Hi everyone!! 
Could you help me with this issue:
Traceback (most recent call last):
File "/home/mfcs/mestrado_projeto/Colorization-mfcs/code/train.py", line 134, in <module>
main(args)
File "/home/mfcs/mestrado_projeto/Colorization-mfcs/code/train.py", line 56, in main
for i, (images, img_ab, filename) in enumerate(data_loader):
File "/home/mfcs/.local/lib/python3.6/site-packages/torch/utils/data/dataloader.py", line 345, in __next__
data = self._next_data()
File "/home/mfcs/.local/lib/python3.6/site-packages/torch/utils/data/dataloader.py", line 856, in _next_data
return self._process_data(data)
File "/home/mfcs/.local/lib/python3.6/site-packages/torch/utils/data/dataloader.py", line 881, in _process_data
data.reraise()
File "/home/mfcs/.local/lib/python3.6/site-packages/torch/_utils.py", line 395, in reraise
raise self.exc_type(msg)
TypeError: Caught TypeError in DataLoader worker process 0.
Original Traceback (most recent call last):
File "/home/mfcs/.local/lib/python3.6/site-packages/torch/utils/data/_utils/worker.py", line 178, in _worker_loop
data = fetcher.fetch(index)
File "/home/mfcs/.local/lib/python3.6/site-packages/torch/utils/data/_utils/fetch.py", line 44, in fetch
data = [self.dataset[idx] for idx in possibly_batched_index]
File "/home/mfcs/.local/lib/python3.6/site-packages/torch/utils/data/_utils/fetch.py", line 44, in <listcomp>
data = [self.dataset[idx] for idx in possibly_batched_index]
File "/home/mfcs/mestrado_projeto/Colorization-mfcs/code/data_loader.py", line 52, in __getitem__
img_l = self.tensor_to_PIL(img_l) # Convert to PIL object image to be possible apply the following transform
File "/home/mfcs/.local/lib/python3.6/site-packages/torchvision/transforms/transforms.py", line 127, in __call__
return F.to_pil_image(pic, self.mode)
File "/home/mfcs/.local/lib/python3.6/site-packages/torchvision/transforms/functional.py", line 167, in to_pil_image
raise TypeError('Input type {} is not supported'.format(npimg.dtype))
TypeError: Input type int64 is not supported
All code that I am using works fine when I run it on Microsoft Windows 10, but on Linux Ubuntu this error happens…Does anyone know what the problem might be?
The DataLoader code is showed below:
#######################################
########## DATA LOADER ###############
#######################################
from torchvision import datasets, transforms
from torch.utils.data import Dataset
from skimage.color import rgb2lab, rgb2gray
from skimage import io
import torch.utils.data as data
import torch
import torch.nn as nn
import torch.nn.functional as F
import numpy as np
from PIL import Image
import os
scale_transform = transforms.Compose([
transforms.Resize((256,256),2),
#transforms.RandomCrop(224),
#transforms.ToTensor()
])
class CustomDataset(Dataset):
"""Custom Dataset."""
def __init__(self, root_dir, transform=None):
"""
Args:
root_dir (string): Directory with all the images.
transform (callable, optional): Optional transform to be applied
on a sample.
"""
self.root_dir = root_dir
self.transform = transform
self.file_list=os.listdir(root_dir)
self.tensor_to_PIL = transforms.ToPILImage()
def __len__(self):
return len(self.file_list)
def __getitem__(self, idx):
img = Image.open(self.root_dir+'/'+self.file_list[idx]).convert('RGB') # Read the image
if self.transform is not None:
img_rgb_resized = transforms.Resize((56,56),2)(img)
img_rgb_ori = np.array(img)
img_lab_ori = rgb2lab(img_rgb_ori) # Convert to CIE Lab color space
img_lab_resized = rgb2lab(img_rgb_resized) # Convert to CIE Lab color space
img_rgb_transposed = img_rgb_ori.transpose(2, 0, 1) #(C, W, H)
img_lab_transposed = img_lab_ori.transpose(2, 0, 1) #(C, W, H)
img_lab_resized_transposed = img_lab_resized.transpose(2, 0, 1) #(C, W, H)
img_l = (np.round(img_lab_transposed[0,:,:])).astype(np.int) # L channel
img_l = self.tensor_to_PIL(img_l) # Convert to PIL object image to be possible apply the following transform
img_l_resized = self.transform(img_l)
img_l_resized = np.array(img_l_resized) # Convert to numpy array
img_l_resized = img_l_resized - 50
img_l_resized = torch.from_numpy(img_l_resized) # Convert to torch tensor
img_ab_resized = (np.round(img_lab_resized_transposed[1:3, :, :])).astype(np.int) # (a,b) channels with int intensity values
img_ab_resized = np.array(img_ab_resized) # Convert to numpy array
img_ab_resized = torch.from_numpy(img_ab_resized) # Convert to torch tensor
filename = self.root_dir+'/'+self.file_list[idx]
return img_l_resized, img_ab_resized, filename # img_l_resized -> 1x256x256 and img_ab_resized -> 2x64x64
Best regards,
Matheus Santos.
