I created embeddings for my patches and then feed them to the vanilla vision transformer for binary classification.
Here’s the forward method:
def forward(self, x):
        #x = self.to_patch_embedding(img)
        b, n, _ = x.shape

        cls_tokens = repeat(self.cls_token, '() n d -> b n d', b = b)
        x = torch.cat((cls_tokens, x), dim=1)
        #x += self.pos_embedding[:, :(n + 1)]
        x = self.dropout(x)

        x = self.transformer(x)

        x = x.mean(dim = 1) if self.pool == 'mean' else x[:, 0]

        x = self.to_latent(x)
        
        x = self.mlp_head(x)
        
        print('x is: ', x)
        return x

However, I am not getting labels as a result. How should I convert this to binary labels?
I get this error consequently:
x is:  tensor([[-8.7743e-01, -1.1380e-01],
        [-4.8789e-01,  5.5360e-04],
        [-7.1857e-01,  3.6758e-01],
        [-5.9797e-01,  2.3756e-01],
        [-6.1892e-01, -1.2594e-02],
        [-4.2626e-01,  1.5825e-01],
        [-8.1902e-01, -1.5155e-01],
        [-5.5616e-01,  1.7184e-02]], device='cuda:0', grad_fn=<AddmmBackward0>)
out shape is:  torch.Size([8, 2])
out is:  tensor([[-8.7743e-01, -1.1380e-01],
        [-4.8789e-01,  5.5360e-04],
        [-7.1857e-01,  3.6758e-01],
        [-5.9797e-01,  2.3756e-01],
        [-6.1892e-01, -1.2594e-02],
        [-4.2626e-01,  1.5825e-01],
        [-8.1902e-01, -1.5155e-01],
        [-5.5616e-01,  1.7184e-02]], device='cuda:0', grad_fn=<AddmmBackward0>)
labels are:  tensor([1, 1, 1, 1, 1, 1, 1, 1], dtype=torch.int32)

Traceback (most recent call last):
  File "main_classifier.py", line 250, in <module>
    pred,label,loss = trainer.train(sample_batched, model)

    pred, labels, loss = model.forward(feats, labels)
 File "/home/jalal/research/venv/dpcc/lib/python3.8/site-packages/torch/nn/parallel/data_parallel.py", line 166, in forward
    return self.module(*inputs[0], **kwargs[0])
  File "/home/jalal/research/venv/dpcc/lib/python3.8/site-packages/torch/nn/modules/module.py", line 1102, in _call_impl
    return forward_call(*input, **kwargs)

    loss = self.criterion(out.cuda(), labels.cuda())

 File "/home/jalal/research/venv/dpcc/lib/python3.8/site-packages/torch/nn/modules/module.py", line 1102, in _call_impl
    return forward_call(*input, **kwargs)
  File "/home/jalal/research/venv/dpcc/lib/python3.8/site-packages/torch/nn/modules/loss.py", line 1150, in forward
    return F.cross_entropy(input, target, weight=self.weight,
  File "/home/jalal/research/venv/dpcc/lib/python3.8/site-packages/torch/nn/functional.py", line 2846, in cross_entropy
    return torch._C._nn.cross_entropy_loss(input, target, weight, _Reduction.get_enum(reduction), ignore_index, label_smoothing)
RuntimeError: "nll_loss_forward_reduce_cuda_kernel_2d_index" not implemented for 'Int'

I also call the transformer like this:
        labels = torch.IntTensor(labels)
        print('labels :', labels)
        stacked_X = torch.stack(X)
        out = self.transformer(stacked_X)
        print('out shape is: ', out.shape)
        print('out is: ', out)
        # loss
        print('labels are: ', labels)
        print(type(labels))
        loss = self.criterion(out.cuda(), labels.cuda())

Here’s the full code of vanilla vision transformer:
import torch
from torch import nn

from einops import rearrange, repeat
from einops.layers.torch import Rearrange

# helpers

def pair(t):
    return t if isinstance(t, tuple) else (t, t)

# classes

class PreNorm(nn.Module):
    def __init__(self, dim, fn):
        super().__init__()
        self.norm = nn.LayerNorm(dim)
        self.fn = fn
    def forward(self, x, **kwargs):
        return self.fn(self.norm(x), **kwargs)

class FeedForward(nn.Module):
    def __init__(self, dim, hidden_dim, dropout = 0.):
        super().__init__()
        self.net = nn.Sequential(
            nn.Linear(dim, hidden_dim),
            nn.GELU(),
            nn.Dropout(dropout),
            nn.Linear(hidden_dim, dim),
            nn.Dropout(dropout)
        )
    def forward(self, x):
        return self.net(x)

class Attention(nn.Module):
    def __init__(self, dim, heads = 8, dim_head = 64, dropout = 0.):
        super().__init__()
        inner_dim = dim_head *  heads
        project_out = not (heads == 1 and dim_head == dim)

        self.heads = heads
        self.scale = dim_head ** -0.5

        self.attend = nn.Softmax(dim = -1)
        self.to_qkv = nn.Linear(dim, inner_dim * 3, bias = False)

        self.to_out = nn.Sequential(
            nn.Linear(inner_dim, dim),
            nn.Dropout(dropout)
        ) if project_out else nn.Identity()

    def forward(self, x):
        qkv = self.to_qkv(x).chunk(3, dim = -1)
        q, k, v = map(lambda t: rearrange(t, 'b n (h d) -> b h n d', h = self.heads), qkv)

        dots = torch.matmul(q, k.transpose(-1, -2)) * self.scale

        attn = self.attend(dots)

        out = torch.matmul(attn, v)
        out = rearrange(out, 'b h n d -> b n (h d)')
        return self.to_out(out)

class Transformer(nn.Module):
    def __init__(self, dim, depth, heads, dim_head, mlp_dim, dropout = 0.):
        super().__init__()
        self.layers = nn.ModuleList([])
        for _ in range(depth):
            self.layers.append(nn.ModuleList([
                PreNorm(dim, Attention(dim, heads = heads, dim_head = dim_head, dropout = dropout)),
                PreNorm(dim, FeedForward(dim, mlp_dim, dropout = dropout))
            ]))
    def forward(self, x):
        for attn, ff in self.layers:
            x = attn(x) + x
            x = ff(x) + x
        return x

class VisionTransformer(nn.Module):
    def __init__(self, *, image_size=256, patch_size=16, dim=512, depth=4, heads=12, mlp_dim=256, dropout=0.25, pool = 'cls', channels = 3, dim_head=64, emb_dropout = 0., num_classes=2):
        super().__init__()
        image_height, image_width = pair(image_size)
        patch_height, patch_width = pair(patch_size)

        assert image_height % patch_height == 0 and image_width % patch_width == 0, 'Image dimensions must be divisible by the patch size.'

        num_patches = (image_height // patch_height) * (image_width // patch_width)
        patch_dim = channels * patch_height * patch_width
        assert pool in {'cls', 'mean'}, 'pool type must be either cls (cls token) or mean (mean pooling)'

        self.to_patch_embedding = nn.Sequential(
            Rearrange('b c (h p1) (w p2) -> b (h w) (p1 p2 c)', p1 = patch_height, p2 = patch_width),
            nn.Linear(patch_dim, dim),
        )

        self.pos_embedding = nn.Parameter(torch.randn(1, num_patches + 1, dim))
        self.cls_token = nn.Parameter(torch.randn(1, 1, dim))
        self.dropout = nn.Dropout(emb_dropout)

        self.transformer = Transformer(dim, depth, heads, dim_head, mlp_dim, dropout)

        self.pool = pool
        self.to_latent = nn.Identity()

        self.mlp_head = nn.Sequential(
            nn.LayerNorm(dim),
            nn.Linear(dim, num_classes)
        )

    def forward(self, x):
        #x = self.to_patch_embedding(img)
        b, n, _ = x.shape

        cls_tokens = repeat(self.cls_token, '() n d -> b n d', b = b)
        x = torch.cat((cls_tokens, x), dim=1)
        #x += self.pos_embedding[:, :(n + 1)]
        x = self.dropout(x)

        x = self.transformer(x)

        x = x.mean(dim = 1) if self.pool == 'mean' else x[:, 0]

        x = self.to_latent(x)
        
        x = self.mlp_head(x)
        
        print('x is: ', x)
        return x 

My batch size here is 8 and labels are 0 and 1.