Remove merge artifacts

aidge_export_cpp/operators.py

@@ -192,7 +192,6 @@ class MaxPoolCPP(ExportNodeCpp):
 
         # No padding with MaxPooling
         # Use PaddedMaxPooling to add padding attribute
-<<<<<<< HEAD
         self.attributes["padding"] = [0, 0]
         self.attributes["pool_type"] = "Max"
         self.attributes["activation"] = "Linear"
@@ -281,175 +280,3 @@ class FcCPP(ExportNodeCpp):
             str(ROOT / "kernels" / "activation.hpp"),
             str(ROOT / "kernels" / "rescaling.hpp")
         ]
-=======
-        self.padding = [0, 0]
-
-        if len(self.inputs_dims[0]) == 4:
-            # if dims == [batch, nb_channels, height, width]
-            # transform to [nb_channels, height, width]
-            self.inputs_dims[0] = self.inputs_dims[0][1:]
-
-        if len(self.outputs_dims[0]) == 4:
-            # if dims == [batch, nb_outputs]
-            # transform to [nb_outputs, 1, 1]
-            self.outputs_dims[0] = self.outputs_dims[0][1:]
-
-    def export(self, export_folder:Path, list_configs:list):
-
-        copyfile(str(ROOT / "kernels" / "pooling.hpp"),
-                 str(export_folder / "include" / "kernels"))
-
-        list_configs.append("kernels/pooling.hpp")
-        list_configs.append(f"layers/{self.name}.h")
-
-        generate_file(
-            str(export_folder / "layers" / f"{self.name}.h"),
-            str(ROOT / "templates" / "configuration" / "pooling_config.jinja"),
-            name=self.name,
-            input_dims=self.inputs_dims[0],
-            output_dims=self.outputs_dims[0],
-            kernel=self.kernel,
-            stride=self.stride,
-            padding=self.padding,
-            pool_type="Max",
-            activation="Linear")
-
-        return list_configs
-
-    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" / "pooling_forward.jinja"),
-            name=self.name,
-            input_name=self.inputs[0].name() if self.inputs[0] else self.name + "_input",
-            output_name=self.name
-        ))
-        return list_actions
-
-@operator_register("FC")
-class FcCPP(ExportNode):
-    def __init__(self, node):
-        super().__init__(node)
-
-        if len(self.inputs_dims[0]) == 4:
-            # if dims == [batch, nb_channels, height, width]
-            # transform to [nb_channels, height, width]
-            self.inputs_dims[0] = self.inputs_dims[0][1:]
-        elif len(self.inputs_dims[0]) == 2:
-            # if dims == [batch, nb_channels]
-            # transform to [nb_channels, 1, 1]
-            self.inputs_dims[0] = [self.inputs_dims[0][1], 1, 1]
-
-        if len(self.outputs_dims[0]) == 2:
-            # if dims == [batch, nb_outputs]
-            # transform to [nb_outputs, 1, 1]
-            self.outputs_dims[0] = [self.outputs_dims[0][1], 1, 1]
-
-
-    def export(self, export_folder:Path, list_configs:list):
-
-        copyfile(str(ROOT / "kernels" / "fullyconnected.hpp"),
-                 str(export_folder / "include" / "kernels"))
-        copyfile(str(ROOT / "kernels" / "macs.hpp"),
-                 str(export_folder / "include" / "kernels"))
-        copyfile(str(ROOT / "kernels" / "activation.hpp"),
-                 str(export_folder / "include" / "kernels"))
-
-        # Add to config list the include of configurations
-        list_configs.append("kernels/fullyconnected.hpp")
-        list_configs.append(f"layers/{self.name}.h")
-
-        # Export configuration file
-        generate_file(
-            str(export_folder / "layers" / f"{self.name}.h"),
-            str(ROOT / "templates" / "configuration" / "fullyconnected_config.jinja"),
-            name=self.name,
-            input_dims=self.inputs_dims[0],
-            output_dims=self.outputs_dims[0],
-            activation="Linear",
-            rescaling="NoScaling")
-
-        return list_configs
-
-    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() 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
-        ))
-        return list_actions
-
-@operator_register("MatMul")
-class MatMulCPP(ExportNode):
-    def __init__(self, node):
-        super().__init__(node)
-
-        dims0, dims1, outdims = [tuple(x) for x in [self.inputs_dims[0], self.inputs_dims[1], self.outputs_dims[0]]]
-
-        # TODO: MatMul aidge operator supports N-D multi broadcast dimensions where N > 2
-        assert len(dims0) <= 2 and len(dims1) <= 2, (
-            f"MatMul export do not support yet dimensions above 2D:  inputs shapes are: {dims0}, {dims1}")
-
-        # Cast to at least 1D
-        # Note that from MatMul::forwardDims(), scalar inputs are supported
-        # which is actually more general than np.matmul
-        dims0 = dims0 if len(dims0) >= 1 else (1, 1)
-        dims1 = dims1 if len(dims1) >= 1 else (1, 1)
-
-        # Cast to at least 2D
-        dims0 = dims0 if len(dims0) >= 2 else (1, dims0[0])
-        dims1 = dims1 if len(dims1) >= 2 else (dims1[0], 1)
-        assert dims0[1] == dims1[0], (
-            f"MatMul input dimensions do no match, expected (m, k), (k, n): inputs shapes are: {dims0}, {dims1}")
-
-        outdims = outdims if len(outdims) > 0 else (1, 1)
-        assert outdims == (dims0[0], dims1[1]), (
-            f"MatMul output dimensions do no match, expected (m, n) for inputs (m, k) (k, n): output shape is: {outdims}, inputs shapes are: {dims0}, {dims1}")
-
-        self.matmul_inputs_dims = dims0, dims1
-        self.matmul_output_dims = outdims
-
-    def export(self, export_folder:Path, list_configs:list):
-
-        copyfile(str(ROOT / "kernels" / "matmul.hpp"),
-                 str(export_folder / "include" / "kernels"))
-        copyfile(str(ROOT / "kernels" / "activation.hpp"),
-                 str(export_folder / "include" / "kernels"))
-
-        # Add to config list the include of configurations
-        list_configs.append("kernels/matmul.hpp")
-        list_configs.append(f"layers/{self.name}.h")
-
-        # Export configuration file
-        generate_file(
-            str(export_folder / "layers" / f"{self.name}.h"),
-            str(ROOT / "templates" / "configuration" / "matmul_config.jinja"),
-            name=self.name,
-            inputs_dims=self.matmul_inputs_dims,
-            output_dims=self.matmul_output_dims,
-            activation="Linear",
-            rescaling="NoScaling",
-        )
-
-        return list_configs
-
-    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" / "matmul_forward.jinja"),
-            name=self.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",
-            outputs_name=self.name
-        ))
-        return list_actions
->>>>>>> origin/dev