diff --git a/aidge_export_cpp/export.py b/aidge_export_cpp/export.py
index de20f3b4eee728a48cdef7849cd5e4894ee0e3d1..b8fe28a54fd5ccc2cd8e380722b431b51a3e1aa8 100644
--- a/aidge_export_cpp/export.py
+++ b/aidge_export_cpp/export.py
@@ -77,7 +77,7 @@ def export(export_folder_name, graphview, scheduler):
list_inputs_name = []
for node in graphview.get_nodes():
if node.type() == "Producer":
- if not node.get_operator().get_attr("Constant"):
+ if not node.get_operator().attr.constant:
export_type = aidge_datatype2ctype(node.get_operator().get_output(0).dtype())
list_inputs_name.append((export_type, node.name()))
diff --git a/aidge_export_cpp/operators.py b/aidge_export_cpp/operators.py
index 86f61f22607558231bc192a35bc5bc8a4644bd32..6dba9a8f37995b4bb387689e89c4841a5311b5c5 100644
--- a/aidge_export_cpp/operators.py
+++ b/aidge_export_cpp/operators.py
@@ -71,7 +71,7 @@ class ProducerCPP(ExportNode):
def __init__(self, node):
super().__init__(node)
- self.constant = self.operator.get_attr("Constant")
+ self.constant = self.operator.attr.constant
self.values = np.array(self.operator.get_output(0))
if len(self.values.shape) == 4:
@@ -130,7 +130,7 @@ class ReLUCPP(ExportNode):
list_actions.append(generate_str(
str(ROOT / "templates" / "kernel_forward" / "activation_forward.jinja"),
name=self.name,
- input_name=self.inputs[0].name(),
+ input_name=self.inputs[0].name() if self.inputs[0] else self.name + "_input",
output_name=self.name
))
return list_actions
@@ -141,14 +141,17 @@ class ConvCPP(ExportNode):
def __init__(self, node):
super().__init__(node)
- self.kernel = node.get_operator().get_attr("KernelDims")
- self.stride = node.get_operator().get_attr("StrideDims")
- self.dilation = node.get_operator().get_attr("DilationDims")
+ self.kernel = node.get_operator().attr.kernel_dims
+ self.stride = node.get_operator().attr.stride_dims
+ self.dilation = node.get_operator().attr.dilation_dims
# No padding with Conv
# Use PaddedConv to add padding attribute
self.padding = [0, 0]
+ self.nb_channels = node.get_operator().in_channels()
+ self.nb_outputs = node.get_operator().out_channels()
+
if len(self.inputs_dims[0]) == 4:
# if dims == [batch, nb_channels, height, width]
# transform to [nb_channels, height, width]
@@ -193,7 +196,7 @@ class ConvCPP(ExportNode):
list_actions.append(generate_str(
str(ROOT / "templates" / "kernel_forward" / "convolution_forward.jinja"),
name=self.name,
- input_name=self.inputs[0].name(),
+ input_name=self.inputs[0].name() if self.inputs[0] else self.name + "_input",
output_name=self.name,
weights_name=self.inputs[1].name(),
biases_name=self.inputs[2].name()
@@ -208,11 +211,11 @@ class PaddedConvCPP(ConvCPP):
for n in self.operator.get_micro_graph().get_nodes():
if n.type() == "Pad":
- self.padding = n.get_operator().get_attr("BeginEndBorders")
+ self.padding = n.get_operator().attr.begin_end_borders
if n.type() == "Conv":
- self.kernel = n.get_operator().get_attr("KernelDims")
- self.stride = n.get_operator().get_attr("StrideDims")
- self.dilation = n.get_operator().get_attr("DilationDims")
+ self.kernel = n.get_operator().attr.kernel_dims
+ self.stride = n.get_operator().attr.stride_dims
+ self.dilation = n.get_operator().attr.dilation_dims
if len(self.inputs_dims[0]) == 4:
# if dims == [batch, nb_channels, height, width]
@@ -253,8 +256,8 @@ class AddCPP(ExportNode):
list_actions.append(generate_str(
str(ROOT / "templates" / "kernel_forward" / "elemwise_forward.jinja"),
name=self.name,
- inputs1_name=self.inputs[0].name(),
- inputs2_name=self.inputs[1].name(),
+ inputs1_name=self.parents[0].name() if self.parents[0] else self.name + "_input1",
+ inputs2_name=self.parents[1].name() if self.parents[1] else self.name + "_input2",
output_name=self.name
))
return list_actions
@@ -286,8 +289,8 @@ class SubCPP(ExportNode):
list_actions.append(generate_str(
str(ROOT / "templates" / "kernel_forward" / "elemwise_forward.jinja"),
name=self.name,
- inputs1_name=self.inputs[0].name(),
- inputs2_name=self.inputs[1].name(),
+ inputs1_name=self.inputs[0].name() if self.inputs[0] else self.name + "_input1",
+ inputs2_name=self.inputs[1].name() if self.inputs[1] else self.name + "_input2",
output_name=self.name
))
return list_actions
@@ -297,8 +300,8 @@ class MaxPoolCPP(ExportNode):
def __init__(self, node):
super().__init__(node)
- self.kernel = node.get_operator().get_attr("KernelDims")
- self.stride = node.get_operator().get_attr("StrideDims")
+ self.kernel = node.get_operator().attr.kernel_dims
+ self.stride = node.get_operator().attr.stride_dims
# No padding with MaxPooling
# Use PaddedMaxPooling to add padding attribute
@@ -344,7 +347,7 @@ class MaxPoolCPP(ExportNode):
list_actions.append(generate_str(
str(ROOT / "templates" / "kernel_forward" / "pooling_forward.jinja"),
name=self.name,
- input_name=self.inputs[0].name(),
+ input_name=self.inputs[0].name() if self.inputs[0] else self.name + "_input",
output_name=self.name
))
return list_actions
@@ -397,11 +400,10 @@ class FcCPP(ExportNode):
def forward(self, list_actions:list):
if not self.is_last:
list_actions.append(set_up_output(self.name, "float"))
-
list_actions.append(generate_str(
str(ROOT / "templates" / "kernel_forward" / "fullyconnected_forward.jinja"),
name=self.name,
- inputs_name=self.inputs[0].name(),
+ inputs_name= self.inputs[0].name() if (self.inputs[0] is not None) else self.name + '_input',
weights_name=self.inputs[1].name(),
biases_name=self.inputs[2].name(),
outputs_name=self.name
diff --git a/aidge_export_cpp/utils/converter.py b/aidge_export_cpp/utils/converter.py
index d706d5a26f103316adfca0cd16f1146284e8177c..0fe7a675a9a00aad6d5a9447097e744b6493bc5c 100644
--- a/aidge_export_cpp/utils/converter.py
+++ b/aidge_export_cpp/utils/converter.py
@@ -17,18 +17,18 @@ def numpy_dtype2ctype(dtype):
# Add more dtype mappings as needed
else:
raise ValueError(f"Unsupported {dtype} dtype")
-
+
def aidge_datatype2ctype(datatype):
- if datatype == aidge_core.DataType.Int8:
+ if datatype == aidge_core.dtype.int8:
return "int8_t"
- elif datatype == aidge_core.DataType.Int32:
+ elif datatype == aidge_core.dtype.int32:
return "int32_t"
- elif datatype == aidge_core.DataType.Int64:
+ elif datatype == aidge_core.dtype.int64:
return "int64_t"
- elif datatype == aidge_core.DataType.Float32:
+ elif datatype == aidge_core.dtype.float32:
return "float"
- elif datatype == aidge_core.DataType.Float64:
+ elif datatype == aidge_core.dtype.float64:
return "double"
# Add more dtype mappings as needed
else:
diff --git a/examples/add_custom_operator/add_custom_operator.ipynb b/examples/add_custom_operator/add_custom_operator.ipynb
index 0687e322831f045a17e729b2706ef518f3db6a7a..5477cdaefa00334a64472810e71c04031c06ab0f 100644
--- a/examples/add_custom_operator/add_custom_operator.ipynb
+++ b/examples/add_custom_operator/add_custom_operator.ipynb
@@ -89,7 +89,7 @@
"model.add(input_node)\n",
"\n",
"# Configuration for the model + forward dimensions\n",
- "model.compile(\"cpu\", aidge_core.DataType.Float32)"
+ "model.compile(\"cpu\", aidge_core.dtype.float32)"
]
},
{
@@ -231,9 +231,9 @@
" def __init__(self, node):\n",
" super().__init__(node)\n",
"\n",
- " self.betas = self.operator.get_attr(\"betas\")\n",
+ " self.betas: float = self.operator.get_attr(\"betas\")\n",
"\n",
- " def export(self, export_folder:str, list_configs:list):\n",
+ " def export(self, export_folder: str, list_configs: list[str]) -> list[str]:\n",
"\n",
" copyfile(\"for_export/swish_kernel.hpp\",\n",
" f\"{export_folder}/include/kernels/\")\n",
@@ -248,7 +248,7 @@
"\n",
" return list_configs\n",
"\n",
- " def forward(self, list_actions:list):\n",
+ " def forward(self, list_actions:list[str]) -> list[str]:\n",
"\n",
" if not self.is_last:\n",
" list_actions.append(set_up_output(self.name, \"float\"))\n",
@@ -296,7 +296,7 @@
"outputs": [],
"source": [
"digit = np.load(\"digit.npy\")\n",
- "cpp.generate_input_file(\"inputs\", digit.reshape(-1), \"myexport/inputs.h\")"
+ "cpp.generate_input_file(array_name=\"inputs\", array=digit.reshape(-1), folder_path=\"myexport\")"
]
},
{
@@ -334,7 +334,7 @@
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
- "version": "3.9.16"
+ "version": "3.1.undefined"
}
},
"nbformat": 4,
diff --git a/examples/export_LeNet/export_lenet_fp32.ipynb b/examples/export_LeNet/export_lenet_fp32.ipynb
index 24fe205a9d781bc6f6adfbe8c9a13a996ca674e7..b10ca6b9dbe4fecf1e56ec04ead0c6c774aaf66c 100644
--- a/examples/export_LeNet/export_lenet_fp32.ipynb
+++ b/examples/export_LeNet/export_lenet_fp32.ipynb
@@ -98,7 +98,7 @@
"model.add(input_node)\n",
"\n",
"# Configuration for the model + forward dimensions\n",
- "model.compile(\"cpu\", aidge_core.DataType.Float32)"
+ "model.compile(\"cpu\", aidge_core.dtype.float32)"
]
},
{
@@ -290,7 +290,7 @@
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
- "version": "3.9.16"
+ "version": "3.10.14"
}
},
"nbformat": 4,