Eurekaer
commented
1 week ago @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.
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!!!