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Commit d7c91a38 authored by Cyril Moineau's avatar Cyril Moineau
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Remove unused functions.

parent db126426
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3 merge requests!17v0.1.0,!12v0.4.0,!11Export refactor
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aidge_export_arm_cortexm/operators.py
+ 1
− 253
View file @ d7c91a38
......@@ -78,9 +78,6 @@ class Producer_ARMCortexM(ExportNode):
# A Producer does nothing during forward
return []
@classmethod
def exportable(cls, node):
return True # TODO add check i/o NCHW
class Scaling():
class ScalingMode:
......@@ -272,9 +269,7 @@ class Pooling_ARMCortexM(ExportNodeCpp):
]
self.kernel = node.get_operator().attr.kernel_dims
self.stride = node.get_operator().attr.stride_dims
@classmethod
def exportable(cls, node):
return True # TODO add check i/o NCHW
@ExportLibAidgeARM.register("FC", aidge_core.ImplSpec(aidge_core.IOSpec(aidge_core.dtype.float32)))
class FC_ARMCortexM(ExportNodeCpp):
......@@ -292,9 +287,6 @@ class FC_ARMCortexM(ExportNodeCpp):
self.kernels_to_copy = [
str(ROOT / "_Aidge_Arm" / "kernels" / "FullyConnected" / "Fc.hpp")
]
@classmethod
def exportable(cls, node):
return True # TODO add check i/o NCHW
@ExportLibAidgeARM.register("MaxPooling2D", aidge_core.ImplSpec(aidge_core.IOSpec(aidge_core.dtype.float32)))
class MaxPooling_ARMCortexM(Pooling_ARMCortexM):
......@@ -431,247 +423,3 @@ class MatMul_ARMCortexM(ExportNodeCpp):
self.kernels_to_copy = [
str(ROOT / "_Aidge_Arm" / "kernels" / "Matmul" / "aidge_matmul_chw_float32.h"),
]
# TODO: Is this used ?
# @register("ConvReluScaling")
# class ConvReluScaling_ARMCortexM(Conv_ARMCortexM):
# def __init__(self, node, board, library):
# super(Conv_ARMCortexM, self).__init__(node, board, library)
# if self.operator.has_attr("Begin_End_Borders"):
# self.padding = self.operator.attr.begin_end_borders
# self.activation = "Rectifier"
# # Should do this line but there is a bug while changing the dtype of generic operator...
# # self.dtype = aidge2c(node.get_operator().get_output(0).dtype())
# # Do this instead
# if self.operator.attr.quantized_nb_bits == 8:
# if self.operator.attr.is_output_unsigned:
# self.dtype = aidge2c(aidge_core.dtype.uint8)
# else:
# self.dtype = aidge2c(aidge_core.dtype.int8)
# # Impose Single Shift (perhaps change it to have a more modular system)
# self.scaling = Scaling(self.operator.attr.scaling_factor,
# self.operator.attr.quantized_nb_bits)("floating_point")
# @register("BatchNorm")
# class BatchNorm2D_ARMCortexM(ExportNode):
# def __init__(self, node, board, library):
# super().__init__(node)
# self.board = board
# self.library = library
# self.dataformat = aidge_datatype2dataformat(node.get_operator().get_output(0).dtype())
# self.dtype = aidge_datatype2ctype(node.get_operator().get_output(0).dtype())
# self.epsilon = node.get_operator().attr.epsilon
# self.producers = []
# for i in range(0, len(node.inputs())):
# if node.input(i)[0].type()=="Producer":
# producer = node.input(i)[0]
# self.producers.append(Producer_ARMCortexM(producer))
# def export(self, export_folder:Path,list_configs:list):
# for i in range(len(self.producers)):
# self.producers[i].export(export_folder / "parameters" / f"{self.producers[i].name}.h")
# list_configs.append(f"parameters/{self.producers[i].name}.h")
# list_configs.append(f"layers/{self.name}.h")
# if self.library == "aidge":
# if self.dataformat == "float32":
# copyfile(str(ROOT / "_Aidge_Arm" / "kernels" / "BatchNorm" / "aidge_batchnorm2d_chw_float32.c"),
# str(export_folder / "src" / "kernels"))
# generate_file(
# str(export_folder / "layers" / f"{self.name}.h"),
# str(ROOT / "_Aidge_Arm" / "templates" / "configuration" / "batchnorm2d.jinja"),
# name=self.name,
# epsilon=self.epsilon,
# input_dims = self.inputs_dims[0])
# return list_configs
# def forward(self, list_actions:list):
# if not self.is_last:
# list_actions.append(set_up_output(self.name, self.dtype))
# if self.library == "aidge":
# list_actions.append(generate_str(
# str(ROOT / "_Aidge_Arm" / "templates" / "forward_call" / "batchnorm2d.jinja"),
# name=self.name,
# dataformat=self.dataformat,
# input_name=self.inputs[0].name(),
# running_mean_name=self.inputs[3].name(),
# running_var_name=self.inputs[4].name(),
# weight_name=self.inputs[1].name(),
# bias_name=self.inputs[2].name(),
# output_name=self.name
# ))
# return list_actions
# @register("Reshape")
# class Reshape_ARMCortexM(ExportNode):
# def __init__(self, node, board, library):
# super().__init__(node)
# self.board = board
# self.library = library
# # node.set_name(self.inputs[0].name())
# self.dataformat = aidge_datatype2dataformat(node.get_operator().get_output(0).dtype())
# self.dtype = aidge_datatype2ctype(node.get_operator().get_output(0).dtype())
# def export(self, export_folder:Path, list_configs:list):
# list_configs.append(f"layers/{self.name}.h")
# if self.library == "aidge":
# if self.dataformat == "float32":
# copyfile(str(ROOT / "_Aidge_Arm" / "kernels" / "Reshape" / "aidge_reshape_chw_float32.c"),
# str(export_folder / "src" / "kernels"))
# generate_file(
# str(export_folder / "layers" / f"{self.name}.h"),
# str(ROOT / "_Aidge_Arm" / "templates" / "configuration" / "reshape.jinja"),
# name=self.name,
# nb_inputs=np.prod(self.inputs_dims[0]),
# nb_outputs=np.prod(self.outputs_dims[0]))
# return list_configs
# def forward(self, list_actions:list):
# if not self.is_last:
# list_actions.append(set_up_output(self.name, self.dtype))
# if self.library == "aidge":
# list_actions.append(generate_str(
# str(ROOT / "_Aidge_Arm" / "templates" / "forward_call" / "reshape.jinja"),
# name=self.name,
# dataformat=self.dataformat,
# input_name=self.inputs[0].name(),
# output_name=self.name,
# ))
# return list_actions
# @register("Gather")
# class Gather_ARMCortexM(ExportNode):
# def __init__(self, node, board, library):
# super().__init__(node)
# self.board = board
# self.library = library
# self.dataformat = aidge_datatype2dataformat(node.get_operator().get_output(0).dtype())
# self.dtype = aidge_datatype2ctype(node.get_operator().get_output(0).dtype())
# self.indices = node.get_operator().attr.indices
# self.axis = node.get_operator().attr.axis
# def export(self, export_folder:Path, list_configs:list):
# list_configs.append(f"layers/{self.name}.h")
# export_params(f"{self.inputs[0].name()}_DIMS", np.array(self.inputs_dims[0],dtype=np.int32),export_folder / "dimensions" / f"{self.inputs[0].name()}_DIMS.h")
# list_configs.append(f"dimensions/{self.inputs[0].name()}_DIMS.h")
# export_params(f"{self.name}_INDEXES", np.array(self.indices,dtype=np.int32),export_folder / "dimensions" / f"{self.name}_INDEXES.h")
# list_configs.append(f"dimensions/{self.name}_INDEXES.h")
# if self.library == "aidge":
# if self.dataformat == "float32":
# copyfile(str(ROOT / "_Aidge_Arm" / "kernels" / "Transform" / "Gather" / "aidge_gather_chw_float32.c"),
# str(export_folder / "src" / "kernels"))
# generate_file(
# str(export_folder / "layers" / f"{self.name}.h"),
# str(ROOT / "_Aidge_Arm" / "templates" / "configuration" / "gather.jinja"),
# name=self.name,
# axis = self.axis,
# indices = self.indices,
# input_dims=self.inputs_dims[0],
# nb_outputs=np.prod(self.outputs_dims[0])
# )
# return list_configs
# def forward(self, list_actions:list):
# if not self.is_last:
# list_actions.append(set_up_output(self.name, self.dtype))
# if self.library == "aidge":
# list_actions.append(generate_str(
# str(ROOT / "_Aidge_Arm" / "templates" / "forward_call" / "gather.jinja"),
# name=self.name,
# dataformat=self.dataformat,
# input_name=self.inputs[0].name(),
# output_name=self.name
# ))
# return list_actions
# @register("Transpose")
# class Transpose_ARMCortexM(ExportNode):
# def __init__(self, node, board, library):
# super().__init__(node)
# self.board = board
# self.library = library
# self.dataformat = aidge_datatype2dataformat(node.get_operator().get_output(0).dtype())
# self.dtype = aidge_datatype2ctype(node.get_operator().get_output(0).dtype())
# self.perm = node.get_operator().attr.output_dims_order
# def export(self, export_folder:Path, list_configs:list):
# list_configs.append(f"layers/{self.name}.h")
# export_params(f"{self.inputs[0].name()}_DIMS", np.array(self.inputs_dims[0],dtype=np.int32),export_folder / "dimensions" / f"{self.inputs[0].name()}_DIMS.h")
# list_configs.append(f"dimensions/{self.inputs[0].name()}_DIMS.h")
# export_params(f"{self.name}_PERMUTATIONS", np.array(self.perm,dtype=np.int32),export_folder / "dimensions" / f"{self.name}_PERMUTATIONS.h")
# list_configs.append(f"dimensions/{self.name}_PERMUTATIONS.h")
# export_params(f"{self.name}_DIMS", np.array(self.outputs_dims[0],dtype=np.int32),export_folder / "dimensions" / f"{self.name}_DIMS.h")
# list_configs.append(f"dimensions/{self.name}_DIMS.h")
# if self.library == "aidge":
# if self.dataformat == "float32":
# copyfile(str(ROOT / "_Aidge_Arm" / "kernels" / "Transform" / "Transpose" / "aidge_transpose_chw_float32.c"),
# str(export_folder / "src" / "kernels"))
# generate_file(
# str(export_folder / "layers" / f"{self.name}.h"),
# str(ROOT / "_Aidge_Arm" / "templates" / "configuration" / "transpose.jinja"),
# name=self.name,
# perm = self.perm,
# input_dims=self.inputs_dims[0],
# output_dims=self.outputs_dims[0],
# nb_outputs=np.prod(self.outputs_dims[0])
# )
# # print(self.outputs_dims)
# return list_configs
# def forward(self, list_actions:list):
# if not self.is_last:
# list_actions.append(set_up_output(self.name, self.dtype))
# if self.library == "aidge":
# list_actions.append(generate_str(
# str(ROOT / "_Aidge_Arm" / "templates" / "forward_call" / "transpose.jinja"),
# name=self.name,
# dataformat=self.dataformat,
# input_name=self.inputs[0].name(),
# output_name=self.name
# ))
# return list_actions
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