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|
ir_version: 7
producer_name: "pytorch"
producer_version: "CURRENT_VERSION"
graph {
node {
output: "onnx::Pad_1"
name: "Constant_0"
op_type: "Constant"
attribute {
name: "value"
t {
dims: 8
data_type: 7
raw_data: "\000\000\000\000\000\000\000\000\000\000\000\000\000\000\000\000\000\000\000\000\000\000\000\000\000\000\000\000\000\000\000\000\000\000\000\000\000\000\000\000\000\000\000\000\000\000\000\000\000\000\000\000\000\000\000\000\000\000\000\000\000\000\000\000"
}
type: TENSOR
}
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node {
input: "onnx::Pad_0"
input: "onnx::Pad_1"
output: "onnx::AveragePool_2"
name: "Pad_1"
op_type: "Pad"
attribute {
name: "mode"
s: "constant"
type: STRING
}
}
node {
input: "onnx::AveragePool_2"
output: "3"
name: "AveragePool_2"
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attribute {
name: "ceil_mode"
i: 0
type: INT
}
attribute {
name: "kernel_shape"
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ints: 3
type: INTS
}
attribute {
name: "pads"
ints: 0
ints: 0
ints: 0
ints: 0
type: INTS
}
attribute {
name: "strides"
ints: 2
ints: 2
type: INTS
}
}
name: "torch_jit"
input {
name: "onnx::Pad_0"
type {
tensor_type {
elem_type: 1
shape {
dim {
dim_value: 20
}
dim {
dim_value: 16
}
dim {
dim_value: 50
}
dim {
dim_value: 32
}
}
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}
}
output {
name: "3"
type {
tensor_type {
elem_type: 1
shape {
dim {
dim_value: 20
}
dim {
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}
dim {
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dim {
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}
opset_import {
version: 14
}
|
