49 lines
2 KiB
Markdown
49 lines
2 KiB
Markdown
# Convolutional Neural Network (CNN) VGG7 implementation
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This example provides an implementation of a convolutional neural network (CNN) that enables for image resolution upscaling, which means that images can improve their quality through purely the machine learning implementation.
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This example demonstrates performing image upscaling using Kompute on the test image below.
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In this example we will be doing the following:
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* Import pre-trained model
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* Create Kompute code that loads model weights
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* Create Kompute shader that performs inference on image
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* Run model against image to perform upscale
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## Import pre-trained model
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To import the no-noise-compensation VGG7 model (into `model-kipper`):
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```
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curl -o model.json https://raw.githubusercontent.com/nagadomi/waifu2x/master/models/vgg_7/art/scale2.0x_model.json
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python3 import_vgg7.py model.json
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```
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Other models from the vgg\_7 set (such as `https://raw.githubusercontent.com/nagadomi/waifu2x/master/models/vgg_7/photo/noise0_model.json`) can be subsituted as desired.
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## Create code that loads model weights
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We implement the kompute logic under run_vgg7 that loads the model weights and coordinates the execution of the inference.
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## Create Kompute shader that performs inference on image
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Similarly, we created a compute shader that performs an inference iteration on an image provided to perfrom upscaling.
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## Run model against image to perfrom upscale
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We now execute model against an image created by us to show how upscaling works. The image used will be the one below:
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To execute that model no tiling is performed, so be careful about image sizes.
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We can now run the command below to perform inference against the image blow.
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`python3 run_vgg7.py w2wbinit.png out.png`
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This would successfully upscale the resolution using the machine learning model, and the result is below:
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