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activations.py
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activations.py
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__all__ = ['OnnxErf', 'OnnxHardSigmoid', 'OnnxSoftmaxV1V11']
import numpy as np
import torch
from torch import nn
from onnx2torch.node_converters.registry import add_converter
from onnx2torch.onnx_graph import OnnxGraph
from onnx2torch.onnx_node import OnnxNode
from onnx2torch.utils.common import OnnxToTorchModule
from onnx2torch.utils.common import OperationConverterResult
from onnx2torch.utils.common import onnx_mapping_from_node
class OnnxErf(nn.Module, OnnxToTorchModule):
def forward(self, input_tensor: torch.Tensor) -> torch.Tensor:
return torch.erf(input_tensor)
class OnnxHardSigmoid(nn.Module, OnnxToTorchModule):
def __init__(self, alpha: float = 0.2, beta: float = 0.5):
super().__init__()
self.alpha = alpha
self.beta = beta
def forward(self, input_tensor: torch.Tensor) -> torch.Tensor:
return torch.clip(self.alpha * input_tensor + self.beta, min=0.0, max=1.0)
class OnnxSoftmaxV1V11(nn.Module, OnnxToTorchModule):
def __init__(self, axis: int = 1, is_log: bool = False):
super().__init__()
self.axis = axis
self.is_log = is_log
def forward(self, input_tensor: torch.Tensor) -> torch.Tensor:
shape = input_tensor.shape
result = torch.flatten(input_tensor, start_dim=self.axis)
result = torch.log_softmax(result, -1) if self.is_log else torch.softmax(result, -1)
return torch.reshape(result, shape)
@add_converter(operation_type='Erf', version=9)
@add_converter(operation_type='Erf', version=13)
def _(node: OnnxNode, graph: OnnxGraph) -> OperationConverterResult: # pylint: disable=unused-argument
return OperationConverterResult(
torch_module=OnnxErf(),
onnx_mapping=onnx_mapping_from_node(node=node),
)
@add_converter(operation_type='HardSigmoid', version=1)
@add_converter(operation_type='HardSigmoid', version=6)
def _(node: OnnxNode, graph: OnnxGraph) -> OperationConverterResult: # pylint: disable=unused-argument
alpha = node.attributes.get('alpha', 0.2)
beta = node.attributes.get('beta', 0.5)
return OperationConverterResult(
torch_module=OnnxHardSigmoid(alpha=alpha, beta=beta),
onnx_mapping=onnx_mapping_from_node(node=node),
)
@add_converter(operation_type='HardSwish', version=14)
def _(node: OnnxNode, graph: OnnxGraph) -> OperationConverterResult: # pylint: disable=unused-argument
return OperationConverterResult(
torch_module=nn.Hardswish(),
onnx_mapping=onnx_mapping_from_node(node=node),
)
@add_converter(operation_type='LeakyRelu', version=1)
@add_converter(operation_type='LeakyRelu', version=6)
def _(node: OnnxNode, graph: OnnxGraph) -> OperationConverterResult: # pylint: disable=unused-argument
alpha = node.attributes.get('alpha', 0.01)
return OperationConverterResult(
torch_module=nn.LeakyReLU(negative_slope=alpha),
onnx_mapping=onnx_mapping_from_node(node=node),
)
@add_converter(operation_type='LogSoftmax', version=13)
def _(node: OnnxNode, graph: OnnxGraph) -> OperationConverterResult: # pylint: disable=unused-argument
dim = node.attributes.get('axis', -1)
return OperationConverterResult(
torch_module=nn.LogSoftmax(dim=dim),
onnx_mapping=onnx_mapping_from_node(node=node),
)
@add_converter(operation_type='LogSoftmax', version=1)
@add_converter(operation_type='LogSoftmax', version=11)
def _(node: OnnxNode, graph: OnnxGraph) -> OperationConverterResult: # pylint: disable=unused-argument
axis = node.attributes.get('axis', 1)
return OperationConverterResult(
torch_module=OnnxSoftmaxV1V11(axis=axis, is_log=True),
onnx_mapping=onnx_mapping_from_node(node=node),
)
@add_converter(operation_type='Relu', version=6)
@add_converter(operation_type='Relu', version=13)
@add_converter(operation_type='Relu', version=14)
def _(node: OnnxNode, graph: OnnxGraph) -> OperationConverterResult: # pylint: disable=unused-argument
return OperationConverterResult(
torch_module=nn.ReLU(),
onnx_mapping=onnx_mapping_from_node(node=node),
)
@add_converter(operation_type='Elu', version=6)
def _(node: OnnxNode, graph: OnnxGraph) -> OperationConverterResult: # pylint: disable=unused-argument
alpha = node.attributes.get('alpha', 1.0)
return OperationConverterResult(
torch_module=nn.ELU(alpha=alpha),
onnx_mapping=onnx_mapping_from_node(node=node),
)
@add_converter(operation_type='Celu', version=12)
def _(node: OnnxNode, graph: OnnxGraph) -> OperationConverterResult: # pylint: disable=unused-argument
alpha = node.attributes.get('alpha', 1.0)
return OperationConverterResult(
torch_module=nn.CELU(alpha=alpha),
onnx_mapping=onnx_mapping_from_node(node=node),
)
@add_converter(operation_type='Selu', version=6)
def _(node: OnnxNode, graph: OnnxGraph) -> OperationConverterResult: # pylint: disable=unused-argument
default_alpha = 1.67326319217681884765625
default_gamma = 1.05070102214813232421875
alpha = node.attributes.get('alpha', default_alpha)
gamma = node.attributes.get('gamma', default_gamma)
if not np.isclose(alpha, default_alpha):
raise ValueError(f'alpha parameter must be {default_alpha}, not {alpha}')
if not np.isclose(gamma, default_gamma):
raise ValueError(f'gamma parameter must be {default_gamma}, not {gamma}')
return OperationConverterResult(
torch_module=nn.SELU(),
onnx_mapping=onnx_mapping_from_node(node=node),
)
@add_converter(operation_type='Sigmoid', version=1)
@add_converter(operation_type='Sigmoid', version=6)
@add_converter(operation_type='Sigmoid', version=13)
def _(node: OnnxNode, graph: OnnxGraph) -> OperationConverterResult: # pylint: disable=unused-argument
return OperationConverterResult(
torch_module=nn.Sigmoid(),
onnx_mapping=onnx_mapping_from_node(node=node),
)
@add_converter(operation_type='Softmax', version=1)
@add_converter(operation_type='Softmax', version=11)
def _(node: OnnxNode, graph: OnnxGraph) -> OperationConverterResult: # pylint: disable=unused-argument
axis = node.attributes.get('axis', 1)
return OperationConverterResult(
torch_module=OnnxSoftmaxV1V11(axis=axis),
onnx_mapping=onnx_mapping_from_node(node=node),
)
@add_converter(operation_type='Softmax', version=13)
def _(node: OnnxNode, graph: OnnxGraph) -> OperationConverterResult: # pylint: disable=unused-argument
dim = node.attributes.get('axis', -1)
return OperationConverterResult(
torch_module=torch.nn.Softmax(dim=dim),
onnx_mapping=onnx_mapping_from_node(node=node),
)
@add_converter(operation_type='Softsign', version=1)
def _(node: OnnxNode, graph: OnnxGraph) -> OperationConverterResult: # pylint: disable=unused-argument
return OperationConverterResult(
torch_module=torch.nn.Softsign(),
onnx_mapping=onnx_mapping_from_node(node=node),
)
@add_converter(operation_type='Softplus', version=1)
def _(node: OnnxNode, graph: OnnxGraph) -> OperationConverterResult: # pylint: disable=unused-argument
beta = node.attributes.get('beta', 1.0)
threshold = node.attributes.get('threshold', 20.0)
return OperationConverterResult(
torch_module=torch.nn.Softplus(beta=beta, threshold=threshold),
onnx_mapping=onnx_mapping_from_node(node=node),
)