# Error in python’s multiprocessing library

**URL:** <https://discuss.pytorch.org/t/error-in-python-s-multiprocessing-library/31355>\
**Category:** vision\
**Created:** [December 5, 2018, 1:31pm UTC](https://discuss.pytorch.org/t/error-in-python-s-multiprocessing-library/31355 "2018-12-05T13:31:27Z")\
**Posts on this page:** 20\
**Page:** 1

<div class="post-metadata">

**Author:** ![Neda](https://discuss.pytorch.org/user_avatar/discuss.pytorch.org/neda/32/7779_2.png) [@Neda](https://discuss.pytorch.org/u/Neda)\
**Post date:** [December 5, 2018, 1:31pm UTC](https://discuss.pytorch.org/t/error-in-python-s-multiprocessing-library/31355/1 "2018-12-05T13:31:27Z")

</div>

This is the Unet architecture for the customised dataset for semantic segmentation based on [this paper](https://arxiv.org/pdf/1505.04597.pdf). I am getting this error when the num\_worker = 2

```auto
`
  File "C:\Users\Neda\Anaconda3\lib\multiprocessing\spawn.py", line 172, in get_preparation_data
    main_mod_name = getattr(main_module. __spec__ , "name", None)

AttributeError: module ' __main__' has no attribute ' __spec__'`

```

while with the num\_worker = 0, I will get this error

```auto
raise TypeError((error_msg.format(type(batch[0]))))

TypeError: batch must contain tensors, numbers, dicts or lists; found <class 'PIL.TiffImagePlugin.TiffImageFile'>

```

The output of print(device) is cuda:0 I don’t have any clue where I am doing wrong. Thank you in advance.

```auto
import torch
import torch.nn as nn
import torch.nn.functional as F
import torch.optim as optim
from torch.utils.data.dataset import Dataset # For custom data-sets
import torchvision.transforms as transforms
from PIL import Image
import glob

print(torch.version)
device = torch.device("cuda:0" if torch.cuda.is_available() else "cpu")
print(device)

# get all the image and mask path and number of images
folder_data = glob.glob("D:\\Neda\\Pytorch\\U-net\\BMMCdata\\data\\*.tif")
folder_mask = glob.glob("D:\\Neda\\Pytorch\\U-net\\BMMCmasks\\masks\\*.tif")

# split these path using a certain percentage
len_data = len(folder_data)
print(len_data)
train_size = 0.6

train_image_paths = folder_data[:int(len_data*train_size)]
# print(train_image_paths) # output is 25 image for train
test_image_paths = folder_data[int(len_data*train_size):]
#print(test_image_paths) # output is 18 image for test

train_mask_paths = folder_mask[:int(len_data*train_size)]
test_mask_paths = folder_mask[int(len_data*train_size):]

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()

    def __getitem__ (self, index):

        image = Image.open(self.image_paths[index])
        mask = Image.open(self.target_paths[index])
        t_image = self.transforms(image)
        return t_image, mask

    def __len__ (self): # return count of sample we have

        return len(self.image_paths)

train_dataset = CustomDataset(train_image_paths, train_mask_paths)
train_loader = torch.utils.data.DataLoader(train_dataset, batch_size=4, shuffle=True, num_workers=2)

test_dataset = CustomDataset(test_image_paths, test_mask_paths)
test_loader = torch.utils.data.DataLoader(test_dataset, batch_size=4, shuffle=False, num_workers=2)

class ConvRelu(nn.Module):
    def __init__ (self, in_channels, out_channels, kernel_size, padding, stride):
        super(ConvRelu, self). __init__ ()
        self.conv = nn.Conv2d(in_channels, out_channels, kernel_size=kernel_size,
                             padding=padding, stride=stride)
        self.relu = nn.ReLU()

    def forward(self, x):
        x = self.conv(x)
        x = self.relu(x)
        return x

class UNet(nn.Module):
    def __init__ (self):
        super(UNet, self). __init__ ()
        #channels, height, width = in_shape

        self.down1 = nn.Sequential(
            ConvRelu(1, 64, kernel_size=(3, 3), stride=1, padding=0),
            ConvRelu(64, 64, kernel_size=(3, 3), stride=1, padding=0)
            )
        self.maxPool1 = nn.MaxPool2d(kernel_size=(2, 2), stride=2)

        self.down2 = nn.Sequential(
            ConvRelu(64, 128, kernel_size=(3, 3), stride=1, padding=0),
            ConvRelu(128, 128, kernel_size=(3, 3), stride=1, padding=0)
            )
        self.maxPool2 = nn.MaxPool2d(kernel_size=(2, 2), stride=2)

        self.down3 = nn.Sequential(
            ConvRelu(128, 256, kernel_size=(3, 3), stride=1, padding=0),
            ConvRelu(256, 256, kernel_size=(3, 3), stride=1, padding=0)
        )
        self.maxPool3 = nn.MaxPool2d(kernel_size=(2, 2), stride=2)

        self.down4 = nn.Sequential(
            ConvRelu(256, 512, kernel_size=(3, 3), stride=1, padding=0),
            ConvRelu(512, 512, kernel_size=(3, 3), stride=1, padding=0)
        )

        self.maxPool4 = nn.MaxPool2d(kernel_size=(2, 2), stride=2)

        self.center = nn.Sequential(
            ConvRelu(512, 1024, kernel_size=(3, 3), stride=1, padding=0),
            ConvRelu(1024, 1024, kernel_size=(3, 3), stride=1, padding=0)
        )
        self.upSample1 = nn.ConvTranspose2d(1024, 1024, 2, stride=2)

        self.up1 = nn.Sequential(
            ConvRelu(1024, 512, kernel_size=(2, 2), stride=1, padding=0),
            ConvRelu(512, 512, kernel_size=(2, 2), stride=1, padding=0)
        )

        self.upSample2 = nn.ConvTranspose2d(512, 512, 2, stride=2)

        self.up2 = nn.Sequential(
            ConvRelu(512, 256, kernel_size=(2, 2), stride=1, padding=0),
            ConvRelu(256, 256, kernel_size=(2, 2), stride=1, padding=0)
        )

        self.upSample3 = nn.ConvTranspose2d(256, 256, 2, stride=2)

        self.up3 = nn.Sequential(
            ConvRelu(256, 128, kernel_size=(2, 2), stride=1, padding=0),
            ConvRelu(128, 128, kernel_size=(2, 2), stride=1, padding=0)
        )

        self.upSample4 = nn.ConvTranspose2d(128, 128, 2, stride=2)

        self.up4 = nn.Sequential(
            ConvRelu(128, 64, kernel_size=(2, 2), stride=1, padding=0),
        )
        # 1x1 convolution at the last layer
        self.output_seg_map = nn.Conv2d(64, 2, kernel_size=(1, 1), padding=0, stride=1)

    def crop_concat(self, upsampled, bypass, crop=False):
        if crop:
            c = (bypass.size()[2] - upsampled.size()[2]) // 2
            # -c is amount of pad which will add on each side for all dimension
            bypass = F.pad(bypass, (-c, -c, -c, -c)) # (padLeft, padRight, padTop, padBottom)
        return torch.cat((upsampled, bypass), 1)

    def forward(self, x):
        x = self.down1(x)
        out_down1 = x

        x = self.down2(x)
        out_down2 = x
        x = self.maxPool2(x)

        x = self.down3(x)
        out_down3 = x
        x = self.maxPool3(x)

        x = self.down4(x)
        out_down4 = x
        x = self.maxPool4(x)

        x = self.center(x)

        x = self.upSample1(x)
        x = self.up1(x)
        self.crop_concat(x, out_down4)

        x = self.upSample2(x)
        x = self.up2(x)
        self.crop_concat(x, out_down3)

        x = self.upSample3(x)
        x = self.up3(x)
        self.crop_concat(x, out_down2)

        x = self.upSample4(x)
        x = self.up4(x)
        self.crop_concat(x, out_down1)

        out = self.output_seg_map(x)

        return F.log_softmax(self.output_seg_map(out)) # applies log-softmax on last layer

net = UNet()
net = net.to(device)

criterion = nn.NLLLoss()
optimizer = optim.SGD(net.parameters(), lr=0.001, momentum=0.99)

def main():
    for epoch in range(2): # loop over the dataset multiple times

        running_loss = 0.0
        for i, data in enumerate(train_loader, 0):
            # get the inputs
            t_image, mask = data
            t_image, mask = t_image.to(device), mask.to(device)

            # zero the parameter gradients
            optimizer.zero_grad()

           # forward + backward + optimize
            outputs = net(t_image)
            loss = criterion(outputs, mask)
            loss.backward()
            optimizer.step()

            # print statistics
            running_loss += loss.item()
            if i % 2000 == 1999: # print every 2000 mini-batches
                print('[%d, %5d] loss: %.3f' %
                  (epoch + 1, i + 1, running_loss / 2000))
                running_loss = 0.0

print('Finished Training')

if __name__ ==' __main__':
    main()

```

---

<div class="post-metadata">

**Author:** ![ptrblck](https://discuss.pytorch.org/user_avatar/discuss.pytorch.org/ptrblck/32/1823_2.png) [@ptrblck](https://discuss.pytorch.org/u/ptrblck)\
**Post date:** [December 5, 2018, 1:55pm UTC](https://discuss.pytorch.org/t/error-in-python-s-multiprocessing-library/31355/2 "2018-12-05T13:55:29Z")

</div>

The error is most likely thrown because your mask is still a `PIL.Image` instead of a tensor.  
I guess using `transforms.ToTensor()` wouldn’t be the best idea in this case, as you are dealing with a semantic segmentation task, i.e. your mask should contain class indices for all pixels.

Does you current mask hold a specific color code so that we could use it to create a mask tensor with class indices?

---

<div class="post-metadata">

**Author:** ![Neda](https://discuss.pytorch.org/user_avatar/discuss.pytorch.org/neda/32/7779_2.png) [@Neda](https://discuss.pytorch.org/u/Neda)\
**Post date:** [December 5, 2018, 3:31pm UTC](https://discuss.pytorch.org/t/error-in-python-s-multiprocessing-library/31355/3 "2018-12-05T15:31:41Z")

</div>

@ptrblck when I close the Spyder and run the code again I got this error `raise TypeError((error_msg.format(type(batch[0])))) TypeError: batch must contain tensors, numbers, dicts or lists; found <class 'PIL.TiffImagePlugin.TiffImageFile'>`  
I guess should be about a mask that didn’t transform to tensor.

I did check the masks. They include mainly 85 and 170 value, and also some of them have 85,170, and 255 value. [The mask looks like this](https://universityoflincoln-my.sharepoint.com/:i:/r/personal/nazarmehr_lincoln_ac_uk/Documents/BMMC_1.tif_segmentation.tifnomalized.tif?csf=1&e=8ZBXsQ). How can I create a mask tensor with class indices?

---

<div class="post-metadata">

**Author:** ![ptrblck](https://discuss.pytorch.org/user_avatar/discuss.pytorch.org/ptrblck/32/1823_2.png) [@ptrblck](https://discuss.pytorch.org/u/ptrblck)\
**Post date:** [December 5, 2018, 5:13pm UTC](https://discuss.pytorch.org/t/error-in-python-s-multiprocessing-library/31355/4 "2018-12-05T17:13:25Z")

</div>

Yeah, I think the mask is the problem as described earlier.  
Could you upload one sample somewhere else? I need some account to download the image from your university.

I guess you are currently loading a “normal” image using a specific color code for certain classes, so that we would need a mapping to decode them to class indices.

Which dataset are you using? Did you create it yourself?

---

<div class="post-metadata">

**Author:** ![Neda](https://discuss.pytorch.org/user_avatar/discuss.pytorch.org/neda/32/7779_2.png) [@Neda](https://discuss.pytorch.org/u/Neda)\
**Post date:** [December 5, 2018, 5:25pm UTC](https://discuss.pytorch.org/t/error-in-python-s-multiprocessing-library/31355/5 "2018-12-05T17:25:09Z")

</div>

@ptrblck yes, at the moment I am loading a normal image. Currently, I am using the TEM dataset in [this link](https://lmb.informatik.uni-freiburg.de/resources/datasets/tem.en.html). I downloaded the BMMC dataset and BMMC segmentation as a mask. For now, I am using this dataset to understand how should I solve segmentation task. Possibly my data will have a class of 0 and 1. 0 is background and 1 is left ventricle.

My dataset will be different. It’s cardiac ultrasound images and mask of the left ventricle that needs to be annotated by a cardiologist.

---

<div class="post-metadata">

**Author:** ![ptrblck](https://discuss.pytorch.org/user_avatar/discuss.pytorch.org/ptrblck/32/1823_2.png) [@ptrblck](https://discuss.pytorch.org/u/ptrblck)\
**Post date:** [December 5, 2018, 5:46pm UTC](https://discuss.pytorch.org/t/error-in-python-s-multiprocessing-library/31355/6 "2018-12-05T17:46:06Z")

</div>

Thanks for the link!  
I’ve downloaded the BMMC dataset with the masks and it seems three different color codes are used for the masks as you already explained.  
This code should load masks and convert these values to class indices:

```python
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 = {
            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)
        return t_image, mask

    def __len__ (self): # return count of sample we have
        return len(self.image_paths)

train_dataset = CustomDataset(train_image_paths, train_mask_paths)
train_loader = torch.utils.data.DataLoader(train_dataset, batch_size=4, shuffle=True, num_workers=2)

for data, target in train_loader:
    print(torch.unique(target))

```

You can of course change the mapping, e.g. if the pixel value 85 should be mapped to another class index.  
Just make sure your classes start with 0 and end with `num_classes-1` in case you would like to use a classification criterion like `nn.CrossEntropyLoss`.

---

<div class="post-metadata">

**Author:** ![Neda](https://discuss.pytorch.org/user_avatar/discuss.pytorch.org/neda/32/7779_2.png) [@Neda](https://discuss.pytorch.org/u/Neda)\
**Post date:** [December 5, 2018, 6:11pm UTC](https://discuss.pytorch.org/t/error-in-python-s-multiprocessing-library/31355/7 "2018-12-05T18:11:56Z")

</div>

@ptrblck Thanks a lot for the effort you put in this forum. I really appreciate it. At the moment I am getting CUDA error: out of memory. I will sort out memory issue and will update you.

---

<div class="post-metadata">

**Author:** ![Neda](https://discuss.pytorch.org/user_avatar/discuss.pytorch.org/neda/32/7779_2.png) [@Neda](https://discuss.pytorch.org/u/Neda)\
**Post date:** [December 5, 2018, 6:41pm UTC](https://discuss.pytorch.org/t/error-in-python-s-multiprocessing-library/31355/8 "2018-12-05T18:41:59Z")

</div>

@ptrblck my dedicated video memory is 8192 MB. Shouldn’t be enough to train this UNet model? Although image size is really big 1024\*1024

---

<div class="post-metadata">

**Author:** ![vmirly1](https://discuss.pytorch.org/user_avatar/discuss.pytorch.org/vmirly1/32/3309_2.png) [@vmirly1](https://discuss.pytorch.org/u/vmirly1)\
**Post date:** [December 5, 2018, 7:37pm UTC](https://discuss.pytorch.org/t/error-in-python-s-multiprocessing-library/31355/9 "2018-12-05T19:37:49Z")

</div>

What’s the batch\_size? You may need to use a smaller batch\_size.

---

<div class="post-metadata">

**Author:** ![Neda](https://discuss.pytorch.org/user_avatar/discuss.pytorch.org/neda/32/7779_2.png) [@Neda](https://discuss.pytorch.org/u/Neda)\
**Post date:** [December 5, 2018, 7:39pm UTC](https://discuss.pytorch.org/t/error-in-python-s-multiprocessing-library/31355/10 "2018-12-05T19:39:55Z")

</div>

@vmirly1 bach\_size is 4. I tried with smaller and got out of memory error.

---

<div class="post-metadata">

**Author:** ![vmirly1](https://discuss.pytorch.org/user_avatar/discuss.pytorch.org/vmirly1/32/3309_2.png) [@vmirly1](https://discuss.pytorch.org/u/vmirly1)\
**Post date:** [December 5, 2018, 7:43pm UTC](https://discuss.pytorch.org/t/error-in-python-s-multiprocessing-library/31355/11 "2018-12-05T19:43:35Z")

</div>

O I see!

I have never worked with UNet. Do you really need to work with such big image sizes? At least you can downsample the input images for debugging purposes.

---

<div class="post-metadata">

**Author:** ![ptrblck](https://discuss.pytorch.org/user_avatar/discuss.pytorch.org/ptrblck/32/1823_2.png) [@ptrblck](https://discuss.pytorch.org/u/ptrblck)\
**Post date:** [December 5, 2018, 9:43pm UTC](https://discuss.pytorch.org/t/error-in-python-s-multiprocessing-library/31355/12 "2018-12-05T21:43:28Z")

</div>

1024 is pretty large for this kind of model.  
In the original paper the authors also used a sliding window approach to get their model working.

I tried to debug the model as @vmirly1 suggested, however, there seem to be a few issues in your code.  
If I resize the images to `512` or `256`, I get a size mismatch error even if I use `crop=True` in the `crop_concat` layers. I fixed it with an ugly hack to get it working.  
However, I also realized that you are actually never using the concatenated output of the `crop_conat` layers.  
If you assign the result back to `x`, you’ll get another error, since the number on input channels won’t match for your conv layers.

If I use [my implementation](https://github.com/ptrblck/pytorch_misc/blob/master/unet_demo.py) as a drop-in replacement for your model, I can get the model working with a batch size of 1 for the original image shapes using approx. 7GB of GPU memory.  
Note that the implementations differ a bit, e.g. your model seem to have an additional layer in the bottleneck, so that will most likely explain the difference in memory usage.

---

<div class="post-metadata">

**Author:** ![Neda](https://discuss.pytorch.org/user_avatar/discuss.pytorch.org/neda/32/7779_2.png) [@Neda](https://discuss.pytorch.org/u/Neda)\
**Post date:** [December 6, 2018, 12:30pm UTC](https://discuss.pytorch.org/t/error-in-python-s-multiprocessing-library/31355/13 "2018-12-06T12:30:48Z")

</div>

@ptrblck Thank you for spending time and explain to me about issues in my code. Does your unet model works for images with 1 channel? I think your dataset was RGB when you have written this model. what is in\_channels\_skip in your model?

---

<div class="post-metadata">

**Author:** ![ptrblck](https://discuss.pytorch.org/user_avatar/discuss.pytorch.org/ptrblck/32/1823_2.png) [@ptrblck](https://discuss.pytorch.org/u/ptrblck)\
**Post date:** [December 6, 2018, 1:03pm UTC](https://discuss.pytorch.org/t/error-in-python-s-multiprocessing-library/31355/14 "2018-12-06T13:03:45Z")

</div>

Yes, you can pass the number of input channels using `in_channels`.  
This should work for your dataset:

```python
model = UNet(in_channels=1,
             out_channels=64,
             n_class=3,
             kernel_size=3,
             padding=1,
             stride=1)

```

`in_channels_skip` is the number of channels from the block in the downward path which should be concatenated with the other input to the current up block.

So lets take `up3` as an example.  
This block takes an activation from `down3` as its input (`in_channels = 8 * out_channels` ) and additionally concatenates it with the output of `down2` (`in_channels_skip = 4 * out_channels` ).  
So you’ll have `12 * out_channels` channels for your conv layer in `up3` now, which is why it’s defined in this way:

```python
self.conv_block = BaseConv(
    in_channels=in_channels + in_channels_skip,
...

```

Does this example make it clear in any way or do you need more information?

---

<div class="post-metadata">

**Author:** ![Neda](https://discuss.pytorch.org/user_avatar/discuss.pytorch.org/neda/32/7779_2.png) [@Neda](https://discuss.pytorch.org/u/Neda)\
**Post date:** [December 6, 2018, 6:02pm UTC](https://discuss.pytorch.org/t/error-in-python-s-multiprocessing-library/31355/15 "2018-12-06T18:02:39Z")

</div>

@ptrblck thanks a lot for example. Before I make sure I understand this, I have two more questions. One is what  
is the second in\_channels in self.conv\_trans1?

```auto
class UpConv(nn.Module):
    def __init__ (self, in_channels, in_channels_skip, out_channels, kernel_size, padding, stride):
        super(UpConv, self). __init__ ()
        self.conv_trans1 = nn.ConvTranspose2d(in_channels, in_channels, kernel_size=2, padding=0, stride=2)

```

and also why you did `2 * out_channels in self.down1 layer in UNet class`?  
sorry, I am asking a lot of question. I am follwoing your model before I go to debug mine.

---

<div class="post-metadata">

**Author:** ![ptrblck](https://discuss.pytorch.org/user_avatar/discuss.pytorch.org/ptrblck/32/1823_2.png) [@ptrblck](https://discuss.pytorch.org/u/ptrblck)\
**Post date:** [December 6, 2018, 6:15pm UTC](https://discuss.pytorch.org/t/error-in-python-s-multiprocessing-library/31355/16 "2018-12-06T18:15:01Z")

</div>

The transposed conv layer just doubles the spatial size without changing the number of channels, so that is why I’m passing `in_channels` twice. I realize that the naming of my variables is really bad, sorry about that.

I’m basically multiply the number of output channels by a power of 2 for each down conv, i.e. `2*out_channels, 4*out_channels, 8*out_channels`.  
You might want to change the multiplicative factor, if you want to increase the number of channels. `out_channels` is passed to the ` __init__ ` of the model.

---

<div class="post-metadata">

**Author:** ![Neda](https://discuss.pytorch.org/user_avatar/discuss.pytorch.org/neda/32/7779_2.png) [@Neda](https://discuss.pytorch.org/u/Neda)\
**Post date:** [December 6, 2018, 8:52pm UTC](https://discuss.pytorch.org/t/error-in-python-s-multiprocessing-library/31355/17 "2018-12-06T20:52:57Z")

</div>

@ptrblck Thanks a lot for clarifying on this. Do you have any figure for your model like fig 1 in [this paper](https://arxiv.org/pdf/1505.04597.pdf)? Just wanted to know did you written this model based on a research paper?

---

<div class="post-metadata">

**Author:** ![ptrblck](https://discuss.pytorch.org/user_avatar/discuss.pytorch.org/ptrblck/32/1823_2.png) [@ptrblck](https://discuss.pytorch.org/u/ptrblck)\
**Post date:** [December 6, 2018, 8:57pm UTC](https://discuss.pytorch.org/t/error-in-python-s-multiprocessing-library/31355/18 "2018-12-06T20:57:38Z")

</div>

No, unfortunately I didn’t create such a nice figure for this implementation.  
Yes, I used UNet as the base model and have rewritten it a bit to work with differently shaped inputs and also to output the same spatial size (instead of a cropped version like in the original implementation).

---

<div class="post-metadata">

**Author:** ![Neda](https://discuss.pytorch.org/user_avatar/discuss.pytorch.org/neda/32/7779_2.png) [@Neda](https://discuss.pytorch.org/u/Neda)\
**Post date:** [December 6, 2018, 9:06pm UTC](https://discuss.pytorch.org/t/error-in-python-s-multiprocessing-library/31355/19 "2018-12-06T21:06:33Z")

</div>

@ptrblck That’s great. Does it mean in your model the size of output will be same as input size? I can see you used Adam as an optimizer and in the paper which I sent earlier, they used SGD. Any specific reason that you chose this as an optimizer?

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<div class="post-metadata">

**Author:** ![ptrblck](https://discuss.pytorch.org/user_avatar/discuss.pytorch.org/ptrblck/32/1823_2.png) [@ptrblck](https://discuss.pytorch.org/u/ptrblck)\
**Post date:** [December 6, 2018, 9:12pm UTC](https://discuss.pytorch.org/t/error-in-python-s-multiprocessing-library/31355/20 "2018-12-06T21:12:11Z")

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Yes, the output size should match the input size, which makes it a bit easier for segmentation tasks, especially if you have valid classes near the borders.

Yeah, just my default optimizer based on my biased decisions for a good baseline. 😉

[Next page](https://discuss.pytorch.org/t/error-in-python-s-multiprocessing-library/31355.md?page=2)