new error:
NotImplementedError
trainer.py
if i % (num_img_tr // 10) == 0:
global_step = i + num_img_tr * epoch
self.summary.visualize_image(self.writer, self.config[‘dataset’][‘dataset_name’], image, target, output, global_step)
summaries.py
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)
datagen_utils.py
def decode_segmap(label_mask, dataset, plot=False):
“”“Decode segmentation class labels into a color image
Args:
label_mask (np.ndarray): an (M,N) array of integer values denoting
the class label at each spatial location.
plot (bool, optional): whether to show the resulting color image
in a figure.
Returns:
(np.ndarray, optional): the resulting decoded color image.
“””
if dataset == ‘pascal’ or dataset == ‘coco’:
n_classes = 21
label_colours = get_pascal_labels()
elif dataset == ‘cityscapes’:
n_classes = 19
label_colours = get_cityscapes_labels()
elif dataset == 'deepfashion':
    n_classes = 13
    label_colours = get_deepfashion_labels()

elif dataset == 'braintumor':
    n_classes = 3
    label_colours = get_braintumor_labels()


**else:**

**        raise NotImplementedError**