Comments (3)
@juandavid212 No, I had found the https://github.com/fateshelled/unimatch_onnx, It was okay for me as PoV design
from unimatch.
Added more context
torch.onnx.export(model_without_ddp, # model being run
args=(right, left, 'self_swin2d_cross_swin1d',
[2, 8], [-1, 4],
[-1, 1], 3,
False, 'stereo', None, None,
1. / 0.5, 1. / 10, 64,
False, False), # model input (or a tuple for multiple inputs)
f="super_resolution.onnx", # where to save the model (can be a file or file-like object)
verbose=True,
export_params=True, # store the trained parameter weights inside the model file
opset_version=16, # the ONNX version to export the model to
do_constant_folding=True, # whether to execute constant folding for optimization
input_names = ['img0', 'img1', 'attn_type',
'attn_splits_list', 'corr_radius_list',
'prop_radius_list', 'num_reg_refine',
'pred_bidir_flow' , 'task', 'intrinsics', 'pose',
'min_depth', 'max_depth', 'num_depth_candidates',
'depth_from_argmax', 'pred_bidir_depth'], # the model's input names
output_names = ['flow_preds'])
RuntimeError Traceback (most recent call last)
Cell In[21], line 1
----> 1 torch.onnx.export(model_without_ddp, # model being run
2 args=(right, left, 'self_swin2d_cross_swin1d',
3 [2, 8], [-1, 4],
4 [-1, 1], 3,
5 False, 'stereo', None, None,
6 1. / 0.5, 1. / 10, 64,
7 False, False), # model input (or a tuple for multiple inputs)
8 f="super_resolution.onnx", # where to save the model (can be a file or file-like object)
9 verbose=True,
10 export_params=True, # store the trained parameter weights inside the model file
11 opset_version=16, # the ONNX version to export the model to
12 do_constant_folding=True, # whether to execute constant folding for optimization
13 input_names = ['img0', 'img1', 'attn_type',
14 'attn_splits_list', 'corr_radius_list',
15 'prop_radius_list', 'num_reg_refine',
16 'pred_bidir_flow' , 'task', 'intrinsics', 'pose',
17 'min_depth', 'max_depth', 'num_depth_candidates',
18 'depth_from_argmax', 'pred_bidir_depth'], # the model's input names
19 output_names = ['flow_preds'])
File c:\ProgramData\Anaconda3\lib\site-packages\torch\onnx\utils.py:506, in export(model, args, f, export_params, verbose, training, input_names, output_names, operator_export_type, opset_version, do_constant_folding, dynamic_axes, keep_initializers_as_inputs, custom_opsets, export_modules_as_functions)
188 @_beartype.beartype
189 def export(
190 model: Union[torch.nn.Module, torch.jit.ScriptModule, torch.jit.ScriptFunction],
(...)
206 export_modules_as_functions: Union[bool, Collection[Type[torch.nn.Module]]] = False,
207 ) -> None:
208 r"""Exports a model into ONNX format.
209
210 If model
is not a :class:torch.jit.ScriptModule
nor a
(...)
503 All errors are subclasses of :class:errors.OnnxExporterError
.
504 """
--> 506 _export(
507 model,
508 args,
509 f,
510 export_params,
511 verbose,
512 training,
513 input_names,
514 output_names,
515 operator_export_type=operator_export_type,
516 opset_version=opset_version,
517 do_constant_folding=do_constant_folding,
518 dynamic_axes=dynamic_axes,
519 keep_initializers_as_inputs=keep_initializers_as_inputs,
520 custom_opsets=custom_opsets,
521 export_modules_as_functions=export_modules_as_functions,
522 )
File c:\ProgramData\Anaconda3\lib\site-packages\torch\onnx\utils.py:1548, in _export(model, args, f, export_params, verbose, training, input_names, output_names, operator_export_type, export_type, opset_version, do_constant_folding, dynamic_axes, keep_initializers_as_inputs, fixed_batch_size, custom_opsets, add_node_names, onnx_shape_inference, export_modules_as_functions)
1545 dynamic_axes = {}
1546 _validate_dynamic_axes(dynamic_axes, model, input_names, output_names)
-> 1548 graph, params_dict, torch_out = _model_to_graph(
1549 model,
1550 args,
1551 verbose,
1552 input_names,
1553 output_names,
1554 operator_export_type,
1555 val_do_constant_folding,
1556 fixed_batch_size=fixed_batch_size,
1557 training=training,
1558 dynamic_axes=dynamic_axes,
1559 )
1561 # TODO: Don't allocate a in-memory string for the protobuf
1562 defer_weight_export = (
1563 export_type is not _exporter_states.ExportTypes.PROTOBUF_FILE
1564 )
File c:\ProgramData\Anaconda3\lib\site-packages\torch\onnx\utils.py:1160, in _model_to_graph(model, args, verbose, input_names, output_names, operator_export_type, do_constant_folding, _disable_torch_constant_prop, fixed_batch_size, training, dynamic_axes)
1156 # assign_output_shape pass is not compatible with quantized outputs.
1157 # Quantized outputs are flattened to 3 values in ONNX, while packed as
1158 # single value in PyTorch.
1159 if not any(getattr(out, "is_quantized", False) for out in output_tensors):
-> 1160 _C._jit_pass_onnx_assign_output_shape(
1161 graph,
1162 output_tensors,
1163 out_desc,
1164 GLOBALS.onnx_shape_inference,
1165 is_script,
1166 GLOBALS.export_onnx_opset_version,
1167 )
1169 _set_input_and_output_names(graph, input_names, output_names)
1170 params_dict = _get_named_param_dict(graph, params)
RuntimeError: Expected a sequence type, but received a non-iterable type in graph output index 0
from unimatch.
Do you have any updates about this?
from unimatch.
Related Issues (20)
- FEATURE REQUEST: Support 'depth' task using two images with different Intrinsics. HOT 5
- About weights HOT 2
- is this method suitable for panoramas which is about up and down?? HOT 1
- nice work! and plan to live camera gui? HOT 2
- Resetting hidden state during refinement HOT 2
- Running in Windows? HOT 1
- Why the value of optical flow is opposite? HOT 1
- gmstereo_scale1 vs gmstereo_scale2 Inference speed HOT 1
- Decoding RGB Optical Flow for Motion Information Extraction in Video Action Recognition HOT 3
- What is the best resolution for inferencing optical flow? HOT 4
- The optical flow between two identical images looks like random noise. Is this normal? HOT 2
- Request for new weights HOT 1
- gmstereo_scale1_train.sh HOT 1
- The problem of optical flow prediction HOT 1
- unimatch.py line 257 HOT 1
- [GMflow] What do the ’train_dataset ‘ equation coefficients mean when stage=sintel? HOT 1
- Question about model and model_without_ddp HOT 2
- loss curve HOT 2
- Question on upsampling during training. HOT 4
- tartan air dataset HOT 5
Recommend Projects
-
React
A declarative, efficient, and flexible JavaScript library for building user interfaces.
-
Vue.js
🖖 Vue.js is a progressive, incrementally-adoptable JavaScript framework for building UI on the web.
-
Typescript
TypeScript is a superset of JavaScript that compiles to clean JavaScript output.
-
TensorFlow
An Open Source Machine Learning Framework for Everyone
-
Django
The Web framework for perfectionists with deadlines.
-
Laravel
A PHP framework for web artisans
-
D3
Bring data to life with SVG, Canvas and HTML. 📊📈🎉
-
Recommend Topics
-
javascript
JavaScript (JS) is a lightweight interpreted programming language with first-class functions.
-
web
Some thing interesting about web. New door for the world.
-
server
A server is a program made to process requests and deliver data to clients.
-
Machine learning
Machine learning is a way of modeling and interpreting data that allows a piece of software to respond intelligently.
-
Visualization
Some thing interesting about visualization, use data art
-
Game
Some thing interesting about game, make everyone happy.
Recommend Org
-
Facebook
We are working to build community through open source technology. NB: members must have two-factor auth.
-
Microsoft
Open source projects and samples from Microsoft.
-
Google
Google ❤️ Open Source for everyone.
-
Alibaba
Alibaba Open Source for everyone
-
D3
Data-Driven Documents codes.
-
Tencent
China tencent open source team.
from unimatch.