| import numpy as np | |
| import torch | |
| def seed_everything(seed): | |
| torch.manual_seed(seed) | |
| torch.cuda.manual_seed(seed) | |
| torch.cuda.manual_seed_all(seed) | |
| torch.backends.cudnn.deterministic = True | |
| torch.backends.cudnn.benchmark = False | |
| def get_views(panorama_height, panorama_width, window_size=64, stride=8): | |
| panorama_height /= 8 | |
| panorama_width /= 8 | |
| num_blocks_height = (panorama_height - window_size) // stride + 1 | |
| num_blocks_width = (panorama_width - window_size) // stride + 1 | |
| total_num_blocks = int(num_blocks_height * num_blocks_width) | |
| views = [] | |
| for i in range(total_num_blocks): | |
| h_start = int((i // num_blocks_width) * stride) | |
| h_end = h_start + window_size | |
| w_start = int((i % num_blocks_width) * stride) | |
| w_end = w_start + window_size | |
| views.append((h_start, h_end, w_start, w_end)) | |
| return views | |
| def exponential_decay_list(init_weight, decay_rate, num_steps): | |
| weights = [init_weight * (decay_rate ** i) for i in range(num_steps)] | |
| return torch.tensor(weights) |