try to recognize cat. do not succeed

examples/efficientnet.py

@@ -96,11 +96,11 @@ class EfficientNet:
 
   def forward(self, x):
     x = x.pad2d(padding=(0,1,0,1))
-    x = self._bn0(x.conv2d(self._conv_stem, stride=2))
+    x = swish(self._bn0(x.conv2d(self._conv_stem, stride=2)))
     for b in self._blocks:
       print(x.shape)
       x = b(x)
-    x = self._bn1(x.conv2d(self._conv_head))
+    x = swish(self._bn1(x.conv2d(self._conv_head)))
     x = x.avg_pool2d(kernel_size=x.shape[2:4]).reshape(shape=(-1, 1280))
     #x = x.dropout(0.2)
     return swish(x.dot(self._fc).add(self._fc_bias))
@@ -130,8 +130,15 @@ if __name__ == "__main__":
         mv = eval(mk.replace(".bias", "_bias"))
     mv.data[:] = v.numpy() if k != '_fc.weight' else v.numpy().T
 
-  #b0 = pickle.loads(b0)
-  img = np.zeros((1, 3, 224, 224), np.float32) + 0.5
-  out = model.forward(Tensor(img))
-  print(out.data[:, 0:10])
+  # load cat image
+  from PIL import Image
+  img = Image.open(io.BytesIO(fetch("https://c.files.bbci.co.uk/12A9B/production/_111434467_gettyimages-1143489763.jpg")))
+  img = img.resize((224, 224))
+  img = np.moveaxis(np.array(img), [2,0,1], [0,1,2])
+  img = img.astype(np.float32).reshape(1,3,224,224)
+  print(img.shape)
+
+  #b0 = pickle.loads(b0)
+  out = model.forward(Tensor(img))
+  print(np.argmax(out.data), np.max(out.data))
 

tinygrad/ops.py

@@ -167,7 +167,7 @@ class Conv2D(Function):
 
     cout,cin,H,W = w.shape
     if groups > 1:
-      w = np.repeat(w, groups, axis=1)
+      w = np.repeat(w, groups, axis=1) / groups
     tw = w.reshape(cout, -1).T
     ys,xs = ctx.stride
     bs,oy,ox = x.shape[0], (x.shape[2]-(H-ys))//ys, (x.shape[3]-(W-xs))//xs