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Types & classes
23 in github.com/Huage001/AdaAttN
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Functions
138
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Types & classes
23
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Endpoints
1
↓ 3 callers
Class
AttnAdaIN
user_specify_demo.py:54
↓ 2 callers
Class
AdaAttN
models/networks.py:111
↓ 2 callers
Class
TestOptions
This class includes test options. It also includes shared options defined in BaseOptions.
options/test_options.py:4
↓ 1 callers
Class
AttnAdaINCos
inference_frame.py:80
↓ 1 callers
Class
CustomDatasetDataLoader
data/__init__.py:47
↓ 1 callers
Class
Decoder
user_specify_demo.py:128
↓ 1 callers
Class
Decoder
inference_frame.py:123
↓ 1 callers
Class
HTML
This HTML class allows us to save images and write texts into a single HTML file. It consists of functions such as <add_header> (add a text head
util/html.py:6
↓ 1 callers
Class
TrainOptions
This class includes training options. It also includes shared options defined in BaseOptions.
options/train_options.py:4
↓ 1 callers
Class
Transformer
user_specify_demo.py:109
↓ 1 callers
Class
Visualizer
This class includes several functions that can display/save images and print/save logging information. It uses a Python library 'visdom' for disp
util/visualizer.py:46
Class
AdaAttNModel
models/adaattn_model.py:8
Class
AttnAdaIN
inference_frame.py:39
Class
BaseDataset
This class is an abstract base class (ABC) for datasets. To create a subclass, you need to implement the following four functions: -- <__init
data/base_dataset.py:14
Class
BaseModel
This class is an abstract base class (ABC) for models. To create a subclass, you need to implement the following five functions: -- <__ini
models/base_model.py:8
Class
BaseOptions
This class defines options used during both training and test time. It also implements several helper functions such as parsing, printing, and sa
options/base_options.py:9
Class
Decoder
models/networks.py:170
Class
GetData
A Python script for downloading CycleGAN or pix2pix datasets. Parameters: technique (str) -- One of: 'cyclegan' or 'pix2pix'. ver
util/get_data.py:11
Class
ImageFolder
data/image_folder.py:39
Class
ImagePool
This class implements an image buffer that stores previously generated images. This buffer enables us to update discriminators using a history of
util/image_pool.py:5
Class
Predictor
predict.py:14
Class
Transformer
models/networks.py:152
Class
UnalignedDataset
data/unaligned_dataset.py:14