eval_linear.py [160:176]:
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        inp = inp.cuda(non_blocking=True)
        target = target.cuda(non_blocking=True)

        # forward
        with torch.no_grad():
            if "vit" in args.arch:
                intermediate_output = model.get_intermediate_layers(inp, n)
                output = torch.cat([x[:, 0] for x in intermediate_output], dim=-1)
                if avgpool:
                    output = torch.cat((output.unsqueeze(-1), torch.mean(intermediate_output[-1][:, 1:], dim=1).unsqueeze(-1)), dim=-1)
                    output = output.reshape(output.shape[0], -1)
            else:
                output = model(inp)
        output = linear_classifier(output)

        # compute cross entropy loss
        loss = nn.CrossEntropyLoss()(output, target)
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eval_linear.py [202:216]:
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        inp = inp.cuda(non_blocking=True)
        target = target.cuda(non_blocking=True)

        # forward
        with torch.no_grad():
            if "vit" in args.arch:
                intermediate_output = model.get_intermediate_layers(inp, n)
                output = torch.cat([x[:, 0] for x in intermediate_output], dim=-1)
                if avgpool:
                    output = torch.cat((output.unsqueeze(-1), torch.mean(intermediate_output[-1][:, 1:], dim=1).unsqueeze(-1)), dim=-1)
                    output = output.reshape(output.shape[0], -1)
            else:
                output = model(inp)
        output = linear_classifier(output)
        loss = nn.CrossEntropyLoss()(output, target)
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