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44 lines
1.0 KiB
Markdown
44 lines
1.0 KiB
Markdown
# Inception v3
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Inception v3 Inference on CUDANet
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## Usage
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1. Export pytorch Inception v3 weights pretrained on ImageNet (requires pytorch and torchvision):
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```sh
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python inception_v3.py
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```
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2. Follow the instructions from the repository root to build the CUDANet library.
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3. Build Inception v3 (requires [OpenCV](https://opencv.org/) for image loading and preprocessing):
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```sh
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mkdir build
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cd build
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cmake -S ..
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make
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```
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4. (Optional) Run tests
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Generate test input/output and resources by running `inception_blocks.py` in the `test` folder
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Build and run tests (requires [Google Test](https://github.com/google/googletest))
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```sh
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cd build
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make test_inception_v3
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./tests/test_inception_v3
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```
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5. Run Inception v3 inference:
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```sh
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inception_v3 ../inception_v3_weights.bin ../image.jpg
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```
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## Note on Preprocessing
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The image preprocessing in this implementation uses OpenCV, which may produce slightly different results compared to PyTorch's Pillow-based preprocessing due to differences in interpolation methods during resizing.
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