【Hackathon 8th No.23】Improved Training of Wasserstein GANs 论文复现 #1146
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Thanks for your contribution! |
XvLingWYY
changed the title
Update wgangp_toy.py
Update functions.py
lijialin03
reviewed
Apr 29, 2025
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| # WGANGP | |||
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请一并修改mkdocs.yml和docs/index.md
docs/zh/examples/wgan_gp.md
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| python wgangp_cifar10.py | ||
| ``` | ||
| ```sh | ||
| python wgangp_minst.py |
docs/zh/examples/wgan_gp.md
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| python wgangp_cifar10.py model=eval | ||
| ``` | ||
| ```sh | ||
| python wgangp_mnist.py model=eval | ||
| ``` | ||
| ```sh | ||
| python wgangp_toy.py model=eval |
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| epochs: 5 | ||
| epochs_dis: 1 | ||
| iters_per_epoch_dis: 1 | ||
| epochs_gen: 1 | ||
| iters_per_epoch_gen: 1 |
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将相关参数修改为可以得到正确结果的参数，其他案例也一样
examples/wgangp/wgangp_mnist.py
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| show_mnist( | ||
| fake_data[i], | ||
| f"{cfg.output_dir}/image{batch_idx}_{i}.png", | ||
| ) |
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将target也保存一下，以便直观显示模型效果，如添加代码：
show_mnist(
input_["real_data"][i],
f"{cfg.output_dir}/image_real_{batch_idx}_{i}.png",
)
examples/wgangp/wgangp_mnist.py
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| for i in range( | ||
| cfg["EVAL"]["batch_size"] | ||
| if cfg["EVAL"]["batch_size"] < cfg.VIS.num | ||
| else cfg.VIS.num | ||
| ): |
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是否可以将eval的batch_size设置为1，以去掉这层循环
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| import math | |||
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看名称，模型和案例强相关，如果不能提出base_model的话，建议分开三个文件
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所有模型和训练日志 |
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