| from visdom import Visdom
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| import numpy as np
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| import torch
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| def setup_visdom(port=8002):
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| return Visdom(port=port)
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|
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| def visdom_plot_loss(win_name, loss, cur_viz):
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| loss_np = np.array(loss)
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| x = np.arange(1, 1 + len(loss))
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| cur_viz.line(win=win_name,
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| X=x,
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| Y=loss_np,
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| opts=dict(showlegend=True, legend=[win_name]))
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| def guassian_light(light_tensor):
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| light_tensor = light_tensor.detach().cpu()
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| channel = light_tensor.size()[0]
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| tensor_ret = torch.zeros(light_tensor.size())
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| for i in range(channel):
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| light_np = light_tensor[0].numpy() * 100.0
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| light_np = gaussian_filter(light_np, sigma=2)
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| tensor_ret[i] = torch.from_numpy(light_np)
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| tensor_ret[i] = torch.clamp(tensor_ret[i], 0.0, 1.0)
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| return tensor_ret
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| def normalize_img(imgs):
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| b,c,h,w = imgs.shape
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| gt_batch = b//2
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| for i in range(gt_batch):
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| factor = torch.max(imgs[i])
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| imgs[i] = imgs[i]/factor
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| imgs[gt_batch + i] = imgs[gt_batch + i]/factor
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| imgs = torch.clamp(imgs, 0.0,1.0)
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| return imgs
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| def visdom_show_batch(imgs, cur_viz, win_name=None, nrow=2, normalize=True):
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| if normalize:
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| imgs = normalize_img(imgs)
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| if win_name is None:
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| cur_viz.images(imgs, win="batch visualize",nrow=nrow)
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| else:
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| cur_viz.images(imgs, win=win_name, opts=dict(title=win_name),nrow=nrow)
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| def visdom_log(log_info, viz, win_name='logger'):
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| viz.text(log_info, win=win_name) |
