Skorch RuntimeError: Input type (torch.cuda.ByteTensor) and weight type (torch.cuda.FloatTensor) should be the same
I'm trying to develop an image segmentation model. In the below code I keep hitting a RuntimeError: Input type (torch.cuda.ByteTensor) and weight type (torch.cuda.FloatTensor) should be the same. I'm not sure why as I've tried to load both my data and my UNet model to the GPU using .cuda() (although not the skorch model-- not sure how to do that). I'm using a library for active learning, modAL, which wraps skorch.
from modAL.models import ActiveLearner
import numpy as np
import torch

from torch import nn
from torch import Tensor
from torch.utils.data import DataLoader
from torch.utils.data import Dataset

from skorch.net import NeuralNet

from modAL.models import ActiveLearner
from modAL.uncertainty import classifier_uncertainty, classifier_margin
from modAL.utils.combination import make_linear_combination, make_product
from modAL.utils.selection import multi_argmax
from modAL.uncertainty import uncertainty_sampling

from model import UNet
from skorch.net import NeuralNet
from skorch.helper import predefined_split
from torch.optim import SGD

import cv2


# Map style dataset, 
class ImagesDataset(Dataset):
    &quot;&quot;&quot;Constructs dataset of satellite images + masks&quot;&quot;&quot;
    def __init__(self, image_paths):
        super().__init__()
        self.image_paths = image_paths

    def __len__(self):
        return len(self.image_paths)

    def __getitem__(self, idx):  
        if torch.is_tensor(idx):
            idx = idx.tolist()
        print(&quot;idx:&quot;, idx)
        sample_dir = self.image_paths[idx]
        img_path = sample_dir +&quot;/images/&quot;+ Path(sample_dir).name +'.png'
        mask_path = sample_dir +'/mask.png'
        img, mask = cv2.imread(img_path), cv2.imread(mask_path)
        print(&quot;shape of img&quot;, img.shape)
        return img, mask

# turn data into dataset
train_ds = ImagesDataset(train_dirs)
val_ds = ImagesDataset(valid_dirs)

train_loader = torch.utils.data.DataLoader(train_ds, batch_size=3, shuffle=True, pin_memory=True)
val_loader = torch.utils.data.DataLoader(val_ds, batch_size=1, shuffle=True, pin_memory=True)

# make sure data loaded in cuda for train, validation
for i, (tr, val) in enumerate(train_loader):
    tr, val = tr.cuda(), val.cuda()

for i, (tr2, val2) in enumerate(val_loader):
    tr2, val2 = tr2.cuda(), val2.cuda()

X, y = next(iter(train_loader))
X_train = np.array(X.reshape(3,3,1024,1024))
y_train = np.array(y.reshape(3,3,1024,1024))

X2, y2 = next(iter(val_loader))
X_test = np.array(X2.reshape(1,3,1024,1024))
y_test = np.array(y2.reshape(1,3,1024,1024))


module = UNet(pretrained=True)
if torch.cuda.is_available():
    module = module.cuda()
    
# create the classifier

net = NeuralNet(
    module,
    criterion=torch.nn.NLLLoss,
    batch_size=32,
    max_epochs=20,
    optimizer=SGD,
    optimizer__momentum=0.9,
    iterator_train__shuffle=True,
    iterator_train__num_workers=4,
    iterator_valid__shuffle=False,
    iterator_valid__num_workers=4,
    train_split=predefined_split(val_ds),
    device='cuda',
)

# assemble initial data
n_initial = 1
initial_idx = np.random.choice(range(len(X_train)), size=n_initial, replace=False)
X_initial = X_train[initial_idx]
y_initial = y_train[initial_idx]

# generate the pool, remove the initial data from the training dataset
X_pool = np.delete(X_train, initial_idx, axis=0)
y_pool = np.delete(y_train, initial_idx, axis=0)

# train the activelearner
# shape of 4D matrix is 'batch', 'channel', 'width', 'height')
learner = ActiveLearner(
    estimator= net,
    X_training=X_initial, y_training=y_initial,
)

The full error trace is:
    ---------------------------------------------------------------------------
RuntimeError                              Traceback (most recent call last)
&lt;ipython-input-83-0af6007b6b72&gt; in &lt;module&gt;
      8 learner = ActiveLearner(
      9     estimator= net,
---&gt; 10     X_training=X_initial, y_training=y_initial,
     11     # X_training=X_initial, y_training=y_initial,
     12 )

~/.local/lib/python3.7/site-packages/modAL/models/learners.py in __init__(self, estimator, query_strategy, X_training, y_training, bootstrap_init, on_transformed, **fit_kwargs)
     80                  ) -&gt; None:
     81         super().__init__(estimator, query_strategy,
---&gt; 82                          X_training, y_training, bootstrap_init, on_transformed, **fit_kwargs)
     83 
     84     def teach(self, X: modALinput, y: modALinput, bootstrap: bool = False, only_new: bool = False, **fit_kwargs) -&gt; None:

~/.local/lib/python3.7/site-packages/modAL/models/base.py in __init__(self, estimator, query_strategy, X_training, y_training, bootstrap_init, on_transformed, force_all_finite, **fit_kwargs)
     70         self.y_training = y_training
     71         if X_training is not None:
---&gt; 72             self._fit_to_known(bootstrap=bootstrap_init, **fit_kwargs)
     73             self.Xt_training = self.transform_without_estimating(self.X_training) if self.on_transformed else None
     74 

~/.local/lib/python3.7/site-packages/modAL/models/base.py in _fit_to_known(self, bootstrap, **fit_kwargs)
    160         &quot;&quot;&quot;
    161         if not bootstrap:
--&gt; 162             self.estimator.fit(self.X_training, self.y_training, **fit_kwargs)
    163         else:
    164             n_instances = self.X_training.shape[0]

~/.local/lib/python3.7/site-packages/skorch/net.py in fit(self, X, y, **fit_params)
    901             self.initialize()
    902 
--&gt; 903         self.partial_fit(X, y, **fit_params)
    904         return self
    905 

~/.local/lib/python3.7/site-packages/skorch/net.py in partial_fit(self, X, y, classes, **fit_params)
    860         self.notify('on_train_begin', X=X, y=y)
    861         try:
--&gt; 862             self.fit_loop(X, y, **fit_params)
    863         except KeyboardInterrupt:
    864             pass

~/.local/lib/python3.7/site-packages/skorch/net.py in fit_loop(self, X, y, epochs, **fit_params)
    774 
    775             self.run_single_epoch(dataset_train, training=True, prefix=&quot;train&quot;,
--&gt; 776                                   step_fn=self.train_step, **fit_params)
    777 
    778             if dataset_valid is not None:

~/.local/lib/python3.7/site-packages/skorch/net.py in run_single_epoch(self, dataset, training, prefix, step_fn, **fit_params)
    810             yi_res = yi if not is_placeholder_y else None
    811             self.notify(&quot;on_batch_begin&quot;, X=Xi, y=yi_res, training=training)
--&gt; 812             step = step_fn(Xi, yi, **fit_params)
    813             self.history.record_batch(prefix + &quot;_loss&quot;, step[&quot;loss&quot;].item())
    814             self.history.record_batch(prefix + &quot;_batch_size&quot;, get_len(Xi))

~/.local/lib/python3.7/site-packages/skorch/net.py in train_step(self, Xi, yi, **fit_params)
    707             return step['loss']
    708 
--&gt; 709         self.optimizer_.step(step_fn)
    710         return step_accumulator.get_step()
    711 

~/.local/lib/python3.7/site-packages/torch/autograd/grad_mode.py in decorate_context(*args, **kwargs)
     24         def decorate_context(*args, **kwargs):
     25             with self.__class__():
---&gt; 26                 return func(*args, **kwargs)
     27         return cast(F, decorate_context)
     28 

~/.local/lib/python3.7/site-packages/torch/optim/sgd.py in step(self, closure)
     84         if closure is not None:
     85             with torch.enable_grad():
---&gt; 86                 loss = closure()
     87 
     88         for group in self.param_groups:

~/.local/lib/python3.7/site-packages/skorch/net.py in step_fn()
    703         def step_fn():
    704             self.optimizer_.zero_grad()
--&gt; 705             step = self.train_step_single(Xi, yi, **fit_params)
    706             step_accumulator.store_step(step)
    707             return step['loss']

~/.local/lib/python3.7/site-packages/skorch/net.py in train_step_single(self, Xi, yi, **fit_params)
    643         &quot;&quot;&quot;
    644         self.module_.train()
--&gt; 645         y_pred = self.infer(Xi, **fit_params)
    646         loss = self.get_loss(y_pred, yi, X=Xi, training=True)
    647         loss.backward()

~/.local/lib/python3.7/site-packages/skorch/net.py in infer(self, x, **fit_params)
   1046             x_dict = self._merge_x_and_fit_params(x, fit_params)
   1047             return self.module_(**x_dict)
-&gt; 1048         return self.module_(x, **fit_params)
   1049 
   1050     def _get_predict_nonlinearity(self):

~/.local/lib/python3.7/site-packages/torch/nn/modules/module.py in _call_impl(self, *input, **kwargs)
    725             result = self._slow_forward(*input, **kwargs)
    726         else:
--&gt; 727             result = self.forward(*input, **kwargs)
    728         for hook in itertools.chain(
    729                 _global_forward_hooks.values(),

~/al/model.py in forward(self, x)
     51 
     52     def forward(self, x):
---&gt; 53         conv1 = self.conv1(x)
     54         conv2 = self.conv2(conv1)
     55         conv3 = self.conv3(conv2)

~/.local/lib/python3.7/site-packages/torch/nn/modules/module.py in _call_impl(self, *input, **kwargs)
    725             result = self._slow_forward(*input, **kwargs)
    726         else:
--&gt; 727             result = self.forward(*input, **kwargs)
    728         for hook in itertools.chain(
    729                 _global_forward_hooks.values(),

~/.local/lib/python3.7/site-packages/torch/nn/modules/container.py in forward(self, input)
    115     def forward(self, input):
    116         for module in self:
--&gt; 117             input = module(input)
    118         return input
    119 

~/.local/lib/python3.7/site-packages/torch/nn/modules/module.py in _call_impl(self, *input, **kwargs)
    725             result = self._slow_forward(*input, **kwargs)
    726         else:
--&gt; 727             result = self.forward(*input, **kwargs)
    728         for hook in itertools.chain(
    729                 _global_forward_hooks.values(),

~/.local/lib/python3.7/site-packages/torch/nn/modules/conv.py in forward(self, input)
    421 
    422     def forward(self, input: Tensor) -&gt; Tensor:
--&gt; 423         return self._conv_forward(input, self.weight)
    424 
    425 class Conv3d(_ConvNd):

~/.local/lib/python3.7/site-packages/torch/nn/modules/conv.py in _conv_forward(self, input, weight)
    418                             _pair(0), self.dilation, self.groups)
    419         return F.conv2d(input, weight, self.bias, self.stride,
--&gt; 420                         self.padding, self.dilation, self.groups)
    421 
    422     def forward(self, input: Tensor) -&gt; Tensor:

RuntimeError: Input type (torch.cuda.ByteTensor) and weight type (torch.cuda.FloatTensor) should be the same

If anyone could help that would be so so so appreciated! I've been really stuck despite searching all over-- casting my UNet model to floats has not helped and I think I've called .cuda() where I'm supposed to.
Specific things I've tried:

RuntimeError: Input type (torch.FloatTensor) and weight type (torch.cuda.FloatTensor) should be the same loading entries of my DataLoader to CUDA
adding pin_memory to my DataLoader
loading my skorch NeuralNetwork to CUDA as stated here Pytorch, INPUT (normal tensor) and WEIGHT (cuda tensor) mismatch (which didn't work because it's not a function in skorch)
Casting my data to float (https://discuss.pytorch.org/t/input-type-torch-cuda-doubletensor-and-weight-type-torch-cuda-floattensor-should-be-the-same/22704)


cv2.imread gives you np.uint8 data type which will be converted to PyTorch's byte. The byte type cannot be used with the float type (which is most probably used by your model).
You need to convert the byte type to float type (and to Tensor), by modifying the dataset
import torchvision.transforms as transforms
class ImagesDataset(Dataset):
    &quot;&quot;&quot;Constructs dataset of satellite images + masks&quot;&quot;&quot;
    def __init__(self, image_paths):
        super().__init__()
        self.image_paths = image_paths
        self.transform = transforms.Compose([transforms.ToTensor()])

    def __len__(self):
        return len(self.image_paths)

    def __getitem__(self, idx):  
        if torch.is_tensor(idx):
            idx = idx.tolist()
        print(&quot;idx:&quot;, idx)
        sample_dir = self.image_paths[idx]
        img_path = sample_dir +&quot;/images/&quot;+ Path(sample_dir).name +'.png'
        mask_path = sample_dir +'/mask.png'
        img, mask = cv2.imread(img_path), cv2.imread(mask_path)
        img = self.transform(img)
        mask = self.transform(mask)
        print(&quot;shape of img&quot;, img.shape)
        return img, mask

