✨ Add GANomaly #70
✨ Add GANomaly #70
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|
Nice 🎁 |
anomalib/models/ganomaly/config.yaml
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| tiling: | ||
| apply: false | ||
| tile_size: null | ||
| stride: null | ||
| remove_border_count: 0 | ||
| use_random_tiling: False | ||
| random_tile_count: 16 |
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I think tiling should be on by default. The encoder and decoder network in this model is capable of handling 64x64 images max. So we need to use either 32x32 or 64x64 windows.
| self.model.add_module( | ||
| f"initial-conv-{num_input_channels}-{n_features}", | ||
| nn.Conv2d(num_input_channels, n_features, kernel_size=4, stride=2, padding=1, bias=False), | ||
| ) | ||
| self.model.add_module(f"initial-relu-{n_features}", nn.LeakyReLU(0.2, inplace=True)) |
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I find this module a bit hard to read. Maybe instead of collecting all the layers in the self.model attribute, you could separate them into multiple modules, and then call them in the forward method? Something like
def __init__():
self.initial_conv = nn.Conv2d(...)
self.initial_relu = nn.LeakyReLU(...)
self.extra_layers = nn.Sequential(...)
self.pyramid_features = nn.Sequential(...)
...
def forward(x):
x = self.init_conv(x)
x = self.init_relu(x)
x = self.extra_layers(x)
x = self.pyramid_features(x)
...
I always like to be able to understand the data flow in a torch module by just looking at the forward mnethod.
| self.model.add_module( | ||
| f"initial-{latent_vec_size}-{n_input_features}-convt", | ||
| nn.ConvTranspose2d(latent_vec_size, n_input_features, kernel_size=4, stride=1, padding=0, bias=False), | ||
| ) | ||
| self.model.add_module(f"initial-{n_input_features}-batchnorm", nn.BatchNorm2d(n_input_features)) | ||
| self.model.add_module(f"initial-{n_input_features}-relu", nn.ReLU(True)) |
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Same as in Encoder. I think it would be good to split self.model up into smaller chunks.
| @@ -0,0 +1,227 @@ | |||
| """Torch models defining encoder, decoder, Generator and Discriminator.""" | |||
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Reminder: We need to check if this code contains any duplication from DCGAN repo. If it does, we should either acknowledge the authors (depending on the license) or write our own implementation.
| """Return latent vectors.""" | ||
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| output = self.input_layers(input_tensor) | ||
| if len(self.extra_layers) > 0: |
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I don't think this check is needed. If the sequential module is empty, calling it will just return the unaltered input.
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Thanks! I didn't know this. I have updated the PR.
| """Return generated image.""" | ||
| output = self.latent_input(input_tensor) | ||
| output = self.inverse_pyramid(output) | ||
| if len(self.extra_layers) > 0: |
Description
Add GANomaly algorithm.
Checklist: