(batchNorm, in_planes, out_planes, kernel_size=3, stride=1)

(self, string)

(in_planes)

(self, requires_grad=False)

(self, label)

(in_planes, out_planes)

(dir)

(image_tensor, imtype=np.uint8, normalize=True)

(A, Ai, nF)

(opt, params, method=Image.BICUBIC, normalize=True, toTensor=True)

(self, image, flow)

(A_paths, B_paths)

(modules, weights, biases, param_prefix='net2_')

(self, args=None, batchNorm=False, div_flow = 20., fp16=False)

(self, args, is_cropped = False, root = '', dstype = 'clean', replicates = 1)

(self, img)

(self, scale_T, tensors_list, dummy_bs=0)

(batchNorm, in_planes, out_planes, kernel_size=3, stride=1, bias = True)

(self, ims, txts, links, width=400, height=0)

(self, tensors, dim=0)

(im, x, y, bw=1, color=(255,255,255), draw_end_points=False)

(modules, weights, biases)

(input_flow, target_flow)

(self)

(ctx, grad_output)

(self, tensor, nearest=False)

(self, input_A, input_B, inst_A, fake_B_prev, dummy_bs=0)

(opt, size)

(B_all, B, t_scales, tD)

(B_all, B, t_scales, tD, n_frames_load, i, is_flow=False)

(x, y)

(self, network, network_label, epoch_label, save_dir='')

(n, base=32.0)

(self)

(self, network, network_label, epoch_label, gpu_ids)

(self, epoch, model)

(self, ratio)

(self, str)

(self, netD, real_A, real_B, fake_B)

(self, Ai)

(self, A_path, size, params, input_type)

(self, A_path, transform_scaleA, is_label=False)

(self, A_path, transform_scaleA, is_label=False)

(opt, n_frames_total, cur_seq_len, index)

(self, A_paths)

(self, opt)

(opt, loss, optimizer)

(self, t)

(self, tensors, remove_size=0)

(self, keypoints, part, index, sym=False)

(self, A_paths, index)

(self, message)

(opt)

(self, pred_real, pred_fake)

(self, im1, im2)

(self, real_As, ts, te=None)

(opt)

(id, destination)

(id, destination)

(self, input_map, real_image, inst_map=None)

(self, real_A, real_B, pool_map=None)

(self, real_A)

(batchsize, rows, cols, gpu_id=0, dtype=torch.float32)

(norm_type='instance')

(self, input, target_is_real)

(filename)

()

(path)

(self, save=True)

(modules, weights, biases, param_prefix='netsd_')

(self, ids)

(self, image, flow)

(opt, epoch, epoch_iter, total_steps, visualizer, iter_path, modelG, modelD, end_of_epoch=False)

(im, yy, xx, color)

(output, imtype=np.uint8)

(args, epoch, start_iteration, data_loader, model, optimizer, logger, is_validate=False, offset=0)

(file_name, unzip_path)

(self)

(opt, modelG, modelD, flowNet)

(opt)

(img, size, pos)

(img, flip)

(self)

(self, pad_size=0, kernel_size=0, max_displacement=0, stride1=1, stride2=2, corr_multiply=1)

(self)

(img, size, method=Image.BICUBIC)

(self, tensors, add_size=0)

(self, border=1)

(self, dim, padding_type, norm_layer, activation, use_dropout)

(self, real_B_prev, fake_B_last, fake_B)

(self, real_B, fake_B, flow_ref, conf_ref, scale_T)

(pts, edge_lists, size, random_drop_prob, remove_face_labels, basic_point_only)

(opt, models)

(basic_point_only)

(self, visuals, epoch, step)

(self, a, b, num=1)

(id, destination)

(self, keypoints, part_list, transform_A, size, add_dist_map)

(pts, edge_lists)

(self, input1)