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- import os
- import torch
- from torchvision.utils import make_grid
- from tensorboardX import SummaryWriter
- from dataloaders.utils import decode_seg_map_sequence
-
-
- class TensorboardSummary(object):
- def __init__(self, directory):
- self.directory = directory
-
- def create_summary(self):
- writer = SummaryWriter(log_dir=os.path.join(self.directory))
- return writer
-
- def visualize_image(self, writer, dataset, image, target, output, global_step):
- grid_image = make_grid(image[:3].clone().cpu().data, 3, normalize=True)
- writer.add_image('Image', grid_image, global_step)
- grid_image = make_grid(decode_seg_map_sequence(torch.max(output[:3], 1)[1].detach().cpu().numpy(),
- dataset=dataset), 3, normalize=False, range=(0, 255))
- writer.add_image('Predicted label', grid_image, global_step)
- grid_image = make_grid(decode_seg_map_sequence(torch.squeeze(target[:3], 1).detach().cpu().numpy(),
- dataset=dataset), 3, normalize=False, range=(0, 255))
- writer.add_image('Groundtruth label', grid_image, global_step)
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