I am wondering why I am getting this error when I start to train the model while I used this model before to train another dataset.
mask = torch.from_numpy(np.array(mask))
TypeError: can't convert np.ndarray of type numpy.bool_. The only supported types are: double, float, float16, int64, int32, and uint8.
The error is about how mask defined in a customised dataset but I don’t have a clue how can I fix that.
this is how customised dataset created.
import torch
from torch.utils.data.dataset import Dataset # For custom data-sets
import torchvision.transforms as transforms
from PIL import Image
import numpy as np
class CustomDataset(Dataset):
def __init__(self, image_paths, target_paths): # initial logic happens like transform
self.image_paths = image_paths
self.target_paths = target_paths
self.transforms = transforms.ToTensor()
self.mapping = {
0: 0,
255: 1
# 85: 0,
# 170: 1,
# 255: 2
}
def mask_to_class(self, mask):
for k in self.mapping:
mask[mask==k] = self.mapping[k]
return mask
def __getitem__(self, index):
image = Image.open(self.image_paths[index])
mask = Image.open(self.target_paths[index])
t_image = self.transforms(image)
mask = torch.from_numpy(np.array(mask))
mask = self.mask_to_class(mask)
mask = mask.long()
return t_image, mask
def __len__(self): # return count of sample we have
return len(self.image_paths)
also, one example of my mask is attached.
mask value is 0-255 and it’s uint8
