From d6cf46746df4ed1f091a06eed69e15fc561e3476 Mon Sep 17 00:00:00 2001
From: cmoineau <cyril.moineau@cea.fr>
Date: Fri, 11 Oct 2024 12:00:06 +0000
Subject: [PATCH] Remove is_input and is_output from node_export.
---
aidge_export_cpp/operators.py | 46 +++++++++----------
.../kernel_forward/_mem_offset.jinja | 3 ++
.../kernel_forward/activation_forward.jinja | 3 +-
.../kernel_forward/batchnorm_forward.jinja | 3 +-
.../kernel_forward/convolution_forward.jinja | 3 +-
.../kernel_forward/elemwise_forward.jinja | 3 +-
.../fullyconnected_forward.jinja | 3 +-
.../kernel_forward/leakyrelu_forward.jinja | 3 +-
.../kernel_forward/pooling_forward.jinja | 3 +-
9 files changed, 40 insertions(+), 30 deletions(-)
diff --git a/aidge_export_cpp/operators.py b/aidge_export_cpp/operators.py
index 8bdc421..9654a20 100644
--- a/aidge_export_cpp/operators.py
+++ b/aidge_export_cpp/operators.py
@@ -37,8 +37,8 @@ def export_params(name: str,
@ExportLibCpp.register("Producer", aidge_core.ImplSpec(aidge_core.IOSpec(aidge_core.dtype.any)))
class ProducerCPP(ExportNode):
- def __init__(self, node, mem_info, is_input, is_output):
- super().__init__(node, mem_info, is_input, is_output)
+ def __init__(self, node, mem_info):
+ super().__init__(node, mem_info)
self.values = np.array(self.operator.get_output(0))
if len(self.values.shape) == 4: # Note: export in HWC
@@ -59,14 +59,14 @@ 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):
- def __init__(self, node, mem_info, is_input, is_output):
+ def __init__(self, node, mem_info):
raise NotImplementedError("Pad2D nodes is not implemented")
@ExportLibCpp.register("ReLU", aidge_core.ImplSpec(aidge_core.IOSpec(aidge_core.dtype.float32)))
class ReLUCPP(ExportNodeCpp):
- def __init__(self, node, mem_info, is_input, is_output):
- super().__init__(node, mem_info, is_input, is_output)
+ def __init__(self, node, mem_info):
+ super().__init__(node, mem_info)
self.attributes["activation"] = "Rectifier"
self.attributes["rescaling"] = "NoScaling"
self.config_template = str(
@@ -81,8 +81,8 @@ class ReLUCPP(ExportNodeCpp):
@ExportLibCpp.register("Conv2D", aidge_core.ImplSpec(aidge_core.IOSpec(aidge_core.dtype.float32)))
class ConvCPP(ExportNodeCpp):
- def __init__(self, node, mem_info, is_input, is_output):
- super().__init__(node, mem_info, is_input, is_output)
+ def __init__(self, node, mem_info):
+ super().__init__(node, mem_info)
# No padding with Conv
# Use PaddedConv to add padding attribute
self.attributes["padding"] = [0, 0]
@@ -102,8 +102,8 @@ class ConvCPP(ExportNodeCpp):
@ExportLibCpp.register_metaop("PaddedConv2D", aidge_core.ImplSpec(aidge_core.IOSpec(aidge_core.dtype.float32)))
class PaddedConvCPP(ExportNodeCpp):
- def __init__(self, node, mem_info, is_input, is_output):
- super().__init__(node, mem_info, is_input, is_output)
+ def __init__(self, node, mem_info):
+ super().__init__(node, mem_info)
# TODO find a way to retrive attr for meta op
for n in self.operator.get_micro_graph().get_nodes():
if n.type() == "Pad2D":
@@ -132,8 +132,8 @@ class PaddedConvCPP(ExportNodeCpp):
@ExportLibCpp.register("Add", aidge_core.ImplSpec(aidge_core.IOSpec(aidge_core.dtype.float32)))
class AddCPP(ExportNodeCpp):
- def __init__(self, node, mem_info, is_input, is_output):
- super().__init__(node, mem_info, is_input, is_output)
+ def __init__(self, node, mem_info):
+ super().__init__(node, mem_info)
self.attributes["elemwise_op"] = "Add"
self.attributes["activation"] = "Linear"
self.attributes["rescaling"] = "NoScaling"
@@ -150,8 +150,8 @@ class AddCPP(ExportNodeCpp):
@ExportLibCpp.register("Sub", aidge_core.ImplSpec(aidge_core.IOSpec(aidge_core.dtype.float32)))
class SubCPP(ExportNodeCpp):
- def __init__(self, node, mem_info, is_input, is_output):
- super().__init__(node, mem_info, is_input, is_output)
+ def __init__(self, node, mem_info):
+ super().__init__(node, mem_info)
self.attributes["elemwise_op"] = "Sub"
self.attributes["activation"] = "Linear"
self.attributes["rescaling"] = "NoScaling"
@@ -169,8 +169,8 @@ class SubCPP(ExportNodeCpp):
@ExportLibCpp.register("Mul", aidge_core.ImplSpec(aidge_core.IOSpec(aidge_core.dtype.float32)))
class MulCPP(ExportNodeCpp):
- def __init__(self, node, mem_info, is_input, is_output):
- super().__init__(node, mem_info, is_input, is_output)
+ def __init__(self, node, mem_info):
+ super().__init__(node, mem_info)
self.attributes["elemwise_op"] = "Mul"
self.attributes["activation"] = "Linear"
self.attributes["rescaling"] = "NoScaling"
@@ -187,8 +187,8 @@ class MulCPP(ExportNodeCpp):
@ExportLibCpp.register("MaxPooling2D", aidge_core.ImplSpec(aidge_core.IOSpec(aidge_core.dtype.float32)))
class MaxPoolCPP(ExportNodeCpp):
- def __init__(self, node, mem_info, is_input, is_output):
- super().__init__(node, mem_info, is_input, is_output)
+ def __init__(self, node, mem_info):
+ super().__init__(node, mem_info)
# No padding with MaxPooling
# Use PaddedMaxPooling to add padding attribute
@@ -210,8 +210,8 @@ class MaxPoolCPP(ExportNodeCpp):
@ExportLibCpp.register_metaop("PaddedMaxPooling2D", aidge_core.ImplSpec(aidge_core.IOSpec(aidge_core.dtype.float32)))
class PaddedMaxPoolCPP(ExportNodeCpp):
- def __init__(self, node, mem_info, is_input, is_output):
- super().__init__(node, mem_info, is_input, is_output)
+ def __init__(self, node, mem_info):
+ super().__init__(node, mem_info)
for n in self.operator.get_micro_graph().get_nodes():
if n.type() == "Pad2D":
self.attributes["padding"] = n.get_operator(
@@ -238,8 +238,8 @@ class PaddedMaxPoolCPP(ExportNodeCpp):
@ExportLibCpp.register("GlobalAveragePooling", aidge_core.ImplSpec(aidge_core.IOSpec(aidge_core.dtype.float32)))
class GlobalAveragePoolCPP(ExportNodeCpp):
- def __init__(self, node, mem_info, is_input, is_output):
- super().__init__(node, mem_info, is_input, is_output)
+ def __init__(self, node, mem_info):
+ super().__init__(node, mem_info)
self.attributes["stride_dims"] = [1, 1]
# No padding with MaxPooling
@@ -265,8 +265,8 @@ class GlobalAveragePoolCPP(ExportNodeCpp):
@ExportLibCpp.register("FC", aidge_core.ImplSpec(aidge_core.IOSpec(aidge_core.dtype.float32)))
class FcCPP(ExportNodeCpp):
- def __init__(self, node, mem_info, is_input, is_output):
- super().__init__(node, mem_info, is_input, is_output)
+ def __init__(self, node, mem_info):
+ super().__init__(node, mem_info)
self.attributes["activation"] = "Linear"
self.attributes["rescaling"] = "NoScaling"
self.config_template = str(
diff --git a/aidge_export_cpp/templates/kernel_forward/_mem_offset.jinja b/aidge_export_cpp/templates/kernel_forward/_mem_offset.jinja
index 4f44773..b85aae8 100644
--- a/aidge_export_cpp/templates/kernel_forward/_mem_offset.jinja
+++ b/aidge_export_cpp/templates/kernel_forward/_mem_offset.jinja
@@ -1,3 +1,6 @@
+{% filter indent(width=4, first=False) %}
+
{% for outidx in range(nb_out) -%}
{{out_cdtype[outidx]}}* {{out_name[outidx]}} = ({{out_cdtype[outidx]}}*) mem + {{out_name[outidx]|upper}}_OFFSET;
{% endfor %}
+{% endfilter %}
diff --git a/aidge_export_cpp/templates/kernel_forward/activation_forward.jinja b/aidge_export_cpp/templates/kernel_forward/activation_forward.jinja
index 61c6ba5..9a39495 100644
--- a/aidge_export_cpp/templates/kernel_forward/activation_forward.jinja
+++ b/aidge_export_cpp/templates/kernel_forward/activation_forward.jinja
@@ -1,6 +1,7 @@
+{% filter indent(width=4, first=False) %}
{% include "./_mem_offset.jinja" %}
-
activation_forward<{{name|upper}}_NB_DATA,
{{name|upper}}_ACTIVATION>
({{in_name[0]}}, {{out_name[0]}}, {{name|upper}}_RESCALING);
{% include "./_save_outputs.jinja" %}
+{% endfilter %}
diff --git a/aidge_export_cpp/templates/kernel_forward/batchnorm_forward.jinja b/aidge_export_cpp/templates/kernel_forward/batchnorm_forward.jinja
index 2714aa9..5a759b8 100644
--- a/aidge_export_cpp/templates/kernel_forward/batchnorm_forward.jinja
+++ b/aidge_export_cpp/templates/kernel_forward/batchnorm_forward.jinja
@@ -1,8 +1,9 @@
+{% filter indent(width=4, first=False) %}
{% include "./_mem_offset.jinja" %}
-
batchnorm_forward<{{ out_name[0]|upper }}_NB_OUTPUTS,
{{ out_name[0]|upper }}_OUT_HEIGHT,
{{ out_name[0]|upper }}_OUT_WIDTH,
{{name|upper}}_ACTIVATION>
({{in_name[0]}}, {{out_name[0]}}, {{in_name[1]}}, {{in_name[2]}}, {{in_name[3]}}, {{in_name[4]}}, {{name|upper}}_EPSILON);
{% include "./_save_outputs.jinja" %}
+{% endfilter %}
diff --git a/aidge_export_cpp/templates/kernel_forward/convolution_forward.jinja b/aidge_export_cpp/templates/kernel_forward/convolution_forward.jinja
index 87d9433..421013b 100644
--- a/aidge_export_cpp/templates/kernel_forward/convolution_forward.jinja
+++ b/aidge_export_cpp/templates/kernel_forward/convolution_forward.jinja
@@ -1,5 +1,5 @@
+{% 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,
@@ -17,3 +17,4 @@ convolution_forward<{{ in_name[0]|upper }}_NB_CHANNELS,
{{name|upper}}_ACTIVATION>
({{in_name[0]}}, {{out_name[0]}}, {{in_name[1]}}, {{in_name[2]}}, {{name|upper}}_RESCALING);
{% include "./_save_outputs.jinja" %}
+{% endfilter %}
diff --git a/aidge_export_cpp/templates/kernel_forward/elemwise_forward.jinja b/aidge_export_cpp/templates/kernel_forward/elemwise_forward.jinja
index 9ccc67e..f60d163 100644
--- a/aidge_export_cpp/templates/kernel_forward/elemwise_forward.jinja
+++ b/aidge_export_cpp/templates/kernel_forward/elemwise_forward.jinja
@@ -1,7 +1,8 @@
+{% filter indent(width=4, first=False) %}
{% include "./_mem_offset.jinja" %}
-
elemwise_forward<{{name|upper}}_NB_ELTS,
{{name|upper}}_ELEM_OP,
{{name|upper}}_ACTIVATION>
({{out_name[0]}}, {{name|upper}}_RESCALING, {{in_name[0]}}, {{in_name[1]}});
{% include "./_save_outputs.jinja" %}
+{% endfilter %}
diff --git a/aidge_export_cpp/templates/kernel_forward/fullyconnected_forward.jinja b/aidge_export_cpp/templates/kernel_forward/fullyconnected_forward.jinja
index be46657..cac97de 100644
--- a/aidge_export_cpp/templates/kernel_forward/fullyconnected_forward.jinja
+++ b/aidge_export_cpp/templates/kernel_forward/fullyconnected_forward.jinja
@@ -1,5 +1,5 @@
+{% filter indent(width=4, first=False) %}
{% include "./_mem_offset.jinja" %}
-
fullyconnected_forward<{{ in_name[0]|upper }}_NB_CHANNELS,
{{ in_name[0]|upper }}_IN_HEIGHT,
{{ in_name[0]|upper }}_IN_WIDTH,
@@ -9,3 +9,4 @@ fullyconnected_forward<{{ in_name[0]|upper }}_NB_CHANNELS,
{{name|upper}}_ACTIVATION>
({{in_name[0]}}, {{out_name[0]}}, {{in_name[1]}}, {{in_name[2]}}, {{name|upper}}_RESCALING);
{% include "./_save_outputs.jinja" %}
+{% endfilter %}
diff --git a/aidge_export_cpp/templates/kernel_forward/leakyrelu_forward.jinja b/aidge_export_cpp/templates/kernel_forward/leakyrelu_forward.jinja
index bbd514c..591fafe 100644
--- a/aidge_export_cpp/templates/kernel_forward/leakyrelu_forward.jinja
+++ b/aidge_export_cpp/templates/kernel_forward/leakyrelu_forward.jinja
@@ -1,5 +1,6 @@
+{% filter indent(width=4, first=False) %}
{% include "./_mem_offset.jinja" %}
-
leakyrelu_forward<{{name|upper}}_NB_DATA>
({{input_name}}, {{output_name}}, {{name|upper}}_ALPHA);
{% include "./_save_outputs.jinja" %}
+{% endfilter %}
diff --git a/aidge_export_cpp/templates/kernel_forward/pooling_forward.jinja b/aidge_export_cpp/templates/kernel_forward/pooling_forward.jinja
index b4312f7..c730923 100644
--- a/aidge_export_cpp/templates/kernel_forward/pooling_forward.jinja
+++ b/aidge_export_cpp/templates/kernel_forward/pooling_forward.jinja
@@ -1,5 +1,5 @@
+{% filter indent(width=4, first=False) %}
{% include "./_mem_offset.jinja" %}
-
pooling_forward<{{ in_name[0]|upper }}_NB_CHANNELS,
{{ in_name[0]|upper }}_IN_HEIGHT,
{{ in_name[0]|upper }}_IN_WIDTH,
@@ -16,3 +16,4 @@ pooling_forward<{{ in_name[0]|upper }}_NB_CHANNELS,
{{name|upper}}_ACTIVATION>
({{in_name[0]}}, {{out_name[0]}});
{% include "./_save_outputs.jinja" %}
+{% endfilter %}
--
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