diff --git a/aidge_export_cpp/operators_old.py b/aidge_export_cpp/operators_old.py
deleted file mode 100644
index aa780fb293168800714d8fea827642510bc8597f..0000000000000000000000000000000000000000
--- a/aidge_export_cpp/operators_old.py
+++ /dev/null
@@ -1,474 +0,0 @@
-from aidge_core import ExportNode
-import numpy as np
-from jinja2 import Environment, FileSystemLoader
-import os
-import shutil
-from aidge_export_cpp.register import export_cpp_register
-
-dirpath = os.path.dirname(__file__)
-
-class KERNELS:
- ACTIVATION = dirpath + "/kernels/activation.hpp"
- BATCHNORM = dirpath + "/kernels/batchnorm.hpp"
- CONV = dirpath + "/kernels/convolution.hpp"
- ADD = dirpath + "/kernels/elemwise.hpp"
- FC = dirpath + "/kernels//fullyconnected.hpp"
- POOLING = dirpath + "/kernels/pooling.hpp"
- LEAKYRELU = dirpath + "/kernels/leakyrelu.hpp"
-
-class KERNELS_FORWARD:
- ACTIVATION = dirpath + "/templates/kernel_forward/activation_forward.jinja"
- BATCHNORM = dirpath + "/templates/kernel_forward/batchnorm_forward.jinja"
- CONV = dirpath + "/templates/kernel_forward/convolution_forward.jinja"
- ADD = dirpath + "/templates/kernel_forward/elemwise_forward.jinja"
- FC = dirpath + "/templates/kernel_forward/fullyconnected_forward.jinja"
- POOLING = dirpath + "/templates/kernel_forward/pooling_forward.jinja"
- LEAKYRELU = dirpath + "/templates/kernel_forward/leakyrelu_forward.jinja"
-
-class CONFIGURATIONS:
- ACTIVATION = dirpath + "/templates/configuration/activation_config.jinja"
- BATCHNORM = dirpath + "/templates/configuration/batchnorm_config.jinja"
- CONV = dirpath + "/templates/configuration/convolution_config.jinja"
- ADD = dirpath + "/templates/configuration/elemwise_config.jinja"
- FC = dirpath + "/templates/configuration//fullyconnected_config.jinja"
- POOLING = dirpath + "/templates/configuration/pooling_config.jinja"
- LEAKYRELU = dirpath + "/templates/configuration/leakyrelu_config.jinja"
-
-##############################################
-############## Export functions ##############
-##############################################
-
-def generate_file(filename, templatename, **kwargs):
-
- # Get directory name of the file
- dirname = os.path.dirname(filename)
-
- # If directory doesn't exist, create it
- if not os.path.exists(dirname):
- os.makedirs(dirname)
-
- # Get directory name and name of the template
- template_dir = os.path.dirname(templatename)
- template_name = os.path.basename(templatename)
-
- # Select template
- template = Environment(loader=FileSystemLoader(template_dir)).get_template(template_name)
-
- # Generate file
- content = template.render(kwargs)
- with open(filename, mode="w", encoding="utf-8") as message:
- message.write(content)
-
-
-def generate_action(template_path, **kwargs):
- dirname = os.path.dirname(template_path)
- filename = os.path.basename(template_path)
- template = Environment(loader=FileSystemLoader(dirname)).get_template(filename)
- return template.render(kwargs)
-
-
-def copyfile(filename, dst_folder):
-
- # If directory doesn't exist, create it
- if not os.path.exists(dst_folder):
- os.makedirs(dst_folder)
-
- shutil.copy(filename, dst_folder)
-
-
-def export_to_static(name, array, filepath):
-
- # Get directory name of the file
- dirname = os.path.dirname(filepath)
-
- # If directory doesn't exist, create it
- if not os.path.exists(dirname):
- os.makedirs(dirname)
-
- generate_file(
- filepath,
- dirpath + "/templates/data/data_static.jinja",
- dims = array.shape,
- data_t = "float",
- name = name,
- values = array.tolist()
- )
-
-
-##############################################
-################### Utils ####################
-##############################################
-
-def get_node_parents(node):
- parents = []
- for parent in node.get_parents():
- if parent.type() != "Producer":
- parents.append(parent)
- return parents
-
-def get_producer_parents(node):
- parents = []
- for parent in node.get_parents():
- if parent.type() == "Producer":
- parents.append(parent)
- return parents
-
-
-##############################################
-################### Actions ##################
-##############################################
-
-def set_up_output(name, datatype):
- return f"{datatype}* {name} = ({datatype}*) mem + {name.upper()}_OFFSET;"
-
-
-##############################################
-############## Operators helper ##############
-##############################################
-
-@export_cpp_register("Conv")
-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")
-
- # Not working anymore because Padding is a standalone operator
- # self.padding = node.get_operator().get_attr("PaddingDims")
- self.padding = [1, 1]
- self.dilation = node.get_operator().get_attr("DilationDims")
- self.nb_channels = node.get_operator().get_attr("InChannels")
- self.nb_outputs = node.get_operator().get_attr("OutChannels")
-
- def export(self, export_folder:str, list_configs:list):
-
- copyfile(KERNELS.CONV, f"{export_folder}/include/kernels/")
- copyfile(dirpath + "/kernels/macs.hpp", f"{export_folder}/include/kernels/")
- copyfile(dirpath + "/kernels/activation.hpp", f"{export_folder}/include/kernels/")
-
- list_configs.append("kernels/convolution.hpp")
- list_configs.append(f"layers/{self.name}.h")
- generate_file(
- f"{export_folder}/layers/{self.name}.h",
- CONFIGURATIONS.CONV,
- name=self.name,
- input_dims=self.inputs_dims[0][1:],
- output_dims=self.outputs_dims[0][1:],
- kernel=self.kernel,
- stride=self.stride,
- padding=self.padding,
- dilation=self.dilation,
- 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_action(
- KERNELS_FORWARD.CONV,
- name=self.name,
- input_name=self.inputs[0].name(),
- output_name=self.name,
- weights_name=self.inputs[1].name(),
- biases_name=self.inputs[2].name()
- ))
- return list_actions
-
-
-class BatchNormCPP:
- def __init__(self, node):
- self.name = node.name()
- self.epsilon = node.get_operator().get_attr("Epsilon")
-
- self.producers = get_producer_parents(node)
-
- self.scales = np.array(self.producers[0].get_operator().get_output(0)).reshape(-1).tolist()
- self.biases = np.array(self.producers[1].get_operator().get_output(0)).reshape(-1).tolist()
- self.means = np.array(self.producers[2].get_operator().get_output(0)).reshape(-1).tolist()
- self.vars = np.array(self.producers[3].get_operator().get_output(0)).reshape(-1).tolist()
-
- parents = get_node_parents(node)
- if len(parents) == 0:
- self.input_name = "in"
- else :
- self.input_name = parents[0].name()
-
- def export(self, export_folder:str, list_configs:list):
- list_configs.append(f"layers/{self.name}.h")
- generate_file(
- f"{export_folder}/layers/{self.name}.h",
- "tensorrt/templates/configuration/batchnorm_config.jinja",
- name=self.name,
- input_dims=[0, 0, 0],
- output_dims=[0, 0, 0],
- activation="Linear",
- epsilon=self.epsilon)
-
- # export the batchnorm parameters
-
- return list_configs
-
- def forward(self, list_actions:list):
-
- list_actions.append(set_up_output(self.name, "float"))
- list_actions.append(generate_action(
- "cpp/templates/kernel_forward/batchnorm_forward.jinja",
- name=self.name,
- input_name=self.input_name,
- output_name=self.name,
- biases_name=self.producers[0].name(),
- variances_name=self.producers[1].name(),
- means_name=self.producers[2].name(),
- scales_name=self.producers[3].name()
- ))
- return list_actions
-
-@export_cpp_register("ReLU")
-class ReLUCPP(ExportNode):
- def __init__(self, node):
- super().__init__(node)
-
- self.nb_data = 1
- for i in self.inputs_dims[0]:
- self.nb_data *= i
-
- def export(self, export_folder:str, list_configs:list):
-
- copyfile(KERNELS.ACTIVATION, f"{export_folder}/include/kernels/")
-
- list_configs.append("kernels/activation.hpp")
- list_configs.append(f"layers/{self.name}.h")
- generate_file(
- f"{export_folder}/layers/{self.name}.h",
- CONFIGURATIONS.ACTIVATION,
- name=self.name,
- nb_data=self.nb_data,
- activation="Rectifier",
- 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_action(
- KERNELS_FORWARD.ACTIVATION,
- name=self.name,
- input_name=self.inputs[0].name(),
- output_name=self.name
- ))
- return list_actions
-
-
-@export_cpp_register("LeakyReLU")
-class LeakyReLUCPP(ExportNode):
- def __init__(self, node):
- super().__init__(node)
-
- self.alpha = node.get_operator().get_attr("NegativeSlope")
-
- self.nb_data = 1
- for i in self.inputs_dims[0]:
- self.nb_data *= i
-
- def export(self, export_folder:str, list_configs:list):
-
- copyfile(KERNELS.LEAKYRELU, f"{export_folder}/include/kernels/")
-
- list_configs.append("kernels/activation.hpp")
- list_configs.append(f"layers/{self.name}.h")
- generate_file(
- f"{export_folder}/layers/{self.name}.h",
- CONFIGURATIONS.LEAKYRELU,
- name=self.name,
- nb_data=self.nb_data,
- alpha = self.alpha)
-
- 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_action(
- KERNELS_FORWARD.LEAKYRELU,
- name=self.name,
- input_name=self.inputs[0].name(),
- output_name=self.name
- ))
- return list_actions
-
-
-class AddCPP:
- def __init__(self, node):
- self.name = node.name()
- self.parents = get_node_parents(node)
-
- def export(self, export_folder:str, list_configs:list):
- list_configs.append(f"layers/{self.name}.h")
- generate_file(
- f"{export_folder}/layers/{self.name}.h",
- CONFIGURATIONS.ADD,
- name=self.name,
- input_dims=[0, 0, 0],
- output_dims=[0, 0, 0],
- activation="Linear",
- elemwise_op="Sum")
-
- return list_configs
-
- def forward(self, list_actions:list):
-
- list_actions.append(set_up_output(self.name, "float"))
- list_actions.append(generate_action(
- "cpp/templates/kernel_forward/elemwise_forward.jinja",
- name=self.name,
- input1_name=self.parents[0].name(),
- input2_name=self.parents[1].name(),
- output_name=self.name
- ))
- return list_actions
-
-
-@export_cpp_register("MaxPooling")
-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")
-
- # Not supported by the core...
- # self.padding = node.get_operator().get_attr("PaddingDims")
- self.padding = [0, 0]
-
- def export(self, export_folder:str, list_configs:list):
-
- copyfile(KERNELS.POOLING, f"{export_folder}/include/kernels/")
-
- list_configs.append("kernels/pooling.hpp")
- list_configs.append(f"layers/{self.name}.h")
- generate_file(
- f"{export_folder}/layers/{self.name}.h",
- CONFIGURATIONS.POOLING,
- 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_action(
- KERNELS_FORWARD.POOLING,
- name=self.name,
- input_name=self.inputs[0].name(),
- output_name=self.name
- ))
- return list_actions
-
-
-class GlobalAvgPoolCPP:
- def __init__(self, node):
-
- # node.get_operator().set_compute_output_dims(lambda x: [[x[0][0], x[0][1], 1, 1]])
- pass
-
- def export(self, export_folder:str, list_configs:list):
-
- return list_configs
-
- def forward(self, list_actions:list):
-
- list_actions.append(set_up_output(self.name, "float"))
- list_actions.append(generate_action(
- "cpp/templates/kernel_forward/pooling_forward.jinja",
- name=self.name,
- input_name=self.input_name,
- output_name=self.name
- ))
- return list_actions
-
-@export_cpp_register("FC")
-class FcCPP(ExportNode):
- def __init__(self, node):
- super().__init__(node)
-
- if len(self.inputs_dims[0]) == 2:
- self.inputs_dims[0] = [self.inputs_dims[0][1], 1, 1]
- elif len(self.inputs_dims[0]) == 4:
- self.inputs_dims[0] = self.inputs_dims[0][1:]
-
- if len(self.outputs_dims[0]) == 2:
- self.outputs_dims[0] = [self.outputs_dims[0][1], 1, 1]
-
- def export(self, export_folder:str, list_configs:list):
-
- copyfile(KERNELS.FC, f"{export_folder}/include/kernels/")
- copyfile(dirpath + "/kernels/macs.hpp", f"{export_folder}/include/kernels/")
- copyfile(dirpath + "/kernels/activation.hpp", f"{export_folder}/include/kernels/")
-
- list_configs.append("kernels/fullyconnected.hpp")
- list_configs.append(f"layers/{self.name}.h")
- generate_file(
- f"{export_folder}/layers/{self.name}.h",
- CONFIGURATIONS.FC,
- 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_action(
- KERNELS_FORWARD.FC,
- name=self.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()
- ))
- return list_actions
-
-
-@export_cpp_register("Producer")
-class ProducerCPP(ExportNode):
- def __init__(self, node):
- super().__init__(node)
-
- self.values = np.array(self.operator.get_output(0))
-
- def export(self, export_folder:str, list_configs:list):
-
- list_configs.append(f"parameters/{self.name}.h")
- export_to_static(self.name,
- self.values.reshape(-1),
- f"{export_folder}/parameters/{self.name}.h")
-
- return list_configs
-
- def forward(self, list_actions:list):
- return list_actions