import torch
import torch. nn as nnclass LSTM ( nn. Module) : def __init__ ( self, input_size, output_size, out_channels, num_layers, device) : super ( LSTM, self) . __init__( ) self. device = deviceself. input_size = input_sizeself. hidden_size = input_sizeself. num_layers = num_layersself. output_size = output_sizeself. lstm = nn. LSTM( input_size= self. input_size, hidden_size= self. hidden_size, num_layers= self. num_layers, batch_first= True ) self. out_channels = out_channelsself. fc = nn. Linear( self. hidden_size, self. output_size) def forward ( self, x) : h0 = torch. zeros( self. num_layers, x. size( 0 ) , self. hidden_size) . to( self. device) c0 = torch. zeros( self. num_layers, x. size( 0 ) , self. hidden_size) . to( self. device) out, _ = self. lstm( x, ( h0, c0) ) if self. out_channels == 1 : out = out[ : , - 1 , : ] return outreturn outbatch_size = 20
input_size = 10
output_size = 10
num_layers = 2
out_channels = 1 model = LSTM( input_size, output_size, out_channels, num_layers, "cpu" )
model. eval ( ) input_names = [ "input" ]
output_names = [ "output" ] x = torch. randn( ( batch_size, input_size, output_size) )
print ( x. shape)
y = model( x)
print ( y. shape) torch. onnx. export( model, x, 'LSTM.onnx' , verbose= True , input_names= input_names, output_names= output_names, dynamic_axes= { 'input' : [ 0 ] , 'output' : [ 0 ] } ) import onnx
model = onnx. load( "LSTM.onnx" )
print ( "load model done." )
onnx. checker. check_model( model)
print ( onnx. helper. printable_graph( model. graph) )
print ( "check model done." ) torch. Size( [ 20, 10, 10] )
torch. Size( [ 20, 10] )
/ home/ubuntu/anaconda3/envs/py37/lib/python3. 7/site-packages/torch/onnx/utils. py:2041: UserWarning: No names were found for specified dynamic axes of provided input. Automatically generated names will be applied to each dynamic axes of input input"No names were found for specified dynamic axes of provided input."
/ home/ubuntu/anaconda3/envs/py37/lib/python3. 7/site-packages/torch/onnx/utils. py:2041: UserWarning: No names were found for specified dynamic axes of provided input. Automatically generated names will be applied to each dynamic axes of input output"No names were found for specified dynamic axes of provided input."
/ home/ubuntu/anaconda3/envs/py37/lib/python3. 7/site-packages/torch/onnx/symbolic_opset9. py:4322: UserWarning: Exporting a model to ONNX with a batch_size other than 1, with a variable length with LSTM can cause an error when running the ONNX model with a different batch size. Make sure to save the model with a batch size of 1, or define the initial states ( h0/c0) as inputs of the model. + "or define the initial states (h0/c0) as inputs of the model. "
/ home/ubuntu/anaconda3/envs/py37/lib/python3. 7/site-packages/torch/onnx/_internal/jit_utils. py:258: UserWarning: The shape inference of prim::Constant type is missing, so it may result in wrong shape inference for the exported graph. Please consider adding it in symbolic function . ( Triggered internally at . . / torch/csrc/jit/passes/onnx/shape_type_inference. cpp :1884. ) _C. _jit_pass_onnx_node_shape_type_inference( node, params_dict, opset_version)
/ home/ubuntu/anaconda3/envs/py37/lib/python3. 7/site-packages/torch/onnx/utils. py:688: UserWarning: The shape inference of prim::Constant type is missing, so it may result in wrong shape inference for the exported graph. Please consider adding it in symbolic function . ( Triggered internally at . . / torch/csrc/jit/passes/onnx/shape_type_inference. cpp :1884. ) graph, params_dict, GLOBALS. export_onnx_opset_version
/ home/ubuntu/anaconda3/envs/py37/lib/python3. 7/site-packages/torch/onnx/utils. py:1179: UserWarning: The shape inference of prim::Constant type is missing, so it may result in wrong shape inference for the exported graph. Please consider adding it in symbolic function . ( Triggered internally at . . / torch/csrc/jit/passes/onnx/shape_type_inference. cpp :1884. ) graph, params_dict, GLOBALS. export_onnx_opset_version
Exported graph: graph( % input : Float( * , 10, 10, strides=[ 100, 10, 1] , requires_grad=0, device=cpu) , % onnx::LSTM_193 : Float( 1, 40, 10, strides=[ 400, 10, 1] , requires_grad=0, device=cpu) , % onnx::LSTM_194 : Float( 1, 40, 10, strides=[ 400, 10, 1] , requires_grad=0, device=cpu) , % onnx::LSTM_195 : Float( 1, 80, strides=[ 80, 1] , requires_grad=0, device=cpu) , % onnx::LSTM_213 : Float( 1, 40, 10, strides=[ 400, 10, 1] , requires_grad=0, device=cpu) , % onnx::LSTM_214 : Float( 1, 40, 10, strides=[ 400, 10, 1] , requires_grad=0, device=cpu) , % onnx::LSTM_215 : Float( 1, 80, strides=[ 80, 1] , requires_grad=0, device=cpu) ) :% / Shape_output_0 : Long( 3, strides=[ 1] , device=cpu) = onnx::Shape[ onnx_name="/Shape" ] ( % input) , scope: __main__. LSTM:: % / Constant_output_0 : Long( device=cpu) = onnx::Constant[ value={ 0} , onnx_name="/Constant" ] ( ) , scope: __main__. LSTM:: % / Gather_output_0 : Long( device=cpu) = onnx::Gather[ axis=0, onnx_name="/Gather" ] ( % / Shape_output_0, % / Constant_output_0) , scope: __main__. LSTM:: % / Constant_1_output_0 : Long( 1, strides=[ 1] , requires_grad=0, device=cpu) = onnx::Constant[ value={ 2} , onnx_name="/Constant_1" ] ( ) , scope: __main__. LSTM::% onnx::Unsqueeze_18 : Long( 1, strides=[ 1] , device=cpu) = onnx::Constant[value={0}] ( ) % / Unsqueeze_output_0 : Long( 1, strides=[ 1] , device=cpu) = onnx::Unsqueeze[ onnx_name="/Unsqueeze" ] ( % / Gather_output_0, % onnx::Unsqueeze_18) , scope: __main__. LSTM::% / Constant_2_output_0 : Long( 1, strides=[ 1] , requires_grad=0, device=cpu) = onnx::Constant[ value={ 10} , onnx_name="/Constant_2" ] ( ) , scope: __main__. LSTM::% / Concat_output_0 : Long( 3, strides=[ 1] , device=cpu) = onnx::Concat[ axis=0, onnx_name="/Concat" ] ( % / Constant_1_output_0, % / Unsqueeze_output_0, % / Constant_2_output_0) , scope: __main__. LSTM:: % / ConstantOfShape_output_0 : Float( * , * , * , strides=[ 200, 10, 1] , requires_grad=0, device=cpu) = onnx::ConstantOfShape[ value={ 0} , onnx_name="/ConstantOfShape" ] ( % / Concat_output_0) , scope: __main__. LSTM:: % / Cast_output_0 : Float( * , * , * , strides=[ 200, 10, 1] , requires_grad=0, device=cpu) = onnx::Cast[ to=1, onnx_name="/Cast" ] ( % / ConstantOfShape_output_0) , scope: __main__. LSTM:: % / lstm/Transpose_output_0 : Float( 10, * , 10, device=cpu) = onnx::Transpose[ perm=[ 1, 0, 2] , onnx_name="/lstm/Transpose" ] ( % input) , scope: __main__. LSTM::/ torch. nn. modules. rnn. LSTM::lstm % onnx::LSTM_26 : Tensor? = prim::Constant( ) , scope: __main__. LSTM::/ torch. nn. modules. rnn. LSTM::lstm % / lstm/Constant_output_0 : Long( 1, strides=[ 1] , device=cpu) = onnx::Constant[ value={ 0} , onnx_name="/lstm/Constant" ] ( ) , scope: __main__. LSTM::/ torch. nn. modules. rnn. LSTM::lstm % / lstm/Constant_1_output_0 : Long( 1, strides=[ 1] , device=cpu) = onnx::Constant[ value={ 0} , onnx_name="/lstm/Constant_1" ] ( ) , scope: __main__. LSTM::/ torch. nn. modules. rnn. LSTM::lstm % / lstm/Constant_2_output_0 : Long( 1, strides=[ 1] , device=cpu) = onnx::Constant[ value={ 1} , onnx_name="/lstm/Constant_2" ] ( ) , scope: __main__. LSTM::/ torch. nn. modules. rnn. LSTM::lstm % / lstm/Slice_output_0 : Float( * , * , * , device=cpu) = onnx::Slice[ onnx_name="/lstm/Slice" ] ( % / Cast_output_0, % / lstm/Constant_1_output_0, % / lstm/Constant_2_output_0, % / lstm/Constant_output_0) , scope: __main__. LSTM::/ torch. nn. modules. rnn. LSTM::lstm % / lstm/Constant_3_output_0 : Long( 1, strides=[ 1] , device=cpu) = onnx::Constant[ value={ 0} , onnx_name="/lstm/Constant_3" ] ( ) , scope: __main__. LSTM::/ torch. nn. modules. rnn. LSTM::lstm % / lstm/Constant_4_output_0 : Long( 1, strides=[ 1] , device=cpu) = onnx::Constant[ value={ 0} , onnx_name="/lstm/Constant_4" ] ( ) , scope: __main__. LSTM::/ torch. nn. modules. rnn. LSTM::lstm % / lstm/Constant_5_output_0 : Long( 1, strides=[ 1] , device=cpu) = onnx::Constant[ value={ 1} , onnx_name="/lstm/Constant_5" ] ( ) , scope: __main__. LSTM::/ torch. nn. modules. rnn. LSTM::lstm % / lstm/Slice_1_output_0 : Float( * , * , * , device=cpu) = onnx::Slice[ onnx_name="/lstm/Slice_1" ] ( % / Cast_output_0, % / lstm/Constant_4_output_0, % / lstm/Constant_5_output_0, % / lstm/Constant_3_output_0) , scope: __main__. LSTM::/ torch. nn. modules. rnn. LSTM::lstm % / lstm/LSTM_output_0 : Float( 10, 1, * , 10, device=cpu) , % / lstm/LSTM_output_1 : Float( 1, * , 10, device=cpu) , % / lstm/LSTM_output_2 : Float( 1, * , 10, device=cpu) = onnx::LSTM[ hidden_size=10, onnx_name="/lstm/LSTM" ] ( % / lstm/Transpose_output_0, % onnx::LSTM_193, % onnx::LSTM_194, % onnx::LSTM_195, % onnx::LSTM_26, % / lstm/Slice_output_0, % / lstm/Slice_1_output_0) , scope: __main__. LSTM::/ torch. nn. modules. rnn. LSTM::lstm % / lstm/Constant_6_output_0 : Long( 1, strides=[ 1] , device=cpu) = onnx::Constant[ value={ 1} , onnx_name="/lstm/Constant_6" ] ( ) , scope: __main__. LSTM::/ torch. nn. modules. rnn. LSTM::lstm % / lstm/Squeeze_output_0 : Float( 10, * , 10, device=cpu) = onnx::Squeeze[ onnx_name="/lstm/Squeeze" ] ( % / lstm/LSTM_output_0, % / lstm/Constant_6_output_0) , scope: __main__. LSTM::/ torch. nn. modules. rnn. LSTM::lstm % / lstm/Constant_7_output_0 : Long( 1, strides=[ 1] , device=cpu) = onnx::Constant[ value={ 0} , onnx_name="/lstm/Constant_7" ] ( ) , scope: __main__. LSTM::/ torch. nn. modules. rnn. LSTM::lstm % / lstm/Constant_8_output_0 : Long( 1, strides=[ 1] , device=cpu) = onnx::Constant[ value={ 1} , onnx_name="/lstm/Constant_8" ] ( ) , scope: __main__. LSTM::/ torch. nn. modules. rnn. LSTM::lstm % / lstm/Constant_9_output_0 : Long( 1, strides=[ 1] , device=cpu) = onnx::Constant[ value={ 2} , onnx_name="/lstm/Constant_9" ] ( ) , scope: __main__. LSTM::/ torch. nn. modules. rnn. LSTM::lstm % / lstm/Slice_2_output_0 : Float( * , * , * , device=cpu) = onnx::Slice[ onnx_name="/lstm/Slice_2" ] ( % / Cast_output_0, % / lstm/Constant_8_output_0, % / lstm/Constant_9_output_0, % / lstm/Constant_7_output_0) , scope: __main__. LSTM::/ torch. nn. modules. rnn. LSTM::lstm % / lstm/Constant_10_output_0 : Long( 1, strides=[ 1] , device=cpu) = onnx::Constant[ value={ 0} , onnx_name="/lstm/Constant_10" ] ( ) , scope: __main__. LSTM::/ torch. nn. modules. rnn. LSTM::lstm % / lstm/Constant_11_output_0 : Long( 1, strides=[ 1] , device=cpu) = onnx::Constant[ value={ 1} , onnx_name="/lstm/Constant_11" ] ( ) , scope: __main__. LSTM::/ torch. nn. modules. rnn. LSTM::lstm % / lstm/Constant_12_output_0 : Long( 1, strides=[ 1] , device=cpu) = onnx::Constant[ value={ 2} , onnx_name="/lstm/Constant_12" ] ( ) , scope: __main__. LSTM::/ torch. nn. modules. rnn. LSTM::lstm % / lstm/Slice_3_output_0 : Float( * , * , * , device=cpu) = onnx::Slice[ onnx_name="/lstm/Slice_3" ] ( % / Cast_output_0, % / lstm/Constant_11_output_0, % / lstm/Constant_12_output_0, % / lstm/Constant_10_output_0) , scope: __main__. LSTM::/ torch. nn. modules. rnn. LSTM::lstm % / lstm/LSTM_1_output_0 : Float( 10, 1, * , 10, device=cpu) , % / lstm/LSTM_1_output_1 : Float( 1, * , 10, device=cpu) , % / lstm/LSTM_1_output_2 : Float( 1, * , 10, device=cpu) = onnx::LSTM[ hidden_size=10, onnx_name="/lstm/LSTM_1" ] ( % / lstm/Squeeze_output_0, % onnx::LSTM_213, % onnx::LSTM_214, % onnx::LSTM_215, % onnx::LSTM_26, % / lstm/Slice_2_output_0, % / lstm/Slice_3_output_0) , scope: __main__. LSTM::/ torch. nn. modules. rnn. LSTM::lstm % / lstm/Constant_13_output_0 : Long( 1, strides=[ 1] , device=cpu) = onnx::Constant[ value={ 1} , onnx_name="/lstm/Constant_13" ] ( ) , scope: __main__. LSTM::/ torch. nn. modules. rnn. LSTM::lstm % / lstm/Squeeze_1_output_0 : Float( 10, * , 10, device=cpu) = onnx::Squeeze[ onnx_name="/lstm/Squeeze_1" ] ( % / lstm/LSTM_1_output_0, % / lstm/Constant_13_output_0) , scope: __main__. LSTM::/ torch. nn. modules. rnn. LSTM::lstm % / lstm/Transpose_1_output_0 : Float( * , 10, 10, strides=[ 10, 200, 1] , requires_grad=1, device=cpu) = onnx::Transpose[ perm=[ 1, 0, 2] , onnx_name="/lstm/Transpose_1" ] ( % / lstm/Squeeze_1_output_0) , scope: __main__. LSTM::/ torch. nn. modules. rnn. LSTM::lstm % / Constant_3_output_0 : Long( device=cpu) = onnx::Constant[ value={ - 1} , onnx_name="/Constant_3" ] ( ) , scope: __main__. LSTM::% output : Float( * , 10, strides=[ 10, 1] , requires_grad=1, device=cpu) = onnx::Gather[ axis=1, onnx_name="/Gather_1" ] ( % / lstm/Transpose_1_output_0, % / Constant_3_output_0) , scope: __main__. LSTM:: return ( % output) load model done.
graph torch_jit ( % input[FLOAT, input_dynamic_axes_1x10x10]
) initializers ( % onnx::LSTM_193[FLOAT, 1x40x10] % onnx::LSTM_194[FLOAT, 1x40x10] % onnx::LSTM_195[FLOAT, 1x80] % onnx::LSTM_213[FLOAT, 1x40x10] % onnx::LSTM_214[FLOAT, 1x40x10] % onnx::LSTM_215[FLOAT, 1x80]
) { % / Shape_output_0 = Shape( % input) % / Constant_output_0 = Constant[value = <Scalar Tensor []>] ( ) % / Gather_output_0 = Gather[axis = 0] ( % / Shape_output_0, % / Constant_output_0) % / Constant_1_output_0 = Constant[value = <Tensor>] ( ) % onnx::Unsqueeze_18 = Constant[value = <Tensor>] ( ) % / Unsqueeze_output_0 = Unsqueeze( % / Gather_output_0, % onnx::Unsqueeze_18) % / Constant_2_output_0 = Constant[value = <Tensor>] ( ) % / Concat_output_0 = Concat[axis = 0] ( % / Constant_1_output_0, % / Unsqueeze_output_0, % / Constant_2_output_0) % / ConstantOfShape_output_0 = ConstantOfShape[value = <Tensor>] ( % / Concat_output_0) % / Cast_output_0 = Cast[to = 1] ( % / ConstantOfShape_output_0) % / lstm/Transpose_output_0 = Transpose[perm = [1, 0, 2]] ( % input) % / lstm/Constant_output_0 = Constant[value = <Tensor>] ( ) % / lstm/Constant_1_output_0 = Constant[value = <Tensor>] ( ) % / lstm/Constant_2_output_0 = Constant[value = <Tensor>] ( ) % / lstm/Slice_output_0 = Slice( % / Cast_output_0, % / lstm/Constant_1_output_0, % / lstm/Constant_2_output_0, % / lstm/Constant_output_0) % / lstm/Constant_3_output_0 = Constant[value = <Tensor>] ( ) % / lstm/Constant_4_output_0 = Constant[value = <Tensor>] ( ) % / lstm/Constant_5_output_0 = Constant[value = <Tensor>] ( ) % / lstm/Slice_1_output_0 = Slice( % / Cast_output_0, % / lstm/Constant_4_output_0, % / lstm/Constant_5_output_0, % / lstm/Constant_3_output_0) % / lstm/LSTM_output_0, % / lstm/LSTM_output_1, % / lstm/LSTM_output_2 = LSTM[hidden_size = 10] ( % / lstm/Transpose_output_0, % onnx::LSTM_193, % onnx::LSTM_194, % onnx::LSTM_195, % , % / lstm/Slice_output_0, % / lstm/Slice_1_output_0) % / lstm/Constant_6_output_0 = Constant[value = <Tensor>] ( ) % / lstm/Squeeze_output_0 = Squeeze( % / lstm/LSTM_output_0, % / lstm/Constant_6_output_0) % / lstm/Constant_7_output_0 = Constant[value = <Tensor>] ( ) % / lstm/Constant_8_output_0 = Constant[value = <Tensor>] ( ) % / lstm/Constant_9_output_0 = Constant[value = <Tensor>] ( ) % / lstm/Slice_2_output_0 = Slice( % / Cast_output_0, % / lstm/Constant_8_output_0, % / lstm/Constant_9_output_0, % / lstm/Constant_7_output_0) % / lstm/Constant_10_output_0 = Constant[value = <Tensor>] ( ) % / lstm/Constant_11_output_0 = Constant[value = <Tensor>] ( ) % / lstm/Constant_12_output_0 = Constant[value = <Tensor>] ( ) % / lstm/Slice_3_output_0 = Slice( % / Cast_output_0, % / lstm/Constant_11_output_0, % / lstm/Constant_12_output_0, % / lstm/Constant_10_output_0) % / lstm/LSTM_1_output_0, % / lstm/LSTM_1_output_1, % / lstm/LSTM_1_output_2 = LSTM[hidden_size = 10] ( % / lstm/Squeeze_output_0, % onnx::LSTM_213, % onnx::LSTM_214, % onnx::LSTM_215, % , % / lstm/Slice_2_output_0, % / lstm/Slice_3_output_0) % / lstm/Constant_13_output_0 = Constant[value = <Tensor>] ( ) % / lstm/Squeeze_1_output_0 = Squeeze( % / lstm/LSTM_1_output_0, % / lstm/Constant_13_output_0) % / lstm/Transpose_1_output_0 = Transpose[perm = [1, 0, 2]] ( % / lstm/Squeeze_1_output_0) % / Constant_3_output_0 = Constant[value = <Scalar Tensor []>] ( ) % output = Gather[axis = 1] ( % / lstm/Transpose_1_output_0, % / Constant_3_output_0) return % output
}
check model done.
vector< float > testOnnxLSTM ( std:: vector< std:: vector< std:: vector< float >> > & inputs)
{ Ort:: Env env ( ORT_LOGGING_LEVEL_WARNING, "Default" ) ; Ort:: SessionOptions session_options; session_options. SetIntraOpNumThreads ( 5 ) ; session_options. SetGraphOptimizationLevel ( GraphOptimizationLevel:: ORT_ENABLE_ALL) ; auto memory_info = Ort:: MemoryInfo :: CreateCpu ( OrtDeviceAllocator, OrtMemTypeCPU) ; # ifdef _WIN32 const wchar_t * model_path = L"C:\\Users\\xxx\\Desktop\\LSTM.onnx" ; # else const char * model_path = "C:\\Users\\xxx\\Desktop\\LSTM.onnx" ; # endif wprintf ( L"%s\n" , model_path) ; Ort:: Session session ( env, model_path, session_options) ; const char * input_names[ ] = { "input" } ; const char * output_names[ ] = { "output" } ; const int input_size = 10 ; const int output_size = 10 ; const int batch_size = 1 ; const int seq_len = 10 ; std:: array< float , batch_size* seq_len* input_size> input_matrix; std:: array< float , batch_size* output_size> output_matrix; std:: array< int64_t , 3 > input_shape{ batch_size, seq_len, input_size } ; std:: array< int64_t , 2 > output_shape{ batch_size, output_size } ; for ( int i = 0 ; i < batch_size; i++ ) for ( int j = 0 ; j < seq_len; j++ ) for ( int k = 0 ; k < input_size; k++ ) input_matrix[ i * seq_len * input_size + j * input_size + k] = inputs[ i] [ j] [ k] ; Ort:: Value input_tensor = Ort:: Value:: CreateTensor < float > ( memory_info, input_matrix. data ( ) , input_matrix. size ( ) , input_shape. data ( ) , input_shape. size ( ) ) ; try { Ort:: Value output_tensor = Ort:: Value:: CreateTensor < float > ( memory_info, output_matrix. data ( ) , output_matrix. size ( ) , output_shape. data ( ) , output_shape. size ( ) ) ; session. Run ( Ort:: RunOptions{ nullptr } , input_names, & input_tensor, 1 , output_names, & output_tensor, 1 ) ; } catch ( const std:: exception& e) { std:: cout << e. what ( ) << std:: endl; } std:: cout << "get data from LSTM onnx: \n" ; vector< float > ret; for ( int i = 0 ; i < output_size; i++ ) { ret. emplace_back ( output_matrix[ i] ) ; std:: cout << ret[ i] << "\t" ; } std:: cout << "\n" ; return ret;
}
std:: vector< std:: vector< std:: vector< float >> > data; for ( int i = 0 ; i < 1 ; i++ ) { std:: vector< std:: vector< float >> t1; for ( int j = 0 ; j < 10 ; j++ ) { std:: vector< float > t2; for ( int k = 0 ; k < 10 ; k++ ) { t2. push_back ( 1.0 * k * j / 20 ) ; } t1. push_back ( t2) ; } data. push_back ( t1) ; } for ( auto & i : data) { for ( auto & j : i) { for ( auto & k : j) { std:: cout << k << "\t" ; } std:: cout << "\n" ; } std:: cout << "\n" ; } auto ret = testOnnxLSTM ( data) ;
0 0 0 0 0 0 0 0 0 0
0 0.05 0.1 0.15 0.2 0.25 0.3 0.35 0.4 0.45
0 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9
0 0.15 0.3 0.45 0.6 0.75 0.9 1.05 1.2 1.35
0 0.2 0.4 0.6 0.8 1 1.2 1.4 1.6 1.8
0 0.25 0.5 0.75 1 1.25 1.5 1.75 2 2.25
0 0.3 0.6 0.9 1.2 1.5 1.8 2.1 2.4 2.7
0 0.35 0.7 1.05 1.4 1.75 2.1 2.45 2.8 3.15
0 0.4 0.8 1.2 1.6 2 2.4 2.8 3.2 3.6
0 0.45 0.9 1.35 1.8 2.25 2.7 3.15 3.6 4.05 C:\ Users\ xxx\ Desktop\ LSTM.onnx
get data from LSTM onnx:
0.000401703 0.00102207 0.0011015 -0.000503412 -0.000911839 -0.0011367 -0.000309185 0.000591398 -0.000362981 -4.81475e-05
