✅ [Add] test for helper and basic module in model

tests/test_model/test_module.py

@@ -0,0 +1,66 @@
+import sys
+from pathlib import Path
+
+import torch
+from torch import nn
+
+project_root = Path(__file__).resolve().parent.parent.parent
+sys.path.append(str(project_root))
+from yolo.model.module import SPPELAN, ADown, CBLinear, Conv, Pool
+
+STRIDE = 2
+KERNEL_SIZE = 3
+IN_CHANNELS = 64
+OUT_CHANNELS = 128
+NECK_CHANNELS = 64
+
+
+def test_conv():
+    conv = Conv(IN_CHANNELS, OUT_CHANNELS, KERNEL_SIZE)
+    x = torch.randn(1, IN_CHANNELS, 64, 64)
+    out = conv(x)
+    assert out.shape == (1, OUT_CHANNELS, 64, 64)
+
+
+def test_pool_max():
+    pool = Pool("max", 2, stride=2)
+    x = torch.randn(1, IN_CHANNELS, 64, 64)
+    out = pool(x)
+    assert out.shape == (1, IN_CHANNELS, 32, 32)
+
+
+def test_pool_avg():
+    pool = Pool("avg", 2, stride=2)
+    x = torch.randn(1, IN_CHANNELS, 64, 64)
+    out = pool(x)
+    assert out.shape == (1, IN_CHANNELS, 32, 32)
+
+
+def test_adown():
+    adown = ADown(IN_CHANNELS, OUT_CHANNELS)
+    x = torch.randn(1, IN_CHANNELS, 64, 64)
+    out = adown(x)
+    assert out.shape == (1, OUT_CHANNELS, 32, 32)
+
+
+def test_adown():
+    adown = ADown(IN_CHANNELS, OUT_CHANNELS)
+    x = torch.randn(1, IN_CHANNELS, 64, 64)
+    out = adown(x)
+    assert out.shape == (1, OUT_CHANNELS, 32, 32)
+
+
+def test_cblinear():
+    cblinear = CBLinear(IN_CHANNELS, [5, 5])
+    x = torch.randn(1, IN_CHANNELS, 64, 64)
+    outs = cblinear(x)
+    assert len(outs) == 2
+    assert outs[0].shape == (1, 5, 64, 64)
+    assert outs[1].shape == (1, 5, 64, 64)
+
+
+def test_sppelan():
+    sppelan = SPPELAN(IN_CHANNELS, OUT_CHANNELS, NECK_CHANNELS)
+    x = torch.randn(1, IN_CHANNELS, 64, 64)
+    out = sppelan(x)
+    assert out.shape == (1, OUT_CHANNELS, 64, 64)

tests/test_tools/test_module_helper.py

@@ -0,0 +1,38 @@
+import sys
+from pathlib import Path
+
+import pytest
+import torch
+from torch import nn
+
+project_root = Path(__file__).resolve().parent.parent.parent
+sys.path.append(str(project_root))
+from yolo.tools.module_helper import auto_pad, get_activation
+
+
+@pytest.mark.parametrize(
+    "kernel_size, dilation, expected",
+    [
+        (3, 1, (1, 1)),
+        ((3, 3), (1, 1), (1, 1)),
+        (3, (2, 2), (2, 2)),
+        ((5, 5), 1, (2, 2)),
+        ((3, 5), (2, 1), (2, 2)),
+    ],
+)
+def test_auto_pad(kernel_size, dilation, expected):
+    assert auto_pad(kernel_size, dilation) == expected, "auto_pad does not calculate padding correctly"
+
+
+@pytest.mark.parametrize(
+    "activation_name, expected_type",
+    [("ReLU", nn.ReLU), ("leakyrelu", nn.LeakyReLU), ("none", nn.Identity), (None, nn.Identity), (False, nn.Identity)],
+)
+def test_get_activation(activation_name, expected_type):
+    result = get_activation(activation_name)
+    assert isinstance(result, expected_type), f"get_activation does not return correct type for {activation_name}"
+
+
+def test_get_activation_invalid():
+    with pytest.raises(ValueError):
+        get_activation("unsupported_activation")