VQVAE生成模型梗概

VQVAE生成模型梗概 VQVAE generated model outline 模型 VQ VAE 具有以下基本模型组件 1. Encoder 类并定义了 x - ze 2. 将编码器输出转换为离散的独热向量的类 VectorQuantizer，该向量是最近嵌入向量的索引 ze - zq 3. Decoder 类并定义映射 zq - xhat 并重建原始图像的类 编码器/解码器类是卷积和逆卷积堆栈，其架构中包含残差块 参见 ResNet 论文. 其中残差模块由 ResidualLayer 和 ResidualStack 类定义。 这些组件按以下文件夹结构组织： This is a PyTorch implementation of the VQVAE generated model (<https://arxiv.org/abs/1711.00937). You can find the author's original implementation in Tensorflow here(https://github.com/deepmind/sonnet/blob/master/sonnet/python/modules/nets/vqvae.py) which contains an example that can be run in a Jupyter Notebook. Install dependencies To install dependencies, create a conda or virtual environment using Python 3 and run pip install -r requirements.txt. Run VQ VAE To run the VQ-VAE simply run python3 main.py. Make sure to include the -save flag if you want to save your model. You can also add parameters in the command line. The default values are specified below: To run VQ-VAE, just run python3 main.py. If you want to save the model, make sure to include the -save flag. You can also add parameters on the command line. The default values ​​are as follows: