Ignoring NN.modules arguments

ctrl_c_nn.py

@@ -642,6 +642,8 @@ class nn:
     class Module:
 
         def __init__(self, *args, **kwargs):
+            if args != () or kwargs != {}:
+                print('Warning, ignoring', args, kwargs)
             self.cache = None
 
         def __call__(self, x: Tensor):
@@ -694,6 +696,7 @@ def forward(self, x: Tensor):
             start = time.time()
             self.cache = x
             res = x.matmul_T_2d(self.weight) + self.bias
+            print("Linear took ", time.time() - start)
             return res
 
         def backward(self, dout: Tensor):
@@ -766,14 +769,16 @@ def backward(self, dout: Tensor):
             return dout
 
     class Conv2d(Module):
-        def __init__(self, in_channels: int, out_channels: int, kernel_size: int, stride=1, padding=0, bias=True):
+        def __init__(self, in_channels: int, out_channels: int, kernel_size: int, stride=1, padding=0, bias=True, *args, **kwargs):
+            if args != () or kwargs != {}:
+                print('Warning, Conv2dTranspose ignoring', args, kwargs)
             super().__init__()
             self.stride = stride
             self.padding = padding
             self.kernel_size = kernel_size
             self.out_channels = out_channels
             self.weight = Tensor.fill(shape=(out_channels, in_channels, kernel_size, kernel_size), number=0.0)
-            self.bias = Tensor.fill(shape=(out_channels, ), number=0.0 if bias else 0)
+            self.bias = Tensor.fill(shape=(out_channels, ), number=0.0 if bias else 0.0)
 
         def forward(self, x: Tensor):
             return self.forward_gemm(x)
@@ -832,7 +837,9 @@ def im2col(x_pad):
             return res
 
     class Conv2dTranspose(Module):
-        def __init__(self, in_channels: int, out_channels: int, kernel_size: int, stride=1, padding=0, bias=True):
+        def __init__(self, in_channels: int, out_channels: int, kernel_size: int, stride=1, padding=0, bias=True, *args, **kwargs):
+            if args != () or kwargs != {}:
+                print('Warning, Conv2dTranspose ignoring', args, kwargs)
             super().__init__()
             self.stride = stride
             self.padding = padding
@@ -846,14 +853,16 @@ def forward(self, x: Tensor):
 
     class BatchNorm2d(Module):
         def __init__(self, num_features, eps=1e-05, *args, **kwargs):
-            self.weight = Tensor.random_float((num_features,))
-            self.bias = Tensor.random_float((num_features,))
-            self.running_mean = Tensor.zeros((num_features,))
-            self.running_var = Tensor.ones((num_features,))
-            self.num_batches_tracked = Tensor([0])
+            if args != () or kwargs != {}:
+                print('Warning, BatchNorm2d ignoring', args, kwargs)
+            self.weight = Tensor.fill((num_features,), 0.0)
+            self.bias = Tensor.fill((num_features,), 0.0)
+            self.running_mean = Tensor.fill((num_features,), 0.0)
+            self.running_var = Tensor.fill((num_features,), 1.0)
+            self.num_batches_tracked = Tensor([0.0])
             self.C = num_features
             self.eps = eps
-            super().__init__(*args, **kwargs)
+            super().__init__()
 
         def forward(self, x: Tensor):
             start_time = time.time()
@@ -869,7 +878,9 @@ def forward(self, x: Tensor):
             return y
 
     class MaxPool2d(Module):
-        def __init__(self, kernel_size=2, stride=2, padding=0):
+        def __init__(self, kernel_size=2, stride=2, padding=0, *args, **kwargs):
+            if args != () or kwargs != {}:
+                print('Warning, MaxPool2d ignoring', args, kwargs)
             self.kernel_size = kernel_size
             self.stride = stride
             self.padding = padding
@@ -919,6 +930,15 @@ class Dropout(Module):
         def forward(self, x: Tensor):
             return x
 
+    class Upsample(Module):
+        def __init__(self, scale_factor, *args, **kwargs):
+            super().__init__()
+            print('Warning, ignoring', args, kwargs)
+            self.scale_factor = scale_factor
+
+        def forward(self, x: Tensor):
+            return F.interpolate(x, scale_factor=self.scale_factor)
+
     class AbstractLoss:
         def __init__(self):
             self.cache = None