提交 54d35271 编写于 作者: W wjj19950828

support yolox model

上级 1c342e26
...@@ -4748,6 +4748,38 @@ def aten_silu(mapper, graph, node): ...@@ -4748,6 +4748,38 @@ def aten_silu(mapper, graph, node):
return current_inputs, current_outputs return current_inputs, current_outputs
def aten_silu_(mapper, graph, node):
""" 构造Silu激活的PaddleLayer。
TorchScript示例:
%result.3 : Tensor = aten::silu_(%input.5)
参数含义:
%result.3 (Tensor): 输出,Silu后的结果。
%input.5 (Tensor): 需要Silu的Tensor。
注意: inplace这个参数在paddle中未实现
"""
scope_name = mapper.normalize_scope_name(node)
op_name = name_generator("silu", mapper.nn_name2id)
output_name = mapper._get_outputs_name(node)[0]
layer_outputs = [op_name, output_name]
layer_inputs = {}
inputs_name, inputs_node = mapper._get_inputs_name(node)
# 获取当前节点输出的list
current_outputs = [output_name]
# 处理输入0,即%input.5
mapper._check_input(graph, inputs_node[0], inputs_name[0], current_outputs,
scope_name)
layer_inputs["x"] = inputs_name[0]
# 获取当前节点输入的list
current_inputs = list(layer_inputs.values())
graph.add_layer(
"paddle.nn.Silu",
inputs=layer_inputs,
outputs=layer_outputs,
scope_name=scope_name)
return current_inputs, current_outputs
def aten_sin(mapper, graph, node): def aten_sin(mapper, graph, node):
""" 构造数学计算sin的PaddleLayer。 """ 构造数学计算sin的PaddleLayer。
TorchScript示例: TorchScript示例:
...@@ -5732,9 +5764,9 @@ def aten_upsample_nearest2d(mapper, graph, node): ...@@ -5732,9 +5764,9 @@ def aten_upsample_nearest2d(mapper, graph, node):
if_layer.add_block(block) if_layer.add_block(block)
if_layer.inputs["input-0"] = inputs_name[1] if_layer.inputs["input-0"] = inputs_name[1]
if "size" in layer_attrs and layer_attrs["size"] is None: if "size" in layer_attrs and layer_attrs["size"] is None:
mapper._check_input(graph, inputs_node[3], inputs_name[3], mapper._check_input(graph, inputs_node[2], inputs_name[2],
current_outputs, scope_name) current_outputs, scope_name)
layer_inputs["scale_factor"] = inputs_name[3] layer_inputs["scale_factor"] = inputs_name[2]
layer_attrs["align_mode"] = 0 layer_attrs["align_mode"] = 0
layer_attrs["mode"] = string("nearest") layer_attrs["mode"] = string("nearest")
graph.add_layer( graph.add_layer(
......
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