(self, input_dim, output_dim, norm='none', activation='relu')

(self, num_features, eps=1e-5, affine=True)

(self, module, name='weight', power_iterations=1)

(self, pool='max')

(self, dataroot)

(self, dataroot, lambda_L1, lambda_perceptual, perceptual_layers, gpu_ids, percep_is_l1)

(self, web_dir, title, reflesh=0)

(self, opt)

(self, pool_size)

(self)

(self)

(self)

(self)

(self, m)

(cords, img_size, sigma=6)

(path)

(input_nc, ndf, which_model_netD, n_layers_D=3, norm='in', use_sigmoid=False, init_type='normal',

(dataroot, input_nc, ngf, which_model_netG, norm='batch', init_type='normal', gpu_ids=[], n_downs

(net, name='network')

()

(pose_map, threshold=0.1, **kwargs)

(pose_map, threshold=0.1, **kwargs)

(self)

(self, input)

(self, x)

(self, Pose, Img, Sem)

(self, x, sem)

(self, x)

(self, x)

(self, appearance_code, x)

(self, statistics_code, x)

(self, x, statistics_code)

(self, x)

(self, x)

(self, x)

(self, *args)

(self, x, out_keys)

(self, x)

(self, inputs, targets)

(self)

(self)

(self)

(self)

(optimizer, opt)

(all_peaks, subsets)

(m)

(self, opt)

(self, opt)

(self)

(self)

(self, opt)

(epoch)

(self)

(X)

(paths)

(self)

(self)

(self)

(net)

(x, val=True, shp=False)

(self, label)

(image_numpy, image_path)

(self, input)

(image_tensor, imtype=np.uint8)

(self)

(p)

(init_type='gaussian')

(m)

(m)

(m)

(m)