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Types & classes23 in github.com/Huage001/AdaAttN

↓ 3 callersClassAttnAdaIN
user_specify_demo.py:54
↓ 2 callersClassAdaAttN
models/networks.py:111
↓ 2 callersClassTestOptions
This class includes test options. It also includes shared options defined in BaseOptions.
options/test_options.py:4
↓ 1 callersClassAttnAdaINCos
inference_frame.py:80
↓ 1 callersClassCustomDatasetDataLoader
data/__init__.py:47
↓ 1 callersClassDecoder
user_specify_demo.py:128
↓ 1 callersClassDecoder
inference_frame.py:123
↓ 1 callersClassHTML
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 callersClassTrainOptions
This class includes training options. It also includes shared options defined in BaseOptions.
options/train_options.py:4
↓ 1 callersClassTransformer
user_specify_demo.py:109
↓ 1 callersClassVisualizer
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
ClassAdaAttNModel
models/adaattn_model.py:8
ClassAttnAdaIN
inference_frame.py:39
ClassBaseDataset
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
ClassBaseModel
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
ClassBaseOptions
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
ClassDecoder
models/networks.py:170
ClassGetData
A Python script for downloading CycleGAN or pix2pix datasets. Parameters: technique (str) -- One of: 'cyclegan' or 'pix2pix'. ver
util/get_data.py:11
ClassImageFolder
data/image_folder.py:39
ClassImagePool
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
ClassPredictor
predict.py:14
ClassTransformer
models/networks.py:152
ClassUnalignedDataset
data/unaligned_dataset.py:14