anyone else let down by the fast conv?

test/test_mnist.py

@@ -22,7 +22,7 @@ class TinyBobNet:
 # create a model with a conv layer
 class TinyConvNet:
   def __init__(self):
-    conv = 7
+    conv = 5
     chans = 16
     self.c1 = Tensor(layer_init_uniform(chans,1,conv,conv))
     self.l1 = Tensor(layer_init_uniform(((28-conv+1)**2)*chans, 128))

test/test_tensor.py

@@ -1,7 +1,7 @@
 import numpy as np
 import torch
 import unittest
-from tinygrad.tensor import Tensor, Conv2D
+from tinygrad.tensor import Tensor
 from tinygrad.gradcheck import numerical_jacobian, jacobian, gradcheck
 
 x_init = np.random.randn(1,3).astype(np.float32)
@@ -71,7 +71,7 @@ class TestTinygrad(unittest.TestCase):
     wt = Tensor(w.detach().numpy())
 
     out = torch.nn.functional.conv2d(x,w)
-    ret = Conv2D.apply(Conv2D, xt, wt)
+    ret = Tensor.conv2d(xt, wt)
     np.testing.assert_allclose(ret.data, out.detach().numpy(), atol=1e-5)
 
     out.mean().backward()