I’d like to export a pretrained model to ONNX format so that I can run it from a browser with JavaScript. The model uses ReflectionPad and ConvTranspose. If I export with an opset version <=10 JS complains that ConvTranspose is not implemented and if I export with an opset version >= 11 JS complains that there are int64 values in my model which it can’t deal with; there aren’t, but ReflectionPad seems to create them.
The model definition is as follows:
import torch
import onnx
print("pytorch version :", torch.__version__)
print("onnx version :", onnx.__version__)
class MyModule(nn.Module):
def __init__(self):
super(MyModule, self).__init__()
self.reflectionpad = nn.ReflectionPad1d(3)
self.conv = nn.ConvTranspose1d(4, 4, kernel_size=3)
def forward(self, x):
x = self.reflectionpad(x)
return self.conv(x)
model_g = MyModule()
x = torch.randn(1, 4, 4)
The bug_example.js scipt:
async function runExample() {
// Create an ONNX inference session with default backend.
const session = new onnx.InferenceSession();
await session.loadModel("toy_model.onnx");
const x = new Float32Array(1 * 4 * 4).fill(1);
const tensorX = new onnx.Tensor(x, 'float32', [1, 4, 4]);
const outputMap = await session.run([tensorX]);
const outputData = outputMap.get('output');
// Check if result is expected.
console.log('ok');
}
The bug_example.html script:
<html>
<head>
<script src="https://cdn.jsdelivr.net/npm/onnxjs/dist/onnx.min.js"></script>
<script src="./bug_example.js"></script>
</head>
<body>
<div><input type="button" value="Run" onclick="runExample()"/></div>
</body>
</html>
Using opset=10
(opset=9 behaves the same)
torch.onnx.export(model_g, x, "toy_model.onnx", verbose = True, opset_version=10)
output:
pytorch version : 1.5.1
onnx version : 1.7.0
graph(%input : Float(1, 4, 4),
%conv.weight : Float(4, 4, 3),
%conv.bias : Float(4)):
%3 : Float(1, 4, 10) = onnx::Pad[mode="reflect", pads=[0, 0, 3, 0, 0, 3]](%input) # /home/bram/miniconda3/envs/whispp_env/lib/python3.7/site-packages/torch/nn/functional.py:3397:0
%4 : Float(1, 4, 12) = onnx::ConvTranspose[dilations=[1], group=1, kernel_shape=[3], pads=[0, 0], strides=[1]](%3, %conv.weight, %conv.bias) # /home/bram/miniconda3/envs/whispp_env/lib/python3.7/site-packages/torch/nn/modules/conv.py:647:0
return (%4)
Running JS in the browser gives me Uncaught (in promise) TypeError: cannot resolve operator 'ConvTranspose' with opsets: ai.onnx v10
Using opset=11
(opset=12 behaves the same)
torch.onnx.export(model_g, x, "toy_model.onnx", verbose = True, opset_version=11)
output:
pytorch version : 1.5.1
onnx version : 1.7.0
graph(%input : Float(1, 4, 4),
%conv.weight : Float(4, 4, 3),
%conv.bias : Float(4),
%27 : Long(),
%28 : Long(2)):
%3 : int[] = onnx::Constant[value= 3 3 [ CPULongType{2} ]]()
%4 : Tensor = onnx::Constant[value={0}]()
%5 : Tensor = onnx::Shape(%3)
%6 : Tensor = onnx::Gather[axis=0](%5, %4)
%10 : LongTensor = onnx::Sub(%27, %6)
%12 : Tensor = onnx::ConstantOfShape[value={0}](%10)
%13 : Tensor = onnx::Concat[axis=0](%28, %12)
%14 : Tensor = onnx::Constant[value=-1 2 [ CPULongType{2} ]]()
%15 : Tensor = onnx::Reshape(%13, %14)
%16 : Tensor = onnx::Constant[value={0}]()
%17 : Tensor = onnx::Constant[value={-1}]()
%18 : Tensor = onnx::Constant[value={-9223372036854775807}]()
%19 : Tensor = onnx::Constant[value={-1}]()
%20 : Tensor = onnx::Slice(%15, %17, %18, %16, %19)
%21 : Tensor = onnx::Transpose[perm=[1, 0]](%20)
%22 : Tensor = onnx::Constant[value={-1}]()
%23 : Tensor = onnx::Reshape(%21, %22)
%24 : Tensor = onnx::Cast[to=7](%23)
%25 : Float(1, 4, 10) = onnx::Pad[mode="reflect"](%input, %24) # /home/bram/miniconda3/envs/whispp_env/lib/python3.7/site-packages/torch/nn/functional.py:3397:0
%26 : Float(1, 4, 12) = onnx::ConvTranspose[dilations=[1], group=1, kernel_shape=[3], pads=[0, 0], strides=[1]](%25, %conv.weight, %conv.bias) # /home/bram/miniconda3/envs/whispp_env/lib/python3.7/site-packages/torch/nn/modules/conv.py:647:0
return (%26)
Running JS in the browser gives me Uncaught (in promise) TypeError: int64 is not supported
I guess the earlier versions didn’t have all functionalities yet, but the more recent version has a bug related to padding layers somehow? Note that using opset=10 without convTranspose works fine.
What is the advised way to deal with this?