Initialize the class; save the options in the class Parameters: opt (Option class)-- stores all the experiment flags; needs to be

Initialize this class Step 1: create a dataset instance given the name [dataset_mode] Step 2: create a multi-threaded data loader.

Reset the class; indicates the class hasn't been initailized

Initialize the BaseModel class. Parameters: opt (Option class)-- stores all the experiment flags; needs to be a subclass of BaseO

Initialize the pix2pix class. Parameters: opt (Option class)-- stores all the experiment flags; needs to be a subclass of BaseOpt

Construct a Resnet-based generator Parameters: input_nc (int) -- the number of channels in input images output_n

Initialize the Resnet block A resnet block is a conv block with skip connections We construct a conv block with build_conv_block func

Construct a Unet generator Parameters: input_nc (int) -- the number of channels in input images output_nc (int) -- th

Construct a Unet submodule with skip connections. Parameters: outer_nc (int) -- the number of filters in the outer conv layer

Construct a PatchGAN discriminator Parameters: input_nc (int) -- the number of channels in input images ndf (int)

Construct a 1x1 PatchGAN discriminator Parameters: input_nc (int) -- the number of channels in input images ndf (int

Initialize the HTML classes Parameters: web_dir (str) -- a directory that stores the webpage. HTML file will be created at <web_d

Initialize the Visualizer class Parameters: opt -- stores all the experiment flags; needs to be a subclass of BaseOptions

Initialize the ImagePool class Parameters: pool_size (int) -- the size of image buffer, if pool_size=0, no buffer will be created

Add an extra bundle into your bundle, so you could have more bundeled items in your bundle.

Add an element that will not be packed into a bundle, but will be accessible.

Return a batch of data

Return the total number of images.

Return the total number of images in the dataset.

Return the number of data in the dataset

Inverse of quantization depth : NCHW -> N1HW

Modification of timm.models.beit.py: Attention._get_rel_pos_bias to support arbitrary window sizes.

Rezise the sample to ensure the given size. Keeps aspect ratio. Args: sample (dict): sample size (tuple): image size Returns

Rezise the sample to ensure the given size. Keeps aspect ratio. Args: sample (dict): sample size (tuple): image size Returns

Modification of timm.models.beit.py: Attention.forward to support arbitrary window sizes.

Modification of timm.models.beit.py: Beit.forward_features to support arbitrary window sizes.

Modification of timm.models.beit.py: Block.forward to support arbitrary window sizes.

Calculate the gradient penalty loss, used in WGAN-GP paper https://arxiv.org/abs/1704.00028 Arguments: netD (network) -- dis

If the program could not connect to Visdom server, this function will start a new server at port < self.port >

Crops the white and black border of the RGB and depth images. Args: rgb: RGB image, shape (H, W, 3). This image is used to determine the

Create a discriminator Parameters: input_nc (int) -- the number of channels in input images ndf (int) -- the number

Create a generator Parameters: input_nc (int) -- the number of channels in input images output_nc (int) -- the number of channels

Reverses the imagenet normalization applied to the input. Args: x (torch.Tensor - shape(N,3,H,W)): input tensor Returns: tor

Calculate and print the mean of average absolute(gradients) Parameters: net (torch network) -- Torch network name (str) -- the na

Exponential attractor: dc = exp(-alpha*|dx|^gamma) * dx , where dx = a - c, a = attractor point, c = bin center, dc = shift in bin centermmary for exp