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how to convert mnet.25.prototxt and mnet.25.caffemodel to mnet-deconv.prototxt and mnet-deconv.caffemodel
Thanks for your sharing. I convert mnet.25 mxnet model to caffe model successfully by your tools, but I find this caffe model can't used in your project directly, so may you pleasure to help me solve it?
what's your problems?
the error message is here: [100%] Linking CXX executable retinaface [100%] Built target retinaface batchSize:1, channel:3, netHeight:416, netWidth:288. Caffe Parser: Invalid axis in crop layer - only spatial cropping is supported error parsing layer type Crop index 143 retinaface: retinaface/tensorrt/trtnetbase.cpp:279: void TrtNetBase::caffeToTRTModel(const string&, const string&, nvcaffeparser1::IPluginFactory*): Assertion `blobNameToTensor != nullptr' failed. Aborted (core dumped)
and I find there are many differences, why the converted model can not be used directly? $ diff mnet25.prototxt mnet-deconv-0517.prototxt 7c7 < shape: { dim: 1 dim: 3 dim: 416 dim: 288 }
shape: { dim: 1 dim: 3 dim: 320 dim: 320 }
1208a1209
bias_term: true
1247a1249
bias_term: true
1279a1282
bias_term: true
1318a1322
bias_term: true
1350a1355
bias_term: true
1389a1395
bias_term: true
1437a1444
bias_term: true
1448,1453c1455,1460
< dim: 2
< dim: -1
< dim: 0
< }
< axis: 1
< }
dim:0 dim:2 dim:-1 dim:0 }
} 1470,1475c1477,1482 < dim: 4 < dim: -1 < dim: 0 < } < axis: 1
< }
dim:0 dim:4 dim:-1 dim:0 }
} 1487a1495 bias_term: true 1500a1509 bias_term: true 1513a1523 bias_term: true 1544,1545d1553 < bottom: "rf_c3_lateral_relu" < top: "rf_c3_upsampling" 1547a1556,1557 bottom: "rf_c3_lateral_relu" top: "rf_c3_upsampling" 1549,1556c1559,1566 < num_output: 64 < kernel_size: 4 < stride: 2 < pad: 1 < group: 64 < bias_term: false < weight_filler: { < type: "bilinear"
kernel_size: 4 stride: 2 pad: 1 num_output: 64 group: 64 bias_term: false weight_filler { type: "bilinear"
1558a1569
param { lr_mult: 0 decay_mult: 0 } 1568,1569c1579 < axis: 1 < offset: 0
axis: 2
1593a1604
bias_term: true
1632a1644
bias_term: true
1664a1677
bias_term: true
1703a1717
bias_term: true
1735a1750
bias_term: true
1774a1790
bias_term: true
1822a1839
bias_term: true
1833,1838c1850,1855
< dim: 2
< dim: -1
< dim: 0
< }
< axis: 1
< }
dim:0 dim:2 dim:-1 dim:0 }
} 1855,1860c1872,1877 < dim: 4 < dim: -1 < dim: 0 < } < axis: 1
< }
dim:0 dim:4 dim:-1 dim:0 }
} 1872a1890 bias_term: true 1885a1904 bias_term: true 1898a1918 bias_term: true 1929,1930d1948 < bottom: "rf_c2_aggr_relu" < top: "rf_c2_upsampling" 1932a1951,1952 bottom: "rf_c2_aggr_relu" top: "rf_c2_upsampling" 1934,1941c1954,1961 < num_output: 64 < kernel_size: 4 < stride: 2 < pad: 1 < group: 64 < bias_term: false < weight_filler: { < type: "bilinear"
kernel_size: 4 stride: 2 pad: 1 num_output: 64 group: 64 bias_term: false weight_filler { type: "bilinear"
1943a1964
param { lr_mult: 0 decay_mult: 0 } 1953,1954c1974 < axis: 1 < offset: 0
axis: 2
1978a1999
bias_term: true
2017a2039
bias_term: true
2049a2072
bias_term: true
2088a2112
bias_term: true
2120a2145
bias_term: true
2159a2185
bias_term: true
2207a2234
bias_term: true
2218,2223c2245,2250
< dim: 2
< dim: -1
< dim: 0
< }
< axis: 1
< }
dim:0 dim:2 dim:-1 dim:0 }
} 2240,2245c2267,2272 < dim: 4 < dim: -1 < dim: 0 < } < axis: 1
< }
dim:0 dim:4 dim:-1 dim:0 }
} 2257a2285 bias_term: true 2270a2299 bias_term: true
can you use caffe model directly instead of use tensorRT model ? if caffe model is not occur error , you should set shape in .prototxt the same as the code.
can you use caffe model directly instead of use tensorRT model ? if caffe model is not occur error , you should set shape in .prototxt the same as the code.
我自己训的模型怎么能变成mnet-deconv那样,转换的时候报错: Caffe Parser: Invalid axis in crop layer - only spatial cropping is supported
@Royzon Hi where did you download the mobilenet25 mxnet model?could you give me the website?thank u
@clancylian , I encountered this error when I tried using your mnet25.caffemodel and ment25.prototxt to do inferencing on DeepStream. Deepstream auto convert the caffe model to tensorRT by generating engine plan file. The caffe model itself is working fine, the error occurs when it comes to tensorRT. Any thoughts on what might be the reason? Thanks
@JaydonChion have you solve this?