Add tests for ConstantPad1d and ConstantPad2d layers in PyTorch converter

Author: NALozano1

test/pytest/test_pytorch_constpadmapping.py

@@ -0,0 +1,48 @@
+from pathlib import Path
+import torch
+import torch.nn as nn
+from hls4ml.converters import convert_from_pytorch_model
+from hls4ml.utils.config import config_from_pytorch_model
+
+def test_pytorch_constantpad_1d_2d():
+    class Pad1DModel(nn.Module):
+        def __init__(self):
+            super().__init__()
+            self.pad = nn.ConstantPad1d((2, 3), 0)  # pad 2 left, 3 right
+
+        def forward(self, x):
+            return self.pad(x)
+
+    class Pad2DModel(nn.Module):
+        def __init__(self):
+            super().__init__()
+            self.pad = nn.ConstantPad2d((1, 2, 3, 4), 0)  # left, right, top, bottom
+
+        def forward(self, x):
+            return self.pad(x)
+
+    # 1D test: batch=1, channels=2, width=4, values 1,2,3,4
+    x1d = torch.tensor([[[1., 2., 3., 4.],
+                         [4., 3., 2., 1.]]])  # shape (1, 2, 4)
+    model_1d = Pad1DModel()
+    model_1d.eval()
+    config_1d = config_from_pytorch_model(model_1d, (2, 4))
+    hls_model_1d = convert_from_pytorch_model(model_1d, hls_config=config_1d)
+    print("1D Padding Model Layers:")
+    for layer in hls_model_1d.get_layers():
+        print(f"{layer.name}: {layer.class_name}")
+
+    # 2D test: batch=1, channels=1, height=2, width=4, values 1,2,3,4,5,6,7,8
+    x2d = torch.tensor([[[[1., 2., 3., 4.],
+                          [5., 6., 7., 8.]]]])  # shape (1, 1, 2, 4)
+    model_2d = Pad2DModel()
+    model_2d.eval()
+    config_2d = config_from_pytorch_model(model_2d, (1, 2, 4))
+    hls_model_2d = convert_from_pytorch_model(model_2d, hls_config=config_2d)
+    print("2D Padding Model Layers:")
+    for layer in hls_model_2d.get_layers():
+        print(f"{layer.name}: {layer.class_name}")
+
+    # Write the HLS projects, cannot compile on Windows
+    hls_model_1d.write()
+    hls_model_2d.write()