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Platform (like ubuntu 16.04/win10): win10
Python version: 3.10
Source framework with version (like Tensorflow 1.4.1 with GPU): caffe
Destination framework with version (like CNTK 2.3 with GPU): pytorch
Pre-trained model path (webpath or webdisk path): https://github.com/CSAILVision/places365, vgg16 hybrid1365
Running scripts: mmconvert --srcFramework caffe --inputWeight vgg16_hybrid1365.caffemodel --inputNetwork deploy_vgg16_hybrid1365.prototxt --dstFramework pytorch -om vgg16_hybrid1365.pth
The following error:
------------------------------------------------------------ WARNING: PyCaffe not found! Falling back to a pure protocol buffer implementation. * Conversions will be drastically slower. * This backend is UNTESTED! ------------------------------------------------------------ Type Name Param Output ---------------------------------------------------------------------------------------------- Data data -- (10, 3, 224, 224) Convolution conv1_1 (3, 3, 3, 64) (10, 64, 224, 224) ReLU relu1_1 -- (10, 64, 224, 224) Convolution conv1_2 (3, 3, 64, 64) (10, 64, 224, 224) ReLU relu1_2 -- (10, 64, 224, 224) Pooling pool1 -- (10, 64, 112, 112) Convolution conv2_1 (3, 3, 64, 128) (10, 128, 112, 112) ReLU relu2_1 -- (10, 128, 112, 112) Convolution conv2_2 (3, 3, 128, 128) (10, 128, 112, 112) ReLU relu2_2 -- (10, 128, 112, 112) Pooling pool2 -- (10, 128, 56, 56) Convolution conv3_1 (3, 3, 128, 256) (10, 256, 56, 56) ReLU relu3_1 -- (10, 256, 56, 56) Convolution conv3_2 (3, 3, 256, 256) (10, 256, 56, 56) ReLU relu3_2 -- (10, 256, 56, 56) Convolution conv3_3 (3, 3, 256, 256) (10, 256, 56, 56) ReLU relu3_3 -- (10, 256, 56, 56) Pooling pool3 -- (10, 256, 28, 28) Convolution conv4_1 (3, 3, 256, 512) (10, 512, 28, 28) ReLU relu4_1 -- (10, 512, 28, 28) Convolution conv4_2 (3, 3, 512, 512) (10, 512, 28, 28) ReLU relu4_2 -- (10, 512, 28, 28) Convolution conv4_3 (3, 3, 512, 512) (10, 512, 28, 28) ReLU relu4_3 -- (10, 512, 28, 28) Pooling pool4 -- (10, 512, 14, 14) Convolution conv5_1 (3, 3, 512, 512) (10, 512, 14, 14) ReLU relu5_1 -- (10, 512, 14, 14) Convolution conv5_2 (3, 3, 512, 512) (10, 512, 14, 14) ReLU relu5_2 -- (10, 512, 14, 14) Convolution conv5_3 (3, 3, 512, 512) (10, 512, 14, 14) ReLU relu5_3 -- (10, 512, 14, 14) Pooling pool5 -- (10, 512, 7, 7) InnerProduct fc6 (25088, 4096) (10, 4096, 1, 1) ReLU relu6 -- (10, 4096, 1, 1) Dropout drop6 -- (10, 4096, 1, 1) InnerProduct fc7 (4096, 4096) (10, 4096, 1, 1) ReLU relu7 -- (10, 4096, 1, 1) Dropout drop7 -- (10, 4096, 1, 1) InnerProduct fc8a (4096, 1365) (10, 1365, 1, 1) Softmax prob -- (10, 1365, 1, 1) IR network structure is saved as [09e40723c6e34cd4b0c799409703de9b.json]. IR network structure is saved as [09e40723c6e34cd4b0c799409703de9b.pb]. IR weights are saved as [09e40723c6e34cd4b0c799409703de9b.npy]. Parse file [09e40723c6e34cd4b0c799409703de9b.pb] with binary format successfully. Target network code snippet is saved as [vgg16_hybrid1365.py]. Target weights are saved as [09e40723c6e34cd4b0c799409703de9b.npy]. Traceback (most recent call last): File "D:\Users\KalburgS\Anaconda3\lib\runpy.py", line 197, in _run_module_as_main return _run_code(code, main_globals, None, File "D:\Users\KalburgS\Anaconda3\lib\runpy.py", line 87, in _run_code exec(code, run_globals) File "D:\Users\KalburgS\Anaconda3\Scripts\mmconvert.exe\__main__.py", line 7, in <module> File "D:\Users\KalburgS\Anaconda3\lib\site-packages\mmdnn\conversion\_script\convert.py", line 112, in _main dump_code(args.dstFramework, network_filename + '.py', temp_filename + '.npy', args.outputModel, args.dump_tag) File "D:\Users\KalburgS\Anaconda3\lib\site-packages\mmdnn\conversion\_script\dump_code.py", line 32, in dump_code save_model(MainModel, network_filepath, weight_filepath, dump_filepath) File "D:\Users\KalburgS\Anaconda3\lib\site-packages\mmdnn\conversion\pytorch\saver.py", line 5, in save_model model = MainModel.KitModel(weight_filepath) File "vgg16_hybrid1365.py", line 28, in __init__ self.conv1_1 = self.__conv(2, name='conv1_1', in_channels=3, out_channels=64, kernel_size=(3, 3), stride=(1, 1), groups=1, bias=True) File "vgg16_hybrid1365.py", line 126, in __conv layer.state_dict()['bias'].copy_(torch.from_numpy(_weights_dict[name]['bias'])) RuntimeError: output with shape [64] doesn't match the broadcast shape [1, 1, 1, 64]
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Platform (like ubuntu 16.04/win10): win10
Python version: 3.10
Source framework with version (like Tensorflow 1.4.1 with GPU): caffe
Destination framework with version (like CNTK 2.3 with GPU): pytorch
Pre-trained model path (webpath or webdisk path): https://github.com/CSAILVision/places365, vgg16 hybrid1365
Running scripts: mmconvert --srcFramework caffe --inputWeight vgg16_hybrid1365.caffemodel --inputNetwork deploy_vgg16_hybrid1365.prototxt --dstFramework pytorch -om vgg16_hybrid1365.pth
The following error:
The text was updated successfully, but these errors were encountered: