opset_version建议设成10,默认不设的话可能会报错(ONNX export of Slice with dynamic inputs) 如果你在data loader里设置了collate func来进行dynamic padding的话(不同batch的文本长度可能不一样),一定要设置dynamic_axes,否则之后加载推断时会出错(因为它会要求你推断时输入的各个维度与你保存ONNX模型时的输入纬...
opset_version=10版本不够高时,可能会报错:RuntimeError: Unsupported: ONNX export of Slice with dynamic inputs. DynamicSlice is a deprecated experimental op. Please use statically allocated variables or export to a higher opset version. 另外,导出的是在 huggingface 的 bert_...
opset_version建议设成10,默认不设的话可能会报错(ONNX export of Slice with dynamic inputs) 如果你在data loader里设置了collate func来进行dynamic padding的话(不同batch的文本长度可能不一样),一定要设置dynamic_axes,否则之后加载推断时会出错(因为它会要求你推断时输入的各个维度与你保存ONNX模型时的输入纬...
dynamic_axes={"inputs": {0:"batch_size",2:"height",3:"width"},# 改成 "inputs",以匹配 input_names"pred_logits": {0:"batch_size"},# 改成 "pred_logits" 和 "pred_boxes""pred_boxes": {0:"batch_size"} } torch.onnx.export( model, torch.randn(1,3,800,1200).to(device),# ...
(scroll up for full backtrace): File "/home/justinchu/anaconda3/envs/onnx/lib/python3.11/site-packages/torch/_decomp/decompositions.py", line 746, in slice_forward if end_val < 0: For more information, run with TORCH_LOGS="dynamic" For extended logs when we create symbols, also add ...
(s): (op_type:Add, node name: n3): [TypeInferenceError] Input 0 expected to have type but instead is null (op_type:models_stylegan2_op_fused_act_FusedLeakyReLU_decoder_convs_slice_None__None__2___3_activate_1, node name: models_stylegan2_op_fused_act_FusedLeakyReLU_decoder_convs...
onnx.export(model, (dummy_input, loop_count), 'loop.onnx', verbose=True) With trace-based exporter, we get the result ONNX graph which unrolls the for loop: graph(%0 : Long(2, 3), %1 : Long()): %2 : Tensor = onnx::Constant[value={1}]() %3 : Tensor = onnx::Add(...
x,export_onnx_file,opset_version=10,do_constant_folding=True,# 是否执行常量折叠优化 input_names=["input"],# 输入名 output_names=["output"],# 输出名 dynamic_axes={"input":{0:"batch_size"},# 批处理变量"output":{0:"batch_size"}}) ...
torch.onnx.export(model, inputs, onnx_path, verbose=False, opset_version=12, input_names=['images'], output_names=['det_out', 'drive_area_seg', 'lane_line_seg']) print('convert', onnx_path, 'to onnx finish!!!') # Checks ...
%31 : Dynamic = onnx::Slice[axes=[0], ends=[1], starts=[0]](%30), scope: AlexNet %32 : Long() = onnx::Squeeze[axes=[0]](%31), scope: AlexNet %33 : Long() = onnx::Constant[value={9216}](), scope: AlexNet # --- omitted for brevity --- %output1 : Float(10, 10...