |
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coverage.xml |
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*.cover |
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*.py,cover |
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.hypothesis/ |
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# Translations |
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*.mo |
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*.pot |
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# Django stuff: |
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*.log |
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local_settings.py |
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db.sqlite3 |
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# Flask stuff: |
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# Scrapy stuff: |
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.scrapy |
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# Sphinx documentation |
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docs/_build/ |
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# PyBuilder |
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# Jupyter Notebook |
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# IPython |
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profile_default/ |
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# pyenv |
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.python-version |
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# pipenv |
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# According to pypa/pipenv#598, it is recommended to include Pipfile.lock in version control. |
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# having no cross-platform support, pipenv may install dependencies that don't work, or not |
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# install all needed dependencies. |
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#Pipfile.lock |
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# PEP 582; used by e.g. github.com/David-OConnor/pyflow |
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__pypackages__/ |
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# Celery stuff |
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celerybeat-schedule |
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celerybeat.pid |
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# SageMath parsed files |
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*.sage.py |
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# Environments |
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.env |
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.venv |
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env/ |
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ENV/ |
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env.bak/ |
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venv.bak/ |
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# Spyder project settings |
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# Rope project settings |
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.ropeproject |
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# mkdocs documentation |
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/site |
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# mypy |
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.mypy_cache/ |
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.dmypy.json |
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dmypy.json |
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# Pyre type checker |
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.pyre/ |
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# vscode |
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.vscode/ |
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output/ |
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outputs/ |
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subs/ |
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logs/ |
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|
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grounding/config/configs |
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grounding/version.py |
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vis/ |
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tmp/ |
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@ -0,0 +1,125 @@ |
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import argparse |
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from functools import partial |
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import cv2 |
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import requests |
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import os |
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from io import BytesIO |
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from PIL import Image |
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import numpy as np |
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from pathlib import Path |
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|
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|
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import warnings |
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|
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import torch |
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|
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# prepare the environment |
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os.system("python setup.py build develop --user") |
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os.system("pip install packaging==21.3") |
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os.system("pip install gradio") |
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|
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|
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warnings.filterwarnings("ignore") |
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|
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import gradio as gr |
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|
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from groundingdino.models import build_model |
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from groundingdino.util.slconfig import SLConfig |
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from groundingdino.util.utils import clean_state_dict |
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from groundingdino.util.inference import annotate, load_image, predict |
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import groundingdino.datasets.transforms as T |
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|
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from huggingface_hub import hf_hub_download |
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|
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|
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|
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# Use this command for evaluate the Grounding DINO model |
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config_file = "groundingdino/config/GroundingDINO_SwinT_OGC.py" |
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ckpt_repo_id = "ShilongLiu/GroundingDINO" |
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ckpt_filenmae = "groundingdino_swint_ogc.pth" |
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|
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|
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def load_model_hf(model_config_path, repo_id, filename, device='cpu'): |
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args = SLConfig.fromfile(model_config_path) |
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model = build_model(args) |
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args.device = device |
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|
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cache_file = hf_hub_download(repo_id=repo_id, filename=filename) |
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checkpoint = torch.load(cache_file, map_location='cpu') |
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log = model.load_state_dict(clean_state_dict(checkpoint['model']), strict=False) |
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print("Model loaded from {} \n => {}".format(cache_file, log)) |
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_ = model.eval() |
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return model |
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|
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def image_transform_grounding(init_image): |
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transform = T.Compose([ |
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T.RandomResize([800], max_size=1333), |
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T.ToTensor(), |
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T.Normalize([0.485, 0.456, 0.406], [0.229, 0.224, 0.225]) |
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]) |
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image, _ = transform(init_image, None) # 3, h, w |
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return init_image, image |
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|
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def image_transform_grounding_for_vis(init_image): |
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transform = T.Compose([ |
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T.RandomResize([800], max_size=1333), |
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]) |
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image, _ = transform(init_image, None) # 3, h, w |
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return image |
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|
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model = load_model_hf(config_file, ckpt_repo_id, ckpt_filenmae) |
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|
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def run_grounding(input_image, grounding_caption, box_threshold, text_threshold): |
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init_image = input_image.convert("RGB") |
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original_size = init_image.size |
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|
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_, image_tensor = image_transform_grounding(init_image) |
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image_pil: Image = image_transform_grounding_for_vis(init_image) |
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|
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# run grounidng |
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boxes, logits, phrases = predict(model, image_tensor, grounding_caption, box_threshold, text_threshold, device='cpu') |
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annotated_frame = annotate(image_source=np.asarray(image_pil), boxes=boxes, logits=logits, phrases=phrases) |
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image_with_box = Image.fromarray(cv2.cvtColor(annotated_frame, cv2.COLOR_BGR2RGB)) |
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return image_with_box |
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|
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if __name__ == "__main__": |
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|
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parser = argparse.ArgumentParser("Grounding DINO demo", add_help=True) |
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parser.add_argument("--debug", action="store_true", help="using debug mode") |
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parser.add_argument("--share", action="store_true", help="share the app") |
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args = parser.parse_args() |
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|
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block = gr.Blocks().queue() |
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with block: |
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gr.Markdown("# [Grounding DINO](https://github.com/IDEA-Research/GroundingDINO)") |
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gr.Markdown("### Open-World Detection with Grounding DINO") |
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|
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with gr.Row(): |
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with gr.Column(): |
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input_image = gr.Image(source='upload', type="pil") |
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grounding_caption = gr.Textbox(label="Detection Prompt") |
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run_button = gr.Button(label="Run") |
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with gr.Accordion("Advanced options", open=False): |
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box_threshold = gr.Slider( |
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label="Box Threshold", minimum=0.0, maximum=1.0, value=0.25, step=0.001 |
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) |
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text_threshold = gr.Slider( |
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label="Text Threshold", minimum=0.0, maximum=1.0, value=0.25, step=0.001 |
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) |
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|
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with gr.Column(): |
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gallery = gr.outputs.Image( |
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type="pil", |
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# label="grounding results" |
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).style(full_width=True, full_height=True) |
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# gallery = gr.Gallery(label="Generated images", show_label=False).style( |
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# grid=[1], height="auto", container=True, full_width=True, full_height=True) |
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|
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run_button.click(fn=run_grounding, inputs=[ |
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input_image, grounding_caption, box_threshold, text_threshold], outputs=[gallery]) |
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|
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|
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block.launch(server_name='0.0.0.0', server_port=7579, debug=args.debug, share=args.share) |
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|
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@ -0,0 +1,172 @@ |
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import argparse |
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import os |
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import sys |
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|
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import numpy as np |
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import torch |
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from PIL import Image, ImageDraw, ImageFont |
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|
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import groundingdino.datasets.transforms as T |
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from groundingdino.models import build_model |
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from groundingdino.util import box_ops |
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from groundingdino.util.slconfig import SLConfig |
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from groundingdino.util.utils import clean_state_dict, get_phrases_from_posmap |
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|
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|
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def plot_boxes_to_image(image_pil, tgt): |
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H, W = tgt["size"] |
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boxes = tgt["boxes"] |
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labels = tgt["labels"] |
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assert len(boxes) == len(labels), "boxes and labels must have same length" |
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|
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draw = ImageDraw.Draw(image_pil) |
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mask = Image.new("L", image_pil.size, 0) |
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mask_draw = ImageDraw.Draw(mask) |
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|
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# draw boxes and masks |
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for box, label in zip(boxes, labels): |
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# from 0..1 to 0..W, 0..H |
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box = box * torch.Tensor([W, H, W, H]) |
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# from xywh to xyxy |
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box[:2] -= box[2:] / 2 |
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box[2:] += box[:2] |
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# random color |
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color = tuple(np.random.randint(0, 255, size=3).tolist()) |
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# draw |
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x0, y0, x1, y1 = box |
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x0, y0, x1, y1 = int(x0), int(y0), int(x1), int(y1) |
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draw.rectangle([x0, y0, x1, y1], outline=color, width=6) |
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# draw.text((x0, y0), str(label), fill=color) |
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|
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font = ImageFont.load_default() |
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if hasattr(font, "getbbox"): |
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bbox = draw.textbbox((x0, y0), str(label), font) |
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else: |
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w, h = draw.textsize(str(label), font) |
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bbox = (x0, y0, w + x0, y0 + h) |
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# bbox = draw.textbbox((x0, y0), str(label)) |
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draw.rectangle(bbox, fill=color) |
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draw.text((x0, y0), str(label), fill="white") |
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|
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mask_draw.rectangle([x0, y0, x1, y1], fill=255, width=6) |
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return image_pil, mask |
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|
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|
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def load_image(image_path): |
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# load image |
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image_pil = Image.open(image_path).convert("RGB") # load image |
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|
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transform = T.Compose( |
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[ |
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T.RandomResize([800], max_size=1333), |
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T.ToTensor(), |
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T.Normalize([0.485, 0.456, 0.406], [0.229, 0.224, 0.225]), |
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] |
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) |
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image, _ = transform(image_pil, None) # 3, h, w |
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return image_pil, image |
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|
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|
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def load_model(model_config_path, model_checkpoint_path, cpu_only=False): |
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args = SLConfig.fromfile(model_config_path) |
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args.device = "cuda" if not cpu_only else "cpu" |
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model = build_model(args) |
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checkpoint = torch.load(model_checkpoint_path, map_location="cpu") |
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load_res = model.load_state_dict(clean_state_dict(checkpoint["model"]), strict=False) |
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print(load_res) |
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_ = model.eval() |
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return model |
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|
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|
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def get_grounding_output(model, image, caption, box_threshold, text_threshold, with_logits=True, cpu_only=False): |
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caption = caption.lower() |
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caption = caption.strip() |
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if not caption.endswith("."): |
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caption = caption + "." |
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device = "cuda" if not cpu_only else "cpu" |
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model = model.to(device) |
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image = image.to(device) |
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with torch.no_grad(): |
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outputs = model(image[None], captions=[caption]) |
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logits = outputs["pred_logits"].cpu().sigmoid()[0] # (nq, 256) |
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boxes = outputs["pred_boxes"].cpu()[0] # (nq, 4) |
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logits.shape[0] |
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|
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# filter output |
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logits_filt = logits.clone() |
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boxes_filt = boxes.clone() |
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filt_mask = logits_filt.max(dim=1)[0] > box_threshold |
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logits_filt = logits_filt[filt_mask] # num_filt, 256 |
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boxes_filt = boxes_filt[filt_mask] # num_filt, 4 |
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logits_filt.shape[0] |
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|
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# get phrase |
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tokenlizer = model.tokenizer |
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tokenized = tokenlizer(caption) |
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# build pred |
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pred_phrases = [] |
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for logit, box in zip(logits_filt, boxes_filt): |
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pred_phrase = get_phrases_from_posmap(logit > text_threshold, tokenized, tokenlizer) |
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if with_logits: |
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pred_phrases.append(pred_phrase + f"({str(logit.max().item())[:4]})") |
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else: |
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pred_phrases.append(pred_phrase) |
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|
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return boxes_filt, pred_phrases |
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|
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|
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if __name__ == "__main__": |
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|
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parser = argparse.ArgumentParser("Grounding DINO example", add_help=True) |
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parser.add_argument("--config_file", "-c", type=str, required=True, help="path to config file") |
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parser.add_argument( |
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"--checkpoint_path", "-p", type=str, required=True, help="path to checkpoint file" |
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) |
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parser.add_argument("--image_path", "-i", type=str, required=True, help="path to image file") |
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parser.add_argument("--text_prompt", "-t", type=str, required=True, help="text prompt") |
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parser.add_argument( |
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"--output_dir", "-o", type=str, default="outputs", required=True, help="output directory" |
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) |
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|
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parser.add_argument("--box_threshold", type=float, default=0.3, help="box threshold") |
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parser.add_argument("--text_threshold", type=float, default=0.25, help="text threshold") |
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|
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parser.add_argument("--cpu-only", action="store_true", help="running on cpu only!, default=False") |
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args = parser.parse_args() |
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|
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# cfg |
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config_file = args.config_file # change the path of the model config file |
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checkpoint_path = args.checkpoint_path # change the path of the model |
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image_path = args.image_path |
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text_prompt = args.text_prompt |
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output_dir = args.output_dir |
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box_threshold = args.box_threshold |
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text_threshold = args.box_threshold |
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|
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# make dir |
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os.makedirs(output_dir, exist_ok=True) |
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# load image |
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image_pil, image = load_image(image_path) |
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# load model |
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model = load_model(config_file, checkpoint_path, cpu_only=args.cpu_only) |
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|
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# visualize raw image |
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image_pil.save(os.path.join(output_dir, "raw_image.jpg")) |
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|
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# run model |
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boxes_filt, pred_phrases = get_grounding_output( |
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model, image, text_prompt, box_threshold, text_threshold, cpu_only=args.cpu_only |
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) |
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|
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# visualize pred |
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size = image_pil.size |
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pred_dict = { |
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"boxes": boxes_filt, |
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"size": [size[1], size[0]], # H,W |
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"labels": pred_phrases, |
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} |
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# import ipdb; ipdb.set_trace() |
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image_with_box = plot_boxes_to_image(image_pil, pred_dict)[0] |
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image_with_box.save(os.path.join(output_dir, "pred.jpg")) |
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@ -0,0 +1,43 @@ |
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batch_size = 1 |
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modelname = "groundingdino" |
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backbone = "swin_B_384_22k" |
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position_embedding = "sine" |
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pe_temperatureH = 20 |
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pe_temperatureW = 20 |
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return_interm_indices = [1, 2, 3] |
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backbone_freeze_keywords = None |
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enc_layers = 6 |
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dec_layers = 6 |
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pre_norm = False |
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dim_feedforward = 2048 |
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hidden_dim = 256 |
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dropout = 0.0 |
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nheads = 8 |
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num_queries = 900 |
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query_dim = 4 |
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num_patterns = 0 |
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num_feature_levels = 4 |
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enc_n_points = 4 |
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dec_n_points = 4 |
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two_stage_type = "standard" |
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two_stage_bbox_embed_share = False |
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two_stage_class_embed_share = False |
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transformer_activation = "relu" |
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dec_pred_bbox_embed_share = True |
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dn_box_noise_scale = 1.0 |
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dn_label_noise_ratio = 0.5 |
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dn_label_coef = 1.0 |
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dn_bbox_coef = 1.0 |
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embed_init_tgt = True |
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dn_labelbook_size = 2000 |
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max_text_len = 256 |
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text_encoder_type = "bert-base-uncased" |
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use_text_enhancer = True |
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use_fusion_layer = True |
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use_checkpoint = True |
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use_transformer_ckpt = True |
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use_text_cross_attention = True |
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text_dropout = 0.0 |
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fusion_dropout = 0.0 |
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fusion_droppath = 0.1 |
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sub_sentence_present = True |
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@ -0,0 +1,43 @@ |
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batch_size = 1 |
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modelname = "groundingdino" |
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backbone = "swin_T_224_1k" |
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position_embedding = "sine" |
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pe_temperatureH = 20 |
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pe_temperatureW = 20 |
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return_interm_indices = [1, 2, 3] |
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backbone_freeze_keywords = None |
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enc_layers = 6 |
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dec_layers = 6 |
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pre_norm = False |
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dim_feedforward = 2048 |
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hidden_dim = 256 |
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dropout = 0.0 |
||||
nheads = 8 |
||||
num_queries = 900 |
||||
query_dim = 4 |
||||
num_patterns = 0 |
||||
num_feature_levels = 4 |
||||
enc_n_points = 4 |
||||
dec_n_points = 4 |
||||
two_stage_type = "standard" |
||||
two_stage_bbox_embed_share = False |
||||
two_stage_class_embed_share = False |
||||
transformer_activation = "relu" |
||||
dec_pred_bbox_embed_share = True |
||||
dn_box_noise_scale = 1.0 |
||||
dn_label_noise_ratio = 0.5 |
||||
dn_label_coef = 1.0 |
||||
dn_bbox_coef = 1.0 |
||||
embed_init_tgt = True |
||||
dn_labelbook_size = 2000 |
||||
max_text_len = 256 |
||||
text_encoder_type = "bert-base-uncased" |
||||
use_text_enhancer = True |
||||
use_fusion_layer = True |
||||
use_checkpoint = True |
||||
use_transformer_ckpt = True |
||||
use_text_cross_attention = True |
||||
text_dropout = 0.0 |
||||
fusion_dropout = 0.0 |
||||
fusion_droppath = 0.1 |
||||
sub_sentence_present = True |
||||
@ -0,0 +1,311 @@ |
||||
# Copyright (c) Facebook, Inc. and its affiliates. All Rights Reserved |
||||
""" |
||||
Transforms and data augmentation for both image + bbox. |
||||
""" |
||||
import os |
||||
import random |
||||
|
||||
import PIL |
||||
import torch |
||||
import torchvision.transforms as T |
||||
import torchvision.transforms.functional as F |
||||
|
||||
from groundingdino.util.box_ops import box_xyxy_to_cxcywh |
||||
from groundingdino.util.misc import interpolate |
||||
|
||||
|
||||
def crop(image, target, region): |
||||
cropped_image = F.crop(image, *region) |
||||
|
||||
target = target.copy() |
||||
i, j, h, w = region |
||||
|
||||
# should we do something wrt the original size? |
||||
target["size"] = torch.tensor([h, w]) |
||||
|
||||
fields = ["labels", "area", "iscrowd", "positive_map"] |
||||
|
||||
if "boxes" in target: |
||||
boxes = target["boxes"] |
||||
max_size = torch.as_tensor([w, h], dtype=torch.float32) |
||||
cropped_boxes = boxes - torch.as_tensor([j, i, j, i]) |
||||
cropped_boxes = torch.min(cropped_boxes.reshape(-1, 2, 2), max_size) |
||||
cropped_boxes = cropped_boxes.clamp(min=0) |
||||
area = (cropped_boxes[:, 1, :] - cropped_boxes[:, 0, :]).prod(dim=1) |
||||
target["boxes"] = cropped_boxes.reshape(-1, 4) |
||||
target["area"] = area |
||||
fields.append("boxes") |
||||
|
||||
if "masks" in target: |
||||
# FIXME should we update the area here if there are no boxes? |
||||
target["masks"] = target["masks"][:, i : i + h, j : j + w] |
||||
fields.append("masks") |
||||
|
||||
# remove elements for which the boxes or masks that have zero area |
||||
if "boxes" in target or "masks" in target: |
||||
# favor boxes selection when defining which elements to keep |
||||
# this is compatible with previous implementation |
||||
if "boxes" in target: |
||||
cropped_boxes = target["boxes"].reshape(-1, 2, 2) |
||||
keep = torch.all(cropped_boxes[:, 1, :] > cropped_boxes[:, 0, :], dim=1) |
||||
else: |
||||
keep = target["masks"].flatten(1).any(1) |
||||
|
||||
for field in fields: |
||||
if field in target: |
||||
target[field] = target[field][keep] |
||||
|
||||
if os.environ.get("IPDB_SHILONG_DEBUG", None) == "INFO": |
||||
# for debug and visualization only. |
||||
if "strings_positive" in target: |
||||
target["strings_positive"] = [ |
||||
_i for _i, _j in zip(target["strings_positive"], keep) if _j |
||||
] |
||||
|
||||
return cropped_image, target |
||||
|
||||
|
||||
def hflip(image, target): |
||||
flipped_image = F.hflip(image) |
||||
|
||||
w, h = image.size |
||||
|
||||
target = target.copy() |
||||
if "boxes" in target: |
||||
boxes = target["boxes"] |
||||
boxes = boxes[:, [2, 1, 0, 3]] * torch.as_tensor([-1, 1, -1, 1]) + torch.as_tensor( |
||||
[w, 0, w, 0] |
||||
) |
||||
target["boxes"] = boxes |
||||
|
||||
if "masks" in target: |
||||
target["masks"] = target["masks"].flip(-1) |
||||
|
||||
return flipped_image, target |
||||
|
||||
|
||||
def resize(image, target, size, max_size=None): |
||||
# size can be min_size (scalar) or (w, h) tuple |
||||
|
||||
def get_size_with_aspect_ratio(image_size, size, max_size=None): |
||||
w, h = image_size |
||||
if max_size is not None: |
||||
min_original_size = float(min((w, h))) |
||||
max_original_size = float(max((w, h))) |
||||
if max_original_size / min_original_size * size > max_size: |
||||
size = int(round(max_size * min_original_size / max_original_size)) |
||||
|
||||
if (w <= h and w == size) or (h <= w and h == size): |
||||
return (h, w) |
||||
|
||||
if w < h: |
||||
ow = size |
||||
oh = int(size * h / w) |
||||
else: |
||||
oh = size |
||||
ow = int(size * w / h) |
||||
|
||||
return (oh, ow) |
||||
|
||||
def get_size(image_size, size, max_size=None): |
||||
if isinstance(size, (list, tuple)): |
||||
return size[::-1] |
||||
else: |
||||
return get_size_with_aspect_ratio(image_size, size, max_size) |
||||
|
||||
size = get_size(image.size, size, max_size) |
||||
rescaled_image = F.resize(image, size) |
||||
|
||||
if target is None: |
||||
return rescaled_image, None |
||||
|
||||
ratios = tuple(float(s) / float(s_orig) for s, s_orig in zip(rescaled_image.size, image.size)) |
||||
ratio_width, ratio_height = ratios |
||||
|
||||
target = target.copy() |
||||
if "boxes" in target: |
||||
boxes = target["boxes"] |
||||
scaled_boxes = boxes * torch.as_tensor( |
||||
[ratio_width, ratio_height, ratio_width, ratio_height] |
||||
) |
||||
target["boxes"] = scaled_boxes |
||||
|
||||
if "area" in target: |
||||
area = target["area"] |
||||
scaled_area = area * (ratio_width * ratio_height) |
||||
target["area"] = scaled_area |
||||
|
||||
h, w = size |
||||
target["size"] = torch.tensor([h, w]) |
||||
|
||||
if "masks" in target: |
||||
target["masks"] = ( |
||||
interpolate(target["masks"][:, None].float(), size, mode="nearest")[:, 0] > 0.5 |
||||
) |
||||
|
||||
return rescaled_image, target |
||||
|
||||
|
||||
def pad(image, target, padding): |
||||
# assumes that we only pad on the bottom right corners |
||||
padded_image = F.pad(image, (0, 0, padding[0], padding[1])) |
||||
if target is None: |
||||
return padded_image, None |
||||
target = target.copy() |
||||
# should we do something wrt the original size? |
||||
target["size"] = torch.tensor(padded_image.size[::-1]) |
||||
if "masks" in target: |
||||
target["masks"] = torch.nn.functional.pad(target["masks"], (0, padding[0], 0, padding[1])) |
||||
return padded_image, target |
||||
|
||||
|
||||
class ResizeDebug(object): |
||||
def __init__(self, size): |
||||
self.size = size |
||||
|
||||
def __call__(self, img, target): |
||||
return resize(img, target, self.size) |
||||
|
||||
|
||||
class RandomCrop(object): |
||||
def __init__(self, size): |
||||
self.size = size |
||||
|
||||
def __call__(self, img, target): |
||||
region = T.RandomCrop.get_params(img, self.size) |
||||
return crop(img, target, region) |
||||
|
||||
|
||||
class RandomSizeCrop(object): |
||||
def __init__(self, min_size: int, max_size: int, respect_boxes: bool = False): |
||||
# respect_boxes: True to keep all boxes |
||||
# False to tolerence box filter |
||||
self.min_size = min_size |
||||
self.max_size = max_size |
||||
self.respect_boxes = respect_boxes |
||||
|
||||
def __call__(self, img: PIL.Image.Image, target: dict): |
||||
init_boxes = len(target["boxes"]) |
||||
max_patience = 10 |
||||
for i in range(max_patience): |
||||
w = random.randint(self.min_size, min(img.width, self.max_size)) |
||||
h = random.randint(self.min_size, min(img.height, self.max_size)) |
||||
region = T.RandomCrop.get_params(img, [h, w]) |
||||
result_img, result_target = crop(img, target, region) |
||||
if ( |
||||
not self.respect_boxes |
||||
or len(result_target["boxes"]) == init_boxes |
||||
or i == max_patience - 1 |
||||
): |
||||
return result_img, result_target |
||||
return result_img, result_target |
||||
|
||||
|
||||
class CenterCrop(object): |
||||
def __init__(self, size): |
||||
self.size = size |
||||
|
||||
def __call__(self, img, target): |
||||
image_width, image_height = img.size |
||||
crop_height, crop_width = self.size |
||||
crop_top = int(round((image_height - crop_height) / 2.0)) |
||||
crop_left = int(round((image_width - crop_width) / 2.0)) |
||||
return crop(img, target, (crop_top, crop_left, crop_height, crop_width)) |
||||
|
||||
|
||||
class RandomHorizontalFlip(object): |
||||
def __init__(self, p=0.5): |
||||
self.p = p |
||||
|
||||
def __call__(self, img, target): |
||||
if random.random() < self.p: |
||||
return hflip(img, target) |
||||
return img, target |
||||
|
||||
|
||||
class RandomResize(object): |
||||
def __init__(self, sizes, max_size=None): |
||||
assert isinstance(sizes, (list, tuple)) |
||||
self.sizes = sizes |
||||
self.max_size = max_size |
||||
|
||||
def __call__(self, img, target=None): |
||||
size = random.choice(self.sizes) |
||||
return resize(img, target, size, self.max_size) |
||||
|
||||
|
||||
class RandomPad(object): |
||||
def __init__(self, max_pad): |
||||
self.max_pad = max_pad |
||||
|
||||
def __call__(self, img, target): |
||||
pad_x = random.randint(0, self.max_pad) |
||||
pad_y = random.randint(0, self.max_pad) |
||||
return pad(img, target, (pad_x, pad_y)) |
||||
|
||||
|
||||
class RandomSelect(object): |
||||
""" |
||||
Randomly selects between transforms1 and transforms2, |
||||
with probability p for transforms1 and (1 - p) for transforms2 |
||||
""" |
||||
|
||||
def __init__(self, transforms1, transforms2, p=0.5): |
||||
self.transforms1 = transforms1 |
||||
self.transforms2 = transforms2 |
||||
self.p = p |
||||
|
||||
def __call__(self, img, target): |
||||
if random.random() < self.p: |
||||
return self.transforms1(img, target) |
||||
return self.transforms2(img, target) |
||||
|
||||
|
||||
class ToTensor(object): |
||||
def __call__(self, img, target): |
||||
return F.to_tensor(img), target |
||||
|
||||
|
||||
class RandomErasing(object): |
||||
def __init__(self, *args, **kwargs): |
||||
self.eraser = T.RandomErasing(*args, **kwargs) |
||||
|
||||
def __call__(self, img, target): |
||||
return self.eraser(img), target |
||||
|
||||
|
||||
class Normalize(object): |
||||
def __init__(self, mean, std): |
||||
self.mean = mean |
||||
self.std = std |
||||
|
||||
def __call__(self, image, target=None): |
||||
image = F.normalize(image, mean=self.mean, std=self.std) |
||||
if target is None: |
||||
return image, None |
||||
target = target.copy() |
||||
h, w = image.shape[-2:] |
||||
if "boxes" in target: |
||||
boxes = target["boxes"] |
||||
boxes = box_xyxy_to_cxcywh(boxes) |
||||
boxes = boxes / torch.tensor([w, h, w, h], dtype=torch.float32) |
||||
target["boxes"] = boxes |
||||
return image, target |
||||
|
||||
|
||||
class Compose(object): |
||||
def __init__(self, transforms): |
||||
self.transforms = transforms |
||||
|
||||
def __call__(self, image, target): |
||||
for t in self.transforms: |
||||
image, target = t(image, target) |
||||
return image, target |
||||
|
||||
def __repr__(self): |
||||
format_string = self.__class__.__name__ + "(" |
||||
for t in self.transforms: |
||||
format_string += "\n" |
||||
format_string += " {0}".format(t) |
||||
format_string += "\n)" |
||||
return format_string |
||||
@ -0,0 +1,15 @@ |
||||
# ------------------------------------------------------------------------ |
||||
# Grounding DINO |
||||
# url: https://github.com/IDEA-Research/GroundingDINO |
||||
# Copyright (c) 2023 IDEA. All Rights Reserved. |
||||
# Licensed under the Apache License, Version 2.0 [see LICENSE for details] |
||||
# ------------------------------------------------------------------------ |
||||
# Conditional DETR |
||||
# Copyright (c) 2021 Microsoft. All Rights Reserved. |
||||
# Licensed under the Apache License, Version 2.0 [see LICENSE for details] |
||||
# ------------------------------------------------------------------------ |
||||
# Copied from DETR (https://github.com/facebookresearch/detr) |
||||
# Copyright (c) Facebook, Inc. and its affiliates. All Rights Reserved. |
||||
# ------------------------------------------------------------------------ |
||||
|
||||
from .groundingdino import build_groundingdino |
||||
@ -0,0 +1 @@ |
||||
from .backbone import build_backbone |
||||
@ -0,0 +1,221 @@ |
||||
# ------------------------------------------------------------------------ |
||||
# Grounding DINO |
||||
# url: https://github.com/IDEA-Research/GroundingDINO |
||||
# Copyright (c) 2023 IDEA. All Rights Reserved. |
||||
# Licensed under the Apache License, Version 2.0 [see LICENSE for details] |
||||
# ------------------------------------------------------------------------ |
||||
# Conditional DETR |
||||
# Copyright (c) 2021 Microsoft. All Rights Reserved. |
||||
# Licensed under the Apache License, Version 2.0 [see LICENSE for details] |
||||
# ------------------------------------------------------------------------ |
||||
# Copied from DETR (https://github.com/facebookresearch/detr) |
||||
# Copyright (c) Facebook, Inc. and its affiliates. All Rights Reserved. |
||||
# ------------------------------------------------------------------------ |
||||
|
||||
""" |
||||
Backbone modules. |
||||
""" |
||||
|
||||
from typing import Dict, List |
||||
|
||||
import torch |
||||
import torch.nn.functional as F |
||||
import torchvision |
||||
from torch import nn |
||||
from torchvision.models._utils import IntermediateLayerGetter |
||||
|
||||
from groundingdino.util.misc import NestedTensor, clean_state_dict, is_main_process |
||||
|
||||
from .position_encoding import build_position_encoding |
||||
from .swin_transformer import build_swin_transformer |
||||
|
||||
|
||||
class FrozenBatchNorm2d(torch.nn.Module): |
||||
""" |
||||
BatchNorm2d where the batch statistics and the affine parameters are fixed. |
||||
|
||||
Copy-paste from torchvision.misc.ops with added eps before rqsrt, |
||||
without which any other models than torchvision.models.resnet[18,34,50,101] |
||||
produce nans. |
||||
""" |
||||
|
||||
def __init__(self, n): |
||||
super(FrozenBatchNorm2d, self).__init__() |
||||
self.register_buffer("weight", torch.ones(n)) |
||||
self.register_buffer("bias", torch.zeros(n)) |
||||
self.register_buffer("running_mean", torch.zeros(n)) |
||||
self.register_buffer("running_var", torch.ones(n)) |
||||
|
||||
def _load_from_state_dict( |
||||
self, state_dict, prefix, local_metadata, strict, missing_keys, unexpected_keys, error_msgs |
||||
): |
||||
num_batches_tracked_key = prefix + "num_batches_tracked" |
||||
if num_batches_tracked_key in state_dict: |
||||
del state_dict[num_batches_tracked_key] |
||||
|
||||
super(FrozenBatchNorm2d, self)._load_from_state_dict( |
||||
state_dict, prefix, local_metadata, strict, missing_keys, unexpected_keys, error_msgs |
||||
) |
||||
|
||||
def forward(self, x): |
||||
# move reshapes to the beginning |
||||
# to make it fuser-friendly |
||||
w = self.weight.reshape(1, -1, 1, 1) |
||||
b = self.bias.reshape(1, -1, 1, 1) |
||||
rv = self.running_var.reshape(1, -1, 1, 1) |
||||
rm = self.running_mean.reshape(1, -1, 1, 1) |
||||
eps = 1e-5 |
||||
scale = w * (rv + eps).rsqrt() |
||||
bias = b - rm * scale |
||||
return x * scale + bias |
||||
|
||||
|
||||
class BackboneBase(nn.Module): |
||||
def __init__( |
||||
self, |
||||
backbone: nn.Module, |
||||
train_backbone: bool, |
||||
num_channels: int, |
||||
return_interm_indices: list, |
||||
): |
||||
super().__init__() |
||||
for name, parameter in backbone.named_parameters(): |
||||
if ( |
||||
not train_backbone |
||||
or "layer2" not in name |
||||
and "layer3" not in name |
||||
and "layer4" not in name |
||||
): |
||||
parameter.requires_grad_(False) |
||||
|
||||
return_layers = {} |
||||
for idx, layer_index in enumerate(return_interm_indices): |
||||
return_layers.update( |
||||
{"layer{}".format(5 - len(return_interm_indices) + idx): "{}".format(layer_index)} |
||||
) |
||||
|
||||
# if len: |
||||
# if use_stage1_feature: |
||||
# return_layers = {"layer1": "0", "layer2": "1", "layer3": "2", "layer4": "3"} |
||||
# else: |
||||
# return_layers = {"layer2": "0", "layer3": "1", "layer4": "2"} |
||||
# else: |
||||
# return_layers = {'layer4': "0"} |
||||
self.body = IntermediateLayerGetter(backbone, return_layers=return_layers) |
||||
self.num_channels = num_channels |
||||
|
||||
def forward(self, tensor_list: NestedTensor): |
||||
xs = self.body(tensor_list.tensors) |
||||
out: Dict[str, NestedTensor] = {} |
||||
for name, x in xs.items(): |
||||
m = tensor_list.mask |
||||
assert m is not None |
||||
mask = F.interpolate(m[None].float(), size=x.shape[-2:]).to(torch.bool)[0] |
||||
out[name] = NestedTensor(x, mask) |
||||
# import ipdb; ipdb.set_trace() |
||||
return out |
||||
|
||||
|
||||
class Backbone(BackboneBase): |
||||
"""ResNet backbone with frozen BatchNorm.""" |
||||
|
||||
def __init__( |
||||
self, |
||||
name: str, |
||||
train_backbone: bool, |
||||
dilation: bool, |
||||
return_interm_indices: list, |
||||
batch_norm=FrozenBatchNorm2d, |
||||
): |
||||
if name in ["resnet18", "resnet34", "resnet50", "resnet101"]: |
||||
backbone = getattr(torchvision.models, name)( |
||||
replace_stride_with_dilation=[False, False, dilation], |
||||
pretrained=is_main_process(), |
||||
norm_layer=batch_norm, |
||||
) |
||||
else: |
||||
raise NotImplementedError("Why you can get here with name {}".format(name)) |
||||
# num_channels = 512 if name in ('resnet18', 'resnet34') else 2048 |
||||
assert name not in ("resnet18", "resnet34"), "Only resnet50 and resnet101 are available." |
||||
assert return_interm_indices in [[0, 1, 2, 3], [1, 2, 3], [3]] |
||||
num_channels_all = [256, 512, 1024, 2048] |
||||
num_channels = num_channels_all[4 - len(return_interm_indices) :] |
||||
super().__init__(backbone, train_backbone, num_channels, return_interm_indices) |
||||
|
||||
|
||||
class Joiner(nn.Sequential): |
||||
def __init__(self, backbone, position_embedding): |
||||
super().__init__(backbone, position_embedding) |
||||
|
||||
def forward(self, tensor_list: NestedTensor): |
||||
xs = self[0](tensor_list) |
||||
out: List[NestedTensor] = [] |
||||
pos = [] |
||||
for name, x in xs.items(): |
||||
out.append(x) |
||||
# position encoding |
||||
pos.append(self[1](x).to(x.tensors.dtype)) |
||||
|
||||
return out, pos |
||||
|
||||
|
||||
def build_backbone(args): |
||||
""" |
||||
Useful args: |
||||
- backbone: backbone name |
||||
- lr_backbone: |
||||
- dilation |
||||
- return_interm_indices: available: [0,1,2,3], [1,2,3], [3] |
||||
- backbone_freeze_keywords: |
||||
- use_checkpoint: for swin only for now |
||||
|
||||
""" |
||||
position_embedding = build_position_encoding(args) |
||||
train_backbone = True |
||||
if not train_backbone: |
||||
raise ValueError("Please set lr_backbone > 0") |
||||
return_interm_indices = args.return_interm_indices |
||||
assert return_interm_indices in [[0, 1, 2, 3], [1, 2, 3], [3]] |
||||
args.backbone_freeze_keywords |
||||
use_checkpoint = getattr(args, "use_checkpoint", False) |
||||
|
||||
if args.backbone in ["resnet50", "resnet101"]: |
||||
backbone = Backbone( |
||||
args.backbone, |
||||
train_backbone, |
||||
args.dilation, |
||||
return_interm_indices, |
||||
batch_norm=FrozenBatchNorm2d, |
||||
) |
||||
bb_num_channels = backbone.num_channels |
||||
elif args.backbone in [ |
||||
"swin_T_224_1k", |
||||
"swin_B_224_22k", |
||||
"swin_B_384_22k", |
||||
"swin_L_224_22k", |
||||
"swin_L_384_22k", |
||||
]: |
||||
pretrain_img_size = int(args.backbone.split("_")[-2]) |
||||
backbone = build_swin_transformer( |
||||
args.backbone, |
||||
pretrain_img_size=pretrain_img_size, |
||||
out_indices=tuple(return_interm_indices), |
||||
dilation=False, |
||||
use_checkpoint=use_checkpoint, |
||||
) |
||||
|
||||
bb_num_channels = backbone.num_features[4 - len(return_interm_indices) :] |
||||
else: |
||||
raise NotImplementedError("Unknown backbone {}".format(args.backbone)) |
||||
|
||||
assert len(bb_num_channels) == len( |
||||
return_interm_indices |
||||
), f"len(bb_num_channels) {len(bb_num_channels)} != len(return_interm_indices) {len(return_interm_indices)}" |
||||
|
||||
model = Joiner(backbone, position_embedding) |
||||
model.num_channels = bb_num_channels |
||||
assert isinstance( |
||||
bb_num_channels, List |
||||
), "bb_num_channels is expected to be a List but {}".format(type(bb_num_channels)) |
||||
# import ipdb; ipdb.set_trace() |
||||
return model |
||||
@ -0,0 +1,186 @@ |
||||
# ------------------------------------------------------------------------ |
||||
# Grounding DINO |
||||
# url: https://github.com/IDEA-Research/GroundingDINO |
||||
# Copyright (c) 2023 IDEA. All Rights Reserved. |
||||
# Licensed under the Apache License, Version 2.0 [see LICENSE for details] |
||||
# ------------------------------------------------------------------------ |
||||
# DINO |
||||
# Copyright (c) 2022 IDEA. All Rights Reserved. |
||||
# Licensed under the Apache License, Version 2.0 [see LICENSE for details] |
||||
# ------------------------------------------------------------------------ |
||||
# Conditional DETR |
||||
# Copyright (c) 2021 Microsoft. All Rights Reserved. |
||||
# Licensed under the Apache License, Version 2.0 [see LICENSE for details] |
||||
# ------------------------------------------------------------------------ |
||||
# Copied from DETR (https://github.com/facebookresearch/detr) |
||||
# Copyright (c) Facebook, Inc. and its affiliates. All Rights Reserved. |
||||
# ------------------------------------------------------------------------ |
||||
|
||||
""" |
||||
Various positional encodings for the transformer. |
||||
""" |
||||
import math |
||||
|
||||
import torch |
||||
from torch import nn |
||||
|
||||
from groundingdino.util.misc import NestedTensor |
||||
|
||||
|
||||
class PositionEmbeddingSine(nn.Module): |
||||
""" |
||||
This is a more standard version of the position embedding, very similar to the one |
||||
used by the Attention is all you need paper, generalized to work on images. |
||||
""" |
||||
|
||||
def __init__(self, num_pos_feats=64, temperature=10000, normalize=False, scale=None): |
||||
super().__init__() |
||||
self.num_pos_feats = num_pos_feats |
||||
self.temperature = temperature |
||||
self.normalize = normalize |
||||
if scale is not None and normalize is False: |
||||
raise ValueError("normalize should be True if scale is passed") |
||||
if scale is None: |
||||
scale = 2 * math.pi |
||||
self.scale = scale |
||||
|
||||
def forward(self, tensor_list: NestedTensor): |
||||
x = tensor_list.tensors |
||||
mask = tensor_list.mask |
||||
assert mask is not None |
||||
not_mask = ~mask |
||||
y_embed = not_mask.cumsum(1, dtype=torch.float32) |
||||
x_embed = not_mask.cumsum(2, dtype=torch.float32) |
||||
if self.normalize: |
||||
eps = 1e-6 |
||||
# if os.environ.get("SHILONG_AMP", None) == '1': |
||||
# eps = 1e-4 |
||||
# else: |
||||
# eps = 1e-6 |
||||
y_embed = y_embed / (y_embed[:, -1:, :] + eps) * self.scale |
||||
x_embed = x_embed / (x_embed[:, :, -1:] + eps) * self.scale |
||||
|
||||
dim_t = torch.arange(self.num_pos_feats, dtype=torch.float32, device=x.device) |
||||
dim_t = self.temperature ** (2 * (dim_t // 2) / self.num_pos_feats) |
||||
|
||||
pos_x = x_embed[:, :, :, None] / dim_t |
||||
pos_y = y_embed[:, :, :, None] / dim_t |
||||
pos_x = torch.stack( |
||||
(pos_x[:, :, :, 0::2].sin(), pos_x[:, :, :, 1::2].cos()), dim=4 |
||||
).flatten(3) |
||||
pos_y = torch.stack( |
||||
(pos_y[:, :, :, 0::2].sin(), pos_y[:, :, :, 1::2].cos()), dim=4 |
||||
).flatten(3) |
||||
pos = torch.cat((pos_y, pos_x), dim=3).permute(0, 3, 1, 2) |
||||
return pos |
||||
|
||||
|
||||
class PositionEmbeddingSineHW(nn.Module): |
||||
""" |
||||
This is a more standard version of the position embedding, very similar to the one |
||||
used by the Attention is all you need paper, generalized to work on images. |
||||
""" |
||||
|
||||
def __init__( |
||||
self, num_pos_feats=64, temperatureH=10000, temperatureW=10000, normalize=False, scale=None |
||||
): |
||||
super().__init__() |
||||
self.num_pos_feats = num_pos_feats |
||||
self.temperatureH = temperatureH |
||||
self.temperatureW = temperatureW |
||||
self.normalize = normalize |
||||
if scale is not None and normalize is False: |
||||
raise ValueError("normalize should be True if scale is passed") |
||||
if scale is None: |
||||
scale = 2 * math.pi |
||||
self.scale = scale |
||||
|
||||
def forward(self, tensor_list: NestedTensor): |
||||
x = tensor_list.tensors |
||||
mask = tensor_list.mask |
||||
assert mask is not None |
||||
not_mask = ~mask |
||||
y_embed = not_mask.cumsum(1, dtype=torch.float32) |
||||
x_embed = not_mask.cumsum(2, dtype=torch.float32) |
||||
|
||||
# import ipdb; ipdb.set_trace() |
||||
|
||||
if self.normalize: |
||||
eps = 1e-6 |
||||
y_embed = y_embed / (y_embed[:, -1:, :] + eps) * self.scale |
||||
x_embed = x_embed / (x_embed[:, :, -1:] + eps) * self.scale |
||||
|
||||
dim_tx = torch.arange(self.num_pos_feats, dtype=torch.float32, device=x.device) |
||||
dim_tx = self.temperatureW ** (2 * (torch.div(dim_tx, 2, rounding_mode='floor')) / self.num_pos_feats) |
||||
pos_x = x_embed[:, :, :, None] / dim_tx |
||||
|
||||
dim_ty = torch.arange(self.num_pos_feats, dtype=torch.float32, device=x.device) |
||||
dim_ty = self.temperatureH ** (2 * (torch.div(dim_ty, 2, rounding_mode='floor')) / self.num_pos_feats) |
||||
pos_y = y_embed[:, :, :, None] / dim_ty |
||||
|
||||
pos_x = torch.stack( |
||||
(pos_x[:, :, :, 0::2].sin(), pos_x[:, :, :, 1::2].cos()), dim=4 |
||||
).flatten(3) |
||||
pos_y = torch.stack( |
||||
(pos_y[:, :, :, 0::2].sin(), pos_y[:, :, :, 1::2].cos()), dim=4 |
||||
).flatten(3) |
||||
pos = torch.cat((pos_y, pos_x), dim=3).permute(0, 3, 1, 2) |
||||
|
||||
# import ipdb; ipdb.set_trace() |
||||
|
||||
return pos |
||||
|
||||
|
||||
class PositionEmbeddingLearned(nn.Module): |
||||
""" |
||||
Absolute pos embedding, learned. |
||||
""" |
||||
|
||||
def __init__(self, num_pos_feats=256): |
||||
super().__init__() |
||||
self.row_embed = nn.Embedding(50, num_pos_feats) |
||||
self.col_embed = nn.Embedding(50, num_pos_feats) |
||||
self.reset_parameters() |
||||
|
||||
def reset_parameters(self): |
||||
nn.init.uniform_(self.row_embed.weight) |
||||
nn.init.uniform_(self.col_embed.weight) |
||||
|
||||
def forward(self, tensor_list: NestedTensor): |
||||
x = tensor_list.tensors |
||||
h, w = x.shape[-2:] |
||||
i = torch.arange(w, device=x.device) |
||||
j = torch.arange(h, device=x.device) |
||||
x_emb = self.col_embed(i) |
||||
y_emb = self.row_embed(j) |
||||
pos = ( |
||||
torch.cat( |
||||
[ |
||||
x_emb.unsqueeze(0).repeat(h, 1, 1), |
||||
y_emb.unsqueeze(1).repeat(1, w, 1), |
||||
], |
||||
dim=-1, |
||||
) |
||||
.permute(2, 0, 1) |
||||
.unsqueeze(0) |
||||
.repeat(x.shape[0], 1, 1, 1) |
||||
) |
||||
return pos |
||||
|
||||
|
||||
def build_position_encoding(args): |
||||
N_steps = args.hidden_dim // 2 |
||||
if args.position_embedding in ("v2", "sine"): |
||||
# TODO find a better way of exposing other arguments |
||||
position_embedding = PositionEmbeddingSineHW( |
||||
N_steps, |
||||
temperatureH=args.pe_temperatureH, |
||||
temperatureW=args.pe_temperatureW, |
||||
normalize=True, |
||||
) |
||||
elif args.position_embedding in ("v3", "learned"): |
||||
position_embedding = PositionEmbeddingLearned(N_steps) |
||||
else: |
||||
raise ValueError(f"not supported {args.position_embedding}") |
||||
|
||||
return position_embedding |
||||
@ -0,0 +1,802 @@ |
||||
# ------------------------------------------------------------------------ |
||||
# Grounding DINO |
||||
# url: https://github.com/IDEA-Research/GroundingDINO |
||||
# Copyright (c) 2023 IDEA. All Rights Reserved. |
||||
# Licensed under the Apache License, Version 2.0 [see LICENSE for details] |
||||
# ------------------------------------------------------------------------ |
||||
# DINO |
||||
# Copyright (c) 2022 IDEA. All Rights Reserved. |
||||
# Licensed under the Apache License, Version 2.0 [see LICENSE for details] |
||||
# -------------------------------------------------------- |
||||
# modified from https://github.com/SwinTransformer/Swin-Transformer-Object-Detection/blob/master/mmdet/models/backbones/swin_transformer.py |
||||
# -------------------------------------------------------- |
||||
|
||||
import numpy as np |
||||
import torch |
||||
import torch.nn as nn |
||||
import torch.nn.functional as F |
||||
import torch.utils.checkpoint as checkpoint |
||||
from timm.models.layers import DropPath, to_2tuple, trunc_normal_ |
||||
|
||||
from groundingdino.util.misc import NestedTensor |
||||
|
||||
|
||||
class Mlp(nn.Module): |
||||
"""Multilayer perceptron.""" |
||||
|
||||
def __init__( |
||||
self, in_features, hidden_features=None, out_features=None, act_layer=nn.GELU, drop=0.0 |
||||
): |
||||
super().__init__() |
||||
out_features = out_features or in_features |
||||
hidden_features = hidden_features or in_features |
||||
self.fc1 = nn.Linear(in_features, hidden_features) |
||||
self.act = act_layer() |
||||
self.fc2 = nn.Linear(hidden_features, out_features) |
||||
self.drop = nn.Dropout(drop) |
||||
|
||||
def forward(self, x): |
||||
x = self.fc1(x) |
||||
x = self.act(x) |
||||
x = self.drop(x) |
||||
x = self.fc2(x) |
||||
x = self.drop(x) |
||||
return x |
||||
|
||||
|
||||
def window_partition(x, window_size): |
||||
""" |
||||
Args: |
||||
x: (B, H, W, C) |
||||
window_size (int): window size |
||||
Returns: |
||||
windows: (num_windows*B, window_size, window_size, C) |
||||
""" |
||||
B, H, W, C = x.shape |
||||
x = x.view(B, H // window_size, window_size, W // window_size, window_size, C) |
||||
windows = x.permute(0, 1, 3, 2, 4, 5).contiguous().view(-1, window_size, window_size, C) |
||||
return windows |
||||
|
||||
|
||||
def window_reverse(windows, window_size, H, W): |
||||
""" |
||||
Args: |
||||
windows: (num_windows*B, window_size, window_size, C) |
||||
window_size (int): Window size |
||||
H (int): Height of image |
||||
W (int): Width of image |
||||
Returns: |
||||
x: (B, H, W, C) |
||||
""" |
||||
B = int(windows.shape[0] / (H * W / window_size / window_size)) |
||||
x = windows.view(B, H // window_size, W // window_size, window_size, window_size, -1) |
||||
x = x.permute(0, 1, 3, 2, 4, 5).contiguous().view(B, H, W, -1) |
||||
return x |
||||
|
||||
|
||||
class WindowAttention(nn.Module): |
||||
"""Window based multi-head self attention (W-MSA) module with relative position bias. |
||||
It supports both of shifted and non-shifted window. |
||||
Args: |
||||
dim (int): Number of input channels. |
||||
window_size (tuple[int]): The height and width of the window. |
||||
num_heads (int): Number of attention heads. |
||||
qkv_bias (bool, optional): If True, add a learnable bias to query, key, value. Default: True |
||||
qk_scale (float | None, optional): Override default qk scale of head_dim ** -0.5 if set |
||||
attn_drop (float, optional): Dropout ratio of attention weight. Default: 0.0 |
||||
proj_drop (float, optional): Dropout ratio of output. Default: 0.0 |
||||
""" |
||||
|
||||
def __init__( |
||||
self, |
||||
dim, |
||||
window_size, |
||||
num_heads, |
||||
qkv_bias=True, |
||||
qk_scale=None, |
||||
attn_drop=0.0, |
||||
proj_drop=0.0, |
||||
): |
||||
|
||||
super().__init__() |
||||
self.dim = dim |
||||
self.window_size = window_size # Wh, Ww |
||||
self.num_heads = num_heads |
||||
head_dim = dim // num_heads |
||||
self.scale = qk_scale or head_dim**-0.5 |
||||
|
||||
# define a parameter table of relative position bias |
||||
self.relative_position_bias_table = nn.Parameter( |
||||
torch.zeros((2 * window_size[0] - 1) * (2 * window_size[1] - 1), num_heads) |
||||
) # 2*Wh-1 * 2*Ww-1, nH |
||||
|
||||
# get pair-wise relative position index for each token inside the window |
||||
coords_h = torch.arange(self.window_size[0]) |
||||
coords_w = torch.arange(self.window_size[1]) |
||||
coords = torch.stack(torch.meshgrid([coords_h, coords_w])) # 2, Wh, Ww |
||||
coords_flatten = torch.flatten(coords, 1) # 2, Wh*Ww |
||||
relative_coords = coords_flatten[:, :, None] - coords_flatten[:, None, :] # 2, Wh*Ww, Wh*Ww |
||||
relative_coords = relative_coords.permute(1, 2, 0).contiguous() # Wh*Ww, Wh*Ww, 2 |
||||
relative_coords[:, :, 0] += self.window_size[0] - 1 # shift to start from 0 |
||||
relative_coords[:, :, 1] += self.window_size[1] - 1 |
||||
relative_coords[:, :, 0] *= 2 * self.window_size[1] - 1 |
||||
relative_position_index = relative_coords.sum(-1) # Wh*Ww, Wh*Ww |
||||
self.register_buffer("relative_position_index", relative_position_index) |
||||
|
||||
self.qkv = nn.Linear(dim, dim * 3, bias=qkv_bias) |
||||
self.attn_drop = nn.Dropout(attn_drop) |
||||
self.proj = nn.Linear(dim, dim) |
||||
self.proj_drop = nn.Dropout(proj_drop) |
||||
|
||||
trunc_normal_(self.relative_position_bias_table, std=0.02) |
||||
self.softmax = nn.Softmax(dim=-1) |
||||
|
||||
def forward(self, x, mask=None): |
||||
"""Forward function. |
||||
Args: |
||||
x: input features with shape of (num_windows*B, N, C) |
||||
mask: (0/-inf) mask with shape of (num_windows, Wh*Ww, Wh*Ww) or None |
||||
""" |
||||
B_, N, C = x.shape |
||||
qkv = ( |
||||
self.qkv(x) |
||||
.reshape(B_, N, 3, self.num_heads, C // self.num_heads) |
||||
.permute(2, 0, 3, 1, 4) |
||||
) |
||||
q, k, v = qkv[0], qkv[1], qkv[2] # make torchscript happy (cannot use tensor as tuple) |
||||
|
||||
q = q * self.scale |
||||
attn = q @ k.transpose(-2, -1) |
||||
|
||||
relative_position_bias = self.relative_position_bias_table[ |
||||
self.relative_position_index.view(-1) |
||||
].view( |
||||
self.window_size[0] * self.window_size[1], self.window_size[0] * self.window_size[1], -1 |
||||
) # Wh*Ww,Wh*Ww,nH |
||||
relative_position_bias = relative_position_bias.permute( |
||||
2, 0, 1 |
||||
).contiguous() # nH, Wh*Ww, Wh*Ww |
||||
attn = attn + relative_position_bias.unsqueeze(0) |
||||
|
||||
if mask is not None: |
||||
nW = mask.shape[0] |
||||
attn = attn.view(B_ // nW, nW, self.num_heads, N, N) + mask.unsqueeze(1).unsqueeze(0) |
||||
attn = attn.view(-1, self.num_heads, N, N) |
||||
attn = self.softmax(attn) |
||||
else: |
||||
attn = self.softmax(attn) |
||||
|
||||
attn = self.attn_drop(attn) |
||||
|
||||
x = (attn @ v).transpose(1, 2).reshape(B_, N, C) |
||||
x = self.proj(x) |
||||
x = self.proj_drop(x) |
||||
return x |
||||
|
||||
|
||||
class SwinTransformerBlock(nn.Module): |
||||
"""Swin Transformer Block. |
||||
Args: |
||||
dim (int): Number of input channels. |
||||
num_heads (int): Number of attention heads. |
||||
window_size (int): Window size. |
||||
shift_size (int): Shift size for SW-MSA. |
||||
mlp_ratio (float): Ratio of mlp hidden dim to embedding dim. |
||||
qkv_bias (bool, optional): If True, add a learnable bias to query, key, value. Default: True |
||||
qk_scale (float | None, optional): Override default qk scale of head_dim ** -0.5 if set. |
||||
drop (float, optional): Dropout rate. Default: 0.0 |
||||
attn_drop (float, optional): Attention dropout rate. Default: 0.0 |
||||
drop_path (float, optional): Stochastic depth rate. Default: 0.0 |
||||
act_layer (nn.Module, optional): Activation layer. Default: nn.GELU |
||||
norm_layer (nn.Module, optional): Normalization layer. Default: nn.LayerNorm |
||||
""" |
||||
|
||||
def __init__( |
||||
self, |
||||
dim, |
||||
num_heads, |
||||
window_size=7, |
||||
shift_size=0, |
||||
mlp_ratio=4.0, |
||||
qkv_bias=True, |
||||
qk_scale=None, |
||||
drop=0.0, |
||||
attn_drop=0.0, |
||||
drop_path=0.0, |
||||
act_layer=nn.GELU, |
||||
norm_layer=nn.LayerNorm, |
||||
): |
||||
super().__init__() |
||||
self.dim = dim |
||||
self.num_heads = num_heads |
||||
self.window_size = window_size |
||||
self.shift_size = shift_size |
||||
self.mlp_ratio = mlp_ratio |
||||
assert 0 <= self.shift_size < self.window_size, "shift_size must in 0-window_size" |
||||
|
||||
self.norm1 = norm_layer(dim) |
||||
self.attn = WindowAttention( |
||||
dim, |
||||
window_size=to_2tuple(self.window_size), |
||||
num_heads=num_heads, |
||||
qkv_bias=qkv_bias, |
||||
qk_scale=qk_scale, |
||||
attn_drop=attn_drop, |
||||
proj_drop=drop, |
||||
) |
||||
|
||||
self.drop_path = DropPath(drop_path) if drop_path > 0.0 else nn.Identity() |
||||
self.norm2 = norm_layer(dim) |
||||
mlp_hidden_dim = int(dim * mlp_ratio) |
||||
self.mlp = Mlp( |
||||
in_features=dim, hidden_features=mlp_hidden_dim, act_layer=act_layer, drop=drop |
||||
) |
||||
|
||||
self.H = None |
||||
self.W = None |
||||
|
||||
def forward(self, x, mask_matrix): |
||||
"""Forward function. |
||||
Args: |
||||
x: Input feature, tensor size (B, H*W, C). |
||||
H, W: Spatial resolution of the input feature. |
||||
mask_matrix: Attention mask for cyclic shift. |
||||
""" |
||||
B, L, C = x.shape |
||||
H, W = self.H, self.W |
||||
assert L == H * W, "input feature has wrong size" |
||||
|
||||
shortcut = x |
||||
x = self.norm1(x) |
||||
x = x.view(B, H, W, C) |
||||
|
||||
# pad feature maps to multiples of window size |
||||
pad_l = pad_t = 0 |
||||
pad_r = (self.window_size - W % self.window_size) % self.window_size |
||||
pad_b = (self.window_size - H % self.window_size) % self.window_size |
||||
x = F.pad(x, (0, 0, pad_l, pad_r, pad_t, pad_b)) |
||||
_, Hp, Wp, _ = x.shape |
||||
|
||||
# cyclic shift |
||||
if self.shift_size > 0: |
||||
shifted_x = torch.roll(x, shifts=(-self.shift_size, -self.shift_size), dims=(1, 2)) |
||||
attn_mask = mask_matrix |
||||
else: |
||||
shifted_x = x |
||||
attn_mask = None |
||||
|
||||
# partition windows |
||||
x_windows = window_partition( |
||||
shifted_x, self.window_size |
||||
) # nW*B, window_size, window_size, C |
||||
x_windows = x_windows.view( |
||||
-1, self.window_size * self.window_size, C |
||||
) # nW*B, window_size*window_size, C |
||||
|
||||
# W-MSA/SW-MSA |
||||
attn_windows = self.attn(x_windows, mask=attn_mask) # nW*B, window_size*window_size, C |
||||
|
||||
# merge windows |
||||
attn_windows = attn_windows.view(-1, self.window_size, self.window_size, C) |
||||
shifted_x = window_reverse(attn_windows, self.window_size, Hp, Wp) # B H' W' C |
||||
|
||||
# reverse cyclic shift |
||||
if self.shift_size > 0: |
||||
x = torch.roll(shifted_x, shifts=(self.shift_size, self.shift_size), dims=(1, 2)) |
||||
else: |
||||
x = shifted_x |
||||
|
||||
if pad_r > 0 or pad_b > 0: |
||||
x = x[:, :H, :W, :].contiguous() |
||||
|
||||
x = x.view(B, H * W, C) |
||||
|
||||
# FFN |
||||
x = shortcut + self.drop_path(x) |
||||
x = x + self.drop_path(self.mlp(self.norm2(x))) |
||||
|
||||
return x |
||||
|
||||
|
||||
class PatchMerging(nn.Module): |
||||
"""Patch Merging Layer |
||||
Args: |
||||
dim (int): Number of input channels. |
||||
norm_layer (nn.Module, optional): Normalization layer. Default: nn.LayerNorm |
||||
""" |
||||
|
||||
def __init__(self, dim, norm_layer=nn.LayerNorm): |
||||
super().__init__() |
||||
self.dim = dim |
||||
self.reduction = nn.Linear(4 * dim, 2 * dim, bias=False) |
||||
self.norm = norm_layer(4 * dim) |
||||
|
||||
def forward(self, x, H, W): |
||||
"""Forward function. |
||||
Args: |
||||
x: Input feature, tensor size (B, H*W, C). |
||||
H, W: Spatial resolution of the input feature. |
||||
""" |
||||
B, L, C = x.shape |
||||
assert L == H * W, "input feature has wrong size" |
||||
|
||||
x = x.view(B, H, W, C) |
||||
|
||||
# padding |
||||
pad_input = (H % 2 == 1) or (W % 2 == 1) |
||||
if pad_input: |
||||
x = F.pad(x, (0, 0, 0, W % 2, 0, H % 2)) |
||||
|
||||
x0 = x[:, 0::2, 0::2, :] # B H/2 W/2 C |
||||
x1 = x[:, 1::2, 0::2, :] # B H/2 W/2 C |
||||
x2 = x[:, 0::2, 1::2, :] # B H/2 W/2 C |
||||
x3 = x[:, 1::2, 1::2, :] # B H/2 W/2 C |
||||
x = torch.cat([x0, x1, x2, x3], -1) # B H/2 W/2 4*C |
||||
x = x.view(B, -1, 4 * C) # B H/2*W/2 4*C |
||||
|
||||
x = self.norm(x) |
||||
x = self.reduction(x) |
||||
|
||||
return x |
||||
|
||||
|
||||
class BasicLayer(nn.Module): |
||||
"""A basic Swin Transformer layer for one stage. |
||||
Args: |
||||
dim (int): Number of feature channels |
||||
depth (int): Depths of this stage. |
||||
num_heads (int): Number of attention head. |
||||
window_size (int): Local window size. Default: 7. |
||||
mlp_ratio (float): Ratio of mlp hidden dim to embedding dim. Default: 4. |
||||
qkv_bias (bool, optional): If True, add a learnable bias to query, key, value. Default: True |
||||
qk_scale (float | None, optional): Override default qk scale of head_dim ** -0.5 if set. |
||||
drop (float, optional): Dropout rate. Default: 0.0 |
||||
attn_drop (float, optional): Attention dropout rate. Default: 0.0 |
||||
drop_path (float | tuple[float], optional): Stochastic depth rate. Default: 0.0 |
||||
norm_layer (nn.Module, optional): Normalization layer. Default: nn.LayerNorm |
||||
downsample (nn.Module | None, optional): Downsample layer at the end of the layer. Default: None |
||||
use_checkpoint (bool): Whether to use checkpointing to save memory. Default: False. |
||||
""" |
||||
|
||||
def __init__( |
||||
self, |
||||
dim, |
||||
depth, |
||||
num_heads, |
||||
window_size=7, |
||||
mlp_ratio=4.0, |
||||
qkv_bias=True, |
||||
qk_scale=None, |
||||
drop=0.0, |
||||
attn_drop=0.0, |
||||
drop_path=0.0, |
||||
norm_layer=nn.LayerNorm, |
||||
downsample=None, |
||||
use_checkpoint=False, |
||||
): |
||||
super().__init__() |
||||
self.window_size = window_size |
||||
self.shift_size = window_size // 2 |
||||
self.depth = depth |
||||
self.use_checkpoint = use_checkpoint |
||||
|
||||
# build blocks |
||||
self.blocks = nn.ModuleList( |
||||
[ |
||||
SwinTransformerBlock( |
||||
dim=dim, |
||||
num_heads=num_heads, |
||||
window_size=window_size, |
||||
shift_size=0 if (i % 2 == 0) else window_size // 2, |
||||
mlp_ratio=mlp_ratio, |
||||
qkv_bias=qkv_bias, |
||||
qk_scale=qk_scale, |
||||
drop=drop, |
||||
attn_drop=attn_drop, |
||||
drop_path=drop_path[i] if isinstance(drop_path, list) else drop_path, |
||||
norm_layer=norm_layer, |
||||
) |
||||
for i in range(depth) |
||||
] |
||||
) |
||||
|
||||
# patch merging layer |
||||
if downsample is not None: |
||||
self.downsample = downsample(dim=dim, norm_layer=norm_layer) |
||||
else: |
||||
self.downsample = None |
||||
|
||||
def forward(self, x, H, W): |
||||
"""Forward function. |
||||
Args: |
||||
x: Input feature, tensor size (B, H*W, C). |
||||
H, W: Spatial resolution of the input feature. |
||||
""" |
||||
|
||||
# calculate attention mask for SW-MSA |
||||
Hp = int(np.ceil(H / self.window_size)) * self.window_size |
||||
Wp = int(np.ceil(W / self.window_size)) * self.window_size |
||||
img_mask = torch.zeros((1, Hp, Wp, 1), device=x.device) # 1 Hp Wp 1 |
||||
h_slices = ( |
||||
slice(0, -self.window_size), |
||||
slice(-self.window_size, -self.shift_size), |
||||
slice(-self.shift_size, None), |
||||
) |
||||
w_slices = ( |
||||
slice(0, -self.window_size), |
||||
slice(-self.window_size, -self.shift_size), |
||||
slice(-self.shift_size, None), |
||||
) |
||||
cnt = 0 |
||||
for h in h_slices: |
||||
for w in w_slices: |
||||
img_mask[:, h, w, :] = cnt |
||||
cnt += 1 |
||||
|
||||
mask_windows = window_partition( |
||||
img_mask, self.window_size |
||||
) # nW, window_size, window_size, 1 |
||||
mask_windows = mask_windows.view(-1, self.window_size * self.window_size) |
||||
attn_mask = mask_windows.unsqueeze(1) - mask_windows.unsqueeze(2) |
||||
attn_mask = attn_mask.masked_fill(attn_mask != 0, float(-100.0)).masked_fill( |
||||
attn_mask == 0, float(0.0) |
||||
) |
||||
|
||||
for blk in self.blocks: |
||||
blk.H, blk.W = H, W |
||||
if self.use_checkpoint: |
||||
x = checkpoint.checkpoint(blk, x, attn_mask) |
||||
else: |
||||
x = blk(x, attn_mask) |
||||
if self.downsample is not None: |
||||
x_down = self.downsample(x, H, W) |
||||
Wh, Ww = (H + 1) // 2, (W + 1) // 2 |
||||
return x, H, W, x_down, Wh, Ww |
||||
else: |
||||
return x, H, W, x, H, W |
||||
|
||||
|
||||
class PatchEmbed(nn.Module): |
||||
"""Image to Patch Embedding |
||||
Args: |
||||
patch_size (int): Patch token size. Default: 4. |
||||
in_chans (int): Number of input image channels. Default: 3. |
||||
embed_dim (int): Number of linear projection output channels. Default: 96. |
||||
norm_layer (nn.Module, optional): Normalization layer. Default: None |
||||
""" |
||||
|
||||
def __init__(self, patch_size=4, in_chans=3, embed_dim=96, norm_layer=None): |
||||
super().__init__() |
||||
patch_size = to_2tuple(patch_size) |
||||
self.patch_size = patch_size |
||||
|
||||
self.in_chans = in_chans |
||||
self.embed_dim = embed_dim |
||||
|
||||
self.proj = nn.Conv2d(in_chans, embed_dim, kernel_size=patch_size, stride=patch_size) |
||||
if norm_layer is not None: |
||||
self.norm = norm_layer(embed_dim) |
||||
else: |
||||
self.norm = None |
||||
|
||||
def forward(self, x): |
||||
"""Forward function.""" |
||||
# padding |
||||
_, _, H, W = x.size() |
||||
if W % self.patch_size[1] != 0: |
||||
x = F.pad(x, (0, self.patch_size[1] - W % self.patch_size[1])) |
||||
if H % self.patch_size[0] != 0: |
||||
x = F.pad(x, (0, 0, 0, self.patch_size[0] - H % self.patch_size[0])) |
||||
|
||||
x = self.proj(x) # B C Wh Ww |
||||
if self.norm is not None: |
||||
Wh, Ww = x.size(2), x.size(3) |
||||
x = x.flatten(2).transpose(1, 2) |
||||
x = self.norm(x) |
||||
x = x.transpose(1, 2).view(-1, self.embed_dim, Wh, Ww) |
||||
|
||||
return x |
||||
|
||||
|
||||
class SwinTransformer(nn.Module): |
||||
"""Swin Transformer backbone. |
||||
A PyTorch impl of : `Swin Transformer: Hierarchical Vision Transformer using Shifted Windows` - |
||||
https://arxiv.org/pdf/2103.14030 |
||||
Args: |
||||
pretrain_img_size (int): Input image size for training the pretrained model, |
||||
used in absolute postion embedding. Default 224. |
||||
patch_size (int | tuple(int)): Patch size. Default: 4. |
||||
in_chans (int): Number of input image channels. Default: 3. |
||||
embed_dim (int): Number of linear projection output channels. Default: 96. |
||||
depths (tuple[int]): Depths of each Swin Transformer stage. |
||||
num_heads (tuple[int]): Number of attention head of each stage. |
||||
window_size (int): Window size. Default: 7. |
||||
mlp_ratio (float): Ratio of mlp hidden dim to embedding dim. Default: 4. |
||||
qkv_bias (bool): If True, add a learnable bias to query, key, value. Default: True |
||||
qk_scale (float): Override default qk scale of head_dim ** -0.5 if set. |
||||
drop_rate (float): Dropout rate. |
||||
attn_drop_rate (float): Attention dropout rate. Default: 0. |
||||
drop_path_rate (float): Stochastic depth rate. Default: 0.2. |
||||
norm_layer (nn.Module): Normalization layer. Default: nn.LayerNorm. |
||||
ape (bool): If True, add absolute position embedding to the patch embedding. Default: False. |
||||
patch_norm (bool): If True, add normalization after patch embedding. Default: True. |
||||
out_indices (Sequence[int]): Output from which stages. |
||||
frozen_stages (int): Stages to be frozen (stop grad and set eval mode). |
||||
-1 means not freezing any parameters. |
||||
use_checkpoint (bool): Whether to use checkpointing to save memory. Default: False. |
||||
dilation (bool): if True, the output size if 16x downsample, ow 32x downsample. |
||||
""" |
||||
|
||||
def __init__( |
||||
self, |
||||
pretrain_img_size=224, |
||||
patch_size=4, |
||||
in_chans=3, |
||||
embed_dim=96, |
||||
depths=[2, 2, 6, 2], |
||||
num_heads=[3, 6, 12, 24], |
||||
window_size=7, |
||||
mlp_ratio=4.0, |
||||
qkv_bias=True, |
||||
qk_scale=None, |
||||
drop_rate=0.0, |
||||
attn_drop_rate=0.0, |
||||
drop_path_rate=0.2, |
||||
norm_layer=nn.LayerNorm, |
||||
ape=False, |
||||
patch_norm=True, |
||||
out_indices=(0, 1, 2, 3), |
||||
frozen_stages=-1, |
||||
dilation=False, |
||||
use_checkpoint=False, |
||||
): |
||||
super().__init__() |
||||
|
||||
self.pretrain_img_size = pretrain_img_size |
||||
self.num_layers = len(depths) |
||||
self.embed_dim = embed_dim |
||||
self.ape = ape |
||||
self.patch_norm = patch_norm |
||||
self.out_indices = out_indices |
||||
self.frozen_stages = frozen_stages |
||||
self.dilation = dilation |
||||
|
||||
# if use_checkpoint: |
||||
# print("use_checkpoint!!!!!!!!!!!!!!!!!!!!!!!!") |
||||
|
||||
# split image into non-overlapping patches |
||||
self.patch_embed = PatchEmbed( |
||||
patch_size=patch_size, |
||||
in_chans=in_chans, |
||||
embed_dim=embed_dim, |
||||
norm_layer=norm_layer if self.patch_norm else None, |
||||
) |
||||
|
||||
# absolute position embedding |
||||
if self.ape: |
||||
pretrain_img_size = to_2tuple(pretrain_img_size) |
||||
patch_size = to_2tuple(patch_size) |
||||
patches_resolution = [ |
||||
pretrain_img_size[0] // patch_size[0], |
||||
pretrain_img_size[1] // patch_size[1], |
||||
] |
||||
|
||||
self.absolute_pos_embed = nn.Parameter( |
||||
torch.zeros(1, embed_dim, patches_resolution[0], patches_resolution[1]) |
||||
) |
||||
trunc_normal_(self.absolute_pos_embed, std=0.02) |
||||
|
||||
self.pos_drop = nn.Dropout(p=drop_rate) |
||||
|
||||
# stochastic depth |
||||
dpr = [ |
||||
x.item() for x in torch.linspace(0, drop_path_rate, sum(depths)) |
||||
] # stochastic depth decay rule |
||||
|
||||
# build layers |
||||
self.layers = nn.ModuleList() |
||||
# prepare downsample list |
||||
downsamplelist = [PatchMerging for i in range(self.num_layers)] |
||||
downsamplelist[-1] = None |
||||
num_features = [int(embed_dim * 2**i) for i in range(self.num_layers)] |
||||
if self.dilation: |
||||
downsamplelist[-2] = None |
||||
num_features[-1] = int(embed_dim * 2 ** (self.num_layers - 1)) // 2 |
||||
for i_layer in range(self.num_layers): |
||||
layer = BasicLayer( |
||||
# dim=int(embed_dim * 2 ** i_layer), |
||||
dim=num_features[i_layer], |
||||
depth=depths[i_layer], |
||||
num_heads=num_heads[i_layer], |
||||
window_size=window_size, |
||||
mlp_ratio=mlp_ratio, |
||||
qkv_bias=qkv_bias, |
||||
qk_scale=qk_scale, |
||||
drop=drop_rate, |
||||
attn_drop=attn_drop_rate, |
||||
drop_path=dpr[sum(depths[:i_layer]) : sum(depths[: i_layer + 1])], |
||||
norm_layer=norm_layer, |
||||
# downsample=PatchMerging if (i_layer < self.num_layers - 1) else None, |
||||
downsample=downsamplelist[i_layer], |
||||
use_checkpoint=use_checkpoint, |
||||
) |
||||
self.layers.append(layer) |
||||
|
||||
# num_features = [int(embed_dim * 2 ** i) for i in range(self.num_layers)] |
||||
self.num_features = num_features |
||||
|
||||
# add a norm layer for each output |
||||
for i_layer in out_indices: |
||||
layer = norm_layer(num_features[i_layer]) |
||||
layer_name = f"norm{i_layer}" |
||||
self.add_module(layer_name, layer) |
||||
|
||||
self._freeze_stages() |
||||
|
||||
def _freeze_stages(self): |
||||
if self.frozen_stages >= 0: |
||||
self.patch_embed.eval() |
||||
for param in self.patch_embed.parameters(): |
||||
param.requires_grad = False |
||||
|
||||
if self.frozen_stages >= 1 and self.ape: |
||||
self.absolute_pos_embed.requires_grad = False |
||||
|
||||
if self.frozen_stages >= 2: |
||||
self.pos_drop.eval() |
||||
for i in range(0, self.frozen_stages - 1): |
||||
m = self.layers[i] |
||||
m.eval() |
||||
for param in m.parameters(): |
||||
param.requires_grad = False |
||||
|
||||
# def init_weights(self, pretrained=None): |
||||
# """Initialize the weights in backbone. |
||||
# Args: |
||||
# pretrained (str, optional): Path to pre-trained weights. |
||||
# Defaults to None. |
||||
# """ |
||||
|
||||
# def _init_weights(m): |
||||
# if isinstance(m, nn.Linear): |
||||
# trunc_normal_(m.weight, std=.02) |
||||
# if isinstance(m, nn.Linear) and m.bias is not None: |
||||
# nn.init.constant_(m.bias, 0) |
||||
# elif isinstance(m, nn.LayerNorm): |
||||
# nn.init.constant_(m.bias, 0) |
||||
# nn.init.constant_(m.weight, 1.0) |
||||
|
||||
# if isinstance(pretrained, str): |
||||
# self.apply(_init_weights) |
||||
# logger = get_root_logger() |
||||
# load_checkpoint(self, pretrained, strict=False, logger=logger) |
||||
# elif pretrained is None: |
||||
# self.apply(_init_weights) |
||||
# else: |
||||
# raise TypeError('pretrained must be a str or None') |
||||
|
||||
def forward_raw(self, x): |
||||
"""Forward function.""" |
||||
x = self.patch_embed(x) |
||||
|
||||
Wh, Ww = x.size(2), x.size(3) |
||||
if self.ape: |
||||
# interpolate the position embedding to the corresponding size |
||||
absolute_pos_embed = F.interpolate( |
||||
self.absolute_pos_embed, size=(Wh, Ww), mode="bicubic" |
||||
) |
||||
x = (x + absolute_pos_embed).flatten(2).transpose(1, 2) # B Wh*Ww C |
||||
else: |
||||
x = x.flatten(2).transpose(1, 2) |
||||
x = self.pos_drop(x) |
||||
|
||||
outs = [] |
||||
for i in range(self.num_layers): |
||||
layer = self.layers[i] |
||||
x_out, H, W, x, Wh, Ww = layer(x, Wh, Ww) |
||||
# import ipdb; ipdb.set_trace() |
||||
|
||||
if i in self.out_indices: |
||||
norm_layer = getattr(self, f"norm{i}") |
||||
x_out = norm_layer(x_out) |
||||
|
||||
out = x_out.view(-1, H, W, self.num_features[i]).permute(0, 3, 1, 2).contiguous() |
||||
outs.append(out) |
||||
# in: |
||||
# torch.Size([2, 3, 1024, 1024]) |
||||
# outs: |
||||
# [torch.Size([2, 192, 256, 256]), torch.Size([2, 384, 128, 128]), \ |
||||
# torch.Size([2, 768, 64, 64]), torch.Size([2, 1536, 32, 32])] |
||||
return tuple(outs) |
||||
|
||||
def forward(self, tensor_list: NestedTensor): |
||||
x = tensor_list.tensors |
||||
|
||||
"""Forward function.""" |
||||
x = self.patch_embed(x) |
||||
|
||||
Wh, Ww = x.size(2), x.size(3) |
||||
if self.ape: |
||||
# interpolate the position embedding to the corresponding size |
||||
absolute_pos_embed = F.interpolate( |
||||
self.absolute_pos_embed, size=(Wh, Ww), mode="bicubic" |
||||
) |
||||
x = (x + absolute_pos_embed).flatten(2).transpose(1, 2) # B Wh*Ww C |
||||
else: |
||||
x = x.flatten(2).transpose(1, 2) |
||||
x = self.pos_drop(x) |
||||
|
||||
outs = [] |
||||
for i in range(self.num_layers): |
||||
layer = self.layers[i] |
||||
x_out, H, W, x, Wh, Ww = layer(x, Wh, Ww) |
||||
|
||||
if i in self.out_indices: |
||||
norm_layer = getattr(self, f"norm{i}") |
||||
x_out = norm_layer(x_out) |
||||
|
||||
out = x_out.view(-1, H, W, self.num_features[i]).permute(0, 3, 1, 2).contiguous() |
||||
outs.append(out) |
||||
# in: |
||||
# torch.Size([2, 3, 1024, 1024]) |
||||
# out: |
||||
# [torch.Size([2, 192, 256, 256]), torch.Size([2, 384, 128, 128]), \ |
||||
# torch.Size([2, 768, 64, 64]), torch.Size([2, 1536, 32, 32])] |
||||
|
||||
# collect for nesttensors |
||||
outs_dict = {} |
||||
for idx, out_i in enumerate(outs): |
||||
m = tensor_list.mask |
||||
assert m is not None |
||||
mask = F.interpolate(m[None].float(), size=out_i.shape[-2:]).to(torch.bool)[0] |
||||
outs_dict[idx] = NestedTensor(out_i, mask) |
||||
|
||||
return outs_dict |
||||
|
||||
def train(self, mode=True): |
||||
"""Convert the model into training mode while keep layers freezed.""" |
||||
super(SwinTransformer, self).train(mode) |
||||
self._freeze_stages() |
||||
|
||||
|
||||
def build_swin_transformer(modelname, pretrain_img_size, **kw): |
||||
assert modelname in [ |
||||
"swin_T_224_1k", |
||||
"swin_B_224_22k", |
||||
"swin_B_384_22k", |
||||
"swin_L_224_22k", |
||||
"swin_L_384_22k", |
||||
] |
||||
|
||||
model_para_dict = { |
||||
"swin_T_224_1k": dict( |
||||
embed_dim=96, depths=[2, 2, 6, 2], num_heads=[3, 6, 12, 24], window_size=7 |
||||
), |
||||
"swin_B_224_22k": dict( |
||||
embed_dim=128, depths=[2, 2, 18, 2], num_heads=[4, 8, 16, 32], window_size=7 |
||||
), |
||||
"swin_B_384_22k": dict( |
||||
embed_dim=128, depths=[2, 2, 18, 2], num_heads=[4, 8, 16, 32], window_size=12 |
||||
), |
||||
"swin_L_224_22k": dict( |
||||
embed_dim=192, depths=[2, 2, 18, 2], num_heads=[6, 12, 24, 48], window_size=7 |
||||
), |
||||
"swin_L_384_22k": dict( |
||||
embed_dim=192, depths=[2, 2, 18, 2], num_heads=[6, 12, 24, 48], window_size=12 |
||||
), |
||||
} |
||||
kw_cgf = model_para_dict[modelname] |
||||
kw_cgf.update(kw) |
||||
model = SwinTransformer(pretrain_img_size=pretrain_img_size, **kw_cgf) |
||||
return model |
||||
|
||||
|
||||
if __name__ == "__main__": |
||||
model = build_swin_transformer("swin_L_384_22k", 384, dilation=True) |
||||
x = torch.rand(2, 3, 1024, 1024) |
||||
y = model.forward_raw(x) |
||||
import ipdb |
||||
|
||||
ipdb.set_trace() |
||||
x = torch.rand(2, 3, 384, 384) |
||||
y = model.forward_raw(x) |
||||
@ -0,0 +1,273 @@ |
||||
# ------------------------------------------------------------------------ |
||||
# Grounding DINO |
||||
# url: https://github.com/IDEA-Research/GroundingDINO |
||||
# Copyright (c) 2023 IDEA. All Rights Reserved. |
||||
# Licensed under the Apache License, Version 2.0 [see LICENSE for details] |
||||
# ------------------------------------------------------------------------ |
||||
|
||||
import torch |
||||
import torch.nn.functional as F |
||||
import torch.utils.checkpoint as checkpoint |
||||
from torch import Tensor, nn |
||||
from torchvision.ops.boxes import nms |
||||
from transformers import BertConfig, BertModel, BertPreTrainedModel |
||||
from transformers.modeling_outputs import BaseModelOutputWithPoolingAndCrossAttentions |
||||
|
||||
|
||||
class BertModelWarper(nn.Module): |
||||
def __init__(self, bert_model): |
||||
super().__init__() |
||||
# self.bert = bert_modelc |
||||
|
||||
self.config = bert_model.config |
||||
self.embeddings = bert_model.embeddings |
||||
self.encoder = bert_model.encoder |
||||
self.pooler = bert_model.pooler |
||||
|
||||
self.get_extended_attention_mask = bert_model.get_extended_attention_mask |
||||
self.invert_attention_mask = bert_model.invert_attention_mask |
||||
self.get_head_mask = bert_model.get_head_mask |
||||
|
||||
def forward( |
||||
self, |
||||
input_ids=None, |
||||
attention_mask=None, |
||||
token_type_ids=None, |
||||
position_ids=None, |
||||
head_mask=None, |
||||
inputs_embeds=None, |
||||
encoder_hidden_states=None, |
||||
encoder_attention_mask=None, |
||||
past_key_values=None, |
||||
use_cache=None, |
||||
output_attentions=None, |
||||
output_hidden_states=None, |
||||
return_dict=None, |
||||
): |
||||
r""" |
||||
encoder_hidden_states (:obj:`torch.FloatTensor` of shape :obj:`(batch_size, sequence_length, hidden_size)`, `optional`): |
||||
Sequence of hidden-states at the output of the last layer of the encoder. Used in the cross-attention if |
||||
the model is configured as a decoder. |
||||
encoder_attention_mask (:obj:`torch.FloatTensor` of shape :obj:`(batch_size, sequence_length)`, `optional`): |
||||
Mask to avoid performing attention on the padding token indices of the encoder input. This mask is used in |
||||
the cross-attention if the model is configured as a decoder. Mask values selected in ``[0, 1]``: |
||||
|
||||
- 1 for tokens that are **not masked**, |
||||
- 0 for tokens that are **masked**. |
||||
past_key_values (:obj:`tuple(tuple(torch.FloatTensor))` of length :obj:`config.n_layers` with each tuple having 4 tensors of shape :obj:`(batch_size, num_heads, sequence_length - 1, embed_size_per_head)`): |
||||
Contains precomputed key and value hidden states of the attention blocks. Can be used to speed up decoding. |
||||
|
||||
If :obj:`past_key_values` are used, the user can optionally input only the last :obj:`decoder_input_ids` |
||||
(those that don't have their past key value states given to this model) of shape :obj:`(batch_size, 1)` |
||||
instead of all :obj:`decoder_input_ids` of shape :obj:`(batch_size, sequence_length)`. |
||||
use_cache (:obj:`bool`, `optional`): |
||||
If set to :obj:`True`, :obj:`past_key_values` key value states are returned and can be used to speed up |
||||
decoding (see :obj:`past_key_values`). |
||||
""" |
||||
output_attentions = ( |
||||
output_attentions if output_attentions is not None else self.config.output_attentions |
||||
) |
||||
output_hidden_states = ( |
||||
output_hidden_states |
||||
if output_hidden_states is not None |
||||
else self.config.output_hidden_states |
||||
) |
||||
return_dict = return_dict if return_dict is not None else self.config.use_return_dict |
||||
|
||||
if self.config.is_decoder: |
||||
use_cache = use_cache if use_cache is not None else self.config.use_cache |
||||
else: |
||||
use_cache = False |
||||
|
||||
if input_ids is not None and inputs_embeds is not None: |
||||
raise ValueError("You cannot specify both input_ids and inputs_embeds at the same time") |
||||
elif input_ids is not None: |
||||
input_shape = input_ids.size() |
||||
batch_size, seq_length = input_shape |
||||
elif inputs_embeds is not None: |
||||
input_shape = inputs_embeds.size()[:-1] |
||||
batch_size, seq_length = input_shape |
||||
else: |
||||
raise ValueError("You have to specify either input_ids or inputs_embeds") |
||||
|
||||
device = input_ids.device if input_ids is not None else inputs_embeds.device |
||||
|
||||
# past_key_values_length |
||||
past_key_values_length = ( |
||||
past_key_values[0][0].shape[2] if past_key_values is not None else 0 |
||||
) |
||||
|
||||
if attention_mask is None: |
||||
attention_mask = torch.ones( |
||||
((batch_size, seq_length + past_key_values_length)), device=device |
||||
) |
||||
if token_type_ids is None: |
||||
token_type_ids = torch.zeros(input_shape, dtype=torch.long, device=device) |
||||
|
||||
# We can provide a self-attention mask of dimensions [batch_size, from_seq_length, to_seq_length] |
||||
# ourselves in which case we just need to make it broadcastable to all heads. |
||||
extended_attention_mask: torch.Tensor = self.get_extended_attention_mask( |
||||
attention_mask, input_shape, device |
||||
) |
||||
|
||||
# If a 2D or 3D attention mask is provided for the cross-attention |
||||
# we need to make broadcastable to [batch_size, num_heads, seq_length, seq_length] |
||||
if self.config.is_decoder and encoder_hidden_states is not None: |
||||
encoder_batch_size, encoder_sequence_length, _ = encoder_hidden_states.size() |
||||
encoder_hidden_shape = (encoder_batch_size, encoder_sequence_length) |
||||
if encoder_attention_mask is None: |
||||
encoder_attention_mask = torch.ones(encoder_hidden_shape, device=device) |
||||
encoder_extended_attention_mask = self.invert_attention_mask(encoder_attention_mask) |
||||
else: |
||||
encoder_extended_attention_mask = None |
||||
# if os.environ.get('IPDB_SHILONG_DEBUG', None) == 'INFO': |
||||
# import ipdb; ipdb.set_trace() |
||||
|
||||
# Prepare head mask if needed |
||||
# 1.0 in head_mask indicate we keep the head |
||||
# attention_probs has shape bsz x n_heads x N x N |
||||
# input head_mask has shape [num_heads] or [num_hidden_layers x num_heads] |
||||
# and head_mask is converted to shape [num_hidden_layers x batch x num_heads x seq_length x seq_length] |
||||
head_mask = self.get_head_mask(head_mask, self.config.num_hidden_layers) |
||||
|
||||
embedding_output = self.embeddings( |
||||
input_ids=input_ids, |
||||
position_ids=position_ids, |
||||
token_type_ids=token_type_ids, |
||||
inputs_embeds=inputs_embeds, |
||||
past_key_values_length=past_key_values_length, |
||||
) |
||||
|
||||
encoder_outputs = self.encoder( |
||||
embedding_output, |
||||
attention_mask=extended_attention_mask, |
||||
head_mask=head_mask, |
||||
encoder_hidden_states=encoder_hidden_states, |
||||
encoder_attention_mask=encoder_extended_attention_mask, |
||||
past_key_values=past_key_values, |
||||
use_cache=use_cache, |
||||
output_attentions=output_attentions, |
||||
output_hidden_states=output_hidden_states, |
||||
return_dict=return_dict, |
||||
) |
||||
sequence_output = encoder_outputs[0] |
||||
pooled_output = self.pooler(sequence_output) if self.pooler is not None else None |
||||
|
||||
if not return_dict: |
||||
return (sequence_output, pooled_output) + encoder_outputs[1:] |
||||
|
||||
return BaseModelOutputWithPoolingAndCrossAttentions( |
||||
last_hidden_state=sequence_output, |
||||
pooler_output=pooled_output, |
||||
past_key_values=encoder_outputs.past_key_values, |
||||
hidden_states=encoder_outputs.hidden_states, |
||||
attentions=encoder_outputs.attentions, |
||||
cross_attentions=encoder_outputs.cross_attentions, |
||||
) |
||||
|
||||
|
||||
class TextEncoderShell(nn.Module): |
||||
def __init__(self, text_encoder): |
||||
super().__init__() |
||||
self.text_encoder = text_encoder |
||||
self.config = self.text_encoder.config |
||||
|
||||
def forward(self, **kw): |
||||
# feed into text encoder |
||||
return self.text_encoder(**kw) |
||||
|
||||
|
||||
def generate_masks_with_special_tokens(tokenized, special_tokens_list, tokenizer): |
||||
"""Generate attention mask between each pair of special tokens |
||||
Args: |
||||
input_ids (torch.Tensor): input ids. Shape: [bs, num_token] |
||||
special_tokens_mask (list): special tokens mask. |
||||
Returns: |
||||
torch.Tensor: attention mask between each special tokens. |
||||
""" |
||||
input_ids = tokenized["input_ids"] |
||||
bs, num_token = input_ids.shape |
||||
# special_tokens_mask: bs, num_token. 1 for special tokens. 0 for normal tokens |
||||
special_tokens_mask = torch.zeros((bs, num_token), device=input_ids.device).bool() |
||||
for special_token in special_tokens_list: |
||||
special_tokens_mask |= input_ids == special_token |
||||
|
||||
# idxs: each row is a list of indices of special tokens |
||||
idxs = torch.nonzero(special_tokens_mask) |
||||
|
||||
# generate attention mask and positional ids |
||||
attention_mask = ( |
||||
torch.eye(num_token, device=input_ids.device).bool().unsqueeze(0).repeat(bs, 1, 1) |
||||
) |
||||
position_ids = torch.zeros((bs, num_token), device=input_ids.device) |
||||
previous_col = 0 |
||||
for i in range(idxs.shape[0]): |
||||
row, col = idxs[i] |
||||
if (col == 0) or (col == num_token - 1): |
||||
attention_mask[row, col, col] = True |
||||
position_ids[row, col] = 0 |
||||
else: |
||||
attention_mask[row, previous_col + 1 : col + 1, previous_col + 1 : col + 1] = True |
||||
position_ids[row, previous_col + 1 : col + 1] = torch.arange( |
||||
0, col - previous_col, device=input_ids.device |
||||
) |
||||
|
||||
previous_col = col |
||||
|
||||
# # padding mask |
||||
# padding_mask = tokenized['attention_mask'] |
||||
# attention_mask = attention_mask & padding_mask.unsqueeze(1).bool() & padding_mask.unsqueeze(2).bool() |
||||
|
||||
return attention_mask, position_ids.to(torch.long) |
||||
|
||||
|
||||
def generate_masks_with_special_tokens_and_transfer_map(tokenized, special_tokens_list, tokenizer): |
||||
"""Generate attention mask between each pair of special tokens |
||||
Args: |
||||
input_ids (torch.Tensor): input ids. Shape: [bs, num_token] |
||||
special_tokens_mask (list): special tokens mask. |
||||
Returns: |
||||
torch.Tensor: attention mask between each special tokens. |
||||
""" |
||||
input_ids = tokenized["input_ids"] |
||||
bs, num_token = input_ids.shape |
||||
# special_tokens_mask: bs, num_token. 1 for special tokens. 0 for normal tokens |
||||
special_tokens_mask = torch.zeros((bs, num_token), device=input_ids.device).bool() |
||||
for special_token in special_tokens_list: |
||||
special_tokens_mask |= input_ids == special_token |
||||
|
||||
# idxs: each row is a list of indices of special tokens |
||||
idxs = torch.nonzero(special_tokens_mask) |
||||
|
||||
# generate attention mask and positional ids |
||||
attention_mask = ( |
||||
torch.eye(num_token, device=input_ids.device).bool().unsqueeze(0).repeat(bs, 1, 1) |
||||
) |
||||
position_ids = torch.zeros((bs, num_token), device=input_ids.device) |
||||
cate_to_token_mask_list = [[] for _ in range(bs)] |
||||
previous_col = 0 |
||||
for i in range(idxs.shape[0]): |
||||
row, col = idxs[i] |
||||
if (col == 0) or (col == num_token - 1): |
||||
attention_mask[row, col, col] = True |
||||
position_ids[row, col] = 0 |
||||
else: |
||||
attention_mask[row, previous_col + 1 : col + 1, previous_col + 1 : col + 1] = True |
||||
position_ids[row, previous_col + 1 : col + 1] = torch.arange( |
||||
0, col - previous_col, device=input_ids.device |
||||
) |
||||
c2t_maski = torch.zeros((num_token), device=input_ids.device).bool() |
||||
c2t_maski[previous_col + 1 : col] = True |
||||
cate_to_token_mask_list[row].append(c2t_maski) |
||||
previous_col = col |
||||
|
||||
cate_to_token_mask_list = [ |
||||
torch.stack(cate_to_token_mask_listi, dim=0) |
||||
for cate_to_token_mask_listi in cate_to_token_mask_list |
||||
] |
||||
|
||||
# # padding mask |
||||
# padding_mask = tokenized['attention_mask'] |
||||
# attention_mask = attention_mask & padding_mask.unsqueeze(1).bool() & padding_mask.unsqueeze(2).bool() |
||||
|
||||
return attention_mask, position_ids.to(torch.long), cate_to_token_mask_list |
||||
@ -0,0 +1,64 @@ |
||||
/*!
|
||||
************************************************************************************************** |
||||
* Deformable DETR |
||||
* Copyright (c) 2020 SenseTime. All Rights Reserved. |
||||
* Licensed under the Apache License, Version 2.0 [see LICENSE for details] |
||||
************************************************************************************************** |
||||
* Modified from https://github.com/chengdazhi/Deformable-Convolution-V2-PyTorch/tree/pytorch_1.0.0
|
||||
************************************************************************************************** |
||||
*/ |
||||
|
||||
#pragma once |
||||
|
||||
#include "ms_deform_attn_cpu.h" |
||||
|
||||
#ifdef WITH_CUDA |
||||
#include "ms_deform_attn_cuda.h" |
||||
#endif |
||||
|
||||
namespace groundingdino { |
||||
|
||||
at::Tensor |
||||
ms_deform_attn_forward( |
||||
const at::Tensor &value,
|
||||
const at::Tensor &spatial_shapes, |
||||
const at::Tensor &level_start_index, |
||||
const at::Tensor &sampling_loc, |
||||
const at::Tensor &attn_weight, |
||||
const int im2col_step) |
||||
{ |
||||
if (value.type().is_cuda()) |
||||
{ |
||||
#ifdef WITH_CUDA |
||||
return ms_deform_attn_cuda_forward( |
||||
value, spatial_shapes, level_start_index, sampling_loc, attn_weight, im2col_step); |
||||
#else |
||||
AT_ERROR("Not compiled with GPU support"); |
||||
#endif |
||||
} |
||||
AT_ERROR("Not implemented on the CPU"); |
||||
} |
||||
|
||||
std::vector<at::Tensor> |
||||
ms_deform_attn_backward( |
||||
const at::Tensor &value,
|
||||
const at::Tensor &spatial_shapes, |
||||
const at::Tensor &level_start_index, |
||||
const at::Tensor &sampling_loc, |
||||
const at::Tensor &attn_weight, |
||||
const at::Tensor &grad_output, |
||||
const int im2col_step) |
||||
{ |
||||
if (value.type().is_cuda()) |
||||
{ |
||||
#ifdef WITH_CUDA |
||||
return ms_deform_attn_cuda_backward( |
||||
value, spatial_shapes, level_start_index, sampling_loc, attn_weight, grad_output, im2col_step); |
||||
#else |
||||
AT_ERROR("Not compiled with GPU support"); |
||||
#endif |
||||
} |
||||
AT_ERROR("Not implemented on the CPU"); |
||||
} |
||||
|
||||
} // namespace groundingdino
|
||||
@ -0,0 +1,43 @@ |
||||
/*!
|
||||
************************************************************************************************** |
||||
* Deformable DETR |
||||
* Copyright (c) 2020 SenseTime. All Rights Reserved. |
||||
* Licensed under the Apache License, Version 2.0 [see LICENSE for details] |
||||
************************************************************************************************** |
||||
* Modified from https://github.com/chengdazhi/Deformable-Convolution-V2-PyTorch/tree/pytorch_1.0.0
|
||||
************************************************************************************************** |
||||
*/ |
||||
|
||||
#include <vector> |
||||
|
||||
#include <ATen/ATen.h> |
||||
#include <ATen/cuda/CUDAContext.h> |
||||
|
||||
namespace groundingdino { |
||||
|
||||
at::Tensor |
||||
ms_deform_attn_cpu_forward( |
||||
const at::Tensor &value,
|
||||
const at::Tensor &spatial_shapes, |
||||
const at::Tensor &level_start_index, |
||||
const at::Tensor &sampling_loc, |
||||
const at::Tensor &attn_weight, |
||||
const int im2col_step) |
||||
{ |
||||
AT_ERROR("Not implement on cpu"); |
||||
} |
||||
|
||||
std::vector<at::Tensor> |
||||
ms_deform_attn_cpu_backward( |
||||
const at::Tensor &value,
|
||||
const at::Tensor &spatial_shapes, |
||||
const at::Tensor &level_start_index, |
||||
const at::Tensor &sampling_loc, |
||||
const at::Tensor &attn_weight, |
||||
const at::Tensor &grad_output, |
||||
const int im2col_step) |
||||
{ |
||||
AT_ERROR("Not implement on cpu"); |
||||
} |
||||
|
||||
} // namespace groundingdino
|
||||
@ -0,0 +1,35 @@ |
||||
/*!
|
||||
************************************************************************************************** |
||||
* Deformable DETR |
||||
* Copyright (c) 2020 SenseTime. All Rights Reserved. |
||||
* Licensed under the Apache License, Version 2.0 [see LICENSE for details] |
||||
************************************************************************************************** |
||||
* Modified from https://github.com/chengdazhi/Deformable-Convolution-V2-PyTorch/tree/pytorch_1.0.0
|
||||
************************************************************************************************** |
||||
*/ |
||||
|
||||
#pragma once |
||||
#include <torch/extension.h> |
||||
|
||||
namespace groundingdino { |
||||
|
||||
at::Tensor |
||||
ms_deform_attn_cpu_forward( |
||||
const at::Tensor &value,
|
||||
const at::Tensor &spatial_shapes, |
||||
const at::Tensor &level_start_index, |
||||
const at::Tensor &sampling_loc, |
||||
const at::Tensor &attn_weight, |
||||
const int im2col_step); |
||||
|
||||
std::vector<at::Tensor> |
||||
ms_deform_attn_cpu_backward( |
||||
const at::Tensor &value,
|
||||
const at::Tensor &spatial_shapes, |
||||
const at::Tensor &level_start_index, |
||||
const at::Tensor &sampling_loc, |
||||
const at::Tensor &attn_weight, |
||||
const at::Tensor &grad_output, |
||||
const int im2col_step); |
||||
|
||||
} // namespace groundingdino
|
||||
@ -0,0 +1,156 @@ |
||||
/*! |
||||
************************************************************************************************** |
||||
* Deformable DETR |
||||
* Copyright (c) 2020 SenseTime. All Rights Reserved. |
||||
* Licensed under the Apache License, Version 2.0 [see LICENSE for details] |
||||
************************************************************************************************** |
||||
* Modified from https://github.com/chengdazhi/Deformable-Convolution-V2-PyTorch/tree/pytorch_1.0.0 |
||||
************************************************************************************************** |
||||
*/ |
||||
|
||||
#include <vector> |
||||
#include "ms_deform_im2col_cuda.cuh" |
||||
|
||||
#include <ATen/ATen.h> |
||||
#include <ATen/cuda/CUDAContext.h> |
||||
#include <cuda.h> |
||||
#include <cuda_runtime.h> |
||||
|
||||
namespace groundingdino { |
||||
|
||||
at::Tensor ms_deform_attn_cuda_forward( |
||||
const at::Tensor &value, |
||||
const at::Tensor &spatial_shapes, |
||||
const at::Tensor &level_start_index, |
||||
const at::Tensor &sampling_loc, |
||||
const at::Tensor &attn_weight, |
||||
const int im2col_step) |
||||
{ |
||||
AT_ASSERTM(value.is_contiguous(), "value tensor has to be contiguous"); |
||||
AT_ASSERTM(spatial_shapes.is_contiguous(), "spatial_shapes tensor has to be contiguous"); |
||||
AT_ASSERTM(level_start_index.is_contiguous(), "level_start_index tensor has to be contiguous"); |
||||
AT_ASSERTM(sampling_loc.is_contiguous(), "sampling_loc tensor has to be contiguous"); |
||||
AT_ASSERTM(attn_weight.is_contiguous(), "attn_weight tensor has to be contiguous"); |
||||
|
||||
AT_ASSERTM(value.type().is_cuda(), "value must be a CUDA tensor"); |
||||
AT_ASSERTM(spatial_shapes.type().is_cuda(), "spatial_shapes must be a CUDA tensor"); |
||||
AT_ASSERTM(level_start_index.type().is_cuda(), "level_start_index must be a CUDA tensor"); |
||||
AT_ASSERTM(sampling_loc.type().is_cuda(), "sampling_loc must be a CUDA tensor"); |
||||
AT_ASSERTM(attn_weight.type().is_cuda(), "attn_weight must be a CUDA tensor"); |
||||
|
||||
const int batch = value.size(0); |
||||
const int spatial_size = value.size(1); |
||||
const int num_heads = value.size(2); |
||||
const int channels = value.size(3); |
||||
|
||||
const int num_levels = spatial_shapes.size(0); |
||||
|
||||
const int num_query = sampling_loc.size(1); |
||||
const int num_point = sampling_loc.size(4); |
||||
|
||||
const int im2col_step_ = std::min(batch, im2col_step); |
||||
|
||||
AT_ASSERTM(batch % im2col_step_ == 0, "batch(%d) must divide im2col_step(%d)", batch, im2col_step_); |
||||
|
||||
auto output = at::zeros({batch, num_query, num_heads, channels}, value.options()); |
||||
|
||||
const int batch_n = im2col_step_; |
||||
auto output_n = output.view({batch/im2col_step_, batch_n, num_query, num_heads, channels}); |
||||
auto per_value_size = spatial_size * num_heads * channels; |
||||
auto per_sample_loc_size = num_query * num_heads * num_levels * num_point * 2; |
||||
auto per_attn_weight_size = num_query * num_heads * num_levels * num_point; |
||||
for (int n = 0; n < batch/im2col_step_; ++n) |
||||
{ |
||||
auto columns = output_n.select(0, n); |
||||
AT_DISPATCH_FLOATING_TYPES(value.type(), "ms_deform_attn_forward_cuda", ([&] { |
||||
ms_deformable_im2col_cuda(at::cuda::getCurrentCUDAStream(), |
||||
value.data<scalar_t>() + n * im2col_step_ * per_value_size, |
||||
spatial_shapes.data<int64_t>(), |
||||
level_start_index.data<int64_t>(), |
||||
sampling_loc.data<scalar_t>() + n * im2col_step_ * per_sample_loc_size, |
||||
attn_weight.data<scalar_t>() + n * im2col_step_ * per_attn_weight_size, |
||||
batch_n, spatial_size, num_heads, channels, num_levels, num_query, num_point, |
||||
columns.data<scalar_t>()); |
||||
|
||||
})); |
||||
} |
||||
|
||||
output = output.view({batch, num_query, num_heads*channels}); |
||||
|
||||
return output; |
||||
} |
||||
|
||||
|
||||
std::vector<at::Tensor> ms_deform_attn_cuda_backward( |
||||
const at::Tensor &value, |
||||
const at::Tensor &spatial_shapes, |
||||
const at::Tensor &level_start_index, |
||||
const at::Tensor &sampling_loc, |
||||
const at::Tensor &attn_weight, |
||||
const at::Tensor &grad_output, |
||||
const int im2col_step) |
||||
{ |
||||
|
||||
AT_ASSERTM(value.is_contiguous(), "value tensor has to be contiguous"); |
||||
AT_ASSERTM(spatial_shapes.is_contiguous(), "spatial_shapes tensor has to be contiguous"); |
||||
AT_ASSERTM(level_start_index.is_contiguous(), "level_start_index tensor has to be contiguous"); |
||||
AT_ASSERTM(sampling_loc.is_contiguous(), "sampling_loc tensor has to be contiguous"); |
||||
AT_ASSERTM(attn_weight.is_contiguous(), "attn_weight tensor has to be contiguous"); |
||||
AT_ASSERTM(grad_output.is_contiguous(), "grad_output tensor has to be contiguous"); |
||||
|
||||
AT_ASSERTM(value.type().is_cuda(), "value must be a CUDA tensor"); |
||||
AT_ASSERTM(spatial_shapes.type().is_cuda(), "spatial_shapes must be a CUDA tensor"); |
||||
AT_ASSERTM(level_start_index.type().is_cuda(), "level_start_index must be a CUDA tensor"); |
||||
AT_ASSERTM(sampling_loc.type().is_cuda(), "sampling_loc must be a CUDA tensor"); |
||||
AT_ASSERTM(attn_weight.type().is_cuda(), "attn_weight must be a CUDA tensor"); |
||||
AT_ASSERTM(grad_output.type().is_cuda(), "grad_output must be a CUDA tensor"); |
||||
|
||||
const int batch = value.size(0); |
||||
const int spatial_size = value.size(1); |
||||
const int num_heads = value.size(2); |
||||
const int channels = value.size(3); |
||||
|
||||
const int num_levels = spatial_shapes.size(0); |
||||
|
||||
const int num_query = sampling_loc.size(1); |
||||
const int num_point = sampling_loc.size(4); |
||||
|
||||
const int im2col_step_ = std::min(batch, im2col_step); |
||||
|
||||
AT_ASSERTM(batch % im2col_step_ == 0, "batch(%d) must divide im2col_step(%d)", batch, im2col_step_); |
||||
|
||||
auto grad_value = at::zeros_like(value); |
||||
auto grad_sampling_loc = at::zeros_like(sampling_loc); |
||||
auto grad_attn_weight = at::zeros_like(attn_weight); |
||||
|
||||
const int batch_n = im2col_step_; |
||||
auto per_value_size = spatial_size * num_heads * channels; |
||||
auto per_sample_loc_size = num_query * num_heads * num_levels * num_point * 2; |
||||
auto per_attn_weight_size = num_query * num_heads * num_levels * num_point; |
||||
auto grad_output_n = grad_output.view({batch/im2col_step_, batch_n, num_query, num_heads, channels}); |
||||
|
||||
for (int n = 0; n < batch/im2col_step_; ++n) |
||||
{ |
||||
auto grad_output_g = grad_output_n.select(0, n); |
||||
AT_DISPATCH_FLOATING_TYPES(value.type(), "ms_deform_attn_backward_cuda", ([&] { |
||||
ms_deformable_col2im_cuda(at::cuda::getCurrentCUDAStream(), |
||||
grad_output_g.data<scalar_t>(), |
||||
value.data<scalar_t>() + n * im2col_step_ * per_value_size, |
||||
spatial_shapes.data<int64_t>(), |
||||
level_start_index.data<int64_t>(), |
||||
sampling_loc.data<scalar_t>() + n * im2col_step_ * per_sample_loc_size, |
||||
attn_weight.data<scalar_t>() + n * im2col_step_ * per_attn_weight_size, |
||||
batch_n, spatial_size, num_heads, channels, num_levels, num_query, num_point, |
||||
grad_value.data<scalar_t>() + n * im2col_step_ * per_value_size, |
||||
grad_sampling_loc.data<scalar_t>() + n * im2col_step_ * per_sample_loc_size, |
||||
grad_attn_weight.data<scalar_t>() + n * im2col_step_ * per_attn_weight_size); |
||||
|
||||
})); |
||||
} |
||||
|
||||
return { |
||||
grad_value, grad_sampling_loc, grad_attn_weight |
||||
}; |
||||
} |
||||
|
||||
} // namespace groundingdino |
||||
@ -0,0 +1,33 @@ |
||||
/*!
|
||||
************************************************************************************************** |
||||
* Deformable DETR |
||||
* Copyright (c) 2020 SenseTime. All Rights Reserved. |
||||
* Licensed under the Apache License, Version 2.0 [see LICENSE for details] |
||||
************************************************************************************************** |
||||
* Modified from https://github.com/chengdazhi/Deformable-Convolution-V2-PyTorch/tree/pytorch_1.0.0
|
||||
************************************************************************************************** |
||||
*/ |
||||
|
||||
#pragma once |
||||
#include <torch/extension.h> |
||||
|
||||
namespace groundingdino { |
||||
|
||||
at::Tensor ms_deform_attn_cuda_forward( |
||||
const at::Tensor &value,
|
||||
const at::Tensor &spatial_shapes, |
||||
const at::Tensor &level_start_index, |
||||
const at::Tensor &sampling_loc, |
||||
const at::Tensor &attn_weight, |
||||
const int im2col_step); |
||||
|
||||
std::vector<at::Tensor> ms_deform_attn_cuda_backward( |
||||
const at::Tensor &value,
|
||||
const at::Tensor &spatial_shapes, |
||||
const at::Tensor &level_start_index, |
||||
const at::Tensor &sampling_loc, |
||||
const at::Tensor &attn_weight, |
||||
const at::Tensor &grad_output, |
||||
const int im2col_step); |
||||
|
||||
} // namespace groundingdino
|
||||
File diff suppressed because it is too large
Load Diff
@ -0,0 +1,7 @@ |
||||
#include <cuda_runtime_api.h> |
||||
|
||||
namespace groundingdino { |
||||
int get_cudart_version() { |
||||
return CUDART_VERSION; |
||||
} |
||||
} // namespace groundingdino |
||||
@ -0,0 +1,58 @@ |
||||
// Copyright (c) Facebook, Inc. and its affiliates. All Rights Reserved
|
||||
|
||||
#include "MsDeformAttn/ms_deform_attn.h" |
||||
|
||||
namespace groundingdino { |
||||
|
||||
#ifdef WITH_CUDA |
||||
extern int get_cudart_version(); |
||||
#endif |
||||
|
||||
std::string get_cuda_version() { |
||||
#ifdef WITH_CUDA |
||||
std::ostringstream oss; |
||||
|
||||
// copied from
|
||||
// https://github.com/pytorch/pytorch/blob/master/aten/src/ATen/cuda/detail/CUDAHooks.cpp#L231
|
||||
auto printCudaStyleVersion = [&](int v) { |
||||
oss << (v / 1000) << "." << (v / 10 % 100); |
||||
if (v % 10 != 0) { |
||||
oss << "." << (v % 10); |
||||
} |
||||
}; |
||||
printCudaStyleVersion(get_cudart_version()); |
||||
return oss.str(); |
||||
#else |
||||
return std::string("not available"); |
||||
#endif |
||||
} |
||||
|
||||
// similar to
|
||||
// https://github.com/pytorch/pytorch/blob/master/aten/src/ATen/Version.cpp
|
||||
std::string get_compiler_version() { |
||||
std::ostringstream ss; |
||||
#if defined(__GNUC__) |
||||
#ifndef __clang__ |
||||
{ ss << "GCC " << __GNUC__ << "." << __GNUC_MINOR__; } |
||||
#endif |
||||
#endif |
||||
|
||||
#if defined(__clang_major__) |
||||
{ |
||||
ss << "clang " << __clang_major__ << "." << __clang_minor__ << "." |
||||
<< __clang_patchlevel__; |
||||
} |
||||
#endif |
||||
|
||||
#if defined(_MSC_VER) |
||||
{ ss << "MSVC " << _MSC_FULL_VER; } |
||||
#endif |
||||
return ss.str(); |
||||
} |
||||
|
||||
PYBIND11_MODULE(TORCH_EXTENSION_NAME, m) { |
||||
m.def("ms_deform_attn_forward", &ms_deform_attn_forward, "ms_deform_attn_forward"); |
||||
m.def("ms_deform_attn_backward", &ms_deform_attn_backward, "ms_deform_attn_backward"); |
||||
} |
||||
|
||||
} // namespace groundingdino
|
||||
@ -0,0 +1,297 @@ |
||||
# ------------------------------------------------------------------------ |
||||
# Grounding DINO |
||||
# url: https://github.com/IDEA-Research/GroundingDINO |
||||
# Copyright (c) 2023 IDEA. All Rights Reserved. |
||||
# Licensed under the Apache License, Version 2.0 [see LICENSE for details] |
||||
# ------------------------------------------------------------------------ |
||||
|
||||
import torch |
||||
import torch.nn as nn |
||||
import torch.nn.functional as F |
||||
from timm.models.layers import DropPath |
||||
|
||||
|
||||
class FeatureResizer(nn.Module): |
||||
""" |
||||
This class takes as input a set of embeddings of dimension C1 and outputs a set of |
||||
embedding of dimension C2, after a linear transformation, dropout and normalization (LN). |
||||
""" |
||||
|
||||
def __init__(self, input_feat_size, output_feat_size, dropout, do_ln=True): |
||||
super().__init__() |
||||
self.do_ln = do_ln |
||||
# Object feature encoding |
||||
self.fc = nn.Linear(input_feat_size, output_feat_size, bias=True) |
||||
self.layer_norm = nn.LayerNorm(output_feat_size, eps=1e-12) |
||||
self.dropout = nn.Dropout(dropout) |
||||
|
||||
def forward(self, encoder_features): |
||||
x = self.fc(encoder_features) |
||||
if self.do_ln: |
||||
x = self.layer_norm(x) |
||||
output = self.dropout(x) |
||||
return output |
||||
|
||||
|
||||
def l1norm(X, dim, eps=1e-8): |
||||
"""L1-normalize columns of X""" |
||||
norm = torch.abs(X).sum(dim=dim, keepdim=True) + eps |
||||
X = torch.div(X, norm) |
||||
return X |
||||
|
||||
|
||||
def l2norm(X, dim, eps=1e-8): |
||||
"""L2-normalize columns of X""" |
||||
norm = torch.pow(X, 2).sum(dim=dim, keepdim=True).sqrt() + eps |
||||
X = torch.div(X, norm) |
||||
return X |
||||
|
||||
|
||||
def func_attention(query, context, smooth=1, raw_feature_norm="softmax", eps=1e-8): |
||||
""" |
||||
query: (n_context, queryL, d) |
||||
context: (n_context, sourceL, d) |
||||
""" |
||||
batch_size_q, queryL = query.size(0), query.size(1) |
||||
batch_size, sourceL = context.size(0), context.size(1) |
||||
|
||||
# Get attention |
||||
# --> (batch, d, queryL) |
||||
queryT = torch.transpose(query, 1, 2) |
||||
|
||||
# (batch, sourceL, d)(batch, d, queryL) |
||||
# --> (batch, sourceL, queryL) |
||||
attn = torch.bmm(context, queryT) |
||||
if raw_feature_norm == "softmax": |
||||
# --> (batch*sourceL, queryL) |
||||
attn = attn.view(batch_size * sourceL, queryL) |
||||
attn = nn.Softmax()(attn) |
||||
# --> (batch, sourceL, queryL) |
||||
attn = attn.view(batch_size, sourceL, queryL) |
||||
elif raw_feature_norm == "l2norm": |
||||
attn = l2norm(attn, 2) |
||||
elif raw_feature_norm == "clipped_l2norm": |
||||
attn = nn.LeakyReLU(0.1)(attn) |
||||
attn = l2norm(attn, 2) |
||||
else: |
||||
raise ValueError("unknown first norm type:", raw_feature_norm) |
||||
# --> (batch, queryL, sourceL) |
||||
attn = torch.transpose(attn, 1, 2).contiguous() |
||||
# --> (batch*queryL, sourceL) |
||||
attn = attn.view(batch_size * queryL, sourceL) |
||||
attn = nn.Softmax()(attn * smooth) |
||||
# --> (batch, queryL, sourceL) |
||||
attn = attn.view(batch_size, queryL, sourceL) |
||||
# --> (batch, sourceL, queryL) |
||||
attnT = torch.transpose(attn, 1, 2).contiguous() |
||||
|
||||
# --> (batch, d, sourceL) |
||||
contextT = torch.transpose(context, 1, 2) |
||||
# (batch x d x sourceL)(batch x sourceL x queryL) |
||||
# --> (batch, d, queryL) |
||||
weightedContext = torch.bmm(contextT, attnT) |
||||
# --> (batch, queryL, d) |
||||
weightedContext = torch.transpose(weightedContext, 1, 2) |
||||
|
||||
return weightedContext, attnT |
||||
|
||||
|
||||
class BiMultiHeadAttention(nn.Module): |
||||
def __init__(self, v_dim, l_dim, embed_dim, num_heads, dropout=0.1, cfg=None): |
||||
super(BiMultiHeadAttention, self).__init__() |
||||
|
||||
self.embed_dim = embed_dim |
||||
self.num_heads = num_heads |
||||
self.head_dim = embed_dim // num_heads |
||||
self.v_dim = v_dim |
||||
self.l_dim = l_dim |
||||
|
||||
assert ( |
||||
self.head_dim * self.num_heads == self.embed_dim |
||||
), f"embed_dim must be divisible by num_heads (got `embed_dim`: {self.embed_dim} and `num_heads`: {self.num_heads})." |
||||
self.scale = self.head_dim ** (-0.5) |
||||
self.dropout = dropout |
||||
|
||||
self.v_proj = nn.Linear(self.v_dim, self.embed_dim) |
||||
self.l_proj = nn.Linear(self.l_dim, self.embed_dim) |
||||
self.values_v_proj = nn.Linear(self.v_dim, self.embed_dim) |
||||
self.values_l_proj = nn.Linear(self.l_dim, self.embed_dim) |
||||
|
||||
self.out_v_proj = nn.Linear(self.embed_dim, self.v_dim) |
||||
self.out_l_proj = nn.Linear(self.embed_dim, self.l_dim) |
||||
|
||||
self.stable_softmax_2d = True |
||||
self.clamp_min_for_underflow = True |
||||
self.clamp_max_for_overflow = True |
||||
|
||||
self._reset_parameters() |
||||
|
||||
def _shape(self, tensor: torch.Tensor, seq_len: int, bsz: int): |
||||
return tensor.view(bsz, seq_len, self.num_heads, self.head_dim).transpose(1, 2).contiguous() |
||||
|
||||
def _reset_parameters(self): |
||||
nn.init.xavier_uniform_(self.v_proj.weight) |
||||
self.v_proj.bias.data.fill_(0) |
||||
nn.init.xavier_uniform_(self.l_proj.weight) |
||||
self.l_proj.bias.data.fill_(0) |
||||
nn.init.xavier_uniform_(self.values_v_proj.weight) |
||||
self.values_v_proj.bias.data.fill_(0) |
||||
nn.init.xavier_uniform_(self.values_l_proj.weight) |
||||
self.values_l_proj.bias.data.fill_(0) |
||||
nn.init.xavier_uniform_(self.out_v_proj.weight) |
||||
self.out_v_proj.bias.data.fill_(0) |
||||
nn.init.xavier_uniform_(self.out_l_proj.weight) |
||||
self.out_l_proj.bias.data.fill_(0) |
||||
|
||||
def forward(self, v, l, attention_mask_v=None, attention_mask_l=None): |
||||
"""_summary_ |
||||
|
||||
Args: |
||||
v (_type_): bs, n_img, dim |
||||
l (_type_): bs, n_text, dim |
||||
attention_mask_v (_type_, optional): _description_. bs, n_img |
||||
attention_mask_l (_type_, optional): _description_. bs, n_text |
||||
|
||||
Returns: |
||||
_type_: _description_ |
||||
""" |
||||
# if os.environ.get('IPDB_SHILONG_DEBUG', None) == 'INFO': |
||||
# import ipdb; ipdb.set_trace() |
||||
bsz, tgt_len, _ = v.size() |
||||
|
||||
query_states = self.v_proj(v) * self.scale |
||||
key_states = self._shape(self.l_proj(l), -1, bsz) |
||||
value_v_states = self._shape(self.values_v_proj(v), -1, bsz) |
||||
value_l_states = self._shape(self.values_l_proj(l), -1, bsz) |
||||
|
||||
proj_shape = (bsz * self.num_heads, -1, self.head_dim) |
||||
query_states = self._shape(query_states, tgt_len, bsz).view(*proj_shape) |
||||
key_states = key_states.view(*proj_shape) |
||||
value_v_states = value_v_states.view(*proj_shape) |
||||
value_l_states = value_l_states.view(*proj_shape) |
||||
|
||||
src_len = key_states.size(1) |
||||
attn_weights = torch.bmm(query_states, key_states.transpose(1, 2)) # bs*nhead, nimg, ntxt |
||||
|
||||
if attn_weights.size() != (bsz * self.num_heads, tgt_len, src_len): |
||||
raise ValueError( |
||||
f"Attention weights should be of size {(bsz * self.num_heads, tgt_len, src_len)}, but is {attn_weights.size()}" |
||||
) |
||||
|
||||
if self.stable_softmax_2d: |
||||
attn_weights = attn_weights - attn_weights.max() |
||||
|
||||
if self.clamp_min_for_underflow: |
||||
attn_weights = torch.clamp( |
||||
attn_weights, min=-50000 |
||||
) # Do not increase -50000, data type half has quite limited range |
||||
if self.clamp_max_for_overflow: |
||||
attn_weights = torch.clamp( |
||||
attn_weights, max=50000 |
||||
) # Do not increase 50000, data type half has quite limited range |
||||
|
||||
attn_weights_T = attn_weights.transpose(1, 2) |
||||
attn_weights_l = attn_weights_T - torch.max(attn_weights_T, dim=-1, keepdim=True)[0] |
||||
if self.clamp_min_for_underflow: |
||||
attn_weights_l = torch.clamp( |
||||
attn_weights_l, min=-50000 |
||||
) # Do not increase -50000, data type half has quite limited range |
||||
if self.clamp_max_for_overflow: |
||||
attn_weights_l = torch.clamp( |
||||
attn_weights_l, max=50000 |
||||
) # Do not increase 50000, data type half has quite limited range |
||||
|
||||
# mask vison for language |
||||
if attention_mask_v is not None: |
||||
attention_mask_v = ( |
||||
attention_mask_v[:, None, None, :].repeat(1, self.num_heads, 1, 1).flatten(0, 1) |
||||
) |
||||
attn_weights_l.masked_fill_(attention_mask_v, float("-inf")) |
||||
|
||||
attn_weights_l = attn_weights_l.softmax(dim=-1) |
||||
|
||||
# mask language for vision |
||||
if attention_mask_l is not None: |
||||
attention_mask_l = ( |
||||
attention_mask_l[:, None, None, :].repeat(1, self.num_heads, 1, 1).flatten(0, 1) |
||||
) |
||||
attn_weights.masked_fill_(attention_mask_l, float("-inf")) |
||||
attn_weights_v = attn_weights.softmax(dim=-1) |
||||
|
||||
attn_probs_v = F.dropout(attn_weights_v, p=self.dropout, training=self.training) |
||||
attn_probs_l = F.dropout(attn_weights_l, p=self.dropout, training=self.training) |
||||
|
||||
attn_output_v = torch.bmm(attn_probs_v, value_l_states) |
||||
attn_output_l = torch.bmm(attn_probs_l, value_v_states) |
||||
|
||||
if attn_output_v.size() != (bsz * self.num_heads, tgt_len, self.head_dim): |
||||
raise ValueError( |
||||
f"`attn_output_v` should be of size {(bsz, self.num_heads, tgt_len, self.head_dim)}, but is {attn_output_v.size()}" |
||||
) |
||||
|
||||
if attn_output_l.size() != (bsz * self.num_heads, src_len, self.head_dim): |
||||
raise ValueError( |
||||
f"`attn_output_l` should be of size {(bsz, self.num_heads, src_len, self.head_dim)}, but is {attn_output_l.size()}" |
||||
) |
||||
|
||||
attn_output_v = attn_output_v.view(bsz, self.num_heads, tgt_len, self.head_dim) |
||||
attn_output_v = attn_output_v.transpose(1, 2) |
||||
attn_output_v = attn_output_v.reshape(bsz, tgt_len, self.embed_dim) |
||||
|
||||
attn_output_l = attn_output_l.view(bsz, self.num_heads, src_len, self.head_dim) |
||||
attn_output_l = attn_output_l.transpose(1, 2) |
||||
attn_output_l = attn_output_l.reshape(bsz, src_len, self.embed_dim) |
||||
|
||||
attn_output_v = self.out_v_proj(attn_output_v) |
||||
attn_output_l = self.out_l_proj(attn_output_l) |
||||
|
||||
return attn_output_v, attn_output_l |
||||
|
||||
|
||||
# Bi-Direction MHA (text->image, image->text) |
||||
class BiAttentionBlock(nn.Module): |
||||
def __init__( |
||||
self, |
||||
v_dim, |
||||
l_dim, |
||||
embed_dim, |
||||
num_heads, |
||||
dropout=0.1, |
||||
drop_path=0.0, |
||||
init_values=1e-4, |
||||
cfg=None, |
||||
): |
||||
""" |
||||
Inputs: |
||||
embed_dim - Dimensionality of input and attention feature vectors |
||||
hidden_dim - Dimensionality of hidden layer in feed-forward network |
||||
(usually 2-4x larger than embed_dim) |
||||
num_heads - Number of heads to use in the Multi-Head Attention block |
||||
dropout - Amount of dropout to apply in the feed-forward network |
||||
""" |
||||
super(BiAttentionBlock, self).__init__() |
||||
|
||||
# pre layer norm |
||||
self.layer_norm_v = nn.LayerNorm(v_dim) |
||||
self.layer_norm_l = nn.LayerNorm(l_dim) |
||||
self.attn = BiMultiHeadAttention( |
||||
v_dim=v_dim, l_dim=l_dim, embed_dim=embed_dim, num_heads=num_heads, dropout=dropout |
||||
) |
||||
|
||||
# add layer scale for training stability |
||||
self.drop_path = DropPath(drop_path) if drop_path > 0.0 else nn.Identity() |
||||
self.gamma_v = nn.Parameter(init_values * torch.ones((v_dim)), requires_grad=True) |
||||
self.gamma_l = nn.Parameter(init_values * torch.ones((l_dim)), requires_grad=True) |
||||
|
||||
def forward(self, v, l, attention_mask_v=None, attention_mask_l=None): |
||||
v = self.layer_norm_v(v) |
||||
l = self.layer_norm_l(l) |
||||
delta_v, delta_l = self.attn( |
||||
v, l, attention_mask_v=attention_mask_v, attention_mask_l=attention_mask_l |
||||
) |
||||
# v, l = v + delta_v, l + delta_l |
||||
v = v + self.drop_path(self.gamma_v * delta_v) |
||||
l = l + self.drop_path(self.gamma_l * delta_l) |
||||
return v, l |
||||
|
||||
# def forward(self, v:List[torch.Tensor], l, attention_mask_v=None, attention_mask_l=None) |
||||
@ -0,0 +1,395 @@ |
||||
# ------------------------------------------------------------------------ |
||||
# Grounding DINO |
||||
# url: https://github.com/IDEA-Research/GroundingDINO |
||||
# Copyright (c) 2023 IDEA. All Rights Reserved. |
||||
# Licensed under the Apache License, Version 2.0 [see LICENSE for details] |
||||
# ------------------------------------------------------------------------ |
||||
# Conditional DETR model and criterion classes. |
||||
# Copyright (c) 2021 Microsoft. All Rights Reserved. |
||||
# Licensed under the Apache License, Version 2.0 [see LICENSE for details] |
||||
# ------------------------------------------------------------------------ |
||||
# Modified from DETR (https://github.com/facebookresearch/detr) |
||||
# Copyright (c) Facebook, Inc. and its affiliates. All Rights Reserved. |
||||
# ------------------------------------------------------------------------ |
||||
# Modified from Deformable DETR (https://github.com/fundamentalvision/Deformable-DETR) |
||||
# Copyright (c) 2020 SenseTime. All Rights Reserved. |
||||
# ------------------------------------------------------------------------ |
||||
import copy |
||||
from typing import List |
||||
|
||||
import torch |
||||
import torch.nn.functional as F |
||||
from torch import nn |
||||
from torchvision.ops.boxes import nms |
||||
from transformers import AutoTokenizer, BertModel, BertTokenizer, RobertaModel, RobertaTokenizerFast |
||||
|
||||
from groundingdino.util import box_ops, get_tokenlizer |
||||
from groundingdino.util.misc import ( |
||||
NestedTensor, |
||||
accuracy, |
||||
get_world_size, |
||||
interpolate, |
||||
inverse_sigmoid, |
||||
is_dist_avail_and_initialized, |
||||
nested_tensor_from_tensor_list, |
||||
) |
||||
from groundingdino.util.utils import get_phrases_from_posmap |
||||
from groundingdino.util.visualizer import COCOVisualizer |
||||
from groundingdino.util.vl_utils import create_positive_map_from_span |
||||
|
||||
from ..registry import MODULE_BUILD_FUNCS |
||||
from .backbone import build_backbone |
||||
from .bertwarper import ( |
||||
BertModelWarper, |
||||
generate_masks_with_special_tokens, |
||||
generate_masks_with_special_tokens_and_transfer_map, |
||||
) |
||||
from .transformer import build_transformer |
||||
from .utils import MLP, ContrastiveEmbed, sigmoid_focal_loss |
||||
|
||||
|
||||
class GroundingDINO(nn.Module): |
||||
"""This is the Cross-Attention Detector module that performs object detection""" |
||||
|
||||
def __init__( |
||||
self, |
||||
backbone, |
||||
transformer, |
||||
num_queries, |
||||
aux_loss=False, |
||||
iter_update=False, |
||||
query_dim=2, |
||||
num_feature_levels=1, |
||||
nheads=8, |
||||
# two stage |
||||
two_stage_type="no", # ['no', 'standard'] |
||||
dec_pred_bbox_embed_share=True, |
||||
two_stage_class_embed_share=True, |
||||
two_stage_bbox_embed_share=True, |
||||
num_patterns=0, |
||||
dn_number=100, |
||||
dn_box_noise_scale=0.4, |
||||
dn_label_noise_ratio=0.5, |
||||
dn_labelbook_size=100, |
||||
text_encoder_type="bert-base-uncased", |
||||
sub_sentence_present=True, |
||||
max_text_len=256, |
||||
): |
||||
"""Initializes the model. |
||||
Parameters: |
||||
backbone: torch module of the backbone to be used. See backbone.py |
||||
transformer: torch module of the transformer architecture. See transformer.py |
||||
num_queries: number of object queries, ie detection slot. This is the maximal number of objects |
||||
Conditional DETR can detect in a single image. For COCO, we recommend 100 queries. |
||||
aux_loss: True if auxiliary decoding losses (loss at each decoder layer) are to be used. |
||||
""" |
||||
super().__init__() |
||||
self.num_queries = num_queries |
||||
self.transformer = transformer |
||||
self.hidden_dim = hidden_dim = transformer.d_model |
||||
self.num_feature_levels = num_feature_levels |
||||
self.nheads = nheads |
||||
self.max_text_len = 256 |
||||
self.sub_sentence_present = sub_sentence_present |
||||
|
||||
# setting query dim |
||||
self.query_dim = query_dim |
||||
assert query_dim == 4 |
||||
|
||||
# for dn training |
||||
self.num_patterns = num_patterns |
||||
self.dn_number = dn_number |
||||
self.dn_box_noise_scale = dn_box_noise_scale |
||||
self.dn_label_noise_ratio = dn_label_noise_ratio |
||||
self.dn_labelbook_size = dn_labelbook_size |
||||
|
||||
# bert |
||||
self.tokenizer = get_tokenlizer.get_tokenlizer(text_encoder_type) |
||||
self.bert = get_tokenlizer.get_pretrained_language_model(text_encoder_type) |
||||
self.bert.pooler.dense.weight.requires_grad_(False) |
||||
self.bert.pooler.dense.bias.requires_grad_(False) |
||||
self.bert = BertModelWarper(bert_model=self.bert) |
||||
|
||||
self.feat_map = nn.Linear(self.bert.config.hidden_size, self.hidden_dim, bias=True) |
||||
nn.init.constant_(self.feat_map.bias.data, 0) |
||||
nn.init.xavier_uniform_(self.feat_map.weight.data) |
||||
# freeze |
||||
|
||||
# special tokens |
||||
self.specical_tokens = self.tokenizer.convert_tokens_to_ids(["[CLS]", "[SEP]", ".", "?"]) |
||||
|
||||
# prepare input projection layers |
||||
if num_feature_levels > 1: |
||||
num_backbone_outs = len(backbone.num_channels) |
||||
input_proj_list = [] |
||||
for _ in range(num_backbone_outs): |
||||
in_channels = backbone.num_channels[_] |
||||
input_proj_list.append( |
||||
nn.Sequential( |
||||
nn.Conv2d(in_channels, hidden_dim, kernel_size=1), |
||||
nn.GroupNorm(32, hidden_dim), |
||||
) |
||||
) |
||||
for _ in range(num_feature_levels - num_backbone_outs): |
||||
input_proj_list.append( |
||||
nn.Sequential( |
||||
nn.Conv2d(in_channels, hidden_dim, kernel_size=3, stride=2, padding=1), |
||||
nn.GroupNorm(32, hidden_dim), |
||||
) |
||||
) |
||||
in_channels = hidden_dim |
||||
self.input_proj = nn.ModuleList(input_proj_list) |
||||
else: |
||||
assert two_stage_type == "no", "two_stage_type should be no if num_feature_levels=1 !!!" |
||||
self.input_proj = nn.ModuleList( |
||||
[ |
||||
nn.Sequential( |
||||
nn.Conv2d(backbone.num_channels[-1], hidden_dim, kernel_size=1), |
||||
nn.GroupNorm(32, hidden_dim), |
||||
) |
||||
] |
||||
) |
||||
|
||||
self.backbone = backbone |
||||
self.aux_loss = aux_loss |
||||
self.box_pred_damping = box_pred_damping = None |
||||
|
||||
self.iter_update = iter_update |
||||
assert iter_update, "Why not iter_update?" |
||||
|
||||
# prepare pred layers |
||||
self.dec_pred_bbox_embed_share = dec_pred_bbox_embed_share |
||||
# prepare class & box embed |
||||
_class_embed = ContrastiveEmbed() |
||||
|
||||
_bbox_embed = MLP(hidden_dim, hidden_dim, 4, 3) |
||||
nn.init.constant_(_bbox_embed.layers[-1].weight.data, 0) |
||||
nn.init.constant_(_bbox_embed.layers[-1].bias.data, 0) |
||||
|
||||
if dec_pred_bbox_embed_share: |
||||
box_embed_layerlist = [_bbox_embed for i in range(transformer.num_decoder_layers)] |
||||
else: |
||||
box_embed_layerlist = [ |
||||
copy.deepcopy(_bbox_embed) for i in range(transformer.num_decoder_layers) |
||||
] |
||||
class_embed_layerlist = [_class_embed for i in range(transformer.num_decoder_layers)] |
||||
self.bbox_embed = nn.ModuleList(box_embed_layerlist) |
||||
self.class_embed = nn.ModuleList(class_embed_layerlist) |
||||
self.transformer.decoder.bbox_embed = self.bbox_embed |
||||
self.transformer.decoder.class_embed = self.class_embed |
||||
|
||||
# two stage |
||||
self.two_stage_type = two_stage_type |
||||
assert two_stage_type in ["no", "standard"], "unknown param {} of two_stage_type".format( |
||||
two_stage_type |
||||
) |
||||
if two_stage_type != "no": |
||||
if two_stage_bbox_embed_share: |
||||
assert dec_pred_bbox_embed_share |
||||
self.transformer.enc_out_bbox_embed = _bbox_embed |
||||
else: |
||||
self.transformer.enc_out_bbox_embed = copy.deepcopy(_bbox_embed) |
||||
|
||||
if two_stage_class_embed_share: |
||||
assert dec_pred_bbox_embed_share |
||||
self.transformer.enc_out_class_embed = _class_embed |
||||
else: |
||||
self.transformer.enc_out_class_embed = copy.deepcopy(_class_embed) |
||||
|
||||
self.refpoint_embed = None |
||||
|
||||
self._reset_parameters() |
||||
|
||||
def _reset_parameters(self): |
||||
# init input_proj |
||||
for proj in self.input_proj: |
||||
nn.init.xavier_uniform_(proj[0].weight, gain=1) |
||||
nn.init.constant_(proj[0].bias, 0) |
||||
|
||||
def init_ref_points(self, use_num_queries): |
||||
self.refpoint_embed = nn.Embedding(use_num_queries, self.query_dim) |
||||
|
||||
def forward(self, samples: NestedTensor, targets: List = None, **kw): |
||||
"""The forward expects a NestedTensor, which consists of: |
||||
- samples.tensor: batched images, of shape [batch_size x 3 x H x W] |
||||
- samples.mask: a binary mask of shape [batch_size x H x W], containing 1 on padded pixels |
||||
|
||||
It returns a dict with the following elements: |
||||
- "pred_logits": the classification logits (including no-object) for all queries. |
||||
Shape= [batch_size x num_queries x num_classes] |
||||
- "pred_boxes": The normalized boxes coordinates for all queries, represented as |
||||
(center_x, center_y, width, height). These values are normalized in [0, 1], |
||||
relative to the size of each individual image (disregarding possible padding). |
||||
See PostProcess for information on how to retrieve the unnormalized bounding box. |
||||
- "aux_outputs": Optional, only returned when auxilary losses are activated. It is a list of |
||||
dictionnaries containing the two above keys for each decoder layer. |
||||
""" |
||||
if targets is None: |
||||
captions = kw["captions"] |
||||
else: |
||||
captions = [t["caption"] for t in targets] |
||||
len(captions) |
||||
|
||||
# encoder texts |
||||
tokenized = self.tokenizer(captions, padding="longest", return_tensors="pt").to( |
||||
samples.device |
||||
) |
||||
( |
||||
text_self_attention_masks, |
||||
position_ids, |
||||
cate_to_token_mask_list, |
||||
) = generate_masks_with_special_tokens_and_transfer_map( |
||||
tokenized, self.specical_tokens, self.tokenizer |
||||
) |
||||
|
||||
if text_self_attention_masks.shape[1] > self.max_text_len: |
||||
text_self_attention_masks = text_self_attention_masks[ |
||||
:, : self.max_text_len, : self.max_text_len |
||||
] |
||||
position_ids = position_ids[:, : self.max_text_len] |
||||
tokenized["input_ids"] = tokenized["input_ids"][:, : self.max_text_len] |
||||
tokenized["attention_mask"] = tokenized["attention_mask"][:, : self.max_text_len] |
||||
tokenized["token_type_ids"] = tokenized["token_type_ids"][:, : self.max_text_len] |
||||
|
||||
# extract text embeddings |
||||
if self.sub_sentence_present: |
||||
tokenized_for_encoder = {k: v for k, v in tokenized.items() if k != "attention_mask"} |
||||
tokenized_for_encoder["attention_mask"] = text_self_attention_masks |
||||
tokenized_for_encoder["position_ids"] = position_ids |
||||
else: |
||||
# import ipdb; ipdb.set_trace() |
||||
tokenized_for_encoder = tokenized |
||||
|
||||
bert_output = self.bert(**tokenized_for_encoder) # bs, 195, 768 |
||||
|
||||
encoded_text = self.feat_map(bert_output["last_hidden_state"]) # bs, 195, d_model |
||||
text_token_mask = tokenized.attention_mask.bool() # bs, 195 |
||||
# text_token_mask: True for nomask, False for mask |
||||
# text_self_attention_masks: True for nomask, False for mask |
||||
|
||||
if encoded_text.shape[1] > self.max_text_len: |
||||
encoded_text = encoded_text[:, : self.max_text_len, :] |
||||
text_token_mask = text_token_mask[:, : self.max_text_len] |
||||
position_ids = position_ids[:, : self.max_text_len] |
||||
text_self_attention_masks = text_self_attention_masks[ |
||||
:, : self.max_text_len, : self.max_text_len |
||||
] |
||||
|
||||
text_dict = { |
||||
"encoded_text": encoded_text, # bs, 195, d_model |
||||
"text_token_mask": text_token_mask, # bs, 195 |
||||
"position_ids": position_ids, # bs, 195 |
||||
"text_self_attention_masks": text_self_attention_masks, # bs, 195,195 |
||||
} |
||||
|
||||
# import ipdb; ipdb.set_trace() |
||||
|
||||
if isinstance(samples, (list, torch.Tensor)): |
||||
samples = nested_tensor_from_tensor_list(samples) |
||||
features, poss = self.backbone(samples) |
||||
|
||||
srcs = [] |
||||
masks = [] |
||||
for l, feat in enumerate(features): |
||||
src, mask = feat.decompose() |
||||
srcs.append(self.input_proj[l](src)) |
||||
masks.append(mask) |
||||
assert mask is not None |
||||
if self.num_feature_levels > len(srcs): |
||||
_len_srcs = len(srcs) |
||||
for l in range(_len_srcs, self.num_feature_levels): |
||||
if l == _len_srcs: |
||||
src = self.input_proj[l](features[-1].tensors) |
||||
else: |
||||
src = self.input_proj[l](srcs[-1]) |
||||
m = samples.mask |
||||
mask = F.interpolate(m[None].float(), size=src.shape[-2:]).to(torch.bool)[0] |
||||
pos_l = self.backbone[1](NestedTensor(src, mask)).to(src.dtype) |
||||
srcs.append(src) |
||||
masks.append(mask) |
||||
poss.append(pos_l) |
||||
|
||||
input_query_bbox = input_query_label = attn_mask = dn_meta = None |
||||
hs, reference, hs_enc, ref_enc, init_box_proposal = self.transformer( |
||||
srcs, masks, input_query_bbox, poss, input_query_label, attn_mask, text_dict |
||||
) |
||||
|
||||
# deformable-detr-like anchor update |
||||
outputs_coord_list = [] |
||||
for dec_lid, (layer_ref_sig, layer_bbox_embed, layer_hs) in enumerate( |
||||
zip(reference[:-1], self.bbox_embed, hs) |
||||
): |
||||
layer_delta_unsig = layer_bbox_embed(layer_hs) |
||||
layer_outputs_unsig = layer_delta_unsig + inverse_sigmoid(layer_ref_sig) |
||||
layer_outputs_unsig = layer_outputs_unsig.sigmoid() |
||||
outputs_coord_list.append(layer_outputs_unsig) |
||||
outputs_coord_list = torch.stack(outputs_coord_list) |
||||
|
||||
# output |
||||
outputs_class = torch.stack( |
||||
[ |
||||
layer_cls_embed(layer_hs, text_dict) |
||||
for layer_cls_embed, layer_hs in zip(self.class_embed, hs) |
||||
] |
||||
) |
||||
out = {"pred_logits": outputs_class[-1], "pred_boxes": outputs_coord_list[-1]} |
||||
|
||||
# # for intermediate outputs |
||||
# if self.aux_loss: |
||||
# out['aux_outputs'] = self._set_aux_loss(outputs_class, outputs_coord_list) |
||||
|
||||
# # for encoder output |
||||
# if hs_enc is not None: |
||||
# # prepare intermediate outputs |
||||
# interm_coord = ref_enc[-1] |
||||
# interm_class = self.transformer.enc_out_class_embed(hs_enc[-1], text_dict) |
||||
# out['interm_outputs'] = {'pred_logits': interm_class, 'pred_boxes': interm_coord} |
||||
# out['interm_outputs_for_matching_pre'] = {'pred_logits': interm_class, 'pred_boxes': init_box_proposal} |
||||
|
||||
return out |
||||
|
||||
@torch.jit.unused |
||||
def _set_aux_loss(self, outputs_class, outputs_coord): |
||||
# this is a workaround to make torchscript happy, as torchscript |
||||
# doesn't support dictionary with non-homogeneous values, such |
||||
# as a dict having both a Tensor and a list. |
||||
return [ |
||||
{"pred_logits": a, "pred_boxes": b} |
||||
for a, b in zip(outputs_class[:-1], outputs_coord[:-1]) |
||||
] |
||||
|
||||
|
||||
@MODULE_BUILD_FUNCS.registe_with_name(module_name="groundingdino") |
||||
def build_groundingdino(args): |
||||
|
||||
backbone = build_backbone(args) |
||||
transformer = build_transformer(args) |
||||
|
||||
dn_labelbook_size = args.dn_labelbook_size |
||||
dec_pred_bbox_embed_share = args.dec_pred_bbox_embed_share |
||||
sub_sentence_present = args.sub_sentence_present |
||||
|
||||
model = GroundingDINO( |
||||
backbone, |
||||
transformer, |
||||
num_queries=args.num_queries, |
||||
aux_loss=True, |
||||
iter_update=True, |
||||
query_dim=4, |
||||
num_feature_levels=args.num_feature_levels, |
||||
nheads=args.nheads, |
||||
dec_pred_bbox_embed_share=dec_pred_bbox_embed_share, |
||||
two_stage_type=args.two_stage_type, |
||||
two_stage_bbox_embed_share=args.two_stage_bbox_embed_share, |
||||
two_stage_class_embed_share=args.two_stage_class_embed_share, |
||||
num_patterns=args.num_patterns, |
||||
dn_number=0, |
||||
dn_box_noise_scale=args.dn_box_noise_scale, |
||||
dn_label_noise_ratio=args.dn_label_noise_ratio, |
||||
dn_labelbook_size=dn_labelbook_size, |
||||
text_encoder_type=args.text_encoder_type, |
||||
sub_sentence_present=sub_sentence_present, |
||||
max_text_len=args.max_text_len, |
||||
) |
||||
|
||||
return model |
||||
@ -0,0 +1,413 @@ |
||||
# ------------------------------------------------------------------------ |
||||
# Grounding DINO |
||||
# url: https://github.com/IDEA-Research/GroundingDINO |
||||
# Copyright (c) 2023 IDEA. All Rights Reserved. |
||||
# Licensed under the Apache License, Version 2.0 [see LICENSE for details] |
||||
# ------------------------------------------------------------------------ |
||||
# Deformable DETR |
||||
# Copyright (c) 2020 SenseTime. All Rights Reserved. |
||||
# Licensed under the Apache License, Version 2.0 [see LICENSE for details] |
||||
# ------------------------------------------------------------------------------------------------ |
||||
# Modified from: |
||||
# https://github.com/fundamentalvision/Deformable-DETR/blob/main/models/ops/functions/ms_deform_attn_func.py |
||||
# https://github.com/fundamentalvision/Deformable-DETR/blob/main/models/ops/modules/ms_deform_attn.py |
||||
# https://github.com/open-mmlab/mmcv/blob/master/mmcv/ops/multi_scale_deform_attn.py |
||||
# ------------------------------------------------------------------------------------------------ |
||||
|
||||
import math |
||||
import warnings |
||||
from typing import Optional |
||||
|
||||
import torch |
||||
import torch.nn as nn |
||||
import torch.nn.functional as F |
||||
from torch.autograd import Function |
||||
from torch.autograd.function import once_differentiable |
||||
from torch.nn.init import constant_, xavier_uniform_ |
||||
|
||||
try: |
||||
from groundingdino import _C |
||||
except: |
||||
warnings.warn("Failed to load custom C++ ops. Running on CPU mode Only!") |
||||
|
||||
|
||||
# helpers |
||||
def _is_power_of_2(n): |
||||
if (not isinstance(n, int)) or (n < 0): |
||||
raise ValueError("invalid input for _is_power_of_2: {} (type: {})".format(n, type(n))) |
||||
return (n & (n - 1) == 0) and n != 0 |
||||
|
||||
|
||||
class MultiScaleDeformableAttnFunction(Function): |
||||
@staticmethod |
||||
def forward( |
||||
ctx, |
||||
value, |
||||
value_spatial_shapes, |
||||
value_level_start_index, |
||||
sampling_locations, |
||||
attention_weights, |
||||
im2col_step, |
||||
): |
||||
ctx.im2col_step = im2col_step |
||||
output = _C.ms_deform_attn_forward( |
||||
value, |
||||
value_spatial_shapes, |
||||
value_level_start_index, |
||||
sampling_locations, |
||||
attention_weights, |
||||
ctx.im2col_step, |
||||
) |
||||
ctx.save_for_backward( |
||||
value, |
||||
value_spatial_shapes, |
||||
value_level_start_index, |
||||
sampling_locations, |
||||
attention_weights, |
||||
) |
||||
return output |
||||
|
||||
@staticmethod |
||||
@once_differentiable |
||||
def backward(ctx, grad_output): |
||||
( |
||||
value, |
||||
value_spatial_shapes, |
||||
value_level_start_index, |
||||
sampling_locations, |
||||
attention_weights, |
||||
) = ctx.saved_tensors |
||||
grad_value, grad_sampling_loc, grad_attn_weight = _C.ms_deform_attn_backward( |
||||
value, |
||||
value_spatial_shapes, |
||||
value_level_start_index, |
||||
sampling_locations, |
||||
attention_weights, |
||||
grad_output, |
||||
ctx.im2col_step, |
||||
) |
||||
|
||||
return grad_value, None, None, grad_sampling_loc, grad_attn_weight, None |
||||
|
||||
|
||||
def multi_scale_deformable_attn_pytorch( |
||||
value: torch.Tensor, |
||||
value_spatial_shapes: torch.Tensor, |
||||
sampling_locations: torch.Tensor, |
||||
attention_weights: torch.Tensor, |
||||
) -> torch.Tensor: |
||||
|
||||
bs, _, num_heads, embed_dims = value.shape |
||||
_, num_queries, num_heads, num_levels, num_points, _ = sampling_locations.shape |
||||
value_list = value.split([H_ * W_ for H_, W_ in value_spatial_shapes], dim=1) |
||||
sampling_grids = 2 * sampling_locations - 1 |
||||
sampling_value_list = [] |
||||
for level, (H_, W_) in enumerate(value_spatial_shapes): |
||||
# bs, H_*W_, num_heads, embed_dims -> |
||||
# bs, H_*W_, num_heads*embed_dims -> |
||||
# bs, num_heads*embed_dims, H_*W_ -> |
||||
# bs*num_heads, embed_dims, H_, W_ |
||||
value_l_ = ( |
||||
value_list[level].flatten(2).transpose(1, 2).reshape(bs * num_heads, embed_dims, H_, W_) |
||||
) |
||||
# bs, num_queries, num_heads, num_points, 2 -> |
||||
# bs, num_heads, num_queries, num_points, 2 -> |
||||
# bs*num_heads, num_queries, num_points, 2 |
||||
sampling_grid_l_ = sampling_grids[:, :, :, level].transpose(1, 2).flatten(0, 1) |
||||
# bs*num_heads, embed_dims, num_queries, num_points |
||||
sampling_value_l_ = F.grid_sample( |
||||
value_l_, sampling_grid_l_, mode="bilinear", padding_mode="zeros", align_corners=False |
||||
) |
||||
sampling_value_list.append(sampling_value_l_) |
||||
# (bs, num_queries, num_heads, num_levels, num_points) -> |
||||
# (bs, num_heads, num_queries, num_levels, num_points) -> |
||||
# (bs, num_heads, 1, num_queries, num_levels*num_points) |
||||
attention_weights = attention_weights.transpose(1, 2).reshape( |
||||
bs * num_heads, 1, num_queries, num_levels * num_points |
||||
) |
||||
output = ( |
||||
(torch.stack(sampling_value_list, dim=-2).flatten(-2) * attention_weights) |
||||
.sum(-1) |
||||
.view(bs, num_heads * embed_dims, num_queries) |
||||
) |
||||
return output.transpose(1, 2).contiguous() |
||||
|
||||
|
||||
class MultiScaleDeformableAttention(nn.Module): |
||||
"""Multi-Scale Deformable Attention Module used in Deformable-DETR |
||||
|
||||
`Deformable DETR: Deformable Transformers for End-to-End Object Detection. |
||||
<https://arxiv.org/pdf/2010.04159.pdf>`_. |
||||
|
||||
Args: |
||||
embed_dim (int): The embedding dimension of Attention. Default: 256. |
||||
num_heads (int): The number of attention heads. Default: 8. |
||||
num_levels (int): The number of feature map used in Attention. Default: 4. |
||||
num_points (int): The number of sampling points for each query |
||||
in each head. Default: 4. |
||||
img2col_steps (int): The step used in image_to_column. Defualt: 64. |
||||
dropout (float): Dropout layer used in output. Default: 0.1. |
||||
batch_first (bool): if ``True``, then the input and output tensor will be |
||||
provided as `(bs, n, embed_dim)`. Default: False. `(n, bs, embed_dim)` |
||||
""" |
||||
|
||||
def __init__( |
||||
self, |
||||
embed_dim: int = 256, |
||||
num_heads: int = 8, |
||||
num_levels: int = 4, |
||||
num_points: int = 4, |
||||
img2col_step: int = 64, |
||||
batch_first: bool = False, |
||||
): |
||||
super().__init__() |
||||
if embed_dim % num_heads != 0: |
||||
raise ValueError( |
||||
"embed_dim must be divisible by num_heads, but got {} and {}".format( |
||||
embed_dim, num_heads |
||||
) |
||||
) |
||||
head_dim = embed_dim // num_heads |
||||
|
||||
self.batch_first = batch_first |
||||
|
||||
if not _is_power_of_2(head_dim): |
||||
warnings.warn( |
||||
""" |
||||
You'd better set d_model in MSDeformAttn to make sure that |
||||
each dim of the attention head a power of 2, which is more efficient. |
||||
""" |
||||
) |
||||
|
||||
self.im2col_step = img2col_step |
||||
self.embed_dim = embed_dim |
||||
self.num_heads = num_heads |
||||
self.num_levels = num_levels |
||||
self.num_points = num_points |
||||
self.sampling_offsets = nn.Linear(embed_dim, num_heads * num_levels * num_points * 2) |
||||
self.attention_weights = nn.Linear(embed_dim, num_heads * num_levels * num_points) |
||||
self.value_proj = nn.Linear(embed_dim, embed_dim) |
||||
self.output_proj = nn.Linear(embed_dim, embed_dim) |
||||
|
||||
self.init_weights() |
||||
|
||||
def _reset_parameters(self): |
||||
return self.init_weights() |
||||
|
||||
def init_weights(self): |
||||
""" |
||||
Default initialization for Parameters of Module. |
||||
""" |
||||
constant_(self.sampling_offsets.weight.data, 0.0) |
||||
thetas = torch.arange(self.num_heads, dtype=torch.float32) * ( |
||||
2.0 * math.pi / self.num_heads |
||||
) |
||||
grid_init = torch.stack([thetas.cos(), thetas.sin()], -1) |
||||
grid_init = ( |
||||
(grid_init / grid_init.abs().max(-1, keepdim=True)[0]) |
||||
.view(self.num_heads, 1, 1, 2) |
||||
.repeat(1, self.num_levels, self.num_points, 1) |
||||
) |
||||
for i in range(self.num_points): |
||||
grid_init[:, :, i, :] *= i + 1 |
||||
with torch.no_grad(): |
||||
self.sampling_offsets.bias = nn.Parameter(grid_init.view(-1)) |
||||
constant_(self.attention_weights.weight.data, 0.0) |
||||
constant_(self.attention_weights.bias.data, 0.0) |
||||
xavier_uniform_(self.value_proj.weight.data) |
||||
constant_(self.value_proj.bias.data, 0.0) |
||||
xavier_uniform_(self.output_proj.weight.data) |
||||
constant_(self.output_proj.bias.data, 0.0) |
||||
|
||||
def freeze_sampling_offsets(self): |
||||
print("Freeze sampling offsets") |
||||
self.sampling_offsets.weight.requires_grad = False |
||||
self.sampling_offsets.bias.requires_grad = False |
||||
|
||||
def freeze_attention_weights(self): |
||||
print("Freeze attention weights") |
||||
self.attention_weights.weight.requires_grad = False |
||||
self.attention_weights.bias.requires_grad = False |
||||
|
||||
def forward( |
||||
self, |
||||
query: torch.Tensor, |
||||
key: Optional[torch.Tensor] = None, |
||||
value: Optional[torch.Tensor] = None, |
||||
query_pos: Optional[torch.Tensor] = None, |
||||
key_padding_mask: Optional[torch.Tensor] = None, |
||||
reference_points: Optional[torch.Tensor] = None, |
||||
spatial_shapes: Optional[torch.Tensor] = None, |
||||
level_start_index: Optional[torch.Tensor] = None, |
||||
**kwargs |
||||
) -> torch.Tensor: |
||||
|
||||
"""Forward Function of MultiScaleDeformableAttention |
||||
|
||||
Args: |
||||
query (torch.Tensor): Query embeddings with shape |
||||
`(num_query, bs, embed_dim)` |
||||
key (torch.Tensor): Key embeddings with shape |
||||
`(num_key, bs, embed_dim)` |
||||
value (torch.Tensor): Value embeddings with shape |
||||
`(num_key, bs, embed_dim)` |
||||
query_pos (torch.Tensor): The position embedding for `query`. Default: None. |
||||
key_padding_mask (torch.Tensor): ByteTensor for `query`, with shape `(bs, num_key)`, |
||||
indicating which elements within `key` to be ignored in attention. |
||||
reference_points (torch.Tensor): The normalized reference points |
||||
with shape `(bs, num_query, num_levels, 2)`, |
||||
all elements is range in [0, 1], top-left (0, 0), |
||||
bottom-right (1, 1), including padding are. |
||||
or `(N, Length_{query}, num_levels, 4)`, add additional |
||||
two dimensions `(h, w)` to form reference boxes. |
||||
spatial_shapes (torch.Tensor): Spatial shape of features in different levels. |
||||
With shape `(num_levels, 2)`, last dimension represents `(h, w)`. |
||||
level_start_index (torch.Tensor): The start index of each level. A tensor with |
||||
shape `(num_levels, )` which can be represented as |
||||
`[0, h_0 * w_0, h_0 * w_0 + h_1 * w_1, ...]`. |
||||
|
||||
Returns: |
||||
torch.Tensor: forward results with shape `(num_query, bs, embed_dim)` |
||||
""" |
||||
|
||||
if value is None: |
||||
value = query |
||||
|
||||
if query_pos is not None: |
||||
query = query + query_pos |
||||
|
||||
if not self.batch_first: |
||||
# change to (bs, num_query ,embed_dims) |
||||
query = query.permute(1, 0, 2) |
||||
value = value.permute(1, 0, 2) |
||||
|
||||
bs, num_query, _ = query.shape |
||||
bs, num_value, _ = value.shape |
||||
|
||||
assert (spatial_shapes[:, 0] * spatial_shapes[:, 1]).sum() == num_value |
||||
|
||||
value = self.value_proj(value) |
||||
if key_padding_mask is not None: |
||||
value = value.masked_fill(key_padding_mask[..., None], float(0)) |
||||
value = value.view(bs, num_value, self.num_heads, -1) |
||||
sampling_offsets = self.sampling_offsets(query).view( |
||||
bs, num_query, self.num_heads, self.num_levels, self.num_points, 2 |
||||
) |
||||
attention_weights = self.attention_weights(query).view( |
||||
bs, num_query, self.num_heads, self.num_levels * self.num_points |
||||
) |
||||
attention_weights = attention_weights.softmax(-1) |
||||
attention_weights = attention_weights.view( |
||||
bs, |
||||
num_query, |
||||
self.num_heads, |
||||
self.num_levels, |
||||
self.num_points, |
||||
) |
||||
|
||||
# bs, num_query, num_heads, num_levels, num_points, 2 |
||||
if reference_points.shape[-1] == 2: |
||||
offset_normalizer = torch.stack([spatial_shapes[..., 1], spatial_shapes[..., 0]], -1) |
||||
sampling_locations = ( |
||||
reference_points[:, :, None, :, None, :] |
||||
+ sampling_offsets / offset_normalizer[None, None, None, :, None, :] |
||||
) |
||||
elif reference_points.shape[-1] == 4: |
||||
sampling_locations = ( |
||||
reference_points[:, :, None, :, None, :2] |
||||
+ sampling_offsets |
||||
/ self.num_points |
||||
* reference_points[:, :, None, :, None, 2:] |
||||
* 0.5 |
||||
) |
||||
else: |
||||
raise ValueError( |
||||
"Last dim of reference_points must be 2 or 4, but get {} instead.".format( |
||||
reference_points.shape[-1] |
||||
) |
||||
) |
||||
|
||||
if torch.cuda.is_available() and value.is_cuda: |
||||
halffloat = False |
||||
if value.dtype == torch.float16: |
||||
halffloat = True |
||||
value = value.float() |
||||
sampling_locations = sampling_locations.float() |
||||
attention_weights = attention_weights.float() |
||||
|
||||
output = MultiScaleDeformableAttnFunction.apply( |
||||
value, |
||||
spatial_shapes, |
||||
level_start_index, |
||||
sampling_locations, |
||||
attention_weights, |
||||
self.im2col_step, |
||||
) |
||||
|
||||
if halffloat: |
||||
output = output.half() |
||||
else: |
||||
output = multi_scale_deformable_attn_pytorch( |
||||
value, spatial_shapes, sampling_locations, attention_weights |
||||
) |
||||
|
||||
output = self.output_proj(output) |
||||
|
||||
if not self.batch_first: |
||||
output = output.permute(1, 0, 2) |
||||
|
||||
return output |
||||
|
||||
|
||||
def create_dummy_class(klass, dependency, message=""): |
||||
""" |
||||
When a dependency of a class is not available, create a dummy class which throws ImportError |
||||
when used. |
||||
|
||||
Args: |
||||
klass (str): name of the class. |
||||
dependency (str): name of the dependency. |
||||
message: extra message to print |
||||
Returns: |
||||
class: a class object |
||||
""" |
||||
err = "Cannot import '{}', therefore '{}' is not available.".format(dependency, klass) |
||||
if message: |
||||
err = err + " " + message |
||||
|
||||
class _DummyMetaClass(type): |
||||
# throw error on class attribute access |
||||
def __getattr__(_, __): # noqa: B902 |
||||
raise ImportError(err) |
||||
|
||||
class _Dummy(object, metaclass=_DummyMetaClass): |
||||
# throw error on constructor |
||||
def __init__(self, *args, **kwargs): |
||||
raise ImportError(err) |
||||
|
||||
return _Dummy |
||||
|
||||
|
||||
def create_dummy_func(func, dependency, message=""): |
||||
""" |
||||
When a dependency of a function is not available, create a dummy function which throws |
||||
ImportError when used. |
||||
|
||||
Args: |
||||
func (str): name of the function. |
||||
dependency (str or list[str]): name(s) of the dependency. |
||||
message: extra message to print |
||||
Returns: |
||||
function: a function object |
||||
""" |
||||
err = "Cannot import '{}', therefore '{}' is not available.".format(dependency, func) |
||||
if message: |
||||
err = err + " " + message |
||||
|
||||
if isinstance(dependency, (list, tuple)): |
||||
dependency = ",".join(dependency) |
||||
|
||||
def _dummy(*args, **kwargs): |
||||
raise ImportError(err) |
||||
|
||||
return _dummy |
||||
@ -0,0 +1,959 @@ |
||||
# ------------------------------------------------------------------------ |
||||
# Grounding DINO |
||||
# url: https://github.com/IDEA-Research/GroundingDINO |
||||
# Copyright (c) 2023 IDEA. All Rights Reserved. |
||||
# Licensed under the Apache License, Version 2.0 [see LICENSE for details] |
||||
# ------------------------------------------------------------------------ |
||||
# DINO |
||||
# Copyright (c) 2022 IDEA. All Rights Reserved. |
||||
# Licensed under the Apache License, Version 2.0 [see LICENSE for details] |
||||
# ------------------------------------------------------------------------ |
||||
# Conditional DETR Transformer class. |
||||
# Copyright (c) 2021 Microsoft. All Rights Reserved. |
||||
# Licensed under the Apache License, Version 2.0 [see LICENSE for details] |
||||
# ------------------------------------------------------------------------ |
||||
# Modified from DETR (https://github.com/facebookresearch/detr) |
||||
# Copyright (c) Facebook, Inc. and its affiliates. All Rights Reserved. |
||||
# ------------------------------------------------------------------------ |
||||
|
||||
from typing import Optional |
||||
|
||||
import torch |
||||
import torch.utils.checkpoint as checkpoint |
||||
from torch import Tensor, nn |
||||
|
||||
from groundingdino.util.misc import inverse_sigmoid |
||||
|
||||
from .fuse_modules import BiAttentionBlock |
||||
from .ms_deform_attn import MultiScaleDeformableAttention as MSDeformAttn |
||||
from .transformer_vanilla import TransformerEncoderLayer |
||||
from .utils import ( |
||||
MLP, |
||||
_get_activation_fn, |
||||
_get_clones, |
||||
gen_encoder_output_proposals, |
||||
gen_sineembed_for_position, |
||||
get_sine_pos_embed, |
||||
) |
||||
|
||||
|
||||
class Transformer(nn.Module): |
||||
def __init__( |
||||
self, |
||||
d_model=256, |
||||
nhead=8, |
||||
num_queries=300, |
||||
num_encoder_layers=6, |
||||
num_unicoder_layers=0, |
||||
num_decoder_layers=6, |
||||
dim_feedforward=2048, |
||||
dropout=0.0, |
||||
activation="relu", |
||||
normalize_before=False, |
||||
return_intermediate_dec=False, |
||||
query_dim=4, |
||||
num_patterns=0, |
||||
# for deformable encoder |
||||
num_feature_levels=1, |
||||
enc_n_points=4, |
||||
dec_n_points=4, |
||||
# init query |
||||
learnable_tgt_init=False, |
||||
# two stage |
||||
two_stage_type="no", # ['no', 'standard', 'early', 'combine', 'enceachlayer', 'enclayer1'] |
||||
embed_init_tgt=False, |
||||
# for text |
||||
use_text_enhancer=False, |
||||
use_fusion_layer=False, |
||||
use_checkpoint=False, |
||||
use_transformer_ckpt=False, |
||||
use_text_cross_attention=False, |
||||
text_dropout=0.1, |
||||
fusion_dropout=0.1, |
||||
fusion_droppath=0.0, |
||||
): |
||||
super().__init__() |
||||
self.num_feature_levels = num_feature_levels |
||||
self.num_encoder_layers = num_encoder_layers |
||||
self.num_unicoder_layers = num_unicoder_layers |
||||
self.num_decoder_layers = num_decoder_layers |
||||
self.num_queries = num_queries |
||||
assert query_dim == 4 |
||||
|
||||
# choose encoder layer type |
||||
encoder_layer = DeformableTransformerEncoderLayer( |
||||
d_model, dim_feedforward, dropout, activation, num_feature_levels, nhead, enc_n_points |
||||
) |
||||
|
||||
if use_text_enhancer: |
||||
text_enhance_layer = TransformerEncoderLayer( |
||||
d_model=d_model, |
||||
nhead=nhead // 2, |
||||
dim_feedforward=dim_feedforward // 2, |
||||
dropout=text_dropout, |
||||
) |
||||
else: |
||||
text_enhance_layer = None |
||||
|
||||
if use_fusion_layer: |
||||
feature_fusion_layer = BiAttentionBlock( |
||||
v_dim=d_model, |
||||
l_dim=d_model, |
||||
embed_dim=dim_feedforward // 2, |
||||
num_heads=nhead // 2, |
||||
dropout=fusion_dropout, |
||||
drop_path=fusion_droppath, |
||||
) |
||||
else: |
||||
feature_fusion_layer = None |
||||
|
||||
encoder_norm = nn.LayerNorm(d_model) if normalize_before else None |
||||
assert encoder_norm is None |
||||
self.encoder = TransformerEncoder( |
||||
encoder_layer, |
||||
num_encoder_layers, |
||||
d_model=d_model, |
||||
num_queries=num_queries, |
||||
text_enhance_layer=text_enhance_layer, |
||||
feature_fusion_layer=feature_fusion_layer, |
||||
use_checkpoint=use_checkpoint, |
||||
use_transformer_ckpt=use_transformer_ckpt, |
||||
) |
||||
|
||||
# choose decoder layer type |
||||
decoder_layer = DeformableTransformerDecoderLayer( |
||||
d_model, |
||||
dim_feedforward, |
||||
dropout, |
||||
activation, |
||||
num_feature_levels, |
||||
nhead, |
||||
dec_n_points, |
||||
use_text_cross_attention=use_text_cross_attention, |
||||
) |
||||
|
||||
decoder_norm = nn.LayerNorm(d_model) |
||||
self.decoder = TransformerDecoder( |
||||
decoder_layer, |
||||
num_decoder_layers, |
||||
decoder_norm, |
||||
return_intermediate=return_intermediate_dec, |
||||
d_model=d_model, |
||||
query_dim=query_dim, |
||||
num_feature_levels=num_feature_levels, |
||||
) |
||||
|
||||
self.d_model = d_model |
||||
self.nhead = nhead |
||||
self.dec_layers = num_decoder_layers |
||||
self.num_queries = num_queries # useful for single stage model only |
||||
self.num_patterns = num_patterns |
||||
if not isinstance(num_patterns, int): |
||||
Warning("num_patterns should be int but {}".format(type(num_patterns))) |
||||
self.num_patterns = 0 |
||||
|
||||
if num_feature_levels > 1: |
||||
if self.num_encoder_layers > 0: |
||||
self.level_embed = nn.Parameter(torch.Tensor(num_feature_levels, d_model)) |
||||
else: |
||||
self.level_embed = None |
||||
|
||||
self.learnable_tgt_init = learnable_tgt_init |
||||
assert learnable_tgt_init, "why not learnable_tgt_init" |
||||
self.embed_init_tgt = embed_init_tgt |
||||
if (two_stage_type != "no" and embed_init_tgt) or (two_stage_type == "no"): |
||||
self.tgt_embed = nn.Embedding(self.num_queries, d_model) |
||||
nn.init.normal_(self.tgt_embed.weight.data) |
||||
else: |
||||
self.tgt_embed = None |
||||
|
||||
# for two stage |
||||
self.two_stage_type = two_stage_type |
||||
assert two_stage_type in ["no", "standard"], "unknown param {} of two_stage_type".format( |
||||
two_stage_type |
||||
) |
||||
if two_stage_type == "standard": |
||||
# anchor selection at the output of encoder |
||||
self.enc_output = nn.Linear(d_model, d_model) |
||||
self.enc_output_norm = nn.LayerNorm(d_model) |
||||
self.two_stage_wh_embedding = None |
||||
|
||||
if two_stage_type == "no": |
||||
self.init_ref_points(num_queries) # init self.refpoint_embed |
||||
|
||||
self.enc_out_class_embed = None |
||||
self.enc_out_bbox_embed = None |
||||
|
||||
self._reset_parameters() |
||||
|
||||
def _reset_parameters(self): |
||||
for p in self.parameters(): |
||||
if p.dim() > 1: |
||||
nn.init.xavier_uniform_(p) |
||||
for m in self.modules(): |
||||
if isinstance(m, MSDeformAttn): |
||||
m._reset_parameters() |
||||
if self.num_feature_levels > 1 and self.level_embed is not None: |
||||
nn.init.normal_(self.level_embed) |
||||
|
||||
def get_valid_ratio(self, mask): |
||||
_, H, W = mask.shape |
||||
valid_H = torch.sum(~mask[:, :, 0], 1) |
||||
valid_W = torch.sum(~mask[:, 0, :], 1) |
||||
valid_ratio_h = valid_H.float() / H |
||||
valid_ratio_w = valid_W.float() / W |
||||
valid_ratio = torch.stack([valid_ratio_w, valid_ratio_h], -1) |
||||
return valid_ratio |
||||
|
||||
def init_ref_points(self, use_num_queries): |
||||
self.refpoint_embed = nn.Embedding(use_num_queries, 4) |
||||
|
||||
def forward(self, srcs, masks, refpoint_embed, pos_embeds, tgt, attn_mask=None, text_dict=None): |
||||
""" |
||||
Input: |
||||
- srcs: List of multi features [bs, ci, hi, wi] |
||||
- masks: List of multi masks [bs, hi, wi] |
||||
- refpoint_embed: [bs, num_dn, 4]. None in infer |
||||
- pos_embeds: List of multi pos embeds [bs, ci, hi, wi] |
||||
- tgt: [bs, num_dn, d_model]. None in infer |
||||
|
||||
""" |
||||
# prepare input for encoder |
||||
src_flatten = [] |
||||
mask_flatten = [] |
||||
lvl_pos_embed_flatten = [] |
||||
spatial_shapes = [] |
||||
for lvl, (src, mask, pos_embed) in enumerate(zip(srcs, masks, pos_embeds)): |
||||
bs, c, h, w = src.shape |
||||
spatial_shape = (h, w) |
||||
spatial_shapes.append(spatial_shape) |
||||
|
||||
src = src.flatten(2).transpose(1, 2) # bs, hw, c |
||||
mask = mask.flatten(1) # bs, hw |
||||
pos_embed = pos_embed.flatten(2).transpose(1, 2) # bs, hw, c |
||||
if self.num_feature_levels > 1 and self.level_embed is not None: |
||||
lvl_pos_embed = pos_embed + self.level_embed[lvl].view(1, 1, -1) |
||||
else: |
||||
lvl_pos_embed = pos_embed |
||||
lvl_pos_embed_flatten.append(lvl_pos_embed) |
||||
src_flatten.append(src) |
||||
mask_flatten.append(mask) |
||||
src_flatten = torch.cat(src_flatten, 1) # bs, \sum{hxw}, c |
||||
mask_flatten = torch.cat(mask_flatten, 1) # bs, \sum{hxw} |
||||
lvl_pos_embed_flatten = torch.cat(lvl_pos_embed_flatten, 1) # bs, \sum{hxw}, c |
||||
spatial_shapes = torch.as_tensor( |
||||
spatial_shapes, dtype=torch.long, device=src_flatten.device |
||||
) |
||||
level_start_index = torch.cat( |
||||
(spatial_shapes.new_zeros((1,)), spatial_shapes.prod(1).cumsum(0)[:-1]) |
||||
) |
||||
valid_ratios = torch.stack([self.get_valid_ratio(m) for m in masks], 1) |
||||
|
||||
# two stage |
||||
enc_topk_proposals = enc_refpoint_embed = None |
||||
|
||||
######################################################### |
||||
# Begin Encoder |
||||
######################################################### |
||||
memory, memory_text = self.encoder( |
||||
src_flatten, |
||||
pos=lvl_pos_embed_flatten, |
||||
level_start_index=level_start_index, |
||||
spatial_shapes=spatial_shapes, |
||||
valid_ratios=valid_ratios, |
||||
key_padding_mask=mask_flatten, |
||||
memory_text=text_dict["encoded_text"], |
||||
text_attention_mask=~text_dict["text_token_mask"], |
||||
# we ~ the mask . False means use the token; True means pad the token |
||||
position_ids=text_dict["position_ids"], |
||||
text_self_attention_masks=text_dict["text_self_attention_masks"], |
||||
) |
||||
######################################################### |
||||
# End Encoder |
||||
# - memory: bs, \sum{hw}, c |
||||
# - mask_flatten: bs, \sum{hw} |
||||
# - lvl_pos_embed_flatten: bs, \sum{hw}, c |
||||
# - enc_intermediate_output: None or (nenc+1, bs, nq, c) or (nenc, bs, nq, c) |
||||
# - enc_intermediate_refpoints: None or (nenc+1, bs, nq, c) or (nenc, bs, nq, c) |
||||
######################################################### |
||||
text_dict["encoded_text"] = memory_text |
||||
# if os.environ.get("SHILONG_AMP_INFNAN_DEBUG") == '1': |
||||
# if memory.isnan().any() | memory.isinf().any(): |
||||
# import ipdb; ipdb.set_trace() |
||||
|
||||
if self.two_stage_type == "standard": |
||||
output_memory, output_proposals = gen_encoder_output_proposals( |
||||
memory, mask_flatten, spatial_shapes |
||||
) |
||||
output_memory = self.enc_output_norm(self.enc_output(output_memory)) |
||||
|
||||
if text_dict is not None: |
||||
enc_outputs_class_unselected = self.enc_out_class_embed(output_memory, text_dict) |
||||
else: |
||||
enc_outputs_class_unselected = self.enc_out_class_embed(output_memory) |
||||
|
||||
topk_logits = enc_outputs_class_unselected.max(-1)[0] |
||||
enc_outputs_coord_unselected = ( |
||||
self.enc_out_bbox_embed(output_memory) + output_proposals |
||||
) # (bs, \sum{hw}, 4) unsigmoid |
||||
topk = self.num_queries |
||||
|
||||
topk_proposals = torch.topk(topk_logits, topk, dim=1)[1] # bs, nq |
||||
|
||||
# gather boxes |
||||
refpoint_embed_undetach = torch.gather( |
||||
enc_outputs_coord_unselected, 1, topk_proposals.unsqueeze(-1).repeat(1, 1, 4) |
||||
) # unsigmoid |
||||
refpoint_embed_ = refpoint_embed_undetach.detach() |
||||
init_box_proposal = torch.gather( |
||||
output_proposals, 1, topk_proposals.unsqueeze(-1).repeat(1, 1, 4) |
||||
).sigmoid() # sigmoid |
||||
|
||||
# gather tgt |
||||
tgt_undetach = torch.gather( |
||||
output_memory, 1, topk_proposals.unsqueeze(-1).repeat(1, 1, self.d_model) |
||||
) |
||||
if self.embed_init_tgt: |
||||
tgt_ = ( |
||||
self.tgt_embed.weight[:, None, :].repeat(1, bs, 1).transpose(0, 1) |
||||
) # nq, bs, d_model |
||||
else: |
||||
tgt_ = tgt_undetach.detach() |
||||
|
||||
if refpoint_embed is not None: |
||||
refpoint_embed = torch.cat([refpoint_embed, refpoint_embed_], dim=1) |
||||
tgt = torch.cat([tgt, tgt_], dim=1) |
||||
else: |
||||
refpoint_embed, tgt = refpoint_embed_, tgt_ |
||||
|
||||
elif self.two_stage_type == "no": |
||||
tgt_ = ( |
||||
self.tgt_embed.weight[:, None, :].repeat(1, bs, 1).transpose(0, 1) |
||||
) # nq, bs, d_model |
||||
refpoint_embed_ = ( |
||||
self.refpoint_embed.weight[:, None, :].repeat(1, bs, 1).transpose(0, 1) |
||||
) # nq, bs, 4 |
||||
|
||||
if refpoint_embed is not None: |
||||
refpoint_embed = torch.cat([refpoint_embed, refpoint_embed_], dim=1) |
||||
tgt = torch.cat([tgt, tgt_], dim=1) |
||||
else: |
||||
refpoint_embed, tgt = refpoint_embed_, tgt_ |
||||
|
||||
if self.num_patterns > 0: |
||||
tgt_embed = tgt.repeat(1, self.num_patterns, 1) |
||||
refpoint_embed = refpoint_embed.repeat(1, self.num_patterns, 1) |
||||
tgt_pat = self.patterns.weight[None, :, :].repeat_interleave( |
||||
self.num_queries, 1 |
||||
) # 1, n_q*n_pat, d_model |
||||
tgt = tgt_embed + tgt_pat |
||||
|
||||
init_box_proposal = refpoint_embed_.sigmoid() |
||||
|
||||
else: |
||||
raise NotImplementedError("unknown two_stage_type {}".format(self.two_stage_type)) |
||||
######################################################### |
||||
# End preparing tgt |
||||
# - tgt: bs, NQ, d_model |
||||
# - refpoint_embed(unsigmoid): bs, NQ, d_model |
||||
######################################################### |
||||
|
||||
######################################################### |
||||
# Begin Decoder |
||||
######################################################### |
||||
hs, references = self.decoder( |
||||
tgt=tgt.transpose(0, 1), |
||||
memory=memory.transpose(0, 1), |
||||
memory_key_padding_mask=mask_flatten, |
||||
pos=lvl_pos_embed_flatten.transpose(0, 1), |
||||
refpoints_unsigmoid=refpoint_embed.transpose(0, 1), |
||||
level_start_index=level_start_index, |
||||
spatial_shapes=spatial_shapes, |
||||
valid_ratios=valid_ratios, |
||||
tgt_mask=attn_mask, |
||||
memory_text=text_dict["encoded_text"], |
||||
text_attention_mask=~text_dict["text_token_mask"], |
||||
# we ~ the mask . False means use the token; True means pad the token |
||||
) |
||||
######################################################### |
||||
# End Decoder |
||||
# hs: n_dec, bs, nq, d_model |
||||
# references: n_dec+1, bs, nq, query_dim |
||||
######################################################### |
||||
|
||||
######################################################### |
||||
# Begin postprocess |
||||
######################################################### |
||||
if self.two_stage_type == "standard": |
||||
hs_enc = tgt_undetach.unsqueeze(0) |
||||
ref_enc = refpoint_embed_undetach.sigmoid().unsqueeze(0) |
||||
else: |
||||
hs_enc = ref_enc = None |
||||
######################################################### |
||||
# End postprocess |
||||
# hs_enc: (n_enc+1, bs, nq, d_model) or (1, bs, nq, d_model) or (n_enc, bs, nq, d_model) or None |
||||
# ref_enc: (n_enc+1, bs, nq, query_dim) or (1, bs, nq, query_dim) or (n_enc, bs, nq, d_model) or None |
||||
######################################################### |
||||
|
||||
return hs, references, hs_enc, ref_enc, init_box_proposal |
||||
# hs: (n_dec, bs, nq, d_model) |
||||
# references: sigmoid coordinates. (n_dec+1, bs, bq, 4) |
||||
# hs_enc: (n_enc+1, bs, nq, d_model) or (1, bs, nq, d_model) or None |
||||
# ref_enc: sigmoid coordinates. \ |
||||
# (n_enc+1, bs, nq, query_dim) or (1, bs, nq, query_dim) or None |
||||
|
||||
|
||||
class TransformerEncoder(nn.Module): |
||||
def __init__( |
||||
self, |
||||
encoder_layer, |
||||
num_layers, |
||||
d_model=256, |
||||
num_queries=300, |
||||
enc_layer_share=False, |
||||
text_enhance_layer=None, |
||||
feature_fusion_layer=None, |
||||
use_checkpoint=False, |
||||
use_transformer_ckpt=False, |
||||
): |
||||
"""_summary_ |
||||
|
||||
Args: |
||||
encoder_layer (_type_): _description_ |
||||
num_layers (_type_): _description_ |
||||
norm (_type_, optional): _description_. Defaults to None. |
||||
d_model (int, optional): _description_. Defaults to 256. |
||||
num_queries (int, optional): _description_. Defaults to 300. |
||||
enc_layer_share (bool, optional): _description_. Defaults to False. |
||||
|
||||
""" |
||||
super().__init__() |
||||
# prepare layers |
||||
self.layers = [] |
||||
self.text_layers = [] |
||||
self.fusion_layers = [] |
||||
if num_layers > 0: |
||||
self.layers = _get_clones(encoder_layer, num_layers, layer_share=enc_layer_share) |
||||
|
||||
if text_enhance_layer is not None: |
||||
self.text_layers = _get_clones( |
||||
text_enhance_layer, num_layers, layer_share=enc_layer_share |
||||
) |
||||
if feature_fusion_layer is not None: |
||||
self.fusion_layers = _get_clones( |
||||
feature_fusion_layer, num_layers, layer_share=enc_layer_share |
||||
) |
||||
else: |
||||
self.layers = [] |
||||
del encoder_layer |
||||
|
||||
if text_enhance_layer is not None: |
||||
self.text_layers = [] |
||||
del text_enhance_layer |
||||
if feature_fusion_layer is not None: |
||||
self.fusion_layers = [] |
||||
del feature_fusion_layer |
||||
|
||||
self.query_scale = None |
||||
self.num_queries = num_queries |
||||
self.num_layers = num_layers |
||||
self.d_model = d_model |
||||
|
||||
self.use_checkpoint = use_checkpoint |
||||
self.use_transformer_ckpt = use_transformer_ckpt |
||||
|
||||
@staticmethod |
||||
def get_reference_points(spatial_shapes, valid_ratios, device): |
||||
reference_points_list = [] |
||||
for lvl, (H_, W_) in enumerate(spatial_shapes): |
||||
|
||||
ref_y, ref_x = torch.meshgrid( |
||||
torch.linspace(0.5, H_ - 0.5, H_, dtype=torch.float32, device=device), |
||||
torch.linspace(0.5, W_ - 0.5, W_, dtype=torch.float32, device=device), |
||||
) |
||||
ref_y = ref_y.reshape(-1)[None] / (valid_ratios[:, None, lvl, 1] * H_) |
||||
ref_x = ref_x.reshape(-1)[None] / (valid_ratios[:, None, lvl, 0] * W_) |
||||
ref = torch.stack((ref_x, ref_y), -1) |
||||
reference_points_list.append(ref) |
||||
reference_points = torch.cat(reference_points_list, 1) |
||||
reference_points = reference_points[:, :, None] * valid_ratios[:, None] |
||||
return reference_points |
||||
|
||||
def forward( |
||||
self, |
||||
# for images |
||||
src: Tensor, |
||||
pos: Tensor, |
||||
spatial_shapes: Tensor, |
||||
level_start_index: Tensor, |
||||
valid_ratios: Tensor, |
||||
key_padding_mask: Tensor, |
||||
# for texts |
||||
memory_text: Tensor = None, |
||||
text_attention_mask: Tensor = None, |
||||
pos_text: Tensor = None, |
||||
text_self_attention_masks: Tensor = None, |
||||
position_ids: Tensor = None, |
||||
): |
||||
""" |
||||
Input: |
||||
- src: [bs, sum(hi*wi), 256] |
||||
- pos: pos embed for src. [bs, sum(hi*wi), 256] |
||||
- spatial_shapes: h,w of each level [num_level, 2] |
||||
- level_start_index: [num_level] start point of level in sum(hi*wi). |
||||
- valid_ratios: [bs, num_level, 2] |
||||
- key_padding_mask: [bs, sum(hi*wi)] |
||||
|
||||
- memory_text: bs, n_text, 256 |
||||
- text_attention_mask: bs, n_text |
||||
False for no padding; True for padding |
||||
- pos_text: bs, n_text, 256 |
||||
|
||||
- position_ids: bs, n_text |
||||
Intermedia: |
||||
- reference_points: [bs, sum(hi*wi), num_level, 2] |
||||
Outpus: |
||||
- output: [bs, sum(hi*wi), 256] |
||||
""" |
||||
|
||||
output = src |
||||
|
||||
# preparation and reshape |
||||
if self.num_layers > 0: |
||||
reference_points = self.get_reference_points( |
||||
spatial_shapes, valid_ratios, device=src.device |
||||
) |
||||
|
||||
if self.text_layers: |
||||
# generate pos_text |
||||
bs, n_text, text_dim = memory_text.shape |
||||
if pos_text is None and position_ids is None: |
||||
pos_text = ( |
||||
torch.arange(n_text, device=memory_text.device) |
||||
.float() |
||||
.unsqueeze(0) |
||||
.unsqueeze(-1) |
||||
.repeat(bs, 1, 1) |
||||
) |
||||
pos_text = get_sine_pos_embed(pos_text, num_pos_feats=256, exchange_xy=False) |
||||
if position_ids is not None: |
||||
pos_text = get_sine_pos_embed( |
||||
position_ids[..., None], num_pos_feats=256, exchange_xy=False |
||||
) |
||||
|
||||
# main process |
||||
for layer_id, layer in enumerate(self.layers): |
||||
# if output.isnan().any() or memory_text.isnan().any(): |
||||
# if os.environ.get('IPDB_SHILONG_DEBUG', None) == 'INFO': |
||||
# import ipdb; ipdb.set_trace() |
||||
if self.fusion_layers: |
||||
if self.use_checkpoint: |
||||
output, memory_text = checkpoint.checkpoint( |
||||
self.fusion_layers[layer_id], |
||||
output, |
||||
memory_text, |
||||
key_padding_mask, |
||||
text_attention_mask, |
||||
) |
||||
else: |
||||
output, memory_text = self.fusion_layers[layer_id]( |
||||
v=output, |
||||
l=memory_text, |
||||
attention_mask_v=key_padding_mask, |
||||
attention_mask_l=text_attention_mask, |
||||
) |
||||
|
||||
if self.text_layers: |
||||
memory_text = self.text_layers[layer_id]( |
||||
src=memory_text.transpose(0, 1), |
||||
src_mask=~text_self_attention_masks, # note we use ~ for mask here |
||||
src_key_padding_mask=text_attention_mask, |
||||
pos=(pos_text.transpose(0, 1) if pos_text is not None else None), |
||||
).transpose(0, 1) |
||||
|
||||
# main process |
||||
if self.use_transformer_ckpt: |
||||
output = checkpoint.checkpoint( |
||||
layer, |
||||
output, |
||||
pos, |
||||
reference_points, |
||||
spatial_shapes, |
||||
level_start_index, |
||||
key_padding_mask, |
||||
) |
||||
else: |
||||
output = layer( |
||||
src=output, |
||||
pos=pos, |
||||
reference_points=reference_points, |
||||
spatial_shapes=spatial_shapes, |
||||
level_start_index=level_start_index, |
||||
key_padding_mask=key_padding_mask, |
||||
) |
||||
|
||||
return output, memory_text |
||||
|
||||
|
||||
class TransformerDecoder(nn.Module): |
||||
def __init__( |
||||
self, |
||||
decoder_layer, |
||||
num_layers, |
||||
norm=None, |
||||
return_intermediate=False, |
||||
d_model=256, |
||||
query_dim=4, |
||||
num_feature_levels=1, |
||||
): |
||||
super().__init__() |
||||
if num_layers > 0: |
||||
self.layers = _get_clones(decoder_layer, num_layers) |
||||
else: |
||||
self.layers = [] |
||||
self.num_layers = num_layers |
||||
self.norm = norm |
||||
self.return_intermediate = return_intermediate |
||||
assert return_intermediate, "support return_intermediate only" |
||||
self.query_dim = query_dim |
||||
assert query_dim in [2, 4], "query_dim should be 2/4 but {}".format(query_dim) |
||||
self.num_feature_levels = num_feature_levels |
||||
|
||||
self.ref_point_head = MLP(query_dim // 2 * d_model, d_model, d_model, 2) |
||||
self.query_pos_sine_scale = None |
||||
|
||||
self.query_scale = None |
||||
self.bbox_embed = None |
||||
self.class_embed = None |
||||
|
||||
self.d_model = d_model |
||||
|
||||
self.ref_anchor_head = None |
||||
|
||||
def forward( |
||||
self, |
||||
tgt, |
||||
memory, |
||||
tgt_mask: Optional[Tensor] = None, |
||||
memory_mask: Optional[Tensor] = None, |
||||
tgt_key_padding_mask: Optional[Tensor] = None, |
||||
memory_key_padding_mask: Optional[Tensor] = None, |
||||
pos: Optional[Tensor] = None, |
||||
refpoints_unsigmoid: Optional[Tensor] = None, # num_queries, bs, 2 |
||||
# for memory |
||||
level_start_index: Optional[Tensor] = None, # num_levels |
||||
spatial_shapes: Optional[Tensor] = None, # bs, num_levels, 2 |
||||
valid_ratios: Optional[Tensor] = None, |
||||
# for text |
||||
memory_text: Optional[Tensor] = None, |
||||
text_attention_mask: Optional[Tensor] = None, |
||||
): |
||||
""" |
||||
Input: |
||||
- tgt: nq, bs, d_model |
||||
- memory: hw, bs, d_model |
||||
- pos: hw, bs, d_model |
||||
- refpoints_unsigmoid: nq, bs, 2/4 |
||||
- valid_ratios/spatial_shapes: bs, nlevel, 2 |
||||
""" |
||||
output = tgt |
||||
|
||||
intermediate = [] |
||||
reference_points = refpoints_unsigmoid.sigmoid() |
||||
ref_points = [reference_points] |
||||
|
||||
for layer_id, layer in enumerate(self.layers): |
||||
|
||||
if reference_points.shape[-1] == 4: |
||||
reference_points_input = ( |
||||
reference_points[:, :, None] |
||||
* torch.cat([valid_ratios, valid_ratios], -1)[None, :] |
||||
) # nq, bs, nlevel, 4 |
||||
else: |
||||
assert reference_points.shape[-1] == 2 |
||||
reference_points_input = reference_points[:, :, None] * valid_ratios[None, :] |
||||
query_sine_embed = gen_sineembed_for_position( |
||||
reference_points_input[:, :, 0, :] |
||||
) # nq, bs, 256*2 |
||||
|
||||
# conditional query |
||||
raw_query_pos = self.ref_point_head(query_sine_embed) # nq, bs, 256 |
||||
pos_scale = self.query_scale(output) if self.query_scale is not None else 1 |
||||
query_pos = pos_scale * raw_query_pos |
||||
# if os.environ.get("SHILONG_AMP_INFNAN_DEBUG") == '1': |
||||
# if query_pos.isnan().any() | query_pos.isinf().any(): |
||||
# import ipdb; ipdb.set_trace() |
||||
|
||||
# main process |
||||
output = layer( |
||||
tgt=output, |
||||
tgt_query_pos=query_pos, |
||||
tgt_query_sine_embed=query_sine_embed, |
||||
tgt_key_padding_mask=tgt_key_padding_mask, |
||||
tgt_reference_points=reference_points_input, |
||||
memory_text=memory_text, |
||||
text_attention_mask=text_attention_mask, |
||||
memory=memory, |
||||
memory_key_padding_mask=memory_key_padding_mask, |
||||
memory_level_start_index=level_start_index, |
||||
memory_spatial_shapes=spatial_shapes, |
||||
memory_pos=pos, |
||||
self_attn_mask=tgt_mask, |
||||
cross_attn_mask=memory_mask, |
||||
) |
||||
if output.isnan().any() | output.isinf().any(): |
||||
print(f"output layer_id {layer_id} is nan") |
||||
try: |
||||
num_nan = output.isnan().sum().item() |
||||
num_inf = output.isinf().sum().item() |
||||
print(f"num_nan {num_nan}, num_inf {num_inf}") |
||||
except Exception as e: |
||||
print(e) |
||||
# if os.environ.get("SHILONG_AMP_INFNAN_DEBUG") == '1': |
||||
# import ipdb; ipdb.set_trace() |
||||
|
||||
# iter update |
||||
if self.bbox_embed is not None: |
||||
# box_holder = self.bbox_embed(output) |
||||
# box_holder[..., :self.query_dim] += inverse_sigmoid(reference_points) |
||||
# new_reference_points = box_holder[..., :self.query_dim].sigmoid() |
||||
|
||||
reference_before_sigmoid = inverse_sigmoid(reference_points) |
||||
delta_unsig = self.bbox_embed[layer_id](output) |
||||
outputs_unsig = delta_unsig + reference_before_sigmoid |
||||
new_reference_points = outputs_unsig.sigmoid() |
||||
|
||||
reference_points = new_reference_points.detach() |
||||
# if layer_id != self.num_layers - 1: |
||||
ref_points.append(new_reference_points) |
||||
|
||||
intermediate.append(self.norm(output)) |
||||
|
||||
return [ |
||||
[itm_out.transpose(0, 1) for itm_out in intermediate], |
||||
[itm_refpoint.transpose(0, 1) for itm_refpoint in ref_points], |
||||
] |
||||
|
||||
|
||||
class DeformableTransformerEncoderLayer(nn.Module): |
||||
def __init__( |
||||
self, |
||||
d_model=256, |
||||
d_ffn=1024, |
||||
dropout=0.1, |
||||
activation="relu", |
||||
n_levels=4, |
||||
n_heads=8, |
||||
n_points=4, |
||||
): |
||||
super().__init__() |
||||
|
||||
# self attention |
||||
self.self_attn = MSDeformAttn( |
||||
embed_dim=d_model, |
||||
num_levels=n_levels, |
||||
num_heads=n_heads, |
||||
num_points=n_points, |
||||
batch_first=True, |
||||
) |
||||
self.dropout1 = nn.Dropout(dropout) |
||||
self.norm1 = nn.LayerNorm(d_model) |
||||
|
||||
# ffn |
||||
self.linear1 = nn.Linear(d_model, d_ffn) |
||||
self.activation = _get_activation_fn(activation, d_model=d_ffn) |
||||
self.dropout2 = nn.Dropout(dropout) |
||||
self.linear2 = nn.Linear(d_ffn, d_model) |
||||
self.dropout3 = nn.Dropout(dropout) |
||||
self.norm2 = nn.LayerNorm(d_model) |
||||
|
||||
@staticmethod |
||||
def with_pos_embed(tensor, pos): |
||||
return tensor if pos is None else tensor + pos |
||||
|
||||
def forward_ffn(self, src): |
||||
src2 = self.linear2(self.dropout2(self.activation(self.linear1(src)))) |
||||
src = src + self.dropout3(src2) |
||||
src = self.norm2(src) |
||||
return src |
||||
|
||||
def forward( |
||||
self, src, pos, reference_points, spatial_shapes, level_start_index, key_padding_mask=None |
||||
): |
||||
# self attention |
||||
# import ipdb; ipdb.set_trace() |
||||
src2 = self.self_attn( |
||||
query=self.with_pos_embed(src, pos), |
||||
reference_points=reference_points, |
||||
value=src, |
||||
spatial_shapes=spatial_shapes, |
||||
level_start_index=level_start_index, |
||||
key_padding_mask=key_padding_mask, |
||||
) |
||||
src = src + self.dropout1(src2) |
||||
src = self.norm1(src) |
||||
|
||||
# ffn |
||||
src = self.forward_ffn(src) |
||||
|
||||
return src |
||||
|
||||
|
||||
class DeformableTransformerDecoderLayer(nn.Module): |
||||
def __init__( |
||||
self, |
||||
d_model=256, |
||||
d_ffn=1024, |
||||
dropout=0.1, |
||||
activation="relu", |
||||
n_levels=4, |
||||
n_heads=8, |
||||
n_points=4, |
||||
use_text_feat_guide=False, |
||||
use_text_cross_attention=False, |
||||
): |
||||
super().__init__() |
||||
|
||||
# cross attention |
||||
self.cross_attn = MSDeformAttn( |
||||
embed_dim=d_model, |
||||
num_levels=n_levels, |
||||
num_heads=n_heads, |
||||
num_points=n_points, |
||||
batch_first=True, |
||||
) |
||||
self.dropout1 = nn.Dropout(dropout) if dropout > 0 else nn.Identity() |
||||
self.norm1 = nn.LayerNorm(d_model) |
||||
|
||||
# cross attention text |
||||
if use_text_cross_attention: |
||||
self.ca_text = nn.MultiheadAttention(d_model, n_heads, dropout=dropout) |
||||
self.catext_dropout = nn.Dropout(dropout) if dropout > 0 else nn.Identity() |
||||
self.catext_norm = nn.LayerNorm(d_model) |
||||
|
||||
# self attention |
||||
self.self_attn = nn.MultiheadAttention(d_model, n_heads, dropout=dropout) |
||||
self.dropout2 = nn.Dropout(dropout) if dropout > 0 else nn.Identity() |
||||
self.norm2 = nn.LayerNorm(d_model) |
||||
|
||||
# ffn |
||||
self.linear1 = nn.Linear(d_model, d_ffn) |
||||
self.activation = _get_activation_fn(activation, d_model=d_ffn, batch_dim=1) |
||||
self.dropout3 = nn.Dropout(dropout) if dropout > 0 else nn.Identity() |
||||
self.linear2 = nn.Linear(d_ffn, d_model) |
||||
self.dropout4 = nn.Dropout(dropout) if dropout > 0 else nn.Identity() |
||||
self.norm3 = nn.LayerNorm(d_model) |
||||
|
||||
self.key_aware_proj = None |
||||
self.use_text_feat_guide = use_text_feat_guide |
||||
assert not use_text_feat_guide |
||||
self.use_text_cross_attention = use_text_cross_attention |
||||
|
||||
def rm_self_attn_modules(self): |
||||
self.self_attn = None |
||||
self.dropout2 = None |
||||
self.norm2 = None |
||||
|
||||
@staticmethod |
||||
def with_pos_embed(tensor, pos): |
||||
return tensor if pos is None else tensor + pos |
||||
|
||||
def forward_ffn(self, tgt): |
||||
with torch.cuda.amp.autocast(enabled=False): |
||||
tgt2 = self.linear2(self.dropout3(self.activation(self.linear1(tgt)))) |
||||
tgt = tgt + self.dropout4(tgt2) |
||||
tgt = self.norm3(tgt) |
||||
return tgt |
||||
|
||||
def forward( |
||||
self, |
||||
# for tgt |
||||
tgt: Optional[Tensor], # nq, bs, d_model |
||||
tgt_query_pos: Optional[Tensor] = None, # pos for query. MLP(Sine(pos)) |
||||
tgt_query_sine_embed: Optional[Tensor] = None, # pos for query. Sine(pos) |
||||
tgt_key_padding_mask: Optional[Tensor] = None, |
||||
tgt_reference_points: Optional[Tensor] = None, # nq, bs, 4 |
||||
memory_text: Optional[Tensor] = None, # bs, num_token, d_model |
||||
text_attention_mask: Optional[Tensor] = None, # bs, num_token |
||||
# for memory |
||||
memory: Optional[Tensor] = None, # hw, bs, d_model |
||||
memory_key_padding_mask: Optional[Tensor] = None, |
||||
memory_level_start_index: Optional[Tensor] = None, # num_levels |
||||
memory_spatial_shapes: Optional[Tensor] = None, # bs, num_levels, 2 |
||||
memory_pos: Optional[Tensor] = None, # pos for memory |
||||
# sa |
||||
self_attn_mask: Optional[Tensor] = None, # mask used for self-attention |
||||
cross_attn_mask: Optional[Tensor] = None, # mask used for cross-attention |
||||
): |
||||
""" |
||||
Input: |
||||
- tgt/tgt_query_pos: nq, bs, d_model |
||||
- |
||||
""" |
||||
assert cross_attn_mask is None |
||||
|
||||
# self attention |
||||
if self.self_attn is not None: |
||||
# import ipdb; ipdb.set_trace() |
||||
q = k = self.with_pos_embed(tgt, tgt_query_pos) |
||||
tgt2 = self.self_attn(q, k, tgt, attn_mask=self_attn_mask)[0] |
||||
tgt = tgt + self.dropout2(tgt2) |
||||
tgt = self.norm2(tgt) |
||||
|
||||
if self.use_text_cross_attention: |
||||
tgt2 = self.ca_text( |
||||
self.with_pos_embed(tgt, tgt_query_pos), |
||||
memory_text.transpose(0, 1), |
||||
memory_text.transpose(0, 1), |
||||
key_padding_mask=text_attention_mask, |
||||
)[0] |
||||
tgt = tgt + self.catext_dropout(tgt2) |
||||
tgt = self.catext_norm(tgt) |
||||
|
||||
tgt2 = self.cross_attn( |
||||
query=self.with_pos_embed(tgt, tgt_query_pos).transpose(0, 1), |
||||
reference_points=tgt_reference_points.transpose(0, 1).contiguous(), |
||||
value=memory.transpose(0, 1), |
||||
spatial_shapes=memory_spatial_shapes, |
||||
level_start_index=memory_level_start_index, |
||||
key_padding_mask=memory_key_padding_mask, |
||||
).transpose(0, 1) |
||||
tgt = tgt + self.dropout1(tgt2) |
||||
tgt = self.norm1(tgt) |
||||
|
||||
# ffn |
||||
tgt = self.forward_ffn(tgt) |
||||
|
||||
return tgt |
||||
|
||||
|
||||
def build_transformer(args): |
||||
return Transformer( |
||||
d_model=args.hidden_dim, |
||||
dropout=args.dropout, |
||||
nhead=args.nheads, |
||||
num_queries=args.num_queries, |
||||
dim_feedforward=args.dim_feedforward, |
||||
num_encoder_layers=args.enc_layers, |
||||
num_decoder_layers=args.dec_layers, |
||||
normalize_before=args.pre_norm, |
||||
return_intermediate_dec=True, |
||||
query_dim=args.query_dim, |
||||
activation=args.transformer_activation, |
||||
num_patterns=args.num_patterns, |
||||
num_feature_levels=args.num_feature_levels, |
||||
enc_n_points=args.enc_n_points, |
||||
dec_n_points=args.dec_n_points, |
||||
learnable_tgt_init=True, |
||||
# two stage |
||||
two_stage_type=args.two_stage_type, # ['no', 'standard', 'early'] |
||||
embed_init_tgt=args.embed_init_tgt, |
||||
use_text_enhancer=args.use_text_enhancer, |
||||
use_fusion_layer=args.use_fusion_layer, |
||||
use_checkpoint=args.use_checkpoint, |
||||
use_transformer_ckpt=args.use_transformer_ckpt, |
||||
use_text_cross_attention=args.use_text_cross_attention, |
||||
text_dropout=args.text_dropout, |
||||
fusion_dropout=args.fusion_dropout, |
||||
fusion_droppath=args.fusion_droppath, |
||||
) |
||||
@ -0,0 +1,123 @@ |
||||
# ------------------------------------------------------------------------ |
||||
# Grounding DINO |
||||
# url: https://github.com/IDEA-Research/GroundingDINO |
||||
# Copyright (c) 2023 IDEA. All Rights Reserved. |
||||
# Licensed under the Apache License, Version 2.0 [see LICENSE for details] |
||||
# ------------------------------------------------------------------------ |
||||
# Copyright (c) Aishwarya Kamath & Nicolas Carion. Licensed under the Apache License 2.0. All Rights Reserved |
||||
# Copyright (c) Facebook, Inc. and its affiliates. All Rights Reserved |
||||
""" |
||||
DETR Transformer class. |
||||
|
||||
Copy-paste from torch.nn.Transformer with modifications: |
||||
* positional encodings are passed in MHattention |
||||
* extra LN at the end of encoder is removed |
||||
* decoder returns a stack of activations from all decoding layers |
||||
""" |
||||
from typing import Optional |
||||
|
||||
import torch |
||||
import torch.nn.functional as F |
||||
from torch import Tensor, nn |
||||
|
||||
from .utils import ( |
||||
MLP, |
||||
_get_activation_fn, |
||||
_get_clones, |
||||
gen_encoder_output_proposals, |
||||
gen_sineembed_for_position, |
||||
sigmoid_focal_loss, |
||||
) |
||||
|
||||
|
||||
class TextTransformer(nn.Module): |
||||
def __init__(self, num_layers, d_model=256, nheads=8, dim_feedforward=2048, dropout=0.1): |
||||
super().__init__() |
||||
self.num_layers = num_layers |
||||
self.d_model = d_model |
||||
self.nheads = nheads |
||||
self.dim_feedforward = dim_feedforward |
||||
self.norm = None |
||||
|
||||
single_encoder_layer = TransformerEncoderLayer( |
||||
d_model=d_model, nhead=nheads, dim_feedforward=dim_feedforward, dropout=dropout |
||||
) |
||||
self.layers = _get_clones(single_encoder_layer, num_layers) |
||||
|
||||
def forward(self, memory_text: torch.Tensor, text_attention_mask: torch.Tensor): |
||||
""" |
||||
|
||||
Args: |
||||
text_attention_mask: bs, num_token |
||||
memory_text: bs, num_token, d_model |
||||
|
||||
Raises: |
||||
RuntimeError: _description_ |
||||
|
||||
Returns: |
||||
output: bs, num_token, d_model |
||||
""" |
||||
|
||||
output = memory_text.transpose(0, 1) |
||||
|
||||
for layer in self.layers: |
||||
output = layer(output, src_key_padding_mask=text_attention_mask) |
||||
|
||||
if self.norm is not None: |
||||
output = self.norm(output) |
||||
|
||||
return output.transpose(0, 1) |
||||
|
||||
|
||||
class TransformerEncoderLayer(nn.Module): |
||||
def __init__( |
||||
self, |
||||
d_model, |
||||
nhead, |
||||
dim_feedforward=2048, |
||||
dropout=0.1, |
||||
activation="relu", |
||||
normalize_before=False, |
||||
): |
||||
super().__init__() |
||||
self.self_attn = nn.MultiheadAttention(d_model, nhead, dropout=dropout) |
||||
# Implementation of Feedforward model |
||||
self.linear1 = nn.Linear(d_model, dim_feedforward) |
||||
self.dropout = nn.Dropout(dropout) |
||||
self.linear2 = nn.Linear(dim_feedforward, d_model) |
||||
|
||||
self.norm1 = nn.LayerNorm(d_model) |
||||
self.norm2 = nn.LayerNorm(d_model) |
||||
self.dropout1 = nn.Dropout(dropout) |
||||
self.dropout2 = nn.Dropout(dropout) |
||||
|
||||
self.activation = _get_activation_fn(activation) |
||||
self.normalize_before = normalize_before |
||||
self.nhead = nhead |
||||
|
||||
def with_pos_embed(self, tensor, pos: Optional[Tensor]): |
||||
return tensor if pos is None else tensor + pos |
||||
|
||||
def forward( |
||||
self, |
||||
src, |
||||
src_mask: Optional[Tensor] = None, |
||||
src_key_padding_mask: Optional[Tensor] = None, |
||||
pos: Optional[Tensor] = None, |
||||
): |
||||
# repeat attn mask |
||||
if src_mask.dim() == 3 and src_mask.shape[0] == src.shape[1]: |
||||
# bs, num_q, num_k |
||||
src_mask = src_mask.repeat(self.nhead, 1, 1) |
||||
|
||||
q = k = self.with_pos_embed(src, pos) |
||||
|
||||
src2 = self.self_attn(q, k, value=src, attn_mask=src_mask)[0] |
||||
|
||||
# src2 = self.self_attn(q, k, value=src, attn_mask=src_mask, key_padding_mask=src_key_padding_mask)[0] |
||||
src = src + self.dropout1(src2) |
||||
src = self.norm1(src) |
||||
src2 = self.linear2(self.dropout(self.activation(self.linear1(src)))) |
||||
src = src + self.dropout2(src2) |
||||
src = self.norm2(src) |
||||
return src |
||||
@ -0,0 +1,268 @@ |
||||
# ------------------------------------------------------------------------ |
||||
# Grounding DINO |
||||
# url: https://github.com/IDEA-Research/GroundingDINO |
||||
# Copyright (c) 2023 IDEA. All Rights Reserved. |
||||
# Licensed under the Apache License, Version 2.0 [see LICENSE for details] |
||||
# ------------------------------------------------------------------------ |
||||
|
||||
import copy |
||||
import math |
||||
|
||||
import torch |
||||
import torch.nn.functional as F |
||||
from torch import Tensor, nn |
||||
|
||||
|
||||
def _get_clones(module, N, layer_share=False): |
||||
# import ipdb; ipdb.set_trace() |
||||
if layer_share: |
||||
return nn.ModuleList([module for i in range(N)]) |
||||
else: |
||||
return nn.ModuleList([copy.deepcopy(module) for i in range(N)]) |
||||
|
||||
|
||||
def get_sine_pos_embed( |
||||
pos_tensor: torch.Tensor, |
||||
num_pos_feats: int = 128, |
||||
temperature: int = 10000, |
||||
exchange_xy: bool = True, |
||||
): |
||||
"""generate sine position embedding from a position tensor |
||||
Args: |
||||
pos_tensor (torch.Tensor): shape: [..., n]. |
||||
num_pos_feats (int): projected shape for each float in the tensor. |
||||
temperature (int): temperature in the sine/cosine function. |
||||
exchange_xy (bool, optional): exchange pos x and pos y. \ |
||||
For example, input tensor is [x,y], the results will be [pos(y), pos(x)]. Defaults to True. |
||||
Returns: |
||||
pos_embed (torch.Tensor): shape: [..., n*num_pos_feats]. |
||||
""" |
||||
scale = 2 * math.pi |
||||
dim_t = torch.arange(num_pos_feats, dtype=torch.float32, device=pos_tensor.device) |
||||
dim_t = temperature ** (2 * torch.div(dim_t, 2, rounding_mode="floor") / num_pos_feats) |
||||
|
||||
def sine_func(x: torch.Tensor): |
||||
sin_x = x * scale / dim_t |
||||
sin_x = torch.stack((sin_x[..., 0::2].sin(), sin_x[..., 1::2].cos()), dim=3).flatten(2) |
||||
return sin_x |
||||
|
||||
pos_res = [sine_func(x) for x in pos_tensor.split([1] * pos_tensor.shape[-1], dim=-1)] |
||||
if exchange_xy: |
||||
pos_res[0], pos_res[1] = pos_res[1], pos_res[0] |
||||
pos_res = torch.cat(pos_res, dim=-1) |
||||
return pos_res |
||||
|
||||
|
||||
def gen_encoder_output_proposals( |
||||
memory: Tensor, memory_padding_mask: Tensor, spatial_shapes: Tensor, learnedwh=None |
||||
): |
||||
""" |
||||
Input: |
||||
- memory: bs, \sum{hw}, d_model |
||||
- memory_padding_mask: bs, \sum{hw} |
||||
- spatial_shapes: nlevel, 2 |
||||
- learnedwh: 2 |
||||
Output: |
||||
- output_memory: bs, \sum{hw}, d_model |
||||
- output_proposals: bs, \sum{hw}, 4 |
||||
""" |
||||
N_, S_, C_ = memory.shape |
||||
proposals = [] |
||||
_cur = 0 |
||||
for lvl, (H_, W_) in enumerate(spatial_shapes): |
||||
mask_flatten_ = memory_padding_mask[:, _cur : (_cur + H_ * W_)].view(N_, H_, W_, 1) |
||||
valid_H = torch.sum(~mask_flatten_[:, :, 0, 0], 1) |
||||
valid_W = torch.sum(~mask_flatten_[:, 0, :, 0], 1) |
||||
|
||||
# import ipdb; ipdb.set_trace() |
||||
|
||||
grid_y, grid_x = torch.meshgrid( |
||||
torch.linspace(0, H_ - 1, H_, dtype=torch.float32, device=memory.device), |
||||
torch.linspace(0, W_ - 1, W_, dtype=torch.float32, device=memory.device), |
||||
) |
||||
grid = torch.cat([grid_x.unsqueeze(-1), grid_y.unsqueeze(-1)], -1) # H_, W_, 2 |
||||
|
||||
scale = torch.cat([valid_W.unsqueeze(-1), valid_H.unsqueeze(-1)], 1).view(N_, 1, 1, 2) |
||||
grid = (grid.unsqueeze(0).expand(N_, -1, -1, -1) + 0.5) / scale |
||||
|
||||
if learnedwh is not None: |
||||
# import ipdb; ipdb.set_trace() |
||||
wh = torch.ones_like(grid) * learnedwh.sigmoid() * (2.0**lvl) |
||||
else: |
||||
wh = torch.ones_like(grid) * 0.05 * (2.0**lvl) |
||||
|
||||
# scale = torch.cat([W_[None].unsqueeze(-1), H_[None].unsqueeze(-1)], 1).view(1, 1, 1, 2).repeat(N_, 1, 1, 1) |
||||
# grid = (grid.unsqueeze(0).expand(N_, -1, -1, -1) + 0.5) / scale |
||||
# wh = torch.ones_like(grid) / scale |
||||
proposal = torch.cat((grid, wh), -1).view(N_, -1, 4) |
||||
proposals.append(proposal) |
||||
_cur += H_ * W_ |
||||
# import ipdb; ipdb.set_trace() |
||||
output_proposals = torch.cat(proposals, 1) |
||||
output_proposals_valid = ((output_proposals > 0.01) & (output_proposals < 0.99)).all( |
||||
-1, keepdim=True |
||||
) |
||||
output_proposals = torch.log(output_proposals / (1 - output_proposals)) # unsigmoid |
||||
output_proposals = output_proposals.masked_fill(memory_padding_mask.unsqueeze(-1), float("inf")) |
||||
output_proposals = output_proposals.masked_fill(~output_proposals_valid, float("inf")) |
||||
|
||||
output_memory = memory |
||||
output_memory = output_memory.masked_fill(memory_padding_mask.unsqueeze(-1), float(0)) |
||||
output_memory = output_memory.masked_fill(~output_proposals_valid, float(0)) |
||||
|
||||
# output_memory = output_memory.masked_fill(memory_padding_mask.unsqueeze(-1), float('inf')) |
||||
# output_memory = output_memory.masked_fill(~output_proposals_valid, float('inf')) |
||||
|
||||
return output_memory, output_proposals |
||||
|
||||
|
||||
class RandomBoxPerturber: |
||||
def __init__( |
||||
self, x_noise_scale=0.2, y_noise_scale=0.2, w_noise_scale=0.2, h_noise_scale=0.2 |
||||
) -> None: |
||||
self.noise_scale = torch.Tensor( |
||||
[x_noise_scale, y_noise_scale, w_noise_scale, h_noise_scale] |
||||
) |
||||
|
||||
def __call__(self, refanchors: Tensor) -> Tensor: |
||||
nq, bs, query_dim = refanchors.shape |
||||
device = refanchors.device |
||||
|
||||
noise_raw = torch.rand_like(refanchors) |
||||
noise_scale = self.noise_scale.to(device)[:query_dim] |
||||
|
||||
new_refanchors = refanchors * (1 + (noise_raw - 0.5) * noise_scale) |
||||
return new_refanchors.clamp_(0, 1) |
||||
|
||||
|
||||
def sigmoid_focal_loss( |
||||
inputs, targets, num_boxes, alpha: float = 0.25, gamma: float = 2, no_reduction=False |
||||
): |
||||
""" |
||||
Loss used in RetinaNet for dense detection: https://arxiv.org/abs/1708.02002. |
||||
Args: |
||||
inputs: A float tensor of arbitrary shape. |
||||
The predictions for each example. |
||||
targets: A float tensor with the same shape as inputs. Stores the binary |
||||
classification label for each element in inputs |
||||
(0 for the negative class and 1 for the positive class). |
||||
alpha: (optional) Weighting factor in range (0,1) to balance |
||||
positive vs negative examples. Default = -1 (no weighting). |
||||
gamma: Exponent of the modulating factor (1 - p_t) to |
||||
balance easy vs hard examples. |
||||
Returns: |
||||
Loss tensor |
||||
""" |
||||
prob = inputs.sigmoid() |
||||
ce_loss = F.binary_cross_entropy_with_logits(inputs, targets, reduction="none") |
||||
p_t = prob * targets + (1 - prob) * (1 - targets) |
||||
loss = ce_loss * ((1 - p_t) ** gamma) |
||||
|
||||
if alpha >= 0: |
||||
alpha_t = alpha * targets + (1 - alpha) * (1 - targets) |
||||
loss = alpha_t * loss |
||||
|
||||
if no_reduction: |
||||
return loss |
||||
|
||||
return loss.mean(1).sum() / num_boxes |
||||
|
||||
|
||||
class MLP(nn.Module): |
||||
"""Very simple multi-layer perceptron (also called FFN)""" |
||||
|
||||
def __init__(self, input_dim, hidden_dim, output_dim, num_layers): |
||||
super().__init__() |
||||
self.num_layers = num_layers |
||||
h = [hidden_dim] * (num_layers - 1) |
||||
self.layers = nn.ModuleList( |
||||
nn.Linear(n, k) for n, k in zip([input_dim] + h, h + [output_dim]) |
||||
) |
||||
|
||||
def forward(self, x): |
||||
for i, layer in enumerate(self.layers): |
||||
x = F.relu(layer(x)) if i < self.num_layers - 1 else layer(x) |
||||
return x |
||||
|
||||
|
||||
def _get_activation_fn(activation, d_model=256, batch_dim=0): |
||||
"""Return an activation function given a string""" |
||||
if activation == "relu": |
||||
return F.relu |
||||
if activation == "gelu": |
||||
return F.gelu |
||||
if activation == "glu": |
||||
return F.glu |
||||
if activation == "prelu": |
||||
return nn.PReLU() |
||||
if activation == "selu": |
||||
return F.selu |
||||
|
||||
raise RuntimeError(f"activation should be relu/gelu, not {activation}.") |
||||
|
||||
|
||||
def gen_sineembed_for_position(pos_tensor): |
||||
# n_query, bs, _ = pos_tensor.size() |
||||
# sineembed_tensor = torch.zeros(n_query, bs, 256) |
||||
scale = 2 * math.pi |
||||
dim_t = torch.arange(128, dtype=torch.float32, device=pos_tensor.device) |
||||
dim_t = 10000 ** (2 * (torch.div(dim_t, 2, rounding_mode='floor')) / 128) |
||||
x_embed = pos_tensor[:, :, 0] * scale |
||||
y_embed = pos_tensor[:, :, 1] * scale |
||||
pos_x = x_embed[:, :, None] / dim_t |
||||
pos_y = y_embed[:, :, None] / dim_t |
||||
pos_x = torch.stack((pos_x[:, :, 0::2].sin(), pos_x[:, :, 1::2].cos()), dim=3).flatten(2) |
||||
pos_y = torch.stack((pos_y[:, :, 0::2].sin(), pos_y[:, :, 1::2].cos()), dim=3).flatten(2) |
||||
if pos_tensor.size(-1) == 2: |
||||
pos = torch.cat((pos_y, pos_x), dim=2) |
||||
elif pos_tensor.size(-1) == 4: |
||||
w_embed = pos_tensor[:, :, 2] * scale |
||||
pos_w = w_embed[:, :, None] / dim_t |
||||
pos_w = torch.stack((pos_w[:, :, 0::2].sin(), pos_w[:, :, 1::2].cos()), dim=3).flatten(2) |
||||
|
||||
h_embed = pos_tensor[:, :, 3] * scale |
||||
pos_h = h_embed[:, :, None] / dim_t |
||||
pos_h = torch.stack((pos_h[:, :, 0::2].sin(), pos_h[:, :, 1::2].cos()), dim=3).flatten(2) |
||||
|
||||
pos = torch.cat((pos_y, pos_x, pos_w, pos_h), dim=2) |
||||
else: |
||||
raise ValueError("Unknown pos_tensor shape(-1):{}".format(pos_tensor.size(-1))) |
||||
return pos |
||||
|
||||
|
||||
class ContrastiveEmbed(nn.Module): |
||||
def __init__(self, max_text_len=256): |
||||
""" |
||||
Args: |
||||
max_text_len: max length of text. |
||||
""" |
||||
super().__init__() |
||||
self.max_text_len = max_text_len |
||||
|
||||
def forward(self, x, text_dict): |
||||
"""_summary_ |
||||
|
||||
Args: |
||||
x (_type_): _description_ |
||||
text_dict (_type_): _description_ |
||||
{ |
||||
'encoded_text': encoded_text, # bs, 195, d_model |
||||
'text_token_mask': text_token_mask, # bs, 195 |
||||
# True for used tokens. False for padding tokens |
||||
} |
||||
Returns: |
||||
_type_: _description_ |
||||
""" |
||||
assert isinstance(text_dict, dict) |
||||
|
||||
y = text_dict["encoded_text"] |
||||
text_token_mask = text_dict["text_token_mask"] |
||||
|
||||
res = x @ y.transpose(-1, -2) |
||||
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 |
||||
@ -0,0 +1,18 @@ |
||||
# ------------------------------------------------------------------------ |
||||
# Grounding DINO |
||||
# url: https://github.com/IDEA-Research/GroundingDINO |
||||
# Copyright (c) 2023 IDEA. All Rights Reserved. |
||||
# Licensed under the Apache License, Version 2.0 [see LICENSE for details] |
||||
# ------------------------------------------------------------------------ |
||||
# Copyright (c) Facebook, Inc. and its affiliates. All Rights Reserved |
||||
from .GroundingDINO import build_groundingdino |
||||
|
||||
|
||||
def build_model(args): |
||||
# we use register to maintain models from catdet6 on. |
||||
from .registry import MODULE_BUILD_FUNCS |
||||
|
||||
assert args.modelname in MODULE_BUILD_FUNCS._module_dict |
||||
build_func = MODULE_BUILD_FUNCS.get(args.modelname) |
||||
model = build_func(args) |
||||
return model |
||||
@ -0,0 +1,66 @@ |
||||
# ------------------------------------------------------------------------ |
||||
# Grounding DINO |
||||
# url: https://github.com/IDEA-Research/GroundingDINO |
||||
# Copyright (c) 2023 IDEA. All Rights Reserved. |
||||
# Licensed under the Apache License, Version 2.0 [see LICENSE for details] |
||||
# ------------------------------------------------------------------------ |
||||
# -*- coding: utf-8 -*- |
||||
# @Author: Yihao Chen |
||||
# @Date: 2021-08-16 16:03:17 |
||||
# @Last Modified by: Shilong Liu |
||||
# @Last Modified time: 2022-01-23 15:26 |
||||
# modified from mmcv |
||||
|
||||
import inspect |
||||
from functools import partial |
||||
|
||||
|
||||
class Registry(object): |
||||
def __init__(self, name): |
||||
self._name = name |
||||
self._module_dict = dict() |
||||
|
||||
def __repr__(self): |
||||
format_str = self.__class__.__name__ + "(name={}, items={})".format( |
||||
self._name, list(self._module_dict.keys()) |
||||
) |
||||
return format_str |
||||
|
||||
def __len__(self): |
||||
return len(self._module_dict) |
||||
|
||||
@property |
||||
def name(self): |
||||
return self._name |
||||
|
||||
@property |
||||
def module_dict(self): |
||||
return self._module_dict |
||||
|
||||
def get(self, key): |
||||
return self._module_dict.get(key, None) |
||||
|
||||
def registe_with_name(self, module_name=None, force=False): |
||||
return partial(self.register, module_name=module_name, force=force) |
||||
|
||||
def register(self, module_build_function, module_name=None, force=False): |
||||
"""Register a module build function. |
||||
Args: |
||||
module (:obj:`nn.Module`): Module to be registered. |
||||
""" |
||||
if not inspect.isfunction(module_build_function): |
||||
raise TypeError( |
||||
"module_build_function must be a function, but got {}".format( |
||||
type(module_build_function) |
||||
) |
||||
) |
||||
if module_name is None: |
||||
module_name = module_build_function.__name__ |
||||
if not force and module_name in self._module_dict: |
||||
raise KeyError("{} is already registered in {}".format(module_name, self.name)) |
||||
self._module_dict[module_name] = module_build_function |
||||
|
||||
return module_build_function |
||||
|
||||
|
||||
MODULE_BUILD_FUNCS = Registry("model build functions") |
||||
@ -0,0 +1 @@ |
||||
# Copyright (c) Facebook, Inc. and its affiliates. All Rights Reserved |
||||
@ -0,0 +1,140 @@ |
||||
# Copyright (c) Facebook, Inc. and its affiliates. All Rights Reserved |
||||
""" |
||||
Utilities for bounding box manipulation and GIoU. |
||||
""" |
||||
import torch |
||||
from torchvision.ops.boxes import box_area |
||||
|
||||
|
||||
def box_cxcywh_to_xyxy(x): |
||||
x_c, y_c, w, h = x.unbind(-1) |
||||
b = [(x_c - 0.5 * w), (y_c - 0.5 * h), (x_c + 0.5 * w), (y_c + 0.5 * h)] |
||||
return torch.stack(b, dim=-1) |
||||
|
||||
|
||||
def box_xyxy_to_cxcywh(x): |
||||
x0, y0, x1, y1 = x.unbind(-1) |
||||
b = [(x0 + x1) / 2, (y0 + y1) / 2, (x1 - x0), (y1 - y0)] |
||||
return torch.stack(b, dim=-1) |
||||
|
||||
|
||||
# modified from torchvision to also return the union |
||||
def box_iou(boxes1, boxes2): |
||||
area1 = box_area(boxes1) |
||||
area2 = box_area(boxes2) |
||||
|
||||
# import ipdb; ipdb.set_trace() |
||||
lt = torch.max(boxes1[:, None, :2], boxes2[:, :2]) # [N,M,2] |
||||
rb = torch.min(boxes1[:, None, 2:], boxes2[:, 2:]) # [N,M,2] |
||||
|
||||
wh = (rb - lt).clamp(min=0) # [N,M,2] |
||||
inter = wh[:, :, 0] * wh[:, :, 1] # [N,M] |
||||
|
||||
union = area1[:, None] + area2 - inter |
||||
|
||||
iou = inter / (union + 1e-6) |
||||
return iou, union |
||||
|
||||
|
||||
def generalized_box_iou(boxes1, boxes2): |
||||
""" |
||||
Generalized IoU from https://giou.stanford.edu/ |
||||
|
||||
The boxes should be in [x0, y0, x1, y1] format |
||||
|
||||
Returns a [N, M] pairwise matrix, where N = len(boxes1) |
||||
and M = len(boxes2) |
||||
""" |
||||
# degenerate boxes gives inf / nan results |
||||
# so do an early check |
||||
assert (boxes1[:, 2:] >= boxes1[:, :2]).all() |
||||
assert (boxes2[:, 2:] >= boxes2[:, :2]).all() |
||||
# except: |
||||
# import ipdb; ipdb.set_trace() |
||||
iou, union = box_iou(boxes1, boxes2) |
||||
|
||||
lt = torch.min(boxes1[:, None, :2], boxes2[:, :2]) |
||||
rb = torch.max(boxes1[:, None, 2:], boxes2[:, 2:]) |
||||
|
||||
wh = (rb - lt).clamp(min=0) # [N,M,2] |
||||
area = wh[:, :, 0] * wh[:, :, 1] |
||||
|
||||
return iou - (area - union) / (area + 1e-6) |
||||
|
||||
|
||||
# modified from torchvision to also return the union |
||||
def box_iou_pairwise(boxes1, boxes2): |
||||
area1 = box_area(boxes1) |
||||
area2 = box_area(boxes2) |
||||
|
||||
lt = torch.max(boxes1[:, :2], boxes2[:, :2]) # [N,2] |
||||
rb = torch.min(boxes1[:, 2:], boxes2[:, 2:]) # [N,2] |
||||
|
||||
wh = (rb - lt).clamp(min=0) # [N,2] |
||||
inter = wh[:, 0] * wh[:, 1] # [N] |
||||
|
||||
union = area1 + area2 - inter |
||||
|
||||
iou = inter / union |
||||
return iou, union |
||||
|
||||
|
||||
def generalized_box_iou_pairwise(boxes1, boxes2): |
||||
""" |
||||
Generalized IoU from https://giou.stanford.edu/ |
||||
|
||||
Input: |
||||
- boxes1, boxes2: N,4 |
||||
Output: |
||||
- giou: N, 4 |
||||
""" |
||||
# degenerate boxes gives inf / nan results |
||||
# so do an early check |
||||
assert (boxes1[:, 2:] >= boxes1[:, :2]).all() |
||||
assert (boxes2[:, 2:] >= boxes2[:, :2]).all() |
||||
assert boxes1.shape == boxes2.shape |
||||
iou, union = box_iou_pairwise(boxes1, boxes2) # N, 4 |
||||
|
||||
lt = torch.min(boxes1[:, :2], boxes2[:, :2]) |
||||
rb = torch.max(boxes1[:, 2:], boxes2[:, 2:]) |
||||
|
||||
wh = (rb - lt).clamp(min=0) # [N,2] |
||||
area = wh[:, 0] * wh[:, 1] |
||||
|
||||
return iou - (area - union) / area |
||||
|
||||
|
||||
def masks_to_boxes(masks): |
||||
"""Compute the bounding boxes around the provided masks |
||||
|
||||
The masks should be in format [N, H, W] where N is the number of masks, (H, W) are the spatial dimensions. |
||||
|
||||
Returns a [N, 4] tensors, with the boxes in xyxy format |
||||
""" |
||||
if masks.numel() == 0: |
||||
return torch.zeros((0, 4), device=masks.device) |
||||
|
||||
h, w = masks.shape[-2:] |
||||
|
||||
y = torch.arange(0, h, dtype=torch.float) |
||||
x = torch.arange(0, w, dtype=torch.float) |
||||
y, x = torch.meshgrid(y, x) |
||||
|
||||
x_mask = masks * x.unsqueeze(0) |
||||
x_max = x_mask.flatten(1).max(-1)[0] |
||||
x_min = x_mask.masked_fill(~(masks.bool()), 1e8).flatten(1).min(-1)[0] |
||||
|
||||
y_mask = masks * y.unsqueeze(0) |
||||
y_max = y_mask.flatten(1).max(-1)[0] |
||||
y_min = y_mask.masked_fill(~(masks.bool()), 1e8).flatten(1).min(-1)[0] |
||||
|
||||
return torch.stack([x_min, y_min, x_max, y_max], 1) |
||||
|
||||
|
||||
if __name__ == "__main__": |
||||
x = torch.rand(5, 4) |
||||
y = torch.rand(3, 4) |
||||
iou, union = box_iou(x, y) |
||||
import ipdb |
||||
|
||||
ipdb.set_trace() |
||||
@ -0,0 +1,26 @@ |
||||
from transformers import AutoTokenizer, BertModel, BertTokenizer, RobertaModel, RobertaTokenizerFast |
||||
|
||||
|
||||
def get_tokenlizer(text_encoder_type): |
||||
if not isinstance(text_encoder_type, str): |
||||
# print("text_encoder_type is not a str") |
||||
if hasattr(text_encoder_type, "text_encoder_type"): |
||||
text_encoder_type = text_encoder_type.text_encoder_type |
||||
elif text_encoder_type.get("text_encoder_type", False): |
||||
text_encoder_type = text_encoder_type.get("text_encoder_type") |
||||
else: |
||||
raise ValueError( |
||||
"Unknown type of text_encoder_type: {}".format(type(text_encoder_type)) |
||||
) |
||||
print("final text_encoder_type: {}".format(text_encoder_type)) |
||||
|
||||
tokenizer = AutoTokenizer.from_pretrained(text_encoder_type) |
||||
return tokenizer |
||||
|
||||
|
||||
def get_pretrained_language_model(text_encoder_type): |
||||
if text_encoder_type == "bert-base-uncased": |
||||
return BertModel.from_pretrained(text_encoder_type) |
||||
if text_encoder_type == "roberta-base": |
||||
return RobertaModel.from_pretrained(text_encoder_type) |
||||
raise ValueError("Unknown text_encoder_type {}".format(text_encoder_type)) |
||||
@ -0,0 +1,242 @@ |
||||
from typing import Tuple, List |
||||
|
||||
import cv2 |
||||
import numpy as np |
||||
import supervision as sv |
||||
import torch |
||||
from PIL import Image |
||||
from torchvision.ops import box_convert |
||||
|
||||
import groundingdino.datasets.transforms as T |
||||
from groundingdino.models import build_model |
||||
from groundingdino.util.misc import clean_state_dict |
||||
from groundingdino.util.slconfig import SLConfig |
||||
from groundingdino.util.utils import get_phrases_from_posmap |
||||
|
||||
# ---------------------------------------------------------------------------------------------------------------------- |
||||
# OLD API |
||||
# ---------------------------------------------------------------------------------------------------------------------- |
||||
|
||||
|
||||
def preprocess_caption(caption: str) -> str: |
||||
result = caption.lower().strip() |
||||
if result.endswith("."): |
||||
return result |
||||
return result + "." |
||||
|
||||
|
||||
def load_model(model_config_path: str, model_checkpoint_path: str, device: str = "cuda"): |
||||
args = SLConfig.fromfile(model_config_path) |
||||
args.device = device |
||||
model = build_model(args) |
||||
checkpoint = torch.load(model_checkpoint_path, map_location="cpu") |
||||
model.load_state_dict(clean_state_dict(checkpoint["model"]), strict=False) |
||||
model.eval() |
||||
return model |
||||
|
||||
|
||||
def load_image(image_path: str) -> Tuple[np.array, torch.Tensor]: |
||||
transform = T.Compose( |
||||
[ |
||||
T.RandomResize([800], max_size=1333), |
||||
T.ToTensor(), |
||||
T.Normalize([0.485, 0.456, 0.406], [0.229, 0.224, 0.225]), |
||||
] |
||||
) |
||||
image_source = Image.open(image_path).convert("RGB") |
||||
image = np.asarray(image_source) |
||||
image_transformed, _ = transform(image_source, None) |
||||
return image, image_transformed |
||||
|
||||
|
||||
def predict( |
||||
model, |
||||
image: torch.Tensor, |
||||
caption: str, |
||||
box_threshold: float, |
||||
text_threshold: float, |
||||
device: str = "cuda" |
||||
) -> Tuple[torch.Tensor, torch.Tensor, List[str]]: |
||||
caption = preprocess_caption(caption=caption) |
||||
|
||||
model = model.to(device) |
||||
image = image.to(device) |
||||
|
||||
with torch.no_grad(): |
||||
outputs = model(image[None], captions=[caption]) |
||||
|
||||
prediction_logits = outputs["pred_logits"].cpu().sigmoid()[0] # prediction_logits.shape = (nq, 256) |
||||
prediction_boxes = outputs["pred_boxes"].cpu()[0] # prediction_boxes.shape = (nq, 4) |
||||
|
||||
mask = prediction_logits.max(dim=1)[0] > box_threshold |
||||
logits = prediction_logits[mask] # logits.shape = (n, 256) |
||||
boxes = prediction_boxes[mask] # boxes.shape = (n, 4) |
||||
|
||||
tokenizer = model.tokenizer |
||||
tokenized = tokenizer(caption) |
||||
|
||||
phrases = [ |
||||
get_phrases_from_posmap(logit > text_threshold, tokenized, tokenizer).replace('.', '') |
||||
for logit |
||||
in logits |
||||
] |
||||
|
||||
return boxes, logits.max(dim=1)[0], phrases |
||||
|
||||
|
||||
def annotate(image_source: np.ndarray, boxes: torch.Tensor, logits: torch.Tensor, phrases: List[str]) -> np.ndarray: |
||||
h, w, _ = image_source.shape |
||||
boxes = boxes * torch.Tensor([w, h, w, h]) |
||||
xyxy = box_convert(boxes=boxes, in_fmt="cxcywh", out_fmt="xyxy").numpy() |
||||
detections = sv.Detections(xyxy=xyxy) |
||||
|
||||
labels = [ |
||||
f"{phrase} {logit:.2f}" |
||||
for phrase, logit |
||||
in zip(phrases, logits) |
||||
] |
||||
|
||||
box_annotator = sv.BoxAnnotator() |
||||
annotated_frame = cv2.cvtColor(image_source, cv2.COLOR_RGB2BGR) |
||||
annotated_frame = box_annotator.annotate(scene=annotated_frame, detections=detections, labels=labels) |
||||
return annotated_frame |
||||
|
||||
|
||||
# ---------------------------------------------------------------------------------------------------------------------- |
||||
# NEW API |
||||
# ---------------------------------------------------------------------------------------------------------------------- |
||||
|
||||
|
||||
class Model: |
||||
|
||||
def __init__( |
||||
self, |
||||
model_config_path: str, |
||||
model_checkpoint_path: str, |
||||
device: str = "cuda" |
||||
): |
||||
self.model = load_model( |
||||
model_config_path=model_config_path, |
||||
model_checkpoint_path=model_checkpoint_path, |
||||
device=device |
||||
).to(device) |
||||
self.device = device |
||||
|
||||
def predict_with_caption( |
||||
self, |
||||
image: np.ndarray, |
||||
caption: str, |
||||
box_threshold: float = 0.35, |
||||
text_threshold: float = 0.25 |
||||
) -> Tuple[sv.Detections, List[str]]: |
||||
""" |
||||
import cv2 |
||||
|
||||
image = cv2.imread(IMAGE_PATH) |
||||
|
||||
model = Model(model_config_path=CONFIG_PATH, model_checkpoint_path=WEIGHTS_PATH) |
||||
detections, labels = model.predict_with_caption( |
||||
image=image, |
||||
caption=caption, |
||||
box_threshold=BOX_THRESHOLD, |
||||
text_threshold=TEXT_THRESHOLD |
||||
) |
||||
|
||||
import supervision as sv |
||||
|
||||
box_annotator = sv.BoxAnnotator() |
||||
annotated_image = box_annotator.annotate(scene=image, detections=detections, labels=labels) |
||||
""" |
||||
processed_image = Model.preprocess_image(image_bgr=image).to(self.device) |
||||
boxes, logits, phrases = predict( |
||||
model=self.model, |
||||
image=processed_image, |
||||
caption=caption, |
||||
box_threshold=box_threshold, |
||||
text_threshold=text_threshold) |
||||
source_h, source_w, _ = image.shape |
||||
detections = Model.post_process_result( |
||||
source_h=source_h, |
||||
source_w=source_w, |
||||
boxes=boxes, |
||||
logits=logits) |
||||
return detections, phrases |
||||
|
||||
def predict_with_classes( |
||||
self, |
||||
image: np.ndarray, |
||||
classes: List[str], |
||||
box_threshold: float, |
||||
text_threshold: float |
||||
) -> sv.Detections: |
||||
""" |
||||
import cv2 |
||||
|
||||
image = cv2.imread(IMAGE_PATH) |
||||
|
||||
model = Model(model_config_path=CONFIG_PATH, model_checkpoint_path=WEIGHTS_PATH) |
||||
detections = model.predict_with_classes( |
||||
image=image, |
||||
classes=CLASSES, |
||||
box_threshold=BOX_THRESHOLD, |
||||
text_threshold=TEXT_THRESHOLD |
||||
) |
||||
|
||||
|
||||
import supervision as sv |
||||
|
||||
box_annotator = sv.BoxAnnotator() |
||||
annotated_image = box_annotator.annotate(scene=image, detections=detections) |
||||
""" |
||||
caption = ", ".join(classes) |
||||
processed_image = Model.preprocess_image(image_bgr=image).to(self.device) |
||||
boxes, logits, phrases = predict( |
||||
model=self.model, |
||||
image=processed_image, |
||||
caption=caption, |
||||
box_threshold=box_threshold, |
||||
text_threshold=text_threshold) |
||||
source_h, source_w, _ = image.shape |
||||
detections = Model.post_process_result( |
||||
source_h=source_h, |
||||
source_w=source_w, |
||||
boxes=boxes, |
||||
logits=logits) |
||||
class_id = Model.phrases2classes(phrases=phrases, classes=classes) |
||||
detections.class_id = class_id |
||||
return detections |
||||
|
||||
@staticmethod |
||||
def preprocess_image(image_bgr: np.ndarray) -> torch.Tensor: |
||||
transform = T.Compose( |
||||
[ |
||||
T.RandomResize([800], max_size=1333), |
||||
T.ToTensor(), |
||||
T.Normalize([0.485, 0.456, 0.406], [0.229, 0.224, 0.225]), |
||||
] |
||||
) |
||||
image_pillow = Image.fromarray(cv2.cvtColor(image_bgr, cv2.COLOR_BGR2RGB)) |
||||
image_transformed, _ = transform(image_pillow, None) |
||||
return image_transformed |
||||
|
||||
@staticmethod |
||||
def post_process_result( |
||||
source_h: int, |
||||
source_w: int, |
||||
boxes: torch.Tensor, |
||||
logits: torch.Tensor |
||||
) -> sv.Detections: |
||||
boxes = boxes * torch.Tensor([source_w, source_h, source_w, source_h]) |
||||
xyxy = box_convert(boxes=boxes, in_fmt="cxcywh", out_fmt="xyxy").numpy() |
||||
confidence = logits.numpy() |
||||
return sv.Detections(xyxy=xyxy, confidence=confidence) |
||||
|
||||
@staticmethod |
||||
def phrases2classes(phrases: List[str], classes: List[str]) -> np.ndarray: |
||||
class_ids = [] |
||||
for phrase in phrases: |
||||
try: |
||||
class_ids.append(classes.index(phrase)) |
||||
except ValueError: |
||||
class_ids.append(None) |
||||
return np.array(class_ids) |
||||
@ -0,0 +1,93 @@ |
||||
# Copyright (c) Facebook, Inc. and its affiliates. All Rights Reserved |
||||
import functools |
||||
import logging |
||||
import os |
||||
import sys |
||||
|
||||
from termcolor import colored |
||||
|
||||
|
||||
class _ColorfulFormatter(logging.Formatter): |
||||
def __init__(self, *args, **kwargs): |
||||
self._root_name = kwargs.pop("root_name") + "." |
||||
self._abbrev_name = kwargs.pop("abbrev_name", "") |
||||
if len(self._abbrev_name): |
||||
self._abbrev_name = self._abbrev_name + "." |
||||
super(_ColorfulFormatter, self).__init__(*args, **kwargs) |
||||
|
||||
def formatMessage(self, record): |
||||
record.name = record.name.replace(self._root_name, self._abbrev_name) |
||||
log = super(_ColorfulFormatter, self).formatMessage(record) |
||||
if record.levelno == logging.WARNING: |
||||
prefix = colored("WARNING", "red", attrs=["blink"]) |
||||
elif record.levelno == logging.ERROR or record.levelno == logging.CRITICAL: |
||||
prefix = colored("ERROR", "red", attrs=["blink", "underline"]) |
||||
else: |
||||
return log |
||||
return prefix + " " + log |
||||
|
||||
|
||||
# so that calling setup_logger multiple times won't add many handlers |
||||
@functools.lru_cache() |
||||
def setup_logger(output=None, distributed_rank=0, *, color=True, name="imagenet", abbrev_name=None): |
||||
""" |
||||
Initialize the detectron2 logger and set its verbosity level to "INFO". |
||||
|
||||
Args: |
||||
output (str): a file name or a directory to save log. If None, will not save log file. |
||||
If ends with ".txt" or ".log", assumed to be a file name. |
||||
Otherwise, logs will be saved to `output/log.txt`. |
||||
name (str): the root module name of this logger |
||||
|
||||
Returns: |
||||
logging.Logger: a logger |
||||
""" |
||||
logger = logging.getLogger(name) |
||||
logger.setLevel(logging.DEBUG) |
||||
logger.propagate = False |
||||
|
||||
if abbrev_name is None: |
||||
abbrev_name = name |
||||
|
||||
plain_formatter = logging.Formatter( |
||||
"[%(asctime)s.%(msecs)03d]: %(message)s", datefmt="%m/%d %H:%M:%S" |
||||
) |
||||
# stdout logging: master only |
||||
if distributed_rank == 0: |
||||
ch = logging.StreamHandler(stream=sys.stdout) |
||||
ch.setLevel(logging.DEBUG) |
||||
if color: |
||||
formatter = _ColorfulFormatter( |
||||
colored("[%(asctime)s.%(msecs)03d]: ", "green") + "%(message)s", |
||||
datefmt="%m/%d %H:%M:%S", |
||||
root_name=name, |
||||
abbrev_name=str(abbrev_name), |
||||
) |
||||
else: |
||||
formatter = plain_formatter |
||||
ch.setFormatter(formatter) |
||||
logger.addHandler(ch) |
||||
|
||||
# file logging: all workers |
||||
if output is not None: |
||||
if output.endswith(".txt") or output.endswith(".log"): |
||||
filename = output |
||||
else: |
||||
filename = os.path.join(output, "log.txt") |
||||
if distributed_rank > 0: |
||||
filename = filename + f".rank{distributed_rank}" |
||||
os.makedirs(os.path.dirname(filename), exist_ok=True) |
||||
|
||||
fh = logging.StreamHandler(_cached_log_stream(filename)) |
||||
fh.setLevel(logging.DEBUG) |
||||
fh.setFormatter(plain_formatter) |
||||
logger.addHandler(fh) |
||||
|
||||
return logger |
||||
|
||||
|
||||
# cache the opened file object, so that different calls to `setup_logger` |
||||
# with the same file name can safely write to the same file. |
||||
@functools.lru_cache(maxsize=None) |
||||
def _cached_log_stream(filename): |
||||
return open(filename, "a") |
||||
@ -0,0 +1,717 @@ |
||||
# Copyright (c) Facebook, Inc. and its affiliates. All Rights Reserved |
||||
""" |
||||
Misc functions, including distributed helpers. |
||||
|
||||
Mostly copy-paste from torchvision references. |
||||
""" |
||||
import colorsys |
||||
import datetime |
||||
import functools |
||||
import io |
||||
import json |
||||
import os |
||||
import pickle |
||||
import subprocess |
||||
import time |
||||
from collections import OrderedDict, defaultdict, deque |
||||
from typing import List, Optional |
||||
|
||||
import numpy as np |
||||
import torch |
||||
import torch.distributed as dist |
||||
|
||||
# needed due to empty tensor bug in pytorch and torchvision 0.5 |
||||
import torchvision |
||||
from torch import Tensor |
||||
|
||||
__torchvision_need_compat_flag = float(torchvision.__version__.split(".")[1]) < 7 |
||||
if __torchvision_need_compat_flag: |
||||
from torchvision.ops import _new_empty_tensor |
||||
from torchvision.ops.misc import _output_size |
||||
|
||||
|
||||
class SmoothedValue(object): |
||||
"""Track a series of values and provide access to smoothed values over a |
||||
window or the global series average. |
||||
""" |
||||
|
||||
def __init__(self, window_size=20, fmt=None): |
||||
if fmt is None: |
||||
fmt = "{median:.4f} ({global_avg:.4f})" |
||||
self.deque = deque(maxlen=window_size) |
||||
self.total = 0.0 |
||||
self.count = 0 |
||||
self.fmt = fmt |
||||
|
||||
def update(self, value, n=1): |
||||
self.deque.append(value) |
||||
self.count += n |
||||
self.total += value * n |
||||
|
||||
def synchronize_between_processes(self): |
||||
""" |
||||
Warning: does not synchronize the deque! |
||||
""" |
||||
if not is_dist_avail_and_initialized(): |
||||
return |
||||
t = torch.tensor([self.count, self.total], dtype=torch.float64, device="cuda") |
||||
dist.barrier() |
||||
dist.all_reduce(t) |
||||
t = t.tolist() |
||||
self.count = int(t[0]) |
||||
self.total = t[1] |
||||
|
||||
@property |
||||
def median(self): |
||||
d = torch.tensor(list(self.deque)) |
||||
if d.shape[0] == 0: |
||||
return 0 |
||||
return d.median().item() |
||||
|
||||
@property |
||||
def avg(self): |
||||
d = torch.tensor(list(self.deque), dtype=torch.float32) |
||||
return d.mean().item() |
||||
|
||||
@property |
||||
def global_avg(self): |
||||
if os.environ.get("SHILONG_AMP", None) == "1": |
||||
eps = 1e-4 |
||||
else: |
||||
eps = 1e-6 |
||||
return self.total / (self.count + eps) |
||||
|
||||
@property |
||||
def max(self): |
||||
return max(self.deque) |
||||
|
||||
@property |
||||
def value(self): |
||||
return self.deque[-1] |
||||
|
||||
def __str__(self): |
||||
return self.fmt.format( |
||||
median=self.median, |
||||
avg=self.avg, |
||||
global_avg=self.global_avg, |
||||
max=self.max, |
||||
value=self.value, |
||||
) |
||||
|
||||
|
||||
@functools.lru_cache() |
||||
def _get_global_gloo_group(): |
||||
""" |
||||
Return a process group based on gloo backend, containing all the ranks |
||||
The result is cached. |
||||
""" |
||||
|
||||
if dist.get_backend() == "nccl": |
||||
return dist.new_group(backend="gloo") |
||||
|
||||
return dist.group.WORLD |
||||
|
||||
|
||||
def all_gather_cpu(data): |
||||
""" |
||||
Run all_gather on arbitrary picklable data (not necessarily tensors) |
||||
Args: |
||||
data: any picklable object |
||||
Returns: |
||||
list[data]: list of data gathered from each rank |
||||
""" |
||||
|
||||
world_size = get_world_size() |
||||
if world_size == 1: |
||||
return [data] |
||||
|
||||
cpu_group = _get_global_gloo_group() |
||||
|
||||
buffer = io.BytesIO() |
||||
torch.save(data, buffer) |
||||
data_view = buffer.getbuffer() |
||||
device = "cuda" if cpu_group is None else "cpu" |
||||
tensor = torch.ByteTensor(data_view).to(device) |
||||
|
||||
# obtain Tensor size of each rank |
||||
local_size = torch.tensor([tensor.numel()], device=device, dtype=torch.long) |
||||
size_list = [torch.tensor([0], device=device, dtype=torch.long) for _ in range(world_size)] |
||||
if cpu_group is None: |
||||
dist.all_gather(size_list, local_size) |
||||
else: |
||||
print("gathering on cpu") |
||||
dist.all_gather(size_list, local_size, group=cpu_group) |
||||
size_list = [int(size.item()) for size in size_list] |
||||
max_size = max(size_list) |
||||
assert isinstance(local_size.item(), int) |
||||
local_size = int(local_size.item()) |
||||
|
||||
# receiving Tensor from all ranks |
||||
# we pad the tensor because torch all_gather does not support |
||||
# gathering tensors of different shapes |
||||
tensor_list = [] |
||||
for _ in size_list: |
||||
tensor_list.append(torch.empty((max_size,), dtype=torch.uint8, device=device)) |
||||
if local_size != max_size: |
||||
padding = torch.empty(size=(max_size - local_size,), dtype=torch.uint8, device=device) |
||||
tensor = torch.cat((tensor, padding), dim=0) |
||||
if cpu_group is None: |
||||
dist.all_gather(tensor_list, tensor) |
||||
else: |
||||
dist.all_gather(tensor_list, tensor, group=cpu_group) |
||||
|
||||
data_list = [] |
||||
for size, tensor in zip(size_list, tensor_list): |
||||
tensor = torch.split(tensor, [size, max_size - size], dim=0)[0] |
||||
buffer = io.BytesIO(tensor.cpu().numpy()) |
||||
obj = torch.load(buffer) |
||||
data_list.append(obj) |
||||
|
||||
return data_list |
||||
|
||||
|
||||
def all_gather(data): |
||||
""" |
||||
Run all_gather on arbitrary picklable data (not necessarily tensors) |
||||
Args: |
||||
data: any picklable object |
||||
Returns: |
||||
list[data]: list of data gathered from each rank |
||||
""" |
||||
|
||||
if os.getenv("CPU_REDUCE") == "1": |
||||
return all_gather_cpu(data) |
||||
|
||||
world_size = get_world_size() |
||||
if world_size == 1: |
||||
return [data] |
||||
|
||||
# serialized to a Tensor |
||||
buffer = pickle.dumps(data) |
||||
storage = torch.ByteStorage.from_buffer(buffer) |
||||
tensor = torch.ByteTensor(storage).to("cuda") |
||||
|
||||
# obtain Tensor size of each rank |
||||
local_size = torch.tensor([tensor.numel()], device="cuda") |
||||
size_list = [torch.tensor([0], device="cuda") for _ in range(world_size)] |
||||
dist.all_gather(size_list, local_size) |
||||
size_list = [int(size.item()) for size in size_list] |
||||
max_size = max(size_list) |
||||
|
||||
# receiving Tensor from all ranks |
||||
# we pad the tensor because torch all_gather does not support |
||||
# gathering tensors of different shapes |
||||
tensor_list = [] |
||||
for _ in size_list: |
||||
tensor_list.append(torch.empty((max_size,), dtype=torch.uint8, device="cuda")) |
||||
if local_size != max_size: |
||||
padding = torch.empty(size=(max_size - local_size,), dtype=torch.uint8, device="cuda") |
||||
tensor = torch.cat((tensor, padding), dim=0) |
||||
dist.all_gather(tensor_list, tensor) |
||||
|
||||
data_list = [] |
||||
for size, tensor in zip(size_list, tensor_list): |
||||
buffer = tensor.cpu().numpy().tobytes()[:size] |
||||
data_list.append(pickle.loads(buffer)) |
||||
|
||||
return data_list |
||||
|
||||
|
||||
def reduce_dict(input_dict, average=True): |
||||
""" |
||||
Args: |
||||
input_dict (dict): all the values will be reduced |
||||
average (bool): whether to do average or sum |
||||
Reduce the values in the dictionary from all processes so that all processes |
||||
have the averaged results. Returns a dict with the same fields as |
||||
input_dict, after reduction. |
||||
""" |
||||
world_size = get_world_size() |
||||
if world_size < 2: |
||||
return input_dict |
||||
with torch.no_grad(): |
||||
names = [] |
||||
values = [] |
||||
# sort the keys so that they are consistent across processes |
||||
for k in sorted(input_dict.keys()): |
||||
names.append(k) |
||||
values.append(input_dict[k]) |
||||
values = torch.stack(values, dim=0) |
||||
dist.all_reduce(values) |
||||
if average: |
||||
values /= world_size |
||||
reduced_dict = {k: v for k, v in zip(names, values)} |
||||
return reduced_dict |
||||
|
||||
|
||||
class MetricLogger(object): |
||||
def __init__(self, delimiter="\t"): |
||||
self.meters = defaultdict(SmoothedValue) |
||||
self.delimiter = delimiter |
||||
|
||||
def update(self, **kwargs): |
||||
for k, v in kwargs.items(): |
||||
if isinstance(v, torch.Tensor): |
||||
v = v.item() |
||||
assert isinstance(v, (float, int)) |
||||
self.meters[k].update(v) |
||||
|
||||
def __getattr__(self, attr): |
||||
if attr in self.meters: |
||||
return self.meters[attr] |
||||
if attr in self.__dict__: |
||||
return self.__dict__[attr] |
||||
raise AttributeError("'{}' object has no attribute '{}'".format(type(self).__name__, attr)) |
||||
|
||||
def __str__(self): |
||||
loss_str = [] |
||||
for name, meter in self.meters.items(): |
||||
# print(name, str(meter)) |
||||
# import ipdb;ipdb.set_trace() |
||||
if meter.count > 0: |
||||
loss_str.append("{}: {}".format(name, str(meter))) |
||||
return self.delimiter.join(loss_str) |
||||
|
||||
def synchronize_between_processes(self): |
||||
for meter in self.meters.values(): |
||||
meter.synchronize_between_processes() |
||||
|
||||
def add_meter(self, name, meter): |
||||
self.meters[name] = meter |
||||
|
||||
def log_every(self, iterable, print_freq, header=None, logger=None): |
||||
if logger is None: |
||||
print_func = print |
||||
else: |
||||
print_func = logger.info |
||||
|
||||
i = 0 |
||||
if not header: |
||||
header = "" |
||||
start_time = time.time() |
||||
end = time.time() |
||||
iter_time = SmoothedValue(fmt="{avg:.4f}") |
||||
data_time = SmoothedValue(fmt="{avg:.4f}") |
||||
space_fmt = ":" + str(len(str(len(iterable)))) + "d" |
||||
if torch.cuda.is_available(): |
||||
log_msg = self.delimiter.join( |
||||
[ |
||||
header, |
||||
"[{0" + space_fmt + "}/{1}]", |
||||
"eta: {eta}", |
||||
"{meters}", |
||||
"time: {time}", |
||||
"data: {data}", |
||||
"max mem: {memory:.0f}", |
||||
] |
||||
) |
||||
else: |
||||
log_msg = self.delimiter.join( |
||||
[ |
||||
header, |
||||
"[{0" + space_fmt + "}/{1}]", |
||||
"eta: {eta}", |
||||
"{meters}", |
||||
"time: {time}", |
||||
"data: {data}", |
||||
] |
||||
) |
||||
MB = 1024.0 * 1024.0 |
||||
for obj in iterable: |
||||
data_time.update(time.time() - end) |
||||
yield obj |
||||
# import ipdb; ipdb.set_trace() |
||||
iter_time.update(time.time() - end) |
||||
if i % print_freq == 0 or i == len(iterable) - 1: |
||||
eta_seconds = iter_time.global_avg * (len(iterable) - i) |
||||
eta_string = str(datetime.timedelta(seconds=int(eta_seconds))) |
||||
if torch.cuda.is_available(): |
||||
print_func( |
||||
log_msg.format( |
||||
i, |
||||
len(iterable), |
||||
eta=eta_string, |
||||
meters=str(self), |
||||
time=str(iter_time), |
||||
data=str(data_time), |
||||
memory=torch.cuda.max_memory_allocated() / MB, |
||||
) |
||||
) |
||||
else: |
||||
print_func( |
||||
log_msg.format( |
||||
i, |
||||
len(iterable), |
||||
eta=eta_string, |
||||
meters=str(self), |
||||
time=str(iter_time), |
||||
data=str(data_time), |
||||
) |
||||
) |
||||
i += 1 |
||||
end = time.time() |
||||
total_time = time.time() - start_time |
||||
total_time_str = str(datetime.timedelta(seconds=int(total_time))) |
||||
print_func( |
||||
"{} Total time: {} ({:.4f} s / it)".format( |
||||
header, total_time_str, total_time / len(iterable) |
||||
) |
||||
) |
||||
|
||||
|
||||
def get_sha(): |
||||
cwd = os.path.dirname(os.path.abspath(__file__)) |
||||
|
||||
def _run(command): |
||||
return subprocess.check_output(command, cwd=cwd).decode("ascii").strip() |
||||
|
||||
sha = "N/A" |
||||
diff = "clean" |
||||
branch = "N/A" |
||||
try: |
||||
sha = _run(["git", "rev-parse", "HEAD"]) |
||||
subprocess.check_output(["git", "diff"], cwd=cwd) |
||||
diff = _run(["git", "diff-index", "HEAD"]) |
||||
diff = "has uncommited changes" if diff else "clean" |
||||
branch = _run(["git", "rev-parse", "--abbrev-ref", "HEAD"]) |
||||
except Exception: |
||||
pass |
||||
message = f"sha: {sha}, status: {diff}, branch: {branch}" |
||||
return message |
||||
|
||||
|
||||
def collate_fn(batch): |
||||
# import ipdb; ipdb.set_trace() |
||||
batch = list(zip(*batch)) |
||||
batch[0] = nested_tensor_from_tensor_list(batch[0]) |
||||
return tuple(batch) |
||||
|
||||
|
||||
def _max_by_axis(the_list): |
||||
# type: (List[List[int]]) -> List[int] |
||||
maxes = the_list[0] |
||||
for sublist in the_list[1:]: |
||||
for index, item in enumerate(sublist): |
||||
maxes[index] = max(maxes[index], item) |
||||
return maxes |
||||
|
||||
|
||||
class NestedTensor(object): |
||||
def __init__(self, tensors, mask: Optional[Tensor]): |
||||
self.tensors = tensors |
||||
self.mask = mask |
||||
if mask == "auto": |
||||
self.mask = torch.zeros_like(tensors).to(tensors.device) |
||||
if self.mask.dim() == 3: |
||||
self.mask = self.mask.sum(0).to(bool) |
||||
elif self.mask.dim() == 4: |
||||
self.mask = self.mask.sum(1).to(bool) |
||||
else: |
||||
raise ValueError( |
||||
"tensors dim must be 3 or 4 but {}({})".format( |
||||
self.tensors.dim(), self.tensors.shape |
||||
) |
||||
) |
||||
|
||||
def imgsize(self): |
||||
res = [] |
||||
for i in range(self.tensors.shape[0]): |
||||
mask = self.mask[i] |
||||
maxH = (~mask).sum(0).max() |
||||
maxW = (~mask).sum(1).max() |
||||
res.append(torch.Tensor([maxH, maxW])) |
||||
return res |
||||
|
||||
def to(self, device): |
||||
# type: (Device) -> NestedTensor # noqa |
||||
cast_tensor = self.tensors.to(device) |
||||
mask = self.mask |
||||
if mask is not None: |
||||
assert mask is not None |
||||
cast_mask = mask.to(device) |
||||
else: |
||||
cast_mask = None |
||||
return NestedTensor(cast_tensor, cast_mask) |
||||
|
||||
def to_img_list_single(self, tensor, mask): |
||||
assert tensor.dim() == 3, "dim of tensor should be 3 but {}".format(tensor.dim()) |
||||
maxH = (~mask).sum(0).max() |
||||
maxW = (~mask).sum(1).max() |
||||
img = tensor[:, :maxH, :maxW] |
||||
return img |
||||
|
||||
def to_img_list(self): |
||||
"""remove the padding and convert to img list |
||||
|
||||
Returns: |
||||
[type]: [description] |
||||
""" |
||||
if self.tensors.dim() == 3: |
||||
return self.to_img_list_single(self.tensors, self.mask) |
||||
else: |
||||
res = [] |
||||
for i in range(self.tensors.shape[0]): |
||||
tensor_i = self.tensors[i] |
||||
mask_i = self.mask[i] |
||||
res.append(self.to_img_list_single(tensor_i, mask_i)) |
||||
return res |
||||
|
||||
@property |
||||
def device(self): |
||||
return self.tensors.device |
||||
|
||||
def decompose(self): |
||||
return self.tensors, self.mask |
||||
|
||||
def __repr__(self): |
||||
return str(self.tensors) |
||||
|
||||
@property |
||||
def shape(self): |
||||
return {"tensors.shape": self.tensors.shape, "mask.shape": self.mask.shape} |
||||
|
||||
|
||||
def nested_tensor_from_tensor_list(tensor_list: List[Tensor]): |
||||
# TODO make this more general |
||||
if tensor_list[0].ndim == 3: |
||||
if torchvision._is_tracing(): |
||||
# nested_tensor_from_tensor_list() does not export well to ONNX |
||||
# call _onnx_nested_tensor_from_tensor_list() instead |
||||
return _onnx_nested_tensor_from_tensor_list(tensor_list) |
||||
|
||||
# TODO make it support different-sized images |
||||
max_size = _max_by_axis([list(img.shape) for img in tensor_list]) |
||||
# min_size = tuple(min(s) for s in zip(*[img.shape for img in tensor_list])) |
||||
batch_shape = [len(tensor_list)] + max_size |
||||
b, c, h, w = batch_shape |
||||
dtype = tensor_list[0].dtype |
||||
device = tensor_list[0].device |
||||
tensor = torch.zeros(batch_shape, dtype=dtype, device=device) |
||||
mask = torch.ones((b, h, w), dtype=torch.bool, device=device) |
||||
for img, pad_img, m in zip(tensor_list, tensor, mask): |
||||
pad_img[: img.shape[0], : img.shape[1], : img.shape[2]].copy_(img) |
||||
m[: img.shape[1], : img.shape[2]] = False |
||||
else: |
||||
raise ValueError("not supported") |
||||
return NestedTensor(tensor, mask) |
||||
|
||||
|
||||
# _onnx_nested_tensor_from_tensor_list() is an implementation of |
||||
# nested_tensor_from_tensor_list() that is supported by ONNX tracing. |
||||
@torch.jit.unused |
||||
def _onnx_nested_tensor_from_tensor_list(tensor_list: List[Tensor]) -> NestedTensor: |
||||
max_size = [] |
||||
for i in range(tensor_list[0].dim()): |
||||
max_size_i = torch.max( |
||||
torch.stack([img.shape[i] for img in tensor_list]).to(torch.float32) |
||||
).to(torch.int64) |
||||
max_size.append(max_size_i) |
||||
max_size = tuple(max_size) |
||||
|
||||
# work around for |
||||
# pad_img[: img.shape[0], : img.shape[1], : img.shape[2]].copy_(img) |
||||
# m[: img.shape[1], :img.shape[2]] = False |
||||
# which is not yet supported in onnx |
||||
padded_imgs = [] |
||||
padded_masks = [] |
||||
for img in tensor_list: |
||||
padding = [(s1 - s2) for s1, s2 in zip(max_size, tuple(img.shape))] |
||||
padded_img = torch.nn.functional.pad(img, (0, padding[2], 0, padding[1], 0, padding[0])) |
||||
padded_imgs.append(padded_img) |
||||
|
||||
m = torch.zeros_like(img[0], dtype=torch.int, device=img.device) |
||||
padded_mask = torch.nn.functional.pad(m, (0, padding[2], 0, padding[1]), "constant", 1) |
||||
padded_masks.append(padded_mask.to(torch.bool)) |
||||
|
||||
tensor = torch.stack(padded_imgs) |
||||
mask = torch.stack(padded_masks) |
||||
|
||||
return NestedTensor(tensor, mask=mask) |
||||
|
||||
|
||||
def setup_for_distributed(is_master): |
||||
""" |
||||
This function disables printing when not in master process |
||||
""" |
||||
import builtins as __builtin__ |
||||
|
||||
builtin_print = __builtin__.print |
||||
|
||||
def print(*args, **kwargs): |
||||
force = kwargs.pop("force", False) |
||||
if is_master or force: |
||||
builtin_print(*args, **kwargs) |
||||
|
||||
__builtin__.print = print |
||||
|
||||
|
||||
def is_dist_avail_and_initialized(): |
||||
if not dist.is_available(): |
||||
return False |
||||
if not dist.is_initialized(): |
||||
return False |
||||
return True |
||||
|
||||
|
||||
def get_world_size(): |
||||
if not is_dist_avail_and_initialized(): |
||||
return 1 |
||||
return dist.get_world_size() |
||||
|
||||
|
||||
def get_rank(): |
||||
if not is_dist_avail_and_initialized(): |
||||
return 0 |
||||
return dist.get_rank() |
||||
|
||||
|
||||
def is_main_process(): |
||||
return get_rank() == 0 |
||||
|
||||
|
||||
def save_on_master(*args, **kwargs): |
||||
if is_main_process(): |
||||
torch.save(*args, **kwargs) |
||||
|
||||
|
||||
def init_distributed_mode(args): |
||||
if "WORLD_SIZE" in os.environ and os.environ["WORLD_SIZE"] != "": # 'RANK' in os.environ and |
||||
args.rank = int(os.environ["RANK"]) |
||||
args.world_size = int(os.environ["WORLD_SIZE"]) |
||||
args.gpu = args.local_rank = int(os.environ["LOCAL_RANK"]) |
||||
|
||||
# launch by torch.distributed.launch |
||||
# Single node |
||||
# python -m torch.distributed.launch --nproc_per_node=8 main.py --world-size 1 --rank 0 ... |
||||
# Multi nodes |
||||
# python -m torch.distributed.launch --nproc_per_node=8 main.py --world-size 2 --rank 0 --dist-url 'tcp://IP_OF_NODE0:FREEPORT' ... |
||||
# python -m torch.distributed.launch --nproc_per_node=8 main.py --world-size 2 --rank 1 --dist-url 'tcp://IP_OF_NODE0:FREEPORT' ... |
||||
# args.rank = int(os.environ.get('OMPI_COMM_WORLD_RANK')) |
||||
# local_world_size = int(os.environ['GPU_PER_NODE_COUNT']) |
||||
# args.world_size = args.world_size * local_world_size |
||||
# args.gpu = args.local_rank = int(os.environ['LOCAL_RANK']) |
||||
# args.rank = args.rank * local_world_size + args.local_rank |
||||
print( |
||||
"world size: {}, rank: {}, local rank: {}".format( |
||||
args.world_size, args.rank, args.local_rank |
||||
) |
||||
) |
||||
print(json.dumps(dict(os.environ), indent=2)) |
||||
elif "SLURM_PROCID" in os.environ: |
||||
args.rank = int(os.environ["SLURM_PROCID"]) |
||||
args.gpu = args.local_rank = int(os.environ["SLURM_LOCALID"]) |
||||
args.world_size = int(os.environ["SLURM_NPROCS"]) |
||||
|
||||
print( |
||||
"world size: {}, world rank: {}, local rank: {}, device_count: {}".format( |
||||
args.world_size, args.rank, args.local_rank, torch.cuda.device_count() |
||||
) |
||||
) |
||||
else: |
||||
print("Not using distributed mode") |
||||
args.distributed = False |
||||
args.world_size = 1 |
||||
args.rank = 0 |
||||
args.local_rank = 0 |
||||
return |
||||
|
||||
print("world_size:{} rank:{} local_rank:{}".format(args.world_size, args.rank, args.local_rank)) |
||||
args.distributed = True |
||||
torch.cuda.set_device(args.local_rank) |
||||
args.dist_backend = "nccl" |
||||
print("| distributed init (rank {}): {}".format(args.rank, args.dist_url), flush=True) |
||||
|
||||
torch.distributed.init_process_group( |
||||
backend=args.dist_backend, |
||||
world_size=args.world_size, |
||||
rank=args.rank, |
||||
init_method=args.dist_url, |
||||
) |
||||
|
||||
print("Before torch.distributed.barrier()") |
||||
torch.distributed.barrier() |
||||
print("End torch.distributed.barrier()") |
||||
setup_for_distributed(args.rank == 0) |
||||
|
||||
|
||||
@torch.no_grad() |
||||
def accuracy(output, target, topk=(1,)): |
||||
"""Computes the precision@k for the specified values of k""" |
||||
if target.numel() == 0: |
||||
return [torch.zeros([], device=output.device)] |
||||
maxk = max(topk) |
||||
batch_size = target.size(0) |
||||
|
||||
_, pred = output.topk(maxk, 1, True, True) |
||||
pred = pred.t() |
||||
correct = pred.eq(target.view(1, -1).expand_as(pred)) |
||||
|
||||
res = [] |
||||
for k in topk: |
||||
correct_k = correct[:k].view(-1).float().sum(0) |
||||
res.append(correct_k.mul_(100.0 / batch_size)) |
||||
return res |
||||
|
||||
|
||||
@torch.no_grad() |
||||
def accuracy_onehot(pred, gt): |
||||
"""_summary_ |
||||
|
||||
Args: |
||||
pred (_type_): n, c |
||||
gt (_type_): n, c |
||||
""" |
||||
tp = ((pred - gt).abs().sum(-1) < 1e-4).float().sum() |
||||
acc = tp / gt.shape[0] * 100 |
||||
return acc |
||||
|
||||
|
||||
def interpolate(input, size=None, scale_factor=None, mode="nearest", align_corners=None): |
||||
# type: (Tensor, Optional[List[int]], Optional[float], str, Optional[bool]) -> Tensor |
||||
""" |
||||
Equivalent to nn.functional.interpolate, but with support for empty batch sizes. |
||||
This will eventually be supported natively by PyTorch, and this |
||||
class can go away. |
||||
""" |
||||
if __torchvision_need_compat_flag < 0.7: |
||||
if input.numel() > 0: |
||||
return torch.nn.functional.interpolate(input, size, scale_factor, mode, align_corners) |
||||
|
||||
output_shape = _output_size(2, input, size, scale_factor) |
||||
output_shape = list(input.shape[:-2]) + list(output_shape) |
||||
return _new_empty_tensor(input, output_shape) |
||||
else: |
||||
return torchvision.ops.misc.interpolate(input, size, scale_factor, mode, align_corners) |
||||
|
||||
|
||||
class color_sys: |
||||
def __init__(self, num_colors) -> None: |
||||
self.num_colors = num_colors |
||||
colors = [] |
||||
for i in np.arange(0.0, 360.0, 360.0 / num_colors): |
||||
hue = i / 360.0 |
||||
lightness = (50 + np.random.rand() * 10) / 100.0 |
||||
saturation = (90 + np.random.rand() * 10) / 100.0 |
||||
colors.append( |
||||
tuple([int(j * 255) for j in colorsys.hls_to_rgb(hue, lightness, saturation)]) |
||||
) |
||||
self.colors = colors |
||||
|
||||
def __call__(self, idx): |
||||
return self.colors[idx] |
||||
|
||||
|
||||
def inverse_sigmoid(x, eps=1e-3): |
||||
x = x.clamp(min=0, max=1) |
||||
x1 = x.clamp(min=eps) |
||||
x2 = (1 - x).clamp(min=eps) |
||||
return torch.log(x1 / x2) |
||||
|
||||
|
||||
def clean_state_dict(state_dict): |
||||
new_state_dict = OrderedDict() |
||||
for k, v in state_dict.items(): |
||||
if k[:7] == "module.": |
||||
k = k[7:] # remove `module.` |
||||
new_state_dict[k] = v |
||||
return new_state_dict |
||||
@ -0,0 +1,427 @@ |
||||
# ========================================================== |
||||
# Modified from mmcv |
||||
# ========================================================== |
||||
import ast |
||||
import os |
||||
import os.path as osp |
||||
import shutil |
||||
import sys |
||||
import tempfile |
||||
from argparse import Action |
||||
from importlib import import_module |
||||
|
||||
from addict import Dict |
||||
from yapf.yapflib.yapf_api import FormatCode |
||||
|
||||
BASE_KEY = "_base_" |
||||
DELETE_KEY = "_delete_" |
||||
RESERVED_KEYS = ["filename", "text", "pretty_text", "get", "dump", "merge_from_dict"] |
||||
|
||||
|
||||
def check_file_exist(filename, msg_tmpl='file "{}" does not exist'): |
||||
if not osp.isfile(filename): |
||||
raise FileNotFoundError(msg_tmpl.format(filename)) |
||||
|
||||
|
||||
class ConfigDict(Dict): |
||||
def __missing__(self, name): |
||||
raise KeyError(name) |
||||
|
||||
def __getattr__(self, name): |
||||
try: |
||||
value = super(ConfigDict, self).__getattr__(name) |
||||
except KeyError: |
||||
ex = AttributeError(f"'{self.__class__.__name__}' object has no " f"attribute '{name}'") |
||||
except Exception as e: |
||||
ex = e |
||||
else: |
||||
return value |
||||
raise ex |
||||
|
||||
|
||||
class SLConfig(object): |
||||
""" |
||||
config files. |
||||
only support .py file as config now. |
||||
|
||||
ref: mmcv.utils.config |
||||
|
||||
Example: |
||||
>>> cfg = Config(dict(a=1, b=dict(b1=[0, 1]))) |
||||
>>> cfg.a |
||||
1 |
||||
>>> cfg.b |
||||
{'b1': [0, 1]} |
||||
>>> cfg.b.b1 |
||||
[0, 1] |
||||
>>> cfg = Config.fromfile('tests/data/config/a.py') |
||||
>>> cfg.filename |
||||
"/home/kchen/projects/mmcv/tests/data/config/a.py" |
||||
>>> cfg.item4 |
||||
'test' |
||||
>>> cfg |
||||
"Config [path: /home/kchen/projects/mmcv/tests/data/config/a.py]: " |
||||
"{'item1': [1, 2], 'item2': {'a': 0}, 'item3': True, 'item4': 'test'}" |
||||
""" |
||||
|
||||
@staticmethod |
||||
def _validate_py_syntax(filename): |
||||
with open(filename) as f: |
||||
content = f.read() |
||||
try: |
||||
ast.parse(content) |
||||
except SyntaxError: |
||||
raise SyntaxError("There are syntax errors in config " f"file {filename}") |
||||
|
||||
@staticmethod |
||||
def _file2dict(filename): |
||||
filename = osp.abspath(osp.expanduser(filename)) |
||||
check_file_exist(filename) |
||||
if filename.lower().endswith(".py"): |
||||
with tempfile.TemporaryDirectory() as temp_config_dir: |
||||
temp_config_file = tempfile.NamedTemporaryFile(dir=temp_config_dir, suffix=".py") |
||||
temp_config_name = osp.basename(temp_config_file.name) |
||||
if os.name == 'nt': |
||||
temp_config_file.close() |
||||
shutil.copyfile(filename, osp.join(temp_config_dir, temp_config_name)) |
||||
temp_module_name = osp.splitext(temp_config_name)[0] |
||||
sys.path.insert(0, temp_config_dir) |
||||
SLConfig._validate_py_syntax(filename) |
||||
mod = import_module(temp_module_name) |
||||
sys.path.pop(0) |
||||
cfg_dict = { |
||||
name: value for name, value in mod.__dict__.items() if not name.startswith("__") |
||||
} |
||||
# delete imported module |
||||
del sys.modules[temp_module_name] |
||||
# close temp file |
||||
temp_config_file.close() |
||||
elif filename.lower().endswith((".yml", ".yaml", ".json")): |
||||
from .slio import slload |
||||
|
||||
cfg_dict = slload(filename) |
||||
else: |
||||
raise IOError("Only py/yml/yaml/json type are supported now!") |
||||
|
||||
cfg_text = filename + "\n" |
||||
with open(filename, "r") as f: |
||||
cfg_text += f.read() |
||||
|
||||
# parse the base file |
||||
if BASE_KEY in cfg_dict: |
||||
cfg_dir = osp.dirname(filename) |
||||
base_filename = cfg_dict.pop(BASE_KEY) |
||||
base_filename = base_filename if isinstance(base_filename, list) else [base_filename] |
||||
|
||||
cfg_dict_list = list() |
||||
cfg_text_list = list() |
||||
for f in base_filename: |
||||
_cfg_dict, _cfg_text = SLConfig._file2dict(osp.join(cfg_dir, f)) |
||||
cfg_dict_list.append(_cfg_dict) |
||||
cfg_text_list.append(_cfg_text) |
||||
|
||||
base_cfg_dict = dict() |
||||
for c in cfg_dict_list: |
||||
if len(base_cfg_dict.keys() & c.keys()) > 0: |
||||
raise KeyError("Duplicate key is not allowed among bases") |
||||
# TODO Allow the duplicate key while warnning user |
||||
base_cfg_dict.update(c) |
||||
|
||||
base_cfg_dict = SLConfig._merge_a_into_b(cfg_dict, base_cfg_dict) |
||||
cfg_dict = base_cfg_dict |
||||
|
||||
# merge cfg_text |
||||
cfg_text_list.append(cfg_text) |
||||
cfg_text = "\n".join(cfg_text_list) |
||||
|
||||
return cfg_dict, cfg_text |
||||
|
||||
@staticmethod |
||||
def _merge_a_into_b(a, b): |
||||
"""merge dict `a` into dict `b` (non-inplace). |
||||
values in `a` will overwrite `b`. |
||||
copy first to avoid inplace modification |
||||
|
||||
Args: |
||||
a ([type]): [description] |
||||
b ([type]): [description] |
||||
|
||||
Returns: |
||||
[dict]: [description] |
||||
""" |
||||
# import ipdb; ipdb.set_trace() |
||||
if not isinstance(a, dict): |
||||
return a |
||||
|
||||
b = b.copy() |
||||
for k, v in a.items(): |
||||
if isinstance(v, dict) and k in b and not v.pop(DELETE_KEY, False): |
||||
|
||||
if not isinstance(b[k], dict) and not isinstance(b[k], list): |
||||
# if : |
||||
# import ipdb; ipdb.set_trace() |
||||
raise TypeError( |
||||
f"{k}={v} in child config cannot inherit from base " |
||||
f"because {k} is a dict in the child config but is of " |
||||
f"type {type(b[k])} in base config. You may set " |
||||
f"`{DELETE_KEY}=True` to ignore the base config" |
||||
) |
||||
b[k] = SLConfig._merge_a_into_b(v, b[k]) |
||||
elif isinstance(b, list): |
||||
try: |
||||
_ = int(k) |
||||
except: |
||||
raise TypeError( |
||||
f"b is a list, " f"index {k} should be an int when input but {type(k)}" |
||||
) |
||||
b[int(k)] = SLConfig._merge_a_into_b(v, b[int(k)]) |
||||
else: |
||||
b[k] = v |
||||
|
||||
return b |
||||
|
||||
@staticmethod |
||||
def fromfile(filename): |
||||
cfg_dict, cfg_text = SLConfig._file2dict(filename) |
||||
return SLConfig(cfg_dict, cfg_text=cfg_text, filename=filename) |
||||
|
||||
def __init__(self, cfg_dict=None, cfg_text=None, filename=None): |
||||
if cfg_dict is None: |
||||
cfg_dict = dict() |
||||
elif not isinstance(cfg_dict, dict): |
||||
raise TypeError("cfg_dict must be a dict, but " f"got {type(cfg_dict)}") |
||||
for key in cfg_dict: |
||||
if key in RESERVED_KEYS: |
||||
raise KeyError(f"{key} is reserved for config file") |
||||
|
||||
super(SLConfig, self).__setattr__("_cfg_dict", ConfigDict(cfg_dict)) |
||||
super(SLConfig, self).__setattr__("_filename", filename) |
||||
if cfg_text: |
||||
text = cfg_text |
||||
elif filename: |
||||
with open(filename, "r") as f: |
||||
text = f.read() |
||||
else: |
||||
text = "" |
||||
super(SLConfig, self).__setattr__("_text", text) |
||||
|
||||
@property |
||||
def filename(self): |
||||
return self._filename |
||||
|
||||
@property |
||||
def text(self): |
||||
return self._text |
||||
|
||||
@property |
||||
def pretty_text(self): |
||||
|
||||
indent = 4 |
||||
|
||||
def _indent(s_, num_spaces): |
||||
s = s_.split("\n") |
||||
if len(s) == 1: |
||||
return s_ |
||||
first = s.pop(0) |
||||
s = [(num_spaces * " ") + line for line in s] |
||||
s = "\n".join(s) |
||||
s = first + "\n" + s |
||||
return s |
||||
|
||||
def _format_basic_types(k, v, use_mapping=False): |
||||
if isinstance(v, str): |
||||
v_str = f"'{v}'" |
||||
else: |
||||
v_str = str(v) |
||||
|
||||
if use_mapping: |
||||
k_str = f"'{k}'" if isinstance(k, str) else str(k) |
||||
attr_str = f"{k_str}: {v_str}" |
||||
else: |
||||
attr_str = f"{str(k)}={v_str}" |
||||
attr_str = _indent(attr_str, indent) |
||||
|
||||
return attr_str |
||||
|
||||
def _format_list(k, v, use_mapping=False): |
||||
# check if all items in the list are dict |
||||
if all(isinstance(_, dict) for _ in v): |
||||
v_str = "[\n" |
||||
v_str += "\n".join( |
||||
f"dict({_indent(_format_dict(v_), indent)})," for v_ in v |
||||
).rstrip(",") |
||||
if use_mapping: |
||||
k_str = f"'{k}'" if isinstance(k, str) else str(k) |
||||
attr_str = f"{k_str}: {v_str}" |
||||
else: |
||||
attr_str = f"{str(k)}={v_str}" |
||||
attr_str = _indent(attr_str, indent) + "]" |
||||
else: |
||||
attr_str = _format_basic_types(k, v, use_mapping) |
||||
return attr_str |
||||
|
||||
def _contain_invalid_identifier(dict_str): |
||||
contain_invalid_identifier = False |
||||
for key_name in dict_str: |
||||
contain_invalid_identifier |= not str(key_name).isidentifier() |
||||
return contain_invalid_identifier |
||||
|
||||
def _format_dict(input_dict, outest_level=False): |
||||
r = "" |
||||
s = [] |
||||
|
||||
use_mapping = _contain_invalid_identifier(input_dict) |
||||
if use_mapping: |
||||
r += "{" |
||||
for idx, (k, v) in enumerate(input_dict.items()): |
||||
is_last = idx >= len(input_dict) - 1 |
||||
end = "" if outest_level or is_last else "," |
||||
if isinstance(v, dict): |
||||
v_str = "\n" + _format_dict(v) |
||||
if use_mapping: |
||||
k_str = f"'{k}'" if isinstance(k, str) else str(k) |
||||
attr_str = f"{k_str}: dict({v_str}" |
||||
else: |
||||
attr_str = f"{str(k)}=dict({v_str}" |
||||
attr_str = _indent(attr_str, indent) + ")" + end |
||||
elif isinstance(v, list): |
||||
attr_str = _format_list(k, v, use_mapping) + end |
||||
else: |
||||
attr_str = _format_basic_types(k, v, use_mapping) + end |
||||
|
||||
s.append(attr_str) |
||||
r += "\n".join(s) |
||||
if use_mapping: |
||||
r += "}" |
||||
return r |
||||
|
||||
cfg_dict = self._cfg_dict.to_dict() |
||||
text = _format_dict(cfg_dict, outest_level=True) |
||||
# copied from setup.cfg |
||||
yapf_style = dict( |
||||
based_on_style="pep8", |
||||
blank_line_before_nested_class_or_def=True, |
||||
split_before_expression_after_opening_paren=True, |
||||
) |
||||
text, _ = FormatCode(text, style_config=yapf_style, verify=True) |
||||
|
||||
return text |
||||
|
||||
def __repr__(self): |
||||
return f"Config (path: {self.filename}): {self._cfg_dict.__repr__()}" |
||||
|
||||
def __len__(self): |
||||
return len(self._cfg_dict) |
||||
|
||||
def __getattr__(self, name): |
||||
# # debug |
||||
# print('+'*15) |
||||
# print('name=%s' % name) |
||||
# print("addr:", id(self)) |
||||
# # print('type(self):', type(self)) |
||||
# print(self.__dict__) |
||||
# print('+'*15) |
||||
# if self.__dict__ == {}: |
||||
# raise ValueError |
||||
|
||||
return getattr(self._cfg_dict, name) |
||||
|
||||
def __getitem__(self, name): |
||||
return self._cfg_dict.__getitem__(name) |
||||
|
||||
def __setattr__(self, name, value): |
||||
if isinstance(value, dict): |
||||
value = ConfigDict(value) |
||||
self._cfg_dict.__setattr__(name, value) |
||||
|
||||
def __setitem__(self, name, value): |
||||
if isinstance(value, dict): |
||||
value = ConfigDict(value) |
||||
self._cfg_dict.__setitem__(name, value) |
||||
|
||||
def __iter__(self): |
||||
return iter(self._cfg_dict) |
||||
|
||||
def dump(self, file=None): |
||||
# import ipdb; ipdb.set_trace() |
||||
if file is None: |
||||
return self.pretty_text |
||||
else: |
||||
with open(file, "w") as f: |
||||
f.write(self.pretty_text) |
||||
|
||||
def merge_from_dict(self, options): |
||||
"""Merge list into cfg_dict |
||||
|
||||
Merge the dict parsed by MultipleKVAction into this cfg. |
||||
|
||||
Examples: |
||||
>>> options = {'model.backbone.depth': 50, |
||||
... 'model.backbone.with_cp':True} |
||||
>>> cfg = Config(dict(model=dict(backbone=dict(type='ResNet')))) |
||||
>>> cfg.merge_from_dict(options) |
||||
>>> cfg_dict = super(Config, self).__getattribute__('_cfg_dict') |
||||
>>> assert cfg_dict == dict( |
||||
... model=dict(backbone=dict(depth=50, with_cp=True))) |
||||
|
||||
Args: |
||||
options (dict): dict of configs to merge from. |
||||
""" |
||||
option_cfg_dict = {} |
||||
for full_key, v in options.items(): |
||||
d = option_cfg_dict |
||||
key_list = full_key.split(".") |
||||
for subkey in key_list[:-1]: |
||||
d.setdefault(subkey, ConfigDict()) |
||||
d = d[subkey] |
||||
subkey = key_list[-1] |
||||
d[subkey] = v |
||||
|
||||
cfg_dict = super(SLConfig, self).__getattribute__("_cfg_dict") |
||||
super(SLConfig, self).__setattr__( |
||||
"_cfg_dict", SLConfig._merge_a_into_b(option_cfg_dict, cfg_dict) |
||||
) |
||||
|
||||
# for multiprocess |
||||
def __setstate__(self, state): |
||||
self.__init__(state) |
||||
|
||||
def copy(self): |
||||
return SLConfig(self._cfg_dict.copy()) |
||||
|
||||
def deepcopy(self): |
||||
return SLConfig(self._cfg_dict.deepcopy()) |
||||
|
||||
|
||||
class DictAction(Action): |
||||
""" |
||||
argparse action to split an argument into KEY=VALUE form |
||||
on the first = and append to a dictionary. List options should |
||||
be passed as comma separated values, i.e KEY=V1,V2,V3 |
||||
""" |
||||
|
||||
@staticmethod |
||||
def _parse_int_float_bool(val): |
||||
try: |
||||
return int(val) |
||||
except ValueError: |
||||
pass |
||||
try: |
||||
return float(val) |
||||
except ValueError: |
||||
pass |
||||
if val.lower() in ["true", "false"]: |
||||
return True if val.lower() == "true" else False |
||||
if val.lower() in ["none", "null"]: |
||||
return None |
||||
return val |
||||
|
||||
def __call__(self, parser, namespace, values, option_string=None): |
||||
options = {} |
||||
for kv in values: |
||||
key, val = kv.split("=", maxsplit=1) |
||||
val = [self._parse_int_float_bool(v) for v in val.split(",")] |
||||
if len(val) == 1: |
||||
val = val[0] |
||||
options[key] = val |
||||
setattr(namespace, self.dest, options) |
||||
@ -0,0 +1,177 @@ |
||||
# ========================================================== |
||||
# Modified from mmcv |
||||
# ========================================================== |
||||
|
||||
import json |
||||
import pickle |
||||
from abc import ABCMeta, abstractmethod |
||||
from pathlib import Path |
||||
|
||||
import yaml |
||||
|
||||
try: |
||||
from yaml import CLoader as Loader, CDumper as Dumper |
||||
except ImportError: |
||||
from yaml import Loader, Dumper |
||||
|
||||
|
||||
# =========================== |
||||
# Rigister handler |
||||
# =========================== |
||||
|
||||
|
||||
class BaseFileHandler(metaclass=ABCMeta): |
||||
@abstractmethod |
||||
def load_from_fileobj(self, file, **kwargs): |
||||
pass |
||||
|
||||
@abstractmethod |
||||
def dump_to_fileobj(self, obj, file, **kwargs): |
||||
pass |
||||
|
||||
@abstractmethod |
||||
def dump_to_str(self, obj, **kwargs): |
||||
pass |
||||
|
||||
def load_from_path(self, filepath, mode="r", **kwargs): |
||||
with open(filepath, mode) as f: |
||||
return self.load_from_fileobj(f, **kwargs) |
||||
|
||||
def dump_to_path(self, obj, filepath, mode="w", **kwargs): |
||||
with open(filepath, mode) as f: |
||||
self.dump_to_fileobj(obj, f, **kwargs) |
||||
|
||||
|
||||
class JsonHandler(BaseFileHandler): |
||||
def load_from_fileobj(self, file): |
||||
return json.load(file) |
||||
|
||||
def dump_to_fileobj(self, obj, file, **kwargs): |
||||
json.dump(obj, file, **kwargs) |
||||
|
||||
def dump_to_str(self, obj, **kwargs): |
||||
return json.dumps(obj, **kwargs) |
||||
|
||||
|
||||
class PickleHandler(BaseFileHandler): |
||||
def load_from_fileobj(self, file, **kwargs): |
||||
return pickle.load(file, **kwargs) |
||||
|
||||
def load_from_path(self, filepath, **kwargs): |
||||
return super(PickleHandler, self).load_from_path(filepath, mode="rb", **kwargs) |
||||
|
||||
def dump_to_str(self, obj, **kwargs): |
||||
kwargs.setdefault("protocol", 2) |
||||
return pickle.dumps(obj, **kwargs) |
||||
|
||||
def dump_to_fileobj(self, obj, file, **kwargs): |
||||
kwargs.setdefault("protocol", 2) |
||||
pickle.dump(obj, file, **kwargs) |
||||
|
||||
def dump_to_path(self, obj, filepath, **kwargs): |
||||
super(PickleHandler, self).dump_to_path(obj, filepath, mode="wb", **kwargs) |
||||
|
||||
|
||||
class YamlHandler(BaseFileHandler): |
||||
def load_from_fileobj(self, file, **kwargs): |
||||
kwargs.setdefault("Loader", Loader) |
||||
return yaml.load(file, **kwargs) |
||||
|
||||
def dump_to_fileobj(self, obj, file, **kwargs): |
||||
kwargs.setdefault("Dumper", Dumper) |
||||
yaml.dump(obj, file, **kwargs) |
||||
|
||||
def dump_to_str(self, obj, **kwargs): |
||||
kwargs.setdefault("Dumper", Dumper) |
||||
return yaml.dump(obj, **kwargs) |
||||
|
||||
|
||||
file_handlers = { |
||||
"json": JsonHandler(), |
||||
"yaml": YamlHandler(), |
||||
"yml": YamlHandler(), |
||||
"pickle": PickleHandler(), |
||||
"pkl": PickleHandler(), |
||||
} |
||||
|
||||
# =========================== |
||||
# load and dump |
||||
# =========================== |
||||
|
||||
|
||||
def is_str(x): |
||||
"""Whether the input is an string instance. |
||||
|
||||
Note: This method is deprecated since python 2 is no longer supported. |
||||
""" |
||||
return isinstance(x, str) |
||||
|
||||
|
||||
def slload(file, file_format=None, **kwargs): |
||||
"""Load data from json/yaml/pickle files. |
||||
|
||||
This method provides a unified api for loading data from serialized files. |
||||
|
||||
Args: |
||||
file (str or :obj:`Path` or file-like object): Filename or a file-like |
||||
object. |
||||
file_format (str, optional): If not specified, the file format will be |
||||
inferred from the file extension, otherwise use the specified one. |
||||
Currently supported formats include "json", "yaml/yml" and |
||||
"pickle/pkl". |
||||
|
||||
Returns: |
||||
The content from the file. |
||||
""" |
||||
if isinstance(file, Path): |
||||
file = str(file) |
||||
if file_format is None and is_str(file): |
||||
file_format = file.split(".")[-1] |
||||
if file_format not in file_handlers: |
||||
raise TypeError(f"Unsupported format: {file_format}") |
||||
|
||||
handler = file_handlers[file_format] |
||||
if is_str(file): |
||||
obj = handler.load_from_path(file, **kwargs) |
||||
elif hasattr(file, "read"): |
||||
obj = handler.load_from_fileobj(file, **kwargs) |
||||
else: |
||||
raise TypeError('"file" must be a filepath str or a file-object') |
||||
return obj |
||||
|
||||
|
||||
def sldump(obj, file=None, file_format=None, **kwargs): |
||||
"""Dump data to json/yaml/pickle strings or files. |
||||
|
||||
This method provides a unified api for dumping data as strings or to files, |
||||
and also supports custom arguments for each file format. |
||||
|
||||
Args: |
||||
obj (any): The python object to be dumped. |
||||
file (str or :obj:`Path` or file-like object, optional): If not |
||||
specified, then the object is dump to a str, otherwise to a file |
||||
specified by the filename or file-like object. |
||||
file_format (str, optional): Same as :func:`load`. |
||||
|
||||
Returns: |
||||
bool: True for success, False otherwise. |
||||
""" |
||||
if isinstance(file, Path): |
||||
file = str(file) |
||||
if file_format is None: |
||||
if is_str(file): |
||||
file_format = file.split(".")[-1] |
||||
elif file is None: |
||||
raise ValueError("file_format must be specified since file is None") |
||||
if file_format not in file_handlers: |
||||
raise TypeError(f"Unsupported format: {file_format}") |
||||
|
||||
handler = file_handlers[file_format] |
||||
if file is None: |
||||
return handler.dump_to_str(obj, **kwargs) |
||||
elif is_str(file): |
||||
handler.dump_to_path(obj, file, **kwargs) |
||||
elif hasattr(file, "write"): |
||||
handler.dump_to_fileobj(obj, file, **kwargs) |
||||
else: |
||||
raise TypeError('"file" must be a filename str or a file-object') |
||||
@ -0,0 +1,62 @@ |
||||
import json |
||||
import time |
||||
|
||||
|
||||
class TimeCounter: |
||||
def __init__(self) -> None: |
||||
pass |
||||
|
||||
def clear(self): |
||||
self.timedict = {} |
||||
self.basetime = time.perf_counter() |
||||
|
||||
def timeit(self, name): |
||||
nowtime = time.perf_counter() - self.basetime |
||||
self.timedict[name] = nowtime |
||||
self.basetime = time.perf_counter() |
||||
|
||||
|
||||
class TimeHolder: |
||||
def __init__(self) -> None: |
||||
self.timedict = {} |
||||
|
||||
def update(self, _timedict: dict): |
||||
for k, v in _timedict.items(): |
||||
if k not in self.timedict: |
||||
self.timedict[k] = AverageMeter(name=k, val_only=True) |
||||
self.timedict[k].update(val=v) |
||||
|
||||
def final_res(self): |
||||
return {k: v.avg for k, v in self.timedict.items()} |
||||
|
||||
def __str__(self): |
||||
return json.dumps(self.final_res(), indent=2) |
||||
|
||||
|
||||
class AverageMeter(object): |
||||
"""Computes and stores the average and current value""" |
||||
|
||||
def __init__(self, name, fmt=":f", val_only=False): |
||||
self.name = name |
||||
self.fmt = fmt |
||||
self.val_only = val_only |
||||
self.reset() |
||||
|
||||
def reset(self): |
||||
self.val = 0 |
||||
self.avg = 0 |
||||
self.sum = 0 |
||||
self.count = 0 |
||||
|
||||
def update(self, val, n=1): |
||||
self.val = val |
||||
self.sum += val * n |
||||
self.count += n |
||||
self.avg = self.sum / self.count |
||||
|
||||
def __str__(self): |
||||
if self.val_only: |
||||
fmtstr = "{name} {val" + self.fmt + "}" |
||||
else: |
||||
fmtstr = "{name} {val" + self.fmt + "} ({avg" + self.fmt + "})" |
||||
return fmtstr.format(**self.__dict__) |
||||
@ -0,0 +1,608 @@ |
||||
import argparse |
||||
import json |
||||
import warnings |
||||
from collections import OrderedDict |
||||
from copy import deepcopy |
||||
from typing import Any, Dict, List |
||||
|
||||
import numpy as np |
||||
import torch |
||||
from transformers import AutoTokenizer |
||||
|
||||
from groundingdino.util.slconfig import SLConfig |
||||
|
||||
|
||||
def slprint(x, name="x"): |
||||
if isinstance(x, (torch.Tensor, np.ndarray)): |
||||
print(f"{name}.shape:", x.shape) |
||||
elif isinstance(x, (tuple, list)): |
||||
print("type x:", type(x)) |
||||
for i in range(min(10, len(x))): |
||||
slprint(x[i], f"{name}[{i}]") |
||||
elif isinstance(x, dict): |
||||
for k, v in x.items(): |
||||
slprint(v, f"{name}[{k}]") |
||||
else: |
||||
print(f"{name}.type:", type(x)) |
||||
|
||||
|
||||
def clean_state_dict(state_dict): |
||||
new_state_dict = OrderedDict() |
||||
for k, v in state_dict.items(): |
||||
if k[:7] == "module.": |
||||
k = k[7:] # remove `module.` |
||||
new_state_dict[k] = v |
||||
return new_state_dict |
||||
|
||||
|
||||
def renorm( |
||||
img: torch.FloatTensor, mean=[0.485, 0.456, 0.406], std=[0.229, 0.224, 0.225] |
||||
) -> torch.FloatTensor: |
||||
# img: tensor(3,H,W) or tensor(B,3,H,W) |
||||
# return: same as img |
||||
assert img.dim() == 3 or img.dim() == 4, "img.dim() should be 3 or 4 but %d" % img.dim() |
||||
if img.dim() == 3: |
||||
assert img.size(0) == 3, 'img.size(0) shoule be 3 but "%d". (%s)' % ( |
||||
img.size(0), |
||||
str(img.size()), |
||||
) |
||||
img_perm = img.permute(1, 2, 0) |
||||
mean = torch.Tensor(mean) |
||||
std = torch.Tensor(std) |
||||
img_res = img_perm * std + mean |
||||
return img_res.permute(2, 0, 1) |
||||
else: # img.dim() == 4 |
||||
assert img.size(1) == 3, 'img.size(1) shoule be 3 but "%d". (%s)' % ( |
||||
img.size(1), |
||||
str(img.size()), |
||||
) |
||||
img_perm = img.permute(0, 2, 3, 1) |
||||
mean = torch.Tensor(mean) |
||||
std = torch.Tensor(std) |
||||
img_res = img_perm * std + mean |
||||
return img_res.permute(0, 3, 1, 2) |
||||
|
||||
|
||||
class CocoClassMapper: |
||||
def __init__(self) -> None: |
||||
self.category_map_str = { |
||||
"1": 1, |
||||
"2": 2, |
||||
"3": 3, |
||||
"4": 4, |
||||
"5": 5, |
||||
"6": 6, |
||||
"7": 7, |
||||
"8": 8, |
||||
"9": 9, |
||||
"10": 10, |
||||
"11": 11, |
||||
"13": 12, |
||||
"14": 13, |
||||
"15": 14, |
||||
"16": 15, |
||||
"17": 16, |
||||
"18": 17, |
||||
"19": 18, |
||||
"20": 19, |
||||
"21": 20, |
||||
"22": 21, |
||||
"23": 22, |
||||
"24": 23, |
||||
"25": 24, |
||||
"27": 25, |
||||
"28": 26, |
||||
"31": 27, |
||||
"32": 28, |
||||
"33": 29, |
||||
"34": 30, |
||||
"35": 31, |
||||
"36": 32, |
||||
"37": 33, |
||||
"38": 34, |
||||
"39": 35, |
||||
"40": 36, |
||||
"41": 37, |
||||
"42": 38, |
||||
"43": 39, |
||||
"44": 40, |
||||
"46": 41, |
||||
"47": 42, |
||||
"48": 43, |
||||
"49": 44, |
||||
"50": 45, |
||||
"51": 46, |
||||
"52": 47, |
||||
"53": 48, |
||||
"54": 49, |
||||
"55": 50, |
||||
"56": 51, |
||||
"57": 52, |
||||
"58": 53, |
||||
"59": 54, |
||||
"60": 55, |
||||
"61": 56, |
||||
"62": 57, |
||||
"63": 58, |
||||
"64": 59, |
||||
"65": 60, |
||||
"67": 61, |
||||
"70": 62, |
||||
"72": 63, |
||||
"73": 64, |
||||
"74": 65, |
||||
"75": 66, |
||||
"76": 67, |
||||
"77": 68, |
||||
"78": 69, |
||||
"79": 70, |
||||
"80": 71, |
||||
"81": 72, |
||||
"82": 73, |
||||
"84": 74, |
||||
"85": 75, |
||||
"86": 76, |
||||
"87": 77, |
||||
"88": 78, |
||||
"89": 79, |
||||
"90": 80, |
||||
} |
||||
self.origin2compact_mapper = {int(k): v - 1 for k, v in self.category_map_str.items()} |
||||
self.compact2origin_mapper = {int(v - 1): int(k) for k, v in self.category_map_str.items()} |
||||
|
||||
def origin2compact(self, idx): |
||||
return self.origin2compact_mapper[int(idx)] |
||||
|
||||
def compact2origin(self, idx): |
||||
return self.compact2origin_mapper[int(idx)] |
||||
|
||||
|
||||
def to_device(item, device): |
||||
if isinstance(item, torch.Tensor): |
||||
return item.to(device) |
||||
elif isinstance(item, list): |
||||
return [to_device(i, device) for i in item] |
||||
elif isinstance(item, dict): |
||||
return {k: to_device(v, device) for k, v in item.items()} |
||||
else: |
||||
raise NotImplementedError( |
||||
"Call Shilong if you use other containers! type: {}".format(type(item)) |
||||
) |
||||
|
||||
|
||||
# |
||||
def get_gaussian_mean(x, axis, other_axis, softmax=True): |
||||
""" |
||||
|
||||
Args: |
||||
x (float): Input images(BxCxHxW) |
||||
axis (int): The index for weighted mean |
||||
other_axis (int): The other index |
||||
|
||||
Returns: weighted index for axis, BxC |
||||
|
||||
""" |
||||
mat2line = torch.sum(x, axis=other_axis) |
||||
# mat2line = mat2line / mat2line.mean() * 10 |
||||
if softmax: |
||||
u = torch.softmax(mat2line, axis=2) |
||||
else: |
||||
u = mat2line / (mat2line.sum(2, keepdim=True) + 1e-6) |
||||
size = x.shape[axis] |
||||
ind = torch.linspace(0, 1, size).to(x.device) |
||||
batch = x.shape[0] |
||||
channel = x.shape[1] |
||||
index = ind.repeat([batch, channel, 1]) |
||||
mean_position = torch.sum(index * u, dim=2) |
||||
return mean_position |
||||
|
||||
|
||||
def get_expected_points_from_map(hm, softmax=True): |
||||
"""get_gaussian_map_from_points |
||||
B,C,H,W -> B,N,2 float(0, 1) float(0, 1) |
||||
softargmax function |
||||
|
||||
Args: |
||||
hm (float): Input images(BxCxHxW) |
||||
|
||||
Returns: |
||||
weighted index for axis, BxCx2. float between 0 and 1. |
||||
|
||||
""" |
||||
# hm = 10*hm |
||||
B, C, H, W = hm.shape |
||||
y_mean = get_gaussian_mean(hm, 2, 3, softmax=softmax) # B,C |
||||
x_mean = get_gaussian_mean(hm, 3, 2, softmax=softmax) # B,C |
||||
# return torch.cat((x_mean.unsqueeze(-1), y_mean.unsqueeze(-1)), 2) |
||||
return torch.stack([x_mean, y_mean], dim=2) |
||||
|
||||
|
||||
# Positional encoding (section 5.1) |
||||
# borrow from nerf |
||||
class Embedder: |
||||
def __init__(self, **kwargs): |
||||
self.kwargs = kwargs |
||||
self.create_embedding_fn() |
||||
|
||||
def create_embedding_fn(self): |
||||
embed_fns = [] |
||||
d = self.kwargs["input_dims"] |
||||
out_dim = 0 |
||||
if self.kwargs["include_input"]: |
||||
embed_fns.append(lambda x: x) |
||||
out_dim += d |
||||
|
||||
max_freq = self.kwargs["max_freq_log2"] |
||||
N_freqs = self.kwargs["num_freqs"] |
||||
|
||||
if self.kwargs["log_sampling"]: |
||||
freq_bands = 2.0 ** torch.linspace(0.0, max_freq, steps=N_freqs) |
||||
else: |
||||
freq_bands = torch.linspace(2.0**0.0, 2.0**max_freq, steps=N_freqs) |
||||
|
||||
for freq in freq_bands: |
||||
for p_fn in self.kwargs["periodic_fns"]: |
||||
embed_fns.append(lambda x, p_fn=p_fn, freq=freq: p_fn(x * freq)) |
||||
out_dim += d |
||||
|
||||
self.embed_fns = embed_fns |
||||
self.out_dim = out_dim |
||||
|
||||
def embed(self, inputs): |
||||
return torch.cat([fn(inputs) for fn in self.embed_fns], -1) |
||||
|
||||
|
||||
def get_embedder(multires, i=0): |
||||
import torch.nn as nn |
||||
|
||||
if i == -1: |
||||
return nn.Identity(), 3 |
||||
|
||||
embed_kwargs = { |
||||
"include_input": True, |
||||
"input_dims": 3, |
||||
"max_freq_log2": multires - 1, |
||||
"num_freqs": multires, |
||||
"log_sampling": True, |
||||
"periodic_fns": [torch.sin, torch.cos], |
||||
} |
||||
|
||||
embedder_obj = Embedder(**embed_kwargs) |
||||
embed = lambda x, eo=embedder_obj: eo.embed(x) |
||||
return embed, embedder_obj.out_dim |
||||
|
||||
|
||||
class APOPMeter: |
||||
def __init__(self) -> None: |
||||
self.tp = 0 |
||||
self.fp = 0 |
||||
self.tn = 0 |
||||
self.fn = 0 |
||||
|
||||
def update(self, pred, gt): |
||||
""" |
||||
Input: |
||||
pred, gt: Tensor() |
||||
""" |
||||
assert pred.shape == gt.shape |
||||
self.tp += torch.logical_and(pred == 1, gt == 1).sum().item() |
||||
self.fp += torch.logical_and(pred == 1, gt == 0).sum().item() |
||||
self.tn += torch.logical_and(pred == 0, gt == 0).sum().item() |
||||
self.tn += torch.logical_and(pred == 1, gt == 0).sum().item() |
||||
|
||||
def update_cm(self, tp, fp, tn, fn): |
||||
self.tp += tp |
||||
self.fp += fp |
||||
self.tn += tn |
||||
self.tn += fn |
||||
|
||||
|
||||
def inverse_sigmoid(x, eps=1e-5): |
||||
x = x.clamp(min=0, max=1) |
||||
x1 = x.clamp(min=eps) |
||||
x2 = (1 - x).clamp(min=eps) |
||||
return torch.log(x1 / x2) |
||||
|
||||
|
||||
def get_raw_dict(args): |
||||
""" |
||||
return the dicf contained in args. |
||||
|
||||
e.g: |
||||
>>> with open(path, 'w') as f: |
||||
json.dump(get_raw_dict(args), f, indent=2) |
||||
""" |
||||
if isinstance(args, argparse.Namespace): |
||||
return vars(args) |
||||
elif isinstance(args, dict): |
||||
return args |
||||
elif isinstance(args, SLConfig): |
||||
return args._cfg_dict |
||||
else: |
||||
raise NotImplementedError("Unknown type {}".format(type(args))) |
||||
|
||||
|
||||
def stat_tensors(tensor): |
||||
assert tensor.dim() == 1 |
||||
tensor_sm = tensor.softmax(0) |
||||
entropy = (tensor_sm * torch.log(tensor_sm + 1e-9)).sum() |
||||
|
||||
return { |
||||
"max": tensor.max(), |
||||
"min": tensor.min(), |
||||
"mean": tensor.mean(), |
||||
"var": tensor.var(), |
||||
"std": tensor.var() ** 0.5, |
||||
"entropy": entropy, |
||||
} |
||||
|
||||
|
||||
class NiceRepr: |
||||
"""Inherit from this class and define ``__nice__`` to "nicely" print your |
||||
objects. |
||||
|
||||
Defines ``__str__`` and ``__repr__`` in terms of ``__nice__`` function |
||||
Classes that inherit from :class:`NiceRepr` should redefine ``__nice__``. |
||||
If the inheriting class has a ``__len__``, method then the default |
||||
``__nice__`` method will return its length. |
||||
|
||||
Example: |
||||
>>> class Foo(NiceRepr): |
||||
... def __nice__(self): |
||||
... return 'info' |
||||
>>> foo = Foo() |
||||
>>> assert str(foo) == '<Foo(info)>' |
||||
>>> assert repr(foo).startswith('<Foo(info) at ') |
||||
|
||||
Example: |
||||
>>> class Bar(NiceRepr): |
||||
... pass |
||||
>>> bar = Bar() |
||||
>>> import pytest |
||||
>>> with pytest.warns(None) as record: |
||||
>>> assert 'object at' in str(bar) |
||||
>>> assert 'object at' in repr(bar) |
||||
|
||||
Example: |
||||
>>> class Baz(NiceRepr): |
||||
... def __len__(self): |
||||
... return 5 |
||||
>>> baz = Baz() |
||||
>>> assert str(baz) == '<Baz(5)>' |
||||
""" |
||||
|
||||
def __nice__(self): |
||||
"""str: a "nice" summary string describing this module""" |
||||
if hasattr(self, "__len__"): |
||||
# It is a common pattern for objects to use __len__ in __nice__ |
||||
# As a convenience we define a default __nice__ for these objects |
||||
return str(len(self)) |
||||
else: |
||||
# In all other cases force the subclass to overload __nice__ |
||||
raise NotImplementedError(f"Define the __nice__ method for {self.__class__!r}") |
||||
|
||||
def __repr__(self): |
||||
"""str: the string of the module""" |
||||
try: |
||||
nice = self.__nice__() |
||||
classname = self.__class__.__name__ |
||||
return f"<{classname}({nice}) at {hex(id(self))}>" |
||||
except NotImplementedError as ex: |
||||
warnings.warn(str(ex), category=RuntimeWarning) |
||||
return object.__repr__(self) |
||||
|
||||
def __str__(self): |
||||
"""str: the string of the module""" |
||||
try: |
||||
classname = self.__class__.__name__ |
||||
nice = self.__nice__() |
||||
return f"<{classname}({nice})>" |
||||
except NotImplementedError as ex: |
||||
warnings.warn(str(ex), category=RuntimeWarning) |
||||
return object.__repr__(self) |
||||
|
||||
|
||||
def ensure_rng(rng=None): |
||||
"""Coerces input into a random number generator. |
||||
|
||||
If the input is None, then a global random state is returned. |
||||
|
||||
If the input is a numeric value, then that is used as a seed to construct a |
||||
random state. Otherwise the input is returned as-is. |
||||
|
||||
Adapted from [1]_. |
||||
|
||||
Args: |
||||
rng (int | numpy.random.RandomState | None): |
||||
if None, then defaults to the global rng. Otherwise this can be an |
||||
integer or a RandomState class |
||||
Returns: |
||||
(numpy.random.RandomState) : rng - |
||||
a numpy random number generator |
||||
|
||||
References: |
||||
.. [1] https://gitlab.kitware.com/computer-vision/kwarray/blob/master/kwarray/util_random.py#L270 # noqa: E501 |
||||
""" |
||||
|
||||
if rng is None: |
||||
rng = np.random.mtrand._rand |
||||
elif isinstance(rng, int): |
||||
rng = np.random.RandomState(rng) |
||||
else: |
||||
rng = rng |
||||
return rng |
||||
|
||||
|
||||
def random_boxes(num=1, scale=1, rng=None): |
||||
"""Simple version of ``kwimage.Boxes.random`` |
||||
|
||||
Returns: |
||||
Tensor: shape (n, 4) in x1, y1, x2, y2 format. |
||||
|
||||
References: |
||||
https://gitlab.kitware.com/computer-vision/kwimage/blob/master/kwimage/structs/boxes.py#L1390 |
||||
|
||||
Example: |
||||
>>> num = 3 |
||||
>>> scale = 512 |
||||
>>> rng = 0 |
||||
>>> boxes = random_boxes(num, scale, rng) |
||||
>>> print(boxes) |
||||
tensor([[280.9925, 278.9802, 308.6148, 366.1769], |
||||
[216.9113, 330.6978, 224.0446, 456.5878], |
||||
[405.3632, 196.3221, 493.3953, 270.7942]]) |
||||
""" |
||||
rng = ensure_rng(rng) |
||||
|
||||
tlbr = rng.rand(num, 4).astype(np.float32) |
||||
|
||||
tl_x = np.minimum(tlbr[:, 0], tlbr[:, 2]) |
||||
tl_y = np.minimum(tlbr[:, 1], tlbr[:, 3]) |
||||
br_x = np.maximum(tlbr[:, 0], tlbr[:, 2]) |
||||
br_y = np.maximum(tlbr[:, 1], tlbr[:, 3]) |
||||
|
||||
tlbr[:, 0] = tl_x * scale |
||||
tlbr[:, 1] = tl_y * scale |
||||
tlbr[:, 2] = br_x * scale |
||||
tlbr[:, 3] = br_y * scale |
||||
|
||||
boxes = torch.from_numpy(tlbr) |
||||
return boxes |
||||
|
||||
|
||||
class ModelEma(torch.nn.Module): |
||||
def __init__(self, model, decay=0.9997, device=None): |
||||
super(ModelEma, self).__init__() |
||||
# make a copy of the model for accumulating moving average of weights |
||||
self.module = deepcopy(model) |
||||
self.module.eval() |
||||
|
||||
# import ipdb; ipdb.set_trace() |
||||
|
||||
self.decay = decay |
||||
self.device = device # perform ema on different device from model if set |
||||
if self.device is not None: |
||||
self.module.to(device=device) |
||||
|
||||
def _update(self, model, update_fn): |
||||
with torch.no_grad(): |
||||
for ema_v, model_v in zip( |
||||
self.module.state_dict().values(), model.state_dict().values() |
||||
): |
||||
if self.device is not None: |
||||
model_v = model_v.to(device=self.device) |
||||
ema_v.copy_(update_fn(ema_v, model_v)) |
||||
|
||||
def update(self, model): |
||||
self._update(model, update_fn=lambda e, m: self.decay * e + (1.0 - self.decay) * m) |
||||
|
||||
def set(self, model): |
||||
self._update(model, update_fn=lambda e, m: m) |
||||
|
||||
|
||||
class BestMetricSingle: |
||||
def __init__(self, init_res=0.0, better="large") -> None: |
||||
self.init_res = init_res |
||||
self.best_res = init_res |
||||
self.best_ep = -1 |
||||
|
||||
self.better = better |
||||
assert better in ["large", "small"] |
||||
|
||||
def isbetter(self, new_res, old_res): |
||||
if self.better == "large": |
||||
return new_res > old_res |
||||
if self.better == "small": |
||||
return new_res < old_res |
||||
|
||||
def update(self, new_res, ep): |
||||
if self.isbetter(new_res, self.best_res): |
||||
self.best_res = new_res |
||||
self.best_ep = ep |
||||
return True |
||||
return False |
||||
|
||||
def __str__(self) -> str: |
||||
return "best_res: {}\t best_ep: {}".format(self.best_res, self.best_ep) |
||||
|
||||
def __repr__(self) -> str: |
||||
return self.__str__() |
||||
|
||||
def summary(self) -> dict: |
||||
return { |
||||
"best_res": self.best_res, |
||||
"best_ep": self.best_ep, |
||||
} |
||||
|
||||
|
||||
class BestMetricHolder: |
||||
def __init__(self, init_res=0.0, better="large", use_ema=False) -> None: |
||||
self.best_all = BestMetricSingle(init_res, better) |
||||
self.use_ema = use_ema |
||||
if use_ema: |
||||
self.best_ema = BestMetricSingle(init_res, better) |
||||
self.best_regular = BestMetricSingle(init_res, better) |
||||
|
||||
def update(self, new_res, epoch, is_ema=False): |
||||
""" |
||||
return if the results is the best. |
||||
""" |
||||
if not self.use_ema: |
||||
return self.best_all.update(new_res, epoch) |
||||
else: |
||||
if is_ema: |
||||
self.best_ema.update(new_res, epoch) |
||||
return self.best_all.update(new_res, epoch) |
||||
else: |
||||
self.best_regular.update(new_res, epoch) |
||||
return self.best_all.update(new_res, epoch) |
||||
|
||||
def summary(self): |
||||
if not self.use_ema: |
||||
return self.best_all.summary() |
||||
|
||||
res = {} |
||||
res.update({f"all_{k}": v for k, v in self.best_all.summary().items()}) |
||||
res.update({f"regular_{k}": v for k, v in self.best_regular.summary().items()}) |
||||
res.update({f"ema_{k}": v for k, v in self.best_ema.summary().items()}) |
||||
return res |
||||
|
||||
def __repr__(self) -> str: |
||||
return json.dumps(self.summary(), indent=2) |
||||
|
||||
def __str__(self) -> str: |
||||
return self.__repr__() |
||||
|
||||
|
||||
def targets_to(targets: List[Dict[str, Any]], device): |
||||
"""Moves the target dicts to the given device.""" |
||||
excluded_keys = [ |
||||
"questionId", |
||||
"tokens_positive", |
||||
"strings_positive", |
||||
"tokens", |
||||
"dataset_name", |
||||
"sentence_id", |
||||
"original_img_id", |
||||
"nb_eval", |
||||
"task_id", |
||||
"original_id", |
||||
"token_span", |
||||
"caption", |
||||
"dataset_type", |
||||
] |
||||
return [ |
||||
{k: v.to(device) if k not in excluded_keys else v for k, v in t.items()} for t in targets |
||||
] |
||||
|
||||
|
||||
def get_phrases_from_posmap( |
||||
posmap: torch.BoolTensor, tokenized: Dict, tokenizer: AutoTokenizer |
||||
): |
||||
assert isinstance(posmap, torch.Tensor), "posmap must be torch.Tensor" |
||||
if posmap.dim() == 1: |
||||
non_zero_idx = posmap.nonzero(as_tuple=True)[0].tolist() |
||||
token_ids = [tokenized["input_ids"][i] for i in non_zero_idx] |
||||
return tokenizer.decode(token_ids) |
||||
else: |
||||
raise NotImplementedError("posmap must be 1-dim") |
||||
@ -0,0 +1,318 @@ |
||||
# -*- coding: utf-8 -*- |
||||
""" |
||||
@File : visualizer.py |
||||
@Time : 2022/04/05 11:39:33 |
||||
@Author : Shilong Liu |
||||
@Contact : slongliu86@gmail.com |
||||
""" |
||||
|
||||
import datetime |
||||
import os |
||||
|
||||
import cv2 |
||||
import matplotlib.pyplot as plt |
||||
import numpy as np |
||||
import torch |
||||
from matplotlib import transforms |
||||
from matplotlib.collections import PatchCollection |
||||
from matplotlib.patches import Polygon |
||||
from pycocotools import mask as maskUtils |
||||
|
||||
|
||||
def renorm( |
||||
img: torch.FloatTensor, mean=[0.485, 0.456, 0.406], std=[0.229, 0.224, 0.225] |
||||
) -> torch.FloatTensor: |
||||
# img: tensor(3,H,W) or tensor(B,3,H,W) |
||||
# return: same as img |
||||
assert img.dim() == 3 or img.dim() == 4, "img.dim() should be 3 or 4 but %d" % img.dim() |
||||
if img.dim() == 3: |
||||
assert img.size(0) == 3, 'img.size(0) shoule be 3 but "%d". (%s)' % ( |
||||
img.size(0), |
||||
str(img.size()), |
||||
) |
||||
img_perm = img.permute(1, 2, 0) |
||||
mean = torch.Tensor(mean) |
||||
std = torch.Tensor(std) |
||||
img_res = img_perm * std + mean |
||||
return img_res.permute(2, 0, 1) |
||||
else: # img.dim() == 4 |
||||
assert img.size(1) == 3, 'img.size(1) shoule be 3 but "%d". (%s)' % ( |
||||
img.size(1), |
||||
str(img.size()), |
||||
) |
||||
img_perm = img.permute(0, 2, 3, 1) |
||||
mean = torch.Tensor(mean) |
||||
std = torch.Tensor(std) |
||||
img_res = img_perm * std + mean |
||||
return img_res.permute(0, 3, 1, 2) |
||||
|
||||
|
||||
class ColorMap: |
||||
def __init__(self, basergb=[255, 255, 0]): |
||||
self.basergb = np.array(basergb) |
||||
|
||||
def __call__(self, attnmap): |
||||
# attnmap: h, w. np.uint8. |
||||
# return: h, w, 4. np.uint8. |
||||
assert attnmap.dtype == np.uint8 |
||||
h, w = attnmap.shape |
||||
res = self.basergb.copy() |
||||
res = res[None][None].repeat(h, 0).repeat(w, 1) # h, w, 3 |
||||
attn1 = attnmap.copy()[..., None] # h, w, 1 |
||||
res = np.concatenate((res, attn1), axis=-1).astype(np.uint8) |
||||
return res |
||||
|
||||
|
||||
def rainbow_text(x, y, ls, lc, **kw): |
||||
""" |
||||
Take a list of strings ``ls`` and colors ``lc`` and place them next to each |
||||
other, with text ls[i] being shown in color lc[i]. |
||||
|
||||
This example shows how to do both vertical and horizontal text, and will |
||||
pass all keyword arguments to plt.text, so you can set the font size, |
||||
family, etc. |
||||
""" |
||||
t = plt.gca().transData |
||||
fig = plt.gcf() |
||||
plt.show() |
||||
|
||||
# horizontal version |
||||
for s, c in zip(ls, lc): |
||||
text = plt.text(x, y, " " + s + " ", color=c, transform=t, **kw) |
||||
text.draw(fig.canvas.get_renderer()) |
||||
ex = text.get_window_extent() |
||||
t = transforms.offset_copy(text._transform, x=ex.width, units="dots") |
||||
|
||||
# #vertical version |
||||
# for s,c in zip(ls,lc): |
||||
# text = plt.text(x,y," "+s+" ",color=c, transform=t, |
||||
# rotation=90,va='bottom',ha='center',**kw) |
||||
# text.draw(fig.canvas.get_renderer()) |
||||
# ex = text.get_window_extent() |
||||
# t = transforms.offset_copy(text._transform, y=ex.height, units='dots') |
||||
|
||||
|
||||
class COCOVisualizer: |
||||
def __init__(self, coco=None, tokenlizer=None) -> None: |
||||
self.coco = coco |
||||
|
||||
def visualize(self, img, tgt, caption=None, dpi=180, savedir="vis"): |
||||
""" |
||||
img: tensor(3, H, W) |
||||
tgt: make sure they are all on cpu. |
||||
must have items: 'image_id', 'boxes', 'size' |
||||
""" |
||||
plt.figure(dpi=dpi) |
||||
plt.rcParams["font.size"] = "5" |
||||
ax = plt.gca() |
||||
img = renorm(img).permute(1, 2, 0) |
||||
# if os.environ.get('IPDB_SHILONG_DEBUG', None) == 'INFO': |
||||
# import ipdb; ipdb.set_trace() |
||||
ax.imshow(img) |
||||
|
||||
self.addtgt(tgt) |
||||
|
||||
if tgt is None: |
||||
image_id = 0 |
||||
elif "image_id" not in tgt: |
||||
image_id = 0 |
||||
else: |
||||
image_id = tgt["image_id"] |
||||
|
||||
if caption is None: |
||||
savename = "{}/{}-{}.png".format( |
||||
savedir, int(image_id), str(datetime.datetime.now()).replace(" ", "-") |
||||
) |
||||
else: |
||||
savename = "{}/{}-{}-{}.png".format( |
||||
savedir, caption, int(image_id), str(datetime.datetime.now()).replace(" ", "-") |
||||
) |
||||
print("savename: {}".format(savename)) |
||||
os.makedirs(os.path.dirname(savename), exist_ok=True) |
||||
plt.savefig(savename) |
||||
plt.close() |
||||
|
||||
def addtgt(self, tgt): |
||||
""" """ |
||||
if tgt is None or not "boxes" in tgt: |
||||
ax = plt.gca() |
||||
|
||||
if "caption" in tgt: |
||||
ax.set_title(tgt["caption"], wrap=True) |
||||
|
||||
ax.set_axis_off() |
||||
return |
||||
|
||||
ax = plt.gca() |
||||
H, W = tgt["size"] |
||||
numbox = tgt["boxes"].shape[0] |
||||
|
||||
color = [] |
||||
polygons = [] |
||||
boxes = [] |
||||
for box in tgt["boxes"].cpu(): |
||||
unnormbbox = box * torch.Tensor([W, H, W, H]) |
||||
unnormbbox[:2] -= unnormbbox[2:] / 2 |
||||
[bbox_x, bbox_y, bbox_w, bbox_h] = unnormbbox.tolist() |
||||
boxes.append([bbox_x, bbox_y, bbox_w, bbox_h]) |
||||
poly = [ |
||||
[bbox_x, bbox_y], |
||||
[bbox_x, bbox_y + bbox_h], |
||||
[bbox_x + bbox_w, bbox_y + bbox_h], |
||||
[bbox_x + bbox_w, bbox_y], |
||||
] |
||||
np_poly = np.array(poly).reshape((4, 2)) |
||||
polygons.append(Polygon(np_poly)) |
||||
c = (np.random.random((1, 3)) * 0.6 + 0.4).tolist()[0] |
||||
color.append(c) |
||||
|
||||
p = PatchCollection(polygons, facecolor=color, linewidths=0, alpha=0.1) |
||||
ax.add_collection(p) |
||||
p = PatchCollection(polygons, facecolor="none", edgecolors=color, linewidths=2) |
||||
ax.add_collection(p) |
||||
|
||||
if "strings_positive" in tgt and len(tgt["strings_positive"]) > 0: |
||||
assert ( |
||||
len(tgt["strings_positive"]) == numbox |
||||
), f"{len(tgt['strings_positive'])} = {numbox}, " |
||||
for idx, strlist in enumerate(tgt["strings_positive"]): |
||||
cate_id = int(tgt["labels"][idx]) |
||||
_string = str(cate_id) + ":" + " ".join(strlist) |
||||
bbox_x, bbox_y, bbox_w, bbox_h = boxes[idx] |
||||
# ax.text(bbox_x, bbox_y, _string, color='black', bbox={'facecolor': 'yellow', 'alpha': 1.0, 'pad': 1}) |
||||
ax.text( |
||||
bbox_x, |
||||
bbox_y, |
||||
_string, |
||||
color="black", |
||||
bbox={"facecolor": color[idx], "alpha": 0.6, "pad": 1}, |
||||
) |
||||
|
||||
if "box_label" in tgt: |
||||
assert len(tgt["box_label"]) == numbox, f"{len(tgt['box_label'])} = {numbox}, " |
||||
for idx, bl in enumerate(tgt["box_label"]): |
||||
_string = str(bl) |
||||
bbox_x, bbox_y, bbox_w, bbox_h = boxes[idx] |
||||
# ax.text(bbox_x, bbox_y, _string, color='black', bbox={'facecolor': 'yellow', 'alpha': 1.0, 'pad': 1}) |
||||
ax.text( |
||||
bbox_x, |
||||
bbox_y, |
||||
_string, |
||||
color="black", |
||||
bbox={"facecolor": color[idx], "alpha": 0.6, "pad": 1}, |
||||
) |
||||
|
||||
if "caption" in tgt: |
||||
ax.set_title(tgt["caption"], wrap=True) |
||||
# plt.figure() |
||||
# rainbow_text(0.0,0.0,"all unicorns poop rainbows ! ! !".split(), |
||||
# ['red', 'orange', 'brown', 'green', 'blue', 'purple', 'black']) |
||||
|
||||
if "attn" in tgt: |
||||
# if os.environ.get('IPDB_SHILONG_DEBUG', None) == 'INFO': |
||||
# import ipdb; ipdb.set_trace() |
||||
if isinstance(tgt["attn"], tuple): |
||||
tgt["attn"] = [tgt["attn"]] |
||||
for item in tgt["attn"]: |
||||
attn_map, basergb = item |
||||
attn_map = (attn_map - attn_map.min()) / (attn_map.max() - attn_map.min() + 1e-3) |
||||
attn_map = (attn_map * 255).astype(np.uint8) |
||||
cm = ColorMap(basergb) |
||||
heatmap = cm(attn_map) |
||||
ax.imshow(heatmap) |
||||
ax.set_axis_off() |
||||
|
||||
def showAnns(self, anns, draw_bbox=False): |
||||
""" |
||||
Display the specified annotations. |
||||
:param anns (array of object): annotations to display |
||||
:return: None |
||||
""" |
||||
if len(anns) == 0: |
||||
return 0 |
||||
if "segmentation" in anns[0] or "keypoints" in anns[0]: |
||||
datasetType = "instances" |
||||
elif "caption" in anns[0]: |
||||
datasetType = "captions" |
||||
else: |
||||
raise Exception("datasetType not supported") |
||||
if datasetType == "instances": |
||||
ax = plt.gca() |
||||
ax.set_autoscale_on(False) |
||||
polygons = [] |
||||
color = [] |
||||
for ann in anns: |
||||
c = (np.random.random((1, 3)) * 0.6 + 0.4).tolist()[0] |
||||
if "segmentation" in ann: |
||||
if type(ann["segmentation"]) == list: |
||||
# polygon |
||||
for seg in ann["segmentation"]: |
||||
poly = np.array(seg).reshape((int(len(seg) / 2), 2)) |
||||
polygons.append(Polygon(poly)) |
||||
color.append(c) |
||||
else: |
||||
# mask |
||||
t = self.imgs[ann["image_id"]] |
||||
if type(ann["segmentation"]["counts"]) == list: |
||||
rle = maskUtils.frPyObjects( |
||||
[ann["segmentation"]], t["height"], t["width"] |
||||
) |
||||
else: |
||||
rle = [ann["segmentation"]] |
||||
m = maskUtils.decode(rle) |
||||
img = np.ones((m.shape[0], m.shape[1], 3)) |
||||
if ann["iscrowd"] == 1: |
||||
color_mask = np.array([2.0, 166.0, 101.0]) / 255 |
||||
if ann["iscrowd"] == 0: |
||||
color_mask = np.random.random((1, 3)).tolist()[0] |
||||
for i in range(3): |
||||
img[:, :, i] = color_mask[i] |
||||
ax.imshow(np.dstack((img, m * 0.5))) |
||||
if "keypoints" in ann and type(ann["keypoints"]) == list: |
||||
# turn skeleton into zero-based index |
||||
sks = np.array(self.loadCats(ann["category_id"])[0]["skeleton"]) - 1 |
||||
kp = np.array(ann["keypoints"]) |
||||
x = kp[0::3] |
||||
y = kp[1::3] |
||||
v = kp[2::3] |
||||
for sk in sks: |
||||
if np.all(v[sk] > 0): |
||||
plt.plot(x[sk], y[sk], linewidth=3, color=c) |
||||
plt.plot( |
||||
x[v > 0], |
||||
y[v > 0], |
||||
"o", |
||||
markersize=8, |
||||
markerfacecolor=c, |
||||
markeredgecolor="k", |
||||
markeredgewidth=2, |
||||
) |
||||
plt.plot( |
||||
x[v > 1], |
||||
y[v > 1], |
||||
"o", |
||||
markersize=8, |
||||
markerfacecolor=c, |
||||
markeredgecolor=c, |
||||
markeredgewidth=2, |
||||
) |
||||
|
||||
if draw_bbox: |
||||
[bbox_x, bbox_y, bbox_w, bbox_h] = ann["bbox"] |
||||
poly = [ |
||||
[bbox_x, bbox_y], |
||||
[bbox_x, bbox_y + bbox_h], |
||||
[bbox_x + bbox_w, bbox_y + bbox_h], |
||||
[bbox_x + bbox_w, bbox_y], |
||||
] |
||||
np_poly = np.array(poly).reshape((4, 2)) |
||||
polygons.append(Polygon(np_poly)) |
||||
color.append(c) |
||||
|
||||
# p = PatchCollection(polygons, facecolor=color, linewidths=0, alpha=0.4) |
||||
# ax.add_collection(p) |
||||
p = PatchCollection(polygons, facecolor="none", edgecolors=color, linewidths=2) |
||||
ax.add_collection(p) |
||||
elif datasetType == "captions": |
||||
for ann in anns: |
||||
print(ann["caption"]) |
||||
@ -0,0 +1,100 @@ |
||||
import os |
||||
import random |
||||
from typing import List |
||||
|
||||
import torch |
||||
|
||||
|
||||
def create_positive_map_from_span(tokenized, token_span, max_text_len=256): |
||||
"""construct a map such that positive_map[i,j] = True iff box i is associated to token j |
||||
Input: |
||||
- tokenized: |
||||
- input_ids: Tensor[1, ntokens] |
||||
- attention_mask: Tensor[1, ntokens] |
||||
- token_span: list with length num_boxes. |
||||
- each item: [start_idx, end_idx] |
||||
""" |
||||
positive_map = torch.zeros((len(token_span), max_text_len), dtype=torch.float) |
||||
for j, tok_list in enumerate(token_span): |
||||
for (beg, end) in tok_list: |
||||
beg_pos = tokenized.char_to_token(beg) |
||||
end_pos = tokenized.char_to_token(end - 1) |
||||
if beg_pos is None: |
||||
try: |
||||
beg_pos = tokenized.char_to_token(beg + 1) |
||||
if beg_pos is None: |
||||
beg_pos = tokenized.char_to_token(beg + 2) |
||||
except: |
||||
beg_pos = None |
||||
if end_pos is None: |
||||
try: |
||||
end_pos = tokenized.char_to_token(end - 2) |
||||
if end_pos is None: |
||||
end_pos = tokenized.char_to_token(end - 3) |
||||
except: |
||||
end_pos = None |
||||
if beg_pos is None or end_pos is None: |
||||
continue |
||||
|
||||
assert beg_pos is not None and end_pos is not None |
||||
if os.environ.get("SHILONG_DEBUG_ONLY_ONE_POS", None) == "TRUE": |
||||
positive_map[j, beg_pos] = 1 |
||||
break |
||||
else: |
||||
positive_map[j, beg_pos : end_pos + 1].fill_(1) |
||||
|
||||
return positive_map / (positive_map.sum(-1)[:, None] + 1e-6) |
||||
|
||||
|
||||
def build_captions_and_token_span(cat_list, force_lowercase): |
||||
""" |
||||
Return: |
||||
captions: str |
||||
cat2tokenspan: dict |
||||
{ |
||||
'dog': [[0, 2]], |
||||
... |
||||
} |
||||
""" |
||||
|
||||
cat2tokenspan = {} |
||||
captions = "" |
||||
for catname in cat_list: |
||||
class_name = catname |
||||
if force_lowercase: |
||||
class_name = class_name.lower() |
||||
if "/" in class_name: |
||||
class_name_list: List = class_name.strip().split("/") |
||||
class_name_list.append(class_name) |
||||
class_name: str = random.choice(class_name_list) |
||||
|
||||
tokens_positive_i = [] |
||||
subnamelist = [i.strip() for i in class_name.strip().split(" ")] |
||||
for subname in subnamelist: |
||||
if len(subname) == 0: |
||||
continue |
||||
if len(captions) > 0: |
||||
captions = captions + " " |
||||
strat_idx = len(captions) |
||||
end_idx = strat_idx + len(subname) |
||||
tokens_positive_i.append([strat_idx, end_idx]) |
||||
captions = captions + subname |
||||
|
||||
if len(tokens_positive_i) > 0: |
||||
captions = captions + " ." |
||||
cat2tokenspan[class_name] = tokens_positive_i |
||||
|
||||
return captions, cat2tokenspan |
||||
|
||||
|
||||
def build_id2posspan_and_caption(category_dict: dict): |
||||
"""Build id2pos_span and caption from category_dict |
||||
|
||||
Args: |
||||
category_dict (dict): category_dict |
||||
""" |
||||
cat_list = [item["name"].lower() for item in category_dict] |
||||
id2catname = {item["id"]: item["name"].lower() for item in category_dict} |
||||
caption, cat2posspan = build_captions_and_token_span(cat_list, force_lowercase=True) |
||||
id2posspan = {catid: cat2posspan[catname] for catid, catname in id2catname.items()} |
||||
return id2posspan, caption |
||||
@ -0,0 +1 @@ |
||||
__version__ = "0.1.0" |
||||
@ -0,0 +1,10 @@ |
||||
torch |
||||
torchvision |
||||
transformers |
||||
addict |
||||
yapf |
||||
timm |
||||
numpy |
||||
opencv-python |
||||
supervision==0.4.0 |
||||
pycocotools |
||||
@ -0,0 +1,208 @@ |
||||
# coding=utf-8 |
||||
# Copyright 2022 The IDEA Authors. 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. |
||||
# ------------------------------------------------------------------------------------------------ |
||||
# Modified from |
||||
# https://github.com/fundamentalvision/Deformable-DETR/blob/main/models/ops/setup.py |
||||
# https://github.com/facebookresearch/detectron2/blob/main/setup.py |
||||
# https://github.com/open-mmlab/mmdetection/blob/master/setup.py |
||||
# https://github.com/Oneflow-Inc/libai/blob/main/setup.py |
||||
# ------------------------------------------------------------------------------------------------ |
||||
|
||||
import glob |
||||
import os |
||||
import subprocess |
||||
|
||||
import torch |
||||
from setuptools import find_packages, setup |
||||
from torch.utils.cpp_extension import CUDA_HOME, CppExtension, CUDAExtension |
||||
|
||||
# groundingdino version info |
||||
version = "0.1.0" |
||||
package_name = "groundingdino" |
||||
cwd = os.path.dirname(os.path.abspath(__file__)) |
||||
|
||||
|
||||
sha = "Unknown" |
||||
try: |
||||
sha = subprocess.check_output(["git", "rev-parse", "HEAD"], cwd=cwd).decode("ascii").strip() |
||||
except Exception: |
||||
pass |
||||
|
||||
|
||||
def write_version_file(): |
||||
version_path = os.path.join(cwd, "groundingdino", "version.py") |
||||
with open(version_path, "w") as f: |
||||
f.write(f"__version__ = '{version}'\n") |
||||
# f.write(f"git_version = {repr(sha)}\n") |
||||
|
||||
|
||||
requirements = ["torch", "torchvision"] |
||||
|
||||
torch_ver = [int(x) for x in torch.__version__.split(".")[:2]] |
||||
|
||||
|
||||
def get_extensions(): |
||||
this_dir = os.path.dirname(os.path.abspath(__file__)) |
||||
extensions_dir = os.path.join(this_dir, "groundingdino", "models", "GroundingDINO", "csrc") |
||||
|
||||
main_source = os.path.join(extensions_dir, "vision.cpp") |
||||
sources = glob.glob(os.path.join(extensions_dir, "**", "*.cpp")) |
||||
source_cuda = glob.glob(os.path.join(extensions_dir, "**", "*.cu")) + glob.glob( |
||||
os.path.join(extensions_dir, "*.cu") |
||||
) |
||||
|
||||
sources = [main_source] + sources |
||||
|
||||
extension = CppExtension |
||||
|
||||
extra_compile_args = {"cxx": []} |
||||
define_macros = [] |
||||
|
||||
if torch.cuda.is_available() and CUDA_HOME is not None: |
||||
print("Compiling with CUDA") |
||||
extension = CUDAExtension |
||||
sources += source_cuda |
||||
define_macros += [("WITH_CUDA", None)] |
||||
extra_compile_args["nvcc"] = [ |
||||
"-DCUDA_HAS_FP16=1", |
||||
"-D__CUDA_NO_HALF_OPERATORS__", |
||||
"-D__CUDA_NO_HALF_CONVERSIONS__", |
||||
"-D__CUDA_NO_HALF2_OPERATORS__", |
||||
] |
||||
else: |
||||
print("Compiling without CUDA") |
||||
define_macros += [("WITH_HIP", None)] |
||||
extra_compile_args["nvcc"] = [] |
||||
return None |
||||
|
||||
sources = [os.path.join(extensions_dir, s) for s in sources] |
||||
include_dirs = [extensions_dir] |
||||
|
||||
ext_modules = [ |
||||
extension( |
||||
"groundingdino._C", |
||||
sources, |
||||
include_dirs=include_dirs, |
||||
define_macros=define_macros, |
||||
extra_compile_args=extra_compile_args, |
||||
) |
||||
] |
||||
|
||||
return ext_modules |
||||
|
||||
|
||||
def parse_requirements(fname="requirements.txt", with_version=True): |
||||
"""Parse the package dependencies listed in a requirements file but strips |
||||
specific versioning information. |
||||
|
||||
Args: |
||||
fname (str): path to requirements file |
||||
with_version (bool, default=False): if True include version specs |
||||
|
||||
Returns: |
||||
List[str]: list of requirements items |
||||
|
||||
CommandLine: |
||||
python -c "import setup; print(setup.parse_requirements())" |
||||
""" |
||||
import re |
||||
import sys |
||||
from os.path import exists |
||||
|
||||
require_fpath = fname |
||||
|
||||
def parse_line(line): |
||||
"""Parse information from a line in a requirements text file.""" |
||||
if line.startswith("-r "): |
||||
# Allow specifying requirements in other files |
||||
target = line.split(" ")[1] |
||||
for info in parse_require_file(target): |
||||
yield info |
||||
else: |
||||
info = {"line": line} |
||||
if line.startswith("-e "): |
||||
info["package"] = line.split("#egg=")[1] |
||||
elif "@git+" in line: |
||||
info["package"] = line |
||||
else: |
||||
# Remove versioning from the package |
||||
pat = "(" + "|".join([">=", "==", ">"]) + ")" |
||||
parts = re.split(pat, line, maxsplit=1) |
||||
parts = [p.strip() for p in parts] |
||||
|
||||
info["package"] = parts[0] |
||||
if len(parts) > 1: |
||||
op, rest = parts[1:] |
||||
if ";" in rest: |
||||
# Handle platform specific dependencies |
||||
# http://setuptools.readthedocs.io/en/latest/setuptools.html#declaring-platform-specific-dependencies |
||||
version, platform_deps = map(str.strip, rest.split(";")) |
||||
info["platform_deps"] = platform_deps |
||||
else: |
||||
version = rest # NOQA |
||||
info["version"] = (op, version) |
||||
yield info |
||||
|
||||
def parse_require_file(fpath): |
||||
with open(fpath, "r") as f: |
||||
for line in f.readlines(): |
||||
line = line.strip() |
||||
if line and not line.startswith("#"): |
||||
for info in parse_line(line): |
||||
yield info |
||||
|
||||
def gen_packages_items(): |
||||
if exists(require_fpath): |
||||
for info in parse_require_file(require_fpath): |
||||
parts = [info["package"]] |
||||
if with_version and "version" in info: |
||||
parts.extend(info["version"]) |
||||
if not sys.version.startswith("3.4"): |
||||
# apparently package_deps are broken in 3.4 |
||||
platform_deps = info.get("platform_deps") |
||||
if platform_deps is not None: |
||||
parts.append(";" + platform_deps) |
||||
item = "".join(parts) |
||||
yield item |
||||
|
||||
packages = list(gen_packages_items()) |
||||
return packages |
||||
|
||||
|
||||
if __name__ == "__main__": |
||||
print(f"Building wheel {package_name}-{version}") |
||||
|
||||
with open("LICENSE", "r", encoding="utf-8") as f: |
||||
license = f.read() |
||||
|
||||
write_version_file() |
||||
|
||||
setup( |
||||
name="groundingdino", |
||||
version="0.1.0", |
||||
author="International Digital Economy Academy, Shilong Liu", |
||||
url="https://github.com/IDEA-Research/GroundingDINO", |
||||
description="open-set object detector", |
||||
license=license, |
||||
install_requires=parse_requirements("requirements.txt"), |
||||
packages=find_packages( |
||||
exclude=( |
||||
"configs", |
||||
"tests", |
||||
) |
||||
), |
||||
ext_modules=get_extensions(), |
||||
cmdclass={"build_ext": torch.utils.cpp_extension.BuildExtension}, |
||||
) |
||||
Loading…
Reference in new issue