Initial code import
Browse files- .gitattributes +1 -0
- .gitignore +2 -0
- LICENSE +22 -0
- README.md +225 -0
- cliponnx/__init__.py +0 -0
- cliponnx/bpe_simple_vocab_16e6.txt.gz +3 -0
- cliponnx/models.py +159 -0
- cliponnx/simple_tokenizer.py +134 -0
- convert.py +104 -0
- example.py +66 -0
- flowers.jpg +3 -0
- heavy-industry.jpg +3 -0
- poetry.lock +841 -0
- poetry.toml +2 -0
- pyproject.toml +32 -0
- variants.py +108 -0
.gitattributes
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LICENSE
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MIT License
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Copyright (c) 2022 Miha Lunar (other scripts)
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Copyright (c) 2021 OpenAI (models, simple_tokenizer.py, bpe_simple_vocab_16e6.txt.gz, parts of models.py)
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Permission is hereby granted, free of charge, to any person obtaining a copy
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of this software and associated documentation files (the "Software"), to deal
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in the Software without restriction, including without limitation the rights
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to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
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copies of the Software, and to permit persons to whom the Software is
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furnished to do so, subject to the following conditions:
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The above copyright notice and this permission notice shall be included in all
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copies or substantial portions of the Software.
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THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
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IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
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FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
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AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
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LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
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OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
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SOFTWARE.
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README.md
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---
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| 2 |
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language:
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- en
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license: mit
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tags:
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- clip
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- vision
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---
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| 9 |
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# CLIP Variants
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_The CLIP model was developed by researchers at OpenAI to learn about what contributes to robustness in computer vision tasks. The model was also developed to test the ability of models to generalize to arbitrary image classification tasks in a zero-shot manner. It was not developed for general model deployment - to deploy models like CLIP, researchers will first need to carefully study their capabilities in relation to the specific context they’re being deployed within._
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See the original [CLIP Model Card][clip-model-card] for more details on limitations and biases.
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This repository holds [OpenAI's CLIP][clip] models converted into many other variants, see below for more details.
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## Disclaimer & License
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I haven't done many tests on these conversions. I've briefly tried the float16 versions, which seem very similar to the original float32, however the similarity seems to drop more with the qint8/quint8 versions as expected. I couldn't try qint8 as it seemed unsupported for some operations, but I'm including it for completeness. From a brief test the quint8 version seemed to work fine.
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The license for the conversion code is MIT, the license for the models is the same as the original license for the OpenAI models (🤷♂️). I have no affiliation with OpenAI.
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## Acknowledgements
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| 25 |
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* [OpenAI CLIP][clip]
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* [OpenAI CLIP JavaScript by josephrocca](https://github.com/josephrocca/openai-clip-js)
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| 27 |
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* [CLIP-ONNX by Lednik7](https://github.com/Lednik7/CLIP-ONNX)
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| 28 |
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* [Exporting a Model from PyTorch to ONNX and Running it using ONNX Runtime](https://pytorch.org/tutorials/advanced/super_resolution_with_onnxruntime.html)
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| 29 |
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* [imgbeddings by minimaxir](https://github.com/minimaxir/imgbeddings)
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| 30 |
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* ... probably more
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| 31 |
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## Example
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| 33 |
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See [example.py](./example.py)
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| 35 |
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| 36 |
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```
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❯ python .\example.py
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Loading visual model: models/clip-vit-base-patch32-visual-float16.onnx
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Visual inference ready, input size 224, type tensor(float16)
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Images shape: (2, 3, 224, 224)
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Embeddings shape: (2, 512)
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Loading textual model: models/clip-vit-base-patch32-textual-float16.onnx
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Textual inference ready, input size 77, type tensor(int32)
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Texts shape: (14, 77)
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Embeddings shape: (14, 512)
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flowers.jpg
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-------- -------- ---------------------------------------------------------------
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| 50 |
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0.294922 >>>>>>>> a close up photo of a cherry blossom
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0.267578 >>>>>>>> cherry blossom
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0.249878 >>>>>>> flowers
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0.242554 >>>>>>> a photo taken on a bright and sunny day
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0.228882 >>>>>> bees
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0.222778 >>>>>> plant
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0.216187 >>>>>> a photo taken on a dark and cloudy day
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0.201538 >>>>>> ruhrgebiet
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0.196655 >>>>> processing plant
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0.192139 >>>>> a photo taken at midnight
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0.18689 >>>>> industry
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0.177856 >>>>> cars
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0.176636 >>>>> dogs and cats
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0.111267 >>> a large industrial plant with many pipes, walkways and railings
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-------- -------- ---------------------------------------------------------------
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heavy-industry.jpg
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-------- ---------- ---------------------------------------------------------------
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0.336182 >>>>>>>>>> a large industrial plant with many pipes, walkways and railings
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0.316895 >>>>>>>>> processing plant
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0.302002 >>>>>>>>> industry
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0.27417 >>>>>>>> ruhrgebiet
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0.254883 >>>>>>> plant
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| 73 |
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0.22876 >>>>>> a photo taken on a dark and cloudy day
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0.219482 >>>>>> a photo taken on a bright and sunny day
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0.211304 >>>>>> a photo taken at midnight
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0.198608 >>>>> cars
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0.190552 >>>>> flowers
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0.181885 >>>>> bees
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0.180542 >>>>> cherry blossom
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0.174438 >>>>> dogs and cats
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0.14917 >>>> a close up photo of a cherry blossom
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-------- ---------- ---------------------------------------------------------------
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```
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## Parameters
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| 86 |
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The only format supported right now is [Open Neural Network Exchange (ONNX)][onnx].
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All the currently available OpenAI models have been converted. Some of the IDs were taken from [Open AI models on Hugging Face](https://huggingface.co/openai), others were made up following the same format.
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| Model name | Model ID |
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| --- | --- |
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| RN50 | resnet-50 |
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| RN101 | resnet-101 |
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| 95 |
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| RN50x4 | resnet-50x4 |
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| RN50x16 | resnet-50x16 |
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| RN50x64 | resnet-50x64 |
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| 98 |
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| RN50 | resnet-50 |
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| RN50 | resnet-50 |
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| RN50 | resnet-50 |
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| ViT-B/16 | vit-base-patch16 |
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| ViT-B/32 | vit-base-patch32 |
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| 103 |
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| ViT-L/14 | vit-large-patch14 |
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| ViT-L/14@336px | vit-large-patch14-336 |
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| 105 |
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As CLIP is a multimodal model, the original models are split into two separate "modes", one for processing images
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and the other for processing text.
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| Mode |
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|---------|
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| visual |
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| textual |
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The models were converted into multiple data types as well.
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| Data Type |
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|-------------|
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| float16 |
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| qint8 |
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| 120 |
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| quint8 |
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| 121 |
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## Variants
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| 123 |
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| Path | Model ID | Mode | Data Type | Available | Size (MB) |
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| 125 |
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|--------------------------------------------------------|-----------------------|---------|--------------------|-------------|-------------|
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| 126 |
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| models/clip-resnet-50-visual.onnx | resnet-50 | visual | float32 (original) | ✅ | 153 |
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| 127 |
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| models/clip-resnet-50-visual-float16.onnx | resnet-50 | visual | float16 | ✅ | 77 |
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| 128 |
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| models/clip-resnet-50-visual-qint8.onnx | resnet-50 | visual | qint8 | ✅ | 39 |
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| 129 |
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| models/clip-resnet-50-visual-quint8.onnx | resnet-50 | visual | quint8 | ✅ | 39 |
|
| 130 |
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| models/clip-resnet-50-textual.onnx | resnet-50 | textual | float32 (original) | ✅ | 255 |
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| 131 |
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| models/clip-resnet-50-textual-float16.onnx | resnet-50 | textual | float16 | ✅ | 128 |
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| 132 |
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| models/clip-resnet-50-textual-qint8.onnx | resnet-50 | textual | qint8 | ✅ | 64 |
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| 133 |
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| models/clip-resnet-50-textual-quint8.onnx | resnet-50 | textual | quint8 | ✅ | 64 |
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| 134 |
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| models/clip-resnet-101-visual.onnx | resnet-101 | visual | float32 (original) | ✅ | 225 |
|
| 135 |
+
| models/clip-resnet-101-visual-float16.onnx | resnet-101 | visual | float16 | ✅ | 112 |
|
| 136 |
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| models/clip-resnet-101-visual-qint8.onnx | resnet-101 | visual | qint8 | ✅ | 57 |
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| 137 |
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| models/clip-resnet-101-visual-quint8.onnx | resnet-101 | visual | quint8 | ✅ | 57 |
|
| 138 |
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| models/clip-resnet-101-textual.onnx | resnet-101 | textual | float32 (original) | ✅ | 254 |
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| 139 |
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| models/clip-resnet-101-textual-float16.onnx | resnet-101 | textual | float16 | ✅ | 127 |
|
| 140 |
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| models/clip-resnet-101-textual-qint8.onnx | resnet-101 | textual | qint8 | ✅ | 64 |
|
| 141 |
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| models/clip-resnet-101-textual-quint8.onnx | resnet-101 | textual | quint8 | ✅ | 64 |
|
| 142 |
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| models/clip-resnet-50x4-visual.onnx | resnet-50x4 | visual | float32 (original) | ✅ | 348 |
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| 143 |
+
| models/clip-resnet-50x4-visual-float16.onnx | resnet-50x4 | visual | float16 | ✅ | 174 |
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| 144 |
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| models/clip-resnet-50x4-visual-qint8.onnx | resnet-50x4 | visual | qint8 | ✅ | 88 |
|
| 145 |
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| models/clip-resnet-50x4-visual-quint8.onnx | resnet-50x4 | visual | quint8 | ✅ | 88 |
|
| 146 |
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| models/clip-resnet-50x4-textual.onnx | resnet-50x4 | textual | float32 (original) | ✅ | 365 |
|
| 147 |
+
| models/clip-resnet-50x4-textual-float16.onnx | resnet-50x4 | textual | float16 | ✅ | 183 |
|
| 148 |
+
| models/clip-resnet-50x4-textual-qint8.onnx | resnet-50x4 | textual | qint8 | ✅ | 92 |
|
| 149 |
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| models/clip-resnet-50x4-textual-quint8.onnx | resnet-50x4 | textual | quint8 | ✅ | 92 |
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| 150 |
+
| models/clip-resnet-50x16-visual.onnx | resnet-50x16 | visual | float32 (original) | ✅ | 669 |
|
| 151 |
+
| models/clip-resnet-50x16-visual-float16.onnx | resnet-50x16 | visual | float16 | ✅ | 335 |
|
| 152 |
+
| models/clip-resnet-50x16-visual-qint8.onnx | resnet-50x16 | visual | qint8 | ✅ | 169 |
|
| 153 |
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| models/clip-resnet-50x16-visual-quint8.onnx | resnet-50x16 | visual | quint8 | ✅ | 169 |
|
| 154 |
+
| models/clip-resnet-50x16-textual.onnx | resnet-50x16 | textual | float32 (original) | ✅ | 495 |
|
| 155 |
+
| models/clip-resnet-50x16-textual-float16.onnx | resnet-50x16 | textual | float16 | ✅ | 248 |
|
| 156 |
+
| models/clip-resnet-50x16-textual-qint8.onnx | resnet-50x16 | textual | qint8 | ✅ | 124 |
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| 157 |
+
| models/clip-resnet-50x16-textual-quint8.onnx | resnet-50x16 | textual | quint8 | ✅ | 124 |
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| 158 |
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| models/clip-resnet-50x64-visual.onnx | resnet-50x64 | visual | float32 (original) | ✅ | 1681 |
|
| 159 |
+
| models/clip-resnet-50x64-visual-float16.onnx | resnet-50x64 | visual | float16 | ✅ | 840 |
|
| 160 |
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| models/clip-resnet-50x64-visual-qint8.onnx | resnet-50x64 | visual | qint8 | ✅ | 424 |
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| 161 |
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| models/clip-resnet-50x64-visual-quint8.onnx | resnet-50x64 | visual | quint8 | ✅ | 424 |
|
| 162 |
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| models/clip-resnet-50x64-textual.onnx | resnet-50x64 | textual | float32 (original) | ✅ | 812 |
|
| 163 |
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| models/clip-resnet-50x64-textual-float16.onnx | resnet-50x64 | textual | float16 | ✅ | 406 |
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| 164 |
+
| models/clip-resnet-50x64-textual-qint8.onnx | resnet-50x64 | textual | qint8 | ✅ | 204 |
|
| 165 |
+
| models/clip-resnet-50x64-textual-quint8.onnx | resnet-50x64 | textual | quint8 | ✅ | 204 |
|
| 166 |
+
| models/clip-resnet-50-visual.onnx | resnet-50 | visual | float32 (original) | ✅ | 153 |
|
| 167 |
+
| models/clip-resnet-50-visual-float16.onnx | resnet-50 | visual | float16 | ✅ | 77 |
|
| 168 |
+
| models/clip-resnet-50-visual-qint8.onnx | resnet-50 | visual | qint8 | ✅ | 39 |
|
| 169 |
+
| models/clip-resnet-50-visual-quint8.onnx | resnet-50 | visual | quint8 | ✅ | 39 |
|
| 170 |
+
| models/clip-resnet-50-textual.onnx | resnet-50 | textual | float32 (original) | ✅ | 255 |
|
| 171 |
+
| models/clip-resnet-50-textual-float16.onnx | resnet-50 | textual | float16 | ✅ | 128 |
|
| 172 |
+
| models/clip-resnet-50-textual-qint8.onnx | resnet-50 | textual | qint8 | ✅ | 64 |
|
| 173 |
+
| models/clip-resnet-50-textual-quint8.onnx | resnet-50 | textual | quint8 | ✅ | 64 |
|
| 174 |
+
| models/clip-resnet-50-visual.onnx | resnet-50 | visual | float32 (original) | ✅ | 153 |
|
| 175 |
+
| models/clip-resnet-50-visual-float16.onnx | resnet-50 | visual | float16 | ✅ | 77 |
|
| 176 |
+
| models/clip-resnet-50-visual-qint8.onnx | resnet-50 | visual | qint8 | ✅ | 39 |
|
| 177 |
+
| models/clip-resnet-50-visual-quint8.onnx | resnet-50 | visual | quint8 | ✅ | 39 |
|
| 178 |
+
| models/clip-resnet-50-textual.onnx | resnet-50 | textual | float32 (original) | ✅ | 255 |
|
| 179 |
+
| models/clip-resnet-50-textual-float16.onnx | resnet-50 | textual | float16 | ✅ | 128 |
|
| 180 |
+
| models/clip-resnet-50-textual-qint8.onnx | resnet-50 | textual | qint8 | ✅ | 64 |
|
| 181 |
+
| models/clip-resnet-50-textual-quint8.onnx | resnet-50 | textual | quint8 | ✅ | 64 |
|
| 182 |
+
| models/clip-resnet-50-visual.onnx | resnet-50 | visual | float32 (original) | ✅ | 153 |
|
| 183 |
+
| models/clip-resnet-50-visual-float16.onnx | resnet-50 | visual | float16 | ✅ | 77 |
|
| 184 |
+
| models/clip-resnet-50-visual-qint8.onnx | resnet-50 | visual | qint8 | ✅ | 39 |
|
| 185 |
+
| models/clip-resnet-50-visual-quint8.onnx | resnet-50 | visual | quint8 | ✅ | 39 |
|
| 186 |
+
| models/clip-resnet-50-textual.onnx | resnet-50 | textual | float32 (original) | ✅ | 255 |
|
| 187 |
+
| models/clip-resnet-50-textual-float16.onnx | resnet-50 | textual | float16 | ✅ | 128 |
|
| 188 |
+
| models/clip-resnet-50-textual-qint8.onnx | resnet-50 | textual | qint8 | ✅ | 64 |
|
| 189 |
+
| models/clip-resnet-50-textual-quint8.onnx | resnet-50 | textual | quint8 | ✅ | 64 |
|
| 190 |
+
| models/clip-vit-base-patch16-visual.onnx | vit-base-patch16 | visual | float32 (original) | ✅ | 345 |
|
| 191 |
+
| models/clip-vit-base-patch16-visual-float16.onnx | vit-base-patch16 | visual | float16 | ✅ | 173 |
|
| 192 |
+
| models/clip-vit-base-patch16-visual-qint8.onnx | vit-base-patch16 | visual | qint8 | ✅ | 87 |
|
| 193 |
+
| models/clip-vit-base-patch16-visual-quint8.onnx | vit-base-patch16 | visual | quint8 | ✅ | 87 |
|
| 194 |
+
| models/clip-vit-base-patch16-textual.onnx | vit-base-patch16 | textual | float32 (original) | ✅ | 254 |
|
| 195 |
+
| models/clip-vit-base-patch16-textual-float16.onnx | vit-base-patch16 | textual | float16 | ✅ | 127 |
|
| 196 |
+
| models/clip-vit-base-patch16-textual-qint8.onnx | vit-base-patch16 | textual | qint8 | ✅ | 64 |
|
| 197 |
+
| models/clip-vit-base-patch16-textual-quint8.onnx | vit-base-patch16 | textual | quint8 | ✅ | 64 |
|
| 198 |
+
| models/clip-vit-base-patch32-visual.onnx | vit-base-patch32 | visual | float32 (original) | ✅ | 352 |
|
| 199 |
+
| models/clip-vit-base-patch32-visual-float16.onnx | vit-base-patch32 | visual | float16 | ✅ | 176 |
|
| 200 |
+
| models/clip-vit-base-patch32-visual-qint8.onnx | vit-base-patch32 | visual | qint8 | ✅ | 89 |
|
| 201 |
+
| models/clip-vit-base-patch32-visual-quint8.onnx | vit-base-patch32 | visual | quint8 | ✅ | 89 |
|
| 202 |
+
| models/clip-vit-base-patch32-textual.onnx | vit-base-patch32 | textual | float32 (original) | ✅ | 254 |
|
| 203 |
+
| models/clip-vit-base-patch32-textual-float16.onnx | vit-base-patch32 | textual | float16 | ✅ | 127 |
|
| 204 |
+
| models/clip-vit-base-patch32-textual-qint8.onnx | vit-base-patch32 | textual | qint8 | ✅ | 64 |
|
| 205 |
+
| models/clip-vit-base-patch32-textual-quint8.onnx | vit-base-patch32 | textual | quint8 | ✅ | 64 |
|
| 206 |
+
| models/clip-vit-large-patch14-visual.onnx | vit-large-patch14 | visual | float32 (original) | ✅ | 1216 |
|
| 207 |
+
| models/clip-vit-large-patch14-visual-float16.onnx | vit-large-patch14 | visual | float16 | ✅ | 608 |
|
| 208 |
+
| models/clip-vit-large-patch14-visual-qint8.onnx | vit-large-patch14 | visual | qint8 | ✅ | 306 |
|
| 209 |
+
| models/clip-vit-large-patch14-visual-quint8.onnx | vit-large-patch14 | visual | quint8 | ✅ | 306 |
|
| 210 |
+
| models/clip-vit-large-patch14-textual.onnx | vit-large-patch14 | textual | float32 (original) | ✅ | 495 |
|
| 211 |
+
| models/clip-vit-large-patch14-textual-float16.onnx | vit-large-patch14 | textual | float16 | ✅ | 247 |
|
| 212 |
+
| models/clip-vit-large-patch14-textual-qint8.onnx | vit-large-patch14 | textual | qint8 | ✅ | 124 |
|
| 213 |
+
| models/clip-vit-large-patch14-textual-quint8.onnx | vit-large-patch14 | textual | quint8 | ✅ | 124 |
|
| 214 |
+
| models/clip-vit-large-patch14-336-visual.onnx | vit-large-patch14-336 | visual | float32 (original) | ✅ | 1217 |
|
| 215 |
+
| models/clip-vit-large-patch14-336-visual-float16.onnx | vit-large-patch14-336 | visual | float16 | ✅ | 609 |
|
| 216 |
+
| models/clip-vit-large-patch14-336-visual-qint8.onnx | vit-large-patch14-336 | visual | qint8 | ✅ | 307 |
|
| 217 |
+
| models/clip-vit-large-patch14-336-visual-quint8.onnx | vit-large-patch14-336 | visual | quint8 | ✅ | 307 |
|
| 218 |
+
| models/clip-vit-large-patch14-336-textual.onnx | vit-large-patch14-336 | textual | float32 (original) | ✅ | 495 |
|
| 219 |
+
| models/clip-vit-large-patch14-336-textual-float16.onnx | vit-large-patch14-336 | textual | float16 | ✅ | 247 |
|
| 220 |
+
| models/clip-vit-large-patch14-336-textual-qint8.onnx | vit-large-patch14-336 | textual | qint8 | ✅ | 124 |
|
| 221 |
+
| models/clip-vit-large-patch14-336-textual-quint8.onnx | vit-large-patch14-336 | textual | quint8 | ✅ | 124 |
|
| 222 |
+
|
| 223 |
+
[onnx]: https://onnx.ai/
|
| 224 |
+
[clip]: https://github.com/openai/CLIP
|
| 225 |
+
[clip-model-card]: https://github.com/openai/CLIP/blob/b4ae44927b78d0093b556e3ce43cbdcff422017a/model-card.md
|
cliponnx/__init__.py
ADDED
|
File without changes
|
cliponnx/bpe_simple_vocab_16e6.txt.gz
ADDED
|
@@ -0,0 +1,3 @@
|
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| 1 |
+
version https://git-lfs.github.com/spec/v1
|
| 2 |
+
oid sha256:924691ac288e54409236115652ad4aa250f48203de50a9e4722a6ecd48d6804a
|
| 3 |
+
size 1356917
|
cliponnx/models.py
ADDED
|
@@ -0,0 +1,159 @@
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|
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|
|
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|
|
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|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
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|
|
|
|
|
|
|
|
|
|
|
|
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|
|
|
|
|
|
|
|
|
|
|
|
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|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
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|
|
|
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|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
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|
|
|
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|
|
|
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|
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|
|
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|
|
|
|
|
|
|
|
|
|
|
|
| 1 |
+
# Based on https://github.com/openai/CLIP/blob/main/clip/model.py
|
| 2 |
+
|
| 3 |
+
import onnxruntime
|
| 4 |
+
import numpy as np
|
| 5 |
+
from typing import List, Union
|
| 6 |
+
from PIL import Image
|
| 7 |
+
|
| 8 |
+
from clip.simple_tokenizer import SimpleTokenizer
|
| 9 |
+
|
| 10 |
+
def onnx_node_type_np_type(type):
|
| 11 |
+
if type == "tensor(float)":
|
| 12 |
+
return np.float32
|
| 13 |
+
if type == "tensor(float16)":
|
| 14 |
+
return np.float16
|
| 15 |
+
if type == "tensor(int32)":
|
| 16 |
+
return np.int32
|
| 17 |
+
if type == "tensor(int64)":
|
| 18 |
+
return np.int64
|
| 19 |
+
raise NotImplementedError(f"Unsupported onnx type: {type}")
|
| 20 |
+
|
| 21 |
+
def ensure_input_type(input, type):
|
| 22 |
+
np_type = onnx_node_type_np_type(type)
|
| 23 |
+
if input.dtype == type:
|
| 24 |
+
return input
|
| 25 |
+
return input.astype(dtype=np_type)
|
| 26 |
+
|
| 27 |
+
class VisualModel:
|
| 28 |
+
def __init__(self, path, providers=None):
|
| 29 |
+
self.path = path
|
| 30 |
+
print(f"Loading visual model: {path}")
|
| 31 |
+
self.sess = onnxruntime.InferenceSession(path, providers=providers)
|
| 32 |
+
self.input = self.sess.get_inputs()[0]
|
| 33 |
+
self.output = self.sess.get_outputs()[0]
|
| 34 |
+
|
| 35 |
+
if len(self.input.shape) != 4 or self.input.shape[2] != self.input.shape[3]:
|
| 36 |
+
raise ValueError(f"unexpected shape {self.input.shape}")
|
| 37 |
+
self.input_size = self.input.shape[2]
|
| 38 |
+
print(f"Visual inference ready, input size {self.input_size}, type {self.input.type}")
|
| 39 |
+
|
| 40 |
+
def encode(self, image_input):
|
| 41 |
+
image_input = ensure_input_type(image_input, self.input.type)
|
| 42 |
+
return self.sess.run([self.output.name], {self.input.name: image_input})[0]
|
| 43 |
+
|
| 44 |
+
def fitted(self, size, w, h):
|
| 45 |
+
short, long = (w, h) if w <= h else (h, w)
|
| 46 |
+
new_short, new_long = size, int(size * long / short)
|
| 47 |
+
new_w, new_h = (new_short, new_long) if w <= h else (new_long, new_short)
|
| 48 |
+
return [new_w, new_h]
|
| 49 |
+
|
| 50 |
+
def resize_to(self, img, size):
|
| 51 |
+
new_size = self.fitted(size, img.width, img.height)
|
| 52 |
+
return img.resize(size=new_size, resample=Image.Resampling.BICUBIC)
|
| 53 |
+
|
| 54 |
+
def center_crop(self, img, size):
|
| 55 |
+
image_height = img.height
|
| 56 |
+
image_width = img.width
|
| 57 |
+
if size > image_width or size > image_height:
|
| 58 |
+
padding_ltrb = [
|
| 59 |
+
(size - image_width) // 2 if size > image_width else 0,
|
| 60 |
+
(size - image_height) // 2 if size > image_height else 0,
|
| 61 |
+
(size - image_width + 1) // 2 if size > image_width else 0,
|
| 62 |
+
(size - image_height + 1) // 2 if size > image_height else 0,
|
| 63 |
+
]
|
| 64 |
+
img = img.pad(img, padding_ltrb, fill=0)
|
| 65 |
+
image_width = img.width
|
| 66 |
+
image_height = img.height
|
| 67 |
+
if size == image_width and size == image_height:
|
| 68 |
+
return img
|
| 69 |
+
top = int(round((image_height - size) / 2.0))
|
| 70 |
+
left = int(round((image_width - size) / 2.0))
|
| 71 |
+
return img.crop((left, top, left + size, top + size))
|
| 72 |
+
|
| 73 |
+
def to_numpy(self, pic):
|
| 74 |
+
mode_to_nptype = {"I": np.int32, "I;16": np.int16, "F": np.float32}
|
| 75 |
+
img = np.array(pic, mode_to_nptype.get(pic.mode, np.uint8), copy=True)
|
| 76 |
+
if pic.mode == "1":
|
| 77 |
+
img = 255 * img
|
| 78 |
+
img = np.transpose(img, (2, 0, 1))
|
| 79 |
+
img = img.astype(np.float32)
|
| 80 |
+
img = np.divide(img, 255)
|
| 81 |
+
return img
|
| 82 |
+
|
| 83 |
+
def normalize(self, img):
|
| 84 |
+
mean = np.array([0.48145466, 0.4578275, 0.40821073]).reshape((-1, 1, 1))
|
| 85 |
+
std = np.array([0.26862954, 0.26130258, 0.27577711]).reshape((-1, 1, 1))
|
| 86 |
+
return np.divide(np.subtract(img, mean), std)
|
| 87 |
+
|
| 88 |
+
def preprocess(self, img):
|
| 89 |
+
img = self.resize_to(img, self.input_size)
|
| 90 |
+
img = self.center_crop(img, self.input_size)
|
| 91 |
+
img = img.convert("RGB")
|
| 92 |
+
img_np = self.to_numpy(img)
|
| 93 |
+
img_np = self.normalize(img_np)
|
| 94 |
+
return img_np
|
| 95 |
+
|
| 96 |
+
def preprocess_images(self, images):
|
| 97 |
+
preprocessed = []
|
| 98 |
+
for img in images:
|
| 99 |
+
if isinstance(img, str):
|
| 100 |
+
img = Image.open(img)
|
| 101 |
+
preprocessed.append(self.preprocess(img))
|
| 102 |
+
return np.stack(preprocessed)
|
| 103 |
+
|
| 104 |
+
class TextualModel:
|
| 105 |
+
def __init__(self, path, providers=None):
|
| 106 |
+
self.path = path
|
| 107 |
+
print(f"Loading textual model: {path}")
|
| 108 |
+
self.sess = onnxruntime.InferenceSession(path, providers=providers)
|
| 109 |
+
self.input = self.sess.get_inputs()[0]
|
| 110 |
+
self.output = self.sess.get_outputs()[0]
|
| 111 |
+
self.tokenizer = SimpleTokenizer()
|
| 112 |
+
|
| 113 |
+
if len(self.input.shape) != 2 or self.input.shape[1] != 77:
|
| 114 |
+
raise ValueError(f"unexpected shape {self.input.shape}")
|
| 115 |
+
self.input_size = self.input.shape[1]
|
| 116 |
+
print(f"Textual inference ready, input size {self.input_size}, type {self.input.type}")
|
| 117 |
+
|
| 118 |
+
def encode(self, texts):
|
| 119 |
+
return self.sess.run([self.output.name], {self.input.name: texts})[0]
|
| 120 |
+
|
| 121 |
+
def tokenize(self, texts: Union[str, List[str]], context_length: int = 77, truncate: bool = False) -> np.array:
|
| 122 |
+
"""
|
| 123 |
+
Returns the tokenized representation of given input string(s)
|
| 124 |
+
|
| 125 |
+
Parameters
|
| 126 |
+
----------
|
| 127 |
+
texts : Union[str, List[str]]
|
| 128 |
+
An input string or a list of input strings to tokenize
|
| 129 |
+
|
| 130 |
+
context_length : int
|
| 131 |
+
The context length to use; all CLIP models use 77 as the context length
|
| 132 |
+
|
| 133 |
+
truncate: bool
|
| 134 |
+
Whether to truncate the text in case its encoding is longer than the context length
|
| 135 |
+
|
| 136 |
+
Returns
|
| 137 |
+
-------
|
| 138 |
+
A two-dimensional tensor containing the resulting tokens, shape = [number of input strings, context_length].
|
| 139 |
+
We return LongTensor when torch version is <1.8.0, since older index_select requires indices to be long.
|
| 140 |
+
"""
|
| 141 |
+
if isinstance(texts, str):
|
| 142 |
+
texts = [texts]
|
| 143 |
+
|
| 144 |
+
sot_token = self.tokenizer.encoder["<|startoftext|>"]
|
| 145 |
+
eot_token = self.tokenizer.encoder["<|endoftext|>"]
|
| 146 |
+
all_tokens = [[sot_token] + self.tokenizer.encode(text) + [eot_token] for text in texts]
|
| 147 |
+
input_type = onnx_node_type_np_type(self.input.type)
|
| 148 |
+
result = np.zeros(shape=(len(all_tokens), context_length), dtype=input_type)
|
| 149 |
+
|
| 150 |
+
for i, tokens in enumerate(all_tokens):
|
| 151 |
+
if len(tokens) > context_length:
|
| 152 |
+
if truncate:
|
| 153 |
+
tokens = tokens[:context_length]
|
| 154 |
+
tokens[-1] = eot_token
|
| 155 |
+
else:
|
| 156 |
+
raise RuntimeError(f"Input {texts[i]} is too long for context length {context_length}")
|
| 157 |
+
result[i, :len(tokens)] = np.array(tokens)
|
| 158 |
+
|
| 159 |
+
return result
|
cliponnx/simple_tokenizer.py
ADDED
|
@@ -0,0 +1,134 @@
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
| 1 |
+
# MIT License - copied from https://github.com/openai/CLIP/blob/main/clip/simple_tokenizer.py
|
| 2 |
+
|
| 3 |
+
import gzip
|
| 4 |
+
import html
|
| 5 |
+
import os
|
| 6 |
+
from functools import lru_cache
|
| 7 |
+
|
| 8 |
+
import ftfy
|
| 9 |
+
import regex as re
|
| 10 |
+
|
| 11 |
+
|
| 12 |
+
@lru_cache()
|
| 13 |
+
def default_bpe():
|
| 14 |
+
return os.path.join(os.path.dirname(os.path.abspath(__file__)), "bpe_simple_vocab_16e6.txt.gz")
|
| 15 |
+
|
| 16 |
+
|
| 17 |
+
@lru_cache()
|
| 18 |
+
def bytes_to_unicode():
|
| 19 |
+
"""
|
| 20 |
+
Returns list of utf-8 byte and a corresponding list of unicode strings.
|
| 21 |
+
The reversible bpe codes work on unicode strings.
|
| 22 |
+
This means you need a large # of unicode characters in your vocab if you want to avoid UNKs.
|
| 23 |
+
When you're at something like a 10B token dataset you end up needing around 5K for decent coverage.
|
| 24 |
+
This is a signficant percentage of your normal, say, 32K bpe vocab.
|
| 25 |
+
To avoid that, we want lookup tables between utf-8 bytes and unicode strings.
|
| 26 |
+
And avoids mapping to whitespace/control characters the bpe code barfs on.
|
| 27 |
+
"""
|
| 28 |
+
bs = list(range(ord("!"), ord("~")+1))+list(range(ord("¡"), ord("¬")+1))+list(range(ord("®"), ord("ÿ")+1))
|
| 29 |
+
cs = bs[:]
|
| 30 |
+
n = 0
|
| 31 |
+
for b in range(2**8):
|
| 32 |
+
if b not in bs:
|
| 33 |
+
bs.append(b)
|
| 34 |
+
cs.append(2**8+n)
|
| 35 |
+
n += 1
|
| 36 |
+
cs = [chr(n) for n in cs]
|
| 37 |
+
return dict(zip(bs, cs))
|
| 38 |
+
|
| 39 |
+
|
| 40 |
+
def get_pairs(word):
|
| 41 |
+
"""Return set of symbol pairs in a word.
|
| 42 |
+
Word is represented as tuple of symbols (symbols being variable-length strings).
|
| 43 |
+
"""
|
| 44 |
+
pairs = set()
|
| 45 |
+
prev_char = word[0]
|
| 46 |
+
for char in word[1:]:
|
| 47 |
+
pairs.add((prev_char, char))
|
| 48 |
+
prev_char = char
|
| 49 |
+
return pairs
|
| 50 |
+
|
| 51 |
+
|
| 52 |
+
def basic_clean(text):
|
| 53 |
+
text = ftfy.fix_text(text)
|
| 54 |
+
text = html.unescape(html.unescape(text))
|
| 55 |
+
return text.strip()
|
| 56 |
+
|
| 57 |
+
|
| 58 |
+
def whitespace_clean(text):
|
| 59 |
+
text = re.sub(r'\s+', ' ', text)
|
| 60 |
+
text = text.strip()
|
| 61 |
+
return text
|
| 62 |
+
|
| 63 |
+
|
| 64 |
+
class SimpleTokenizer(object):
|
| 65 |
+
def __init__(self, bpe_path: str = default_bpe()):
|
| 66 |
+
self.byte_encoder = bytes_to_unicode()
|
| 67 |
+
self.byte_decoder = {v: k for k, v in self.byte_encoder.items()}
|
| 68 |
+
merges = gzip.open(bpe_path).read().decode("utf-8").split('\n')
|
| 69 |
+
merges = merges[1:49152-256-2+1]
|
| 70 |
+
merges = [tuple(merge.split()) for merge in merges]
|
| 71 |
+
vocab = list(bytes_to_unicode().values())
|
| 72 |
+
vocab = vocab + [v+'</w>' for v in vocab]
|
| 73 |
+
for merge in merges:
|
| 74 |
+
vocab.append(''.join(merge))
|
| 75 |
+
vocab.extend(['<|startoftext|>', '<|endoftext|>'])
|
| 76 |
+
self.encoder = dict(zip(vocab, range(len(vocab))))
|
| 77 |
+
self.decoder = {v: k for k, v in self.encoder.items()}
|
| 78 |
+
self.bpe_ranks = dict(zip(merges, range(len(merges))))
|
| 79 |
+
self.cache = {'<|startoftext|>': '<|startoftext|>', '<|endoftext|>': '<|endoftext|>'}
|
| 80 |
+
self.pat = re.compile(r"""<\|startoftext\|>|<\|endoftext\|>|'s|'t|'re|'ve|'m|'ll|'d|[\p{L}]+|[\p{N}]|[^\s\p{L}\p{N}]+""", re.IGNORECASE)
|
| 81 |
+
|
| 82 |
+
def bpe(self, token):
|
| 83 |
+
if token in self.cache:
|
| 84 |
+
return self.cache[token]
|
| 85 |
+
word = tuple(token[:-1]) + ( token[-1] + '</w>',)
|
| 86 |
+
pairs = get_pairs(word)
|
| 87 |
+
|
| 88 |
+
if not pairs:
|
| 89 |
+
return token+'</w>'
|
| 90 |
+
|
| 91 |
+
while True:
|
| 92 |
+
bigram = min(pairs, key = lambda pair: self.bpe_ranks.get(pair, float('inf')))
|
| 93 |
+
if bigram not in self.bpe_ranks:
|
| 94 |
+
break
|
| 95 |
+
first, second = bigram
|
| 96 |
+
new_word = []
|
| 97 |
+
i = 0
|
| 98 |
+
while i < len(word):
|
| 99 |
+
try:
|
| 100 |
+
j = word.index(first, i)
|
| 101 |
+
new_word.extend(word[i:j])
|
| 102 |
+
i = j
|
| 103 |
+
except:
|
| 104 |
+
new_word.extend(word[i:])
|
| 105 |
+
break
|
| 106 |
+
|
| 107 |
+
if word[i] == first and i < len(word)-1 and word[i+1] == second:
|
| 108 |
+
new_word.append(first+second)
|
| 109 |
+
i += 2
|
| 110 |
+
else:
|
| 111 |
+
new_word.append(word[i])
|
| 112 |
+
i += 1
|
| 113 |
+
new_word = tuple(new_word)
|
| 114 |
+
word = new_word
|
| 115 |
+
if len(word) == 1:
|
| 116 |
+
break
|
| 117 |
+
else:
|
| 118 |
+
pairs = get_pairs(word)
|
| 119 |
+
word = ' '.join(word)
|
| 120 |
+
self.cache[token] = word
|
| 121 |
+
return word
|
| 122 |
+
|
| 123 |
+
def encode(self, text):
|
| 124 |
+
bpe_tokens = []
|
| 125 |
+
text = whitespace_clean(basic_clean(text)).lower()
|
| 126 |
+
for token in re.findall(self.pat, text):
|
| 127 |
+
token = ''.join(self.byte_encoder[b] for b in token.encode('utf-8'))
|
| 128 |
+
bpe_tokens.extend(self.encoder[bpe_token] for bpe_token in self.bpe(token).split(' '))
|
| 129 |
+
return bpe_tokens
|
| 130 |
+
|
| 131 |
+
def decode(self, tokens):
|
| 132 |
+
text = ''.join([self.decoder[token] for token in tokens])
|
| 133 |
+
text = bytearray([self.byte_decoder[c] for c in text]).decode('utf-8', errors="replace").replace('</w>', ' ')
|
| 134 |
+
return text
|
convert.py
ADDED
|
@@ -0,0 +1,104 @@
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
| 1 |
+
from cgitb import text
|
| 2 |
+
import os
|
| 3 |
+
|
| 4 |
+
import clip
|
| 5 |
+
import torch.onnx
|
| 6 |
+
import torch
|
| 7 |
+
from torch import nn
|
| 8 |
+
from multiprocessing import Pool
|
| 9 |
+
|
| 10 |
+
class TextTransformer(nn.Module):
|
| 11 |
+
def __init__(self, clip_model):
|
| 12 |
+
super().__init__()
|
| 13 |
+
self.clip_model = clip_model
|
| 14 |
+
|
| 15 |
+
def forward(self, x: torch.Tensor):
|
| 16 |
+
return self.clip_model.encode_text(x)
|
| 17 |
+
|
| 18 |
+
def export(model, input, path):
|
| 19 |
+
print(f"Exporting to {path}")
|
| 20 |
+
torch.onnx.export(
|
| 21 |
+
model, # model being run
|
| 22 |
+
input, # model input (or a tuple for multiple inputs)
|
| 23 |
+
path, # where to save the model (can be a file or file-like object)
|
| 24 |
+
export_params=True, # store the trained parameter weights inside the model file
|
| 25 |
+
opset_version=16, # the ONNX version to export the model to
|
| 26 |
+
do_constant_folding=True, # whether to execute constant folding for optimization
|
| 27 |
+
input_names = ['input'], # the model's input names
|
| 28 |
+
output_names = ['output'], # the model's output names
|
| 29 |
+
dynamic_axes={
|
| 30 |
+
'input' : {0 : 'batch_size'}, # variable length axes
|
| 31 |
+
'output' : {0 : 'batch_size'}
|
| 32 |
+
}
|
| 33 |
+
)
|
| 34 |
+
|
| 35 |
+
def convert(model_name, dashed_name):
|
| 36 |
+
visual_path = f"{output_dir}/clip-{dashed_name}-visual.onnx"
|
| 37 |
+
textual_path = f"{output_dir}/clip-{dashed_name}-textual.onnx"
|
| 38 |
+
visual_exists = os.path.exists(visual_path)
|
| 39 |
+
textual_exists = os.path.exists(textual_path)
|
| 40 |
+
if visual_exists and textual_exists:
|
| 41 |
+
print(f"{visual_path} exists, skipping")
|
| 42 |
+
print(f"{textual_path} exists, skipping")
|
| 43 |
+
return
|
| 44 |
+
|
| 45 |
+
print(f"Model: {model_name}")
|
| 46 |
+
print(f"Loading CLIP")
|
| 47 |
+
model, _ = clip.load(model_name, device=device)
|
| 48 |
+
model = model.to(device=device)
|
| 49 |
+
|
| 50 |
+
|
| 51 |
+
if not visual_exists:
|
| 52 |
+
input_res = model.visual.input_resolution
|
| 53 |
+
export(
|
| 54 |
+
model.visual,
|
| 55 |
+
torch.rand(1, 3, input_res, input_res),
|
| 56 |
+
visual_path,
|
| 57 |
+
)
|
| 58 |
+
else:
|
| 59 |
+
print(f"{visual_path} exists, skipping")
|
| 60 |
+
|
| 61 |
+
if not textual_exists:
|
| 62 |
+
text_transformer = TextTransformer(model)
|
| 63 |
+
export(
|
| 64 |
+
text_transformer,
|
| 65 |
+
clip.tokenize(["hello onnx"]).to(device),
|
| 66 |
+
textual_path,
|
| 67 |
+
)
|
| 68 |
+
else:
|
| 69 |
+
print(f"{textual_path} exists, skipping")
|
| 70 |
+
|
| 71 |
+
device = "cuda" if torch.cuda.is_available() else "cpu"
|
| 72 |
+
device = "cpu"
|
| 73 |
+
output_dir = "models"
|
| 74 |
+
if __name__ == "__main__":
|
| 75 |
+
print(f"Torch device: {device}")
|
| 76 |
+
|
| 77 |
+
available_models = clip.available_models()
|
| 78 |
+
print(f"Available models: {available_models}")
|
| 79 |
+
|
| 80 |
+
models = [
|
| 81 |
+
("RN50", "resnet-50"),
|
| 82 |
+
("RN101", "resnet-101"),
|
| 83 |
+
("RN50x4", "resnet-50x4"),
|
| 84 |
+
("RN50x16", "resnet-50x16"),
|
| 85 |
+
("RN50x64", "resnet-50x64"),
|
| 86 |
+
("RN50", "resnet-50"),
|
| 87 |
+
("RN50", "resnet-50"),
|
| 88 |
+
("RN50", "resnet-50"),
|
| 89 |
+
("ViT-B/16", "vit-base-patch16"),
|
| 90 |
+
("ViT-B/32", "vit-base-patch32"),
|
| 91 |
+
("ViT-L/14", "vit-large-patch14"),
|
| 92 |
+
("ViT-L/14@336px", "vit-large-patch14-336"),
|
| 93 |
+
]
|
| 94 |
+
|
| 95 |
+
print(f"Converting models: {models}")
|
| 96 |
+
|
| 97 |
+
for model in models:
|
| 98 |
+
convert(*model)
|
| 99 |
+
|
| 100 |
+
# For converting multiple models at once
|
| 101 |
+
# with Pool(1) as p:
|
| 102 |
+
# p.starmap(convert, models)
|
| 103 |
+
|
| 104 |
+
print("done")
|
example.py
ADDED
|
@@ -0,0 +1,66 @@
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
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|
| 1 |
+
from operator import itemgetter
|
| 2 |
+
import numpy as np
|
| 3 |
+
from tabulate import tabulate
|
| 4 |
+
|
| 5 |
+
from cliponnx.models import TextualModel, VisualModel
|
| 6 |
+
|
| 7 |
+
def cosine_similarity(a, b):
|
| 8 |
+
return np.dot(a, b) / (np.linalg.norm(a) * np.linalg.norm(b))
|
| 9 |
+
|
| 10 |
+
# With GPU (slower startup, faster inference with supported cards)
|
| 11 |
+
# providers = ['TensorrtExecutionProvider', 'CUDAExecutionProvider', 'CPUExecutionProvider']
|
| 12 |
+
|
| 13 |
+
# CPU only (faster startup, slower inference)
|
| 14 |
+
providers = ['CPUExecutionProvider']
|
| 15 |
+
|
| 16 |
+
images = [
|
| 17 |
+
"flowers.jpg",
|
| 18 |
+
"heavy-industry.jpg",
|
| 19 |
+
]
|
| 20 |
+
|
| 21 |
+
texts = [
|
| 22 |
+
"a close up photo of a cherry blossom",
|
| 23 |
+
"cherry blossom",
|
| 24 |
+
"flowers",
|
| 25 |
+
"plant",
|
| 26 |
+
"processing plant",
|
| 27 |
+
"a large industrial plant with many pipes, walkways and railings",
|
| 28 |
+
"ruhrgebiet",
|
| 29 |
+
"industry",
|
| 30 |
+
"a photo taken on a bright and sunny day",
|
| 31 |
+
"a photo taken on a dark and cloudy day",
|
| 32 |
+
"a photo taken at midnight",
|
| 33 |
+
"bees",
|
| 34 |
+
"cars",
|
| 35 |
+
"dogs and cats",
|
| 36 |
+
]
|
| 37 |
+
|
| 38 |
+
visual = VisualModel("models/clip-vit-base-patch32-visual-float16.onnx", providers=providers)
|
| 39 |
+
images_input = visual.preprocess_images(images)
|
| 40 |
+
print(f"Images shape: {images_input.shape}")
|
| 41 |
+
image_embeddings = visual.encode(images_input)
|
| 42 |
+
print(f"Embeddings shape: {image_embeddings.shape}")
|
| 43 |
+
print()
|
| 44 |
+
|
| 45 |
+
textual = TextualModel("models/clip-vit-base-patch32-textual-float16.onnx", providers=providers)
|
| 46 |
+
texts_input = textual.tokenize(texts)
|
| 47 |
+
print(f"Texts shape: {texts_input.shape}")
|
| 48 |
+
text_embeddings = textual.encode(texts_input)
|
| 49 |
+
print(f"Embeddings shape: {text_embeddings.shape}")
|
| 50 |
+
print()
|
| 51 |
+
|
| 52 |
+
table = [["image", "similarity", "text"]]
|
| 53 |
+
|
| 54 |
+
for ii, image in enumerate(images):
|
| 55 |
+
image_embedding = image_embeddings[ii]
|
| 56 |
+
|
| 57 |
+
similarities = []
|
| 58 |
+
for ti, text in enumerate(texts):
|
| 59 |
+
text_embedding = text_embeddings[ti]
|
| 60 |
+
similarity = cosine_similarity(image_embedding, text_embedding)
|
| 61 |
+
similarities.append([similarity, ">" * int(similarity * 30), text])
|
| 62 |
+
|
| 63 |
+
similarities.sort(reverse=True, key=itemgetter(0))
|
| 64 |
+
print(image)
|
| 65 |
+
print(tabulate(similarities))
|
| 66 |
+
print()
|
flowers.jpg
ADDED
|
Git LFS Details
|
heavy-industry.jpg
ADDED
|
Git LFS Details
|
poetry.lock
ADDED
|
@@ -0,0 +1,841 @@
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| 811 |
+
{file = "torch-1.12.1+cu116-cp38-cp38-linux_x86_64.whl", hash = "sha256:dda312901220895087cc83d3665464a3dc171d04460c61c31af463efbfb54896"},
|
| 812 |
+
{file = "torch-1.12.1+cu116-cp38-cp38-win_amd64.whl", hash = "sha256:b8e8906e770bcad12e67c269e1bcdd7661a8abd96519a4ba643e86440bbcc1bf"},
|
| 813 |
+
{file = "torch-1.12.1+cu116-cp39-cp39-linux_x86_64.whl", hash = "sha256:7725420dabebfcaf44984edce3283eea91f98f0f7d5874bc68c7a164bd8126e3"},
|
| 814 |
+
{file = "torch-1.12.1+cu116-cp39-cp39-win_amd64.whl", hash = "sha256:84f031e4ee25d95368d7531aa58e79da9808d3fa53b4b363ea03a2450b6fd0af"},
|
| 815 |
+
]
|
| 816 |
+
torchvision = [
|
| 817 |
+
{file = "torchvision-0.13.1+cu116-cp310-cp310-linux_x86_64.whl", hash = "sha256:0c9a2b605ac30fcf475d60f79ba378af0073a22de585453f8c3dd6c1452ab9bc"},
|
| 818 |
+
{file = "torchvision-0.13.1+cu116-cp310-cp310-win_amd64.whl", hash = "sha256:ba8b7d3c33f63feb29c7dd8c0db68b735d0c9d924ff4e84121b4b20b17cec7a5"},
|
| 819 |
+
{file = "torchvision-0.13.1+cu116-cp37-cp37m-linux_x86_64.whl", hash = "sha256:dcf32f6d998493e76ec21a38bbb856b7402295cf7a67fb09ce5bde7e7e725756"},
|
| 820 |
+
{file = "torchvision-0.13.1+cu116-cp37-cp37m-win_amd64.whl", hash = "sha256:9ec5654c56a22fe420dc0af0ff5cd31105f583fdb0240043ff26a7cfed7e05fb"},
|
| 821 |
+
{file = "torchvision-0.13.1+cu116-cp38-cp38-linux_x86_64.whl", hash = "sha256:c3ceb2b3f456f0c984af71ef55f8637f178a29dc3e13a66fbb010ceead2891e1"},
|
| 822 |
+
{file = "torchvision-0.13.1+cu116-cp38-cp38-win_amd64.whl", hash = "sha256:8a4c395bb72cf51eb4318c6861c9a5ea490d48ec36a3d767220ef182445449cb"},
|
| 823 |
+
{file = "torchvision-0.13.1+cu116-cp39-cp39-linux_x86_64.whl", hash = "sha256:75986abe572138258eb9795cb4cd73f40b2bdf8374fefa1af6ff6bb0dbc972c6"},
|
| 824 |
+
{file = "torchvision-0.13.1+cu116-cp39-cp39-win_amd64.whl", hash = "sha256:92e4685c6010b6b1c228ebb5fe93105d0a71e5b586483a942e04529a43e0bb42"},
|
| 825 |
+
]
|
| 826 |
+
tqdm = [
|
| 827 |
+
{file = "tqdm-4.64.1-py2.py3-none-any.whl", hash = "sha256:6fee160d6ffcd1b1c68c65f14c829c22832bc401726335ce92c52d395944a6a1"},
|
| 828 |
+
{file = "tqdm-4.64.1.tar.gz", hash = "sha256:5f4f682a004951c1b450bc753c710e9280c5746ce6ffedee253ddbcbf54cf1e4"},
|
| 829 |
+
]
|
| 830 |
+
typing-extensions = [
|
| 831 |
+
{file = "typing_extensions-4.3.0-py3-none-any.whl", hash = "sha256:25642c956049920a5aa49edcdd6ab1e06d7e5d467fc00e0506c44ac86fbfca02"},
|
| 832 |
+
{file = "typing_extensions-4.3.0.tar.gz", hash = "sha256:e6d2677a32f47fc7eb2795db1dd15c1f34eff616bcaf2cfb5e997f854fa1c4a6"},
|
| 833 |
+
]
|
| 834 |
+
urllib3 = [
|
| 835 |
+
{file = "urllib3-1.26.12-py2.py3-none-any.whl", hash = "sha256:b930dd878d5a8afb066a637fbb35144fe7901e3b209d1cd4f524bd0e9deee997"},
|
| 836 |
+
{file = "urllib3-1.26.12.tar.gz", hash = "sha256:3fa96cf423e6987997fc326ae8df396db2a8b7c667747d47ddd8ecba91f4a74e"},
|
| 837 |
+
]
|
| 838 |
+
wcwidth = [
|
| 839 |
+
{file = "wcwidth-0.2.5-py2.py3-none-any.whl", hash = "sha256:beb4802a9cebb9144e99086eff703a642a13d6a0052920003a230f3294bbe784"},
|
| 840 |
+
{file = "wcwidth-0.2.5.tar.gz", hash = "sha256:c4d647b99872929fdb7bdcaa4fbe7f01413ed3d98077df798530e5b04f116c83"},
|
| 841 |
+
]
|
poetry.toml
ADDED
|
@@ -0,0 +1,2 @@
|
|
|
|
|
|
|
|
|
|
| 1 |
+
[virtualenvs]
|
| 2 |
+
in-project = true
|
pyproject.toml
ADDED
|
@@ -0,0 +1,32 @@
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
| 1 |
+
[tool.poetry]
|
| 2 |
+
name = "clip-variants"
|
| 3 |
+
version = "0.1.0"
|
| 4 |
+
description = ""
|
| 5 |
+
authors = ["Miha Lunar <mlunar@gmail.com>"]
|
| 6 |
+
readme = "README.md"
|
| 7 |
+
packages = [{include = "clip_variants"}]
|
| 8 |
+
|
| 9 |
+
[tool.poetry.dependencies]
|
| 10 |
+
python = "^3.9"
|
| 11 |
+
onnxmltools = "^1.11.1"
|
| 12 |
+
packaging = "^21.3"
|
| 13 |
+
torch = "^1.12.1"
|
| 14 |
+
clip = {git = "https://github.com/openai/CLIP.git"}
|
| 15 |
+
torchvision = "^0.13.1"
|
| 16 |
+
ftfy = "^6.1.1"
|
| 17 |
+
regex = "^2022.9.13"
|
| 18 |
+
tqdm = "^4.64.1"
|
| 19 |
+
onnxruntime = "^1.12.1"
|
| 20 |
+
onnxconverter-common = "^1.12.2"
|
| 21 |
+
tabulate = "^0.8.10"
|
| 22 |
+
numpy = "^1.23.3"
|
| 23 |
+
Pillow = "^9.2.0"
|
| 24 |
+
|
| 25 |
+
[build-system]
|
| 26 |
+
requires = ["poetry-core"]
|
| 27 |
+
build-backend = "poetry.core.masonry.api"
|
| 28 |
+
|
| 29 |
+
[[tool.poetry.source]]
|
| 30 |
+
name = "torch"
|
| 31 |
+
url = "https://download.pytorch.org/whl/cu116"
|
| 32 |
+
secondary = true
|
variants.py
ADDED
|
@@ -0,0 +1,108 @@
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
| 1 |
+
import onnx
|
| 2 |
+
import os
|
| 3 |
+
import itertools
|
| 4 |
+
import argparse
|
| 5 |
+
from onnxconverter_common.float16 import convert_float_to_float16
|
| 6 |
+
from onnxruntime.quantization import quantize_dynamic, QuantType
|
| 7 |
+
from multiprocessing import Pool
|
| 8 |
+
from tabulate import tabulate
|
| 9 |
+
|
| 10 |
+
def float16(input, output):
|
| 11 |
+
model = onnx.load(input)
|
| 12 |
+
model_f16 = convert_float_to_float16(model)
|
| 13 |
+
onnx.save(model_f16, output)
|
| 14 |
+
|
| 15 |
+
def qint8(input, output):
|
| 16 |
+
quantize_dynamic(input, output, weight_type=QuantType.QInt8)
|
| 17 |
+
|
| 18 |
+
def quint8(input, output):
|
| 19 |
+
quantize_dynamic(input, output, weight_type=QuantType.QUInt8)
|
| 20 |
+
|
| 21 |
+
def print_table(table):
|
| 22 |
+
print(tabulate(table, headers="keys", tablefmt="github"), "\n")
|
| 23 |
+
|
| 24 |
+
def get_file_mb(path):
|
| 25 |
+
try:
|
| 26 |
+
stat = os.stat(path)
|
| 27 |
+
except FileNotFoundError:
|
| 28 |
+
return "N/A"
|
| 29 |
+
mb = round(stat.st_size / 1_000_000)
|
| 30 |
+
return f"{mb}"
|
| 31 |
+
|
| 32 |
+
def convert(name, mode, f, markdown):
|
| 33 |
+
fname = f.__name__
|
| 34 |
+
input = f"models/clip-{name}-{mode}.onnx"
|
| 35 |
+
output = f"models/clip-{name}-{mode}-{fname}.onnx"
|
| 36 |
+
exists = os.path.exists(output)
|
| 37 |
+
if exists:
|
| 38 |
+
if not markdown:
|
| 39 |
+
print(f"{output} exists")
|
| 40 |
+
else:
|
| 41 |
+
if not markdown:
|
| 42 |
+
print(f"{output} converting")
|
| 43 |
+
f(input, output)
|
| 44 |
+
if not markdown:
|
| 45 |
+
print(f"{output} done")
|
| 46 |
+
return [input, output, name, mode, fname, "✅" if exists else "❌"]
|
| 47 |
+
|
| 48 |
+
if __name__ == '__main__':
|
| 49 |
+
parser = argparse.ArgumentParser(description='Create variants of converted models')
|
| 50 |
+
parser.add_argument(
|
| 51 |
+
'--markdown',
|
| 52 |
+
action='store_true',
|
| 53 |
+
help='Print markdown tables describing the variants'
|
| 54 |
+
)
|
| 55 |
+
args = parser.parse_args()
|
| 56 |
+
names = [
|
| 57 |
+
"resnet-50",
|
| 58 |
+
"resnet-101",
|
| 59 |
+
"resnet-50x4",
|
| 60 |
+
"resnet-50x16",
|
| 61 |
+
"resnet-50x64",
|
| 62 |
+
"resnet-50",
|
| 63 |
+
"resnet-50",
|
| 64 |
+
"resnet-50",
|
| 65 |
+
"vit-base-patch16",
|
| 66 |
+
"vit-base-patch32",
|
| 67 |
+
"vit-large-patch14",
|
| 68 |
+
"vit-large-patch14-336",
|
| 69 |
+
]
|
| 70 |
+
modes = [
|
| 71 |
+
"visual",
|
| 72 |
+
"textual"
|
| 73 |
+
]
|
| 74 |
+
funcs = [
|
| 75 |
+
float16,
|
| 76 |
+
qint8,
|
| 77 |
+
quint8,
|
| 78 |
+
]
|
| 79 |
+
markdown = args.markdown
|
| 80 |
+
if markdown:
|
| 81 |
+
print_table({ "Model ID": names })
|
| 82 |
+
print_table({ "Mode": modes })
|
| 83 |
+
print_table({ "Data Type": [f.__name__ for f in funcs] })
|
| 84 |
+
variants = itertools.product(names, modes, funcs, [markdown])
|
| 85 |
+
|
| 86 |
+
with Pool(8 if not markdown else 1) as p:
|
| 87 |
+
variants_table = p.starmap(convert, variants)
|
| 88 |
+
if markdown:
|
| 89 |
+
# Insert rows for the original models
|
| 90 |
+
prev_input = ""
|
| 91 |
+
variants_table_with_originals = []
|
| 92 |
+
for row in variants_table:
|
| 93 |
+
input = row[0]
|
| 94 |
+
output = row[1]
|
| 95 |
+
if input != prev_input:
|
| 96 |
+
prev_input = input
|
| 97 |
+
variants_table_with_originals.append(
|
| 98 |
+
row[0:1] + row[2:4] + ["float32 (original)", "✅", get_file_mb(input)]
|
| 99 |
+
)
|
| 100 |
+
file_size = get_file_mb(output)
|
| 101 |
+
variants_table_with_originals.append(row[1:] + [file_size])
|
| 102 |
+
# Add header
|
| 103 |
+
variants_table_with_originals.insert(0, ["Path", "Model ID", "Mode", "Data Type", "Available", "Size (MB)"])
|
| 104 |
+
# Print
|
| 105 |
+
print(tabulate(variants_table_with_originals, headers="firstrow", tablefmt="github"))
|
| 106 |
+
else:
|
| 107 |
+
print("done")
|
| 108 |
+
|