add dashborad dataset configuration, add nondist_validation code
huangcaohui
parent
50cb1496d6
commit
a8d01a7e3b
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@ -6,4 +6,5 @@ logs/
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*__pycache__*
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*.sh
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datasets
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basicsr.egg-info
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basicsr.egg-info
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.eggs/
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@ -375,27 +375,120 @@ class ImageRestorationModel(BaseModel):
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for key in metrics_dict:
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metrics_dict[key] /= cnt
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self.metric_results = metrics_dict
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self._log_validation_metric_values(current_iter, dataloader.dataset.opt['name'],
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tb_logger, metrics_dict)
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tb_logger)
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return 0.
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def nondist_validation(self, *args, **kwargs):
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logger = get_root_logger()
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logger.warning('nondist_validation is not implemented. Run dist_validation.')
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self.dist_validation(*args, **kwargs)
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# def nondist_validation(self, *args, **kwargs):
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# logger = get_root_logger()
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# logger.warning('nondist_validation is not implemented. Run dist_validation.')
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# self.dist_validation(*args, **kwargs)
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def nondist_validation(self, dataloader, current_iter, tb_logger,
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save_img, rgb2bgr, use_image):
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dataset_name = dataloader.dataset.opt['name']
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with_metrics = self.opt['val'].get('metrics') is not None
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if with_metrics:
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self.metric_results = {
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metric: 0
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for metric in self.opt['val']['metrics'].keys()
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}
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pbar = tqdm(total=len(dataloader), unit='image')
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cnt = 0
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for idx, val_data in enumerate(dataloader):
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img_name = osp.splitext(osp.basename(val_data['lq_path'][0]))[0]
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# if img_name[-1] != '9':
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# continue
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# print('val_data .. ', val_data['lq'].size(), val_data['gt'].size())
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self.feed_data(val_data)
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if self.opt['val'].get('grids', False):
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self.grids()
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self.test()
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if self.opt['val'].get('grids', False):
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self.grids_inverse()
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visuals = self.get_current_visuals()
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sr_img = tensor2img([visuals['result']], rgb2bgr=rgb2bgr)
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if 'gt' in visuals:
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gt_img = tensor2img([visuals['gt']], rgb2bgr=rgb2bgr)
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del self.gt
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# tentative for out of GPU memory
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del self.lq
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del self.output
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torch.cuda.empty_cache()
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if save_img:
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if self.opt['is_train']:
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save_img_path = osp.join(self.opt['path']['visualization'],
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img_name,
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f'{img_name}_{current_iter}.png')
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save_gt_img_path = osp.join(self.opt['path']['visualization'],
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img_name,
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f'{img_name}_{current_iter}_gt.png')
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else:
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save_img_path = osp.join(
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self.opt['path']['visualization'], dataset_name,
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f'{img_name}.png')
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save_gt_img_path = osp.join(
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self.opt['path']['visualization'], dataset_name,
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f'{img_name}_gt.png')
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imwrite(sr_img, save_img_path)
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imwrite(gt_img, save_gt_img_path)
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if with_metrics:
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# calculate metrics
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opt_metric = deepcopy(self.opt['val']['metrics'])
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if use_image:
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for name, opt_ in opt_metric.items():
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metric_type = opt_.pop('type')
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self.metric_results[name] += getattr(
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metric_module, metric_type)(sr_img, gt_img, **opt_)
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else:
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for name, opt_ in opt_metric.items():
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metric_type = opt_.pop('type')
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self.metric_results[name] += getattr(
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metric_module, metric_type)(visuals['result'], visuals['gt'], **opt_)
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pbar.update(1)
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pbar.set_description(f'Test {img_name}')
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cnt += 1
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# if cnt == 300:
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# break
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pbar.close()
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current_metric = 0.
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if with_metrics:
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for metric in self.metric_results.keys():
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self.metric_results[metric] /= cnt
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current_metric = self.metric_results[metric]
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self._log_validation_metric_values(current_iter, dataset_name,
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tb_logger)
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return current_metric
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def _log_validation_metric_values(self, current_iter, dataset_name,
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tb_logger, metric_dict):
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tb_logger):
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log_str = f'Validation {dataset_name}, \t'
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for metric, value in metric_dict.items():
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for metric, value in self.metric_results.items():
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log_str += f'\t # {metric}: {value:.4f}'
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logger = get_root_logger()
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logger.info(log_str)
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log_dict = OrderedDict()
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# for name, value in loss_dict.items():
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for metric, value in metric_dict.items():
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for metric, value in self.metric_results.items():
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log_dict[f'm_{metric}'] = value
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self.log_dict = log_dict
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@ -155,7 +155,7 @@ def main():
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import os
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try:
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states = os.listdir(state_folder_path)
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except:
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except Exception:
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states = []
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resume_state = None
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@ -1,5 +1,5 @@
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# GENERATED VERSION FILE
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# TIME: Mon Apr 18 21:35:20 2022
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__version__ = '1.2.0+386ca20'
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# TIME: Fri May 5 22:09:31 2023
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__version__ = '1.2.0+50cb149'
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short_version = '1.2.0'
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version_info = (1, 2, 0)
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@ -0,0 +1,60 @@
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# ------------------------------------------------------------------------
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# Copyright (c) 2022 megvii-model. All Rights Reserved.
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# ------------------------------------------------------------------------
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# Modified from BasicSR (https://github.com/xinntao/BasicSR)
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# Copyright 2018-2020 BasicSR Authors
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# ------------------------------------------------------------------------
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# general settings
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name: NAFNet-Dashboard-width32-test
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model_type: ImageRestorationModel
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scale: 1
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num_gpu: 1 # set num_gpu: 0 for cpu mode
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manual_seed: 10
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# dataset and data loader settings
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datasets:
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test:
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name: dashboard-test
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type: PairedImageDataset
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dataroot_gt: E:\HCH\graduate\research_1\NAFNet\datasets\dashboard.v2i.voc\val\gt
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dataroot_lq: E:\HCH\graduate\research_1\NAFNet\datasets\dashboard.v2i.voc\val\lq
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io_backend:
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type: disk
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# network structures
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network_g:
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type: NAFNetLocal
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width: 32
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enc_blk_nums: [1, 1, 1, 14]
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middle_blk_num: 1
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dec_blk_nums: [1, 1, 1, 1]
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# path
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path:
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pretrain_network_g: experiments/pretrained_models/NAFNet-dashboard-width32.pth
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strict_load_g: true
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resume_state: ~
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# validation settings
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val:
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save_img: true
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grids: false
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metrics:
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psnr: # metric name, can be arbitrary
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type: calculate_psnr
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crop_border: 0
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test_y_channel: false
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ssim:
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type: calculate_ssim
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crop_border: 0
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test_y_channel: false
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# dist training settings
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# dist_params:
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# backend: nccl
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# port: 29500
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@ -0,0 +1,108 @@
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# ------------------------------------------------------------------------
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# Copyright (c) 2022 megvii-model. All Rights Reserved.
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# ------------------------------------------------------------------------
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# Modified from BasicSR (https://github.com/xinntao/BasicSR)
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# Copyright 2018-2020 BasicSR Authors
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# ------------------------------------------------------------------------
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# general settings
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name: NAFNet-Dashboard-width32
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model_type: ImageRestorationModel
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scale: 1
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num_gpu: 1
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manual_seed: 10
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datasets:
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train:
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name: dashboard-train
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type: PairedImageDataset
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dataroot_gt: E:\HCH\graduate\research_1\NAFNet\datasets\dashboard.v2i.voc\train\gt
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dataroot_lq: E:\HCH\graduate\research_1\NAFNet\datasets\dashboard.v2i.voc\train\lq
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filename_tmpl: '{}'
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io_backend:
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type: disk
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gt_size: 128 # Cropped patched size for gt patches.
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use_flip: true
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use_rot: true
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# data loader
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use_shuffle: true
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num_worker_per_gpu: 20
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batch_size_per_gpu: 80
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dataset_enlarge_ratio: 1
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prefetch_mode: ~
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val:
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name: dashboard-val
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type: PairedImageDataset
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dataroot_gt: E:\HCH\graduate\research_1\NAFNet\datasets\dashboard.v2i.voc\val\gt
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dataroot_lq: E:\HCH\graduate\research_1\NAFNet\datasets\dashboard.v2i.voc\val\lq
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io_backend:
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type: disk
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network_g:
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type: NAFNetLocal # network name, see in models/archs/NAFNet_arch.py
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width: 32
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enc_blk_nums: [1, 1, 1, 14]
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middle_blk_num: 1
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dec_blk_nums: [1, 1, 1, 1]
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# path
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path:
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pretrain_network_g: ~
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strict_load_g: true
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resume_state: ~
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# training settings
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train:
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optim_g:
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type: AdamW
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lr: !!float 1e-3
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weight_decay: !!float 1e-3
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betas: [0.9, 0.9]
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scheduler:
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type: TrueCosineAnnealingLR
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T_max: 1000
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eta_min: !!float 1e-7
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total_iter: 1000
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warmup_iter: -1 # no warm up
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# losses
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pixel_opt:
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type: PSNRLoss
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loss_weight: 1
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reduction: mean
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# validation settings
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val:
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val_freq: !!float 2e2
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save_img: false
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metrics:
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psnr: # metric name, can be arbitrary
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type: calculate_psnr
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crop_border: 0
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test_y_channel: false
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ssim:
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type: calculate_ssim
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crop_border: 0
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test_y_channel: false
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# logging settings
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logger:
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print_freq: 10
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save_checkpoint_freq: !!float 2e2
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use_tb_logger: true
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wandb:
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project: ~
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resume_id: ~
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# dist training settings
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# dist_params:
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# backend: nccl
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# port: 29500
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