Split python scripts

tools/conv2d_test.py

@@ -0,0 +1,136 @@
+import torch
+
+from utils import print_cpp_vector
+
+def _conv2d(in_channels,
+            out_channels,
+            kernel_size,
+            stride,
+            padding,
+            inputs,
+            weights):
+
+    conv2d = torch.nn.Conv2d(in_channels=in_channels,
+                             out_channels=out_channels,
+                             kernel_size=kernel_size,
+                             stride=stride,
+                             padding=padding,
+                             bias=False)
+    conv2d.weight = torch.nn.Parameter(weights)
+
+    output = conv2d(inputs)
+
+    # Print the output as cpp vector
+    output = torch.flatten(output)
+    return output
+
+def gen_convd_padded_test_result():
+
+    in_channels = 3
+    out_channels = 2
+    kernel_size = 3
+    stride = 1
+    padding = 1
+
+    # Define input and kernel data as tensors
+    inputs = torch.tensor([
+        0.823, 0.217, 0.435, 0.981, 0.742,
+        0.109, 0.518, 0.374, 0.681, 0.147,
+        0.956, 0.729, 0.654, 0.087, 0.392,
+        0.784, 0.921, 0.543, 0.231, 0.816,
+        0.472, 0.614, 0.102, 0.987, 0.398,
+        0.051, 0.756, 0.841, 0.293, 0.128,
+        0.417, 0.632, 0.095, 0.184, 0.529,
+        0.871, 0.958, 0.213, 0.347, 0.725,
+        0.461, 0.012, 0.278, 0.195, 0.649,
+        0.853, 0.707, 0.988, 0.988, 0.322,
+        0.345, 0.123, 0.789, 0.123, 0.456,
+        0.456, 0.789, 0.123, 0.345, 0.123,
+        0.789, 0.123, 0.345, 0.123, 0.456,
+        0.123, 0.345, 0.123, 0.789, 0.123,
+        0.345, 0.123, 0.789, 0.123, 0.456
+    ], dtype=torch.float).reshape(1, 3, 5, 5)
+
+    weights = torch.tensor([
+        0.128, 0.754, 0.987,
+        0.321, 0.412, 0.635,
+        0.298, 0.017, 0.845,
+        0.514, 0.729, 0.952,
+        0.684, 0.378, 0.159,
+        0.823, 0.547, 0.216,
+        0.983, 0.231, 0.456,
+        0.178, 0.654, 0.821,
+        0.345, 0.987, 0.123,
+        0.789, 0.543, 0.210,
+        0.012, 0.371, 0.638,
+        0.456, 0.198, 0.907,
+        0.101, 0.432, 0.759,
+        0.234, 0.567, 0.890,
+        0.543, 0.876, 0.219,
+        0.345, 0.678, 0.011,
+        0.678, 0.011, 0.345,
+        0.011, 0.345, 0.678
+    ], dtype=torch.float).reshape(2, 3, 3, 3)
+
+    output = _conv2d(in_channels,
+                     out_channels,
+                     kernel_size,
+                     stride,
+                     padding,
+                     inputs,
+                     weights)
+    
+    print_cpp_vector(output)
+
+
+def gen_convd_strided_test_result():
+
+    in_channels = 2
+    out_channels = 2
+    kernel_size = 3
+    stride = 2
+    padding = 3
+
+    input = torch.tensor([
+        0.946, 0.879, 0.382, 0.542, 0.453,
+        0.128, 0.860, 0.778, 0.049, 0.974,
+        0.400, 0.874, 0.161, 0.271, 0.580,
+        0.373, 0.078, 0.366, 0.396, 0.181,
+        0.246, 0.112, 0.179, 0.979, 0.026,
+        0.598, 0.458, 0.776, 0.213, 0.199,
+        0.853, 0.170, 0.609, 0.269, 0.777,
+        0.776, 0.694, 0.430, 0.238, 0.968,
+        0.473, 0.303, 0.084, 0.785, 0.444,
+        0.464, 0.413, 0.779, 0.298, 0.783
+    ], dtype=torch.float).reshape(1, 2, 5, 5)
+    weights = torch.tensor([
+        0.744, 0.745, 0.641,
+        0.164, 0.157, 0.127,
+        0.732, 0.761, 0.601,
+        0.475, 0.335, 0.499,
+        0.833, 0.793, 0.176,
+        0.822, 0.163, 0.175,
+        0.918, 0.340, 0.497,
+        0.233, 0.218, 0.847,
+        0.931, 0.926, 0.199,
+        0.510, 0.432, 0.567,
+        0.236, 0.397, 0.739,
+        0.939, 0.891, 0.006
+    ], dtype=torch.float).reshape(2, 2, 3, 3)
+
+    output = _conv2d(in_channels,
+                     out_channels,
+                     kernel_size,
+                     stride,
+                     padding,
+                     input,
+                     weights)
+    
+    print_cpp_vector(output)
+
+if __name__ == "__main__":
+    print("Generating test results...")
+    print("Padded convolution test:")
+    gen_convd_padded_test_result()
+    print("Strided convolution test:")
+    gen_convd_strided_test_result()