I am trying to export my LSTM Anomally-Detection Pytorch model to ONNX, but I’m experiencing errors. Please take a look at my code below.
Note: My data is shaped as [2685, 5, 6].
Here is where I define my model:
class Model(torch.nn.Module):
def __init__(self, input_dim, hidden_dim, layer_dim):
super(Model, self).__init__()
self.hidden_dim = hidden_dim
self.layer_dim = layer_dim
self.lstm = nn.LSTM(input_dim, hidden_dim, layer_dim, batch_first=True)
self.fc1 = torch.nn.Linear(hidden_dim, hidden_dim)
self.fc2 = torch.nn.Linear(hidden_dim, input_dim)
def forward(self, x):
h0 = torch.zeros(self.layer_dim, x.size(0), self.hidden_dim).requires_grad_()
c0 = torch.zeros(self.layer_dim, x.size(0), self.hidden_dim).requires_grad_()
out, (hn, cn) = self.lstm(x, (h0.detach(), c0.detach()))
out = self.fc1(out)
out = self.fc2(out)
return out
input_dim = 6
hidden_dim = 3
layer_dim = 2
model = Model(input_dim, hidden_dim, layer_dim)
I can train it and test with it fine. But the problem comes when exporting:
model.eval()
import torch.onnx
torch_out = torch.onnx.export(model,
torch.randn(2685, 5, 6),
"onnx_model.onnx",
export_params = True
)
But I have the following error:
LSTM(6, 3, num_layers=2, batch_first=True)
Linear(in_features=3, out_features=3, bias=True)
Linear(in_features=3, out_features=6, bias=True)
['input_1', 'l_lstm_LSTM', 'l_fc1_Linear', 'l_fc2_Linear', 'l_lstm_LSTM', 'l_fc1_Linear', 'l_fc2_Linear', 'l_lstm_LSTM', 'l_fc1_Linear', 'l_fc2_Linear', 'l_lstm_LSTM', 'l_fc1_Linear', 'l_fc2_Linear', 'l_lstm_LSTM', 'l_fc1_Linear', 'l_fc2_Linear', 'l_lstm_LSTM', 'l_fc1_Linear', 'l_fc2_Linear', 'l_lstm_LSTM', 'l_fc1_Linear', 'l_fc2_Linear', 'l_lstm_LSTM', 'l_fc1_Linear', 'l_fc2_Linear', 'l_lstm_LSTM', 'l_fc1_Linear', 'l_fc2_Linear', 'l_lstm_LSTM', 'l_fc1_Linear', 'l_fc2_Linear']
/home/ec2-user/anaconda3/envs/pytorch_p36/lib/python3.6/site-packages/torch/onnx/symbolic.py:173: UserWarning: ONNX export failed on RNN/GRU/LSTM because batch_first not supported
warnings.warn("ONNX export failed on " + op + " because " + msg + " not supported")
---------------------------------------------------------------------------
RuntimeError Traceback (most recent call last)
<ipython-input-264-28c6c55537ab> in <module>()
10 torch.randn(2685, 5, 6),
11 "onnx_model.onnx",
---> 12 export_params = True
13 )
~/anaconda3/envs/pytorch_p36/lib/python3.6/site-packages/torch/onnx/__init__.py in export(*args, **kwargs)
23 def export(*args, **kwargs):
24 from torch.onnx import utils
---> 25 return utils.export(*args, **kwargs)
26
27
~/anaconda3/envs/pytorch_p36/lib/python3.6/site-packages/torch/onnx/utils.py in export(model, args, f, export_params, verbose, training, input_names, output_names, aten, export_raw_ir, operator_export_type, opset_version, _retain_param_name, do_constant_folding, strip_doc_string)
129 operator_export_type=operator_export_type, opset_version=opset_version,
130 _retain_param_name=_retain_param_name, do_constant_folding=do_constant_folding,
--> 131 strip_doc_string=strip_doc_string)
132
133
~/anaconda3/envs/pytorch_p36/lib/python3.6/site-packages/torch/onnx/utils.py in _export(model, args, f, export_params, verbose, training, input_names, output_names, operator_export_type, export_type, example_outputs, propagate, opset_version, _retain_param_name, do_constant_folding, strip_doc_string)
367 if export_params:
368 proto, export_map = graph._export_onnx(params_dict, opset_version, defer_weight_export, operator_export_type,
--> 369 strip_doc_string)
370 else:
371 proto, export_map = graph._export_onnx({}, opset_version, False, operator_export_type, strip_doc_string)
RuntimeError: ONNX export failed: Couldn't export operator aten::lstm
Defined at:
/home/ec2-user/anaconda3/envs/pytorch_p36/lib/python3.6/site-packages/torch/nn/modules/rnn.py(522): forward_impl
/home/ec2-user/anaconda3/envs/pytorch_p36/lib/python3.6/site-packages/torch/nn/modules/rnn.py(539): forward_tensor
/home/ec2-user/anaconda3/envs/pytorch_p36/lib/python3.6/site-packages/torch/nn/modules/rnn.py(559): forward
/home/ec2-user/anaconda3/envs/pytorch_p36/lib/python3.6/site-packages/torch/nn/modules/module.py(481): _slow_forward
/home/ec2-user/anaconda3/envs/pytorch_p36/lib/python3.6/site-packages/torch/nn/modules/module.py(491): __call__
<ipython-input-255-468cef410a2c>(14): forward
/home/ec2-user/anaconda3/envs/pytorch_p36/lib/python3.6/site-packages/torch/nn/modules/module.py(481): _slow_forward
/home/ec2-user/anaconda3/envs/pytorch_p36/lib/python3.6/site-packages/torch/nn/modules/module.py(491): __call__
/home/ec2-user/anaconda3/envs/pytorch_p36/lib/python3.6/site-packages/torch/jit/__init__.py(294): forward
/home/ec2-user/anaconda3/envs/pytorch_p36/lib/python3.6/site-packages/torch/nn/modules/module.py(493): __call__
/home/ec2-user/anaconda3/envs/pytorch_p36/lib/python3.6/site-packages/torch/jit/__init__.py(231): get_trace_graph
/home/ec2-user/anaconda3/envs/pytorch_p36/lib/python3.6/site-packages/torch/onnx/utils.py(225): _trace_and_get_graph_from_model
/home/ec2-user/anaconda3/envs/pytorch_p36/lib/python3.6/site-packages/torch/onnx/utils.py(266): _model_to_graph
/home/ec2-user/anaconda3/envs/pytorch_p36/lib/python3.6/site-packages/torch/onnx/utils.py(363): _export
/home/ec2-user/anaconda3/envs/pytorch_p36/lib/python3.6/site-packages/torch/onnx/utils.py(131): export
/home/ec2-user/anaconda3/envs/pytorch_p36/lib/python3.6/site-packages/torch/onnx/__init__.py(25): export
<ipython-input-264-28c6c55537ab>(12): <module>
/home/ec2-user/anaconda3/envs/pytorch_p36/lib/python3.6/site-packages/IPython/core/interactiveshell.py(2963): run_code
/home/ec2-user/anaconda3/envs/pytorch_p36/lib/python3.6/site-packages/IPython/core/interactiveshell.py(2903): run_ast_nodes
/home/ec2-user/anaconda3/envs/pytorch_p36/lib/python3.6/site-packages/IPython/core/interactiveshell.py(2785): _run_cell
/home/ec2-user/anaconda3/envs/pytorch_p36/lib/python3.6/site-packages/IPython/core/interactiveshell.py(2662): run_cell
/home/ec2-user/anaconda3/envs/pytorch_p36/lib/python3.6/site-packages/ipykernel/zmqshell.py(537): run_cell
/home/ec2-user/anaconda3/envs/pytorch_p36/lib/python3.6/site-packages/ipykernel/ipkernel.py(208): do_execute
/home/ec2-user/anaconda3/envs/pytorch_p36/lib/python3.6/site-packages/ipykernel/kernelbase.py(399): execute_request
/home/ec2-user/anaconda3/envs/pytorch_p36/lib/python3.6/site-packages/ipykernel/kernelbase.py(233): dispatch_shell
/home/ec2-user/anaconda3/envs/pytorch_p36/lib/python3.6/site-packages/ipykernel/kernelbase.py(283): dispatcher
/home/ec2-user/anaconda3/envs/pytorch_p36/lib/python3.6/site-packages/tornado/stack_context.py(276): null_wrapper
/home/ec2-user/anaconda3/envs/pytorch_p36/lib/python3.6/site-packages/zmq/eventloop/zmqstream.py(432): _run_callback
/home/ec2-user/anaconda3/envs/pytorch_p36/lib/python3.6/site-packages/zmq/eventloop/zmqstream.py(480): _handle_recv
/home/ec2-user/anaconda3/envs/pytorch_p36/lib/python3.6/site-packages/zmq/eventloop/zmqstream.py(450): _handle_events
/home/ec2-user/anaconda3/envs/pytorch_p36/lib/python3.6/site-packages/tornado/stack_context.py(276): null_wrapper
/home/ec2-user/anaconda3/envs/pytorch_p36/lib/python3.6/site-packages/tornado/platform/asyncio.py(117): _handle_events
/home/ec2-user/anaconda3/envs/pytorch_p36/lib/python3.6/asyncio/events.py(145): _run
/home/ec2-user/anaconda3/envs/pytorch_p36/lib/python3.6/asyncio/base_events.py(1432): _run_once
/home/ec2-user/anaconda3/envs/pytorch_p36/lib/python3.6/asyncio/base_events.py(422): run_forever
/home/ec2-user/anaconda3/envs/pytorch_p36/lib/python3.6/site-packages/tornado/platform/asyncio.py(127): start
/home/ec2-user/anaconda3/envs/pytorch_p36/lib/python3.6/site-packages/ipykernel/kernelapp.py(486): start
/home/ec2-user/anaconda3/envs/pytorch_p36/lib/python3.6/site-packages/traitlets/config/application.py(658): launch_instance
/home/ec2-user/anaconda3/envs/pytorch_p36/lib/python3.6/site-packages/ipykernel/__main__.py(3): <module>
/home/ec2-user/anaconda3/envs/pytorch_p36/lib/python3.6/runpy.py(85): _run_code
/home/ec2-user/anaconda3/envs/pytorch_p36/lib/python3.6/runpy.py(193): _run_module_as_main
Graph we tried to export:
graph(%input.1 : Float(2685, 5, 6),
%lstm.weight_ih_l0 : Float(12, 6),
%lstm.weight_hh_l0 : Float(12, 3),
%lstm.bias_ih_l0 : Float(12),
%lstm.bias_hh_l0 : Float(12),
%lstm.weight_ih_l1 : Float(12, 3),
%lstm.weight_hh_l1 : Float(12, 3),
%lstm.bias_ih_l1 : Float(12),
%lstm.bias_hh_l1 : Float(12),
%fc1.weight : Float(3, 3),
%fc1.bias : Float(3),
%fc2.weight : Float(6, 3),
%fc2.bias : Float(6)):
%13 : Long() = onnx::Constant[value={0}](), scope: Model
%14 : Tensor = onnx::Shape(%input.1), scope: Model
%15 : Long() = onnx::Gather[axis=0](%14, %13), scope: Model
%16 : Long() = onnx::Constant[value={2}](), scope: Model
%17 : Long() = onnx::Constant[value={3}](), scope: Model
%18 : Tensor = onnx::Unsqueeze[axes=[0]](%16)
%19 : Tensor = onnx::Unsqueeze[axes=[0]](%15)
%20 : Tensor = onnx::Unsqueeze[axes=[0]](%17)
%21 : Tensor = onnx::Concat[axis=0](%18, %19, %20)
%22 : Float(2, 2685, 3) = onnx::ConstantOfShape[value={0}](%21), scope: Model
%23 : Long() = onnx::Constant[value={0}](), scope: Model
%24 : Tensor = onnx::Shape(%input.1), scope: Model
%25 : Long() = onnx::Gather[axis=0](%24, %23), scope: Model
%26 : Long() = onnx::Constant[value={2}](), scope: Model
%27 : Long() = onnx::Constant[value={3}](), scope: Model
%28 : Tensor = onnx::Unsqueeze[axes=[0]](%26)
%29 : Tensor = onnx::Unsqueeze[axes=[0]](%25)
%30 : Tensor = onnx::Unsqueeze[axes=[0]](%27)
%31 : Tensor = onnx::Concat[axis=0](%28, %29, %30)
%32 : Float(2, 2685, 3) = onnx::ConstantOfShape[value={0}](%31), scope: Model
%33 : Long() = onnx::Constant[value={1}](), scope: Model/LSTM[lstm]
%34 : Long() = onnx::Constant[value={2}](), scope: Model/LSTM[lstm]
%35 : Double() = onnx::Constant[value={0}](), scope: Model/LSTM[lstm]
%36 : Long() = onnx::Constant[value={0}](), scope: Model/LSTM[lstm]
%37 : Long() = onnx::Constant[value={0}](), scope: Model/LSTM[lstm]
%38 : Long() = onnx::Constant[value={1}](), scope: Model/LSTM[lstm]
%input.2 : Float(2685!, 5!, 3), %40 : Float(2, 2685, 3), %41 : Float(2, 2685, 3) = aten::lstm(%input.1, %22, %32, %lstm.weight_ih_l0, %lstm.weight_hh_l0, %lstm.bias_ih_l0, %lstm.bias_hh_l0, %lstm.weight_ih_l1, %lstm.weight_hh_l1, %lstm.bias_ih_l1, %lstm.bias_hh_l1, %33, %34, %35, %36, %37, %38), scope: Model/LSTM[lstm]
%42 : Float(3!, 3!) = onnx::Transpose[perm=[1, 0]](%fc1.weight), scope: Model/Linear[fc1]
%43 : Float(2685, 5, 3) = onnx::MatMul(%input.2, %42), scope: Model/Linear[fc1]
%44 : Float(2685, 5, 3) = onnx::Add(%43, %fc1.bias), scope: Model/Linear[fc1]
%45 : Float(3!, 6!) = onnx::Transpose[perm=[1, 0]](%fc2.weight), scope: Model/Linear[fc2]
%46 : Float(2685, 5, 6) = onnx::MatMul(%44, %45), scope: Model/Linear[fc2]
%47 : Float(2685, 5, 6) = onnx::Add(%46, %fc2.bias), scope: Model/Linear[fc2]
return (%47)
What does this mean? What should I do to export properly?