Error while trying to run the example

[Junaid199f]

TypeError Traceback (most recent call last)
in
4 # Build HiddenLayer graph
5 # Jupyter Notebook renders it automatically
----> 6 hl.build_graph(model, torch.zeros([1, 3, 224, 224]))

3 frames
/usr/local/lib/python3.7/dist-packages/torch/onnx/utils.py in _optimize_graph(graph, operator_export_type, _disable_torch_constant_prop, fixed_batch_size, params_dict, dynamic_axes, input_names, module)
276 # Unpack quantized weights for conv and linear ops and insert into graph.
277 _C._jit_pass_onnx_unpack_quantized_weights(
--> 278 graph, params_dict, symbolic_helper.is_caffe2_aten_fallback()
279 )
280 if symbolic_helper.is_caffe2_aten_fallback():

TypeError: _jit_pass_onnx_unpack_quantized_weights(): incompatible function arguments. The following argument types are supported:
1. (arg0: torch::jit::Graph, arg1: Dict[str, IValue], arg2: bool) -> Dict[str, IValue]

Invoked with: graph(%input.1 : Float(1, 3, 224, 224, strides=[150528, 50176, 224, 1], requires_grad=0, device=cpu),
%1 : Float(64, 3, 3, 3, strides=[27, 9, 3, 1], requires_grad=1, device=cpu),
%2 : Float(64, strides=[1], requires_grad=1, device=cpu),
%3 : Float(64, 64, 3, 3, strides=[576, 9, 3, 1], requires_grad=1, device=cpu),
%4 : Float(64, strides=[1], requires_grad=1, device=cpu),
%5 : Float(128, 64, 3, 3, strides=[576, 9, 3, 1], requires_grad=1, device=cpu),
%6 : Float(128, strides=[1], requires_grad=1, device=cpu),
%7 : Float(128, 128, 3, 3, strides=[1152, 9, 3, 1], requires_grad=1, device=cpu),
%8 : Float(128, strides=[1], requires_grad=1, device=cpu),
%9 : Float(256, 128, 3, 3, strides=[1152, 9, 3, 1], requires_grad=1, device=cpu),
%10 : Float(256, strides=[1], requires_grad=1, device=cpu),
%11 : Float(256, 256, 3, 3, strides=[2304, 9, 3, 1], requires_grad=1, device=cpu),
%12 : Float(256, strides=[1], requires_grad=1, device=cpu),
%13 : Float(256, 256, 3, 3, strides=[2304, 9, 3, 1], requires_grad=1, device=cpu),
%14 : Float(256, strides=[1], requires_grad=1, device=cpu),
%15 : Float(512, 256, 3, 3, strides=[2304, 9, 3, 1], requires_grad=1, device=cpu),
%16 : Float(512, strides=[1], requires_grad=1, device=cpu),
%17 : Float(512, 512, 3, 3, strides=[4608, 9, 3, 1], requires_grad=1, device=cpu),
%18 : Float(512, strides=[1], requires_grad=1, device=cpu),
%19 : Float(512, 512, 3, 3, strides=[4608, 9, 3, 1], requires_grad=1, device=cpu),
%20 : Float(512, strides=[1], requires_grad=1, device=cpu),
%21 : Float(512, 512, 3, 3, strides=[4608, 9, 3, 1], requires_grad=1, device=cpu),
%22 : Float(512, strides=[1], requires_grad=1, device=cpu),
%23 : Float(512, 512, 3, 3, strides=[4608, 9, 3, 1], requires_grad=1, device=cpu),
%24 : Float(512, strides=[1], requires_grad=1, device=cpu),
%25 : Float(512, 512, 3, 3, strides=[4608, 9, 3, 1], requires_grad=1, device=cpu),
%26 : Float(512, strides=[1], requires_grad=1, device=cpu),
%27 : Float(4096, 25088, strides=[25088, 1], requires_grad=1, device=cpu),
%28 : Float(4096, strides=[1], requires_grad=1, device=cpu),
%29 : Float(4096, 4096, strides=[4096, 1], requires_grad=1, device=cpu),
%30 : Float(4096, strides=[1], requires_grad=1, device=cpu),
%31 : Float(1000, 4096, strides=[4096, 1], requires_grad=1, device=cpu),
%32 : Float(1000, strides=[1], requires_grad=1, device=cpu)):
%459 : int[] = prim::Constantvalue=[1, 1]
%460 : int[] = prim::Constantvalue=[1, 1]
%461 : int[] = prim::Constantvalue=[1, 1]
%108 : bool = prim::Constantvalue=0 # /usr/local/lib/python3.7/dist-packages/torch/nn/modules/conv.py:454:0
%462 : int[] = prim::Constantvalue=[0, 0]
%112 : int = prim::Constantvalue=1 # /usr/local/lib/python3.7/dist-packages/torch/nn/modules/conv.py:454:0
%113 : bool = prim::Constantvalue=0 # /usr/local/lib/python3.7/dist-packages/torch/nn/modules/conv.py:454:0
%114 : bool = prim::Constantvalue=0 # /usr/local/lib/python3.7/dist-packages/torch/nn/modules/conv.py:454:0
%115 : bool = prim::Constantvalue=1 # /usr/local/lib/python3.7/dist-packages/torch/nn/modules/conv.py:454:0
%116 : bool = prim::Constantvalue=1 # /usr/local/lib/python3.7/dist-packages/torch/nn/modules/conv.py:454:0
%input.3 : Float(1, 64, 224, 224, strides=[3211264, 50176, 224, 1], requires_grad=0, device=cpu) = aten::_convolution(%input.1, %1, %2, %459, %460, %461, %108, %462, %112, %113, %114, %115, %116) # /usr/local/lib/python3.7/dist-packages/torch/nn/modules/conv.py:454:0
%532 : Float(1, 64, 224, 224, strides=[3211264, 50176, 224, 1], requires_grad=1, device=cpu) = aten::relu(%input.3) # /usr/local/lib/python3.7/dist-packages/torch/nn/functional.py:1455:0
%463 : int[] = prim::Constantvalue=[1, 1]
%464 : int[] = prim::Constantvalue=[1, 1]
%465 : int[] = prim::Constantvalue=[1, 1]
%128 : bool = prim::Constantvalue=0 # /usr/local/lib/python3.7/dist-packages/torch/nn/modules/conv.py:454:0
%466 : int[] = prim::Constantvalue=[0, 0]
%132 : int = prim::Constantvalue=1 # /usr/local/lib/python3.7/dist-packages/torch/nn/modules/conv.py:454:0
%133 : bool = prim::Constantvalue=0 # /usr/local/lib/python3.7/dist-packages/torch/nn/modules/conv.py:454:0
%134 : bool = prim::Constantvalue=0 # /usr/local/lib/python3.7/dist-packages/torch/nn/modules/conv.py:454:0
%135 : bool = prim::Constantvalue=1 # /usr/local/lib/python3.7/dist-packages/torch/nn/modules/conv.py:454:0
%136 : bool = prim::Constantvalue=1 # /usr/local/lib/python3.7/dist-packages/torch/nn/modules/conv.py:454:0
%input.7 : Float(1, 64, 224, 224, strides=[3211264, 50176, 224, 1], requires_grad=0, device=cpu) = aten::_convolution(%532, %3, %4, %463, %464, %465, %128, %466, %132, %133, %134, %135, %136) # /usr/local/lib/python3.7/dist-packages/torch/nn/modules/conv.py:454:0
%533 : Float(1, 64, 224, 224, strides=[3211264, 50176, 224, 1], requires_grad=1, device=cpu) = aten::relu(%input.7) # /usr/local/lib/python3.7/dist-packages/torch/nn/functional.py:1455:0
%467 : int[] = prim::Constantvalue=[2, 2]
%468 : int[] = prim::Constantvalue=[2, 2]
%469 : int[] = prim::Constantvalue=[0, 0]
%470 : int[] = prim::Constantvalue=[1, 1]
%151 : bool = prim::Constantvalue=0 # /usr/local/lib/python3.7/dist-packages/torch/nn/functional.py:782:0
%input.9 : Float(1, 64, 112, 112, strides=[802816, 12544, 112, 1], requires_grad=1, device=cpu) = aten::max_pool2d(%533, %467, %468, %469, %470, %151) # /usr/local/lib/python3.7/dist-packages/torch/nn/functional.py:782:0
%471 : int[] = prim::Constantvalue=[1, 1]
%472 : int[] = prim::Constantvalue=[1, 1]
%473 : int[] = prim::Constantvalue=[1, 1]
%162 : bool = prim::Constantvalue=0 # /usr/local/lib/python3.7/dist-packages/torch/nn/modules/conv.py:454:0
%474 : int[] = prim::Constantvalue=[0, 0]
%166 : int = prim::Constantvalue=1 # /usr/local/lib/python3.7/dist-packages/torch/nn/modules/conv.py:454:0
%167 : bool = prim::Constantvalue=0 # /usr/local/lib/python3.7/dist-packages/torch/nn/modules/conv.py:454:0
%168 : bool = prim::Constantvalue=0 # /usr/local/lib/python3.7/dist-packages/torch/nn/modules/conv.py:454:0
%169 : bool = prim::Constantvalue=1 # /usr/local/lib/python3.7/dist-packages/torch/nn/modules/conv.py:454:0
%170 : bool = prim::Constantvalue=1 # /usr/local/lib/python3.7/dist-packages/torch/nn/modules/conv.py:454:0
%input.11 : Float(1, 128, 112, 112, strides=[1605632, 12544, 112, 1], requires_grad=0, device=cpu) = aten::_convolution(%input.9, %5, %6, %471, %472, %473, %162, %474, %166, %167, %168, %169, %170) # /usr/local/lib/python3.7/dist-packages/torch/nn/modules/conv.py:454:0
%534 : Float(1, 128, 112, 112, strides=[1605632, 12544, 112, 1], requires_grad=1, device=cpu) = aten::relu(%input.11) # /usr/local/lib/python3.7/dist-packages/torch/nn/functional.py:1455:0
%475 : int[] = prim::Constantvalue=[1, 1]
%476 : int[] = prim::Constantvalue=[1, 1]
%477 : int[] = prim::Constantvalue=[1, 1]
%182 : bool = prim::Constantvalue=0 # /usr/local/lib/python3.7/dist-packages/torch/nn/modules/conv.py:454:0
%478 : int[] = prim::Constantvalue=[0, 0]
%186 : int = prim::Constantvalue=1 # /usr/local/lib/python3.7/dist-packages/torch/nn/modules/conv.py:454:0
%187 : bool = prim::Constantvalue=0 # /usr/local/lib/python3.7/dist-packages/torch/nn/modules/conv.py:454:0
%188 : bool = prim::Constantvalue=0 # /usr/local/lib/python3.7/dist-packages/torch/nn/modules/conv.py:454:0
%189 : bool = prim::Constantvalue=1 # /usr/local/lib/python3.7/dist-packages/torch/nn/modules/conv.py:454:0
%190 : bool = prim::Constantvalue=1 # /usr/local/lib/python3.7/dist-packages/torch/nn/modules/conv.py:454:0
%input.15 : Float(1, 128, 112, 112, strides=[1605632, 12544, 112, 1], requires_grad=0, device=cpu) = aten::_convolution(%534, %7, %8, %475, %476, %477, %182, %478, %186, %187, %188, %189, %190) # /usr/local/lib/python3.7/dist-packages/torch/nn/modules/conv.py:454:0
%535 : Float(1, 128, 112, 112, strides=[1605632, 12544, 112, 1], requires_grad=1, device=cpu) = aten::relu(%input.15) # /usr/local/lib/python3.7/dist-packages/torch/nn/functional.py:1455:0
%479 : int[] = prim::Constantvalue=[2, 2]
%480 : int[] = prim::Constantvalue=[2, 2]
%481 : int[] = prim::Constantvalue=[0, 0]
%482 : int[] = prim::Constantvalue=[1, 1]
%205 : bool = prim::Constantvalue=0 # /usr/local/lib/python3.7/dist-packages/torch/nn/functional.py:782:0
%input.17 : Float(1, 128, 56, 56, strides=[401408, 3136, 56, 1], requires_grad=1, device=cpu) = aten::max_pool2d(%535, %479, %480, %481, %482, %205) # /usr/local/lib/python3.7/dist-packages/torch/nn/functional.py:782:0
%483 : int[] = prim::Constantvalue=[1, 1]
%484 : int[] = prim::Constantvalue=[1, 1]
%485 : int[] = prim::Constantvalue=[1, 1]
%216 : bool = prim::Constantvalue=0 # /usr/local/lib/python3.7/dist-packages/torch/nn/modules/conv.py:454:0
%486 : int[] = prim::Constantvalue=[0, 0]
%220 : int = prim::Constantvalue=1 # /usr/local/lib/python3.7/dist-packages/torch/nn/modules/conv.py:454:0
%221 : bool = prim::Constantvalue=0 # /usr/local/lib/python3.7/dist-packages/torch/nn/modules/conv.py:454:0
%222 : bool = prim::Constantvalue=0 # /usr/local/lib/python3.7/dist-packages/torch/nn/modules/conv.py:454:0
%223 : bool = prim::Constantvalue=1 # /usr/local/lib/python3.7/dist-packages/torch/nn/modules/conv.py:454:0
%224 : bool = prim::Constantvalue=1 # /usr/local/lib/python3.7/dist-packages/torch/nn/modules/conv.py:454:0
%input.19 : Float(1, 256, 56, 56, strides=[802816, 3136, 56, 1], requires_grad=0, device=cpu) = aten::_convolution(%input.17, %9, %10, %483, %484, %485, %216, %486, %220, %221, %222, %223, %224) # /usr/local/lib/python3.7/dist-packages/torch/nn/modules/conv.py:454:0
%536 : Float(1, 256, 56, 56, strides=[802816, 3136, 56, 1], requires_grad=1, device=cpu) = aten::relu(%input.19) # /usr/local/lib/python3.7/dist-packages/torch/nn/functional.py:1455:0
%487 : int[] = prim::Constantvalue=[1, 1]
%488 : int[] = prim::Constantvalue=[1, 1]
%489 : int[] = prim::Constantvalue=[1, 1]
%236 : bool = prim::Constantvalue=0 # /usr/local/lib/python3.7/dist-packages/torch/nn/modules/conv.py:454:0
%490 : int[] = prim::Constantvalue=[0, 0]
%240 : int = prim::Constantvalue=1 # /usr/local/lib/python3.7/dist-packages/torch/nn/modules/conv.py:454:0
%241 : bool = prim::Constantvalue=0 # /usr/local/lib/python3.7/dist-packages/torch/nn/modules/conv.py:454:0
%242 : bool = prim::Constantvalue=0 # /usr/local/lib/python3.7/dist-packages/torch/nn/modules/conv.py:454:0
%243 : bool = prim::Constantvalue=1 # /usr/local/lib/python3.7/dist-packages/torch/nn/modules/conv.py:454:0
%244 : bool = prim::Constantvalue=1 # /usr/local/lib/python3.7/dist-packages/torch/nn/modules/conv.py:454:0
%input.23 : Float(1, 256, 56, 56, strides=[802816, 3136, 56, 1], requires_grad=0, device=cpu) = aten::_convolution(%536, %11, %12, %487, %488, %489, %236, %490, %240, %241, %242, %243, %244) # /usr/local/lib/python3.7/dist-packages/torch/nn/modules/conv.py:454:0
%537 : Float(1, 256, 56, 56, strides=[802816, 3136, 56, 1], requires_grad=1, device=cpu) = aten::relu(%input.23) # /usr/local/lib/python3.7/dist-packages/torch/nn/functional.py:1455:0
%491 : int[] = prim::Constantvalue=[1, 1]
%492 : int[] = prim::Constantvalue=[1, 1]
%493 : int[] = prim::Constantvalue=[1, 1]
%256 : bool = prim::Constantvalue=0 # /usr/local/lib/python3.7/dist-packages/torch/nn/modules/conv.py:454:0
%494 : int[] = prim::Constantvalue=[0, 0]
%260 : int = prim::Constantvalue=1 # /usr/local/lib/python3.7/dist-packages/torch/nn/modules/conv.py:454:0
%261 : bool = prim::Constantvalue=0 # /usr/local/lib/python3.7/dist-packages/torch/nn/modules/conv.py:454:0
%262 : bool = prim::Constantvalue=0 # /usr/local/lib/python3.7/dist-packages/torch/nn/modules/conv.py:454:0
%263 : bool = prim::Constantvalue=1 # /usr/local/lib/python3.7/dist-packages/torch/nn/modules/conv.py:454:0
%264 : bool = prim::Constantvalue=1 # /usr/local/lib/python3.7/dist-packages/torch/nn/modules/conv.py:454:0
%input.27 : Float(1, 256, 56, 56, strides=[802816, 3136, 56, 1], requires_grad=0, device=cpu) = aten::_convolution(%537, %13, %14, %491, %492, %493, %256, %494, %260, %261, %262, %263, %264) # /usr/local/lib/python3.7/dist-packages/torch/nn/modules/conv.py:454:0
%538 : Float(1, 256, 56, 56, strides=[802816, 3136, 56, 1], requires_grad=1, device=cpu) = aten::relu(%input.27) # /usr/local/lib/python3.7/dist-packages/torch/nn/functional.py:1455:0
%495 : int[] = prim::Constantvalue=[2, 2]
%496 : int[] = prim::Constantvalue=[2, 2]
%497 : int[] = prim::Constantvalue=[0, 0]
%498 : int[] = prim::Constantvalue=[1, 1]
%279 : bool = prim::Constantvalue=0 # /usr/local/lib/python3.7/dist-packages/torch/nn/functional.py:782:0
%input.29 : Float(1, 256, 28, 28, strides=[200704, 784, 28, 1], requires_grad=1, device=cpu) = aten::max_pool2d(%538, %495, %496, %497, %498, %279) # /usr/local/lib/python3.7/dist-packages/torch/nn/functional.py:782:0
%499 : int[] = prim::Constantvalue=[1, 1]
%500 : int[] = prim::Constantvalue=[1, 1]
%501 : int[] = prim::Constantvalue=[1, 1]
%290 : bool = prim::Constantvalue=0 # /usr/local/lib/python3.7/dist-packages/torch/nn/modules/conv.py:454:0
%502 : int[] = prim::Constantvalue=[0, 0]
%294 : int = prim::Constantvalue=1 # /usr/local/lib/python3.7/dist-packages/torch/nn/modules/conv.py:454:0
%295 : bool = prim::Constantvalue=0 # /usr/local/lib/python3.7/dist-packages/torch/nn/modules/conv.py:454:0
%296 : bool = prim::Constantvalue=0 # /usr/local/lib/python3.7/dist-packages/torch/nn/modules/conv.py:454:0
%297 : bool = prim::Constantvalue=1 # /usr/local/lib/python3.7/dist-packages/torch/nn/modules/conv.py:454:0
%298 : bool = prim::Constantvalue=1 # /usr/local/lib/python3.7/dist-packages/torch/nn/modules/conv.py:454:0
%input.31 : Float(1, 512, 28, 28, strides=[401408, 784, 28, 1], requires_grad=0, device=cpu) = aten::_convolution(%input.29, %15, %16, %499, %500, %501, %290, %502, %294, %295, %296, %297, %298) # /usr/local/lib/python3.7/dist-packages/torch/nn/modules/conv.py:454:0
%539 : Float(1, 512, 28, 28, strides=[401408, 784, 28, 1], requires_grad=1, device=cpu) = aten::relu(%input.31) # /usr/local/lib/python3.7/dist-packages/torch/nn/functional.py:1455:0
%503 : int[] = prim::Constantvalue=[1, 1]
%504 : int[] = prim::Constantvalue=[1, 1]
%505 : int[] = prim::Constantvalue=[1, 1]
%310 : bool = prim::Constantvalue=0 # /usr/local/lib/python3.7/dist-packages/torch/nn/modules/conv.py:454:0
%506 : int[] = prim::Constantvalue=[0, 0]
%314 : int = prim::Constantvalue=1 # /usr/local/lib/python3.7/dist-packages/torch/nn/modules/conv.py:454:0
%315 : bool = prim::Constantvalue=0 # /usr/local/lib/python3.7/dist-packages/torch/nn/modules/conv.py:454:0
%316 : bool = prim::Constantvalue=0 # /usr/local/lib/python3.7/dist-packages/torch/nn/modules/conv.py:454:0
%317 : bool = prim::Constantvalue=1 # /usr/local/lib/python3.7/dist-packages/torch/nn/modules/conv.py:454:0
%318 : bool = prim::Constantvalue=1 # /usr/local/lib/python3.7/dist-packages/torch/nn/modules/conv.py:454:0
%input.35 : Float(1, 512, 28, 28, strides=[401408, 784, 28, 1], requires_grad=0, device=cpu) = aten::_convolution(%539, %17, %18, %503, %504, %505, %310, %506, %314, %315, %316, %317, %318) # /usr/local/lib/python3.7/dist-packages/torch/nn/modules/conv.py:454:0
%540 : Float(1, 512, 28, 28, strides=[401408, 784, 28, 1], requires_grad=1, device=cpu) = aten::relu(%input.35) # /usr/local/lib/python3.7/dist-packages/torch/nn/functional.py:1455:0
%507 : int[] = prim::Constantvalue=[1, 1]
%508 : int[] = prim::Constantvalue=[1, 1]
%509 : int[] = prim::Constantvalue=[1, 1]
%330 : bool = prim::Constantvalue=0 # /usr/local/lib/python3.7/dist-packages/torch/nn/modules/conv.py:454:0
%510 : int[] = prim::Constantvalue=[0, 0]
%334 : int = prim::Constantvalue=1 # /usr/local/lib/python3.7/dist-packages/torch/nn/modules/conv.py:454:0
%335 : bool = prim::Constantvalue=0 # /usr/local/lib/python3.7/dist-packages/torch/nn/modules/conv.py:454:0
%336 : bool = prim::Constantvalue=0 # /usr/local/lib/python3.7/dist-packages/torch/nn/modules/conv.py:454:0
%337 : bool = prim::Constantvalue=1 # /usr/local/lib/python3.7/dist-packages/torch/nn/modules/conv.py:454:0
%338 : bool = prim::Constantvalue=1 # /usr/local/lib/python3.7/dist-packages/torch/nn/modules/conv.py:454:0
%input.39 : Float(1, 512, 28, 28, strides=[401408, 784, 28, 1], requires_grad=0, device=cpu) = aten::_convolution(%540, %19, %20, %507, %508, %509, %330, %510, %334, %335, %336, %337, %338) # /usr/local/lib/python3.7/dist-packages/torch/nn/modules/conv.py:454:0
%541 : Float(1, 512, 28, 28, strides=[401408, 784, 28, 1], requires_grad=1, device=cpu) = aten::relu(%input.39) # /usr/local/lib/python3.7/dist-packages/torch/nn/functional.py:1455:0
%511 : int[] = prim::Constantvalue=[2, 2]
%512 : int[] = prim::Constantvalue=[2, 2]
%513 : int[] = prim::Constantvalue=[0, 0]
%514 : int[] = prim::Constantvalue=[1, 1]
%353 : bool = prim::Constantvalue=0 # /usr/local/lib/python3.7/dist-packages/torch/nn/functional.py:782:0
%input.41 : Float(1, 512, 14, 14, strides=[100352, 196, 14, 1], requires_grad=1, device=cpu) = aten::max_pool2d(%541, %511, %512, %513, %514, %353) # /usr/local/lib/python3.7/dist-packages/torch/nn/functional.py:782:0
%515 : int[] = prim::Constantvalue=[1, 1]
%516 : int[] = prim::Constantvalue=[1, 1]
%517 : int[] = prim::Constantvalue=[1, 1]
%364 : bool = prim::Constantvalue=0 # /usr/local/lib/python3.7/dist-packages/torch/nn/modules/conv.py:454:0
%518 : int[] = prim::Constantvalue=[0, 0]
%368 : int = prim::Constantvalue=1 # /usr/local/lib/python3.7/dist-packages/torch/nn/modules/conv.py:454:0
%369 : bool = prim::Constantvalue=0 # /usr/local/lib/python3.7/dist-packages/torch/nn/modules/conv.py:454:0
%370 : bool = prim::Constantvalue=0 # /usr/local/lib/python3.7/dist-packages/torch/nn/modules/conv.py:454:0
%371 : bool = prim::Constantvalue=1 # /usr/local/lib/python3.7/dist-packages/torch/nn/modules/conv.py:454:0
%372 : bool = prim::Constantvalue=1 # /usr/local/lib/python3.7/dist-packages/torch/nn/modules/conv.py:454:0
%input.43 : Float(1, 512, 14, 14, strides=[100352, 196, 14, 1], requires_grad=0, device=cpu) = aten::_convolution(%input.41, %21, %22, %515, %516, %517, %364, %518, %368, %369, %370, %371, %372) # /usr/local/lib/python3.7/dist-packages/torch/nn/modules/conv.py:454:0
%542 : Float(1, 512, 14, 14, strides=[100352, 196, 14, 1], requires_grad=1, device=cpu) = aten::relu(%input.43) # /usr/local/lib/python3.7/dist-packages/torch/nn/functional.py:1455:0
%519 : int[] = prim::Constantvalue=[1, 1]
%520 : int[] = prim::Constantvalue=[1, 1]
%521 : int[] = prim::Constantvalue=[1, 1]
%384 : bool = prim::Constantvalue=0 # /usr/local/lib/python3.7/dist-packages/torch/nn/modules/conv.py:454:0
%522 : int[] = prim::Constantvalue=[0, 0]
%388 : int = prim::Constantvalue=1 # /usr/local/lib/python3.7/dist-packages/torch/nn/modules/conv.py:454:0
%389 : bool = prim::Constantvalue=0 # /usr/local/lib/python3.7/dist-packages/torch/nn/modules/conv.py:454:0
%390 : bool = prim::Constantvalue=0 # /usr/local/lib/python3.7/dist-packages/torch/nn/modules/conv.py:454:0
%391 : bool = prim::Constantvalue=1 # /usr/local/lib/python3.7/dist-packages/torch/nn/modules/conv.py:454:0
%392 : bool = prim::Constantvalue=1 # /usr/local/lib/python3.7/dist-packages/torch/nn/modules/conv.py:454:0
%input.47 : Float(1, 512, 14, 14, strides=[100352, 196, 14, 1], requires_grad=0, device=cpu) = aten::_convolution(%542, %23, %24, %519, %520, %521, %384, %522, %388, %389, %390, %391, %392) # /usr/local/lib/python3.7/dist-packages/torch/nn/modules/conv.py:454:0
%543 : Float(1, 512, 14, 14, strides=[100352, 196, 14, 1], requires_grad=1, device=cpu) = aten::relu(%input.47) # /usr/local/lib/python3.7/dist-packages/torch/nn/functional.py:1455:0
%523 : int[] = prim::Constantvalue=[1, 1]
%524 : int[] = prim::Constantvalue=[1, 1]
%525 : int[] = prim::Constantvalue=[1, 1]
%404 : bool = prim::Constantvalue=0 # /usr/local/lib/python3.7/dist-packages/torch/nn/modules/conv.py:454:0
%526 : int[] = prim::Constantvalue=[0, 0]
%408 : int = prim::Constantvalue=1 # /usr/local/lib/python3.7/dist-packages/torch/nn/modules/conv.py:454:0
%409 : bool = prim::Constantvalue=0 # /usr/local/lib/python3.7/dist-packages/torch/nn/modules/conv.py:454:0
%410 : bool = prim::Constantvalue=0 # /usr/local/lib/python3.7/dist-packages/torch/nn/modules/conv.py:454:0
%411 : bool = prim::Constantvalue=1 # /usr/local/lib/python3.7/dist-packages/torch/nn/modules/conv.py:454:0
%412 : bool = prim::Constantvalue=1 # /usr/local/lib/python3.7/dist-packages/torch/nn/modules/conv.py:454:0
%input.51 : Float(1, 512, 14, 14, strides=[100352, 196, 14, 1], requires_grad=0, device=cpu) = aten::_convolution(%543, %25, %26, %523, %524, %525, %404, %526, %408, %409, %410, %411, %412) # /usr/local/lib/python3.7/dist-packages/torch/nn/modules/conv.py:454:0
%544 : Float(1, 512, 14, 14, strides=[100352, 196, 14, 1], requires_grad=1, device=cpu) = aten::relu(%input.51) # /usr/local/lib/python3.7/dist-packages/torch/nn/functional.py:1455:0
%527 : int[] = prim::Constantvalue=[2, 2]
%528 : int[] = prim::Constantvalue=[2, 2]
%529 : int[] = prim::Constantvalue=[0, 0]
%530 : int[] = prim::Constantvalue=[1, 1]
%427 : bool = prim::Constantvalue=0 # /usr/local/lib/python3.7/dist-packages/torch/nn/functional.py:782:0
%input.53 : Float(1, 512, 7, 7, strides=[25088, 49, 7, 1], requires_grad=1, device=cpu) = aten::max_pool2d(%544, %527, %528, %529, %530, %427) # /usr/local/lib/python3.7/dist-packages/torch/nn/functional.py:782:0
%531 : int[] = prim::Constantvalue=[7, 7]
%444 : Float(1, 512, 7, 7, strides=[25088, 49, 7, 1], requires_grad=1, device=cpu) = aten::adaptive_avg_pool2d(%input.53, %531) # /usr/local/lib/python3.7/dist-packages/torch/nn/functional.py:1214:0
%445 : int = prim::Constantvalue=1 # /usr/local/lib/python3.7/dist-packages/torchvision/models/vgg.py:68:0
%446 : int = prim::Constantvalue=-1 # /usr/local/lib/python3.7/dist-packages/torchvision/models/vgg.py:68:0
%447 : Float(1, 25088, strides=[25088, 1], requires_grad=1, device=cpu) = aten::flatten(%444, %445, %446) # /usr/local/lib/python3.7/dist-packages/torchvision/models/vgg.py:68:0
%input.55 : Float(1, 4096, strides=[4096, 1], requires_grad=1, device=cpu) = aten::linear(%447, %27, %28) # /usr/local/lib/python3.7/dist-packages/torch/nn/modules/linear.py:114:0
%545 : Float(1, 4096, strides=[4096, 1], requires_grad=1, device=cpu) = aten::relu(%input.55) # /usr/local/lib/python3.7/dist-packages/torch/nn/functional.py:1455:0
%450 : float = prim::Constantvalue=0.5 # /usr/local/lib/python3.7/dist-packages/torch/nn/functional.py:1252:0
%451 : bool = prim::Constantvalue=1 # /usr/local/lib/python3.7/dist-packages/torch/nn/functional.py:1252:0
%452 : Float(1, 4096, strides=[4096, 1], requires_grad=1, device=cpu) = aten::dropout(%545, %450, %451) # /usr/local/lib/python3.7/dist-packages/torch/nn/functional.py:1252:0
%input.59 : Float(1, 4096, strides=[4096, 1], requires_grad=1, device=cpu) = aten::linear(%452, %29, %30) # /usr/local/lib/python3.7/dist-packages/torch/nn/modules/linear.py:114:0
%546 : Float(1, 4096, strides=[4096, 1], requires_grad=1, device=cpu) = aten::relu(%input.59) # /usr/local/lib/python3.7/dist-packages/torch/nn/functional.py:1455:0
%455 : float = prim::Constantvalue=0.5 # /usr/local/lib/python3.7/dist-packages/torch/nn/functional.py:1252:0
%456 : bool = prim::Constantvalue=1 # /usr/local/lib/python3.7/dist-packages/torch/nn/functional.py:1252:0
%457 : Float(1, 4096, strides=[4096, 1], requires_grad=1, device=cpu) = aten::dropout(%546, %455, %456) # /usr/local/lib/python3.7/dist-packages/torch/nn/functional.py:1252:0
%458 : Float(1, 1000, strides=[1000, 1], requires_grad=1, device=cpu) = aten::linear(%457, %31, %32) # /usr/local/lib/python3.7/dist-packages/torch/nn/modules/linear.py:114:0
return (%458)
, None, False