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Yolo v8 compile error #163

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St-Viacheslavik opened this issue Sep 27, 2024 · 7 comments
Open

Yolo v8 compile error #163

St-Viacheslavik opened this issue Sep 27, 2024 · 7 comments

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@St-Viacheslavik
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I'm trying to compile the model yolov8 small for RK3588
model_on_google_disk

rknn-toolkit2 2.2.0

Compile code simple:

rknn = RKNN(verbose=True)
rknn.config(mean_values=[[0, 0, 0]], std_values=[[255, 255, 255]], target_platform='rk3588')
ret = rknn.load_onnx(model=model_name)
ret = rknn.build(do_quantization=QUANTIZE_ON, dataset=DATASET)
ret = rknn.export_rknn(model_name.replace("onnx", "rknn"))

The detailed log output shows some errors.

E RKNN: [15:38:15.133] REGTASK: The bit width of field value exceeds the limit, target: v2, offset: 0x500c, shift = 0, limit: 0x1fff, value: 0x20cf
E RKNN: [15:38:15.133] REGTASK: The bit width of field value exceeds the limit, target: v2, offset: 0x4038, shift = 0, limit: 0x1fff, value: 0x419f
E RKNN: [15:38:15.133] REGTASK: The bit width of field value exceeds the limit, target: v2, offset: 0x4038, shift = 16, limit: 0x1fff, value: 0x419f
W RKNN: [15:38:15.133] Transpose will fallback to CPU, because input shape has exceeded the max limit, height(4) * width(8400) = 33600, required product no larger than 16384!
E RKNN: [15:38:15.133] REGTASK: The bit width of field value exceeds the limit, target: v2, offset: 0x500c, shift = 0, limit: 0x1fff, value: 0x20cf
D RKNN: [15:38:15.133] >>>>>> start: rknn::RKNNTilingPass
W RKNN: [15:38:15.134] Failed to config layer: 'Conv:/model.22/dfl/conv/Conv' using 3Core fallback to single core mode,
W RKNN: [15:38:15.134] core_num 3 ori_Ih 4 ori_Iw 8400 ori_Ic 16 ori_Ib 1
W RKNN: [15:38:15.134] ori_Kh 1 ori_Kw 1 ori_Kk 1 ori_Kc 16 ori_Ksx 1 ori_Ksy 1
W RKNN: [15:38:15.134] ori_Oh 4 oriOw 8400 oriOc 1 pad_t 0 pad_b 0 pad_l 0 pad_r 0,
W RKNN: [15:38:15.134] Please help report this bug!
W RKNN: [15:38:15.134] Failed to config layer: 'Conv:/model.22/dfl/conv/Conv' using 3Core fallback to single core mode,
W RKNN: [15:38:15.134] core_num 3 ori_Ih 4 ori_Iw 8400 ori_Ic 16 ori_Ib 1
W RKNN: [15:38:15.134] ori_Kh 1 ori_Kw 1 ori_Kk 1 ori_Kc 16 ori_Ksx 1 ori_Ksy 1
W RKNN: [15:38:15.134] ori_Oh 4 oriOw 8400 oriOc 1 pad_t 0 pad_b 0 pad_l 0 pad_r 0,
W RKNN: [15:38:15.134] Please help report this bug!
W RKNN: [15:38:15.134] Failed to config layer: 'Conv:/model.22/dfl/conv/Conv' using 3Core fallback to single core mode,
W RKNN: [15:38:15.134] core_num 3 ori_Ih 4 ori_Iw 8400 ori_Ic 16 ori_Ib 1
W RKNN: [15:38:15.134] ori_Kh 1 ori_Kw 1 ori_Kk 1 ori_Kc 16 ori_Ksx 1 ori_Ksy 1
W RKNN: [15:38:15.134] ori_Oh 4 oriOw 8400 oriOc 1 pad_t 0 pad_b 0 pad_l 0 pad_r 0,
W RKNN: [15:38:15.134] Please help report this bug!
W RKNN: [15:38:15.134] Failed to config layer: 'Conv:/model.22/dfl/conv/Conv' using 3Core fallback to single core mode,
W RKNN: [15:38:15.134] core_num 3 ori_Ih 4 ori_Iw 8400 ori_Ic 16 ori_Ib 1
W RKNN: [15:38:15.134] ori_Kh 1 ori_Kw 1 ori_Kk 1 ori_Kc 16 ori_Ksx 1 ori_Ksy 1
W RKNN: [15:38:15.134] ori_Oh 4 oriOw 8400 oriOc 1 pad_t 0 pad_b 0 pad_l 0 pad_r 0,
W RKNN: [15:38:15.134] Please help report this bug!
W RKNN: [15:38:15.134] Failed to config layer: 'Conv:/model.22/dfl/conv/Conv' using 3Core fallback to single core mode,
W RKNN: [15:38:15.134] core_num 3 ori_Ih 4 ori_Iw 8400 ori_Ic 16 ori_Ib 1
W RKNN: [15:38:15.134] ori_Kh 1 ori_Kw 1 ori_Kk 1 ori_Kc 16 ori_Ksx 1 ori_Ksy 1
W RKNN: [15:38:15.134] ori_Oh 4 oriOw 8400 oriOc 1 pad_t 0 pad_b 0 pad_l 0 pad_r 0,
W RKNN: [15:38:15.134] Please help report this bug!
D RKNN: [15:38:15.134] <<<<<<<< end: rknn::RKNNTilingPass

After running the compilation I get the model. However, when I try to run the inference on the RK3588, I get a vector of zero length
From the detailed output of the model compilation I believe that the model was compiled incorrectly.
Can you help with the solution?
@yuyun2000
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Using rknnlite inference, and then reporting errors？

@St-Viacheslavik
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Using rknnlite inference, and then reporting errors？

I gave an example of an error from rknn-toolkit2 during model compilation. I built the model on an x86 machine

From rknnlite inference on ARM64 i got zero result:
Code sample:

RKNN_MODEL = "yolov8_small.rknn"
rknn = RKNNLite()
ret = rknn.load_rknn(RKNN_MODEL)
ret = rknn.init_runtime()
cap = cv2.VideoCapture(0)
ret, frame = cap.read()
frame = cv2.cvtColor(frame, cv2.COLOR_BGR2RGB)
input_frame = cv2.resize(frame, (640, 640))
input_frame = numpy.expand_dims(input_frame, 0)
outputs = rknn.inference(inputs=[input_frame])
print(outputs)
print(len(outputs))
print(type(outputs))
rknn.release()

Result:

W rknn-toolkit-lite2 version: 2.2.0
-> Start Program <-
--> Load RKNN model
done
--> Init runtime environment
I RKNN: [11:16:40.100] RKNN Runtime Information, librknnrt version: 2.2.0 (c195366594@2024-09-14T12:18:56)
I RKNN: [11:16:40.100] RKNN Driver Information, version: 0.9.6
I RKNN: [11:16:40.100] RKNN Model Information, version: 6, toolkit version: 2.2.0(compiler version: 2.2.0 (c195366594@2024-09-14T12:24:14)), target: RKNPU v2, target platform: rk3588, framework name: ONNX, framework layout: NCHW, model inference type: static_shape
W RKNN: [11:16:40.121] query RKNN_QUERY_INPUT_DYNAMIC_RANGE error, rknn model is static shape type, please export rknn with dynamic_shapes
W Query dynamic range failed. Ret code: RKNN_ERR_MODEL_INVALID. (If it is a static shape RKNN model, please ignore the above warning message.)
done
-> Loading Done <-
[array([[[  0.       ,   0.       ,   0.       , ...,   0.       ,
           0.       ,   0.       ],
        [  0.       ,   0.       ,   0.       , ...,   0.       ,
           0.       ,   0.       ],
        [  0.       ,   0.       ,   0.       , ...,   0.       ,
           0.       ,   0.      ],
        ...,
        [  0.       ,   0.       ,   0.       , ...,   0.       ,
           0.       ,   0.       ],
        [  0.       ,   0.       ,   0.       , ...,   0.       ,
           0.       ,   0.       ],
        [  0.       ,   0.       ,   0.       , ...,   0.       ,
           0.       ,   0.       ]]], dtype=float32)]
1
<class 'list'>

I think that the compilation of the model is incorrect, because when I run the inference of the original model, I get the following values for the test frame:

[array([[[6.4593658e+00, 7.8225298e+00, 1.3197342e+01, ...,
         3.5425361e-07, 1.6246098e-07, 1.0452623e-07],
        [1.6069817e+01, 6.6863852e+00, 3.1918978e+01, ...,
         2.1877072e-07, 1.0333743e-07, 8.5050246e-08],
        [1.8130138e+01, 3.7029805e+00, 3.4330154e+01, ...,
         3.2787889e-07, 8.8689447e-08, 1.2997523e-07],
        ...,
        [5.3248120e+02, 6.0730432e+02, 2.3935321e+02, ...,
         1.0074392e-06, 1.5418524e-06, 1.7887144e-06],
        [5.6285797e+02, 6.0808728e+02, 2.1686890e+02, ...,
         7.3178660e-07, 1.3735065e-06, 1.6389531e-06],
        [6.0336908e+02, 6.0807568e+02, 9.7625366e+01, ...,
         1.2430581e-06, 1.5659134e-06, 1.8450241e-06]]], dtype=float32)]

@St-Viacheslavik
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Mistakenly closed the issue😅

@yuyun2000
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It looks like an internal bug, which is not easy to solve. You can try to convert the model using a lower version of toolkit.

@St-Viacheslavik
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It looks like an internal bug, which is not easy to solve. You can try to convert the model using a lower version of toolkit.

rknn-toolkit2 2.1.0
Got the same compile error

--> Configuring model  yolov8_small.onnx
done
--> Loading model
I Loading : 100%|█████████████████████████████████████████████| 143/143 [00:00<00:00, 195115.64it/s]
done
--> Building model
D base_optimize ...
D base_optimize done.
D
D fold_constant ...
D fold_constant done.
D
D correct_ops ...
D correct_ops done.
D
D fuse_ops ...
D fuse_ops results:
D     replace_exswish: remove node = ['/model.0/act/Sigmoid', '/model.0/act/Mul'], add node = ['/model.0/act/Sigmoid_2swish']
D     replace_exswish: remove node = ['/model.1/act/Sigmoid', '/model.1/act/Mul'], add node = ['/model.1/act/Sigmoid_2swish']
D     replace_exswish: remove node = ['/model.2/cv1/act/Sigmoid', '/model.2/cv1/act/Mul'], add node = ['/model.2/cv1/act/Sigmoid_2swish']
D     replace_exswish: remove node = ['/model.2/m.0/cv1/act/Sigmoid', '/model.2/m.0/cv1/act/Mul'], add node = ['/model.2/m.0/cv1/act/Sigmoid_2swish']
D     replace_exswish: remove node = ['/model.2/m.0/cv2/act/Sigmoid', '/model.2/m.0/cv2/act/Mul'], add node = ['/model.2/m.0/cv2/act/Sigmoid_2swish']
D     replace_exswish: remove node = ['/model.2/cv2/act/Sigmoid', '/model.2/cv2/act/Mul'], add node = ['/model.2/cv2/act/Sigmoid_2swish']
D     replace_exswish: remove node = ['/model.3/act/Sigmoid', '/model.3/act/Mul'], add node = ['/model.3/act/Sigmoid_2swish']
D     replace_exswish: remove node = ['/model.4/cv1/act/Sigmoid', '/model.4/cv1/act/Mul'], add node = ['/model.4/cv1/act/Sigmoid_2swish']
D     replace_exswish: remove node = ['/model.4/m.0/cv1/act/Sigmoid', '/model.4/m.0/cv1/act/Mul'], add node = ['/model.4/m.0/cv1/act/Sigmoid_2swish']
D     replace_exswish: remove node = ['/model.4/m.0/cv2/act/Sigmoid', '/model.4/m.0/cv2/act/Mul'], add node = ['/model.4/m.0/cv2/act/Sigmoid_2swish']
D     replace_exswish: remove node = ['/model.4/m.1/cv1/act/Sigmoid', '/model.4/m.1/cv1/act/Mul'], add node = ['/model.4/m.1/cv1/act/Sigmoid_2swish']
D     replace_exswish: remove node = ['/model.4/m.1/cv2/act/Sigmoid', '/model.4/m.1/cv2/act/Mul'], add node = ['/model.4/m.1/cv2/act/Sigmoid_2swish']
D     replace_exswish: remove node = ['/model.4/cv2/act/Sigmoid', '/model.4/cv2/act/Mul'], add node = ['/model.4/cv2/act/Sigmoid_2swish']
D     replace_exswish: remove node = ['/model.5/act/Sigmoid', '/model.5/act/Mul'], add node = ['/model.5/act/Sigmoid_2swish']
D     replace_exswish: remove node = ['/model.6/cv1/act/Sigmoid', '/model.6/cv1/act/Mul'], add node = ['/model.6/cv1/act/Sigmoid_2swish']
D     replace_exswish: remove node = ['/model.6/m.0/cv1/act/Sigmoid', '/model.6/m.0/cv1/act/Mul'], add node = ['/model.6/m.0/cv1/act/Sigmoid_2swish']
D     replace_exswish: remove node = ['/model.6/m.0/cv2/act/Sigmoid', '/model.6/m.0/cv2/act/Mul'], add node = ['/model.6/m.0/cv2/act/Sigmoid_2swish']
D     replace_exswish: remove node = ['/model.6/m.1/cv1/act/Sigmoid', '/model.6/m.1/cv1/act/Mul'], add node = ['/model.6/m.1/cv1/act/Sigmoid_2swish']
D     replace_exswish: remove node = ['/model.6/m.1/cv2/act/Sigmoid', '/model.6/m.1/cv2/act/Mul'], add node = ['/model.6/m.1/cv2/act/Sigmoid_2swish']
D     replace_exswish: remove node = ['/model.6/cv2/act/Sigmoid', '/model.6/cv2/act/Mul'], add node = ['/model.6/cv2/act/Sigmoid_2swish']
D     replace_exswish: remove node = ['/model.7/act/Sigmoid', '/model.7/act/Mul'], add node = ['/model.7/act/Sigmoid_2swish']
D     replace_exswish: remove node = ['/model.8/cv1/act/Sigmoid', '/model.8/cv1/act/Mul'], add node = ['/model.8/cv1/act/Sigmoid_2swish']
D     replace_exswish: remove node = ['/model.8/m.0/cv1/act/Sigmoid', '/model.8/m.0/cv1/act/Mul'], add node = ['/model.8/m.0/cv1/act/Sigmoid_2swish']
D     replace_exswish: remove node = ['/model.8/m.0/cv2/act/Sigmoid', '/model.8/m.0/cv2/act/Mul'], add node = ['/model.8/m.0/cv2/act/Sigmoid_2swish']
D     replace_exswish: remove node = ['/model.8/cv2/act/Sigmoid', '/model.8/cv2/act/Mul'], add node = ['/model.8/cv2/act/Sigmoid_2swish']
D     replace_exswish: remove node = ['/model.9/cv1/act/Sigmoid', '/model.9/cv1/act/Mul'], add node = ['/model.9/cv1/act/Sigmoid_2swish']
D     replace_exswish: remove node = ['/model.9/cv2/act/Sigmoid', '/model.9/cv2/act/Mul'], add node = ['/model.9/cv2/act/Sigmoid_2swish']
D     replace_exswish: remove node = ['/model.12/cv1/act/Sigmoid', '/model.12/cv1/act/Mul'], add node = ['/model.12/cv1/act/Sigmoid_2swish']
D     replace_exswish: remove node = ['/model.12/m.0/cv1/act/Sigmoid', '/model.12/m.0/cv1/act/Mul'], add node = ['/model.12/m.0/cv1/act/Sigmoid_2swish']
D     replace_exswish: remove node = ['/model.12/m.0/cv2/act/Sigmoid', '/model.12/m.0/cv2/act/Mul'], add node = ['/model.12/m.0/cv2/act/Sigmoid_2swish']
D     replace_exswish: remove node = ['/model.12/cv2/act/Sigmoid', '/model.12/cv2/act/Mul'], add node = ['/model.12/cv2/act/Sigmoid_2swish']
D     replace_exswish: remove node = ['/model.15/cv1/act/Sigmoid', '/model.15/cv1/act/Mul'], add node = ['/model.15/cv1/act/Sigmoid_2swish']
D     replace_exswish: remove node = ['/model.15/m.0/cv1/act/Sigmoid', '/model.15/m.0/cv1/act/Mul'], add node = ['/model.15/m.0/cv1/act/Sigmoid_2swish']
D     replace_exswish: remove node = ['/model.15/m.0/cv2/act/Sigmoid', '/model.15/m.0/cv2/act/Mul'], add node = ['/model.15/m.0/cv2/act/Sigmoid_2swish']
D     replace_exswish: remove node = ['/model.15/cv2/act/Sigmoid', '/model.15/cv2/act/Mul'], add node = ['/model.15/cv2/act/Sigmoid_2swish']
D     replace_exswish: remove node = ['/model.16/act/Sigmoid', '/model.16/act/Mul'], add node = ['/model.16/act/Sigmoid_2swish']
D     replace_exswish: remove node = ['/model.18/cv1/act/Sigmoid', '/model.18/cv1/act/Mul'], add node = ['/model.18/cv1/act/Sigmoid_2swish']
D     replace_exswish: remove node = ['/model.18/m.0/cv1/act/Sigmoid', '/model.18/m.0/cv1/act/Mul'], add node = ['/model.18/m.0/cv1/act/Sigmoid_2swish']
D     replace_exswish: remove node = ['/model.18/m.0/cv2/act/Sigmoid', '/model.18/m.0/cv2/act/Mul'], add node = ['/model.18/m.0/cv2/act/Sigmoid_2swish']
D     replace_exswish: remove node = ['/model.18/cv2/act/Sigmoid', '/model.18/cv2/act/Mul'], add node = ['/model.18/cv2/act/Sigmoid_2swish']
D     replace_exswish: remove node = ['/model.19/act/Sigmoid', '/model.19/act/Mul'], add node = ['/model.19/act/Sigmoid_2swish']
D     replace_exswish: remove node = ['/model.21/cv1/act/Sigmoid', '/model.21/cv1/act/Mul'], add node = ['/model.21/cv1/act/Sigmoid_2swish']
D     replace_exswish: remove node = ['/model.21/m.0/cv1/act/Sigmoid', '/model.21/m.0/cv1/act/Mul'], add node = ['/model.21/m.0/cv1/act/Sigmoid_2swish']
D     replace_exswish: remove node = ['/model.21/m.0/cv2/act/Sigmoid', '/model.21/m.0/cv2/act/Mul'], add node = ['/model.21/m.0/cv2/act/Sigmoid_2swish']
D     replace_exswish: remove node = ['/model.21/cv2/act/Sigmoid', '/model.21/cv2/act/Mul'], add node = ['/model.21/cv2/act/Sigmoid_2swish']
D     replace_exswish: remove node = ['/model.22/cv2.2/cv2.2.0/act/Sigmoid', '/model.22/cv2.2/cv2.2.0/act/Mul'], add node = ['/model.22/cv2.2/cv2.2.0/act/Sigmoid_2swish']
D     replace_exswish: remove node = ['/model.22/cv2.2/cv2.2.1/act/Sigmoid', '/model.22/cv2.2/cv2.2.1/act/Mul'], add node = ['/model.22/cv2.2/cv2.2.1/act/Sigmoid_2swish']
D     replace_exswish: remove node = ['/model.22/cv3.2/cv3.2.0/act/Sigmoid', '/model.22/cv3.2/cv3.2.0/act/Mul'], add node = ['/model.22/cv3.2/cv3.2.0/act/Sigmoid_2swish']
D     replace_exswish: remove node = ['/model.22/cv3.2/cv3.2.1/act/Sigmoid', '/model.22/cv3.2/cv3.2.1/act/Mul'], add node = ['/model.22/cv3.2/cv3.2.1/act/Sigmoid_2swish']
D     replace_exswish: remove node = ['/model.22/cv2.1/cv2.1.0/act/Sigmoid', '/model.22/cv2.1/cv2.1.0/act/Mul'], add node = ['/model.22/cv2.1/cv2.1.0/act/Sigmoid_2swish']
D     replace_exswish: remove node = ['/model.22/cv2.1/cv2.1.1/act/Sigmoid', '/model.22/cv2.1/cv2.1.1/act/Mul'], add node = ['/model.22/cv2.1/cv2.1.1/act/Sigmoid_2swish']
D     replace_exswish: remove node = ['/model.22/cv3.1/cv3.1.0/act/Sigmoid', '/model.22/cv3.1/cv3.1.0/act/Mul'], add node = ['/model.22/cv3.1/cv3.1.0/act/Sigmoid_2swish']
D     replace_exswish: remove node = ['/model.22/cv3.1/cv3.1.1/act/Sigmoid', '/model.22/cv3.1/cv3.1.1/act/Mul'], add node = ['/model.22/cv3.1/cv3.1.1/act/Sigmoid_2swish']
D     replace_exswish: remove node = ['/model.22/cv2.0/cv2.0.0/act/Sigmoid', '/model.22/cv2.0/cv2.0.0/act/Mul'], add node = ['/model.22/cv2.0/cv2.0.0/act/Sigmoid_2swish']
D     replace_exswish: remove node = ['/model.22/cv2.0/cv2.0.1/act/Sigmoid', '/model.22/cv2.0/cv2.0.1/act/Mul'], add node = ['/model.22/cv2.0/cv2.0.1/act/Sigmoid_2swish']
D     replace_exswish: remove node = ['/model.22/cv3.0/cv3.0.0/act/Sigmoid', '/model.22/cv3.0/cv3.0.0/act/Mul'], add node = ['/model.22/cv3.0/cv3.0.0/act/Sigmoid_2swish']
D     replace_exswish: remove node = ['/model.22/cv3.0/cv3.0.1/act/Sigmoid', '/model.22/cv3.0/cv3.0.1/act/Mul'], add node = ['/model.22/cv3.0/cv3.0.1/act/Sigmoid_2swish']
D     replace_parallel_slice_by_split: remove node = ['/model.22/Slice', '/model.22/Slice_1'], add node = ['/model.22/Slice_2sp']
D     unsqueeze_to_4d_concat: remove node = [], add node = ['/model.22/Reshape_output_0_rs', '/model.22/Reshape_1_output_0_rs', '/model.22/Reshape_2_output_0_rs', '/model.22/Concat_3_output_0-rs']
D     unsqueeze_to_4d_split: remove node = [], add node = ['/model.22/Concat_3_output_0_rs', '/model.22/Split_output_0-rs', '/model.22/Split_output_1-rs']
D     convert_softmax_to_exsoftmax13: remove node = ['/model.22/dfl/Softmax'], add node = ['/model.22/dfl/Softmax']
D     unsqueeze_to_4d_split: remove node = [], add node = ['/model.22/dfl/Reshape_1_output_0_rs', '/model.22/Slice_output_0-rs', '/model.22/Slice_1_output_0-rs']
D     unsqueeze_to_4d_sub: remove node = [], add node = ['/model.22/Slice_output_0_rs', '/model.22/Sub_output_0-rs']
D     unsqueeze_to_4d_add: remove node = [], add node = ['/model.22/Slice_1_output_0_rs', '/model.22/Add_1_output_0-rs']
D     unsqueeze_to_4d_add: remove node = [], add node = ['/model.22/Sub_output_0_rs', '/model.22/Add_1_output_0_rs', '/model.22/Add_2_output_0-rs']
D     convert_div_to_mul: remove node = ['/model.22/Div_1'], add node = ['/model.22/Div_1_2mul']
D     unsqueeze_to_4d_sub: remove node = [], add node = ['/model.22/Add_1_output_0_rs#1', '/model.22/Sub_output_0_rs#1', '/model.22/Sub_1_output_0-rs']
D     unsqueeze_to_4d_concat: remove node = [], add node = ['/model.22/Div_1_output_0_rs', '/model.22/Sub_1_output_0_rs', '/model.22/Concat_4_output_0-rs']
D     unsqueeze_to_4d_mul: remove node = [], add node = ['/model.22/Concat_4_output_0_rs', '/model.22/Mul_2_output_0-rs']
D     unsqueeze_to_4d_sigmoid: remove node = [], add node = ['/model.22/Split_output_1_rs', '/model.22/Sigmoid_output_0-rs']
D     unsqueeze_to_4d_concat: remove node = [], add node = ['/model.22/Mul_2_output_0_rs', '/model.22/Sigmoid_output_0_rs', 'output0-rs']
D     unsqueeze_to_4d_mul: remove node = [], add node = ['/model.22/Add_2_output_0_rs', '/model.22/Div_1_output_0-rs']
D     swap_concat_axis_avoid_channel_concat: remove node = [], add node = ['/model.22/Div_1_output_0_rs_rs', '/model.22/Sub_1_output_0_rs_rs', '/model.22/Concat_4_output_0-rs-rs']
D     input_align_4D_mul: remove node = ['/model.22/Mul_2'], add node = ['/model.22/Mul_2']
D     swap_concat_axis_avoid_channel_concat: remove node = [], add node = ['/model.22/Mul_2_output_0_rs_rs', '/model.22/Sigmoid_output_0_rs_rs', 'output0-rs-rs']
D     convert_reshape_to_transpose: remove node = ['/model.22/Sub_1_output_0_rs_rs'], add node = ['/model.22/Sub_1_output_0_rs_rs']
D     input_align_4D_mul: remove node = ['/model.22/Div_1_2mul'], add node = ['/model.22/Div_1_2mul']
D     convert_reshape_to_transpose: remove node = ['/model.22/Div_1_output_0_rs_rs'], add node = ['/model.22/Div_1_output_0_rs_rs']
D     convert_reshape_to_transpose: remove node = ['/model.22/Concat_4_output_0-rs-rs'], add node = ['/model.22/Concat_4_output_0-rs-rs']
D     convert_reshape_to_transpose: remove node = ['/model.22/Mul_2_output_0_rs_rs'], add node = ['/model.22/Mul_2_output_0_rs_rs']
D     convert_reshape_to_transpose: remove node = ['/model.22/Sigmoid_output_0_rs_rs'], add node = ['/model.22/Sigmoid_output_0_rs_rs']
D     convert_reshape_to_transpose: remove node = ['output0-rs-rs'], add node = ['output0-rs-rs']
D     fuse_two_reshape: remove node = ['/model.22/Reshape_2', '/model.22/Reshape_1', '/model.22/Reshape']
D     bypass_two_reshape: remove node = ['/model.22/Concat_3_output_0_rs', '/model.22/Concat_3_output_0-rs']
D     fuse_two_reshape: remove node = ['/model.22/Split_output_0-rs', '/model.22/dfl/Reshape_1']
D     bypass_two_reshape: remove node = ['/model.22/Slice_output_0_rs', '/model.22/Slice_output_0-rs', '/model.22/Sub_output_0_rs#1', '/model.22/Sub_output_0_rs', '/model.22/Sub_output_0-rs', '/model.22/Slice_1_output_0_rs', '/model.22/Slice_1_output_0-rs', '/model.22/Add_1_output_0_rs#1', '/model.22/Add_1_output_0_rs', '/model.22/Add_1_output_0-rs', '/model.22/Add_2_output_0_rs', '/model.22/Add_2_output_0-rs', '/model.22/Div_1_output_0_rs', '/model.22/Div_1_output_0-rs']
D     reduce_tp_in_mesh_concat: remove node = ['/model.22/Div_1_output_0_rs_rs', '/model.22/Sub_1_output_0_rs_rs', '/model.22/Concat_4_output_0-rs-rs']
D     bypass_two_reshape: remove node = ['/model.22/Concat_4_output_0_rs', '/model.22/Concat_4_output_0-rs', '/model.22/Mul_2_output_0_rs', '/model.22/Mul_2_output_0-rs', '/model.22/Split_output_1_rs', '/model.22/Split_output_1-rs', '/model.22/Sigmoid_output_0_rs', '/model.22/Sigmoid_output_0-rs']
D     reduce_tp_in_mesh_concat: remove node = ['/model.22/Mul_2_output_0_rs_rs', '/model.22/Sigmoid_output_0_rs_rs', 'output0-rs-rs']
D     fuse_reshape_transpose: remove node = ['/model.22/dfl/Transpose'], add node = ['/model.22/dfl/Transpose']
D     bypass_two_reshape: remove node = ['/model.22/Sub_1_output_0_rs', '/model.22/Sub_1_output_0-rs']
D     convert_reshape_to_transpose: remove node = ['/model.22/dfl/Reshape_1_output_0_rs'], add node = ['/model.22/dfl/Reshape_1_output_0_rs']
D     fold_constant ...
D     fold_constant done.
D fuse_ops done.
D
W build: found outlier value, this may affect quantization accuracy
                        const name               abs_mean    abs_std     outlier value
                        model.0.conv.weight      4.03        4.41        26.039
D sparse_weight ...
D sparse_weight done.
D
I GraphPreparing : 100%|████████████████████████████████████████| 176/176 [00:00<00:00, 1449.01it/s]
I Quantizating : 100%|████████████████████████████████████████████| 176/176 [00:03<00:00, 52.53it/s]
D
D quant_optimizer ...
D quant_optimizer results:
D     adjust_tanh_sigmoid: ['/model.22/Sigmoid']
D     adjust_concat_split: ['/model.22/Split', '/model.22/Concat_3', '/model.22/Concat', '/model.22/Concat_1', '/model.22/Concat_2']
D     adjust_no_change_node: ['/model.22/Reshape_output_0_rs', '/model.22/Reshape_1_output_0_rs', '/model.22/Reshape_2_output_0_rs', '/model.9/m_2/MaxPool', '/model.9/m_1/MaxPool', '/model.9/m/MaxPool']
D quant_optimizer done.
D
D recover_const_share ...
D recover_const_share done.
D
W build: The default input dtype of 'images' is changed from 'float32' to 'int8' in rknn model for performance!
                       Please take care of this change when deploy rknn model with Runtime API!
W build: The default output dtype of 'output0' is changed from 'float32' to 'int8' in rknn model for performance!
                      Please take care of this change when deploy rknn model with Runtime API!
I rknn building ...
I RKNN: [13:38:22.064] 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 = 1, enable_flash_attention = 0
I RKNN: librknnc version: 2.1.0 (967d001cc8@2024-08-07T11:32:45)
D RKNN: [13:38:22.114] RKNN is invoked
W RKNN: [13:38:22.299] Model initializer tensor data is empty, name: empty_placeholder_0
D RKNN: [13:38:22.301] >>>>>> start: rknn::RKNNExtractCustomOpAttrs
D RKNN: [13:38:22.301] <<<<<<<< end: rknn::RKNNExtractCustomOpAttrs
D RKNN: [13:38:22.301] >>>>>> start: rknn::RKNNSetOpTargetPass
D RKNN: [13:38:22.301] <<<<<<<< end: rknn::RKNNSetOpTargetPass
D RKNN: [13:38:22.301] >>>>>> start: rknn::RKNNBindNorm
D RKNN: [13:38:22.302] <<<<<<<< end: rknn::RKNNBindNorm
D RKNN: [13:38:22.302] >>>>>> start: rknn::RKNNEliminateQATDataConvert
D RKNN: [13:38:22.302] <<<<<<<< end: rknn::RKNNEliminateQATDataConvert
D RKNN: [13:38:22.302] >>>>>> start: rknn::RKNNTileGroupConv
D RKNN: [13:38:22.302] <<<<<<<< end: rknn::RKNNTileGroupConv
D RKNN: [13:38:22.302] >>>>>> start: rknn::RKNNAddConvBias
D RKNN: [13:38:22.302] <<<<<<<< end: rknn::RKNNAddConvBias
D RKNN: [13:38:22.302] >>>>>> start: rknn::RKNNTileChannel
D RKNN: [13:38:22.302] <<<<<<<< end: rknn::RKNNTileChannel
D RKNN: [13:38:22.302] >>>>>> start: rknn::RKNNPerChannelPrep
D RKNN: [13:38:22.303] <<<<<<<< end: rknn::RKNNPerChannelPrep
D RKNN: [13:38:22.303] >>>>>> start: rknn::RKNNBnQuant
D RKNN: [13:38:22.303] <<<<<<<< end: rknn::RKNNBnQuant
D RKNN: [13:38:22.303] >>>>>> start: rknn::RKNNFuseOptimizerPass
D RKNN: [13:38:22.316] <<<<<<<< end: rknn::RKNNFuseOptimizerPass
D RKNN: [13:38:22.316] >>>>>> start: rknn::RKNNTurnAutoPad
D RKNN: [13:38:22.316] <<<<<<<< end: rknn::RKNNTurnAutoPad
D RKNN: [13:38:22.316] >>>>>> start: rknn::RKNNInitRNNConst
D RKNN: [13:38:22.316] <<<<<<<< end: rknn::RKNNInitRNNConst
D RKNN: [13:38:22.316] >>>>>> start: rknn::RKNNInitCastConst
D RKNN: [13:38:22.316] <<<<<<<< end: rknn::RKNNInitCastConst
D RKNN: [13:38:22.316] >>>>>> start: rknn::RKNNMultiSurfacePass
D RKNN: [13:38:22.316] <<<<<<<< end: rknn::RKNNMultiSurfacePass
D RKNN: [13:38:22.316] >>>>>> start: rknn::RKNNReplaceConstantTensorPass
D RKNN: [13:38:22.316] <<<<<<<< end: rknn::RKNNReplaceConstantTensorPass
D RKNN: [13:38:22.316] >>>>>> start: rknn::RKNNSubgraphManager
D RKNN: [13:38:22.316] >>>>>> start: rknn::RKNNSubgraphManager
D RKNN: [13:38:22.316] <<<<<<<< end: rknn::RKNNSubgraphManager
D RKNN: [13:38:22.316] >>>>>> start: rknn::RKNNAddSecondaryNode
D RKNN: [13:38:22.316] <<<<<<<< end: rknn::RKNNAddSecondaryNode
D RKNN: [13:38:22.316] <<<<<<<< end: rknn::RKNNSubgraphManager
D RKNN: [13:38:22.316] >>>>>> start: OpEmit
E RKNN: [13:38:22.316] REGTASK: The bit width of field value exceeds the limit, target: v2, offset: 0x500c, shift = 0, limit: 0x1fff, value: 0x20cf
W RKNN: [13:38:22.316] emitABC_T_BAC_regtask notch_addr overflow
E RKNN: [13:38:22.316] REGTASK: The bit width of field value exceeds the limit, target: v2, offset: 0x4038, shift = 0, limit: 0x1fff, value: 0x419f
E RKNN: [13:38:22.316] REGTASK: The bit width of field value exceeds the limit, target: v2, offset: 0x4038, shift = 16, limit: 0x1fff, value: 0x419f
W RKNN: [13:38:22.316] emitABC_T_BAC_regtask notch_addr overflow
W RKNN: [13:38:22.317] Transpose will fallback to CPU, because input shape has exceeded the max limit, height(4) * width(8400) = 33600, required product no larger than 16384!
E RKNN: [13:38:22.317] REGTASK: The bit width of field value exceeds the limit, target: v2, offset: 0x500c, shift = 0, limit: 0x1fff, value: 0x20cf
D RKNN: [13:38:22.317] <<<<<<<< end: OpEmit
D RKNN: [13:38:22.317] >>>>>> start: rknn::RKNNAddFirstConv
D RKNN: [13:38:22.317] <<<<<<<< end: rknn::RKNNAddFirstConv
D RKNN: [13:38:22.317] >>>>>> start: rknn::RKNNTilingPass
W RKNN: [13:38:22.318] Failed to config layer: 'Conv:/model.22/dfl/conv/Conv' using 3Core fallback to single core mode,
W RKNN: [13:38:22.318] core_num 3 ori_Ih 4 ori_Iw 8400 ori_Ic 16 ori_Ib 1
W RKNN: [13:38:22.318] ori_Kh 1 ori_Kw 1 ori_Kk 1 ori_Kc 16 ori_Ksx 1 ori_Ksy 1
W RKNN: [13:38:22.318] ori_Oh 4 oriOw 8400 oriOc 1 pad_t 0 pad_b 0 pad_l 0 pad_r 0,
W RKNN: [13:38:22.318] Please help report this bug!
W RKNN: [13:38:22.318] Failed to config layer: 'Conv:/model.22/dfl/conv/Conv' using 3Core fallback to single core mode,
W RKNN: [13:38:22.318] core_num 3 ori_Ih 4 ori_Iw 8400 ori_Ic 16 ori_Ib 1
W RKNN: [13:38:22.318] ori_Kh 1 ori_Kw 1 ori_Kk 1 ori_Kc 16 ori_Ksx 1 ori_Ksy 1
W RKNN: [13:38:22.318] ori_Oh 4 oriOw 8400 oriOc 1 pad_t 0 pad_b 0 pad_l 0 pad_r 0,
W RKNN: [13:38:22.318] Please help report this bug!
W RKNN: [13:38:22.318] Failed to config layer: 'Conv:/model.22/dfl/conv/Conv' using 3Core fallback to single core mode,
W RKNN: [13:38:22.318] core_num 3 ori_Ih 4 ori_Iw 8400 ori_Ic 16 ori_Ib 1
W RKNN: [13:38:22.318] ori_Kh 1 ori_Kw 1 ori_Kk 1 ori_Kc 16 ori_Ksx 1 ori_Ksy 1
W RKNN: [13:38:22.318] ori_Oh 4 oriOw 8400 oriOc 1 pad_t 0 pad_b 0 pad_l 0 pad_r 0,
W RKNN: [13:38:22.318] Please help report this bug!
W RKNN: [13:38:22.318] Failed to config layer: 'Conv:/model.22/dfl/conv/Conv' using 3Core fallback to single core mode,
W RKNN: [13:38:22.318] core_num 3 ori_Ih 4 ori_Iw 8400 ori_Ic 16 ori_Ib 1
W RKNN: [13:38:22.318] ori_Kh 1 ori_Kw 1 ori_Kk 1 ori_Kc 16 ori_Ksx 1 ori_Ksy 1
W RKNN: [13:38:22.318] ori_Oh 4 oriOw 8400 oriOc 1 pad_t 0 pad_b 0 pad_l 0 pad_r 0,
W RKNN: [13:38:22.318] Please help report this bug!
W RKNN: [13:38:22.318] Failed to config layer: 'Conv:/model.22/dfl/conv/Conv' using 3Core fallback to single core mode,
W RKNN: [13:38:22.318] core_num 3 ori_Ih 4 ori_Iw 8400 ori_Ic 16 ori_Ib 1
W RKNN: [13:38:22.318] ori_Kh 1 ori_Kw 1 ori_Kk 1 ori_Kc 16 ori_Ksx 1 ori_Ksy 1
W RKNN: [13:38:22.318] ori_Oh 4 oriOw 8400 oriOc 1 pad_t 0 pad_b 0 pad_l 0 pad_r 0,
W RKNN: [13:38:22.318] Please help report this bug!
D RKNN: [13:38:22.318] <<<<<<<< end: rknn::RKNNTilingPass
D RKNN: [13:38:22.318] >>>>>> start: rknn::RKNNLayoutMatchPass
W RKNN: [13:38:22.318] LayoutMatchManager: recursion_depth=3, Logic is Dangerous, Will Force layout to native.
W RKNN: [13:38:22.318] LayoutMatchManager: recursion_depth=3, Logic is Dangerous, Will Force layout to native.
W RKNN: [13:38:22.318] LayoutMatchManager: recursion_depth=3, Logic is Dangerous, Will Force layout to native.
W RKNN: [13:38:22.318] LayoutMatchManager: recursion_depth=3, Logic is Dangerous, Will Force layout to native.
W RKNN: [13:38:22.318] LayoutMatchManager: recursion_depth=3, Logic is Dangerous, Will Force layout to native.
W RKNN: [13:38:22.318] LayoutMatchManager: recursion_depth=3, Logic is Dangerous, Will Force layout to native.
W RKNN: [13:38:22.318] LayoutMatchManager: recursion_depth=3, Logic is Dangerous, Will Force layout to native.
W RKNN: [13:38:22.318] LayoutMatchManager: recursion_depth=3, Logic is Dangerous, Will Force layout to native.
W RKNN: [13:38:22.318] LayoutMatchManager: recursion_depth=3, Logic is Dangerous, Will Force layout to native.
W RKNN: [13:38:22.318] LayoutMatchManager: recursion_depth=3, Logic is Dangerous, Will Force layout to native.
W RKNN: [13:38:22.318] LayoutMatchManager: recursion_depth=3, Logic is Dangerous, Will Force layout to native.
W RKNN: [13:38:22.318] LayoutMatchManager: recursion_depth=3, Logic is Dangerous, Will Force layout to native.
W RKNN: [13:38:22.318] LayoutMatchManager: recursion_depth=3, Logic is Dangerous, Will Force layout to native.
W RKNN: [13:38:22.318] LayoutMatchManager: recursion_depth=3, Logic is Dangerous, Will Force layout to native.
W RKNN: [13:38:22.318] LayoutMatchManager: recursion_depth=3, Logic is Dangerous, Will Force layout to native.
W RKNN: [13:38:22.318] LayoutMatchManager: recursion_depth=3, Logic is Dangerous, Will Force layout to native.
W RKNN: [13:38:22.318] LayoutMatchManager: recursion_depth=3, Logic is Dangerous, Will Force layout to native.
W RKNN: [13:38:22.318] LayoutMatchManager: recursion_depth=3, Logic is Dangerous, Will Force layout to native.
W RKNN: [13:38:22.318] LayoutMatchManager: recursion_depth=3, Logic is Dangerous, Will Force layout to native.
W RKNN: [13:38:22.318] LayoutMatchManager: recursion_depth=3, Logic is Dangerous, Will Force layout to native.
W RKNN: [13:38:22.318] LayoutMatchManager: recursion_depth=3, Logic is Dangerous, Will Force layout to native.
W RKNN: [13:38:22.318] LayoutMatchManager: recursion_depth=3, Logic is Dangerous, Will Force layout to native.
W RKNN: [13:38:22.318] BroadcastLayoutConnFactory: op(Mul:/model.22/Mul_2) has undefined broadcast type, please check this op.
W RKNN: [13:38:22.318] BroadcastLayoutConnFactory: op(Mul:/model.22/Mul_2) has undefined broadcast type, please check this op.
W RKNN: [13:38:22.318] BroadcastLayoutConnFactory: op(Mul:/model.22/Mul_2) has undefined broadcast type, please check this op.
W RKNN: [13:38:22.318] LayoutMatchManager: recursion_depth=3, Logic is Dangerous, Will Force layout to native.
D RKNN: [13:38:22.318] <<<<<<<< end: rknn::RKNNLayoutMatchPass
D RKNN: [13:38:22.318] >>>>>> start: rknn::RKNNAddSecondaryNode
D RKNN: [13:38:22.318] <<<<<<<< end: rknn::RKNNAddSecondaryNode
D RKNN: [13:38:22.318] >>>>>> start: rknn::RKNNAllocateConvCachePass
D RKNN: [13:38:22.318] <<<<<<<< end: rknn::RKNNAllocateConvCachePass
D RKNN: [13:38:22.318] >>>>>> start: OpEmit
D RKNN: [13:38:22.340] finish initComputeZoneMapByStepsVector
D RKNN: [13:38:22.340] finish initComputeZoneMapByStepsVector
D RKNN: [13:38:22.340] finish initComputeZoneMapByStepsVector
D RKNN: [13:38:22.340] finish initComputeZoneMapByStepsVector
D RKNN: [13:38:22.340] finish initComputeZoneMapByStepsVector
D RKNN: [13:38:22.340] finish initComputeZoneMapByStepsVector
D RKNN: [13:38:22.340] finish initComputeZoneMapByStepsVector
D RKNN: [13:38:22.340] finish initComputeZoneMapByStepsVector
D RKNN: [13:38:22.340] finish initComputeZoneMapByStepsVector
D RKNN: [13:38:22.341] finish initComputeZoneMapByStepsVector
D RKNN: [13:38:22.341] finish initComputeZoneMapByStepsVector
D RKNN: [13:38:22.342] finish initComputeZoneMapByStepsVector
D RKNN: [13:38:22.342] finish initComputeZoneMapByStepsVector
D RKNN: [13:38:22.342] <<<<<<<< end: OpEmit
D RKNN: [13:38:22.342] >>>>>> start: rknn::RKNNSubGraphMemoryPlanPass
D RKNN: [13:38:22.342] >>>>>> start: rknn::RKNNSubGraphMemoryPlanPass
D RKNN: [13:38:22.342] <<<<<<<< end: rknn::RKNNSubGraphMemoryPlanPass
D RKNN: [13:38:22.342] >>>>>> start: rknn::RKNNOperatorIdGenPass
D RKNN: [13:38:22.342] <<<<<<<< end: rknn::RKNNOperatorIdGenPass
D RKNN: [13:38:22.343] <<<<<<<< end: rknn::RKNNSubGraphMemoryPlanPass
D RKNN: [13:38:22.343] >>>>>> start: rknn::RKNNProfileAnalysisPass
D RKNN: [13:38:22.343] <<<<<<<< end: rknn::RKNNProfileAnalysisPass
D RKNN: [13:38:22.343] >>>>>> start: rknn::RKNNOperatorIdGenPass
D RKNN: [13:38:22.343] <<<<<<<< end: rknn::RKNNOperatorIdGenPass
D RKNN: [13:38:22.343] >>>>>> start: rknn::RKNNWeightTransposePass
W RKNN: [13:38:22.451] Warning: Tensor onnx::Split_137 need paramter qtype, type is set to float16 by default!
W RKNN: [13:38:22.451] Warning: Tensor onnx::Split_137 need paramter qtype, type is set to float16 by default!
W RKNN: [13:38:22.451] Warning: Tensor onnx::Split_157 need paramter qtype, type is set to float16 by default!
W RKNN: [13:38:22.451] Warning: Tensor onnx::Split_157 need paramter qtype, type is set to float16 by default!
W RKNN: [13:38:22.451] Warning: Tensor onnx::Split_157 need paramter qtype, type is set to float16 by default!
W RKNN: [13:38:22.451] Warning: Tensor onnx::Split_157 need paramter qtype, type is set to float16 by default!
W RKNN: [13:38:22.451] Warning: Tensor onnx::Split_184 need paramter qtype, type is set to float16 by default!
W RKNN: [13:38:22.451] Warning: Tensor onnx::Split_184 need paramter qtype, type is set to float16 by default!
W RKNN: [13:38:22.451] Warning: Tensor onnx::Split_184 need paramter qtype, type is set to float16 by default!
W RKNN: [13:38:22.451] Warning: Tensor onnx::Split_184 need paramter qtype, type is set to float16 by default!
W RKNN: [13:38:22.451] Warning: Tensor onnx::Split_184 need paramter qtype, type is set to float16 by default!
W RKNN: [13:38:22.451] Warning: Tensor onnx::Split_184 need paramter qtype, type is set to float16 by default!
W RKNN: [13:38:22.451] Warning: Tensor onnx::Split_211 need paramter qtype, type is set to float16 by default!
W RKNN: [13:38:22.451] Warning: Tensor onnx::Split_211 need paramter qtype, type is set to float16 by default!
W RKNN: [13:38:22.451] Warning: Tensor onnx::Split_211 need paramter qtype, type is set to float16 by default!
W RKNN: [13:38:22.451] Warning: Tensor onnx::Split_211 need paramter qtype, type is set to float16 by default!
W RKNN: [13:38:22.451] Warning: Tensor /model.10/Constant_output_0 need paramter qtype, type is set to float16 by default!
W RKNN: [13:38:22.451] Warning: Tensor empty_placeholder_0 need paramter qtype, type is set to float16 by default!
W RKNN: [13:38:22.451] Warning: Tensor /model.10/Constant_output_0 need paramter qtype, type is set to float16 by default!
W RKNN: [13:38:22.451] Warning: Tensor empty_placeholder_0 need paramter qtype, type is set to float16 by default!
W RKNN: [13:38:22.451] Warning: Tensor onnx::Split_388 need paramter qtype, type is set to float16 by default!
W RKNN: [13:38:22.451] Warning: Tensor onnx::Split_388 need paramter qtype, type is set to float16 by default!
W RKNN: [13:38:22.451] Warning: Tensor /model.22/dfl/Constant_output_0 need paramter qtype, type is set to float16 by default!
W RKNN: [13:38:22.451] Warning: Tensor /model.22/dfl/Constant_output_0 need paramter qtype, type is set to float16 by default!
W RKNN: [13:38:22.451] Warning: Tensor /model.22/Slice_2sp_split need paramter qtype, type is set to float16 by default!
W RKNN: [13:38:22.451] Warning: Tensor /model.22/Slice_2sp_split need paramter qtype, type is set to float16 by default!
W RKNN: [13:38:22.451] Warning: Tensor /model.22/Reshape_output_0_rs_i1 need paramter qtype, type is set to float16 by default!
W RKNN: [13:38:22.451] Warning: Tensor /model.22/Reshape_output_0_rs_i1 need paramter qtype, type is set to float16 by default!
W RKNN: [13:38:22.451] Warning: Tensor /model.22/Reshape_1_output_0_rs_i1 need paramter qtype, type is set to float16 by default!
W RKNN: [13:38:22.451] Warning: Tensor /model.22/Reshape_1_output_0_rs_i1 need paramter qtype, type is set to float16 by default!
W RKNN: [13:38:22.451] Warning: Tensor /model.22/Reshape_2_output_0_rs_i1 need paramter qtype, type is set to float16 by default!
W RKNN: [13:38:22.451] Warning: Tensor /model.22/Reshape_2_output_0_rs_i1 need paramter qtype, type is set to float16 by default!
D RKNN: [13:38:22.451] <<<<<<<< end: rknn::RKNNWeightTransposePass
D RKNN: [13:38:22.451] >>>>>> start: rknn::RKNNCPUWeightTransposePass
D RKNN: [13:38:22.451] <<<<<<<< end: rknn::RKNNCPUWeightTransposePass
D RKNN: [13:38:22.451] >>>>>> start: rknn::RKNNModelBuildPass
D RKNN: [13:38:22.474] <<<<<<<< end: rknn::RKNNModelBuildPass
D RKNN: [13:38:22.474] >>>>>> start: rknn::RKNNModelRegCmdbuildPass
D RKNN: [13:38:22.478] --------------------------------------------------------------------------------------------------------------------------------------------------------------------------
D RKNN: [13:38:22.478]                                                         Network Layer Information Table
D RKNN: [13:38:22.478] --------------------------------------------------------------------------------------------------------------------------------------------------------------------------
D RKNN: [13:38:22.478] ID   OpType             DataType Target InputShape                               OutputShape            Cycles(DDR/NPU/Total)    RW(KB)       FullName
D RKNN: [13:38:22.478] --------------------------------------------------------------------------------------------------------------------------------------------------------------------------
D RKNN: [13:38:22.478] 0    InputOperator      INT8     CPU    \                                        (1,3,640,640)          0/0/0                    0            InputOperator:images
D RKNN: [13:38:22.478] 1    ConvExSwish        INT8     NPU    (1,3,640,640),(32,3,3,3),(32)            (1,32,320,320)         190686/921600/921600     1204         Conv:/model.0/conv/Conv
D RKNN: [13:38:22.478] 2    ConvExSwish        INT8     NPU    (1,32,320,320),(64,32,3,3),(64)          (1,64,160,160)         208597/460800/460800     3218         Conv:/model.1/conv/Conv
D RKNN: [13:38:22.478] 3    ConvExSwish        INT8     NPU    (1,64,160,160),(64,64,1,1),(64)          (1,64,160,160)         138726/204800/204800     1604         Conv:/model.2/cv1/conv/Conv
D RKNN: [13:38:22.478] 4    Split              INT8     NPU    (1,64,160,160),(2)                       (1,32,160,160),...     0/0/0                    1600         Split:/model.2/Split
D RKNN: [13:38:22.478] 5    ConvExSwish        INT8     NPU    (1,32,160,160),(32,32,3,3),(32)          (1,32,160,160)         69666/230400/230400      809          Conv:/model.2/m.0/cv1/conv/Conv
D RKNN: [13:38:22.478] 6    ConvExSwish        INT8     NPU    (1,32,160,160),(32,32,3,3),(32)          (1,32,160,160)         69666/230400/230400      809          Conv:/model.2/m.0/cv2/conv/Conv
D RKNN: [13:38:22.478] 7    Add                INT8     NPU    (1,32,160,160),(1,32,160,160)            (1,32,160,160)         0/0/0                    1600         Add:/model.2/m.0/Add
D RKNN: [13:38:22.478] 8    Concat             INT8     NPU    (1,32,160,160),(1,32,160,160),...        (1,96,160,160)         0/0/0                    2400         Concat:/model.2/Concat
D RKNN: [13:38:22.478] 9    ConvExSwish        INT8     NPU    (1,96,160,160),(64,96,1,1),(64)          (1,64,160,160)         173445/204800/204800     2406         Conv:/model.2/cv2/conv/Conv
D RKNN: [13:38:22.478] 10   ConvExSwish        INT8     NPU    (1,64,160,160),(128,64,3,3),(128)        (1,128,80,80)          107059/460800/460800     1673         Conv:/model.3/conv/Conv
D RKNN: [13:38:22.478] 11   ConvExSwish        INT8     NPU    (1,128,80,80),(128,128,1,1),(128)        (1,128,80,80)          70002/102400/102400      817          Conv:/model.4/cv1/conv/Conv
D RKNN: [13:38:22.478] 12   Split              INT8     NPU    (1,128,80,80),(2)                        (1,64,80,80),...       0/0/0                    800          Split:/model.4/Split
D RKNN: [13:38:22.478] 13   ConvExSwish        INT8     NPU    (1,64,80,80),(64,64,3,3),(64)            (1,64,80,80)           36213/230400/230400      436          Conv:/model.4/m.0/cv1/conv/Conv
D RKNN: [13:38:22.478] 14   ConvExSwish        INT8     NPU    (1,64,80,80),(64,64,3,3),(64)            (1,64,80,80)           36213/230400/230400      436          Conv:/model.4/m.0/cv2/conv/Conv
D RKNN: [13:38:22.478] 15   Add                INT8     NPU    (1,64,80,80),(1,64,80,80)                (1,64,80,80)           0/0/0                    800          Add:/model.4/m.0/Add
D RKNN: [13:38:22.478] 16   ConvExSwish        INT8     NPU    (1,64,80,80),(64,64,3,3),(64)            (1,64,80,80)           36213/230400/230400      436          Conv:/model.4/m.1/cv1/conv/Conv
D RKNN: [13:38:22.478] 17   ConvExSwish        INT8     NPU    (1,64,80,80),(64,64,3,3),(64)            (1,64,80,80)           36213/230400/230400      436          Conv:/model.4/m.1/cv2/conv/Conv
D RKNN: [13:38:22.478] 18   Add                INT8     NPU    (1,64,80,80),(1,64,80,80)                (1,64,80,80)           0/0/0                    800          Add:/model.4/m.1/Add
D RKNN: [13:38:22.478] 19   Concat             INT8     NPU    (1,64,80,80),(1,64,80,80),...            (1,256,80,80)          0/0/0                    1600         Concat:/model.4/Concat
D RKNN: [13:38:22.478] 20   ConvExSwish        INT8     NPU    (1,256,80,80),(128,256,1,1),(128)        (1,128,80,80)          105327/204800/204800     1633         Conv:/model.4/cv2/conv/Conv
D RKNN: [13:38:22.478] 21   ConvExSwish        INT8     NPU    (1,128,80,80),(256,128,3,3),(256)        (1,256,40,40)          64504/460800/460800      1090         Conv:/model.5/conv/Conv
D RKNN: [13:38:22.478] 22   ConvExSwish        INT8     NPU    (1,256,40,40),(256,256,1,1),(256)        (1,256,40,40)          37490/102400/102400      466          Conv:/model.6/cv1/conv/Conv
D RKNN: [13:38:22.478] 23   Split              INT8     NPU    (1,256,40,40),(2)                        (1,128,40,40),...      0/0/0                    400          Split:/model.6/Split
D RKNN: [13:38:22.478] 24   ConvExSwish        INT8     NPU    (1,128,40,40),(128,128,3,3),(128)        (1,128,40,40)          23594/230400/230400      345          Conv:/model.6/m.0/cv1/conv/Conv
D RKNN: [13:38:22.478] 25   ConvExSwish        INT8     NPU    (1,128,40,40),(128,128,3,3),(128)        (1,128,40,40)          23594/230400/230400      345          Conv:/model.6/m.0/cv2/conv/Conv
D RKNN: [13:38:22.478] 26   Add                INT8     NPU    (1,128,40,40),(1,128,40,40)              (1,128,40,40)          0/0/0                    400          Add:/model.6/m.0/Add
D RKNN: [13:38:22.478] 27   ConvExSwish        INT8     NPU    (1,128,40,40),(128,128,3,3),(128)        (1,128,40,40)          23594/230400/230400      345          Conv:/model.6/m.1/cv1/conv/Conv
D RKNN: [13:38:22.478] 28   ConvExSwish        INT8     NPU    (1,128,40,40),(128,128,3,3),(128)        (1,128,40,40)          23594/230400/230400      345          Conv:/model.6/m.1/cv2/conv/Conv
D RKNN: [13:38:22.478] 29   Add                INT8     NPU    (1,128,40,40),(1,128,40,40)              (1,128,40,40)          0/0/0                    400          Add:/model.6/m.1/Add
D RKNN: [13:38:22.478] 30   Concat             INT8     NPU    (1,128,40,40),(1,128,40,40),...          (1,512,40,40)          0/0/0                    800          Concat:/model.6/Concat
D RKNN: [13:38:22.478] 31   ConvExSwish        INT8     NPU    (1,512,40,40),(256,512,1,1),(256)        (1,256,40,40)          57577/204800/204800      930          Conv:/model.6/cv2/conv/Conv
D RKNN: [13:38:22.478] 32   ConvExSwish        INT8     NPU    (1,256,40,40),(512,256,3,3),(512)        (1,512,20,20)          76019/460800/460800      1556         Conv:/model.7/conv/Conv
D RKNN: [13:38:22.478] 33   ConvExSwish        INT8     NPU    (1,512,20,20),(512,512,1,1),(512)        (1,512,20,20)          28572/102400/102400      460          Conv:/model.8/cv1/conv/Conv
D RKNN: [13:38:22.478] 34   Split              INT8     NPU    (1,512,20,20),(2)                        (1,256,20,20),...      0/0/0                    200          Split:/model.8/Split
D RKNN: [13:38:22.478] 35   ConvExSwish        INT8     NPU    (1,256,20,20),(256,256,3,3),(256)        (1,256,20,20)          33681/230400/230400      678          Conv:/model.8/m.0/cv1/conv/Conv
D RKNN: [13:38:22.478] 36   ConvExSwish        INT8     NPU    (1,256,20,20),(256,256,3,3),(256)        (1,256,20,20)          33681/230400/230400      678          Conv:/model.8/m.0/cv2/conv/Conv
D RKNN: [13:38:22.478] 37   Add                INT8     NPU    (1,256,20,20),(1,256,20,20)              (1,256,20,20)          0/0/0                    200          Add:/model.8/m.0/Add
D RKNN: [13:38:22.478] 38   Concat             INT8     NPU    (1,256,20,20),(1,256,20,20),...          (1,768,20,20)          0/0/0                    300          Concat:/model.8/Concat
D RKNN: [13:38:22.478] 39   ConvExSwish        INT8     NPU    (1,768,20,20),(512,768,1,1),(512)        (1,512,20,20)          38443/153600/153600      688          Conv:/model.8/cv2/conv/Conv
D RKNN: [13:38:22.478] 40   ConvExSwish        INT8     NPU    (1,512,20,20),(256,512,1,1),(256)        (1,256,20,20)          18616/51200/51200        330          Conv:/model.9/cv1/conv/Conv
D RKNN: [13:38:22.478] 41   MaxPool            INT8     NPU    (1,256,20,20)                            (1,256,20,20)          0/0/0                    100          MaxPool:/model.9/m/MaxPool
D RKNN: [13:38:22.478] 42   MaxPool            INT8     NPU    (1,256,20,20)                            (1,256,20,20)          0/0/0                    100          MaxPool:/model.9/m_1/MaxPool
D RKNN: [13:38:22.478] 43   MaxPool            INT8     NPU    (1,256,20,20)                            (1,256,20,20)          0/0/0                    100          MaxPool:/model.9/m_2/MaxPool
D RKNN: [13:38:22.478] 44   Concat             INT8     NPU    (1,256,20,20),(1,256,20,20),...          (1,1024,20,20)         0/0/0                    400          Concat:/model.9/Concat
D RKNN: [13:38:22.478] 45   ConvExSwish        INT8     NPU    (1,1024,20,20),(512,1024,1,1),(512)      (1,512,20,20)          48313/204800/204800      916          Conv:/model.9/cv2/conv/Conv
D RKNN: [13:38:22.478] 46   Resize             INT8     NPU    (1,512,20,20),(1),(4)                    (1,512,40,40)          0/0/0                    200          Resize:/model.10/Resize
D RKNN: [13:38:22.478] 47   Concat             INT8     NPU    (1,512,40,40),(1,256,40,40)              (1,768,40,40)          0/0/0                    1200         Concat:/model.11/Concat
D RKNN: [13:38:22.478] 48   ConvExSwish        INT8     NPU    (1,768,40,40),(256,768,1,1),(256)        (1,256,40,40)          77664/307200/307200      1394         Conv:/model.12/cv1/conv/Conv
D RKNN: [13:38:22.478] 49   Split              INT8     NPU    (1,256,40,40),(2)                        (1,128,40,40),...      0/0/0                    400          Split:/model.12/Split
D RKNN: [13:38:22.478] 50   ConvExSwish        INT8     NPU    (1,128,40,40),(128,128,3,3),(128)        (1,128,40,40)          23594/230400/230400      345          Conv:/model.12/m.0/cv1/conv/Conv
D RKNN: [13:38:22.478] 51   ConvExSwish        INT8     NPU    (1,128,40,40),(128,128,3,3),(128)        (1,128,40,40)          23594/230400/230400      345          Conv:/model.12/m.0/cv2/conv/Conv
D RKNN: [13:38:22.478] 52   Concat             INT8     NPU    (1,128,40,40),(1,128,40,40),...          (1,384,40,40)          0/0/0                    600          Concat:/model.12/Concat
D RKNN: [13:38:22.478] 53   ConvExSwish        INT8     NPU    (1,384,40,40),(256,384,1,1),(256)        (1,256,40,40)          47534/153600/153600      698          Conv:/model.12/cv2/conv/Conv
D RKNN: [13:38:22.478] 54   Resize             INT8     NPU    (1,256,40,40),(1),(4)                    (1,256,80,80)          0/0/0                    400          Resize:/model.13/Resize
D RKNN: [13:38:22.478] 55   Concat             INT8     NPU    (1,256,80,80),(1,128,80,80)              (1,384,80,80)          0/0/0                    2400         Concat:/model.14/Concat
D RKNN: [13:38:22.478] 56   ConvExSwish        INT8     NPU    (1,384,80,80),(128,384,1,1),(128)        (1,128,80,80)          140652/307200/307200     2449         Conv:/model.15/cv1/conv/Conv
D RKNN: [13:38:22.478] 57   Split              INT8     NPU    (1,128,80,80),(2)                        (1,64,80,80),...       0/0/0                    800          Split:/model.15/Split
D RKNN: [13:38:22.478] 58   ConvExSwish        INT8     NPU    (1,64,80,80),(64,64,3,3),(64)            (1,64,80,80)           36213/230400/230400      436          Conv:/model.15/m.0/cv1/conv/Conv
D RKNN: [13:38:22.478] 59   ConvExSwish        INT8     NPU    (1,64,80,80),(64,64,3,3),(64)            (1,64,80,80)           36213/230400/230400      436          Conv:/model.15/m.0/cv2/conv/Conv
D RKNN: [13:38:22.478] 60   Concat             INT8     NPU    (1,64,80,80),(1,64,80,80),...            (1,192,80,80)          0/0/0                    1200         Concat:/model.15/Concat
D RKNN: [13:38:22.478] 61   ConvExSwish        INT8     NPU    (1,192,80,80),(128,192,1,1),(128)        (1,128,80,80)          87664/153600/153600      1225         Conv:/model.15/cv2/conv/Conv
D RKNN: [13:38:22.478] 62   ConvExSwish        INT8     NPU    (1,128,80,80),(128,128,3,3),(128)        (1,128,40,40)          49568/230400/230400      945          Conv:/model.16/conv/Conv
D RKNN: [13:38:22.478] 63   Concat             INT8     NPU    (1,128,40,40),(1,256,40,40)              (1,384,40,40)          0/0/0                    600          Concat:/model.17/Concat
D RKNN: [13:38:22.478] 64   ConvExSwish        INT8     NPU    (1,128,80,80),(64,128,3,3),(64)          (1,64,80,80)           55088/460800/460800      872          Conv:/model.22/cv2.0/cv2.0.0/conv/Conv
D RKNN: [13:38:22.478] 65   ConvExSwish        INT8     NPU    (1,128,80,80),(128,128,3,3),(128)        (1,128,80,80)          75543/921600/921600      945          Conv:/model.22/cv3.0/cv3.0.0/conv/Conv
D RKNN: [13:38:22.478] 66   ConvExSwish        INT8     NPU    (1,384,40,40),(256,384,1,1),(256)        (1,256,40,40)          47534/153600/153600      698          Conv:/model.18/cv1/conv/Conv
D RKNN: [13:38:22.478] 67   Split              INT8     NPU    (1,256,40,40),(2)                        (1,128,40,40),...      0/0/0                    400          Split:/model.18/Split
D RKNN: [13:38:22.478] 68   ConvExSwish        INT8     NPU    (1,64,80,80),(64,64,3,3),(64)            (1,64,80,80)           36213/230400/230400      436          Conv:/model.22/cv2.0/cv2.0.1/conv/Conv
D RKNN: [13:38:22.478] 69   Conv               INT8     NPU    (1,64,80,80),(64,64,1,1),(64)            (1,64,80,80)           34828/51200/51200        404          Conv:/model.22/cv2.0/cv2.0.2/Conv
D RKNN: [13:38:22.478] 70   ConvExSwish        INT8     NPU    (1,128,80,80),(128,128,3,3),(128)        (1,128,80,80)          75543/921600/921600      945          Conv:/model.22/cv3.0/cv3.0.1/conv/Conv
D RKNN: [13:38:22.478] 71   Conv               INT8     NPU    (1,128,80,80),(80,128,1,1),(80)          (1,80,80,80)           56744/76800/76800        810          Conv:/model.22/cv3.0/cv3.0.2/Conv
D RKNN: [13:38:22.478] 72   Concat             INT8     NPU    (1,64,80,80),(1,80,80,80)                (1,144,80,80)          0/0/0                    900          Concat:/model.22/Concat
D RKNN: [13:38:22.478] 73   ConvExSwish        INT8     NPU    (1,128,40,40),(128,128,3,3),(128)        (1,128,40,40)          23594/230400/230400      345          Conv:/model.18/m.0/cv1/conv/Conv
D RKNN: [13:38:22.478] 74   ConvExSwish        INT8     NPU    (1,128,40,40),(128,128,3,3),(128)        (1,128,40,40)          23594/230400/230400      345          Conv:/model.18/m.0/cv2/conv/Conv
D RKNN: [13:38:22.478] 75   Concat             INT8     NPU    (1,128,40,40),(1,128,40,40),...          (1,384,40,40)          0/0/0                    600          Concat:/model.18/Concat
D RKNN: [13:38:22.478] 76   ConvExSwish        INT8     NPU    (1,384,40,40),(256,384,1,1),(256)        (1,256,40,40)          47534/153600/153600      698          Conv:/model.18/cv2/conv/Conv
D RKNN: [13:38:22.478] 77   ConvExSwish        INT8     NPU    (1,256,40,40),(256,256,3,3),(256)        (1,256,20,20)          46668/230400/230400      978          Conv:/model.19/conv/Conv
D RKNN: [13:38:22.478] 78   Concat             INT8     NPU    (1,256,20,20),(1,512,20,20)              (1,768,20,20)          0/0/0                    300          Concat:/model.20/Concat
D RKNN: [13:38:22.478] 79   ConvExSwish        INT8     NPU    (1,256,40,40),(64,256,3,3),(64)          (1,64,40,40)           27901/230400/230400      544          Conv:/model.22/cv2.1/cv2.1.0/conv/Conv
D RKNN: [13:38:22.478] 80   ConvExSwish        INT8     NPU    (1,256,40,40),(128,256,3,3),(128)        (1,128,40,40)          38486/460800/460800      689          Conv:/model.22/cv3.1/cv3.1.0/conv/Conv
D RKNN: [13:38:22.478] 81   ConvExSwish        INT8     NPU    (1,768,20,20),(512,768,1,1),(512)        (1,512,20,20)          38443/153600/153600      688          Conv:/model.21/cv1/conv/Conv
D RKNN: [13:38:22.478] 82   Split              INT8     NPU    (1,512,20,20),(2)                        (1,256,20,20),...      0/0/0                    200          Split:/model.21/Split
D RKNN: [13:38:22.478] 83   ConvExSwish        INT8     NPU    (1,64,40,40),(64,64,3,3),(64)            (1,64,40,40)           10239/57600/57600        136          Conv:/model.22/cv2.1/cv2.1.1/conv/Conv
D RKNN: [13:38:22.478] 84   Conv               INT8     NPU    (1,64,40,40),(64,64,1,1),(64)            (1,64,40,40)           8853/12800/12800         104          Conv:/model.22/cv2.1/cv2.1.2/Conv
D RKNN: [13:38:22.478] 85   ConvExSwish        INT8     NPU    (1,128,40,40),(128,128,3,3),(128)        (1,128,40,40)          23594/230400/230400      345          Conv:/model.22/cv3.1/cv3.1.1/conv/Conv
D RKNN: [13:38:22.478] 86   Conv               INT8     NPU    (1,128,40,40),(80,128,1,1),(80)          (1,80,40,40)           14535/19200/19200        210          Conv:/model.22/cv3.1/cv3.1.2/Conv
D RKNN: [13:38:22.478] 87   Concat             INT8     NPU    (1,64,40,40),(1,80,40,40)                (1,144,40,40)          0/0/0                    225          Concat:/model.22/Concat_1
D RKNN: [13:38:22.478] 88   ConvExSwish        INT8     NPU    (1,256,20,20),(256,256,3,3),(256)        (1,256,20,20)          33681/230400/230400      678          Conv:/model.21/m.0/cv1/conv/Conv
D RKNN: [13:38:22.478] 89   ConvExSwish        INT8     NPU    (1,256,20,20),(256,256,3,3),(256)        (1,256,20,20)          33681/230400/230400      678          Conv:/model.21/m.0/cv2/conv/Conv
D RKNN: [13:38:22.478] 90   Concat             INT8     NPU    (1,256,20,20),(1,256,20,20),...          (1,768,20,20)          0/0/0                    300          Concat:/model.21/Concat
D RKNN: [13:38:22.478] 91   ConvExSwish        INT8     NPU    (1,768,20,20),(512,768,1,1),(512)        (1,512,20,20)          38443/153600/153600      688          Conv:/model.21/cv2/conv/Conv
D RKNN: [13:38:22.478] 92   ConvExSwish        INT8     NPU    (1,512,20,20),(64,512,3,3),(64)          (1,64,20,20)           22230/115200/115200      488          Conv:/model.22/cv2.2/cv2.2.0/conv/Conv
D RKNN: [13:38:22.478] 93   ConvExSwish        INT8     NPU    (1,512,20,20),(128,512,3,3),(128)        (1,128,20,20)          35802/230400/230400      777          Conv:/model.22/cv3.2/cv3.2.0/conv/Conv
D RKNN: [13:38:22.478] 94   ConvExSwish        INT8     NPU    (1,64,20,20),(64,64,3,3),(64)            (1,64,20,20)           3745/14400/14400         61           Conv:/model.22/cv2.2/cv2.2.1/conv/Conv
D RKNN: [13:38:22.478] 95   Conv               INT8     NPU    (1,64,20,20),(64,64,1,1),(64)            (1,64,20,20)           2360/3200/3200           29           Conv:/model.22/cv2.2/cv2.2.2/Conv
D RKNN: [13:38:22.478] 96   ConvExSwish        INT8     NPU    (1,128,20,20),(128,128,3,3),(128)        (1,128,20,20)          10607/57600/57600        195          Conv:/model.22/cv3.2/cv3.2.1/conv/Conv
D RKNN: [13:38:22.478] 97   Conv               INT8     NPU    (1,128,20,20),(80,128,1,1),(80)          (1,80,20,20)           3983/4800/4800           60           Conv:/model.22/cv3.2/cv3.2.2/Conv
D RKNN: [13:38:22.478] 98   Concat             INT8     NPU    (1,64,20,20),(1,80,20,20)                (1,144,20,20)          0/0/0                    56           Concat:/model.22/Concat_2
D RKNN: [13:38:22.478] 99   Reshape            INT8     NPU    (1,144,80,80),(4)                        (1,144,1,6400)         0/0/0                    900          Reshape:/model.22/Reshape_output_0_rs
D RKNN: [13:38:22.478] 100  Reshape            INT8     NPU    (1,144,40,40),(4)                        (1,144,1,1600)         0/0/0                    225          Reshape:/model.22/Reshape_1_output_0_rs
D RKNN: [13:38:22.478] 101  Reshape            INT8     NPU    (1,144,20,20),(4)                        (1,144,1,400)          0/0/0                    56           Reshape:/model.22/Reshape_2_output_0_rs
D RKNN: [13:38:22.478] 102  Concat             INT8     NPU    (1,144,1,6400),(1,144,1,1600),...        (1,144,1,8400)         0/0/0                    1181         Concat:/model.22/Concat_3
D RKNN: [13:38:22.478] 103  Split              INT8     NPU    (1,144,1,8400),(2)                       (1,64,1,8400),...      0/0/0                    1181         Split:/model.22/Split
D RKNN: [13:38:22.478] 104  Sigmoid            INT8     NPU    (1,80,1,8400)                            (1,80,1,8400)          0/0/0                    656          Sigmoid:/model.22/Sigmoid
D RKNN: [13:38:22.478] 105  Reshape            INT8     NPU    (1,64,1,8400),(4)                        (4,16,1,8400)          0/0/0                    525          Reshape:/model.22/dfl/Reshape
D RKNN: [13:38:22.478] 106  Transpose          INT8     NPU    (4,16,1,8400)                            (1,16,4,8400)          0/0/0                    525          Transpose:/model.22/dfl/Transpose
D RKNN: [13:38:22.478] 107  exSoftmax13        INT8     NPU    (1,16,4,8400),(1,16,1,1)                 (1,16,4,8400)          0/0/0                    525          exSoftmax13:/model.22/dfl/Softmax
D RKNN: [13:38:22.478] 108  Conv               INT8     NPU    (1,16,4,8400),(1,16,1,1),(1)             (1,1,4,8400)           45463/134400/134400      525          Conv:/model.22/dfl/conv/Conv
D RKNN: [13:38:22.478] 109  Transpose          INT8     NPU    (1,1,4,8400)                             (1,4,1,8400)           0/0/0                    525          Transpose:/model.22/dfl/Reshape_1_output_0_rs
D RKNN: [13:38:22.478] 110  Split              INT8     NPU    (1,4,1,8400),(2)                         (1,2,1,8400),...       0/0/0                    131          Split:/model.22/Slice_2sp
D RKNN: [13:38:22.478] 111  Sub                INT8     NPU    (1,2,1,8400),(1,2,1,8400)                (1,2,1,8400)           0/0/0                    262          Sub:/model.22/Sub
D RKNN: [13:38:22.478] 112  Add                INT8     NPU    (1,2,1,8400),(1,2,1,8400)                (1,2,1,8400)           0/0/0                    262          Add:/model.22/Add_1
D RKNN: [13:38:22.478] 113  Sub                INT8     NPU    (1,2,1,8400),(1,2,1,8400)                (1,2,1,8400)           0/0/0                    262          Sub:/model.22/Sub_1
D RKNN: [13:38:22.478] 114  Add                INT8     NPU    (1,2,1,8400),(1,2,1,8400)                (1,2,1,8400)           0/0/0                    262          Add:/model.22/Add_2
D RKNN: [13:38:22.478] 115  Mul                INT8     NPU    (1,2,1,8400),(1,1,1,1)                   (1,2,1,8400)           0/0/0                    131          Mul:/model.22/Div_1_2mul
D RKNN: [13:38:22.478] 116  Concat             INT8     NPU    (1,2,1,8400),(1,2,1,8400),(4,32,1,1)     (1,4,1,8400)           0/0/0                    262          Concat:/model.22/Concat_4
D RKNN: [13:38:22.478] 117  Mul                INT8     NPU    (1,4,1,8400),(1,1,1,8400)                (1,4,1,8400)           0/0/0                    262          Mul:/model.22/Mul_2
D RKNN: [13:38:22.478] 118  Concat             INT8     NPU    (1,4,1,8400),(1,80,1,8400),...           (1,84,1,8400)          0/0/0                    795          Concat:/model.22/Concat_5
D RKNN: [13:38:22.478] 119  Reshape            INT8     CPU    (1,84,1,8400),(3)                        (1,84,8400)            0/0/0                    787          Reshape:output0-rs
D RKNN: [13:38:22.478] 120  OutputOperator     INT8     CPU    (1,84,8400)                              \                      0/0/0                    689          OutputOperator:output0
D RKNN: [13:38:22.478] --------------------------------------------------------------------------------------------------------------------------------------------------------------------------
D RKNN: [13:38:22.479] <<<<<<<< end: rknn::RKNNModelRegCmdbuildPass
D RKNN: [13:38:22.479] >>>>>> start: rknn::RKNNModelExportPass
D RKNN: [13:38:22.479] Export RKNN model to /tmp/tmpq2sub6ck/check.rknn
D RKNN: [13:38:22.489] <<<<<<<< end: rknn::RKNNModelExportPass
D RKNN: [13:38:22.489] >>>>>> start: rknn::RKNNMemStatisticsPass
D RKNN: [13:38:22.489] ----------------------------------------------------------------------------------------------------------------------------------------------------------
D RKNN: [13:38:22.489]                                                    Feature Tensor Information Table
D RKNN: [13:38:22.489] ------------------------------------------------------------------------------------------------------------------------+---------------------------------
D RKNN: [13:38:22.489] ID  User           Tensor                                        DataType  DataFormat   OrigShape      NativeShape      |     [Start       End)       Size
D RKNN: [13:38:22.489] ------------------------------------------------------------------------------------------------------------------------+---------------------------------
D RKNN: [13:38:22.489] 1   ConvExSwish    images                                        INT8      NC1HWC2      (1,3,640,640)  (1,1,640,640,3)  | 0x00000000 0x0012c000 0x0012c000
D RKNN: [13:38:22.489] 2   ConvExSwish    /model.0/act/Mul_output_0                     INT8      NC1HWC2      (1,32,320,320) (1,2,320,320,16) | 0x0012c000 0x0044c000 0x00320000
D RKNN: [13:38:22.489] 3   ConvExSwish    /model.1/act/Mul_output_0                     INT8      NC1HWC2      (1,64,160,160) (1,4,160,160,16) | 0x0044c000 0x005dc000 0x00190000
D RKNN: [13:38:22.489] 4   Split          /model.2/cv1/act/Mul_output_0                 INT8      NC1HWC2      (1,64,160,160) (1,4,160,160,16) | 0x00000000 0x00190000 0x00190000
D RKNN: [13:38:22.489] 5   ConvExSwish    /model.2/Split_output_1                       INT8      NC1HWC2      (1,32,160,160) (1,2,160,160,16) | 0x00190000 0x00258000 0x000c8000
D RKNN: [13:38:22.489] 6   ConvExSwish    /model.2/m.0/cv1/act/Mul_output_0             INT8      NC1HWC2      (1,32,160,160) (1,2,160,160,16) | 0x00000000 0x000c8000 0x000c8000
D RKNN: [13:38:22.489] 7   Add            /model.2/Split_output_1                       INT8      NC1HWC2      (1,32,160,160) (1,2,160,160,16) | 0x00190000 0x00258000 0x000c8000
D RKNN: [13:38:22.489] 7   Add            /model.2/m.0/cv2/act/Mul_output_0             INT8      NC1HWC2      (1,32,160,160) (1,2,160,160,16) | 0x000c8000 0x00190000 0x000c8000
D RKNN: [13:38:22.489] 8   Concat         /model.2/Split_output_0                       INT8      NC1HWC2      (1,32,160,160) (1,2,160,160,16) | 0x00258000 0x00320000 0x000c8000
D RKNN: [13:38:22.489] 8   Concat         /model.2/Split_output_1                       INT8      NC1HWC2      (1,32,160,160) (1,2,160,160,16) | 0x00190000 0x00258000 0x000c8000
D RKNN: [13:38:22.489] 8   Concat         /model.2/m.0/Add_output_0                     INT8      NC1HWC2      (1,32,160,160) (1,2,160,160,16) | 0x00000000 0x000c8000 0x000c8000
D RKNN: [13:38:22.489] 9   ConvExSwish    /model.2/Concat_output_0                      INT8      NC1HWC2      (1,96,160,160) (1,6,160,160,16) | 0x00320000 0x00578000 0x00258000
D RKNN: [13:38:22.489] 10  ConvExSwish    /model.2/cv2/act/Mul_output_0                 INT8      NC1HWC2      (1,64,160,160) (1,4,160,160,16) | 0x00000000 0x00190000 0x00190000
D RKNN: [13:38:22.489] 11  ConvExSwish    /model.3/act/Mul_output_0                     INT8      NC1HWC2      (1,128,80,80)  (1,8,80,80,16)   | 0x00190000 0x00258000 0x000c8000
D RKNN: [13:38:22.489] 12  Split          /model.4/cv1/act/Mul_output_0                 INT8      NC1HWC2      (1,128,80,80)  (1,8,80,80,16)   | 0x00000000 0x000c8000 0x000c8000
D RKNN: [13:38:22.489] 13  ConvExSwish    /model.4/Split_output_1                       INT8      NC1HWC2      (1,64,80,80)   (1,4,80,80,16)   | 0x000c8000 0x0012c000 0x00064000
D RKNN: [13:38:22.489] 14  ConvExSwish    /model.4/m.0/cv1/act/Mul_output_0             INT8      NC1HWC2      (1,64,80,80)   (1,4,80,80,16)   | 0x00000000 0x00064000 0x00064000
D RKNN: [13:38:22.489] 15  Add            /model.4/Split_output_1                       INT8      NC1HWC2      (1,64,80,80)   (1,4,80,80,16)   | 0x000c8000 0x0012c000 0x00064000
D RKNN: [13:38:22.489] 15  Add            /model.4/m.0/cv2/act/Mul_output_0             INT8      NC1HWC2      (1,64,80,80)   (1,4,80,80,16)   | 0x00064000 0x000c8000 0x00064000
D RKNN: [13:38:22.489] 16  ConvExSwish    /model.4/m.0/Add_output_0                     INT8      NC1HWC2      (1,64,80,80)   (1,4,80,80,16)   | 0x00000000 0x00064000 0x00064000
D RKNN: [13:38:22.489] 17  ConvExSwish    /model.4/m.1/cv1/act/Mul_output_0             INT8      NC1HWC2      (1,64,80,80)   (1,4,80,80,16)   | 0x00064000 0x000c8000 0x00064000
D RKNN: [13:38:22.489] 18  Add            /model.4/m.0/Add_output_0                     INT8      NC1HWC2      (1,64,80,80)   (1,4,80,80,16)   | 0x00000000 0x00064000 0x00064000
D RKNN: [13:38:22.489] 18  Add            /model.4/m.1/cv2/act/Mul_output_0             INT8      NC1HWC2      (1,64,80,80)   (1,4,80,80,16)   | 0x00190000 0x001f4000 0x00064000
D RKNN: [13:38:22.489] 19  Concat         /model.4/Split_output_0                       INT8      NC1HWC2      (1,64,80,80)   (1,4,80,80,16)   | 0x0012c000 0x00190000 0x00064000
D RKNN: [13:38:22.489] 19  Concat         /model.4/Split_output_1                       INT8      NC1HWC2      (1,64,80,80)   (1,4,80,80,16)   | 0x000c8000 0x0012c000 0x00064000
D RKNN: [13:38:22.489] 19  Concat         /model.4/m.0/Add_output_0                     INT8      NC1HWC2      (1,64,80,80)   (1,4,80,80,16)   | 0x00000000 0x00064000 0x00064000
D RKNN: [13:38:22.489] 19  Concat         /model.4/m.1/Add_output_0                     INT8      NC1HWC2      (1,64,80,80)   (1,4,80,80,16)   | 0x00064000 0x000c8000 0x00064000
D RKNN: [13:38:22.489] 20  ConvExSwish    /model.4/Concat_output_0                      INT8      NC1HWC2      (1,256,80,80)  (1,16,80,80,16)  | 0x00190000 0x00320000 0x00190000
D RKNN: [13:38:22.489] 21  ConvExSwish    /model.4/cv2/act/Mul_output_0                 INT8      NC1HWC2      (1,128,80,80)  (1,8,80,80,16)   | 0x00000000 0x000c8000 0x000c8000
D RKNN: [13:38:22.489] 22  ConvExSwish    /model.5/act/Mul_output_0                     INT8      NC1HWC2      (1,256,40,40)  (1,16,40,40,16)  | 0x000c8000 0x0012c000 0x00064000
D RKNN: [13:38:22.489] 23  Split          /model.6/cv1/act/Mul_output_0                 INT8      NC1HWC2      (1,256,40,40)  (1,16,40,40,16)  | 0x0012c000 0x00190000 0x00064000
D RKNN: [13:38:22.489] 24  ConvExSwish    /model.6/Split_output_1                       INT8      NC1HWC2      (1,128,40,40)  (1,8,40,40,16)   | 0x000c8000 0x000fa000 0x00032000
D RKNN: [13:38:22.489] 25  ConvExSwish    /model.6/m.0/cv1/act/Mul_output_0             INT8      NC1HWC2      (1,128,40,40)  (1,8,40,40,16)   | 0x0012c000 0x0015e000 0x00032000
D RKNN: [13:38:22.489] 26  Add            /model.6/Split_output_1                       INT8      NC1HWC2      (1,128,40,40)  (1,8,40,40,16)   | 0x000c8000 0x000fa000 0x00032000
D RKNN: [13:38:22.489] 26  Add            /model.6/m.0/cv2/act/Mul_output_0             INT8      NC1HWC2      (1,128,40,40)  (1,8,40,40,16)   | 0x0015e000 0x00190000 0x00032000
D RKNN: [13:38:22.489] 27  ConvExSwish    /model.6/m.0/Add_output_0                     INT8      NC1HWC2      (1,128,40,40)  (1,8,40,40,16)   | 0x0012c000 0x0015e000 0x00032000
D RKNN: [13:38:22.489] 28  ConvExSwish    /model.6/m.1/cv1/act/Mul_output_0             INT8      NC1HWC2      (1,128,40,40)  (1,8,40,40,16)   | 0x0015e000 0x00190000 0x00032000
D RKNN: [13:38:22.489] 29  Add            /model.6/m.0/Add_output_0                     INT8      NC1HWC2      (1,128,40,40)  (1,8,40,40,16)   | 0x0012c000 0x0015e000 0x00032000
D RKNN: [13:38:22.489] 29  Add            /model.6/m.1/cv2/act/Mul_output_0             INT8      NC1HWC2      (1,128,40,40)  (1,8,40,40,16)   | 0x00190000 0x001c2000 0x00032000
D RKNN: [13:38:22.489] 30  Concat         /model.6/Split_output_0                       INT8      NC1HWC2      (1,128,40,40)  (1,8,40,40,16)   | 0x000fa000 0x0012c000 0x00032000
D RKNN: [13:38:22.489] 30  Concat         /model.6/Split_output_1                       INT8      NC1HWC2      (1,128,40,40)  (1,8,40,40,16)   | 0x000c8000 0x000fa000 0x00032000
D RKNN: [13:38:22.489] 30  Concat         /model.6/m.0/Add_output_0                     INT8      NC1HWC2      (1,128,40,40)  (1,8,40,40,16)   | 0x0012c000 0x0015e000 0x00032000
D RKNN: [13:38:22.489] 30  Concat         /model.6/m.1/Add_output_0                     INT8      NC1HWC2      (1,128,40,40)  (1,8,40,40,16)   | 0x0015e000 0x00190000 0x00032000
D RKNN: [13:38:22.489] 31  ConvExSwish    /model.6/Concat_output_0                      INT8      NC1HWC2      (1,512,40,40)  (1,32,40,40,16)  | 0x00190000 0x00258000 0x000c8000
D RKNN: [13:38:22.489] 32  ConvExSwish    /model.6/cv2/act/Mul_output_0                 INT8      NC1HWC2      (1,256,40,40)  (1,16,40,40,16)  | 0x000c8000 0x0012c000 0x00064000
D RKNN: [13:38:22.489] 33  ConvExSwish    /model.7/act/Mul_output_0                     INT8      NC1HWC2      (1,512,20,20)  (1,32,20,20,16)  | 0x0012c000 0x0015e000 0x00032000
D RKNN: [13:38:22.489] 34  Split          /model.8/cv1/act/Mul_output_0                 INT8      NC1HWC2      (1,512,20,20)  (1,32,20,20,16)  | 0x0015e000 0x00190000 0x00032000
D RKNN: [13:38:22.489] 35  ConvExSwish    /model.8/Split_output_1                       INT8      NC1HWC2      (1,256,20,20)  (1,16,20,20,16)  | 0x0012c000 0x00145000 0x00019000
D RKNN: [13:38:22.489] 36  ConvExSwish    /model.8/m.0/cv1/act/Mul_output_0             INT8      NC1HWC2      (1,256,20,20)  (1,16,20,20,16)  | 0x0015e000 0x00177000 0x00019000
D RKNN: [13:38:22.489] 37  Add            /model.8/Split_output_1                       INT8      NC1HWC2      (1,256,20,20)  (1,16,20,20,16)  | 0x0012c000 0x00145000 0x00019000
D RKNN: [13:38:22.489] 37  Add            /model.8/m.0/cv2/act/Mul_output_0             INT8      NC1HWC2      (1,256,20,20)  (1,16,20,20,16)  | 0x00177000 0x00190000 0x00019000
D RKNN: [13:38:22.489] 38  Concat         /model.8/Split_output_0                       INT8      NC1HWC2      (1,256,20,20)  (1,16,20,20,16)  | 0x00145000 0x0015e000 0x00019000
D RKNN: [13:38:22.489] 38  Concat         /model.8/Split_output_1                       INT8      NC1HWC2      (1,256,20,20)  (1,16,20,20,16)  | 0x0012c000 0x00145000 0x00019000
D RKNN: [13:38:22.489] 38  Concat         /model.8/m.0/Add_output_0                     INT8      NC1HWC2      (1,256,20,20)  (1,16,20,20,16)  | 0x0015e000 0x00177000 0x00019000
D RKNN: [13:38:22.489] 39  ConvExSwish    /model.8/Concat_output_0                      INT8      NC1HWC2      (1,768,20,20)  (1,48,20,20,16)  | 0x00177000 0x001c2000 0x0004b000
D RKNN: [13:38:22.489] 40  ConvExSwish    /model.8/cv2/act/Mul_output_0                 INT8      NC1HWC2      (1,512,20,20)  (1,32,20,20,16)  | 0x0012c000 0x0015e000 0x00032000
D RKNN: [13:38:22.489] 41  MaxPool        /model.9/cv1/act/Mul_output_0                 INT8      NC1HWC2      (1,256,20,20)  (1,16,20,20,16)  | 0x0015e000 0x00177000 0x00019000
D RKNN: [13:38:22.489] 42  MaxPool        /model.9/m/MaxPool_output_0                   INT8      NC1HWC2      (1,256,20,20)  (1,16,20,20,16)  | 0x0012c000 0x00145000 0x00019000
D RKNN: [13:38:22.489] 43  MaxPool        /model.9/m_1/MaxPool_output_0                 INT8      NC1HWC2      (1,256,20,20)  (1,16,20,20,16)  | 0x00145000 0x0015e000 0x00019000
D RKNN: [13:38:22.489] 44  Concat         /model.9/cv1/act/Mul_output_0                 INT8      NC1HWC2      (1,256,20,20)  (1,16,20,20,16)  | 0x0015e000 0x00177000 0x00019000
D RKNN: [13:38:22.489] 44  Concat         /model.9/m/MaxPool_output_0                   INT8      NC1HWC2      (1,256,20,20)  (1,16,20,20,16)  | 0x0012c000 0x00145000 0x00019000
D RKNN: [13:38:22.489] 44  Concat         /model.9/m_1/MaxPool_output_0                 INT8      NC1HWC2      (1,256,20,20)  (1,16,20,20,16)  | 0x00145000 0x0015e000 0x00019000
D RKNN: [13:38:22.489] 44  Concat         /model.9/m_2/MaxPool_output_0                 INT8      NC1HWC2      (1,256,20,20)  (1,16,20,20,16)  | 0x00177000 0x00190000 0x00019000
D RKNN: [13:38:22.489] 45  ConvExSwish    /model.9/Concat_output_0                      INT8      NC1HWC2      (1,1024,20,20) (1,64,20,20,16)  | 0x00190000 0x001f4000 0x00064000
D RKNN: [13:38:22.489] 46  Resize         /model.9/cv2/act/Mul_output_0                 INT8      NC1HWC2      (1,512,20,20)  (1,32,20,20,16)  | 0x0012c000 0x0015e000 0x00032000
D RKNN: [13:38:22.489] 47  Concat         /model.10/Resize_output_0                     INT8      NC1HWC2      (1,512,40,40)  (1,32,40,40,16)  | 0x0015e040 0x00226040 0x000c8000
D RKNN: [13:38:22.489] 47  Concat         /model.6/cv2/act/Mul_output_0                 INT8      NC1HWC2      (1,256,40,40)  (1,16,40,40,16)  | 0x000c8000 0x0012c000 0x00064000
D RKNN: [13:38:22.489] 48  ConvExSwish    /model.11/Concat_output_0                     INT8      NC1HWC2      (1,768,40,40)  (1,48,40,40,16)  | 0x00226040 0x00352040 0x0012c000
D RKNN: [13:38:22.489] 49  Split          /model.12/cv1/act/Mul_output_0                INT8      NC1HWC2      (1,256,40,40)  (1,16,40,40,16)  | 0x000c8000 0x0012c000 0x00064000
D RKNN: [13:38:22.489] 50  ConvExSwish    /model.12/Split_output_1                      INT8      NC1HWC2      (1,128,40,40)  (1,8,40,40,16)   | 0x00190000 0x001c2000 0x00032000
D RKNN: [13:38:22.489] 51  ConvExSwish    /model.12/m.0/cv1/act/Mul_output_0            INT8      NC1HWC2      (1,128,40,40)  (1,8,40,40,16)   | 0x000c8000 0x000fa000 0x00032000
D RKNN: [13:38:22.489] 52  Concat         /model.12/Split_output_0                      INT8      NC1HWC2      (1,128,40,40)  (1,8,40,40,16)   | 0x0015e000 0x00190000 0x00032000
D RKNN: [13:38:22.489] 52  Concat         /model.12/Split_output_1                      INT8      NC1HWC2      (1,128,40,40)  (1,8,40,40,16)   | 0x00190000 0x001c2000 0x00032000
D RKNN: [13:38:22.489] 52  Concat         /model.12/m.0/cv2/act/Mul_output_0            INT8      NC1HWC2      (1,128,40,40)  (1,8,40,40,16)   | 0x000fa000 0x0012c000 0x00032000
D RKNN: [13:38:22.489] 53  ConvExSwish    /model.12/Concat_output_0                     INT8      NC1HWC2      (1,384,40,40)  (1,24,40,40,16)  | 0x001c2000 0x00258000 0x00096000
D RKNN: [13:38:22.489] 54  Resize         /model.12/cv2/act/Mul_output_0                INT8      NC1HWC2      (1,256,40,40)  (1,16,40,40,16)  | 0x000c8000 0x0012c000 0x00064000
D RKNN: [13:38:22.489] 55  Concat         /model.13/Resize_output_0                     INT8      NC1HWC2      (1,256,80,80)  (1,16,80,80,16)  | 0x0015e040 0x002ee040 0x00190000
D RKNN: [13:38:22.489] 55  Concat         /model.4/cv2/act/Mul_output_0                 INT8      NC1HWC2      (1,128,80,80)  (1,8,80,80,16)   | 0x00000000 0x000c8000 0x000c8000
D RKNN: [13:38:22.489] 56  ConvExSwish    /model.14/Concat_output_0                     INT8      NC1HWC2      (1,384,80,80)  (1,24,80,80,16)  | 0x002ee040 0x00546040 0x00258000
D RKNN: [13:38:22.489] 57  Split          /model.15/cv1/act/Mul_output_0                INT8      NC1HWC2      (1,128,80,80)  (1,8,80,80,16)   | 0x00000000 0x000c8000 0x000c8000
D RKNN: [13:38:22.489] 58  ConvExSwish    /model.15/Split_output_1                      INT8      NC1HWC2      (1,64,80,80)   (1,4,80,80,16)   | 0x001c2000 0x00226000 0x00064000
D RKNN: [13:38:22.489] 59  ConvExSwish    /model.15/m.0/cv1/act/Mul_output_0            INT8      NC1HWC2      (1,64,80,80)   (1,4,80,80,16)   | 0x00000000 0x00064000 0x00064000
D RKNN: [13:38:22.489] 60  Concat         /model.15/Split_output_0                      INT8      NC1HWC2      (1,64,80,80)   (1,4,80,80,16)   | 0x0015e000 0x001c2000 0x00064000
D RKNN: [13:38:22.489] 60  Concat         /model.15/Split_output_1                      INT8      NC1HWC2      (1,64,80,80)   (1,4,80,80,16)   | 0x001c2000 0x00226000 0x00064000
D RKNN: [13:38:22.489] 60  Concat         /model.15/m.0/cv2/act/Mul_output_0            INT8      NC1HWC2      (1,64,80,80)   (1,4,80,80,16)   | 0x00064000 0x000c8000 0x00064000
D RKNN: [13:38:22.489] 61  ConvExSwish    /model.15/Concat_output_0                     INT8      NC1HWC2      (1,192,80,80)  (1,12,80,80,16)  | 0x00226000 0x00352000 0x0012c000
D RKNN: [13:38:22.489] 62  ConvExSwish    /model.15/cv2/act/Mul_output_0                INT8      NC1HWC2      (1,128,80,80)  (1,8,80,80,16)   | 0x00000000 0x000c8000 0x000c8000
D RKNN: [13:38:22.489] 63  Concat         /model.16/act/Mul_output_0                    INT8      NC1HWC2      (1,128,40,40)  (1,8,40,40,16)   | 0x0015e000 0x00190000 0x00032000
D RKNN: [13:38:22.489] 63  Concat         /model.12/cv2/act/Mul_output_0                INT8      NC1HWC2      (1,256,40,40)  (1,16,40,40,16)  | 0x000c8000 0x0012c000 0x00064000
D RKNN: [13:38:22.489] 64  ConvExSwish    /model.15/cv2/act/Mul_output_0                INT8      NC1HWC2      (1,128,80,80)  (1,8,80,80,16)   | 0x00000000 0x000c8000 0x000c8000
D RKNN: [13:38:22.489] 65  ConvExSwish    /model.15/cv2/act/Mul_output_0                INT8      NC1HWC2      (1,128,80,80)  (1,8,80,80,16)   | 0x00000000 0x000c8000 0x000c8000
D RKNN: [13:38:22.489] 66  ConvExSwish    /model.17/Concat_output_0                     INT8      NC1HWC2      (1,384,40,40)  (1,24,40,40,16)  | 0x00190000 0x00226000 0x00096000
D RKNN: [13:38:22.489] 67  Split          /model.18/cv1/act/Mul_output_0                INT8      NC1HWC2      (1,256,40,40)  (1,16,40,40,16)  | 0x00000000 0x00064000 0x00064000
D RKNN: [13:38:22.489] 68  ConvExSwish    /model.22/cv2.0/cv2.0.0/act/Mul_output_0      INT8      NC1HWC2      (1,64,80,80)   (1,4,80,80,16)   | 0x000c8000 0x0012c000 0x00064000
D RKNN: [13:38:22.489] 69  Conv           /model.22/cv2.0/cv2.0.1/act/Mul_output_0      INT8      NC1HWC2      (1,64,80,80)   (1,4,80,80,16)   | 0x00000000 0x00064000 0x00064000
D RKNN: [13:38:22.489] 70  ConvExSwish    /model.22/cv3.0/cv3.0.0/act/Mul_output_0      INT8      NC1HWC2      (1,128,80,80)  (1,8,80,80,16)   | 0x00226000 0x002ee000 0x000c8000
D RKNN: [13:38:22.489] 71  Conv           /model.22/cv3.0/cv3.0.1/act/Mul_output_0      INT8      NC1HWC2      (1,128,80,80)  (1,8,80,80,16)   | 0x0015e000 0x00226000 0x000c8000
D RKNN: [13:38:22.489] 72  Concat         /model.22/cv2.0/cv2.0.2/Conv_output_0         INT8      NC1HWC2      (1,64,80,80)   (1,4,80,80,16)   | 0x000c8000 0x0012c000 0x00064000
D RKNN: [13:38:22.489] 72  Concat         /model.22/cv3.0/cv3.0.2/Conv_output_0         INT8      NC1HWC2      (1,80,80,80)   (1,6,80,80,16)   | 0x00226000 0x002bc000 0x00096000
D RKNN: [13:38:22.489] 73  ConvExSwish    /model.18/Split_output_1                      INT8      NC1HWC2      (1,128,40,40)  (1,8,40,40,16)   | 0x00064000 0x00096000 0x00032000
D RKNN: [13:38:22.489] 74  ConvExSwish    /model.18/m.0/cv1/act/Mul_output_0            INT8      NC1HWC2      (1,128,40,40)  (1,8,40,40,16)   | 0x00000000 0x00032000 0x00032000
D RKNN: [13:38:22.489] 75  Concat         /model.18/Split_output_0                      INT8      NC1HWC2      (1,128,40,40)  (1,8,40,40,16)   | 0x00096000 0x000c8000 0x00032000
D RKNN: [13:38:22.489] 75  Concat         /model.18/Split_output_1                      INT8      NC1HWC2      (1,128,40,40)  (1,8,40,40,16)   | 0x00064000 0x00096000 0x00032000
D RKNN: [13:38:22.489] 75  Concat         /model.18/m.0/cv2/act/Mul_output_0            INT8      NC1HWC2      (1,128,40,40)  (1,8,40,40,16)   | 0x00032000 0x00064000 0x00032000
D RKNN: [13:38:22.489] 76  ConvExSwish    /model.18/Concat_output_0                     INT8      NC1HWC2      (1,384,40,40)  (1,24,40,40,16)  | 0x0015e000 0x001f4000 0x00096000
D RKNN: [13:38:22.489] 77  ConvExSwish    /model.18/cv2/act/Mul_output_0                INT8      NC1HWC2      (1,256,40,40)  (1,16,40,40,16)  | 0x001f4000 0x00258000 0x00064000
D RKNN: [13:38:22.489] 78  Concat         /model.19/act/Mul_output_0                    INT8      NC1HWC2      (1,256,20,20)  (1,16,20,20,16)  | 0x00258000 0x00271000 0x00019000
D RKNN: [13:38:22.489] 78  Concat         /model.9/cv2/act/Mul_output_0                 INT8      NC1HWC2      (1,512,20,20)  (1,32,20,20,16)  | 0x0012c000 0x0015e000 0x00032000
D RKNN: [13:38:22.489] 79  ConvExSwish    /model.18/cv2/act/Mul_output_0                INT8      NC1HWC2      (1,256,40,40)  (1,16,40,40,16)  | 0x001f4000 0x00258000 0x00064000
D RKNN: [13:38:22.489] 80  ConvExSwish    /model.18/cv2/act/Mul_output_0                INT8      NC1HWC2      (1,256,40,40)  (1,16,40,40,16)  | 0x001f4000 0x00258000 0x00064000
D RKNN: [13:38:22.489] 81  ConvExSwish    /model.20/Concat_output_0                     INT8      NC1HWC2      (1,768,20,20)  (1,48,20,20,16)  | 0x00271000 0x002bc000 0x0004b000
D RKNN: [13:38:22.489] 82  Split          /model.21/cv1/act/Mul_output_0                INT8      NC1HWC2      (1,512,20,20)  (1,32,20,20,16)  | 0x00032000 0x00064000 0x00032000
D RKNN: [13:38:22.489] 83  ConvExSwish    /model.22/cv2.1/cv2.1.0/act/Mul_output_0      INT8      NC1HWC2      (1,64,40,40)   (1,4,40,40,16)   | 0x00258000 0x00271000 0x00019000
D RKNN: [13:38:22.489] 84  Conv           /model.22/cv2.1/cv2.1.1/act/Mul_output_0      INT8      NC1HWC2      (1,64,40,40)   (1,4,40,40,16)   | 0x002a3000 0x002bc000 0x00019000
D RKNN: [13:38:22.489] 85  ConvExSwish    /model.22/cv3.1/cv3.1.0/act/Mul_output_0      INT8      NC1HWC2      (1,128,40,40)  (1,8,40,40,16)   | 0x00000000 0x00032000 0x00032000
D RKNN: [13:38:22.489] 86  Conv           /model.22/cv3.1/cv3.1.1/act/Mul_output_0      INT8      NC1HWC2      (1,128,40,40)  (1,8,40,40,16)   | 0x0004b000 0x0007d000 0x00032000
D RKNN: [13:38:22.489] 87  Concat         /model.22/cv2.1/cv2.1.2/Conv_output_0         INT8      NC1HWC2      (1,64,40,40)   (1,4,40,40,16)   | 0x00032000 0x0004b000 0x00019000
D RKNN: [13:38:22.489] 87  Concat         /model.22/cv3.1/cv3.1.2/Conv_output_0         INT8      NC1HWC2      (1,80,40,40)   (1,6,40,40,16)   | 0x00000000 0x00025800 0x00025800
D RKNN: [13:38:22.489] 88  ConvExSwish    /model.21/Split_output_1                      INT8      NC1HWC2      (1,256,20,20)  (1,16,20,20,16)  | 0x0028a000 0x002a3000 0x00019000
D RKNN: [13:38:22.489] 89  ConvExSwish    /model.21/m.0/cv1/act/Mul_output_0            INT8      NC1HWC2      (1,256,20,20)  (1,16,20,20,16)  | 0x002a3000 0x002bc000 0x00019000
D RKNN: [13:38:22.489] 90  Concat         /model.21/Split_output_0                      INT8      NC1HWC2      (1,256,20,20)  (1,16,20,20,16)  | 0x00271000 0x0028a000 0x00019000
D RKNN: [13:38:22.489] 90  Concat         /model.21/Split_output_1                      INT8      NC1HWC2      (1,256,20,20)  (1,16,20,20,16)  | 0x0028a000 0x002a3000 0x00019000
D RKNN: [13:38:22.489] 90  Concat         /model.21/m.0/cv2/act/Mul_output_0            INT8      NC1HWC2      (1,256,20,20)  (1,16,20,20,16)  | 0x00000000 0x00019000 0x00019000
D RKNN: [13:38:22.489] 91  ConvExSwish    /model.21/Concat_output_0                     INT8      NC1HWC2      (1,768,20,20)  (1,48,20,20,16)  | 0x00083400 0x000ce400 0x0004b000
D RKNN: [13:38:22.489] 92  ConvExSwish    /model.21/cv2/act/Mul_output_0                INT8      NC1HWC2      (1,512,20,20)  (1,32,20,20,16)  | 0x00000000 0x00032000 0x00032000
D RKNN: [13:38:22.489] 93  ConvExSwish    /model.21/cv2/act/Mul_output_0                INT8      NC1HWC2      (1,512,20,20)  (1,32,20,20,16)  | 0x00000000 0x00032000 0x00032000
D RKNN: [13:38:22.489] 94  ConvExSwish    /model.22/cv2.2/cv2.2.0/act/Mul_output_0      INT8      NC1HWC2      (1,64,20,20)   (1,4,20,20,16)   | 0x00032000 0x00038400 0x00006400
D RKNN: [13:38:22.489] 95  Conv           /model.22/cv2.2/cv2.2.1/act/Mul_output_0      INT8      NC1HWC2      (1,64,20,20)   (1,4,20,20,16)   | 0x00044c00 0x0004b000 0x00006400
D RKNN: [13:38:22.489] 96  ConvExSwish    /model.22/cv3.2/cv3.2.0/act/Mul_output_0      INT8      NC1HWC2      (1,128,20,20)  (1,8,20,20,16)   | 0x00038400 0x00044c00 0x0000c800
D RKNN: [13:38:22.489] 97  Conv           /model.22/cv3.2/cv3.2.1/act/Mul_output_0      INT8      NC1HWC2      (1,128,20,20)  (1,8,20,20,16)   | 0x00006400 0x00012c00 0x0000c800
D RKNN: [13:38:22.489] 98  Concat         /model.22/cv2.2/cv2.2.2/Conv_output_0         INT8      NC1HWC2      (1,64,20,20)   (1,4,20,20,16)   | 0x00000000 0x00006400 0x00006400
D RKNN: [13:38:22.489] 98  Concat         /model.22/cv3.2/cv3.2.2/Conv_output_0         INT8      NC1HWC2      (1,80,20,20)   (1,6,20,20,16)   | 0x00012c00 0x0001c200 0x00009600
D RKNN: [13:38:22.489] 99  Reshape        /model.22/Concat_output_0                     INT8      NC1HWC2      (1,144,80,80)  (1,9,80,80,16)   | 0x002bc000 0x0039d000 0x000e1000
D RKNN: [13:38:22.489] 99  Reshape        /model.22/Concat_output_0_exSecondary         INT8      NC1HWC2      (1,144,80,80)  (1,9,80,80,16)   | 0x00083400 0x00164400 0x000e1000
D RKNN: [13:38:22.489] 100 Reshape        /model.22/Concat_1_output_0                   INT8      NC1HWC2      (1,144,40,40)  (1,9,40,40,16)   | 0x0004b000 0x00083400 0x00038400
D RKNN: [13:38:22.489] 100 Reshape        /model.22/Concat_1_output_0_exSecondary       INT8      NC1HWC2      (1,144,40,40)  (1,9,40,40,16)   | 0x00083400 0x000bb800 0x00038400
D RKNN: [13:38:22.489] 101 Reshape        /model.22/Concat_2_output_0                   INT8      NC1HWC2      (1,144,20,20)  (1,9,20,20,16)   | 0x0001c200 0x0002a300 0x0000e100
D RKNN: [13:38:22.489] 101 Reshape        /model.22/Concat_2_output_0_exSecondary       INT8      NC1HWC2      (1,144,20,20)  (1,9,20,20,16)   | 0x00000000 0x0000e100 0x0000e100
D RKNN: [13:38:22.489] 102 Concat         /model.22/Reshape_output_0_rs                 INT8      NC1HWC2      (1,144,1,6400) (1,9,1,6400,16)  | 0x002bc000 0x0039d000 0x000e1000
D RKNN: [13:38:22.489] 102 Concat         /model.22/Reshape_1_output_0_rs               INT8      NC1HWC2      (1,144,1,1600) (1,9,1,1600,16)  | 0x0004b000 0x00083400 0x00038400
D RKNN: [13:38:22.489] 102 Concat         /model.22/Reshape_2_output_0_rs               INT8      NC1HWC2      (1,144,1,400)  (1,9,1,400,16)   | 0x0001c200 0x0002a300 0x0000e100
D RKNN: [13:38:22.489] 103 Split          /model.22/Concat_3_output_0-rs                INT8      NC1HWC2      (1,144,1,8400) (1,9,1,8400,16)  | 0x00083400 0x001aa900 0x00127500
D RKNN: [13:38:22.489] 104 Sigmoid        /model.22/Split_output_1-rs                   INT8      NC1HWC2      (1,80,1,8400)  (1,5,1,8400,16)  | 0x001aa900 0x0024ea00 0x000a4100
D RKNN: [13:38:22.489] 105 Reshape        /model.22/Split_output_0-rs                   INT8      NC1HWC2      (1,64,1,8400)  (1,4,1,8400,16)  | 0x00000000 0x00083400 0x00083400
D RKNN: [13:38:22.489] 105 Reshape        /model.22/Split_output_0-rs_exSecondary       INT8      NC1HWC2      (1,64,1,8400)  (1,4,1,8400,16)  | 0x00127500 0x001aa900 0x00083400
D RKNN: [13:38:22.489] 106 Transpose      /model.22/dfl/Reshape_output_0                INT8      NC1HWC2      (4,16,1,8400)  (4,1,1,8400,16)  | 0x00000000 0x00083400 0x00083400
D RKNN: [13:38:22.489] 107 exSoftmax13    /model.22/dfl/Transpose_output_0              INT8      NC1HWC2      (1,16,4,8400)  (1,1,4,8400,16)  | 0x00000000 0x00083400 0x00083400
D RKNN: [13:38:22.489] 107 exSoftmax13    /model.22/dfl/Transpose_output_0_exSecondary  INT8      NC1HWC2      (1,16,4,8400)  (1,7,4,8400,16)  | 0x00127500 0x004be140 0x00396c40
D RKNN: [13:38:22.489] 108 Conv           /model.22/dfl/Softmax_output_0                INT8      NC1HWC2      (1,16,4,8400)  (1,1,4,8400,16)  | 0x004be140 0x00541540 0x00083400
D RKNN: [13:38:22.489] 109 Transpose      /model.22/dfl/conv/Conv_output_0              INT8      NC1HWC2      (1,1,4,8400)   (1,2,4,8400,16)  | 0x00127500 0x0022dd00 0x00106800
D RKNN: [13:38:22.489] 109 Transpose      /model.22/dfl/conv/Conv_output_0_exSecondary  INT8      NC1HWC2      (1,1,4,8400)   (1,4,4,8400,16)  | 0x0022dd00 0x0045ba00 0x0022dd00
D RKNN: [13:38:22.489] 110 Split          /model.22/dfl/Reshape_1_output_0_rs           INT8      NC1HWC2      (1,4,1,8400)   (1,16,1,8400,16) | 0x0045ba00*0x00668a00 0x0020d000
D RKNN: [13:38:22.489] 111 Sub            /model.22/Slice_output_0-rs                   INT8      NC1HWC2      (1,2,1,8400)   (1,1,1,8400,16)  | 0x00041a00 0x00062700 0x00020d00
D RKNN: [13:38:22.489] 112 Add            /model.22/Slice_1_output_0-rs                 INT8      NC1HWC2      (1,2,1,8400)   (1,2,1,8400,16)  | 0x00000000 0x00041a00 0x00041a00
D RKNN: [13:38:22.489] 113 Sub            /model.22/Add_1_output_0-rs                   INT8      NC1HWC2      (1,2,1,8400)   (1,1,1,8400,16)  | 0x00041a00 0x00062700 0x00020d00
D RKNN: [13:38:22.489] 113 Sub            /model.22/Sub_output_0-rs                     INT8      NC1HWC2      (1,2,1,8400)   (1,1,1,8400,16)  | 0x00062700 0x00083400 0x00020d00
D RKNN: [13:38:22.489] 114 Add            /model.22/Sub_output_0-rs                     INT8      NC1HWC2      (1,2,1,8400)   (1,1,1,8400,16)  | 0x00062700 0x00083400 0x00020d00
D RKNN: [13:38:22.489] 114 Add            /model.22/Add_1_output_0-rs                   INT8      NC1HWC2      (1,2,1,8400)   (1,1,1,8400,16)  | 0x00041a00 0x00062700 0x00020d00
D RKNN: [13:38:22.489] 115 Mul            /model.22/Add_2_output_0-rs                   INT8      NC1HWC2      (1,2,1,8400)   (1,1,1,8400,16)  | 0x00020d00 0x00041a00 0x00020d00
D RKNN: [13:38:22.489] 116 Concat         /model.22/Div_1_output_0-rs                   INT8      NC1HWC2      (1,2,1,8400)   (1,1,1,8400,16)  | 0x00041a00 0x00062700 0x00020d00
D RKNN: [13:38:22.489] 116 Concat         /model.22/Sub_1_output_0-rs                   INT8      NC1HWC2      (1,2,1,8400)   (1,1,1,8400,16)  | 0x00000000 0x00020d00 0x00020d00
D RKNN: [13:38:22.489] 116 Concat         /model.22/Div_1_output_0-rs_exSecondary       INT8      NC1HWC2      (1,4,1,8400)   (1,2,1,8400,16)  | 0x00127500 0x00168f00 0x00041a00
D RKNN: [13:38:22.489] 117 Mul            /model.22/Concat_4_output_0-rs-rs             INT8      NC1HWC2      (1,4,1,8400)   (1,2,1,8400,16)  | 0x00168f00 0x001aa900 0x00041a00
D RKNN: [13:38:22.489] 117 Mul            /model.22/Concat_4_output_0-rs-rs_exSecondary INT8      NC1HWC2      (1,4,1,8400)   (1,4,1,8400,16)  | 0x00000000 0x00083400 0x00083400
D RKNN: [13:38:22.489] 118 Concat         /model.22/Mul_2_output_0-rs                   INT8      NC1HWC2      (1,4,1,8400)   (1,1,1,8400,16)  | 0x00127500 0x00148200 0x00020d00
D RKNN: [13:38:22.489] 118 Concat         /model.22/Sigmoid_output_0-rs                 INT8      NC1HWC2      (1,80,1,8400)  (1,5,1,8400,16)  | 0x00083400 0x00127500 0x000a4100
D RKNN: [13:38:22.489] 118 Concat         /model.22/Mul_2_output_0-rs_exSecondary       INT8      NC1HWC2      (1,84,1,8400)  (1,6,1,8400,16)  | 0x00148200 0x0020d000 0x000c4e00
D RKNN: [13:38:22.489] 119 Reshape        output0-rs-rs                                 INT8      NC1HWC2      (1,84,1,8400)  (1,6,1,8400,16)  | 0x0020d000 0x002d1e00 0x000c4e00
D RKNN: [13:38:22.489] 120 OutputOperator output0                                       INT8      UNDEFINED    (1,84,8400)    (1,84,8400)      | 0x00000040 0x000ac480 0x000ac440
D RKNN: [13:38:22.489] ------------------------------------------------------------------------------------------------------------------------+---------------------------------
D RKNN: [13:38:22.489] --------------------------------------------------------------------------------------------------------------
D RKNN: [13:38:22.489]                                     Const Tensor Information Table
D RKNN: [13:38:22.489] ----------------------------------------------------------------------------+---------------------------------
D RKNN: [13:38:22.489] ID  User        Tensor                             DataType  OrigShape      |     [Start       End)       Size
D RKNN: [13:38:22.489] ----------------------------------------------------------------------------+---------------------------------
D RKNN: [13:38:22.489] 1   ConvExSwish model.0.conv.weight                INT8      (32,3,3,3)     | 0x00000000 0x00001200 0x00001200
D RKNN: [13:38:22.489] 1   ConvExSwish model.0.conv.bias                  INT32     (32)           | 0x00001200 0x00001300 0x00000100
D RKNN: [13:38:22.489] 2   ConvExSwish model.1.conv.weight                INT8      (64,32,3,3)    | 0x00001300 0x00005b00 0x00004800
D RKNN: [13:38:22.489] 2   ConvExSwish model.1.conv.bias                  INT32     (64)           | 0x00005b00 0x00005d00 0x00000200
D RKNN: [13:38:22.489] 3   ConvExSwish model.2.cv1.conv.weight            INT8      (64,64,1,1)    | 0x00005d00 0x00006d00 0x00001000
D RKNN: [13:38:22.489] 3   ConvExSwish model.2.cv1.conv.bias              INT32     (64)           | 0x00006d00 0x00006f00 0x00000200
D RKNN: [13:38:22.489] 4   Split       onnx::Split_137                    INT64     (2)            | 0x00ab6a40 0x00ab6a50 0x00000010
D RKNN: [13:38:22.489] 5   ConvExSwish model.2.m.0.cv1.conv.weight        INT8      (32,32,3,3)    | 0x00008900 0x0000ad00 0x00002400
D RKNN: [13:38:22.489] 5   ConvExSwish model.2.m.0.cv1.conv.bias          INT32     (32)           | 0x0000ad00 0x0000ae00 0x00000100
D RKNN: [13:38:22.489] 6   ConvExSwish model.2.m.0.cv2.conv.weight        INT8      (32,32,3,3)    | 0x0000ae00 0x0000d200 0x00002400
D RKNN: [13:38:22.489] 6   ConvExSwish model.2.m.0.cv2.conv.bias          INT32     (32)           | 0x0000d200 0x0000d300 0x00000100
D RKNN: [13:38:22.489] 9   ConvExSwish model.2.cv2.conv.weight            INT8      (64,96,1,1)    | 0x00006f00 0x00008700 0x00001800
D RKNN: [13:38:22.489] 9   ConvExSwish model.2.cv2.conv.bias              INT32     (64)           | 0x00008700 0x00008900 0x00000200
D RKNN: [13:38:22.489] 10  ConvExSwish model.3.conv.weight                INT8      (128,64,3,3)   | 0x0000d300 0x0001f300 0x00012000
D RKNN: [13:38:22.489] 10  ConvExSwish model.3.conv.bias                  INT32     (128)          | 0x0001f300 0x0001f700 0x00000400
D RKNN: [13:38:22.489] 11  ConvExSwish model.4.cv1.conv.weight            INT8      (128,128,1,1)  | 0x0001f700 0x00023700 0x00004000
D RKNN: [13:38:22.489] 11  ConvExSwish model.4.cv1.conv.bias              INT32     (128)          | 0x00023700 0x00023b00 0x00000400
D RKNN: [13:38:22.489] 12  Split       onnx::Split_157                    INT64     (2)            | 0x00ab6a80 0x00ab6a90 0x00000010
D RKNN: [13:38:22.489] 13  ConvExSwish model.4.m.0.cv1.conv.weight        INT8      (64,64,3,3)    | 0x0002bf00 0x00034f00 0x00009000
D RKNN: [13:38:22.489] 13  ConvExSwish model.4.m.0.cv1.conv.bias          INT32     (64)           | 0x00034f00 0x00035100 0x00000200
D RKNN: [13:38:22.489] 14  ConvExSwish model.4.m.0.cv2.conv.weight        INT8      (64,64,3,3)    | 0x00035100 0x0003e100 0x00009000
D RKNN: [13:38:22.489] 14  ConvExSwish model.4.m.0.cv2.conv.bias          INT32     (64)           | 0x0003e100 0x0003e300 0x00000200
D RKNN: [13:38:22.489] 16  ConvExSwish model.4.m.1.cv1.conv.weight        INT8      (64,64,3,3)    | 0x0003e300 0x00047300 0x00009000
D RKNN: [13:38:22.489] 16  ConvExSwish model.4.m.1.cv1.conv.bias          INT32     (64)           | 0x00047300 0x00047500 0x00000200
D RKNN: [13:38:22.489] 17  ConvExSwish model.4.m.1.cv2.conv.weight        INT8      (64,64,3,3)    | 0x00047500 0x00050500 0x00009000
D RKNN: [13:38:22.489] 17  ConvExSwish model.4.m.1.cv2.conv.bias          INT32     (64)           | 0x00050500 0x00050700 0x00000200
D RKNN: [13:38:22.489] 20  ConvExSwish model.4.cv2.conv.weight            INT8      (128,256,1,1)  | 0x00023b00 0x0002bb00 0x00008000
D RKNN: [13:38:22.489] 20  ConvExSwish model.4.cv2.conv.bias              INT32     (128)          | 0x0002bb00 0x0002bf00 0x00000400
D RKNN: [13:38:22.489] 21  ConvExSwish model.5.conv.weight                INT8      (256,128,3,3)  | 0x00050700 0x00098700 0x00048000
D RKNN: [13:38:22.489] 21  ConvExSwish model.5.conv.bias                  INT32     (256)          | 0x00098700 0x00098f00 0x00000800
D RKNN: [13:38:22.489] 22  ConvExSwish model.6.cv1.conv.weight            INT8      (256,256,1,1)  | 0x00098f00 0x000a8f00 0x00010000
D RKNN: [13:38:22.489] 22  ConvExSwish model.6.cv1.conv.bias              INT32     (256)          | 0x000a8f00 0x000a9700 0x00000800
D RKNN: [13:38:22.489] 23  Split       onnx::Split_184                    INT64     (2)            | 0x00ab6ac0 0x00ab6ad0 0x00000010
D RKNN: [13:38:22.489] 24  ConvExSwish model.6.m.0.cv1.conv.weight        INT8      (128,128,3,3)  | 0x000c9f00 0x000edf00 0x00024000
D RKNN: [13:38:22.489] 24  ConvExSwish model.6.m.0.cv1.conv.bias          INT32     (128)          | 0x000edf00 0x000ee300 0x00000400
D RKNN: [13:38:22.489] 25  ConvExSwish model.6.m.0.cv2.conv.weight        INT8      (128,128,3,3)  | 0x000ee300 0x00112300 0x00024000
D RKNN: [13:38:22.489] 25  ConvExSwish model.6.m.0.cv2.conv.bias          INT32     (128)          | 0x00112300 0x00112700 0x00000400
D RKNN: [13:38:22.489] 27  ConvExSwish model.6.m.1.cv1.conv.weight        INT8      (128,128,3,3)  | 0x00112700 0x00136700 0x00024000
D RKNN: [13:38:22.489] 27  ConvExSwish model.6.m.1.cv1.conv.bias          INT32     (128)          | 0x00136700 0x00136b00 0x00000400
D RKNN: [13:38:22.489] 28  ConvExSwish model.6.m.1.cv2.conv.weight        INT8      (128,128,3,3)  | 0x00136b00 0x0015ab00 0x00024000
D RKNN: [13:38:22.489] 28  ConvExSwish model.6.m.1.cv2.conv.bias          INT32     (128)          | 0x0015ab00 0x0015af00 0x00000400
D RKNN: [13:38:22.489] 31  ConvExSwish model.6.cv2.conv.weight            INT8      (256,512,1,1)  | 0x000a9700 0x000c9700 0x00020000
D RKNN: [13:38:22.489] 31  ConvExSwish model.6.cv2.conv.bias              INT32     (256)          | 0x000c9700 0x000c9f00 0x00000800
D RKNN: [13:38:22.489] 32  ConvExSwish model.7.conv.weight                INT8      (512,256,3,3)  | 0x0015af00 0x0027af00 0x00120000
D RKNN: [13:38:22.489] 32  ConvExSwish model.7.conv.bias                  INT32     (512)          | 0x0027af00 0x0027bf00 0x00001000
D RKNN: [13:38:22.489] 33  ConvExSwish model.8.cv1.conv.weight            INT8      (512,512,1,1)  | 0x0027bf00 0x002bbf00 0x00040000
D RKNN: [13:38:22.489] 33  ConvExSwish model.8.cv1.conv.bias              INT32     (512)          | 0x002bbf00 0x002bcf00 0x00001000
D RKNN: [13:38:22.489] 34  Split       onnx::Split_211                    INT64     (2)            | 0x00ab6b00 0x00ab6b10 0x00000010
D RKNN: [13:38:22.489] 35  ConvExSwish model.8.m.0.cv1.conv.weight        INT8      (256,256,3,3)  | 0x0031df00 0x003adf00 0x00090000
D RKNN: [13:38:22.489] 35  ConvExSwish model.8.m.0.cv1.conv.bias          INT32     (256)          | 0x003adf00 0x003ae700 0x00000800
D RKNN: [13:38:22.489] 36  ConvExSwish model.8.m.0.cv2.conv.weight        INT8      (256,256,3,3)  | 0x003ae700 0x0043e700 0x00090000
D RKNN: [13:38:22.489] 36  ConvExSwish model.8.m.0.cv2.conv.bias          INT32     (256)          | 0x0043e700 0x0043ef00 0x00000800
D RKNN: [13:38:22.489] 39  ConvExSwish model.8.cv2.conv.weight            INT8      (512,768,1,1)  | 0x002bcf00 0x0031cf00 0x00060000
D RKNN: [13:38:22.489] 39  ConvExSwish model.8.cv2.conv.bias              INT32     (512)          | 0x0031cf00 0x0031df00 0x00001000
D RKNN: [13:38:22.489] 40  ConvExSwish model.9.cv1.conv.weight            INT8      (256,512,1,1)  | 0x0043ef00 0x0045ef00 0x00020000
D RKNN: [13:38:22.489] 40  ConvExSwish model.9.cv1.conv.bias              INT32     (256)          | 0x0045ef00 0x0045f700 0x00000800
D RKNN: [13:38:22.489] 45  ConvExSwish model.9.cv2.conv.weight            INT8      (512,1024,1,1) | 0x0045f700 0x004df700 0x00080000
D RKNN: [13:38:22.489] 45  ConvExSwish model.9.cv2.conv.bias              INT32     (512)          | 0x004df700 0x004e0700 0x00001000
D RKNN: [13:38:22.489] 46  Resize      empty_placeholder_0                FLOAT     (1)            | 0x00b19580 0x00b19680 0x00000100
D RKNN: [13:38:22.489] 46  Resize      /model.10/Constant_output_0        FLOAT     (4)            | 0x00ab6b40 0x00ab6c40 0x00000100
D RKNN: [13:38:22.489] 48  ConvExSwish model.12.cv1.conv.weight           INT8      (256,768,1,1)  | 0x004e0700 0x00510700 0x00030000
D RKNN: [13:38:22.489] 48  ConvExSwish model.12.cv1.conv.bias             INT32     (256)          | 0x00510700 0x00510f00 0x00000800
D RKNN: [13:38:22.489] 49  Split       onnx::Split_184                    INT64     (2)            | 0x00ab6ac0 0x00ab6ad0 0x00000010
D RKNN: [13:38:22.489] 50  ConvExSwish model.12.m.0.cv1.conv.weight       INT8      (128,128,3,3)  | 0x00529700 0x0054d700 0x00024000
D RKNN: [13:38:22.489] 50  ConvExSwish model.12.m.0.cv1.conv.bias         INT32     (128)          | 0x0054d700 0x0054db00 0x00000400
D RKNN: [13:38:22.489] 51  ConvExSwish model.12.m.0.cv2.conv.weight       INT8      (128,128,3,3)  | 0x0054db00 0x00571b00 0x00024000
D RKNN: [13:38:22.489] 51  ConvExSwish model.12.m.0.cv2.conv.bias         INT32     (128)          | 0x00571b00 0x00571f00 0x00000400
D RKNN: [13:38:22.489] 53  ConvExSwish model.12.cv2.conv.weight           INT8      (256,384,1,1)  | 0x00510f00 0x00528f00 0x00018000
D RKNN: [13:38:22.489] 53  ConvExSwish model.12.cv2.conv.bias             INT32     (256)          | 0x00528f00 0x00529700 0x00000800
D RKNN: [13:38:22.489] 54  Resize      empty_placeholder_0                FLOAT     (1)            | 0x00b19580 0x00b19680 0x00000100
D RKNN: [13:38:22.489] 54  Resize      /model.10/Constant_output_0        FLOAT     (4)            | 0x00ab6b40 0x00ab6c40 0x00000100
D RKNN: [13:38:22.489] 56  ConvExSwish model.15.cv1.conv.weight           INT8      (128,384,1,1)  | 0x00571f00 0x0057df00 0x0000c000
D RKNN: [13:38:22.489] 56  ConvExSwish model.15.cv1.conv.bias             INT32     (128)          | 0x0057df00 0x0057e300 0x00000400
D RKNN: [13:38:22.489] 57  Split       onnx::Split_157                    INT64     (2)            | 0x00ab6a80 0x00ab6a90 0x00000010
D RKNN: [13:38:22.489] 58  ConvExSwish model.15.m.0.cv1.conv.weight       INT8      (64,64,3,3)    | 0x00584700 0x0058d700 0x00009000
D RKNN: [13:38:22.489] 58  ConvExSwish model.15.m.0.cv1.conv.bias         INT32     (64)           | 0x0058d700 0x0058d900 0x00000200
D RKNN: [13:38:22.489] 59  ConvExSwish model.15.m.0.cv2.conv.weight       INT8      (64,64,3,3)    | 0x0058d900 0x00596900 0x00009000
D RKNN: [13:38:22.489] 59  ConvExSwish model.15.m.0.cv2.conv.bias         INT32     (64)           | 0x00596900 0x00596b00 0x00000200
D RKNN: [13:38:22.489] 61  ConvExSwish model.15.cv2.conv.weight           INT8      (128,192,1,1)  | 0x0057e300 0x00584300 0x00006000
D RKNN: [13:38:22.489] 61  ConvExSwish model.15.cv2.conv.bias             INT32     (128)          | 0x00584300 0x00584700 0x00000400
D RKNN: [13:38:22.489] 62  ConvExSwish model.16.conv.weight               INT8      (128,128,3,3)  | 0x00596b00 0x005bab00 0x00024000
D RKNN: [13:38:22.489] 62  ConvExSwish model.16.conv.bias                 INT32     (128)          | 0x005bab00 0x005baf00 0x00000400
D RKNN: [13:38:22.489] 64  ConvExSwish model.22.cv2.0.0.conv.weight       INT8      (64,128,3,3)   | 0x008a7f00 0x008b9f00 0x00012000
D RKNN: [13:38:22.489] 64  ConvExSwish model.22.cv2.0.0.conv.bias         INT32     (64)           | 0x008b9f00 0x008ba100 0x00000200
D RKNN: [13:38:22.489] 65  ConvExSwish model.22.cv3.0.0.conv.weight       INT8      (128,128,3,3)  | 0x00945100 0x00969100 0x00024000
D RKNN: [13:38:22.489] 65  ConvExSwish model.22.cv3.0.0.conv.bias         INT32     (128)          | 0x00969100 0x00969500 0x00000400
D RKNN: [13:38:22.489] 66  ConvExSwish model.18.cv1.conv.weight           INT8      (256,384,1,1)  | 0x005baf00 0x005d2f00 0x00018000
D RKNN: [13:38:22.489] 66  ConvExSwish model.18.cv1.conv.bias             INT32     (256)          | 0x005d2f00 0x005d3700 0x00000800
D RKNN: [13:38:22.489] 67  Split       onnx::Split_184                    INT64     (2)            | 0x00ab6ac0 0x00ab6ad0 0x00000010
D RKNN: [13:38:22.489] 68  ConvExSwish model.22.cv2.0.1.conv.weight       INT8      (64,64,3,3)    | 0x008ba100 0x008c3100 0x00009000
D RKNN: [13:38:22.489] 68  ConvExSwish model.22.cv2.0.1.conv.bias         INT32     (64)           | 0x008c3100 0x008c3300 0x00000200
D RKNN: [13:38:22.489] 69  Conv        model.22.cv2.0.2.weight            INT8      (64,64,1,1)    | 0x008c3300 0x008c4300 0x00001000
D RKNN: [13:38:22.489] 69  Conv        model.22.cv2.0.2.bias              INT32     (64)           | 0x008c4300 0x008c4500 0x00000200
D RKNN: [13:38:22.489] 70  ConvExSwish model.22.cv3.0.1.conv.weight       INT8      (128,128,3,3)  | 0x00969500 0x0098d500 0x00024000
D RKNN: [13:38:22.489] 70  ConvExSwish model.22.cv3.0.1.conv.bias         INT32     (128)          | 0x0098d500 0x0098d900 0x00000400
D RKNN: [13:38:22.489] 71  Conv        model.22.cv3.0.2.weight            INT8      (80,128,1,1)   | 0x0098d900 0x00990100 0x00002800
D RKNN: [13:38:22.489] 71  Conv        model.22.cv3.0.2.bias              INT32     (80)           | 0x00990100 0x00990400 0x00000300
D RKNN: [13:38:22.489] 73  ConvExSwish model.18.m.0.cv1.conv.weight       INT8      (128,128,3,3)  | 0x005ebf00 0x0060ff00 0x00024000
D RKNN: [13:38:22.489] 73  ConvExSwish model.18.m.0.cv1.conv.bias         INT32     (128)          | 0x0060ff00 0x00610300 0x00000400
D RKNN: [13:38:22.489] 74  ConvExSwish model.18.m.0.cv2.conv.weight       INT8      (128,128,3,3)  | 0x00610300 0x00634300 0x00024000
D RKNN: [13:38:22.489] 74  ConvExSwish model.18.m.0.cv2.conv.bias         INT32     (128)          | 0x00634300 0x00634700 0x00000400
D RKNN: [13:38:22.489] 76  ConvExSwish model.18.cv2.conv.weight           INT8      (256,384,1,1)  | 0x005d3700 0x005eb700 0x00018000
D RKNN: [13:38:22.489] 76  ConvExSwish model.18.cv2.conv.bias             INT32     (256)          | 0x005eb700 0x005ebf00 0x00000800
D RKNN: [13:38:22.489] 77  ConvExSwish model.19.conv.weight               INT8      (256,256,3,3)  | 0x00634700 0x006c4700 0x00090000
D RKNN: [13:38:22.489] 77  ConvExSwish model.19.conv.bias                 INT32     (256)          | 0x006c4700 0x006c4f00 0x00000800
D RKNN: [13:38:22.489] 79  ConvExSwish model.22.cv2.1.0.conv.weight       INT8      (64,256,3,3)   | 0x008c4500 0x008e8500 0x00024000
D RKNN: [13:38:22.489] 79  ConvExSwish model.22.cv2.1.0.conv.bias         INT32     (64)           | 0x008e8500 0x008e8700 0x00000200
D RKNN: [13:38:22.489] 80  ConvExSwish model.22.cv3.1.0.conv.weight       INT8      (128,256,3,3)  | 0x00990400 0x009d8400 0x00048000
D RKNN: [13:38:22.489] 80  ConvExSwish model.22.cv3.1.0.conv.bias         INT32     (128)          | 0x009d8400 0x009d8800 0x00000400
D RKNN: [13:38:22.489] 81  ConvExSwish model.21.cv1.conv.weight           INT8      (512,768,1,1)  | 0x006c4f00 0x00724f00 0x00060000
D RKNN: [13:38:22.489] 81  ConvExSwish model.21.cv1.conv.bias             INT32     (512)          | 0x00724f00 0x00725f00 0x00001000
D RKNN: [13:38:22.489] 82  Split       onnx::Split_211                    INT64     (2)            | 0x00ab6b00 0x00ab6b10 0x00000010
D RKNN: [13:38:22.489] 83  ConvExSwish model.22.cv2.1.1.conv.weight       INT8      (64,64,3,3)    | 0x008e8700 0x008f1700 0x00009000
D RKNN: [13:38:22.489] 83  ConvExSwish model.22.cv2.1.1.conv.bias         INT32     (64)           | 0x008f1700 0x008f1900 0x00000200
D RKNN: [13:38:22.489] 84  Conv        model.22.cv2.1.2.weight            INT8      (64,64,1,1)    | 0x008f1900 0x008f2900 0x00001000
D RKNN: [13:38:22.489] 84  Conv        model.22.cv2.1.2.bias              INT32     (64)           | 0x008f2900 0x008f2b00 0x00000200
D RKNN: [13:38:22.489] 85  ConvExSwish model.22.cv3.1.1.conv.weight       INT8      (128,128,3,3)  | 0x009d8800 0x009fc800 0x00024000
D RKNN: [13:38:22.489] 85  ConvExSwish model.22.cv3.1.1.conv.bias         INT32     (128)          | 0x009fc800 0x009fcc00 0x00000400
D RKNN: [13:38:22.489] 86  Conv        model.22.cv3.1.2.weight            INT8      (80,128,1,1)   | 0x009fcc00 0x009ff400 0x00002800
D RKNN: [13:38:22.489] 86  Conv        model.22.cv3.1.2.bias              INT32     (80)           | 0x009ff400 0x009ff700 0x00000300
D RKNN: [13:38:22.489] 88  ConvExSwish model.21.m.0.cv1.conv.weight       INT8      (256,256,3,3)  | 0x00786f00 0x00816f00 0x00090000
D RKNN: [13:38:22.489] 88  ConvExSwish model.21.m.0.cv1.conv.bias         INT32     (256)          | 0x00816f00 0x00817700 0x00000800
D RKNN: [13:38:22.489] 89  ConvExSwish model.21.m.0.cv2.conv.weight       INT8      (256,256,3,3)  | 0x00817700 0x008a7700 0x00090000
D RKNN: [13:38:22.489] 89  ConvExSwish model.21.m.0.cv2.conv.bias         INT32     (256)          | 0x008a7700 0x008a7f00 0x00000800
D RKNN: [13:38:22.489] 91  ConvExSwish model.21.cv2.conv.weight           INT8      (512,768,1,1)  | 0x00725f00 0x00785f00 0x00060000
D RKNN: [13:38:22.489] 91  ConvExSwish model.21.cv2.conv.bias             INT32     (512)          | 0x00785f00 0x00786f00 0x00001000
D RKNN: [13:38:22.489] 92  ConvExSwish model.22.cv2.2.0.conv.weight       INT8      (64,512,3,3)   | 0x008f2b00 0x0093ab00 0x00048000
D RKNN: [13:38:22.489] 92  ConvExSwish model.22.cv2.2.0.conv.bias         INT32     (64)           | 0x0093ab00 0x0093ad00 0x00000200
D RKNN: [13:38:22.489] 93  ConvExSwish model.22.cv3.2.0.conv.weight       INT8      (128,512,3,3)  | 0x009ff700 0x00a8f700 0x00090000
D RKNN: [13:38:22.489] 93  ConvExSwish model.22.cv3.2.0.conv.bias         INT32     (128)          | 0x00a8f700 0x00a8fb00 0x00000400
D RKNN: [13:38:22.489] 94  ConvExSwish model.22.cv2.2.1.conv.weight       INT8      (64,64,3,3)    | 0x0093ad00 0x00943d00 0x00009000
D RKNN: [13:38:22.489] 94  ConvExSwish model.22.cv2.2.1.conv.bias         INT32     (64)           | 0x00943d00 0x00943f00 0x00000200
D RKNN: [13:38:22.489] 95  Conv        model.22.cv2.2.2.weight            INT8      (64,64,1,1)    | 0x00943f00 0x00944f00 0x00001000
D RKNN: [13:38:22.489] 95  Conv        model.22.cv2.2.2.bias              INT32     (64)           | 0x00944f00 0x00945100 0x00000200
D RKNN: [13:38:22.489] 96  ConvExSwish model.22.cv3.2.1.conv.weight       INT8      (128,128,3,3)  | 0x00a8fb00 0x00ab3b00 0x00024000
D RKNN: [13:38:22.489] 96  ConvExSwish model.22.cv3.2.1.conv.bias         INT32     (128)          | 0x00ab3b00 0x00ab3f00 0x00000400
D RKNN: [13:38:22.489] 97  Conv        model.22.cv3.2.2.weight            INT8      (80,128,1,1)   | 0x00ab3f00 0x00ab6700 0x00002800
D RKNN: [13:38:22.489] 97  Conv        model.22.cv3.2.2.bias              INT32     (80)           | 0x00ab6700 0x00ab6a00 0x00000300
D RKNN: [13:38:22.489] 99  Reshape     /model.22/Reshape_output_0_rs_i1   INT64     (4)            | 0x00b19400 0x00b19420 0x00000020
D RKNN: [13:38:22.489] 100 Reshape     /model.22/Reshape_1_output_0_rs_i1 INT64     (4)            | 0x00b19440 0x00b19460 0x00000020
D RKNN: [13:38:22.489] 101 Reshape     /model.22/Reshape_2_output_0_rs_i1 INT64     (4)            | 0x00b19480 0x00b194a0 0x00000020
D RKNN: [13:38:22.489] 103 Split       onnx::Split_388                    INT64     (2)            | 0x00ab6c40 0x00ab6c50 0x00000010
D RKNN: [13:38:22.489] 105 Reshape     /model.22/dfl/Constant_output_0    INT64     (4)            | 0x00ab6c80 0x00ab6ca0 0x00000020
D RKNN: [13:38:22.489] 108 Conv        model.22.dfl.conv.weight           INT8      (1,16,1,1)     | 0x00ab6a00 0x00ab6a20 0x00000020
D RKNN: [13:38:22.489] 108 Conv        model.22.dfl.conv.weight_bias_0    INT32     (1)            | 0x00b19680 0x00b19700 0x00000080
D RKNN: [13:38:22.489] 110 Split       /model.22/Slice_2sp_split          INT64     (2)            | 0x00b193c0 0x00b193d0 0x00000010
D RKNN: [13:38:22.489] 111 Sub         /model.22/Constant_9_output_0      INT8      (1,2,1,8400)   | 0x00ab6cc0 0x00af86c0 0x00041a00
D RKNN: [13:38:22.489] 112 Add         /model.22/Constant_9_output_0      INT8      (1,2,1,8400)   | 0x00ab6cc0 0x00af86c0 0x00041a00
D RKNN: [13:38:22.489] 115 Mul         /model.22/Div_1_2mul_i1            FLOAT     (1,1,1,1)      | 0x00b194c0 0x00b19540 0x00000080
D RKNN: [13:38:22.489] 117 Mul         /model.22/Constant_12_output_0     INT8      (1,1,1,8400)   | 0x00af86c0 0x00b193c0 0x00020d00
D RKNN: [13:38:22.489] 119 Reshape     output0-rs_i1                      INT64     (3)            | 0x00b19540 0x00b19558 0x00000018
D RKNN: [13:38:22.489] ----------------------------------------------------------------------------+---------------------------------
D RKNN: [13:38:22.490] ----------------------------------------
D RKNN: [13:38:22.490] Total Internal Memory Size: 6562.5KB
D RKNN: [13:38:22.490] Total Weight Memory Size: 11367.8KB
D RKNN: [13:38:22.490] ----------------------------------------
D RKNN: [13:38:22.490] <<<<<<<< end: rknn::RKNNMemStatisticsPass
I rknn buiding done.
done
--> Export rknn model
done

rknn-toolkit2 2.0.0-beta
Got an error with numpy

I rknn-toolkit2 version: 2.0.0b0+9bab5682
--> Configuring model  yolov8_small.onnx
done
--> Loading model
I It is recommended onnx opset 19, but your onnx model opset is 17!
I Model converted from pytorch, 'opset_version' should be set 19 in torch.onnx.export for successful convert!
I Loading : 100%|█████████████████████████████████████████████| 143/143 [00:00<00:00, 190650.18it/s]
done
--> Building model
D base_optimize ...
D base_optimize done.
D
D fold_constant ...

A module that was compiled using NumPy 1.x cannot be run in
NumPy 2.1.1 as it may crash. To support both 1.x and 2.x
versions of NumPy, modules must be compiled with NumPy 2.0.
Some module may need to rebuild instead e.g. with 'pybind11>=2.12'.

If you are a user of the module, the easiest solution will be to
downgrade to 'numpy<2' or try to upgrade the affected module.
We expect that some modules will need time to support NumPy 2.

Traceback (most recent call last):  File "/home/vstepanov/RKNN_Compiller/compile.py", line 34, in <module>
    ret = rknn.build(do_quantization=QUANTIZE_ON, dataset=DATASET)
  File "/home/vstepanov/RKNN_Compiller/toolkit2.0-venv/lib/python3.11/site-packages/rknn/api/rknn.py", line 201, in build
    return self.rknn_base.build(do_quantization=do_quantization, dataset=dataset, expand_batch_size=rknn_batch_size)
  File "/home/vstepanov/RKNN_Compiller/toolkit2.0-venv/lib/python3.11/site-packages/onnxruntime/__init__.py", line 23, in <module>
    from onnxruntime.capi._pybind_state import ExecutionMode  # noqa: F401
  File "/home/vstepanov/RKNN_Compiller/toolkit2.0-venv/lib/python3.11/site-packages/onnxruntime/capi/_pybind_state.py", line 32, in <module>
    from .onnxruntime_pybind11_state import *  # noqa
AttributeError: _ARRAY_API not found
ImportError: numpy.core.multiarray failed to import

After downgrade numpy got another error

I rknn-toolkit2 version: 2.0.0b0+9bab5682
--> Configuring model  yolov8_small.onnx
done
--> Loading model
I It is recommended onnx opset 19, but your onnx model opset is 17!
I Model converted from pytorch, 'opset_version' should be set 19 in torch.onnx.export for successful convert!
I Loading : 100%|█████████████████████████████████████████████| 143/143 [00:00<00:00, 197492.75it/s]
done
--> Building model
D base_optimize ...
D base_optimize done.
D
D fold_constant ...
D fold_constant done.
D
D correct_ops ...
D correct_ops done.
D
D fuse_ops ...
D fuse_ops results:
D     replace_exswish: remove node = ['/model.0/act/Sigmoid', '/model.0/act/Mul'], add node = ['/model.0/act/Sigmoid_2swish']
D     replace_exswish: remove node = ['/model.1/act/Sigmoid', '/model.1/act/Mul'], add node = ['/model.1/act/Sigmoid_2swish']
D     replace_exswish: remove node = ['/model.2/cv1/act/Sigmoid', '/model.2/cv1/act/Mul'], add node = ['/model.2/cv1/act/Sigmoid_2swish']
D     replace_exswish: remove node = ['/model.2/m.0/cv1/act/Sigmoid', '/model.2/m.0/cv1/act/Mul'], add node = ['/model.2/m.0/cv1/act/Sigmoid_2swish']
D     replace_exswish: remove node = ['/model.2/m.0/cv2/act/Sigmoid', '/model.2/m.0/cv2/act/Mul'], add node = ['/model.2/m.0/cv2/act/Sigmoid_2swish']
D     replace_exswish: remove node = ['/model.2/cv2/act/Sigmoid', '/model.2/cv2/act/Mul'], add node = ['/model.2/cv2/act/Sigmoid_2swish']
D     replace_exswish: remove node = ['/model.3/act/Sigmoid', '/model.3/act/Mul'], add node = ['/model.3/act/Sigmoid_2swish']
D     replace_exswish: remove node = ['/model.4/cv1/act/Sigmoid', '/model.4/cv1/act/Mul'], add node = ['/model.4/cv1/act/Sigmoid_2swish']
D     replace_exswish: remove node = ['/model.4/m.0/cv1/act/Sigmoid', '/model.4/m.0/cv1/act/Mul'], add node = ['/model.4/m.0/cv1/act/Sigmoid_2swish']
D     replace_exswish: remove node = ['/model.4/m.0/cv2/act/Sigmoid', '/model.4/m.0/cv2/act/Mul'], add node = ['/model.4/m.0/cv2/act/Sigmoid_2swish']
D     replace_exswish: remove node = ['/model.4/m.1/cv1/act/Sigmoid', '/model.4/m.1/cv1/act/Mul'], add node = ['/model.4/m.1/cv1/act/Sigmoid_2swish']
D     replace_exswish: remove node = ['/model.4/m.1/cv2/act/Sigmoid', '/model.4/m.1/cv2/act/Mul'], add node = ['/model.4/m.1/cv2/act/Sigmoid_2swish']
D     replace_exswish: remove node = ['/model.4/cv2/act/Sigmoid', '/model.4/cv2/act/Mul'], add node = ['/model.4/cv2/act/Sigmoid_2swish']
D     replace_exswish: remove node = ['/model.5/act/Sigmoid', '/model.5/act/Mul'], add node = ['/model.5/act/Sigmoid_2swish']
D     replace_exswish: remove node = ['/model.6/cv1/act/Sigmoid', '/model.6/cv1/act/Mul'], add node = ['/model.6/cv1/act/Sigmoid_2swish']
D     replace_exswish: remove node = ['/model.6/m.0/cv1/act/Sigmoid', '/model.6/m.0/cv1/act/Mul'], add node = ['/model.6/m.0/cv1/act/Sigmoid_2swish']
D     replace_exswish: remove node = ['/model.6/m.0/cv2/act/Sigmoid', '/model.6/m.0/cv2/act/Mul'], add node = ['/model.6/m.0/cv2/act/Sigmoid_2swish']
D     replace_exswish: remove node = ['/model.6/m.1/cv1/act/Sigmoid', '/model.6/m.1/cv1/act/Mul'], add node = ['/model.6/m.1/cv1/act/Sigmoid_2swish']
D     replace_exswish: remove node = ['/model.6/m.1/cv2/act/Sigmoid', '/model.6/m.1/cv2/act/Mul'], add node = ['/model.6/m.1/cv2/act/Sigmoid_2swish']
D     replace_exswish: remove node = ['/model.6/cv2/act/Sigmoid', '/model.6/cv2/act/Mul'], add node = ['/model.6/cv2/act/Sigmoid_2swish']
D     replace_exswish: remove node = ['/model.7/act/Sigmoid', '/model.7/act/Mul'], add node = ['/model.7/act/Sigmoid_2swish']
D     replace_exswish: remove node = ['/model.8/cv1/act/Sigmoid', '/model.8/cv1/act/Mul'], add node = ['/model.8/cv1/act/Sigmoid_2swish']
D     replace_exswish: remove node = ['/model.8/m.0/cv1/act/Sigmoid', '/model.8/m.0/cv1/act/Mul'], add node = ['/model.8/m.0/cv1/act/Sigmoid_2swish']
D     replace_exswish: remove node = ['/model.8/m.0/cv2/act/Sigmoid', '/model.8/m.0/cv2/act/Mul'], add node = ['/model.8/m.0/cv2/act/Sigmoid_2swish']
D     replace_exswish: remove node = ['/model.8/cv2/act/Sigmoid', '/model.8/cv2/act/Mul'], add node = ['/model.8/cv2/act/Sigmoid_2swish']
D     replace_exswish: remove node = ['/model.9/cv1/act/Sigmoid', '/model.9/cv1/act/Mul'], add node = ['/model.9/cv1/act/Sigmoid_2swish']
D     replace_exswish: remove node = ['/model.9/cv2/act/Sigmoid', '/model.9/cv2/act/Mul'], add node = ['/model.9/cv2/act/Sigmoid_2swish']
D     replace_exswish: remove node = ['/model.12/cv1/act/Sigmoid', '/model.12/cv1/act/Mul'], add node = ['/model.12/cv1/act/Sigmoid_2swish']
D     replace_exswish: remove node = ['/model.12/m.0/cv1/act/Sigmoid', '/model.12/m.0/cv1/act/Mul'], add node = ['/model.12/m.0/cv1/act/Sigmoid_2swish']
D     replace_exswish: remove node = ['/model.12/m.0/cv2/act/Sigmoid', '/model.12/m.0/cv2/act/Mul'], add node = ['/model.12/m.0/cv2/act/Sigmoid_2swish']
D     replace_exswish: remove node = ['/model.12/cv2/act/Sigmoid', '/model.12/cv2/act/Mul'], add node = ['/model.12/cv2/act/Sigmoid_2swish']
D     replace_exswish: remove node = ['/model.15/cv1/act/Sigmoid', '/model.15/cv1/act/Mul'], add node = ['/model.15/cv1/act/Sigmoid_2swish']
D     replace_exswish: remove node = ['/model.15/m.0/cv1/act/Sigmoid', '/model.15/m.0/cv1/act/Mul'], add node = ['/model.15/m.0/cv1/act/Sigmoid_2swish']
D     replace_exswish: remove node = ['/model.15/m.0/cv2/act/Sigmoid', '/model.15/m.0/cv2/act/Mul'], add node = ['/model.15/m.0/cv2/act/Sigmoid_2swish']
D     replace_exswish: remove node = ['/model.15/cv2/act/Sigmoid', '/model.15/cv2/act/Mul'], add node = ['/model.15/cv2/act/Sigmoid_2swish']
D     replace_exswish: remove node = ['/model.16/act/Sigmoid', '/model.16/act/Mul'], add node = ['/model.16/act/Sigmoid_2swish']
D     replace_exswish: remove node = ['/model.18/cv1/act/Sigmoid', '/model.18/cv1/act/Mul'], add node = ['/model.18/cv1/act/Sigmoid_2swish']
D     replace_exswish: remove node = ['/model.18/m.0/cv1/act/Sigmoid', '/model.18/m.0/cv1/act/Mul'], add node = ['/model.18/m.0/cv1/act/Sigmoid_2swish']
D     replace_exswish: remove node = ['/model.18/m.0/cv2/act/Sigmoid', '/model.18/m.0/cv2/act/Mul'], add node = ['/model.18/m.0/cv2/act/Sigmoid_2swish']
D     replace_exswish: remove node = ['/model.18/cv2/act/Sigmoid', '/model.18/cv2/act/Mul'], add node = ['/model.18/cv2/act/Sigmoid_2swish']
D     replace_exswish: remove node = ['/model.19/act/Sigmoid', '/model.19/act/Mul'], add node = ['/model.19/act/Sigmoid_2swish']
D     replace_exswish: remove node = ['/model.21/cv1/act/Sigmoid', '/model.21/cv1/act/Mul'], add node = ['/model.21/cv1/act/Sigmoid_2swish']
D     replace_exswish: remove node = ['/model.21/m.0/cv1/act/Sigmoid', '/model.21/m.0/cv1/act/Mul'], add node = ['/model.21/m.0/cv1/act/Sigmoid_2swish']
D     replace_exswish: remove node = ['/model.21/m.0/cv2/act/Sigmoid', '/model.21/m.0/cv2/act/Mul'], add node = ['/model.21/m.0/cv2/act/Sigmoid_2swish']
D     replace_exswish: remove node = ['/model.21/cv2/act/Sigmoid', '/model.21/cv2/act/Mul'], add node = ['/model.21/cv2/act/Sigmoid_2swish']
D     replace_exswish: remove node = ['/model.22/cv2.2/cv2.2.0/act/Sigmoid', '/model.22/cv2.2/cv2.2.0/act/Mul'], add node = ['/model.22/cv2.2/cv2.2.0/act/Sigmoid_2swish']
D     replace_exswish: remove node = ['/model.22/cv2.2/cv2.2.1/act/Sigmoid', '/model.22/cv2.2/cv2.2.1/act/Mul'], add node = ['/model.22/cv2.2/cv2.2.1/act/Sigmoid_2swish']
D     replace_exswish: remove node = ['/model.22/cv3.2/cv3.2.0/act/Sigmoid', '/model.22/cv3.2/cv3.2.0/act/Mul'], add node = ['/model.22/cv3.2/cv3.2.0/act/Sigmoid_2swish']
D     replace_exswish: remove node = ['/model.22/cv3.2/cv3.2.1/act/Sigmoid', '/model.22/cv3.2/cv3.2.1/act/Mul'], add node = ['/model.22/cv3.2/cv3.2.1/act/Sigmoid_2swish']
D     replace_exswish: remove node = ['/model.22/cv2.1/cv2.1.0/act/Sigmoid', '/model.22/cv2.1/cv2.1.0/act/Mul'], add node = ['/model.22/cv2.1/cv2.1.0/act/Sigmoid_2swish']
D     replace_exswish: remove node = ['/model.22/cv2.1/cv2.1.1/act/Sigmoid', '/model.22/cv2.1/cv2.1.1/act/Mul'], add node = ['/model.22/cv2.1/cv2.1.1/act/Sigmoid_2swish']
D     replace_exswish: remove node = ['/model.22/cv3.1/cv3.1.0/act/Sigmoid', '/model.22/cv3.1/cv3.1.0/act/Mul'], add node = ['/model.22/cv3.1/cv3.1.0/act/Sigmoid_2swish']
D     replace_exswish: remove node = ['/model.22/cv3.1/cv3.1.1/act/Sigmoid', '/model.22/cv3.1/cv3.1.1/act/Mul'], add node = ['/model.22/cv3.1/cv3.1.1/act/Sigmoid_2swish']
D     replace_exswish: remove node = ['/model.22/cv2.0/cv2.0.0/act/Sigmoid', '/model.22/cv2.0/cv2.0.0/act/Mul'], add node = ['/model.22/cv2.0/cv2.0.0/act/Sigmoid_2swish']
D     replace_exswish: remove node = ['/model.22/cv2.0/cv2.0.1/act/Sigmoid', '/model.22/cv2.0/cv2.0.1/act/Mul'], add node = ['/model.22/cv2.0/cv2.0.1/act/Sigmoid_2swish']
D     replace_exswish: remove node = ['/model.22/cv3.0/cv3.0.0/act/Sigmoid', '/model.22/cv3.0/cv3.0.0/act/Mul'], add node = ['/model.22/cv3.0/cv3.0.0/act/Sigmoid_2swish']
D     replace_exswish: remove node = ['/model.22/cv3.0/cv3.0.1/act/Sigmoid', '/model.22/cv3.0/cv3.0.1/act/Mul'], add node = ['/model.22/cv3.0/cv3.0.1/act/Sigmoid_2swish']
D     replace_parallel_slice_by_split: remove node = ['/model.22/Slice', '/model.22/Slice_1'], add node = ['/model.22/Slice_2split']
D     unsqueeze_to_4d_concat: remove node = [], add node = ['/model.22/Concat_3_0_unsqueeze0', '/model.22/Concat_3_1_unsqueeze0', '/model.22/Concat_3_2_unsqueeze0', '/model.22/Concat_3_0_unsqueeze1']
D     unsqueeze_to_4d_split: remove node = [], add node = ['/model.22/Split_0_unsqueeze0', '/model.22/Split_0_unsqueeze1', '/model.22/Split_1_unsqueeze1']
D     convert_softmax_to_exsoftmax13: remove node = ['/model.22/dfl/Softmax'], add node = ['/model.22/dfl/Softmax']
D     unsqueeze_to_4d_split: remove node = [], add node = ['/model.22/Slice_2split_0_unsqueeze0', '/model.22/Slice_2split_0_unsqueeze1', '/model.22/Slice_2split_1_unsqueeze1']
D     unsqueeze_to_4d_sub: remove node = [], add node = ['/model.22/Sub_1_unsqueeze0', '/model.22/Sub_0_unsqueeze1']
D     unsqueeze_to_4d_add: remove node = [], add node = ['/model.22/Add_1_1_unsqueeze0', '/model.22/Add_1_0_unsqueeze1']
D     unsqueeze_to_4d_add: remove node = [], add node = ['/model.22/Add_2_0_unsqueeze0', '/model.22/Add_2_1_unsqueeze0', '/model.22/Add_2_0_unsqueeze1']
D     convert_div_to_mul: remove node = ['/model.22/Div_1'], add node = ['/model.22/Div_1_2mul']
D     unsqueeze_to_4d_sub: remove node = [], add node = ['/model.22/Sub_1_0_unsqueeze0', '/model.22/Sub_1_1_unsqueeze0', '/model.22/Sub_1_0_unsqueeze1']
D     unsqueeze_to_4d_concat: remove node = [], add node = ['/model.22/Concat_4_0_unsqueeze0', '/model.22/Concat_4_1_unsqueeze0', '/model.22/Concat_4_0_unsqueeze1']
D     unsqueeze_to_4d_mul: remove node = [], add node = ['/model.22/Mul_2_0_unsqueeze0', '/model.22/Mul_2_0_unsqueeze1']
D     unsqueeze_to_4d_sigmoid: remove node = [], add node = ['/model.22/Sigmoid_0_unsqueeze0', '/model.22/Sigmoid_0_unsqueeze1']
D     unsqueeze_to_4d_concat: remove node = [], add node = ['/model.22/Concat_5_0_unsqueeze0', '/model.22/Concat_5_1_unsqueeze0', '/model.22/Concat_5_0_unsqueeze1']
D     fuse_two_reshape: remove node = ['/model.22/Reshape_2', '/model.22/Reshape_1', '/model.22/Reshape', '/model.22/Concat_3_0_unsqueeze1', '/model.22/Split_0_unsqueeze1', '/model.22/dfl/Reshape_1', '/model.22/Slice_2split_0_unsqueeze1']
D     remove_parallel_reshape: remove node = ['/model.22/Add_2_0_unsqueeze0']
D     fuse_two_reshape: remove node = ['/model.22/Slice_2split_1_unsqueeze1']
D     remove_parallel_reshape: remove node = ['/model.22/Add_2_1_unsqueeze0']
D     unsqueeze_to_4d_mul: remove node = [], add node = ['/model.22/Div_1_2mul_0_unsqueeze0', '/model.22/Div_1_2mul_0_unsqueeze1']
D     swap_concat_axis_avoid_channel_concat: remove node = [], add node = ['/model.22/Concat_4_swap_concat_reshape_i0_out', '/model.22/Concat_4_swap_concat_reshape_i1_out', '/model.22/Concat_4_swap_concat_reshape_o0_out']
D     fuse_two_reshape: remove node = ['/model.22/Concat_4_0_unsqueeze1']
D     input_align_4D_mul: remove node = ['/model.22/Mul_2'], add node = ['/model.22/Mul_2']
D     fuse_two_reshape: remove node = ['/model.22/Mul_2_0_unsqueeze1', '/model.22/Split_1_unsqueeze1', '/model.22/Sigmoid_0_unsqueeze1']
D     swap_concat_axis_avoid_channel_concat: remove node = [], add node = ['/model.22/Concat_5_swap_concat_reshape_i0_out', '/model.22/Concat_5_swap_concat_reshape_i1_out', '/model.22/Concat_5_swap_concat_reshape_o0_out']
D     remove_invalid_reshape: remove node = ['/model.22/Split_0_unsqueeze0', '/model.22/Sub_1_unsqueeze0']
D     fuse_two_reshape: remove node = ['/model.22/Sub_0_unsqueeze1']
D     remove_invalid_reshape: remove node = ['/model.22/Add_1_1_unsqueeze0']
D     fuse_two_reshape: remove node = ['/model.22/Add_1_0_unsqueeze1', '/model.22/Sub_1_0_unsqueeze1', '/model.22/Add_2_0_unsqueeze1', '/model.22/Div_1_2mul_0_unsqueeze1', '/model.22/Concat_4_swap_concat_reshape_o0_out', '/model.22/Concat_5_0_unsqueeze0']
D     remove_invalid_reshape: remove node = ['/model.22/Sigmoid_0_unsqueeze0']
D     fuse_two_reshape: remove node = ['/model.22/Concat_5_1_unsqueeze0', '/model.22/Concat_5_swap_concat_reshape_o0_out']
D     remove_invalid_reshape: remove node = ['/model.22/Sub_1_1_unsqueeze0', '/model.22/Sub_1_0_unsqueeze0']
D     fuse_two_reshape: remove node = ['/model.22/Concat_4_1_unsqueeze0']
D     remove_invalid_reshape: remove node = ['/model.22/Div_1_2mul_0_unsqueeze0']
D     fuse_two_reshape: remove node = ['/model.22/Concat_4_0_unsqueeze0']
D     replace_batch_shuffle_transpose_by_gather_after_reshape: remove node = ['/model.22/dfl/Reshape', '/model.22/dfl/Transpose'], add node = ['/model.22/dfl/Reshape', '/model.22/dfl/Transpose_2_gather']
D     convert_reshape_to_transpose: remove node = ['/model.22/Slice_2split_0_unsqueeze0'], add node = ['/model.22/Slice_2split_0_unsqueeze0']
D     convert_reshape_to_transpose: remove node = ['/model.22/Concat_4_swap_concat_reshape_i1_out'], add node = ['/model.22/Concat_4_swap_concat_reshape_i1_out']
D     convert_reshape_to_transpose: remove node = ['/model.22/Concat_4_swap_concat_reshape_i0_out'], add node = ['/model.22/Concat_4_swap_concat_reshape_i0_out']
D     reduce_transpose_op_around_concat: remove node = ['/model.22/Concat_4'], add node = ['/model.22/Concat_4_swap_concat_reshape_i0_out_tp_4/model.22/Concat_4', '/model.22/Concat_4_swap_concat_reshape_i1_out_tp_4/model.22/Concat_4', '/model.22/Concat_4', '/model.22/Concat_4_output_0_shape4_tp_4/model.22/Concat_4']
D     convert_reshape_to_transpose: remove node = ['/model.22/Mul_2_0_unsqueeze0'], add node = ['/model.22/Mul_2_0_unsqueeze0']
D     convert_reshape_to_transpose: remove node = ['/model.22/Concat_5_swap_concat_reshape_i0_out'], add node = ['/model.22/Concat_5_swap_concat_reshape_i0_out']
D     convert_reshape_to_transpose: remove node = ['/model.22/Concat_5_swap_concat_reshape_i1_out'], add node = ['/model.22/Concat_5_swap_concat_reshape_i1_out']
D     reduce_transpose_op_around_concat: remove node = ['/model.22/Concat_5'], add node = ['/model.22/Concat_5_swap_concat_reshape_i0_out_tp_4/model.22/Concat_5', '/model.22/Concat_5_swap_concat_reshape_i1_out_tp_4/model.22/Concat_5', '/model.22/Concat_5', 'output0_shape4_tp_4/model.22/Concat_5']
D     convert_transpose_to_reshape: remove node = ['/model.22/Slice_2split_0_unsqueeze0'], add node = ['/model.22/Slice_2split_0_unsqueeze0_2reshape']
D     bypass_two_transpose: remove node = ['/model.22/Concat_4_swap_concat_reshape_i1_out_tp_4/model.22/Concat_4', '/model.22/Concat_4_swap_concat_reshape_i1_out', '/model.22/Concat_4_swap_concat_reshape_i0_out_tp_4/model.22/Concat_4', '/model.22/Concat_4_swap_concat_reshape_i0_out', '/model.22/Mul_2_0_unsqueeze0', '/model.22/Concat_4_output_0_shape4_tp_4/model.22/Concat_4', '/model.22/Concat_5_swap_concat_reshape_i0_out_tp_4/model.22/Concat_5', '/model.22/Concat_5_swap_concat_reshape_i0_out', '/model.22/Concat_5_swap_concat_reshape_i1_out_tp_4/model.22/Concat_5', '/model.22/Concat_5_swap_concat_reshape_i1_out']
D     convert_transpose_to_reshape: remove node = ['output0_shape4_tp_4/model.22/Concat_5'], add node = ['output0_shape4_tp_4/model.22/Concat_5_2reshape']
D     convert_reshape_to_transpose: remove node = ['/model.22/Slice_2split_0_unsqueeze0_2reshape'], add node = ['/model.22/Slice_2split_0_unsqueeze0_2reshape']
D     fuse_two_reshape: remove node = ['output0_shape4_tp_4/model.22/Concat_5_2reshape']
D     fold_constant ...
D     fold_constant done.
D fuse_ops done.
D
W build: found outlier value, this may affect quantization accuracy
                        const name               abs_mean    abs_std     outlier value
                        model.0.conv.weight      4.03        4.41        26.039
D sparse_weight ...
D sparse_weight done.
D
I GraphPreparing : 100%|████████████████████████████████████████| 176/176 [00:00<00:00, 1900.26it/s]
I Quantizating : 100%|███████████████████████████████████████████| 176/176 [00:00<00:00, 480.21it/s]
D
D quant_optimizer ...
D quant_optimizer results:
D     adjust_tanh_sigmoid: ['/model.22/Sigmoid']
D     adjust_concat_split: ['/model.22/Split', '/model.22/Concat_3', '/model.22/Concat', '/model.22/Concat_1', '/model.22/Concat_2']
D     adjust_no_change_node: ['/model.22/Concat_3_0_unsqueeze0', '/model.22/Concat_3_1_unsqueeze0', '/model.22/Concat_3_2_unsqueeze0', '/model.9/m_2/MaxPool', '/model.9/m_1/MaxPool', '/model.9/m/MaxPool']
D quant_optimizer done.
D
D recover_const_share ...
D recover_const_share done.
D
W build: The default input dtype of 'images' is changed from 'float32' to 'int8' in rknn model for performance!
                       Please take care of this change when deploy rknn model with Runtime API!
W build: The default output dtype of 'output0' is changed from 'float32' to 'int8' in rknn model for performance!
                      Please take care of this change when deploy rknn model with Runtime API!
I rknn building ...
I RKNN: [13:48:42.362] 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
I RKNN: librknnc version: 2.0.0b0 (35a6907d79@2024-03-24T02:34:11)
D RKNN: [13:48:42.412] RKNN is invoked
W RKNN: [13:48:42.597] Model initializer tensor data is empty, name: empty_placeholder_0
D RKNN: [13:48:42.599] >>>>>> start: rknn::RKNNExtractCustomOpAttrs
D RKNN: [13:48:42.600] <<<<<<<< end: rknn::RKNNExtractCustomOpAttrs
D RKNN: [13:48:42.600] >>>>>> start: rknn::RKNNSetOpTargetPass
D RKNN: [13:48:42.600] <<<<<<<< end: rknn::RKNNSetOpTargetPass
D RKNN: [13:48:42.600] >>>>>> start: rknn::RKNNBindNorm
D RKNN: [13:48:42.600] <<<<<<<< end: rknn::RKNNBindNorm
D RKNN: [13:48:42.600] >>>>>> start: rknn::RKNNAddFirstConv
D RKNN: [13:48:42.600] <<<<<<<< end: rknn::RKNNAddFirstConv
D RKNN: [13:48:42.600] >>>>>> start: rknn::RKNNEliminateQATDataConvert
D RKNN: [13:48:42.600] <<<<<<<< end: rknn::RKNNEliminateQATDataConvert
D RKNN: [13:48:42.600] >>>>>> start: rknn::RKNNTileGroupConv
D RKNN: [13:48:42.600] <<<<<<<< end: rknn::RKNNTileGroupConv
D RKNN: [13:48:42.600] >>>>>> start: rknn::RKNNAddConvBias
D RKNN: [13:48:42.601] <<<<<<<< end: rknn::RKNNAddConvBias
D RKNN: [13:48:42.601] >>>>>> start: rknn::RKNNTileChannel
D RKNN: [13:48:42.601] <<<<<<<< end: rknn::RKNNTileChannel
D RKNN: [13:48:42.601] >>>>>> start: rknn::RKNNPerChannelPrep
D RKNN: [13:48:42.601] <<<<<<<< end: rknn::RKNNPerChannelPrep
D RKNN: [13:48:42.601] >>>>>> start: rknn::RKNNBnQuant
D RKNN: [13:48:42.601] <<<<<<<< end: rknn::RKNNBnQuant
D RKNN: [13:48:42.601] >>>>>> start: rknn::RKNNFuseOptimizerPass
D RKNN: [13:48:42.613] <<<<<<<< end: rknn::RKNNFuseOptimizerPass
D RKNN: [13:48:42.613] >>>>>> start: rknn::RKNNTurnAutoPad
D RKNN: [13:48:42.613] <<<<<<<< end: rknn::RKNNTurnAutoPad
D RKNN: [13:48:42.613] >>>>>> start: rknn::RKNNInitRNNConst
D RKNN: [13:48:42.613] <<<<<<<< end: rknn::RKNNInitRNNConst
D RKNN: [13:48:42.613] >>>>>> start: rknn::RKNNInitCastConst
D RKNN: [13:48:42.613] <<<<<<<< end: rknn::RKNNInitCastConst
D RKNN: [13:48:42.613] >>>>>> start: rknn::RKNNMultiSurfacePass
D RKNN: [13:48:42.613] <<<<<<<< end: rknn::RKNNMultiSurfacePass
D RKNN: [13:48:42.613] >>>>>> start: rknn::RKNNReplaceConstantTensorPass
D RKNN: [13:48:42.614] <<<<<<<< end: rknn::RKNNReplaceConstantTensorPass
D RKNN: [13:48:42.614] >>>>>> start: rknn::RKNNTilingPass
D RKNN: [13:48:42.614] DatainEntries overflow, need to tiling more, datain_entries: 2100, limitation: 2048
D RKNN: [13:48:42.614] DatainEntries overflow, need to tiling more, datain_entries: 2100, limitation: 2048
D RKNN: [13:48:42.614] DatainEntries overflow, need to tiling more, datain_entries: 2100, limitation: 2048
D RKNN: [13:48:42.614] DatainEntries overflow, need to tiling more, datain_entries: 2100, limitation: 2048
D RKNN: [13:48:42.614] DatainEntries overflow, need to tiling more, datain_entries: 2100, limitation: 2048
D RKNN: [13:48:42.614] DatainEntries overflow, need to tiling more, datain_entries: 2100, limitation: 2048
D RKNN: [13:48:42.614] DatainEntries overflow, need to tiling more, datain_entries: 2100, limitation: 2048
D RKNN: [13:48:42.614] DatainEntries overflow, need to tiling more, datain_entries: 2100, limitation: 2048
D RKNN: [13:48:42.614] DatainEntries overflow, need to tiling more, datain_entries: 2100, limitation: 2048
D RKNN: [13:48:42.614] DatainEntries overflow, need to tiling more, datain_entries: 2100, limitation: 2048
D RKNN: [13:48:42.614] DatainEntries overflow, need to tiling more, datain_entries: 2100, limitation: 2048
D RKNN: [13:48:42.614] DatainEntries overflow, need to tiling more, datain_entries: 2100, limitation: 2048
D RKNN: [13:48:42.614] DatainEntries overflow, need to tiling more, datain_entries: 2100, limitation: 2048
D RKNN: [13:48:42.614] DatainEntries overflow, need to tiling more, datain_entries: 2100, limitation: 2048
D RKNN: [13:48:42.614] DatainEntries overflow, need to tiling more, datain_entries: 2100, limitation: 2048
D RKNN: [13:48:42.614] DatainEntries overflow, need to tiling more, datain_entries: 2100, limitation: 2048
D RKNN: [13:48:42.614] DatainEntries overflow, need to tiling more, datain_entries: 2100, limitation: 2048
D RKNN: [13:48:42.614] DatainEntries overflow, need to tiling more, datain_entries: 2100, limitation: 2048
D RKNN: [13:48:42.614] DatainEntries overflow, need to tiling more, datain_entries: 2100, limitation: 2048
D RKNN: [13:48:42.614] DatainEntries overflow, need to tiling more, datain_entries: 2100, limitation: 2048
D RKNN: [13:48:42.614] DatainEntries overflow, need to tiling more, datain_entries: 2100, limitation: 2048
D RKNN: [13:48:42.614] DatainEntries overflow, need to tiling more, datain_entries: 2100, limitation: 2048
D RKNN: [13:48:42.614] DatainEntries overflow, need to tiling more, datain_entries: 2100, limitation: 2048
D RKNN: [13:48:42.614] DatainEntries overflow, need to tiling more, datain_entries: 2100, limitation: 2048
D RKNN: [13:48:42.614] DatainEntries overflow, need to tiling more, datain_entries: 2100, limitation: 2048
D RKNN: [13:48:42.614] DatainEntries overflow, need to tiling more, datain_entries: 2100, limitation: 2048
D RKNN: [13:48:42.614] DatainEntries overflow, need to tiling more, datain_entries: 2100, limitation: 2048
D RKNN: [13:48:42.614] DatainEntries overflow, need to tiling more, datain_entries: 2100, limitation: 2048
D RKNN: [13:48:42.614] DatainEntries overflow, need to tiling more, datain_entries: 2100, limitation: 2048
D RKNN: [13:48:42.614] DatainEntries overflow, need to tiling more, datain_entries: 2100, limitation: 2048
W RKNN: [13:48:42.615] Failed to config layer: 'Conv:/model.22/dfl/conv/Conv' using 3Core fallback to single core mode,
W RKNN: [13:48:42.615] core_num 3 ori_Ih 4 ori_Iw 8400 ori_Ic 16 ori_Ib 1
W RKNN: [13:48:42.615] ori_Kh 1 ori_Kw 1 ori_Kk 1 ori_Kc 16 ori_Ksx 1 ori_Ksy 1
W RKNN: [13:48:42.615] ori_Oh 4 oriOw 8400 oriOc 1 pad_t 0 pad_b 0 pad_l 0 pad_r 0,
W RKNN: [13:48:42.615] Please help report this bug!
W RKNN: [13:48:42.615] Failed to config layer: 'Conv:/model.22/dfl/conv/Conv' using 3Core fallback to single core mode,
W RKNN: [13:48:42.615] core_num 3 ori_Ih 4 ori_Iw 8400 ori_Ic 16 ori_Ib 1
W RKNN: [13:48:42.615] ori_Kh 1 ori_Kw 1 ori_Kk 1 ori_Kc 16 ori_Ksx 1 ori_Ksy 1
W RKNN: [13:48:42.615] ori_Oh 4 oriOw 8400 oriOc 1 pad_t 0 pad_b 0 pad_l 0 pad_r 0,
W RKNN: [13:48:42.615] Please help report this bug!
W RKNN: [13:48:42.615] Failed to config layer: 'Conv:/model.22/dfl/conv/Conv' using 3Core fallback to single core mode,
W RKNN: [13:48:42.615] core_num 3 ori_Ih 4 ori_Iw 8400 ori_Ic 16 ori_Ib 1
W RKNN: [13:48:42.615] ori_Kh 1 ori_Kw 1 ori_Kk 1 ori_Kc 16 ori_Ksx 1 ori_Ksy 1
W RKNN: [13:48:42.615] ori_Oh 4 oriOw 8400 oriOc 1 pad_t 0 pad_b 0 pad_l 0 pad_r 0,
W RKNN: [13:48:42.615] Please help report this bug!
W RKNN: [13:48:42.615] Failed to config layer: 'Conv:/model.22/dfl/conv/Conv' using 3Core fallback to single core mode,
W RKNN: [13:48:42.615] core_num 3 ori_Ih 4 ori_Iw 8400 ori_Ic 16 ori_Ib 1
W RKNN: [13:48:42.615] ori_Kh 1 ori_Kw 1 ori_Kk 1 ori_Kc 16 ori_Ksx 1 ori_Ksy 1
W RKNN: [13:48:42.615] ori_Oh 4 oriOw 8400 oriOc 1 pad_t 0 pad_b 0 pad_l 0 pad_r 0,
W RKNN: [13:48:42.615] Please help report this bug!
W RKNN: [13:48:42.615] Failed to config layer: 'Conv:/model.22/dfl/conv/Conv' using 3Core fallback to single core mode,
W RKNN: [13:48:42.615] core_num 3 ori_Ih 4 ori_Iw 8400 ori_Ic 16 ori_Ib 1
W RKNN: [13:48:42.615] ori_Kh 1 ori_Kw 1 ori_Kk 1 ori_Kc 16 ori_Ksx 1 ori_Ksy 1
W RKNN: [13:48:42.615] ori_Oh 4 oriOw 8400 oriOc 1 pad_t 0 pad_b 0 pad_l 0 pad_r 0,
W RKNN: [13:48:42.615] Please help report this bug!
D RKNN: [13:48:42.615] <<<<<<<< end: rknn::RKNNTilingPass
D RKNN: [13:48:42.615] >>>>>> start: rknn::RKNNSubgraphManager
D RKNN: [13:48:42.615] <<<<<<<< end: rknn::RKNNSubgraphManager
D RKNN: [13:48:42.615] >>>>>> start: OpEmit
E RKNN: [13:48:42.615] REGTASK: The bit width of field value exceeds the limit, target: v2, offset: 0x500c, shift = 0, limit: 0x1fff, value: 0x20cf
E RKNN: [13:48:42.615] REGTASK: The bit width of field value exceeds the limit, target: v2, offset: 0x500c, shift = 0, limit: 0x1fff, value: 0x20cf
W RKNN: [13:48:42.615] Transpose will fallback to CPU, because input shape has exceeded the max limit, height(4) * width(8400) = 33600, required product no larger than 16384!
W RKNN: [13:48:42.615] Transpose will fallback to CPU, because input shape has exceeded the max limit, height(16) * width(8400) = 134400, required product no larger than 16384!
D RKNN: [13:48:42.615] <<<<<<<< end: OpEmit
D RKNN: [13:48:42.615] >>>>>> start: rknn::RKNNLayoutMatchPass
I RKNN: [13:48:42.615] AppointLayout: t->setNativeLayout(64), tname:[/model.0/act/Mul_output_0]
I RKNN: [13:48:42.615] AppointLayout: t->setNativeLayout(64), tname:[/model.1/act/Mul_output_0]
I RKNN: [13:48:42.615] AppointLayout: t->setNativeLayout(64), tname:[/model.2/cv1/act/Mul_output_0]
I RKNN: [13:48:42.615] AppointLayout: t->setNativeLayout(64), tname:[/model.2/Split_output_0]
I RKNN: [13:48:42.615] AppointLayout: t->setNativeLayout(64), tname:[/model.2/Split_output_1]
I RKNN: [13:48:42.615] AppointLayout: t->setNativeLayout(64), tname:[/model.2/m.0/cv1/act/Mul_output_0]
I RKNN: [13:48:42.615] AppointLayout: t->setNativeLayout(64), tname:[/model.2/m.0/cv2/act/Mul_output_0]
I RKNN: [13:48:42.615] AppointLayout: t->setNativeLayout(64), tname:[/model.2/m.0/Add_output_0]
I RKNN: [13:48:42.615] AppointLayout: t->setNativeLayout(64), tname:[/model.2/Concat_output_0]
I RKNN: [13:48:42.615] AppointLayout: t->setNativeLayout(64), tname:[/model.2/cv2/act/Mul_output_0]
I RKNN: [13:48:42.615] AppointLayout: t->setNativeLayout(64), tname:[/model.3/act/Mul_output_0]
I RKNN: [13:48:42.615] AppointLayout: t->setNativeLayout(64), tname:[/model.4/cv1/act/Mul_output_0]
I RKNN: [13:48:42.615] AppointLayout: t->setNativeLayout(64), tname:[/model.4/Split_output_0]
I RKNN: [13:48:42.615] AppointLayout: t->setNativeLayout(64), tname:[/model.4/Split_output_1]
I RKNN: [13:48:42.615] AppointLayout: t->setNativeLayout(64), tname:[/model.4/m.0/cv1/act/Mul_output_0]
I RKNN: [13:48:42.615] AppointLayout: t->setNativeLayout(64), tname:[/model.4/m.0/cv2/act/Mul_output_0]
I RKNN: [13:48:42.615] AppointLayout: t->setNativeLayout(64), tname:[/model.4/m.0/Add_output_0]
I RKNN: [13:48:42.615] AppointLayout: t->setNativeLayout(64), tname:[/model.4/m.1/cv1/act/Mul_output_0]
I RKNN: [13:48:42.615] AppointLayout: t->setNativeLayout(64), tname:[/model.4/m.1/cv2/act/Mul_output_0]
I RKNN: [13:48:42.615] AppointLayout: t->setNativeLayout(64), tname:[/model.4/m.1/Add_output_0]
I RKNN: [13:48:42.615] AppointLayout: t->setNativeLayout(64), tname:[/model.4/Concat_output_0]
I RKNN: [13:48:42.615] AppointLayout: t->setNativeLayout(64), tname:[/model.5/act/Mul_output_0]
I RKNN: [13:48:42.616] AppointLayout: t->setNativeLayout(64), tname:[/model.6/cv1/act/Mul_output_0]
I RKNN: [13:48:42.616] AppointLayout: t->setNativeLayout(64), tname:[/model.6/Split_output_0]
I RKNN: [13:48:42.616] AppointLayout: t->setNativeLayout(64), tname:[/model.6/Split_output_1]
I RKNN: [13:48:42.616] AppointLayout: t->setNativeLayout(64), tname:[/model.6/m.0/cv1/act/Mul_output_0]
I RKNN: [13:48:42.616] AppointLayout: t->setNativeLayout(64), tname:[/model.6/m.0/cv2/act/Mul_output_0]
I RKNN: [13:48:42.616] AppointLayout: t->setNativeLayout(64), tname:[/model.6/m.0/Add_output_0]
I RKNN: [13:48:42.616] AppointLayout: t->setNativeLayout(64), tname:[/model.6/m.1/cv1/act/Mul_output_0]
I RKNN: [13:48:42.616] AppointLayout: t->setNativeLayout(64), tname:[/model.6/m.1/cv2/act/Mul_output_0]
I RKNN: [13:48:42.616] AppointLayout: t->setNativeLayout(64), tname:[/model.6/m.1/Add_output_0]
I RKNN: [13:48:42.616] AppointLayout: t->setNativeLayout(64), tname:[/model.6/Concat_output_0]
I RKNN: [13:48:42.616] AppointLayout: t->setNativeLayout(64), tname:[/model.7/act/Mul_output_0]
I RKNN: [13:48:42.616] AppointLayout: t->setNativeLayout(64), tname:[/model.8/cv1/act/Mul_output_0]
I RKNN: [13:48:42.616] AppointLayout: t->setNativeLayout(64), tname:[/model.8/Split_output_0]
I RKNN: [13:48:42.616] AppointLayout: t->setNativeLayout(64), tname:[/model.8/Split_output_1]
I RKNN: [13:48:42.616] AppointLayout: t->setNativeLayout(64), tname:[/model.8/m.0/cv1/act/Mul_output_0]
I RKNN: [13:48:42.616] AppointLayout: t->setNativeLayout(64), tname:[/model.8/m.0/cv2/act/Mul_output_0]
I RKNN: [13:48:42.616] AppointLayout: t->setNativeLayout(64), tname:[/model.8/m.0/Add_output_0]
I RKNN: [13:48:42.616] AppointLayout: t->setNativeLayout(64), tname:[/model.8/Concat_output_0]
I RKNN: [13:48:42.616] AppointLayout: t->setNativeLayout(64), tname:[/model.8/cv2/act/Mul_output_0]
I RKNN: [13:48:42.616] AppointLayout: t->setNativeLayout(64), tname:[/model.9/cv1/act/Mul_output_0]
I RKNN: [13:48:42.616] AppointLayout: t->setNativeLayout(64), tname:[/model.9/m/MaxPool_output_0]
I RKNN: [13:48:42.616] AppointLayout: t->setNativeLayout(64), tname:[/model.9/m_1/MaxPool_output_0]
I RKNN: [13:48:42.616] AppointLayout: t->setNativeLayout(64), tname:[/model.9/m_2/MaxPool_output_0]
I RKNN: [13:48:42.616] AppointLayout: t->setNativeLayout(64), tname:[/model.9/Concat_output_0]
I RKNN: [13:48:42.616] AppointLayout: t->setNativeLayout(64), tname:[/model.12/cv1/act/Mul_output_0]
I RKNN: [13:48:42.616] AppointLayout: t->setNativeLayout(64), tname:[/model.12/Split_output_0]
I RKNN: [13:48:42.616] AppointLayout: t->setNativeLayout(64), tname:[/model.12/Split_output_1]
I RKNN: [13:48:42.616] AppointLayout: t->setNativeLayout(64), tname:[/model.12/m.0/cv1/act/Mul_output_0]
I RKNN: [13:48:42.616] AppointLayout: t->setNativeLayout(64), tname:[/model.12/m.0/cv2/act/Mul_output_0]
I RKNN: [13:48:42.616] AppointLayout: t->setNativeLayout(64), tname:[/model.12/Concat_output_0]
I RKNN: [13:48:42.616] AppointLayout: t->setNativeLayout(64), tname:[/model.15/cv1/act/Mul_output_0]
I RKNN: [13:48:42.616] AppointLayout: t->setNativeLayout(64), tname:[/model.15/Split_output_0]
I RKNN: [13:48:42.616] AppointLayout: t->setNativeLayout(64), tname:[/model.15/Split_output_1]
I RKNN: [13:48:42.616] AppointLayout: t->setNativeLayout(64), tname:[/model.15/m.0/cv1/act/Mul_output_0]
I RKNN: [13:48:42.616] AppointLayout: t->setNativeLayout(64), tname:[/model.15/m.0/cv2/act/Mul_output_0]
I RKNN: [13:48:42.616] AppointLayout: t->setNativeLayout(64), tname:[/model.15/Concat_output_0]
I RKNN: [13:48:42.616] AppointLayout: t->setNativeLayout(64), tname:[/model.15/cv2/act/Mul_output_0]
I RKNN: [13:48:42.616] AppointLayout: t->setNativeLayout(64), tname:[/model.22/cv2.0/cv2.0.0/act/Mul_output_0]
I RKNN: [13:48:42.616] AppointLayout: t->setNativeLayout(64), tname:[/model.22/cv3.0/cv3.0.0/act/Mul_output_0]
I RKNN: [13:48:42.616] AppointLayout: t->setNativeLayout(64), tname:[/model.18/cv1/act/Mul_output_0]
I RKNN: [13:48:42.616] AppointLayout: t->setNativeLayout(64), tname:[/model.18/Split_output_0]
I RKNN: [13:48:42.616] AppointLayout: t->setNativeLayout(64), tname:[/model.18/Split_output_1]
I RKNN: [13:48:42.616] AppointLayout: t->setNativeLayout(64), tname:[/model.22/cv2.0/cv2.0.1/act/Mul_output_0]
W RKNN: [13:48:42.616] LayoutMatchManager: recursion_depth=3, Logic is Dangerous, Will Force layout to native.
W RKNN: [13:48:42.616] LayoutMatchManager: recursion_depth=3, Logic is Dangerous, Will Force layout to native.
W RKNN: [13:48:42.616] LayoutMatchManager: recursion_depth=3, Logic is Dangerous, Will Force layout to native.
I RKNN: [13:48:42.616] AppointLayout: t->setNativeLayout(64), tname:[/model.22/cv2.0/cv2.0.2/Conv_output_0]
I RKNN: [13:48:42.616] AppointLayout: t->setNativeLayout(64), tname:[/model.22/cv3.0/cv3.0.1/act/Mul_output_0]
W RKNN: [13:48:42.616] LayoutMatchManager: recursion_depth=3, Logic is Dangerous, Will Force layout to native.
W RKNN: [13:48:42.616] LayoutMatchManager: recursion_depth=3, Logic is Dangerous, Will Force layout to native.
W RKNN: [13:48:42.616] LayoutMatchManager: recursion_depth=3, Logic is Dangerous, Will Force layout to native.
I RKNN: [13:48:42.616] AppointLayout: t->setNativeLayout(64), tname:[/model.22/cv3.0/cv3.0.2/Conv_output_0]
W RKNN: [13:48:42.616] LayoutMatchManager: recursion_depth=3, Logic is Dangerous, Will Force layout to native.
W RKNN: [13:48:42.616] LayoutMatchManager: recursion_depth=3, Logic is Dangerous, Will Force layout to native.
W RKNN: [13:48:42.616] LayoutMatchManager: recursion_depth=3, Logic is Dangerous, Will Force layout to native.
W RKNN: [13:48:42.616] LayoutMatchManager: recursion_depth=3, Logic is Dangerous, Will Force layout to native.
I RKNN: [13:48:42.616] AppointLayout: t->setNativeLayout(64), tname:[/model.22/Concat_output_0]
I RKNN: [13:48:42.616] AppointLayout: t->setNativeLayout(64), tname:[/model.22/Reshape_output_0_shape4_/model.22/Concat_3]
I RKNN: [13:48:42.616] AppointLayout: t->setNativeLayout(64), tname:[/model.18/m.0/cv1/act/Mul_output_0]
I RKNN: [13:48:42.616] AppointLayout: t->setNativeLayout(64), tname:[/model.18/m.0/cv2/act/Mul_output_0]
I RKNN: [13:48:42.616] AppointLayout: t->setNativeLayout(64), tname:[/model.18/Concat_output_0]
I RKNN: [13:48:42.616] AppointLayout: t->setNativeLayout(64), tname:[/model.18/cv2/act/Mul_output_0]
I RKNN: [13:48:42.616] AppointLayout: t->setNativeLayout(64), tname:[/model.22/cv2.1/cv2.1.0/act/Mul_output_0]
I RKNN: [13:48:42.616] AppointLayout: t->setNativeLayout(64), tname:[/model.22/cv3.1/cv3.1.0/act/Mul_output_0]
I RKNN: [13:48:42.616] AppointLayout: t->setNativeLayout(64), tname:[/model.21/cv1/act/Mul_output_0]
I RKNN: [13:48:42.616] AppointLayout: t->setNativeLayout(64), tname:[/model.21/Split_output_0]
I RKNN: [13:48:42.616] AppointLayout: t->setNativeLayout(64), tname:[/model.21/Split_output_1]
I RKNN: [13:48:42.616] AppointLayout: t->setNativeLayout(64), tname:[/model.22/cv2.1/cv2.1.1/act/Mul_output_0]
W RKNN: [13:48:42.616] LayoutMatchManager: recursion_depth=3, Logic is Dangerous, Will Force layout to native.
I RKNN: [13:48:42.616] AppointLayout: t->setNativeLayout(64), tname:[/model.22/cv2.1/cv2.1.2/Conv_output_0]
I RKNN: [13:48:42.616] AppointLayout: t->setNativeLayout(64), tname:[/model.22/cv3.1/cv3.1.1/act/Mul_output_0]
W RKNN: [13:48:42.616] LayoutMatchManager: recursion_depth=3, Logic is Dangerous, Will Force layout to native.
I RKNN: [13:48:42.616] AppointLayout: t->setNativeLayout(64), tname:[/model.22/cv3.1/cv3.1.2/Conv_output_0]
I RKNN: [13:48:42.616] AppointLayout: t->setNativeLayout(64), tname:[/model.22/Concat_1_output_0]
I RKNN: [13:48:42.616] AppointLayout: t->setNativeLayout(64), tname:[/model.22/Reshape_1_output_0_shape4_/model.22/Concat_3]
I RKNN: [13:48:42.616] AppointLayout: t->setNativeLayout(64), tname:[/model.21/m.0/cv1/act/Mul_output_0]
I RKNN: [13:48:42.616] AppointLayout: t->setNativeLayout(64), tname:[/model.21/m.0/cv2/act/Mul_output_0]
I RKNN: [13:48:42.616] AppointLayout: t->setNativeLayout(64), tname:[/model.21/Concat_output_0]
I RKNN: [13:48:42.616] AppointLayout: t->setNativeLayout(64), tname:[/model.21/cv2/act/Mul_output_0]
I RKNN: [13:48:42.616] AppointLayout: t->setNativeLayout(64), tname:[/model.22/cv2.2/cv2.2.0/act/Mul_output_0]
I RKNN: [13:48:42.616] AppointLayout: t->setNativeLayout(64), tname:[/model.22/cv3.2/cv3.2.0/act/Mul_output_0]
I RKNN: [13:48:42.616] AppointLayout: t->setNativeLayout(64), tname:[/model.22/cv2.2/cv2.2.1/act/Mul_output_0]
W RKNN: [13:48:42.616] LayoutMatchManager: recursion_depth=3, Logic is Dangerous, Will Force layout to native.
I RKNN: [13:48:42.616] AppointLayout: t->setNativeLayout(64), tname:[/model.22/cv2.2/cv2.2.2/Conv_output_0]
I RKNN: [13:48:42.616] AppointLayout: t->setNativeLayout(64), tname:[/model.22/cv3.2/cv3.2.1/act/Mul_output_0]
W RKNN: [13:48:42.616] LayoutMatchManager: recursion_depth=3, Logic is Dangerous, Will Force layout to native.
I RKNN: [13:48:42.616] AppointLayout: t->setNativeLayout(64), tname:[/model.22/cv3.2/cv3.2.2/Conv_output_0]
I RKNN: [13:48:42.616] AppointLayout: t->setNativeLayout(64), tname:[/model.22/Concat_2_output_0]
I RKNN: [13:48:42.616] AppointLayout: t->setNativeLayout(64), tname:[/model.22/Reshape_2_output_0_shape4_/model.22/Concat_3]
W RKNN: [13:48:42.616] LayoutMatchManager: recursion_depth=3, Logic is Dangerous, Will Force layout to native.
W RKNN: [13:48:42.616] LayoutMatchManager: recursion_depth=3, Logic is Dangerous, Will Force layout to native.
W RKNN: [13:48:42.616] LayoutMatchManager: recursion_depth=3, Logic is Dangerous, Will Force layout to native.
W RKNN: [13:48:42.616] LayoutMatchManager: recursion_depth=3, Logic is Dangerous, Will Force layout to native.
W RKNN: [13:48:42.616] LayoutMatchManager: recursion_depth=3, Logic is Dangerous, Will Force layout to native.
W RKNN: [13:48:42.616] LayoutMatchManager: recursion_depth=3, Logic is Dangerous, Will Force layout to native.
I RKNN: [13:48:42.616] AppointLayout: t->setNativeLayout(64), tname:[/model.22/Concat_3_output_0]
W RKNN: [13:48:42.616] LayoutMatchManager: recursion_depth=3, Logic is Dangerous, Will Force layout to native.
I RKNN: [13:48:42.616] AppointLayout: t->setNativeLayout(64), tname:[/model.22/dfl/Softmax_output_0]
I RKNN: [13:48:42.616] AppointLayout: t->setNativeLayout(64), tname:[/model.22/Slice_output_0]
I RKNN: [13:48:42.616] AppointLayout: t->setNativeLayout(64), tname:[/model.22/Slice_1_output_0]
I RKNN: [13:48:42.616] AppointLayout: t->setNativeLayout(64), tname:[/model.22/Sub_output_0]
I RKNN: [13:48:42.616] AppointLayout: t->setNativeLayout(64), tname:[/model.22/Add_1_output_0]
W RKNN: [13:48:42.616] BroadcastLayoutConnFactory: op(Mul:/model.22/Mul_2) has undefined broadcast type, please check this op or contact rk-compiler-developer.
I RKNN: [13:48:42.616] AppointLayout: t->setNativeLayout(64), tname:[/model.22/Sub_1_output_0]
I RKNN: [13:48:42.616] AppointLayout: t->setNativeLayout(64), tname:[/model.22/Add_2_output_0]
W RKNN: [13:48:42.616] BroadcastLayoutConnFactory: op(Mul:/model.22/Mul_2) has undefined broadcast type, please check this op or contact rk-compiler-developer.
I RKNN: [13:48:42.616] AppointLayout: t->setNativeLayout(64), tname:[/model.22/Div_1_output_0]
W RKNN: [13:48:42.616] BroadcastLayoutConnFactory: op(Mul:/model.22/Mul_2) has undefined broadcast type, please check this op or contact rk-compiler-developer.
I RKNN: [13:48:42.616] AppointLayout: t->setNativeLayout(64), tname:[/model.22/Concat_4_output_0_shape4_tp_4/model.22/Concat_4]
W RKNN: [13:48:42.616] LayoutMatchManager: recursion_depth=3, Logic is Dangerous, Will Force layout to native.
D RKNN: [13:48:42.616] <<<<<<<< end: rknn::RKNNLayoutMatchPass
D RKNN: [13:48:42.616] >>>>>> start: rknn::RKNNAddSecondaryNode
D RKNN: [13:48:42.616] <<<<<<<< end: rknn::RKNNAddSecondaryNode
D RKNN: [13:48:42.616] >>>>>> start: OpEmit
D RKNN: [13:48:42.635] finish initComputeZoneMapByStepsVector
D RKNN: [13:48:42.635] finish initComputeZoneMapByStepsVector
D RKNN: [13:48:42.635] finish initComputeZoneMapByStepsVector
D RKNN: [13:48:42.635] finish initComputeZoneMapByStepsVector
D RKNN: [13:48:42.635] not need tranpose
D RKNN: [13:48:42.635] not need tranpose
D RKNN: [13:48:42.635] finish initComputeZoneMap
D RKNN: [13:48:42.635] emit max
E RKNN: [13:48:42.636] failed to config argb mode layer!

rknn-toolkit2 1.6.0

W __init__: rknn-toolkit2 version: 1.6.0+81f21f4d
--> Configuring model  yolov8_small.onnx
done
--> Loading model
W load_onnx: It is recommended onnx opset 19, but your onnx model opset is 17!
W load_onnx: Model converted from pytorch, 'opset_version' should be set 19 in torch.onnx.export for successful convert!
Loading : 100%|███████████████████████████████████████████████| 143/143 [00:00<00:00, 184435.88it/s]
done
--> Building model
I base_optimize ...
I base_optimize done.
I
I fold_constant ...
I fold_constant done.
I
I correct_ops ...
I correct_ops done.
I
I fuse_ops ...
I fuse_ops results:
I     replace_exswish: remove node = ['/model.0/act/Sigmoid', '/model.0/act/Mul'], add node = ['/model.0/act/Sigmoid_2swish']
I     replace_exswish: remove node = ['/model.1/act/Sigmoid', '/model.1/act/Mul'], add node = ['/model.1/act/Sigmoid_2swish']
I     replace_exswish: remove node = ['/model.2/cv1/act/Sigmoid', '/model.2/cv1/act/Mul'], add node = ['/model.2/cv1/act/Sigmoid_2swish']
I     replace_exswish: remove node = ['/model.2/m.0/cv1/act/Sigmoid', '/model.2/m.0/cv1/act/Mul'], add node = ['/model.2/m.0/cv1/act/Sigmoid_2swish']
I     replace_exswish: remove node = ['/model.2/m.0/cv2/act/Sigmoid', '/model.2/m.0/cv2/act/Mul'], add node = ['/model.2/m.0/cv2/act/Sigmoid_2swish']
I     replace_exswish: remove node = ['/model.2/cv2/act/Sigmoid', '/model.2/cv2/act/Mul'], add node = ['/model.2/cv2/act/Sigmoid_2swish']
I     replace_exswish: remove node = ['/model.3/act/Sigmoid', '/model.3/act/Mul'], add node = ['/model.3/act/Sigmoid_2swish']
I     replace_exswish: remove node = ['/model.4/cv1/act/Sigmoid', '/model.4/cv1/act/Mul'], add node = ['/model.4/cv1/act/Sigmoid_2swish']
I     replace_exswish: remove node = ['/model.4/m.0/cv1/act/Sigmoid', '/model.4/m.0/cv1/act/Mul'], add node = ['/model.4/m.0/cv1/act/Sigmoid_2swish']
I     replace_exswish: remove node = ['/model.4/m.0/cv2/act/Sigmoid', '/model.4/m.0/cv2/act/Mul'], add node = ['/model.4/m.0/cv2/act/Sigmoid_2swish']
I     replace_exswish: remove node = ['/model.4/m.1/cv1/act/Sigmoid', '/model.4/m.1/cv1/act/Mul'], add node = ['/model.4/m.1/cv1/act/Sigmoid_2swish']
I     replace_exswish: remove node = ['/model.4/m.1/cv2/act/Sigmoid', '/model.4/m.1/cv2/act/Mul'], add node = ['/model.4/m.1/cv2/act/Sigmoid_2swish']
I     replace_exswish: remove node = ['/model.4/cv2/act/Sigmoid', '/model.4/cv2/act/Mul'], add node = ['/model.4/cv2/act/Sigmoid_2swish']
I     replace_exswish: remove node = ['/model.5/act/Sigmoid', '/model.5/act/Mul'], add node = ['/model.5/act/Sigmoid_2swish']
I     replace_exswish: remove node = ['/model.6/cv1/act/Sigmoid', '/model.6/cv1/act/Mul'], add node = ['/model.6/cv1/act/Sigmoid_2swish']
I     replace_exswish: remove node = ['/model.6/m.0/cv1/act/Sigmoid', '/model.6/m.0/cv1/act/Mul'], add node = ['/model.6/m.0/cv1/act/Sigmoid_2swish']
I     replace_exswish: remove node = ['/model.6/m.0/cv2/act/Sigmoid', '/model.6/m.0/cv2/act/Mul'], add node = ['/model.6/m.0/cv2/act/Sigmoid_2swish']
I     replace_exswish: remove node = ['/model.6/m.1/cv1/act/Sigmoid', '/model.6/m.1/cv1/act/Mul'], add node = ['/model.6/m.1/cv1/act/Sigmoid_2swish']
I     replace_exswish: remove node = ['/model.6/m.1/cv2/act/Sigmoid', '/model.6/m.1/cv2/act/Mul'], add node = ['/model.6/m.1/cv2/act/Sigmoid_2swish']
I     replace_exswish: remove node = ['/model.6/cv2/act/Sigmoid', '/model.6/cv2/act/Mul'], add node = ['/model.6/cv2/act/Sigmoid_2swish']
I     replace_exswish: remove node = ['/model.7/act/Sigmoid', '/model.7/act/Mul'], add node = ['/model.7/act/Sigmoid_2swish']
I     replace_exswish: remove node = ['/model.8/cv1/act/Sigmoid', '/model.8/cv1/act/Mul'], add node = ['/model.8/cv1/act/Sigmoid_2swish']
I     replace_exswish: remove node = ['/model.8/m.0/cv1/act/Sigmoid', '/model.8/m.0/cv1/act/Mul'], add node = ['/model.8/m.0/cv1/act/Sigmoid_2swish']
I     replace_exswish: remove node = ['/model.8/m.0/cv2/act/Sigmoid', '/model.8/m.0/cv2/act/Mul'], add node = ['/model.8/m.0/cv2/act/Sigmoid_2swish']
I     replace_exswish: remove node = ['/model.8/cv2/act/Sigmoid', '/model.8/cv2/act/Mul'], add node = ['/model.8/cv2/act/Sigmoid_2swish']
I     replace_exswish: remove node = ['/model.9/cv1/act/Sigmoid', '/model.9/cv1/act/Mul'], add node = ['/model.9/cv1/act/Sigmoid_2swish']
I     replace_exswish: remove node = ['/model.9/cv2/act/Sigmoid', '/model.9/cv2/act/Mul'], add node = ['/model.9/cv2/act/Sigmoid_2swish']
I     replace_exswish: remove node = ['/model.12/cv1/act/Sigmoid', '/model.12/cv1/act/Mul'], add node = ['/model.12/cv1/act/Sigmoid_2swish']
I     replace_exswish: remove node = ['/model.12/m.0/cv1/act/Sigmoid', '/model.12/m.0/cv1/act/Mul'], add node = ['/model.12/m.0/cv1/act/Sigmoid_2swish']
I     replace_exswish: remove node = ['/model.12/m.0/cv2/act/Sigmoid', '/model.12/m.0/cv2/act/Mul'], add node = ['/model.12/m.0/cv2/act/Sigmoid_2swish']
I     replace_exswish: remove node = ['/model.12/cv2/act/Sigmoid', '/model.12/cv2/act/Mul'], add node = ['/model.12/cv2/act/Sigmoid_2swish']
I     replace_exswish: remove node = ['/model.15/cv1/act/Sigmoid', '/model.15/cv1/act/Mul'], add node = ['/model.15/cv1/act/Sigmoid_2swish']
I     replace_exswish: remove node = ['/model.15/m.0/cv1/act/Sigmoid', '/model.15/m.0/cv1/act/Mul'], add node = ['/model.15/m.0/cv1/act/Sigmoid_2swish']
I     replace_exswish: remove node = ['/model.15/m.0/cv2/act/Sigmoid', '/model.15/m.0/cv2/act/Mul'], add node = ['/model.15/m.0/cv2/act/Sigmoid_2swish']
I     replace_exswish: remove node = ['/model.15/cv2/act/Sigmoid', '/model.15/cv2/act/Mul'], add node = ['/model.15/cv2/act/Sigmoid_2swish']
I     replace_exswish: remove node = ['/model.16/act/Sigmoid', '/model.16/act/Mul'], add node = ['/model.16/act/Sigmoid_2swish']
I     replace_exswish: remove node = ['/model.18/cv1/act/Sigmoid', '/model.18/cv1/act/Mul'], add node = ['/model.18/cv1/act/Sigmoid_2swish']
I     replace_exswish: remove node = ['/model.18/m.0/cv1/act/Sigmoid', '/model.18/m.0/cv1/act/Mul'], add node = ['/model.18/m.0/cv1/act/Sigmoid_2swish']
I     replace_exswish: remove node = ['/model.18/m.0/cv2/act/Sigmoid', '/model.18/m.0/cv2/act/Mul'], add node = ['/model.18/m.0/cv2/act/Sigmoid_2swish']
I     replace_exswish: remove node = ['/model.18/cv2/act/Sigmoid', '/model.18/cv2/act/Mul'], add node = ['/model.18/cv2/act/Sigmoid_2swish']
I     replace_exswish: remove node = ['/model.19/act/Sigmoid', '/model.19/act/Mul'], add node = ['/model.19/act/Sigmoid_2swish']
I     replace_exswish: remove node = ['/model.21/cv1/act/Sigmoid', '/model.21/cv1/act/Mul'], add node = ['/model.21/cv1/act/Sigmoid_2swish']
I     replace_exswish: remove node = ['/model.21/m.0/cv1/act/Sigmoid', '/model.21/m.0/cv1/act/Mul'], add node = ['/model.21/m.0/cv1/act/Sigmoid_2swish']
I     replace_exswish: remove node = ['/model.21/m.0/cv2/act/Sigmoid', '/model.21/m.0/cv2/act/Mul'], add node = ['/model.21/m.0/cv2/act/Sigmoid_2swish']
I     replace_exswish: remove node = ['/model.21/cv2/act/Sigmoid', '/model.21/cv2/act/Mul'], add node = ['/model.21/cv2/act/Sigmoid_2swish']
I     replace_exswish: remove node = ['/model.22/cv2.2/cv2.2.0/act/Sigmoid', '/model.22/cv2.2/cv2.2.0/act/Mul'], add node = ['/model.22/cv2.2/cv2.2.0/act/Sigmoid_2swish']
I     replace_exswish: remove node = ['/model.22/cv2.2/cv2.2.1/act/Sigmoid', '/model.22/cv2.2/cv2.2.1/act/Mul'], add node = ['/model.22/cv2.2/cv2.2.1/act/Sigmoid_2swish']
I     replace_exswish: remove node = ['/model.22/cv3.2/cv3.2.0/act/Sigmoid', '/model.22/cv3.2/cv3.2.0/act/Mul'], add node = ['/model.22/cv3.2/cv3.2.0/act/Sigmoid_2swish']
I     replace_exswish: remove node = ['/model.22/cv3.2/cv3.2.1/act/Sigmoid', '/model.22/cv3.2/cv3.2.1/act/Mul'], add node = ['/model.22/cv3.2/cv3.2.1/act/Sigmoid_2swish']
I     replace_exswish: remove node = ['/model.22/cv2.1/cv2.1.0/act/Sigmoid', '/model.22/cv2.1/cv2.1.0/act/Mul'], add node = ['/model.22/cv2.1/cv2.1.0/act/Sigmoid_2swish']
I     replace_exswish: remove node = ['/model.22/cv2.1/cv2.1.1/act/Sigmoid', '/model.22/cv2.1/cv2.1.1/act/Mul'], add node = ['/model.22/cv2.1/cv2.1.1/act/Sigmoid_2swish']
I     replace_exswish: remove node = ['/model.22/cv3.1/cv3.1.0/act/Sigmoid', '/model.22/cv3.1/cv3.1.0/act/Mul'], add node = ['/model.22/cv3.1/cv3.1.0/act/Sigmoid_2swish']
I     replace_exswish: remove node = ['/model.22/cv3.1/cv3.1.1/act/Sigmoid', '/model.22/cv3.1/cv3.1.1/act/Mul'], add node = ['/model.22/cv3.1/cv3.1.1/act/Sigmoid_2swish']
I     replace_exswish: remove node = ['/model.22/cv2.0/cv2.0.0/act/Sigmoid', '/model.22/cv2.0/cv2.0.0/act/Mul'], add node = ['/model.22/cv2.0/cv2.0.0/act/Sigmoid_2swish']
I     replace_exswish: remove node = ['/model.22/cv2.0/cv2.0.1/act/Sigmoid', '/model.22/cv2.0/cv2.0.1/act/Mul'], add node = ['/model.22/cv2.0/cv2.0.1/act/Sigmoid_2swish']
I     replace_exswish: remove node = ['/model.22/cv3.0/cv3.0.0/act/Sigmoid', '/model.22/cv3.0/cv3.0.0/act/Mul'], add node = ['/model.22/cv3.0/cv3.0.0/act/Sigmoid_2swish']
I     replace_exswish: remove node = ['/model.22/cv3.0/cv3.0.1/act/Sigmoid', '/model.22/cv3.0/cv3.0.1/act/Mul'], add node = ['/model.22/cv3.0/cv3.0.1/act/Sigmoid_2swish']
I     replace_parallel_slice_by_split: remove node = ['/model.22/Slice', '/model.22/Slice_1'], add node = ['/model.22/Slice_2split']
I     unsqueeze_to_4d_concat: remove node = [], add node = ['/model.22/Concat_3_0_unsqueeze0', '/model.22/Concat_3_1_unsqueeze0', '/model.22/Concat_3_2_unsqueeze0', '/model.22/Concat_3_0_unsqueeze1']
I     unsqueeze_to_4d_split: remove node = [], add node = ['/model.22/Split_0_unsqueeze0', '/model.22/Split_0_unsqueeze1', '/model.22/Split_1_unsqueeze1']
I     convert_softmax_to_exsoftmax13: remove node = ['/model.22/dfl/Softmax'], add node = ['/model.22/dfl/Softmax']
I     unsqueeze_to_4d_split: remove node = [], add node = ['/model.22/Slice_2split_0_unsqueeze0', '/model.22/Slice_2split_0_unsqueeze1', '/model.22/Slice_2split_1_unsqueeze1']
I     unsqueeze_to_4d_sub: remove node = [], add node = ['/model.22/Sub_1_unsqueeze0', '/model.22/Sub_0_unsqueeze1']
I     unsqueeze_to_4d_add: remove node = [], add node = ['/model.22/Add_1_1_unsqueeze0', '/model.22/Add_1_0_unsqueeze1']
I     unsqueeze_to_4d_add: remove node = [], add node = ['/model.22/Add_2_0_unsqueeze0', '/model.22/Add_2_1_unsqueeze0', '/model.22/Add_2_0_unsqueeze1']
I     convert_div_to_mul: remove node = ['/model.22/Div_1'], add node = ['/model.22/Div_1_2mul']
I     unsqueeze_to_4d_sub: remove node = [], add node = ['/model.22/Sub_1_0_unsqueeze0', '/model.22/Sub_1_1_unsqueeze0', '/model.22/Sub_1_0_unsqueeze1']
I     unsqueeze_to_4d_concat: remove node = [], add node = ['/model.22/Concat_4_0_unsqueeze0', '/model.22/Concat_4_1_unsqueeze0', '/model.22/Concat_4_0_unsqueeze1']
I     unsqueeze_to_4d_mul: remove node = [], add node = ['/model.22/Mul_2_0_unsqueeze0', '/model.22/Mul_2_0_unsqueeze1']
I     unsqueeze_to_4d_sigmoid: remove node = [], add node = ['/model.22/Sigmoid_0_unsqueeze0', '/model.22/Sigmoid_0_unsqueeze1']
I     unsqueeze_to_4d_concat: remove node = [], add node = ['/model.22/Concat_5_0_unsqueeze0', '/model.22/Concat_5_1_unsqueeze0', '/model.22/Concat_5_0_unsqueeze1']
I     fuse_two_reshape: remove node = ['/model.22/Reshape_2', '/model.22/Reshape_1', '/model.22/Reshape', '/model.22/Concat_3_0_unsqueeze1', '/model.22/Split_0_unsqueeze1', '/model.22/dfl/Reshape_1']
I     convert_split_to_conv_split: remove node = [], add node = ['/model.22/Slice_output_0_shape4_conv', '/model.22/dfl/Reshape_1_output_0_shape4_/model.22/Slice_2split_conv_/model.22/Slice_2split']
I     fuse_two_reshape: remove node = ['/model.22/Slice_2split_0_unsqueeze1']
I     remove_parallel_reshape: remove node = ['/model.22/Add_2_0_unsqueeze0']
I     fuse_two_reshape: remove node = ['/model.22/Slice_2split_1_unsqueeze1']
I     remove_parallel_reshape: remove node = ['/model.22/Add_2_1_unsqueeze0']
I     unsqueeze_to_4d_mul: remove node = [], add node = ['/model.22/Div_1_2mul_0_unsqueeze0', '/model.22/Div_1_2mul_0_unsqueeze1']
I     swap_concat_axis_avoid_channel_concat: remove node = [], add node = ['/model.22/Concat_4_swap_concat_reshape_i0_out', '/model.22/Concat_4_swap_concat_reshape_i1_out', '/model.22/Concat_4_swap_concat_reshape_o0_out']
I     fuse_two_reshape: remove node = ['/model.22/Concat_4_0_unsqueeze1', '/model.22/Mul_2_0_unsqueeze1', '/model.22/Split_1_unsqueeze1', '/model.22/Sigmoid_0_unsqueeze1']
I     swap_concat_axis_avoid_channel_concat: remove node = [], add node = ['/model.22/Concat_5_swap_concat_reshape_i0_out', '/model.22/Concat_5_swap_concat_reshape_i1_out', '/model.22/Concat_5_swap_concat_reshape_o0_out']
I     remove_invalid_reshape: remove node = ['/model.22/Split_0_unsqueeze0', '/model.22/Sub_1_unsqueeze0']
I     fuse_two_reshape: remove node = ['/model.22/Sub_0_unsqueeze1']
I     remove_invalid_reshape: remove node = ['/model.22/Add_1_1_unsqueeze0']
I     fuse_two_reshape: remove node = ['/model.22/Add_1_0_unsqueeze1', '/model.22/Sub_1_0_unsqueeze1', '/model.22/Add_2_0_unsqueeze1', '/model.22/Div_1_2mul_0_unsqueeze1', '/model.22/Concat_4_swap_concat_reshape_o0_out', '/model.22/Concat_5_0_unsqueeze0']
I     remove_invalid_reshape: remove node = ['/model.22/Sigmoid_0_unsqueeze0']
I     fuse_two_reshape: remove node = ['/model.22/Concat_5_1_unsqueeze0', '/model.22/Concat_5_swap_concat_reshape_o0_out']
I     remove_invalid_reshape: remove node = ['/model.22/Sub_1_1_unsqueeze0', '/model.22/Sub_1_0_unsqueeze0']
I     fuse_two_reshape: remove node = ['/model.22/Concat_4_1_unsqueeze0']
I     remove_invalid_reshape: remove node = ['/model.22/Div_1_2mul_0_unsqueeze0']
I     fuse_two_reshape: remove node = ['/model.22/Concat_4_0_unsqueeze0']
I     fuse_reshape_into_conv: remove node = ['/model.22/Slice_2split_0_unsqueeze0', '/model.22/dfl/Reshape_1_output_0_shape4_/model.22/Slice_2split_conv_/model.22/Slice_2split'], add node = ['/model.22/dfl/Reshape_1_output_0_shape4_/model.22/Slice_2split_conv_/model.22/Slice_2split']
I     remove_reshape_after_concat: remove node = ['/model.22/Mul_2_0_unsqueeze0']
I     remove_invalid_reshape: remove node = ['/model.22/Concat_4_swap_concat_reshape_i1_out', '/model.22/Concat_4_swap_concat_reshape_i0_out']
I     convert_concat_to_conv_concat: remove node = [], add node = ['/model.22/Div_1_output_0_conv_/model.22/Concat_4', '/model.22/Concat_4_output_0_shape4_/model.22/Mul_2_conv']
I     fuse_mul_into_conv2: remove node = ['/model.22/Div_1_2mul']
I     fold_constant ...
I     fold_constant done.
I fuse_ops done.
I
W build: found outlier value, this may affect quantization accuracy
const name               abs_mean    abs_std     outlier value
model.0.conv.weight      4.03        4.41        26.039
I sparse_weight ...
I sparse_weight done.
I
GraphPreparing : 100%|██████████████████████████████████████████| 180/180 [00:00<00:00, 2094.71it/s]
Quantizating : 100%|█████████████████████████████████████████████| 180/180 [00:00<00:00, 317.54it/s]
I
I quant_optimizer ...
I quant_optimizer results:
I     adjust_tanh_sigmoid: ['/model.22/Sigmoid']
I     adjust_concat_split: ['/model.22/Split', '/model.22/Concat_3', '/model.22/Concat', '/model.22/Concat_1', '/model.22/Concat_2']
I     adjust_no_change_node: ['/model.22/Concat_3_0_unsqueeze0', '/model.22/Concat_3_1_unsqueeze0', '/model.22/Concat_3_2_unsqueeze0', '/model.9/m_2/MaxPool', '/model.9/m_1/MaxPool', '/model.9/m/MaxPool']
I quant_optimizer done.
I
I recover_const_share ...
I recover_const_share done.
I
W build: The default input dtype of 'images' is changed from 'float32' to 'int8' in rknn model for performance!
                       Please take care of this change when deploy rknn model with Runtime API!
W build: The default output dtype of 'output0' is changed from 'float32' to 'int8' in rknn model for performance!
                      Please take care of this change when deploy rknn model with Runtime API!
I rknn building ...
I RKNN: [13:55:01.955] compress = 0, conv_eltwise_activation_fuse = 1, global_fuse = 1, multi-core-model-mode = 7, output_optimize = 1,enable_argb_group=0 ,layout_match = 0, pipeline_fuse = 0
I RKNN: librknnc version: 1.6.0 (585b3edcf@2023-12-11T08:03:14)
D RKNN: [13:55:01.002] RKNN is invoked
W RKNN: [13:55:02.191] Model initializer tensor data is empty, name: empty_placeholder_0
D RKNN: [13:55:02.194] >>>>>> start: rknn::RKNNExtractCustomOpAttrs
D RKNN: [13:55:02.194] <<<<<<<< end: rknn::RKNNExtractCustomOpAttrs
D RKNN: [13:55:02.194] >>>>>> start: rknn::RKNNSetOpTargetPass
D RKNN: [13:55:02.194] <<<<<<<< end: rknn::RKNNSetOpTargetPass
D RKNN: [13:55:02.194] >>>>>> start: rknn::RKNNBindNorm
D RKNN: [13:55:02.194] <<<<<<<< end: rknn::RKNNBindNorm
D RKNN: [13:55:02.194] >>>>>> start: rknn::RKNNAddFirstConv
D RKNN: [13:55:02.194] <<<<<<<< end: rknn::RKNNAddFirstConv
D RKNN: [13:55:02.194] >>>>>> start: rknn::RKNNEliminateQATDataConvert
D RKNN: [13:55:02.195] <<<<<<<< end: rknn::RKNNEliminateQATDataConvert
D RKNN: [13:55:02.195] >>>>>> start: rknn::RKNNTileGroupConv
D RKNN: [13:55:02.195] <<<<<<<< end: rknn::RKNNTileGroupConv
D RKNN: [13:55:02.195] >>>>>> start: rknn::RKNNAddConvBias
D RKNN: [13:55:02.195] <<<<<<<< end: rknn::RKNNAddConvBias
D RKNN: [13:55:02.195] >>>>>> start: rknn::RKNNTileChannel
D RKNN: [13:55:02.195] <<<<<<<< end: rknn::RKNNTileChannel
D RKNN: [13:55:02.195] >>>>>> start: rknn::RKNNPerChannelPrep
D RKNN: [13:55:02.195] <<<<<<<< end: rknn::RKNNPerChannelPrep
D RKNN: [13:55:02.195] >>>>>> start: rknn::RKNNBnQuant
D RKNN: [13:55:02.195] <<<<<<<< end: rknn::RKNNBnQuant
D RKNN: [13:55:02.195] >>>>>> start: rknn::RKNNFuseOptimizerPass
D RKNN: [13:55:02.209] <<<<<<<< end: rknn::RKNNFuseOptimizerPass
D RKNN: [13:55:02.209] >>>>>> start: rknn::RKNNTurnAutoPad
D RKNN: [13:55:02.209] <<<<<<<< end: rknn::RKNNTurnAutoPad
D RKNN: [13:55:02.209] >>>>>> start: rknn::RKNNInitRNNConst
D RKNN: [13:55:02.209] <<<<<<<< end: rknn::RKNNInitRNNConst
D RKNN: [13:55:02.209] >>>>>> start: rknn::RKNNInitCastConst
D RKNN: [13:55:02.209] <<<<<<<< end: rknn::RKNNInitCastConst
D RKNN: [13:55:02.209] >>>>>> start: rknn::RKNNMultiSurfacePass
D RKNN: [13:55:02.209] <<<<<<<< end: rknn::RKNNMultiSurfacePass
D RKNN: [13:55:02.209] >>>>>> start: rknn::RKNNReplaceConstantTensorPass
D RKNN: [13:55:02.210] <<<<<<<< end: rknn::RKNNReplaceConstantTensorPass
D RKNN: [13:55:02.210] >>>>>> start: rknn::RKNNTilingPass
D RKNN: [13:55:02.210] DatainEntries overflow, need to tiling more, datain_entries: 2100, limitation: 2048
D RKNN: [13:55:02.210] DatainEntries overflow, need to tiling more, datain_entries: 2100, limitation: 2048
D RKNN: [13:55:02.210] DatainEntries overflow, need to tiling more, datain_entries: 2100, limitation: 2048
D RKNN: [13:55:02.210] DatainEntries overflow, need to tiling more, datain_entries: 2100, limitation: 2048
D RKNN: [13:55:02.210] DatainEntries overflow, need to tiling more, datain_entries: 2100, limitation: 2048
D RKNN: [13:55:02.210] DatainEntries overflow, need to tiling more, datain_entries: 2100, limitation: 2048
D RKNN: [13:55:02.210] DatainEntries overflow, need to tiling more, datain_entries: 2100, limitation: 2048
D RKNN: [13:55:02.210] DatainEntries overflow, need to tiling more, datain_entries: 2100, limitation: 2048
D RKNN: [13:55:02.210] DatainEntries overflow, need to tiling more, datain_entries: 2100, limitation: 2048
D RKNN: [13:55:02.210] DatainEntries overflow, need to tiling more, datain_entries: 2100, limitation: 2048
D RKNN: [13:55:02.211] DatainEntries overflow, need to tiling more, datain_entries: 2100, limitation: 2048
D RKNN: [13:55:02.211] DatainEntries overflow, need to tiling more, datain_entries: 2100, limitation: 2048
D RKNN: [13:55:02.211] DatainEntries overflow, need to tiling more, datain_entries: 2100, limitation: 2048
D RKNN: [13:55:02.211] DatainEntries overflow, need to tiling more, datain_entries: 2100, limitation: 2048
D RKNN: [13:55:02.211] DatainEntries overflow, need to tiling more, datain_entries: 2100, limitation: 2048
D RKNN: [13:55:02.211] DatainEntries overflow, need to tiling more, datain_entries: 2100, limitation: 2048
D RKNN: [13:55:02.211] DatainEntries overflow, need to tiling more, datain_entries: 2100, limitation: 2048
D RKNN: [13:55:02.211] DatainEntries overflow, need to tiling more, datain_entries: 2100, limitation: 2048
D RKNN: [13:55:02.211] DatainEntries overflow, need to tiling more, datain_entries: 2100, limitation: 2048
D RKNN: [13:55:02.211] DatainEntries overflow, need to tiling more, datain_entries: 2100, limitation: 2048
D RKNN: [13:55:02.211] DatainEntries overflow, need to tiling more, datain_entries: 2100, limitation: 2048
D RKNN: [13:55:02.211] DatainEntries overflow, need to tiling more, datain_entries: 2100, limitation: 2048
D RKNN: [13:55:02.211] DatainEntries overflow, need to tiling more, datain_entries: 2100, limitation: 2048
D RKNN: [13:55:02.211] DatainEntries overflow, need to tiling more, datain_entries: 2100, limitation: 2048
D RKNN: [13:55:02.211] DatainEntries overflow, need to tiling more, datain_entries: 2100, limitation: 2048
D RKNN: [13:55:02.211] DatainEntries overflow, need to tiling more, datain_entries: 2100, limitation: 2048
D RKNN: [13:55:02.211] DatainEntries overflow, need to tiling more, datain_entries: 2100, limitation: 2048
D RKNN: [13:55:02.211] DatainEntries overflow, need to tiling more, datain_entries: 2100, limitation: 2048
D RKNN: [13:55:02.211] DatainEntries overflow, need to tiling more, datain_entries: 2100, limitation: 2048
D RKNN: [13:55:02.211] DatainEntries overflow, need to tiling more, datain_entries: 2100, limitation: 2048
W RKNN: [13:55:02.211] Failed to config layer: 'Conv:/model.22/dfl/conv/Conv' using 3Core fallback to single core mode,
W RKNN: [13:55:02.211] core_num 3 ori_Ih 4 ori_Iw 8400 ori_Ic 16 ori_Ib 1
W RKNN: [13:55:02.211] ori_Kh 1 ori_Kw 1 ori_Kk 1 ori_Kc 16 ori_Ksx 1 ori_Ksy 1
W RKNN: [13:55:02.211] ori_Oh 4 oriOw 8400 oriOc 1 pad_t 0 pad_b 0 pad_l 0 pad_r 0,
W RKNN: [13:55:02.211] Please help report this bug!
W RKNN: [13:55:02.211] Failed to config layer: 'Conv:/model.22/dfl/conv/Conv' using 3Core fallback to single core mode,
W RKNN: [13:55:02.211] core_num 3 ori_Ih 4 ori_Iw 8400 ori_Ic 16 ori_Ib 1
W RKNN: [13:55:02.211] ori_Kh 1 ori_Kw 1 ori_Kk 1 ori_Kc 16 ori_Ksx 1 ori_Ksy 1
W RKNN: [13:55:02.211] ori_Oh 4 oriOw 8400 oriOc 1 pad_t 0 pad_b 0 pad_l 0 pad_r 0,
W RKNN: [13:55:02.211] Please help report this bug!
W RKNN: [13:55:02.211] Failed to config layer: 'Conv:/model.22/dfl/conv/Conv' using 3Core fallback to single core mode,
W RKNN: [13:55:02.211] core_num 3 ori_Ih 4 ori_Iw 8400 ori_Ic 16 ori_Ib 1
W RKNN: [13:55:02.211] ori_Kh 1 ori_Kw 1 ori_Kk 1 ori_Kc 16 ori_Ksx 1 ori_Ksy 1
W RKNN: [13:55:02.211] ori_Oh 4 oriOw 8400 oriOc 1 pad_t 0 pad_b 0 pad_l 0 pad_r 0,
W RKNN: [13:55:02.211] Please help report this bug!
W RKNN: [13:55:02.211] Failed to config layer: 'Conv:/model.22/dfl/conv/Conv' using 3Core fallback to single core mode,
W RKNN: [13:55:02.211] core_num 3 ori_Ih 4 ori_Iw 8400 ori_Ic 16 ori_Ib 1
W RKNN: [13:55:02.211] ori_Kh 1 ori_Kw 1 ori_Kk 1 ori_Kc 16 ori_Ksx 1 ori_Ksy 1
W RKNN: [13:55:02.211] ori_Oh 4 oriOw 8400 oriOc 1 pad_t 0 pad_b 0 pad_l 0 pad_r 0,
W RKNN: [13:55:02.211] Please help report this bug!
W RKNN: [13:55:02.211] Failed to config layer: 'Conv:/model.22/dfl/conv/Conv' using 3Core fallback to single core mode,
W RKNN: [13:55:02.211] core_num 3 ori_Ih 4 ori_Iw 8400 ori_Ic 16 ori_Ib 1
W RKNN: [13:55:02.211] ori_Kh 1 ori_Kw 1 ori_Kk 1 ori_Kc 16 ori_Ksx 1 ori_Ksy 1
W RKNN: [13:55:02.211] ori_Oh 4 oriOw 8400 oriOc 1 pad_t 0 pad_b 0 pad_l 0 pad_r 0,
W RKNN: [13:55:02.211] Please help report this bug!
D RKNN: [13:55:02.211] DatainEntries overflow, need to tiling more, datain_entries: 2100, limitation: 2048
D RKNN: [13:55:02.211] DatainEntries overflow, need to tiling more, datain_entries: 2100, limitation: 2048
D RKNN: [13:55:02.211] DatainEntries overflow, need to tiling more, datain_entries: 2100, limitation: 2048
D RKNN: [13:55:02.211] DatainEntries overflow, need to tiling more, datain_entries: 2100, limitation: 2048
D RKNN: [13:55:02.211] DatainEntries overflow, need to tiling more, datain_entries: 2100, limitation: 2048
D RKNN: [13:55:02.211] DatainEntries overflow, need to tiling more, datain_entries: 2100, limitation: 2048
D RKNN: [13:55:02.211] DatainEntries overflow, need to tiling more, datain_entries: 2100, limitation: 2048
D RKNN: [13:55:02.211] DatainEntries overflow, need to tiling more, datain_entries: 2100, limitation: 2048
D RKNN: [13:55:02.211] DatainEntries overflow, need to tiling more, datain_entries: 2100, limitation: 2048
D RKNN: [13:55:02.211] DatainEntries overflow, need to tiling more, datain_entries: 2100, limitation: 2048
D RKNN: [13:55:02.211] DatainEntries overflow, need to tiling more, datain_entries: 2100, limitation: 2048
D RKNN: [13:55:02.211] DatainEntries overflow, need to tiling more, datain_entries: 2100, limitation: 2048
D RKNN: [13:55:02.211] DatainEntries overflow, need to tiling more, datain_entries: 2100, limitation: 2048
D RKNN: [13:55:02.211] DatainEntries overflow, need to tiling more, datain_entries: 2100, limitation: 2048
D RKNN: [13:55:02.211] DatainEntries overflow, need to tiling more, datain_entries: 2100, limitation: 2048
D RKNN: [13:55:02.211] DatainEntries overflow, need to tiling more, datain_entries: 2100, limitation: 2048
D RKNN: [13:55:02.211] DatainEntries overflow, need to tiling more, datain_entries: 2100, limitation: 2048
D RKNN: [13:55:02.211] DatainEntries overflow, need to tiling more, datain_entries: 2100, limitation: 2048
D RKNN: [13:55:02.211] DatainEntries overflow, need to tiling more, datain_entries: 2100, limitation: 2048
D RKNN: [13:55:02.211] DatainEntries overflow, need to tiling more, datain_entries: 2100, limitation: 2048
D RKNN: [13:55:02.211] DatainEntries overflow, need to tiling more, datain_entries: 2100, limitation: 2048
D RKNN: [13:55:02.211] DatainEntries overflow, need to tiling more, datain_entries: 2100, limitation: 2048
D RKNN: [13:55:02.211] DatainEntries overflow, need to tiling more, datain_entries: 2100, limitation: 2048
D RKNN: [13:55:02.211] DatainEntries overflow, need to tiling more, datain_entries: 2100, limitation: 2048
D RKNN: [13:55:02.211] DatainEntries overflow, need to tiling more, datain_entries: 2100, limitation: 2048
D RKNN: [13:55:02.211] DatainEntries overflow, need to tiling more, datain_entries: 2100, limitation: 2048
D RKNN: [13:55:02.211] DatainEntries overflow, need to tiling more, datain_entries: 2100, limitation: 2048
D RKNN: [13:55:02.211] DatainEntries overflow, need to tiling more, datain_entries: 2100, limitation: 2048
D RKNN: [13:55:02.211] DatainEntries overflow, need to tiling more, datain_entries: 2100, limitation: 2048
D RKNN: [13:55:02.211] DatainEntries overflow, need to tiling more, datain_entries: 2100, limitation: 2048
D RKNN: [13:55:02.211] DatainEntries overflow, need to tiling more, datain_entries: 2100, limitation: 2048
D RKNN: [13:55:02.211] DatainEntries overflow, need to tiling more, datain_entries: 2100, limitation: 2048
D RKNN: [13:55:02.211] DatainEntries overflow, need to tiling more, datain_entries: 2100, limitation: 2048
D RKNN: [13:55:02.211] DatainEntries overflow, need to tiling more, datain_entries: 2100, limitation: 2048
D RKNN: [13:55:02.211] DatainEntries overflow, need to tiling more, datain_entries: 2100, limitation: 2048
D RKNN: [13:55:02.211] DatainEntries overflow, need to tiling more, datain_entries: 2100, limitation: 2048
D RKNN: [13:55:02.211] DatainEntries overflow, need to tiling more, datain_entries: 2100, limitation: 2048
D RKNN: [13:55:02.211] DatainEntries overflow, need to tiling more, datain_entries: 2100, limitation: 2048
D RKNN: [13:55:02.211] DatainEntries overflow, need to tiling more, datain_entries: 2100, limitation: 2048
D RKNN: [13:55:02.211] DatainEntries overflow, need to tiling more, datain_entries: 2100, limitation: 2048
D RKNN: [13:55:02.211] DatainEntries overflow, need to tiling more, datain_entries: 2100, limitation: 2048
D RKNN: [13:55:02.211] DatainEntries overflow, need to tiling more, datain_entries: 2100, limitation: 2048
D RKNN: [13:55:02.211] DatainEntries overflow, need to tiling more, datain_entries: 2100, limitation: 2048
D RKNN: [13:55:02.211] DatainEntries overflow, need to tiling more, datain_entries: 2100, limitation: 2048
D RKNN: [13:55:02.211] DatainEntries overflow, need to tiling more, datain_entries: 2100, limitation: 2048
D RKNN: [13:55:02.211] DatainEntries overflow, need to tiling more, datain_entries: 2100, limitation: 2048
D RKNN: [13:55:02.211] DatainEntries overflow, need to tiling more, datain_entries: 2100, limitation: 2048
D RKNN: [13:55:02.211] DatainEntries overflow, need to tiling more, datain_entries: 2100, limitation: 2048
D RKNN: [13:55:02.211] DatainEntries overflow, need to tiling more, datain_entries: 2100, limitation: 2048
D RKNN: [13:55:02.211] DatainEntries overflow, need to tiling more, datain_entries: 2100, limitation: 2048
D RKNN: [13:55:02.211] DatainEntries overflow, need to tiling more, datain_entries: 2100, limitation: 2048
D RKNN: [13:55:02.211] DatainEntries overflow, need to tiling more, datain_entries: 2100, limitation: 2048
D RKNN: [13:55:02.211] DatainEntries overflow, need to tiling more, datain_entries: 2100, limitation: 2048
D RKNN: [13:55:02.211] DatainEntries overflow, need to tiling more, datain_entries: 2100, limitation: 2048
D RKNN: [13:55:02.211] DatainEntries overflow, need to tiling more, datain_entries: 4200, limitation: 2048
D RKNN: [13:55:02.211] DatainEntries overflow, need to tiling more, datain_entries: 4200, limitation: 2048
D RKNN: [13:55:02.211] DatainEntries overflow, need to tiling more, datain_entries: 4200, limitation: 2048
D RKNN: [13:55:02.211] DatainEntries overflow, need to tiling more, datain_entries: 2100, limitation: 2048
D RKNN: [13:55:02.211] DatainEntries overflow, need to tiling more, datain_entries: 4200, limitation: 2048
D RKNN: [13:55:02.211] DatainEntries overflow, need to tiling more, datain_entries: 4200, limitation: 2048
D RKNN: [13:55:02.211] DatainEntries overflow, need to tiling more, datain_entries: 4200, limitation: 2048
D RKNN: [13:55:02.211] DatainEntries overflow, need to tiling more, datain_entries: 4200, limitation: 2048
D RKNN: [13:55:02.211] DatainEntries overflow, need to tiling more, datain_entries: 4200, limitation: 2048
D RKNN: [13:55:02.211] DatainEntries overflow, need to tiling more, datain_entries: 4200, limitation: 2048
D RKNN: [13:55:02.211] DatainEntries overflow, need to tiling more, datain_entries: 4200, limitation: 2048
D RKNN: [13:55:02.211] DatainEntries overflow, need to tiling more, datain_entries: 2100, limitation: 2048
D RKNN: [13:55:02.211] DatainEntries overflow, need to tiling more, datain_entries: 4200, limitation: 2048
D RKNN: [13:55:02.211] DatainEntries overflow, need to tiling more, datain_entries: 4200, limitation: 2048
D RKNN: [13:55:02.211] DatainEntries overflow, need to tiling more, datain_entries: 4200, limitation: 2048
D RKNN: [13:55:02.211] DatainEntries overflow, need to tiling more, datain_entries: 4200, limitation: 2048
D RKNN: [13:55:02.211] DatainEntries overflow, need to tiling more, datain_entries: 4200, limitation: 2048
D RKNN: [13:55:02.211] DatainEntries overflow, need to tiling more, datain_entries: 4200, limitation: 2048
D RKNN: [13:55:02.211] DatainEntries overflow, need to tiling more, datain_entries: 4200, limitation: 2048
D RKNN: [13:55:02.211] DatainEntries overflow, need to tiling more, datain_entries: 2100, limitation: 2048
D RKNN: [13:55:02.211] DatainEntries overflow, need to tiling more, datain_entries: 4200, limitation: 2048
D RKNN: [13:55:02.211] DatainEntries overflow, need to tiling more, datain_entries: 4200, limitation: 2048
D RKNN: [13:55:02.211] DatainEntries overflow, need to tiling more, datain_entries: 4200, limitation: 2048
D RKNN: [13:55:02.211] DatainEntries overflow, need to tiling more, datain_entries: 4200, limitation: 2048
D RKNN: [13:55:02.211] <<<<<<<< end: rknn::RKNNTilingPass
D RKNN: [13:55:02.211] >>>>>> start: OpEmit
D RKNN: [13:55:02.211] Transpose will fallback to CPU, because input shape has exceeded the max limit, height(16) * width(8400) = 134400, required product no larger than 16384!
D RKNN: [13:55:02.212] <<<<<<<< end: OpEmit
D RKNN: [13:55:02.212] >>>>>> start: rknn::RKNNLayoutMatchPass
D RKNN: [13:55:02.212] <<<<<<<< end: rknn::RKNNLayoutMatchPass
D RKNN: [13:55:02.212] >>>>>> start: rknn::RKNNAddSecondaryNode
D RKNN: [13:55:02.212] <<<<<<<< end: rknn::RKNNAddSecondaryNode
D RKNN: [13:55:02.212] >>>>>> start: OpEmit
D RKNN: [13:55:02.228] Transpose will fallback to CPU, because input shape has exceeded the max limit, height(16) * width(8400) = 134400, required product no larger than 16384!
D RKNN: [13:55:02.228] not need tranpose
D RKNN: [13:55:02.228] not need tranpose
D RKNN: [13:55:02.228] finish initComputeZoneMap
D RKNN: [13:55:02.228] emit max
E RKNN: [13:55:02.228] failed to config argb mode layer!

None of the versions can convert this model.

@yuyun2000
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@zen-xingle

@zen-xingle
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zen-xingle commented Oct 10, 2024 •
edited
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RKNN-Toolkit 2.2.0 seems OK.

The reason all the outputs were 0 is caused by quantization. When you concat the score(range 0->1) and position value(range 0->height/width), the score value will be suppressed to 0.

Two ways to solve this:

  1. Do not quantize the model.
  2. Do not concat score and position value.

And there is already a yolov8 demo in this repo: https://github.com/airockchip/rknn_model_zoo

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@St-Viacheslavik @zen-xingle @yuyun2000