diff --git a/aidge_export_cpp/kernels/pad.hpp b/aidge_export_cpp/kernels/pad.hpp
index 158c9359c934f2735c12e47cbd186a4f30715554..4e83257c1152b1963dd4b0eefc912216a729de7d 100644
--- a/aidge_export_cpp/kernels/pad.hpp
+++ b/aidge_export_cpp/kernels/pad.hpp
@@ -6,32 +6,44 @@
// Todo add border value and border type (Reflect, Constant, Wrap...) and add the two missing pad value (bottom and right)
-template<int NB_CHANNELS,
+template<int NB_BATCHES, int NB_CHANNELS,
int CHANNELS_HEIGHT, int CHANNELS_WIDTH,
int NB_OUTPUTS,
int OUTPUTS_HEIGHT, int OUTPUTS_WIDTH,
- int PADDING_Y, int PADDING_X,
+ int PADDING_TOP,
+ int PADDING_LEFT,
+ int PADDING_BOTTOM,
+ int PADDING_RIGHT,
typename Input_T, typename Output_T>
__attribute__((always_inline)) inline
-void convolution_forward(
+void pad_forward(
+ double borderValue,
const Input_T* __restrict inputs,
Output_T* __restrict outputs
)
{
- const unsigned int oySize = CHANNELS_HEIGHT + PADDING_Y + PADDING_Y;
- const unsigned int oxSize = CHANNELS_WIDTH + PADDING_X + PADDING_X;
-
- for (unsigned int oy = 0; oy < oySize; ++oy) {
- for (unsigned int ox = 0; ox < oxSize; ++ox) {
- if (oy < PADDING_Y or oy >= CHANNELS_HEIGHT + PADDING_Y or ox < PADDING_X or ox >= CHANNELS_WIDTH + PADDING_X)
- {
- outputs[oy * oySize + ox] = 0.0f;
- }
- else
- {
- outputs[oy * oySize + ox] = inputs[(oy - PADDING_Y) * CHANNELS_HEIGHT + (ox - PADDING_X)];
- }
+ const unsigned int oySize = CHANNELS_HEIGHT + PADDING_TOP + PADDING_BOTTOM;
+ const unsigned int oxSize = CHANNELS_WIDTH + PADDING_LEFT + PADDING_RIGHT;
+
+ for (unsigned int batch = 0; batch < NB_BATCHES; ++batch) {
+ for (unsigned int ch = 0; ch < NB_CHANNELS; ++ch) {
+ const unsigned int preIndex = batch * NB_CHANNELS * CHANNELS_HEIGHT * CHANNELS_WIDTH + ch * CHANNELS_HEIGHT * CHANNELS_WIDTH;
+
+ for (unsigned int oy = 0; oy < oySize; ++oy) {
+ for (unsigned int ox = 0; ox < oxSize; ++ox) {
+ const unsigned int outIndex = batch * NB_CHANNELS * oySize * oxSize + ch * oySize * oxSize + oy * oxSize + ox;
+ outputs[outIndex] = borderValue;
+
+ const unsigned int inputX = ox - PADDING_LEFT;
+ const unsigned int inputY = oy - PADDING_TOP;
+
+ if (inputY >= 0 and inputY < CHANNELS_HEIGHT and inputX >= 0 and inputX < CHANNELS_WIDTH)
+ {
+ outputs[outIndex] = inputs[preIndex + inputY * CHANNELS_WIDTH + inputX];
+ }
+ }
+ }
}
}
}
diff --git a/aidge_export_cpp/operators.py b/aidge_export_cpp/operators.py
index 5abb137d9431e2b21b83ecacd5329ebc496c61f4..a6ad95d26c0d7649611a6940646c7b4cd72364e5 100644
--- a/aidge_export_cpp/operators.py
+++ b/aidge_export_cpp/operators.py
@@ -73,10 +73,16 @@ class ProducerCPP(ExportNode):
# TODO : find a way to remove this dummy exportnode
@ExportLibCpp.register("Pad2D", aidge_core.ImplSpec(aidge_core.IOSpec(aidge_core.dtype.any)))
-class Pad_ARMCortexM(ExportNodeCpp):
+class PadCPP(ExportNodeCpp):
def __init__(self, node, mem_info):
super().__init__(node, mem_info)
self.attributes["padding"] = node.get_operator().attr.begin_end_borders
+ self.attributes["border_type"] = node.get_operator().attr.border_type
+ self.attributes["border_value"] = node.get_operator().attr.border_value
+
+ assert self.attributes["border_type"] == aidge_core.pad_border_type.Constant, (
+ f"export Pad2d: border_type == {node.get_operator().attr.border_type} not implemented"
+ )
self.config_template = str(
ROOT / "templates" / "configuration" / "pad_config.jinja")
@@ -87,8 +93,6 @@ class Pad_ARMCortexM(ExportNodeCpp):
str(ROOT / "kernels" / "pad.hpp")
]
-
-
@ExportLibCpp.register("ReLU", aidge_core.ImplSpec(aidge_core.IOSpec(aidge_core.dtype.float32)))
class ReLUCPP(ExportNodeCpp):
def __init__(self, node, mem_info):
diff --git a/aidge_export_cpp/templates/configuration/pad_config.jinja b/aidge_export_cpp/templates/configuration/pad_config.jinja
index 527e5c08e4e70370616e0794f3508f5c62583124..8b21577fe4d6f52ddb36ae796740f265db3d45cc 100644
--- a/aidge_export_cpp/templates/configuration/pad_config.jinja
+++ b/aidge_export_cpp/templates/configuration/pad_config.jinja
@@ -4,7 +4,10 @@
{# For layer configuration -#}
{% include "./_def_io.jinja" %}
{% include "./_meminfo.jinja" %}
-#define {{ name|upper }}_PADDING_Y {{ padding[1] }}
-#define {{ name|upper }}_PADDING_X {{ padding[0] }}
+#define {{ name|upper }}_PADDING_BOTTOM {{ padding[2] }}
+#define {{ name|upper }}_PADDING_RIGHT {{ padding[3] }}
+#define {{ name|upper }}_PADDING_TOP {{ padding[0] }}
+#define {{ name|upper }}_PADDING_LEFT {{ padding[1] }}
+#define {{ name|upper }}_BORDER_VALUE {{ border_value }}
#endif /* {{ name|upper }}_LAYER_H */
diff --git a/aidge_export_cpp/templates/kernel_forward/pad_forward.jinja b/aidge_export_cpp/templates/kernel_forward/pad_forward.jinja
index 04976e9fbac4a268c02a94f6af6f846f50f783ae..721418709f589d56723156797d7e45afe1259a7b 100644
--- a/aidge_export_cpp/templates/kernel_forward/pad_forward.jinja
+++ b/aidge_export_cpp/templates/kernel_forward/pad_forward.jinja
@@ -1,13 +1,16 @@
{% filter indent(width=4, first=False) %}
{% include "./_mem_offset.jinja" %}
-convolution_forward<{{ in_name[0]|upper }}_NB_CHANNELS,
- {{ in_name[0]|upper }}_IN_HEIGHT,
- {{ in_name[0]|upper }}_IN_WIDTH,
- {{ out_name[0]|upper }}_NB_OUTPUTS,
- {{ out_name[0]|upper }}_OUT_HEIGHT,
- {{ out_name[0]|upper }}_OUT_WIDTH,
- {{name|upper}}_PADDING_Y,
- {{name|upper}}_PADDING_X>
- ({{in_name[0]}}, {{out_name[0]}});
+pad_forward<{{ in_name[0]|upper }}_IN_BATCH,
+ {{ in_name[0]|upper }}_NB_CHANNELS,
+ {{ in_name[0]|upper }}_IN_HEIGHT,
+ {{ in_name[0]|upper }}_IN_WIDTH,
+ {{ out_name[0]|upper }}_NB_OUTPUTS,
+ {{ out_name[0]|upper }}_OUT_HEIGHT,
+ {{ out_name[0]|upper }}_OUT_WIDTH,
+ {{name|upper}}_PADDING_TOP,
+ {{name|upper}}_PADDING_LEFT,
+ {{name|upper}}_PADDING_BOTTOM,
+ {{name|upper}}_PADDING_RIGHT>
+ ({{name|upper}}_BORDER_VALUE, {{in_name[0]}}, {{out_name[0]}});
{% include "./_save_outputs.jinja" %}
{% endfilter %}
diff --git a/aidge_export_cpp/unit_tests/test_export.py b/aidge_export_cpp/unit_tests/test_export.py
index 273c9b4b0481771a2f90a8a6818787e37d28efd4..9ee6443ab6b5143b4df5c6ae373adf4067fa3f6d 100644
--- a/aidge_export_cpp/unit_tests/test_export.py
+++ b/aidge_export_cpp/unit_tests/test_export.py
@@ -211,6 +211,55 @@ class test_operator_export(unittest.TestCase):
self.unit_test_export(model, "Mul", [[1, 5, 5]])
+ def test_export_mul_larger(self):
+ print("MulLarger")
+ model = aidge_core.sequential([
+ aidge_core.Producer([1, 7, 5], name="producer"),
+ aidge_core.Mul(name="mul")
+ ])
+
+ self.unit_test_export(model, "Mul", [[1, 7, 5]])
+
+ def test_export_mul_higher(self):
+ print("MulHigher")
+ model = aidge_core.sequential([
+ aidge_core.Producer([1, 5, 7], name="producer"),
+ aidge_core.Mul(name="mul")
+ ])
+
+ self.unit_test_export(model, "Mul", [[1, 5, 7]])
+
+ # "Broadcast not supported yet in export operator"
+ @unittest.expectedFailure
+ def test_export_mul_simple_broadcast(self):
+ print("MulSimpleBroadcast")
+ model = aidge_core.sequential([
+ aidge_core.Producer([1, 1, 5], name="producer"),
+ aidge_core.Mul(name="mul")
+ ])
+
+ self.unit_test_export(model, "MulSimpleBroadcast", [[1, 7, 5]])
+
+ # "Broadcast not supported yet in export operator"
+ @unittest.expectedFailure
+ def test_export_mul_double_broadcast(self):
+ print("MulDoubleBroadcast")
+ model = aidge_core.sequential([
+ aidge_core.Producer([1, 1, 7], name="producer"),
+ aidge_core.Mul(name="mul")
+ ])
+
+ self.unit_test_export(model, "MulDoubleBroadcast", [[1, 5, 1]])
+
+ def test_export_mul_batch(self):
+ print("MulBatch")
+ model = aidge_core.sequential([
+ aidge_core.Producer([3, 5, 7], name="producer"),
+ aidge_core.Mul(name="mul")
+ ])
+
+ self.unit_test_export(model, "MulBatch", [[3, 5, 7]])
+
def test_export_concat(self):
print("Concat")
model = aidge_core.sequential([
@@ -250,7 +299,56 @@ class test_operator_export(unittest.TestCase):
aidge_core.Pad2D((1, 1, 1, 1), name="pad2d")
])
- self.unit_test_export(model, "Pad2D", [[1, 1, 10, 10]])
+ self.unit_test_export(model, "Pad2D", [[1, 1, 11, 11]])
+
+ def test_export_pad2D_larger(self):
+ print("Pad2DLarger")
+ model = aidge_core.sequential([
+ aidge_core.Pad2D((1, 3, 1, 3), name="pad2d")
+ ])
+
+ self.unit_test_export(model, "Pad2DLarger", [[1, 1, 7, 11]])
+
+ def test_export_pad2D_higher(self):
+ print("Pad2DHigher")
+ model = aidge_core.sequential([
+ aidge_core.Pad2D((3, 1, 3, 1), name="pad2d")
+ ])
+
+ self.unit_test_export(model, "Pad2DHigher", [[1, 1, 11, 7]])
+
+ def test_export_pad2D_mismatch(self):
+ print("Pad2DMismatch")
+ model = aidge_core.sequential([
+ aidge_core.Pad2D((1, 3, 5, 7), name="pad2d")
+ ])
+
+ self.unit_test_export(model, "Pad2DMismatch", [[3, 5, 11, 7]])
+
+ def test_export_pad2D_denser(self):
+ print("Pad2DDenser")
+ model = aidge_core.sequential([
+ aidge_core.Pad2D((3, 3, 3, 3), name="pad2d")
+ ])
+
+ self.unit_test_export(model, "Pad2DDenser", [[1, 5, 7, 11]])
+
+ def test_export_pad2D_with_bigger_batch_size(self):
+ print("Pad2DBiggerBatchSize")
+ model = aidge_core.sequential([
+ aidge_core.Pad2D((1, 1, 1, 1), name="pad2d")
+ ])
+
+ self.unit_test_export(model, "Pad2DBiggerBatchSize", [[3, 5, 7, 11]])
+
+ @unittest.expectedFailure
+ def test_export_pad2D_not_constant(self):
+ print("Pad2DNotConstant")
+ model = aidge_core.sequential([
+ aidge_core.Pad2D((3, 3, 3, 3), border_type=aidge_core.pad_border_type.Wrap, name="pad2d")
+ ])
+
+ self.unit_test_export(model, "Pad2DNotConstant", [[1, 5, 7, 11]])
def test_export_batchnorm2D(self):
print("BatchNormalization2D")
@@ -260,6 +358,38 @@ class test_operator_export(unittest.TestCase):
self.unit_test_export(model, "BatchNorm2D", [[1, 1, 5, 5]], False, False)
+ def test_export_batchnorm2D_Larger(self):
+ print("BatchNormalization2DLarger")
+ model = aidge_core.sequential([
+ aidge_core.BatchNorm2D(nb_features=10, epsilon=2e-5, name="bn")
+ ])
+
+ self.unit_test_export(model, "BatchNorm2DLarger", [[1, 1, 5, 7]], False, False)
+
+ def test_export_batchnorm2D_Higher(self):
+ print("BatchNormalization2DHigher")
+ model = aidge_core.sequential([
+ aidge_core.BatchNorm2D(nb_features=10, epsilon=2e-5, name="bn")
+ ])
+
+ self.unit_test_export(model, "BatchNorm2DHigher", [[1, 1, 7, 5]], False, False)
+
+ def test_export_batchnorm2D_Denser(self):
+ print("BatchNormalization2DDenser")
+ model = aidge_core.sequential([
+ aidge_core.BatchNorm2D(nb_features=10, epsilon=2e-5, name="bn")
+ ])
+
+ self.unit_test_export(model, "BatchNorm2DDenser", [[1, 3, 5, 7]], False, False)
+
+ def test_export_batchnorm2D_with_bigger_batch_size(self):
+ print("BatchNormalization2DBiggerBatchSize")
+ model = aidge_core.sequential([
+ aidge_core.BatchNorm2D(nb_features=10, epsilon=2e-5, name="bn")
+ ])
+
+ self.unit_test_export(model, "BatchNormalization2DBiggerBatchSize", [[4, 3, 5, 7]], False, False)
+
def test_export_cpp(self):
print("Export test to do")