simclr_ds_inference.py

tdg = DSDataGen('test', testimages, testlabels, num_classes=10)

tdl = DataLoader(tdg, batch_size = 32, drop_last = True)

dsmodel.eval()
    
loss_sublist = np.array([])
acc_sublist = np.array([])

with torch.no_grad():
    for x,y in vdl:
        x = x.squeeze().to(device = 'cuda:0', dtype = torch.float)
        y = y.to(device = 'cuda:0')
        z = dsmodel(x)

        val_loss = loss_fn(z,y)

        preds = torch.exp(z.cpu().data)/torch.sum(torch.exp(z.cpu().data))

        loss_sublist = np.append(loss_sublist, val_loss.cpu().data)
        acc_sublist = np.append(acc_sublist,np.array(np.argmax(preds,axis=1)==y.cpu().data.view(-1)).astype('int'),axis=0)

print('TEST BINARY CROSSENTROPY LOSS: ',np.mean(loss_sublist))
print('TEST BINARY ACCURACY: ',np.mean(acc_sublist))