DataLoader not working as expected

username: PURAV_SHAH
cooked: <p>Hi,<br>
I am trying to create a dataset for a siamese CNN and essentially the dataset is a modified version of the MNIST dataset wherein in a list i have 3 images(the first two images are of the same digit and the last image is of another digit),i create 5000 samples of the same kind and save all these samples in a list and so the size of my list is essentially 5000x3.<br>
Now when i pass this to the DataLoader for getting me batches of size 2 the DataLoader should essentially return to me a list which is of the size 2x3 but the DataLoader returns to me a list of images of size 3x2 which is totally wrong.<br>
I have tried to print values inside the <strong>getitem</strong> fuunction to see how the sampling is happening and it is happening correctly but after this sampling the DataLoader is somehow changing the dimensions of my dataset.<br>
The ‘traindata’ and ‘testdata’ is essentially the downloaded MNIST dataset from pytorch<br>
dont know why this is happening or if i am misunderstanding something.<br>
some help would be much appreciated.<br>
Thanks!<br>
The code is below–</p>
<pre><code class="lang-auto">class siameseMNIST(Dataset):
    def __init__(self,images,labels):
        self.x=images
        #print(self.x[0])
        self.y=labels
    def __getitem__(self,index):
        print(index,self.x[index])
        return (self.x[index],self.y[index])
    def __len__(self):
        return len(self.x)

def generateTrainSet(trainData):
    newtrainset=list()
    newtrainlabels=list()
    for i in range(10):
        for j in range(500):
            index=np.argwhere(trainData[1]==i)
            index1=np.argwhere(trainData[1]!=i)
            newtrainset.append([trainData[0][index[0][0]],trainData[0][index[0][random.randint(0,index.shape[1]-1)]],trainData[0][index1[0][random.randint(0,index1.shape[1]-1)]]])
            newtrainlabels.append([1,0])
    return newtrainset,newtrainlabels
def generateTestSet(testData):
    newtestset=list()
    newtestlabels=list()
    for i in range(10):
        for j in range(500):
            index=np.argwhere(trainData[1]==i)
            index1=np.argwhere(trainData[1]!=i)
            newtestset.append([trainData[0][index[0][0]],trainData[0][index[0][random.randint(0,index.shape[1]-1)]],trainData[0][index1[0][random.randint(0,index1.shape[1]-1)]]])
            newtestlabels.append([1,0])
    return newtestset,newtestlabels
newtrainset,newtrainlabels=generateTrainSet(trainData)
newtestset,newtestlabels=generateTestSet(testData)
newtrainlabels=torch.tensor(newtrainlabels).float()
newtestlabels=torch.tensor(newtestlabels).float()
train=siameseMNIST(newtrainset,newtrainlabels)
test=siameseMNIST(newtestset,newtestlabels)
trainLoader=DataLoader(dataset=train,batch_size=2,shuffle=True)
testLoader=DataLoader(dataset=test,batch_size=2,shuffle=False)
#print(newtrainset[0])
example=iter(trainLoader)
features,labels=example.next()
print(len(features))
print(len(features[0]))

</code></pre>
<p>and the output is–</p>
<pre><code class="lang-auto">3
2
</code></pre>
<p>and i need the output to be</p>
<pre><code class="lang-auto">2
3
</code></pre>

username: Dipam_Vasani
cooked: <p>This is weird. Can you show the first few rows of your input and labels?</p>

username: PURAV_SHAH
cooked: <p>Hi,<br>
Yes sure.<br>
My input for to the data loader is as follows for 2 rows-<br>
images–</p>
<pre><code class="lang-auto">[[tensor([[  0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,
           0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0],
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       dtype=torch.uint8), tensor([[  0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,
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         253, 253, 253, 236, 219, 224, 208,  76,  17,   0,   0,   0,   0,   0],
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           0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0],
        [  0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,
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        [  0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,
           0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0],
        [  0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,
           0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0]],
       dtype=torch.uint8)]]
</code></pre>
<p>and the labels are–</p>
<pre><code class="lang-auto">[[1, 0], [1, 0]]
</code></pre>
<p>The labels have been converted into a tensor later on.</p>