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training_linear_model.txt
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training_linear_model.txt
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ir_version: 3
producer_name: "pytorch"
producer_version: "0.4"
graph {
node {
input: "input"
input: "1"
output: "3"
op_type: "MyInferenceFunction"
}
name: "torch-jit-export"
initializer {
dims: 1
dims: 1
data_type: 1
name: "1"
raw_data: "\000T\365\273"
}
input {
name: "input"
type {
tensor_type {
elem_type: 1
shape {
dim {
dim_value: 5
}
dim {
dim_value: 1
}
}
}
}
}
input {
name: "1"
type {
tensor_type {
elem_type: 1
shape {
dim {
dim_value: 1
}
dim {
dim_value: 1
}
}
}
}
}
output {
name: "3"
type {
tensor_type {
elem_type: 1
shape {
dim {
dim_value: 5
}
dim {
dim_value: 1
}
}
}
}
}
}
opset_import {
version: 9
}
function {
name: "MyInferenceFunction"
input: "input"
input: "1"
output: "3"
node {
input: "1"
output: "2"
op_type: "Transpose"
attribute {
name: "perm"
ints: 1
ints: 0
type: INTS
}
doc_string: "/home/chinhuang/venv_py3/lib/python3.6/site-packages/torch/nn/functional.py(1354): linear\n/home/chinhuang/venv_py3/lib/python3.6/site-packages/torch/nn/modules/linear.py(67): forward\n/home/chinhuang/venv_py3/lib/python3.6/site-packages/torch/nn/modules/module.py(477): _slow_forward\n/home/chinhuang/venv_py3/lib/python3.6/site-packages/torch/nn/modules/module.py(487): __call__\n/home/chinhuang/venv_py3/lib/python3.6/site-packages/torch/nn/modules/container.py(92): forward\n/home/chinhuang/venv_py3/lib/python3.6/site-packages/torch/nn/modules/module.py(477): _slow_forward\n/home/chinhuang/venv_py3/lib/python3.6/site-packages/torch/nn/modules/module.py(487): __call__\n/home/chinhuang/venv_py3/lib/python3.6/site-packages/torch/jit/__init__.py(252): forward\n/home/chinhuang/venv_py3/lib/python3.6/site-packages/torch/nn/modules/module.py(489): __call__\n/home/chinhuang/venv_py3/lib/python3.6/site-packages/torch/jit/__init__.py(197): get_trace_graph\n/home/chinhuang/venv_py3/lib/python3.6/site-packages/torch/onnx/utils.py(192): _trace_and_get_graph_from_model\n/home/chinhuang/venv_py3/lib/python3.6/site-packages/torch/onnx/utils.py(224): _model_to_graph\n/home/chinhuang/venv_py3/lib/python3.6/site-packages/torch/onnx/utils.py(281): _export\n/home/chinhuang/venv_py3/lib/python3.6/site-packages/torch/onnx/utils.py(104): export\n/home/chinhuang/venv_py3/lib/python3.6/site-packages/torch/onnx/__init__.py(27): export\ntorch_adagrad_convert.py(183): show_pytorch\ntorch_adagrad_convert.py(217): <module>\n"
}
node {
input: "input"
input: "2"
output: "3"
op_type: "MatMul"
doc_string: "/home/chinhuang/venv_py3/lib/python3.6/site-packages/torch/nn/functional.py(1354): linear\n/home/chinhuang/venv_py3/lib/python3.6/site-packages/torch/nn/modules/linear.py(67): forward\n/home/chinhuang/venv_py3/lib/python3.6/site-packages/torch/nn/modules/module.py(477): _slow_forward\n/home/chinhuang/venv_py3/lib/python3.6/site-packages/torch/nn/modules/module.py(487): __call__\n/home/chinhuang/venv_py3/lib/python3.6/site-packages/torch/nn/modules/container.py(92): forward\n/home/chinhuang/venv_py3/lib/python3.6/site-packages/torch/nn/modules/module.py(477): _slow_forward\n/home/chinhuang/venv_py3/lib/python3.6/site-packages/torch/nn/modules/module.py(487): __call__\n/home/chinhuang/venv_py3/lib/python3.6/site-packages/torch/jit/__init__.py(252): forward\n/home/chinhuang/venv_py3/lib/python3.6/site-packages/torch/nn/modules/module.py(489): __call__\n/home/chinhuang/venv_py3/lib/python3.6/site-packages/torch/jit/__init__.py(197): get_trace_graph\n/home/chinhuang/venv_py3/lib/python3.6/site-packages/torch/onnx/utils.py(192): _trace_and_get_graph_from_model\n/home/chinhuang/venv_py3/lib/python3.6/site-packages/torch/onnx/utils.py(224): _model_to_graph\n/home/chinhuang/venv_py3/lib/python3.6/site-packages/torch/onnx/utils.py(281): _export\n/home/chinhuang/venv_py3/lib/python3.6/site-packages/torch/onnx/utils.py(104): export\n/home/chinhuang/venv_py3/lib/python3.6/site-packages/torch/onnx/__init__.py(27): export\ntorch_adagrad_convert.py(183): show_pytorch\ntorch_adagrad_convert.py(217): <module>\n"
}
}
training_info {
initializer {
data_type: 7
int64_data: 0
name: "T"
}
initializer {
data_type: 1
float_data: 0.20000000298023224
name: "R"
}
initializer {
dims: 1
dims: 1
data_type: 1
float_data: 1.0
name: "squared_grad_sum1"
}
input {
name: "input"
type {
tensor_type {
elem_type: 1
shape {
dim {
dim_value: 5
}
dim {
dim_value: 1
}
}
}
}
}
input {
name: "label"
type {
tensor_type {
elem_type: 1
shape {
dim {
dim_value: 5
}
dim {
dim_value: 1
}
}
}
}
}
output {
name: "loss_value"
type {
tensor_type {
elem_type: 1
shape {
}
}
}
}
output {
name: "new_1"
type {
tensor_type {
elem_type: 1
shape {
dim {
dim_value: 1
}
dim {
dim_value: 1
}
}
}
}
}
output {
name: "new_squared_grad_sum_1"
type {
tensor_type {
elem_type: 1
shape {
dim {
dim_value: 1
}
dim {
dim_value: 1
}
}
}
}
}
algorithm {
name: "MyTrainingFunction"
input: "input"
input: "label"
input: "1"
input: "T"
input: "R"
input: "squared_grad_sum1"
output: "new_1"
output: "new_squared_grad_sum_1"
node {
input: "input"
input: "1"
output: "3"
op_type: "MyInferenceFunction"
}
node {
input: "label"
input: "3"
output: "loss_value"
op_type: "MSE"
}
node {
input: "input"
input: "1"
output: "grad_1"
op_type: "Gradient"
attribute {
name: "xs"
strings: "1"
type: STRINGS
}
attribute {
name: "y"
s: "loss_value"
type: STRING
}
}
node {
input: "R"
input: "T"
input: "1"
input: "grad_1"
input: "squared_grad_sum1"
output: "new_1"
output: "new_squared_grad_sum_1"
name: "Optimizer"
op_type: "Adagrad"
attribute {
name: "decay_factor"
f: 0.0
type: FLOAT
}
attribute {
name: "epsilon"
f: 1.0000000116860974e-07
type: FLOAT
}
attribute {
name: "norm_coefficient"
f: 0.0
type: FLOAT
}
}
}
update_binding {
key: "new_1"
value: "1"
}
update_binding {
key: "new_squared_grad_sum_1"
value: "squared_grad_sum1"
}
}