update tests

minitorch · Nov 14, 2020 · c2bff8d · c2bff8d
1 parent fb1f1d9
commit c2bff8d
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Showing 3 changed files with 34 additions and 12 deletions.
diff --git a/minitorch/fast_conv.py b/minitorch/fast_conv.py
@@ -195,8 +195,8 @@ def tensor_conv2d(
     s1 = input_strides
     s2 = weight_strides
 
-    # TODO: Implement for Task 4.1.
-    raise NotImplementedError('Need to implement for Task 4.1')
+    # TODO: Implement for Task 4.2.
+    raise NotImplementedError('Need to implement for Task 4.2')
 
 
 class Conv2dFun(Function):

diff --git a/project/run_mnist_multiclass.py b/project/run_mnist_multiclass.py
@@ -34,10 +34,8 @@ def __init__(self, in_size, out_size):
         self.out_size = out_size
 
     def forward(self, x):
-        batch, in_size = x.shape
-        return (
-            x.view(batch, 1, in_size) @ self.weights.value.view(in_size, self.out_size)
-        ).view(batch, self.out_size) + self.bias.value
+        # TODO: Implement for Task 4.5.
+        raise NotImplementedError('Need to implement for Task 4.5')
 
 
 class Conv2d(minitorch.Module):
@@ -110,8 +108,12 @@ def make_mnist(start, stop):
     for batch_num, example_num in enumerate(range(0, N, BATCH)):
         if N - example_num <= BATCH:
             continue
-        y = minitorch.tensor_fromlist(ys[example_num : example_num + BATCH], backend=BACKEND)
-        x = minitorch.tensor_fromlist(X[example_num : example_num + BATCH], backend=BACKEND)
+        y = minitorch.tensor_fromlist(
+            ys[example_num : example_num + BATCH], backend=BACKEND
+        )
+        x = minitorch.tensor_fromlist(
+            X[example_num : example_num + BATCH], backend=BACKEND
+        )
         x.requires_grad_(True)
         y.requires_grad_(True)
 
@@ -134,8 +136,12 @@ def make_mnist(start, stop):
 
             correct = 0
             for val_example_num in range(0, 5 * BATCH, BATCH):
-                y = minitorch.tensor_fromlist(val_ys[val_example_num : val_example_num + BATCH], backend=BACKEND)
-                x = minitorch.tensor_fromlist(val_x[val_example_num : val_example_num + BATCH], backend=BACKEND)
+                y = minitorch.tensor_fromlist(
+                    val_ys[val_example_num : val_example_num + BATCH], backend=BACKEND
+                )
+                x = minitorch.tensor_fromlist(
+                    val_x[val_example_num : val_example_num + BATCH], backend=BACKEND
+                )
                 out = model.forward(x.view(BATCH, 1, H, W)).view(BATCH, C)
                 for i in range(BATCH):
                     m = -1000
@@ -147,7 +153,16 @@ def make_mnist(start, stop):
                     if y[i, ind] == 1.0:
                         correct += 1
 
-            print("Epoch ", epoch, " example ", example_num, " loss ", total_loss[0], " accuracy ", correct / float(5 * BATCH))
+            print(
+                "Epoch ",
+                epoch,
+                " example ",
+                example_num,
+                " loss ",
+                total_loss[0],
+                " accuracy ",
+                correct / float(5 * BATCH),
+            )
 
             # Visualize test batch
             for channel in range(4):

diff --git a/tests/test_nn.py b/tests/test_nn.py
@@ -28,8 +28,15 @@ def test_avg(t):
 
 
 @pytest.mark.task4_4
-@given(tensors(shape=(1, 1, 4, 4)))
+@given(tensors(shape=(2, 3, 4)))
 def test_max(t):
+    # TODO: Implement for Task 4.4.
+    raise NotImplementedError('Need to implement for Task 4.4')
+
+
+@pytest.mark.task4_4
+@given(tensors(shape=(1, 1, 4, 4)))
+def test_max_pool(t):
     out = minitorch.maxpool2d(t, (2, 2))
     print(out)
     print(t)