'Tensor' object has no attribute '_trt'

I get error AttributeError: 'Tensor' object has no attribute '_trt' when I meet MaxPool2d

/usr/local/lib/python3.6/dist-packages/torch2trt-0.0.0-py3.6.egg/torch2trt/torch2trt.py in wrapper(*args, **kwargs) 95 96 #print(‘%s : %s’ % (method.qualname, converter.name)) —> 97 converter(ctx) 98 99 # convert to None so conversion will fail for unsupported layers

/usr/local/lib/python3.6/dist-packages/torch2trt-0.0.0-py3.6.egg/torch2trt/converters/MaxPool2d.py in convert_MaxPool2d(ctx) 21 22 layer = ctx.network.add_pooling( —> 23 input=input._trt, type=trt.PoolingType.MAX, window_size=kernel_size) 24 layer.stride = stride 25 layer.padding = padding

AttributeError: ‘Tensor’ object has no attribute ‘_trt’

[vfdev-5]

HI @jaybdub ,

I’m trying to reproduce image segmentation example of this tutorial (Deeplabv3 model) and I have the following issue as in this topic.

Click to see the log

---------------------------------------------------------------------------
AttributeError                            Traceback (most recent call last)
<ipython-input-8-7f1400d9157d> in <module>
----> 1 model_trt = torch2trt.torch2trt(model_w, [data], fp16_mode=True)

~/miniconda3/envs/py35/lib/python3.5/site-packages/torch2trt-0.0.0-py3.5.egg/torch2trt/torch2trt.py in torch2trt(module, inputs, input_names, output_names, log_level, max_batch_size, fp16_mode, max_workspace_size)
    250         ctx.add_inputs(inputs, input_names)
    251 
--> 252         outputs = module(*inputs)
    253 
    254         if not isinstance(outputs, tuple):

~/miniconda3/envs/py35/lib/python3.5/site-packages/torch/nn/modules/module.py in __call__(self, *input, **kwargs)
    491             result = self._slow_forward(*input, **kwargs)
    492         else:
--> 493             result = self.forward(*input, **kwargs)
    494         for hook in self._forward_hooks.values():
    495             hook_result = hook(self, input, result)

<ipython-input-5-405ea0677ccc> in forward(self, x)
      4         self.model = model
      5     def forward(self, x):
----> 6         return model(x)['out']

~/miniconda3/envs/py35/lib/python3.5/site-packages/torch/nn/modules/module.py in __call__(self, *input, **kwargs)
    491             result = self._slow_forward(*input, **kwargs)
    492         else:
--> 493             result = self.forward(*input, **kwargs)
    494         for hook in self._forward_hooks.values():
    495             hook_result = hook(self, input, result)

~/miniconda3/envs/py35/lib/python3.5/site-packages/torchvision/models/segmentation/_utils.py in forward(self, x)
     20         result = OrderedDict()
     21         x = features["out"]
---> 22         x = self.classifier(x)
     23         x = F.interpolate(x, size=input_shape, mode='bilinear', align_corners=False)
     24         result["out"] = x

~/miniconda3/envs/py35/lib/python3.5/site-packages/torch/nn/modules/module.py in __call__(self, *input, **kwargs)
    491             result = self._slow_forward(*input, **kwargs)
    492         else:
--> 493             result = self.forward(*input, **kwargs)
    494         for hook in self._forward_hooks.values():
    495             hook_result = hook(self, input, result)

~/miniconda3/envs/py35/lib/python3.5/site-packages/torch/nn/modules/container.py in forward(self, input)
     90     def forward(self, input):
     91         for module in self._modules.values():
---> 92             input = module(input)
     93         return input
     94 

~/miniconda3/envs/py35/lib/python3.5/site-packages/torch/nn/modules/module.py in __call__(self, *input, **kwargs)
    491             result = self._slow_forward(*input, **kwargs)
    492         else:
--> 493             result = self.forward(*input, **kwargs)
    494         for hook in self._forward_hooks.values():
    495             hook_result = hook(self, input, result)

~/miniconda3/envs/py35/lib/python3.5/site-packages/torchvision/models/segmentation/deeplabv3.py in forward(self, x)
     90         for conv in self.convs:
     91             res.append(conv(x))
---> 92         res = torch.cat(res, dim=1)
     93         return self.project(res)

~/miniconda3/envs/py35/lib/python3.5/site-packages/torch2trt-0.0.0-py3.5.egg/torch2trt/torch2trt.py in wrapper(*args, **kwargs)
     95 
     96             #print('%s : %s' % (method.__qualname__, converter.__name__))
---> 97             converter(ctx)
     98 
     99             # convert to None so conversion will fail for unsupported layers

~/miniconda3/envs/py35/lib/python3.5/site-packages/torch2trt-0.0.0-py3.5.egg/torch2trt/converters/cat.py in convert_cat(ctx)
     12 
     13     output = ctx.method_return
---> 14     trt_inputs = [i._trt for i in inputs]
     15 
     16     layer = ctx.network.add_concatenation(inputs=trt_inputs)

~/miniconda3/envs/py35/lib/python3.5/site-packages/torch2trt-0.0.0-py3.5.egg/torch2trt/converters/cat.py in <listcomp>(.0)
     12 
     13     output = ctx.method_return
---> 14     trt_inputs = [i._trt for i in inputs]
     15 
     16     layer = ctx.network.add_concatenation(inputs=trt_inputs)

AttributeError: 'Tensor' object has no attribute '_trt'

Any ideas why it does not work ?

Packages Versions

``` # packages in environment at /root/miniconda3/envs/py35: # # Name Version Build Channel _libgcc_mutex 0.1 main attrs 19.1.0 pypi_0 pypi backcall 0.1.0 pypi_0 pypi blas 1.0 mkl bleach 3.1.0 pypi_0 pypi ca-certificates 2019.5.15 0 certifi 2018.8.24 py35_1 cffi 1.11.5 py35he75722e_1 cudatoolkit 10.0.130 0 decorator 4.4.0 pypi_0 pypi defusedxml 0.6.0 pypi_0 pypi entrypoints 0.3 pypi_0 pypi freetype 2.9.1 h8a8886c_1 intel-openmp 2019.4 243 ipykernel 5.1.1 pypi_0 pypi ipython 7.6.1 pypi_0 pypi ipython-genutils 0.2.0 pypi_0 pypi ipywidgets 7.5.0 pypi_0 pypi jedi 0.14.1 pypi_0 pypi jinja2 2.10.1 pypi_0 pypi jpeg 9b h024ee3a_2 jsonschema 3.0.1 pypi_0 pypi jupyter 1.0.0 pypi_0 pypi jupyter-client 5.3.1 pypi_0 pypi jupyter-console 6.0.0 pypi_0 pypi jupyter-core 4.5.0 pypi_0 pypi libedit 3.1.20181209 hc058e9b_0 libffi 3.2.1 hd88cf55_4 libgcc-ng 9.1.0 hdf63c60_0 libgfortran-ng 7.3.0 hdf63c60_0 libpng 1.6.37 hbc83047_0 libstdcxx-ng 9.1.0 hdf63c60_0 libtiff 4.0.10 h2733197_2 markupsafe 1.1.1 pypi_0 pypi mistune 0.8.4 pypi_0 pypi mkl 2019.4 243 nbconvert 5.5.0 pypi_0 pypi nbformat 4.4.0 pypi_0 pypi ncurses 6.1 he6710b0_1 ninja 1.8.2 py35h6bb024c_1 notebook 6.0.0 pypi_0 pypi numpy 1.14.2 py35hdbf6ddf_0 olefile 0.46 py35_0 openssl 1.0.2s h7b6447c_0 pandocfilters 1.4.2 pypi_0 pypi parso 0.5.1 pypi_0 pypi pexpect 4.7.0 pypi_0 pypi pickleshare 0.7.5 pypi_0 pypi pillow 5.2.0 py35heded4f4_0 pip 10.0.1 py35_0 prometheus-client 0.7.1 pypi_0 pypi prompt-toolkit 2.0.9 pypi_0 pypi ptyprocess 0.6.0 pypi_0 pypi pycparser 2.19 py35_0 pygments 2.4.2 pypi_0 pypi pyrsistent 0.15.3 pypi_0 pypi python 3.5.6 hc3d631a_0 python-dateutil 2.8.0 pypi_0 pypi pytorch 1.1.0 py3.5_cuda10.0.130_cudnn7.5.1_0 pytorch pyzmq 18.0.2 pypi_0 pypi qtconsole 4.5.2 pypi_0 pypi readline 7.0 h7b6447c_5 send2trash 1.5.0 pypi_0 pypi setuptools 40.2.0 py35_0 six 1.11.0 py35_1 sqlite 3.29.0 h7b6447c_0 terminado 0.8.2 pypi_0 pypi testpath 0.4.2 pypi_0 pypi tk 8.6.8 hbc83047_0 torch2trt 0.0.0 pypi_0 pypi torchvision 0.3.0 py35_cu10.0.130_1 pytorch tornado 6.0.3 pypi_0 pypi traitlets 4.3.2 pypi_0 pypi wcwidth 0.1.7 pypi_0 pypi webencodings 0.5.1 pypi_0 pypi wheel 0.31.1 py35_0 widgetsnbextension 3.5.0 pypi_0 pypi xz 5.2.4 h14c3975_4 zlib 1.2.11 h7b6447c_3 zstd 1.3.7 h0b5b093_0 ```

[jaybdub]

Hi All,

This is most likely because there Is a layer converter that has not been implemented. It is likely the layer before the one that threw the error (because the _trt was not set by the previous converter).

We’re focused on supporting the models in the README, but the coverage may increase over time.

@gavrin-s are you able to share the model you’re attempting to convert?

FYI, You can see the list of registered converters by typing

‘’’python import torch2trt

print(torch2trt.CONVERTERS) ‘’’

It may be possible to add support for the unsupported layers by using the ‘’@tensorrt_converter’’ method described in the README.md.

Please let me know if this helps or you have any other questions.

Best, John