avgpool and test refactor

test/test_tensor.py

@@ -64,44 +64,6 @@ class TestTinygrad(unittest.TestCase):
     # coarse approx. since a "big" eps and the non-linearities of the model
     self.assertFalse(gradcheck(tiny_func, tiny_x, eps = 0.1))
 
-class TestOps(unittest.TestCase):
-  def test_conv2d(self):
-    for cin in [1,2,3]:
-      for H in [2,3,5]:
-        for W in [2,3,5]:
-          x = torch.randn((5,cin,10,7), requires_grad=True)
-          w = torch.randn((4,cin,H,W), requires_grad=True)
-          xt = Tensor(x.detach().numpy())
-          wt = Tensor(w.detach().numpy())
-
-          out = torch.nn.functional.conv2d(x,w)
-          ret = Tensor.conv2d(xt, wt)
-          # TODO: why so inaccurate?
-          np.testing.assert_allclose(ret.data, out.detach().numpy(), atol=1e-5)
-
-          out.relu().mean().backward()
-          ret.relu().mean().backward()
-
-          np.testing.assert_allclose(w.grad, wt.grad, atol=1e-7)
-          np.testing.assert_allclose(x.grad, xt.grad, atol=1e-7)
-
-  def test_maxpool2x2(self):
-    x = torch.randn((5,2,10,8), requires_grad=True)
-    xt = Tensor(x.detach().numpy())
-
-    # in tinygrad
-    ret = xt.max_pool2d()
-    assert ret.shape == (5,2,10//2,8//2)
-    ret.mean().backward()
-
-    # in torch
-    out = torch.nn.MaxPool2d((2,2))(x)
-    out.mean().backward()
-
-    # forward and backward the same
-    np.testing.assert_allclose(ret.data, out.detach().numpy(), atol=1e-5)
-    np.testing.assert_allclose(x.grad, xt.grad, atol=1e-5)
-
 if __name__ == '__main__':
   unittest.main()