mirror of https://github.com/alibaba/EasyCV.git
56 lines
2.2 KiB
Python
56 lines
2.2 KiB
Python
# Copyright (c) Alibaba, Inc. and its affiliates.
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import numpy as np
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import torch
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from mmcv.runner.dist_utils import master_only
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from mmcv.runner.hooks import HOOKS
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from mmcv.runner.hooks import TensorboardLoggerHook as _TensorboardLoggerHook
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@HOOKS.register_module()
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class TensorboardLoggerHookV2(_TensorboardLoggerHook):
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def visualization_log(self, runner):
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"""Images Visulization.
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`visualization_buffer` is a dictionary containing:
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images (list): list of visulaized images.
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img_metas (list of dict, optional): dict containing ori_filename and so on.
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ori_filename will be displayed as the tag of the image by default.
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"""
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visual_results = runner.visualization_buffer.output
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for vis_key, vis_result in visual_results.items():
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images = vis_result.get('images', [])
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img_metas = vis_result.get('img_metas', None)
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if img_metas is not None:
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assert len(images) == len(
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img_metas
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), 'Output `images` and `img_metas` must keep the same length!'
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for i, img in enumerate(images):
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if isinstance(img, np.ndarray):
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img = torch.from_numpy(img)
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else:
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assert isinstance(
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img, torch.Tensor
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), 'Only support np.ndarray and torch.Tensor type!'
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default_name = 'image_%i' % i
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filename = img_metas[i].get(
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'ori_filename',
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default_name) if img_metas is not None else default_name
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self.writer.add_image(
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f'{vis_key}/{filename}',
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img,
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self.get_iter(runner),
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dataformats='HWC')
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@master_only
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def log(self, runner):
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self.visualization_log(runner)
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super(TensorboardLoggerHookV2, self).log(runner)
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def after_train_iter(self, runner):
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super(TensorboardLoggerHookV2, self).after_train_iter(runner)
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# clear visualization_buffer after each iter to ensure that it is only written once,
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# avoiding repeated writing of the same image buffer every self.interval
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runner.visualization_buffer.clear_output()
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