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44 lines
1.7 KiB
44 lines
1.7 KiB
# Preparation for pre-training & ImageNet fine-tuning |
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## Pip dependencies |
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1. Prepare a python environment, e.g.: |
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```shell script |
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$ conda create -n spark python=3.8 -y |
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$ conda activate spark |
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``` |
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2. Install `PyTorch` and `timm` (better to use `torch~=1.10`, `torchvision~=0.11`, and `timm==0.5.4`) then other python packages: |
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```shell script |
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$ pip install torch==1.10.0+cu113 torchvision==0.11.1+cu113 -f https://download.pytorch.org/whl/torch_stable.html |
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$ pip install timm==0.5.4 |
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$ pip install -r requirements.txt |
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``` |
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It is highly recommended to install these versions to ensure a consistent environment for re-implementation. |
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## ImageNet preparation |
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Prepare the [ImageNet-1k](http://image-net.org/) dataset |
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- assume the dataset is in `/path/to/imagenet` |
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- it should look like this: |
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``` |
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/path/to/imagenet/: |
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train/: |
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class1: |
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a_lot_images.jpeg |
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class2: |
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a_lot_images.jpeg |
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val/: |
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class1: |
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a_lot_images.jpeg |
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class2: |
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a_lot_images.jpeg |
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``` |
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- that argument of `--data_path=/path/to/imagenet` should be passed to the training script introduced later |
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> `PS:` In our implementation, we use pytorch built-in operators to simulate the submanifold sparse convolution in [encoder.py](https://github.com/keyu-tian/SparK/blob/main/pretrain/encoder.py) for generality, |
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due to the fact that many convolution operators (e.g., grouped conv and dilated conv) do not yet have efficient sparse implementations on today's hardware. |
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If you want to try those sparse convolution, you may refer to [this](https://github.com/facebookresearch/SparseConvNet) sparse convolution library or [MinkowskiEngine](https://github.com/NVIDIA/MinkowskiEngine).
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