test (nn/convxd): Added tests for padding issue

Xilinx · Sep 4, 2024 · d171072 · d171072
1 parent 9de92f2
commit d171072
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Showing 2 changed files with 68 additions and 6 deletions.
diff --git a/tests/brevitas/nn/test_conv2d.py b/tests/brevitas/nn/test_conv2d.py
@@ -19,10 +19,16 @@
 class TestQuantConv2d:
 
  @pytest_cases.parametrize(
- 'kwargs', [{}, {
+ 'kwargs',
+ [{}, {
  'padding': 'same', 'stride': 1}, {
- 'padding': 'same', 'stride': (1, 1)}],
- ids=['defaults', 'padding="same",stride=1', 'padding="same",stride=(1,1)'])
+ 'padding': 'same', 'stride': (1, 1)}, {
+ 'padding': 'same', 'stride': (2, 1)}],
+ ids=[
+ 'defaults',
+ 'padding="same",stride=1',
+ 'padding="same",stride=(1,1)',
+ 'padding="same",stride=(2,1)'])
  def test_module_init(self, kwargs):
  mod = QuantConv2d(
  out_channels=OUTPUT_CHANNELS,
@@ -108,3 +114,28 @@ def test_internally_scaled_int_bias_after_bn_merge(self):
  merge_bn(mod, bn)
  inp = torch.randn(1, INPUT_CHANNELS, 20, 20)
  mod(inp)
+
+ @pytest_cases.parametrize(
+ 'kwargs',
+ [{
+ 'is_same_padded_strided': False}, {
+ 'padding': 'same', 'stride': 1, 'is_same_padded_strided': False}, {
+ 'padding': 'same', 'stride': (1, 1), 'is_same_padded_strided': False}, {
+ 'padding': 'same', 'stride': (2, 1), 'is_same_padded_strided': True}, {
+ 'padding': 0, 'stride': (2, 1), 'is_same_padded_strided': False}],
+ ids=[
+ 'defaults',
+ 'padding="same",stride=1',
+ 'padding="same",stride=(1,1)',
+ 'padding="same",stride=(2,1)',
+ 'padding=0,stride=(2,1)'])
+ def test_is_same_padded_strided(self, kwargs):
+ is_same_padded_strided = kwargs['is_same_padded_strided']
+ del kwargs['is_same_padded_strided']
+ mod = QuantConv2d(
+ out_channels=OUTPUT_CHANNELS,
+ in_channels=INPUT_CHANNELS,
+ kernel_size=KERNEL_SIZE,
+ bias=False,
+ **kwargs)
+ assert is_same_padded_strided == mod.is_same_padded_strided, f"Expected is_same_padded_strided={is_same_padded_strided}, found is_same_padded_strided={mod.is_same_padded_strided}"
diff --git a/tests/brevitas/nn/test_conv3d.py b/tests/brevitas/nn/test_conv3d.py
@@ -19,10 +19,16 @@
 class TestQuantConv3d:
 
  @pytest_cases.parametrize(
- 'kwargs', [{}, {
+ 'kwargs',
+ [{}, {
  'padding': 'same', 'stride': 1}, {
- 'padding': 'same', 'stride': (1, 1, 1)}],
- ids=['defaults', 'padding="same",stride=1', 'padding="same",stride=(1,1,1)'])
+ 'padding': 'same', 'stride': (1, 1, 1)}, {
+ 'padding': 'same', 'stride': (2, 1, 1)}],
+ ids=[
+ 'defaults',
+ 'padding="same",stride=1',
+ 'padding="same",stride=(1,1,1)',
+ 'padding="same",stride=(2,1,1)'])
  def test_module_init(self, kwargs):
  mod = QuantConv3d(
  out_channels=OUTPUT_CHANNELS,
@@ -108,3 +114,28 @@ def test_internally_scaled_int_bias_after_bn_merge(self):
  merge_bn(mod, bn)
  inp = torch.randn(1, INPUT_CHANNELS, 20, 20, 20)
  mod(inp)
+
+ @pytest_cases.parametrize(
+ 'kwargs',
+ [{
+ 'is_same_padded_strided': False}, {
+ 'padding': 'same', 'stride': 1, 'is_same_padded_strided': False}, {
+ 'padding': 'same', 'stride': (1, 1, 1), 'is_same_padded_strided': False}, {
+ 'padding': 'same', 'stride': (2, 1, 1), 'is_same_padded_strided': True}, {
+ 'padding': 0, 'stride': (2, 1, 1), 'is_same_padded_strided': False}],
+ ids=[
+ 'defaults',
+ 'padding="same",stride=1',
+ 'padding="same",stride=(1,1,1)',
+ 'padding="same",stride=(2,1,1)',
+ 'padding=0,stride=(2,1,1)'])
+ def test_is_same_padded_strided(self, kwargs):
+ is_same_padded_strided = kwargs['is_same_padded_strided']
+ del kwargs['is_same_padded_strided']
+ mod = QuantConv3d(
+ out_channels=OUTPUT_CHANNELS,
+ in_channels=INPUT_CHANNELS,
+ kernel_size=KERNEL_SIZE,
+ bias=False,
+ **kwargs)
+ assert is_same_padded_strided == mod.is_same_padded_strided, f"Expected is_same_padded_strided={is_same_padded_strided}, found is_same_padded_strided={mod.is_same_padded_strided}"