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* add fid code * add link * update readme * Update README.MD * Update README.MD * update readme
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| # @package _global_ | ||
| defaults: | ||
| - _self_ # make sure override | ||
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| fid: | ||
| resize: ${dataset.back_resize} # (h, w) | ||
| padding: ${dataset.back_pad} # left, top, right and bottom | ||
| raw_output: false | ||
| # path relative to `${ROOT}` | ||
| img_gen_dir: ??? | ||
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| runner: | ||
| validation_index: all | ||
| validation_times: 1 | ||
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| log_root_prefix: ../magicdrive-log/fid_gen | ||
| show_box: false |
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| device: null | ||
| num_workers: null | ||
| save_stats: false | ||
| batch_size: 512 | ||
| dims: 2048 | ||
| ratio: -1 | ||
| roota: ./data/nuscenes/samples | ||
| rootb: ??? |
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| defaults: | ||
| - config | ||
| - fid: default | ||
| - _self_ | ||
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| log_root_prefix: ../magicdrive-log/fid |
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| import torch | ||
| from accelerate import Accelerator | ||
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| def concat_from_everyone(accelerator: Accelerator, tmp): | ||
| if not accelerator.use_distributed: | ||
| return tmp | ||
| output = [None for _ in range(accelerator.num_processes)] | ||
| torch.distributed.all_gather_object(output, tmp) | ||
| if accelerator.is_main_process: | ||
| res = [] | ||
| for tmpi in output: | ||
| res += tmpi | ||
| return res | ||
| else: | ||
| return None |
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| import random | ||
| from collections import OrderedDict | ||
| from nuscenes.nuscenes import NuScenes | ||
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| def sample_token_from_scene(ratio_or_num, nusc=None, drop_desc=None): | ||
| """Sample keyframes from each scene according to ratio. | ||
| if ratio_or_num >= 1, treated as num; | ||
| if ratio_or_num < 1, treated as ratio; | ||
| if ratio_or_num == 0, only pick the first frame; | ||
| if ratio_or_num == -1, return None. | ||
| Args: | ||
| ratio (float): sample ratio to each scene. | ||
| Returns: | ||
| sample_flag_dict (dict): Dict[token, bool] | ||
| scene_sample_flag_dict (dict): Dict[scene_name, Dict[token, bool]] | ||
| """ | ||
| if ratio_or_num == -1 and drop_desc is None: | ||
| return None, None | ||
| if nusc is None: | ||
| nusc = NuScenes(version='v1.0-trainval', | ||
| dataroot='./data/nuscenes', verbose=True) | ||
| sample_flag_dict = {} | ||
| scene_sample_flag_dict = {} | ||
| for idx, scene in enumerate(nusc.scene): | ||
| scene_name = scene['name'] | ||
| frames_len = scene['nbr_samples'] | ||
| sample_token = scene['first_sample_token'] | ||
| # iteratively gather sample tokens from one scene | ||
| token_in_this_scene = OrderedDict() | ||
| for fi in range(frames_len): | ||
| token_in_this_scene[sample_token] = False | ||
| sample = nusc.get('sample', sample_token) | ||
| sample_token = sample['next'] | ||
| desc = scene['description'] | ||
| if drop_desc is not None and drop_desc in desc.lower(): | ||
| picked = [] # we pick nothing | ||
| else: | ||
| # pick tokens according to your ratio | ||
| if ratio_or_num == 0: | ||
| # if 0, only pick the first one | ||
| picked = list(token_in_this_scene.keys())[0:1] | ||
| else: | ||
| if ratio_or_num >= 1: | ||
| pick_num = int(ratio_or_num) | ||
| else: | ||
| pick_num = int(frames_len * ratio_or_num) | ||
| picked = random.sample(token_in_this_scene.keys(), pick_num) | ||
| for pick in picked: | ||
| token_in_this_scene[pick] = True | ||
| # now save data for output | ||
| token_in_this_scene = dict(token_in_this_scene) | ||
| scene_sample_flag_dict[scene_name] = token_in_this_scene | ||
| sample_flag_dict.update(token_in_this_scene) | ||
| return sample_flag_dict, scene_sample_flag_dict |
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