convert model from ONNX

[braincxx]

im trying convert model (cnn+lstm) from onnx to rknn for rk3588

My code:
shape = (1, 7, 3, 608, 184)
import numpy as np
img_means = (np.array((0.19007764876619865, 0.15170388157131237, 0.10659445665650864)) * 255).tolist()
img_stds = (np.array((0.2610784009469139, 0.25729316928935814, 0.25163823815039915)) * 255).tolist()

from rknn.api import RKNN
rknn = RKNN(verbose=True)
rknn.config(mean_values=img_means + [0, 0, 0, 0], std_values=img_stds + [0, 0, 0, 0], target_platform='rk3588')

#ret = rknn.load_pytorch(model=path2torch_converted_model, input_size_list=[list(shape)])
ret = rknn.load_onnx(model=path2onnx_model, input_size_list=[list(shape)])

my output:
I rknn-toolkit2 version: 2.2.0
I Loading : 100%|█████████████████████████████████████████████████| 26/26 [00:00<00:00, 1054.44it/s]
D base_optimize ...
D base_optimize done.
D
D fold_constant ...
/root/miniconda3/envs/rknn/lib/python3.10/site-packages/rknn/api/rknn.py:192: RuntimeWarning: divide by zero encountered in divide
return self.rknn_base.build(do_quantization=do_quantization, dataset=dataset, expand_batch_size=rknn_batch_size)
/root/miniconda3/envs/rknn/lib/python3.10/site-packages/rknn/api/rknn.py:192: RuntimeWarning: invalid value encountered in divide
return self.rknn_base.build(do_quantization=do_quantization, dataset=dataset, expand_batch_size=rknn_batch_size)
D fold_constant done.
D fold_constant remove nodes = ['/rnn/Expand_3', '/rnn/Concat_3', 'Unsqueeze_104', '/rnn/Gather_3', '/rnn/Shape_3', '/rnn/Expand_2', '/rnn/Concat_2', 'Unsqueeze_95', '/rnn/Gather_2', '/rnn/Shape_2', '/rnn/Expand_1', '/rnn/Concat_1', 'Unsqueeze_81', '/rnn/Gather_1', '/rnn/Shape_1', '/rnn/Expand', '/rnn/Concat', 'Unsqueeze_72', '/rnn/Gather', '/rnn/Shape']
D Fixed the shape information of some tensor!
D
D correct_ops ...
D correct_ops done.
D
D fuse_ops ...
D fuse_ops results:
D fuse_reshape_transpose: remove node = ['/rnn/Transpose']
D squeeze_to_4d_slice: remove node = [], add node = ['input_rs', '/Slice_output_0-rs']
D squeeze_to_4d_slice: remove node = [], add node = ['input_rs#1', '/Slice_1_output_0-rs']
D squeeze_to_4d_concat: remove node = [], add node = ['/Slice_output_0_rs', '/Slice_1_output_0_rs', '/Concat_output_0-rs']
D convert_squeeze_to_reshape: remove node = ['/rnn/Squeeze'], add node = ['/rnn/Squeeze_2rs']
D convert_squeeze_to_reshape: remove node = ['/rnn/Squeeze_1'], add node = ['/rnn/Squeeze_1_2rs']
D unsqueeze_to_4d_transpose: remove node = [], add node = ['/rnn/Squeeze_1_output_0_rs', '/rnn/Transpose_1_output_0-rs']
D convert_matmul_to_exmatmul: remove node = ['/linear/MatMul'], add node = ['/rnn/Transpose_1_output_0_tp', '/rnn/Transpose_1_output_0_tp_rs', '/linear/MatMul', '/linear/MatMul_output_0_mm_tp', '/linear/MatMul_output_0_mm_tp_rs']
D unsqueeze_to_4d_add: remove node = [], add node = ['/linear/MatMul_output_0_rs', 'output-rs']
D fuse_lstm_transpose_reshape: remove node = ['/rnn/Squeeze_2rs', '/rnn/LSTM'], add node = ['/rnn/LSTM']
D fuse_lstm_transpose_reshape: remove node = ['/rnn/Squeeze_1_2rs', '/rnn/LSTM_1'], add node = ['/rnn/LSTM_1']
D unsqueeze_to_4d_transpose: remove node = [], add node = ['/rnn/Transpose_1_output_0_rs', '/rnn/Transpose_1_output_0_tp-rs']
D input_align_4D_add: remove node = ['/linear/Add'], add node = ['/linear/Add']
D bypass_two_reshape: remove node = ['/Slice_output_0_rs', '/Slice_output_0-rs', '/Slice_1_output_0_rs', '/Slice_1_output_0-rs', '/Reshape', '/Concat_output_0-rs']
D fuse_reshape_transpose: remove node = ['/rnn/Transpose_1']
D fuse_two_reshape: remove node = ['/rnn/Transpose_1_output_0-rs']
D bypass_two_reshape: remove node = ['/rnn/Transpose_1_output_0_tp_rs', '/rnn/Transpose_1_output_0_tp-rs', '/linear/MatMul_output_0_rs', '/linear/MatMul_output_0_mm_tp_rs']
D fuse_two_reshape: remove node = ['/rnn/Squeeze_1_output_0_rs']
D swap_transpose_add: remove node = ['/linear/MatMul_output_0_mm_tp', '/linear/Add'], add node = ['/linear/Add', '/linear/MatMul_output_0_mm_tp']
D fuse_exmatmul_add: remove node = ['/linear/Add', '/linear/MatMul'], add node = ['/linear/MatMul']
D convert_exmatmul_to_conv: remove node = ['/linear/MatMul'], add node = ['/linear/MatMul']
D fold_constant ...
D fold_constant done.
D fuse_ops done.
D
D sparse_weight ...
D sparse_weight done.
D
I rknn building ...
I RKNN: [17:16:46.556] compress = 0, conv_eltwise_activation_fuse = 1, global_fuse = 1, multi-core-model-mode = 7, output_optimize = 1, layout_match = 1, enable_argb_group = 0, pipeline_fuse = 0, enable_flash_attention = 0
I RKNN: librknnc version: 2.2.0 (c195366594@2024-09-14T12:24:14)
D RKNN: [17:16:47.381] RKNN is invoked
W RKNN: [17:16:48.557] Model initializer tensor data is empty, name: empty_placeholder_0
D RKNN: [17:16:48.559] >>>>>> start: rknn::RKNNExtractCustomOpAttrs
D RKNN: [17:16:48.559] <<<<<<<< end: rknn::RKNNExtractCustomOpAttrs
D RKNN: [17:16:48.559] >>>>>> start: rknn::RKNNSetOpTargetPass
D RKNN: [17:16:48.559] <<<<<<<< end: rknn::RKNNSetOpTargetPass
D RKNN: [17:16:48.559] >>>>>> start: rknn::RKNNBindNorm
D RKNN: [17:16:48.559] <<<<<<<< end: rknn::RKNNBindNorm
D RKNN: [17:16:48.559] >>>>>> start: rknn::RKNNEliminateQATDataConvert
D RKNN: [17:16:48.559] <<<<<<<< end: rknn::RKNNEliminateQATDataConvert
D RKNN: [17:16:48.559] >>>>>> start: rknn::RKNNTileGroupConv
D RKNN: [17:16:48.559] <<<<<<<< end: rknn::RKNNTileGroupConv
D RKNN: [17:16:48.559] >>>>>> start: rknn::RKNNAddConvBias
D RKNN: [17:16:48.559] <<<<<<<< end: rknn::RKNNAddConvBias
D RKNN: [17:16:48.559] >>>>>> start: rknn::RKNNTileChannel
D RKNN: [17:16:48.559] <<<<<<<< end: rknn::RKNNTileChannel
D RKNN: [17:16:48.559] >>>>>> start: rknn::RKNNPerChannelPrep
D RKNN: [17:16:48.559] <<<<<<<< end: rknn::RKNNPerChannelPrep
D RKNN: [17:16:48.559] >>>>>> start: rknn::RKNNBnQuant
D RKNN: [17:16:48.559] <<<<<<<< end: rknn::RKNNBnQuant
D RKNN: [17:16:48.559] >>>>>> start: rknn::RKNNFuseOptimizerPass
D RKNN: [17:16:48.560] <<<<<<<< end: rknn::RKNNFuseOptimizerPass
D RKNN: [17:16:48.560] >>>>>> start: rknn::RKNNTurnAutoPad
D RKNN: [17:16:48.560] <<<<<<<< end: rknn::RKNNTurnAutoPad
D RKNN: [17:16:48.560] >>>>>> start: rknn::RKNNInitRNNConst
D RKNN: [17:16:48.562] <<<<<<<< end: rknn::RKNNInitRNNConst
D RKNN: [17:16:48.562] >>>>>> start: rknn::RKNNInitCastConst
D RKNN: [17:16:48.562] <<<<<<<< end: rknn::RKNNInitCastConst
D RKNN: [17:16:48.562] >>>>>> start: rknn::RKNNMultiSurfacePass
D RKNN: [17:16:48.562] <<<<<<<< end: rknn::RKNNMultiSurfacePass
D RKNN: [17:16:48.562] >>>>>> start: rknn::RKNNReplaceConstantTensorPass
D RKNN: [17:16:48.562] <<<<<<<< end: rknn::RKNNReplaceConstantTensorPass
D RKNN: [17:16:48.562] >>>>>> start: rknn::RKNNSubgraphManager
D RKNN: [17:16:48.562] <<<<<<<< end: rknn::RKNNSubgraphManager
D RKNN: [17:16:48.562] >>>>>> start: OpEmit
D RKNN: [17:16:48.564] <<<<<<<< end: OpEmit
D RKNN: [17:16:48.564] >>>>>> start: rknn::RKNNAddFirstConv
D RKNN: [17:16:48.564] <<<<<<<< end: rknn::RKNNAddFirstConv
D RKNN: [17:16:48.564] >>>>>> start: rknn::RKNNTilingPass
D RKNN: [17:16:48.570] <<<<<<<< end: rknn::RKNNTilingPass
D RKNN: [17:16:48.570] >>>>>> start: rknn::RKNNLayoutMatchPass
D RKNN: [17:16:48.570] <<<<<<<< end: rknn::RKNNLayoutMatchPass
D RKNN: [17:16:48.570] >>>>>> start: rknn::RKNNAddSecondaryNode
D RKNN: [17:16:48.570] <<<<<<<< end: rknn::RKNNAddSecondaryNode
D RKNN: [17:16:48.570] >>>>>> start: rknn::RKNNAllocateConvCachePass
D RKNN: [17:16:48.570] <<<<<<<< end: rknn::RKNNAllocateConvCachePass
D RKNN: [17:16:48.570] >>>>>> start: OpEmit
E RKNN: [17:16:52.762] buffer overflow!!!

[Eurekaer]

@braincxx Hi, I recently encountered the same issue as you. May I ask if your input is also 5-dimensional? Have you found a solution to this problem? When my input is [1,3,16,640,640], I ran into the following issue:
ValueError: Traceback (most recent call last):
File "rknn/api/rknn_log.py", line 309, in rknn.api.rknn_log.error_catch_decorator.error_catch_wrapper
File "rknn/api/rknn_base.py", line 1945, in rknn.api.rknn_base.RKNNBase.build
File "rknn/api/rknn_base.py", line 176, in rknn.api.rknn_base.RKNNBase._quantize
File "rknn/api/quantizer.py", line 1397, in rknn.api.quantizer.Quantizer.run
File "rknn/api/quantizer.py", line 899, in rknn.api.quantizer.Quantizer._get_layer_range
File "rknn/api/rknn_utils.py", line 274, in rknn.api.rknn_utils.get_input_img
File "rknn/api/rknn_log.py", line 95, in rknn.api.rknn_log.RKNNLog.e
ValueError: The height_width of r_shape [16, 640, 640] is invalid!

Have you encountered a similar issue? I'm looking forward to your response.