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Hello,
Thanks for the great work. I'm trying to convert this model into onnx, but have met a few issues.
The mv and dot operator used by PyTorch's spectral_norm was one of them. Following onnx/onnx#3006 (comment) I coverted them to matmul in my own implementation of spectral_norm and the issue went away.
Now it's complaining:
Traceback (most recent call last):
out = torch.onnx.export(model, input_dict["image"], "model.onnx", verbose=False, opset_version=11,
File "/usr/local/lib/python3.8/dist-packages/torch/onnx/__init__.py", line 271, in export
return utils.export(model, args, f, export_params, verbose, training,
File "/usr/local/lib/python3.8/dist-packages/torch/onnx/utils.py", line 88, in export
_export(model, args, f, export_params, verbose, training, input_names, output_names,
File "/usr/local/lib/python3.8/dist-packages/torch/onnx/utils.py", line 694, in _export
_model_to_graph(model, args, verbose, input_names,
File "/usr/local/lib/python3.8/dist-packages/torch/onnx/utils.py", line 463, in _model_to_graph
graph = _optimize_graph(graph, operator_export_type,
File "/usr/local/lib/python3.8/dist-packages/torch/onnx/utils.py", line 206, in _optimize_graph
graph = torch._C._jit_pass_onnx(graph, operator_export_type)
File "/usr/local/lib/python3.8/dist-packages/torch/onnx/__init__.py", line 309, in _run_symbolic_function
return utils._run_symbolic_function(*args, **kwargs)
File "/usr/local/lib/python3.8/dist-packages/torch/onnx/utils.py", line 997, in _run_symbolic_function
return symbolic_fn(g, *inputs, **attrs)
File "/usr/local/lib/python3.8/dist-packages/torch/onnx/symbolic_helper.py", line 148, in wrapper
return fn(g, *args, **kwargs)
File "/usr/local/lib/python3.8/dist-packages/torch/onnx/symbolic_opset9.py", line 1285, in batch_norm
if weight is None or sym_help._is_none(weight):
RuntimeError: Unsupported: ONNX export of batch_norm for unknown channel size.
The code to convert this is:
opt = EasyDict(aspect_ratio=1.0,
checkpoints_dir='Face_Enhancement/checkpoints',
contain_dontcare_label=False,
crop_size=256,
gpu_ids=[0],
init_type='xavier',
init_variance=0.02,
injection_layer='all',
isTrain=False,
label_nc=18,
load_size=256,
model='pix2pix',
name='Setting_9_epoch_100',
nef=16,
netG='spade',
ngf=64,
no_flip=True,
no_instance=True,
no_parsing_map=True,
norm_D='spectralinstance',
norm_E='spectralinstance',
# norm_G='spectralspadebatch3x3',
norm_G='spectralspadesyncbatch3x3',
num_upsampling_layers='normal',
output_nc=3,
preprocess_mode='resize',
semantic_nc=18,
use_vae=False,
which_epoch='latest',
z_dim=256)
model = Pix2PixModel(opt)
model.eval()
input_dict = {
"label": torch.zeros((1, 18, 256, 256)),
"image": torch.randn(1, 3, 256, 256),
"path": None,
}
# from torchsummary import summary
# summary(model, (3, 256, 256))
out = torch.onnx.export(model, input_dict, "model.onnx", verbose=False, opset_version=11,
input_names = ['input'],
output_names = ['output'])
I printed out the graph g from https://github.com/pytorch/pytorch/blob/e56d3b023818f54553f2dc5d30b6b7aaf6b6a325/torch/onnx/symbolic_opset9.py#L1337
...
%450 : Long(2, strides=[1], device=cpu) = onnx::Constant[value= 1 1 [ CPULongType{2} ]]()
%451 : Long(requires_grad=0, device=cpu) = onnx::Constant[value={0}]()
%452 : Long(2, strides=[1], device=cpu) = onnx::Constant[value= 0 0 [ CPULongType{2} ]]()
%453 : Long(requires_grad=0, device=cpu) = onnx::Constant[value={1}]()
%454 : Long(requires_grad=0, device=cpu) = onnx::Constant[value={0}]()
%455 : Long(requires_grad=0, device=cpu) = onnx::Constant[value={0}]()
%456 : Long(requires_grad=0, device=cpu) = onnx::Constant[value={1}]()
%457 : Long(requires_grad=0, device=cpu) = onnx::Constant[value={1}]()
%458 : Float(*, 1024, *, *, strides=[65536, 64, 8, 1], requires_grad=0, device=cuda:0) = onnx::Conv[dilations=[1, 1], group=1, kernel_shape=[3, 3], pads=[1, 1, 1, 1], strides=[1, 1]](%436, %447, %netG.head_0.conv_1.bias) # /usr/local/lib/python3.8/dist-packages/torch/nn/modules/conv.py:395:0
%459 : Long(requires_grad=0, device=cpu) = onnx::Constant[value={1}]()
%460 : Float(*, 1024, *, *, strides=[65536, 64, 8, 1], requires_grad=0, device=cuda:0) = onnx::Add(%266, %458) # /workdir/Face_Enhancement/models/networks/architecture.py:56:0
%461 : None = prim::Constant()
%462 : Float(2, strides=[1], device=cpu) = onnx::Constant[value= 2 2 [ CPUFloatType{2} ]]()
%463 : Float(2, strides=[1], device=cpu) = onnx::Constant[value= 1 1 [ CPUFloatType{2} ]]()
%464 : Float(2, strides=[1], device=cpu) = onnx::Constant[value= 2 2 [ CPUFloatType{2} ]]()
%465 : Float(4, strides=[1], device=cpu) = onnx::Concat[axis=0](%463, %464)
%466 : Float(0, strides=[1], device=cpu) = onnx::Constant[value=[ CPUFloatType{0} ]]()
%467 : Float(*, *, *, *, strides=[262144, 256, 16, 1], requires_grad=0, device=cuda:0) = onnx::Resize[coordinate_transformation_mode="asymmetric", cubic_coeff_a=-0.75, mode="nearest", nearest_mode="floor"](%460, %466, %465) # /usr/local/lib/python3.8/dist-packages/torch/nn/functional.py:3535:0
%468 : None = prim::Constant()
%469 : None = prim::Constant()
%470 : Long(requires_grad=0, device=cpu) = onnx::Constant[value={0}]()
%471 : Double(requires_grad=0, device=cpu) = onnx::Constant[value={0.1}]()
%472 : Double(requires_grad=0, device=cpu) = onnx::Constant[value={1e-05}]()
%473 : Long(requires_grad=0, device=cpu) = onnx::Constant[value={1}]()
return ()
ipdb> input
467 defined in (%467 : Float(*, *, *, *, strides=[262144, 256, 16, 1], requires_grad=0, device=cuda:0) = onnx::Resize[coordinate_transformation_mode="asymmetric", cubic_coeff_a=-0.75, mode="nearest", nearest_mode="floor"](%460, %466, %465) # /usr/local/lib/python3.8/dist-packages/torch/nn/functional.py:3535:0
)
ipdb> weight
468 defined in (%468 : None = prim::Constant()
)
ipdb> bias
469 defined in (%469 : None = prim::Constant()
)
Float(*, *, *, * stood out to me but I'm not sure how to interpret this.
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