Split python scripts

.gitignore

@@ -37,3 +37,5 @@ build/
 
 venv
 docs
+
+__pycache__

tools/conv2d_test.py

@@ -1,5 +1,6 @@
 import torch
 
+from utils import print_cpp_vector
 
 def _conv2d(in_channels,
             out_channels,
@@ -23,29 +24,6 @@ def _conv2d(in_channels,
     output = torch.flatten(output)
     return output
 
-
-def _print_cpp_vector(vector):
-    print("std::vector<float> expected = {", end="")
-    for i in range(len(vector)):
-        if i != 0:
-            print(", ", end="")
-        print(str(round(vector[i].item(), 5)) + "f", end="")
-    print("};")
-
-
-def _get_pool_input():
-    return torch.tensor([
-        0.573, 0.619, 0.732, 0.055,
-        0.243, 0.316, 0.573, 0.619,
-        0.712, 0.055, 0.243, 0.316,
-        0.573, 0.619, 0.742, 0.055,
-        0.473, 0.919, 0.107, 0.073,
-        0.073, 0.362, 0.973, 0.059,
-        0.473, 0.455, 0.283, 0.416,
-        0.532, 0.819, 0.732, 0.850
-    ]).reshape(1, 2, 4, 4)
-
-
 def gen_convd_padded_test_result():
 
     in_channels = 3
@@ -101,7 +79,8 @@ def gen_convd_padded_test_result():
                      padding,
                      inputs,
                      weights)
-    _print_cpp_vector(output)
+    
+    print_cpp_vector(output)
 
 
 def gen_convd_strided_test_result():
@@ -146,36 +125,8 @@ def gen_convd_strided_test_result():
                      padding,
                      input,
                      weights)
-    _print_cpp_vector(output)
-
-
-def gen_softmax_test_result():
-    input = torch.tensor([
-        0.573, 0.619, 0.732, 0.055, 0.243
-    ])
-
-    output = torch.nn.Softmax(dim=0)(input)
-    _print_cpp_vector(output)
-
-
-def gen_max_pool_test_result():
-    input = _get_pool_input()
-
-    output = torch.nn.MaxPool2d(kernel_size=2, stride=2)(input)
-    output = torch.flatten(output)
-
-    _print_cpp_vector(output)
-
-
-def gen_avg_pool_test_result():
-
-    input = _get_pool_input()
-
-    output = torch.nn.AvgPool2d(kernel_size=2, stride=2)(input)
-    output = torch.flatten(output)
-
-    _print_cpp_vector(output)
     
+    print_cpp_vector(output)
 
 if __name__ == "__main__":
     print("Generating test results...")
@@ -183,9 +134,3 @@ if __name__ == "__main__":
     gen_convd_padded_test_result()
     print("Strided convolution test:")
     gen_convd_strided_test_result()
-    print("Softmax test:")
-    gen_softmax_test_result()
-    print("Max pool test:")
-    gen_max_pool_test_result()
-    print("Avg pool test:")
-    gen_avg_pool_test_result()

tools/gen_random_vector.py

@@ -0,0 +1,15 @@
+import numpy as np
+import utils
+from sys import argv
+
+def gen_random_vector(size):
+    return np.random.rand(size)
+
+if __name__ == "__main__":
+
+    if len(argv) < 2:
+        print("Usage: python gen_random_vector.py <size>")
+        exit(1)
+
+    vector = gen_random_vector(int(argv[1]))
+    utils.print_cpp_vector(vector)

tools/pooling_test.py

@@ -0,0 +1,41 @@
+import torch
+from utils import print_cpp_vector
+
+
+def _get_pool_input():
+    return torch.tensor([
+        0.573, 0.619, 0.732, 0.055,
+        0.243, 0.316, 0.573, 0.619,
+        0.712, 0.055, 0.243, 0.316,
+        0.573, 0.619, 0.742, 0.055,
+        0.473, 0.919, 0.107, 0.073,
+        0.073, 0.362, 0.973, 0.059,
+        0.473, 0.455, 0.283, 0.416,
+        0.532, 0.819, 0.732, 0.850
+    ]).reshape(1, 2, 4, 4)
+
+def gen_max_pool_test_result():
+    input = _get_pool_input()
+
+    output = torch.nn.MaxPool2d(kernel_size=2, stride=2)(input)
+    output = torch.flatten(output)
+
+    print_cpp_vector(output)
+
+
+def gen_avg_pool_test_result():
+
+    input = _get_pool_input()
+
+    output = torch.nn.AvgPool2d(kernel_size=2, stride=2)(input)
+    output = torch.flatten(output)
+
+    print_cpp_vector(output)
+
+
+if __name__ == "__main__":
+    print("Generating test results...")
+    print("Max pool test:")
+    gen_max_pool_test_result()
+    print("Avg pool test:")
+    gen_avg_pool_test_result()

tools/softmax_tes.py

@@ -0,0 +1,17 @@
+import torch
+from utils import print_cpp_vector
+
+
+def gen_softmax_test_result():
+    input = torch.tensor([
+        0.573, 0.619, 0.732, 0.055, 0.243
+    ])
+
+    output = torch.nn.Softmax(dim=0)(input)
+    print_cpp_vector(output)
+
+
+if __name__ == "__main__":
+    print("Generating test results...")
+    print("Softmax test:")
+    gen_softmax_test_result()

tools/utils.py

@@ -0,0 +1,7 @@
+def print_cpp_vector(vector):
+    print("std::vector<float> expected = {", end="")
+    for i in range(len(vector)):
+        if i != 0:
+            print(", ", end="")
+        print(str(round(vector[i].item(), 5)) + "f", end="")
+    print("};")