Fix Conv2D tests

gigatorch/cnn.py

@@ -97,7 +97,9 @@ def __init__(self, in_channels, out_channels, kernel_size, activation_fn, stride
         self.activation_fn = activation_fn
         self.stride = stride
 
-   def compute(self, input):
+    def compute(self, input):
+        assert len(input.shape) == 4, f"Can't Conv2D {input.shape}"
+
         (batch_size, _, height, width) = input.shape
         output_height = (height - self.kernel_size) // self.stride + 1
         output_width = (width - self.kernel_size) // self.stride + 1
@@ -112,7 +114,7 @@ def compute(self, input):
                         w_start = j * self.stride
                         w_end = w_start + self.kernel_size
                         output[b, k, i, j] = self.activation_fn(
-                            np.sum(input[b, :, h_start:h_end, w_start:w_end] * self.kernels[k])
+                            np.sum((input[b, :, h_start:h_end, w_start:w_end] * self.kernels[k]).data)
                         )
 
         return output

tests/cnn_test.py

@@ -2,32 +2,51 @@
 from pytest import raises
 from gigatorch.cnn import Conv2D, MaxPool2D
 from gigatorch.activation_fn import relu
-
-
-def test_conv2d_success():
-    conv2d = Conv2D(1, 10, 2, relu)
-    conv2d.kernels = Tensor(
+import numpy as np
+
+def test_conv2d_shape_success():
+    # Test case 1: Basic functionality
+    conv2d = Conv2D(in_channels=3, out_channels=2, kernel_size=2, activation_fn=np.tanh, stride=1)
+    input_tensor = Tensor(np.random.rand(1, 3, 4, 4))  # 1 image, 3 channels, 4x4 size
+    output = conv2d.compute(input_tensor)
+    assert output.shape == (1, 2, 3, 3), "Output shape mismatch in Test Case 1"
+
+    # Test case 2: Stride functionality
+    conv2d = Conv2D(in_channels=3, out_channels=2, kernel_size=2, activation_fn=np.tanh, stride=2)
+    input_tensor = Tensor(np.random.rand(1, 3, 4, 4))  # 1 image, 3 channels, 4x4 size
+    output = conv2d.compute(input_tensor)
+    assert output.shape == (1, 2, 2, 2), "Output shape mismatch in Test Case 2"
+
+    # Test case 3: Multiple images
+    conv2d = Conv2D(in_channels=3, out_channels=2, kernel_size=2, activation_fn=np.tanh, stride=1)
+    input_tensor = Tensor(np.random.rand(2, 3, 4, 4))  # 2 images, 3 channels, 4x4 size
+    output = conv2d.compute(input_tensor)
+    assert output.shape == (2, 2, 3, 3), "Output shape mismatch in Test Case 3"
+
+def test_conv2d_compute_success():
+    # Test case: Check actual values
+    conv2d = Conv2D(in_channels=1, out_channels=1, kernel_size=2, activation_fn=lambda x: x, stride=1)
+    conv2d.kernels = Tensor(np.array([
         [
             [
-                [1, 2],
-                [3, 4],
+                [1, 0],
+                [0, 1]
             ]
         ]
-    )
-    sample_data = Tensor(
-        [ # Batch 1
-            [  # Channel 1
-                [1, 1, 1],
-                [1, 1, 1],
-                [1, 1, 1],
+    ]))  # Set a fixed kernel
+    input_tensor = Tensor(np.array([
+        [
+            [
+                [1, 2, 3],
+                [4, 5, 6],
+                [7, 8, 9]
             ]
         ]
-    )
-
-    output = conv2d.compute(sample_data)
-    expected = Tensor([[[10, 10], [10, 10]]])  # for layer 1
+    ]))
 
-    assert all(output.item() == expected)
+    output = conv2d.compute(input_tensor)
+    expected_output = np.array([[[[6, 8], [12, 14]]]])
+    assert np.allclose(output.data, expected_output), "Output values mismatch"
 
 
 def test_conv2d_kernel_size_larger_than_input():