Open Junaid199f opened 1 year ago
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:
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::Constant[value=[1, 1]]() %460 : int[] = prim::Constant[value=[1, 1]]() %461 : int[] = prim::Constant[value=[1, 1]]() %108 : bool = prim::Constant[value=0]() # /usr/local/lib/python3.7/dist-packages/torch/nn/modules/conv.py:454:0 %462 : int[] = prim::Constant[value=[0, 0]]() %112 : int = prim::Constant[value=1]() # /usr/local/lib/python3.7/dist-packages/torch/nn/modules/conv.py:454:0 %113 : bool = prim::Constant[value=0]() # /usr/local/lib/python3.7/dist-packages/torch/nn/modules/conv.py:454:0 %114 : bool = prim::Constant[value=0]() # /usr/local/lib/python3.7/dist-packages/torch/nn/modules/conv.py:454:0 %115 : bool = prim::Constant[value=1]() # /usr/local/lib/python3.7/dist-packages/torch/nn/modules/conv.py:454:0 %116 : bool = prim::Constant[value=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::Constant[value=[1, 1]]() %464 : int[] = prim::Constant[value=[1, 1]]() %465 : int[] = prim::Constant[value=[1, 1]]() %128 : bool = prim::Constant[value=0]() # /usr/local/lib/python3.7/dist-packages/torch/nn/modules/conv.py:454:0 %466 : int[] = prim::Constant[value=[0, 0]]() %132 : int = prim::Constant[value=1]() # /usr/local/lib/python3.7/dist-packages/torch/nn/modules/conv.py:454:0 %133 : bool = prim::Constant[value=0]() # /usr/local/lib/python3.7/dist-packages/torch/nn/modules/conv.py:454:0 %134 : bool = prim::Constant[value=0]() # /usr/local/lib/python3.7/dist-packages/torch/nn/modules/conv.py:454:0 %135 : bool = prim::Constant[value=1]() # /usr/local/lib/python3.7/dist-packages/torch/nn/modules/conv.py:454:0 %136 : bool = prim::Constant[value=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::Constant[value=[2, 2]]() %468 : int[] = prim::Constant[value=[2, 2]]() %469 : int[] = prim::Constant[value=[0, 0]]() %470 : int[] = prim::Constant[value=[1, 1]]() %151 : bool = prim::Constant[value=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::Constant[value=[1, 1]]() %472 : int[] = prim::Constant[value=[1, 1]]() %473 : int[] = prim::Constant[value=[1, 1]]() %162 : bool = prim::Constant[value=0]() # /usr/local/lib/python3.7/dist-packages/torch/nn/modules/conv.py:454:0 %474 : int[] = prim::Constant[value=[0, 0]]() %166 : int = prim::Constant[value=1]() # /usr/local/lib/python3.7/dist-packages/torch/nn/modules/conv.py:454:0 %167 : bool = prim::Constant[value=0]() # /usr/local/lib/python3.7/dist-packages/torch/nn/modules/conv.py:454:0 %168 : bool = prim::Constant[value=0]() # /usr/local/lib/python3.7/dist-packages/torch/nn/modules/conv.py:454:0 %169 : bool = prim::Constant[value=1]() # /usr/local/lib/python3.7/dist-packages/torch/nn/modules/conv.py:454:0 %170 : bool = prim::Constant[value=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::Constant[value=[1, 1]]() %476 : int[] = prim::Constant[value=[1, 1]]() %477 : int[] = prim::Constant[value=[1, 1]]() %182 : bool = prim::Constant[value=0]() # /usr/local/lib/python3.7/dist-packages/torch/nn/modules/conv.py:454:0 %478 : int[] = prim::Constant[value=[0, 0]]() %186 : int = prim::Constant[value=1]() # /usr/local/lib/python3.7/dist-packages/torch/nn/modules/conv.py:454:0 %187 : bool = prim::Constant[value=0]() # /usr/local/lib/python3.7/dist-packages/torch/nn/modules/conv.py:454:0 %188 : bool = prim::Constant[value=0]() # /usr/local/lib/python3.7/dist-packages/torch/nn/modules/conv.py:454:0 %189 : bool = prim::Constant[value=1]() # /usr/local/lib/python3.7/dist-packages/torch/nn/modules/conv.py:454:0 %190 : bool = prim::Constant[value=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::Constant[value=[2, 2]]() %480 : int[] = prim::Constant[value=[2, 2]]() %481 : int[] = prim::Constant[value=[0, 0]]() %482 : int[] = prim::Constant[value=[1, 1]]() %205 : bool = prim::Constant[value=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::Constant[value=[1, 1]]() %484 : int[] = prim::Constant[value=[1, 1]]() %485 : int[] = prim::Constant[value=[1, 1]]() %216 : bool = prim::Constant[value=0]() # /usr/local/lib/python3.7/dist-packages/torch/nn/modules/conv.py:454:0 %486 : int[] = prim::Constant[value=[0, 0]]() %220 : int = prim::Constant[value=1]() # /usr/local/lib/python3.7/dist-packages/torch/nn/modules/conv.py:454:0 %221 : bool = prim::Constant[value=0]() # /usr/local/lib/python3.7/dist-packages/torch/nn/modules/conv.py:454:0 %222 : bool = prim::Constant[value=0]() # /usr/local/lib/python3.7/dist-packages/torch/nn/modules/conv.py:454:0 %223 : bool = prim::Constant[value=1]() # /usr/local/lib/python3.7/dist-packages/torch/nn/modules/conv.py:454:0 %224 : bool = prim::Constant[value=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::Constant[value=[1, 1]]() %488 : int[] = prim::Constant[value=[1, 1]]() %489 : int[] = prim::Constant[value=[1, 1]]() %236 : bool = prim::Constant[value=0]() # /usr/local/lib/python3.7/dist-packages/torch/nn/modules/conv.py:454:0 %490 : int[] = prim::Constant[value=[0, 0]]() %240 : int = prim::Constant[value=1]() # /usr/local/lib/python3.7/dist-packages/torch/nn/modules/conv.py:454:0 %241 : bool = prim::Constant[value=0]() # /usr/local/lib/python3.7/dist-packages/torch/nn/modules/conv.py:454:0 %242 : bool = prim::Constant[value=0]() # /usr/local/lib/python3.7/dist-packages/torch/nn/modules/conv.py:454:0 %243 : bool = prim::Constant[value=1]() # /usr/local/lib/python3.7/dist-packages/torch/nn/modules/conv.py:454:0 %244 : bool = prim::Constant[value=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::Constant[value=[1, 1]]() %492 : int[] = prim::Constant[value=[1, 1]]() %493 : int[] = prim::Constant[value=[1, 1]]() %256 : bool = prim::Constant[value=0]() # /usr/local/lib/python3.7/dist-packages/torch/nn/modules/conv.py:454:0 %494 : int[] = prim::Constant[value=[0, 0]]() %260 : int = prim::Constant[value=1]() # /usr/local/lib/python3.7/dist-packages/torch/nn/modules/conv.py:454:0 %261 : bool = prim::Constant[value=0]() # /usr/local/lib/python3.7/dist-packages/torch/nn/modules/conv.py:454:0 %262 : bool = prim::Constant[value=0]() # /usr/local/lib/python3.7/dist-packages/torch/nn/modules/conv.py:454:0 %263 : bool = prim::Constant[value=1]() # /usr/local/lib/python3.7/dist-packages/torch/nn/modules/conv.py:454:0 %264 : bool = prim::Constant[value=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::Constant[value=[2, 2]]() %496 : int[] = prim::Constant[value=[2, 2]]() %497 : int[] = prim::Constant[value=[0, 0]]() %498 : int[] = prim::Constant[value=[1, 1]]() %279 : bool = prim::Constant[value=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::Constant[value=[1, 1]]() %500 : int[] = prim::Constant[value=[1, 1]]() %501 : int[] = prim::Constant[value=[1, 1]]() %290 : bool = prim::Constant[value=0]() # /usr/local/lib/python3.7/dist-packages/torch/nn/modules/conv.py:454:0 %502 : int[] = prim::Constant[value=[0, 0]]() %294 : int = prim::Constant[value=1]() # /usr/local/lib/python3.7/dist-packages/torch/nn/modules/conv.py:454:0 %295 : bool = prim::Constant[value=0]() # /usr/local/lib/python3.7/dist-packages/torch/nn/modules/conv.py:454:0 %296 : bool = prim::Constant[value=0]() # /usr/local/lib/python3.7/dist-packages/torch/nn/modules/conv.py:454:0 %297 : bool = prim::Constant[value=1]() # /usr/local/lib/python3.7/dist-packages/torch/nn/modules/conv.py:454:0 %298 : bool = prim::Constant[value=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::Constant[value=[1, 1]]() %504 : int[] = prim::Constant[value=[1, 1]]() %505 : int[] = prim::Constant[value=[1, 1]]() %310 : bool = prim::Constant[value=0]() # /usr/local/lib/python3.7/dist-packages/torch/nn/modules/conv.py:454:0 %506 : int[] = prim::Constant[value=[0, 0]]() %314 : int = prim::Constant[value=1]() # /usr/local/lib/python3.7/dist-packages/torch/nn/modules/conv.py:454:0 %315 : bool = prim::Constant[value=0]() # /usr/local/lib/python3.7/dist-packages/torch/nn/modules/conv.py:454:0 %316 : bool = prim::Constant[value=0]() # /usr/local/lib/python3.7/dist-packages/torch/nn/modules/conv.py:454:0 %317 : bool = prim::Constant[value=1]() # /usr/local/lib/python3.7/dist-packages/torch/nn/modules/conv.py:454:0 %318 : bool = prim::Constant[value=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::Constant[value=[1, 1]]() %508 : int[] = prim::Constant[value=[1, 1]]() %509 : int[] = prim::Constant[value=[1, 1]]() %330 : bool = prim::Constant[value=0]() # /usr/local/lib/python3.7/dist-packages/torch/nn/modules/conv.py:454:0 %510 : int[] = prim::Constant[value=[0, 0]]() %334 : int = prim::Constant[value=1]() # /usr/local/lib/python3.7/dist-packages/torch/nn/modules/conv.py:454:0 %335 : bool = prim::Constant[value=0]() # /usr/local/lib/python3.7/dist-packages/torch/nn/modules/conv.py:454:0 %336 : bool = prim::Constant[value=0]() # /usr/local/lib/python3.7/dist-packages/torch/nn/modules/conv.py:454:0 %337 : bool = prim::Constant[value=1]() # /usr/local/lib/python3.7/dist-packages/torch/nn/modules/conv.py:454:0 %338 : bool = prim::Constant[value=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::Constant[value=[2, 2]]() %512 : int[] = prim::Constant[value=[2, 2]]() %513 : int[] = prim::Constant[value=[0, 0]]() %514 : int[] = prim::Constant[value=[1, 1]]() %353 : bool = prim::Constant[value=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::Constant[value=[1, 1]]() %516 : int[] = prim::Constant[value=[1, 1]]() %517 : int[] = prim::Constant[value=[1, 1]]() %364 : bool = prim::Constant[value=0]() # /usr/local/lib/python3.7/dist-packages/torch/nn/modules/conv.py:454:0 %518 : int[] = prim::Constant[value=[0, 0]]() %368 : int = prim::Constant[value=1]() # /usr/local/lib/python3.7/dist-packages/torch/nn/modules/conv.py:454:0 %369 : bool = prim::Constant[value=0]() # /usr/local/lib/python3.7/dist-packages/torch/nn/modules/conv.py:454:0 %370 : bool = prim::Constant[value=0]() # /usr/local/lib/python3.7/dist-packages/torch/nn/modules/conv.py:454:0 %371 : bool = prim::Constant[value=1]() # /usr/local/lib/python3.7/dist-packages/torch/nn/modules/conv.py:454:0 %372 : bool = prim::Constant[value=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::Constant[value=[1, 1]]() %520 : int[] = prim::Constant[value=[1, 1]]() %521 : int[] = prim::Constant[value=[1, 1]]() %384 : bool = prim::Constant[value=0]() # /usr/local/lib/python3.7/dist-packages/torch/nn/modules/conv.py:454:0 %522 : int[] = prim::Constant[value=[0, 0]]() %388 : int = prim::Constant[value=1]() # /usr/local/lib/python3.7/dist-packages/torch/nn/modules/conv.py:454:0 %389 : bool = prim::Constant[value=0]() # /usr/local/lib/python3.7/dist-packages/torch/nn/modules/conv.py:454:0 %390 : bool = prim::Constant[value=0]() # /usr/local/lib/python3.7/dist-packages/torch/nn/modules/conv.py:454:0 %391 : bool = prim::Constant[value=1]() # /usr/local/lib/python3.7/dist-packages/torch/nn/modules/conv.py:454:0 %392 : bool = prim::Constant[value=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::Constant[value=[1, 1]]() %524 : int[] = prim::Constant[value=[1, 1]]() %525 : int[] = prim::Constant[value=[1, 1]]() %404 : bool = prim::Constant[value=0]() # /usr/local/lib/python3.7/dist-packages/torch/nn/modules/conv.py:454:0 %526 : int[] = prim::Constant[value=[0, 0]]() %408 : int = prim::Constant[value=1]() # /usr/local/lib/python3.7/dist-packages/torch/nn/modules/conv.py:454:0 %409 : bool = prim::Constant[value=0]() # /usr/local/lib/python3.7/dist-packages/torch/nn/modules/conv.py:454:0 %410 : bool = prim::Constant[value=0]() # /usr/local/lib/python3.7/dist-packages/torch/nn/modules/conv.py:454:0 %411 : bool = prim::Constant[value=1]() # /usr/local/lib/python3.7/dist-packages/torch/nn/modules/conv.py:454:0 %412 : bool = prim::Constant[value=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::Constant[value=[2, 2]]() %528 : int[] = prim::Constant[value=[2, 2]]() %529 : int[] = prim::Constant[value=[0, 0]]() %530 : int[] = prim::Constant[value=[1, 1]]() %427 : bool = prim::Constant[value=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::Constant[value=[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::Constant[value=1]() # /usr/local/lib/python3.7/dist-packages/torchvision/models/vgg.py:68:0 %446 : int = prim::Constant[value=-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::Constant[value=0.5]() # /usr/local/lib/python3.7/dist-packages/torch/nn/functional.py:1252:0 %451 : bool = prim::Constant[value=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::Constant[value=0.5]() # /usr/local/lib/python3.7/dist-packages/torch/nn/functional.py:1252:0 %456 : bool = prim::Constant[value=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
TypeError Traceback (most recent call last)
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:
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::Constant[value=[1, 1]]() %460 : int[] = prim::Constant[value=[1, 1]]() %461 : int[] = prim::Constant[value=[1, 1]]() %108 : bool = prim::Constant[value=0]() # /usr/local/lib/python3.7/dist-packages/torch/nn/modules/conv.py:454:0 %462 : int[] = prim::Constant[value=[0, 0]]() %112 : int = prim::Constant[value=1]() # /usr/local/lib/python3.7/dist-packages/torch/nn/modules/conv.py:454:0 %113 : bool = prim::Constant[value=0]() # /usr/local/lib/python3.7/dist-packages/torch/nn/modules/conv.py:454:0 %114 : bool = prim::Constant[value=0]() # /usr/local/lib/python3.7/dist-packages/torch/nn/modules/conv.py:454:0 %115 : bool = prim::Constant[value=1]() # /usr/local/lib/python3.7/dist-packages/torch/nn/modules/conv.py:454:0 %116 : bool = prim::Constant[value=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::Constant[value=[1, 1]]() %464 : int[] = prim::Constant[value=[1, 1]]() %465 : int[] = prim::Constant[value=[1, 1]]() %128 : bool = prim::Constant[value=0]() # /usr/local/lib/python3.7/dist-packages/torch/nn/modules/conv.py:454:0 %466 : int[] = prim::Constant[value=[0, 0]]() %132 : int = prim::Constant[value=1]() # /usr/local/lib/python3.7/dist-packages/torch/nn/modules/conv.py:454:0 %133 : bool = prim::Constant[value=0]() # /usr/local/lib/python3.7/dist-packages/torch/nn/modules/conv.py:454:0 %134 : bool = prim::Constant[value=0]() # /usr/local/lib/python3.7/dist-packages/torch/nn/modules/conv.py:454:0 %135 : bool = prim::Constant[value=1]() # /usr/local/lib/python3.7/dist-packages/torch/nn/modules/conv.py:454:0 %136 : bool = prim::Constant[value=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::Constant[value=[2, 2]]() %468 : int[] = prim::Constant[value=[2, 2]]() %469 : int[] = prim::Constant[value=[0, 0]]() %470 : int[] = prim::Constant[value=[1, 1]]() %151 : bool = prim::Constant[value=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::Constant[value=[1, 1]]() %472 : int[] = prim::Constant[value=[1, 1]]() %473 : int[] = prim::Constant[value=[1, 1]]() %162 : bool = prim::Constant[value=0]() # /usr/local/lib/python3.7/dist-packages/torch/nn/modules/conv.py:454:0 %474 : int[] = prim::Constant[value=[0, 0]]() %166 : int = prim::Constant[value=1]() # /usr/local/lib/python3.7/dist-packages/torch/nn/modules/conv.py:454:0 %167 : bool = prim::Constant[value=0]() # /usr/local/lib/python3.7/dist-packages/torch/nn/modules/conv.py:454:0 %168 : bool = prim::Constant[value=0]() # /usr/local/lib/python3.7/dist-packages/torch/nn/modules/conv.py:454:0 %169 : bool = prim::Constant[value=1]() # /usr/local/lib/python3.7/dist-packages/torch/nn/modules/conv.py:454:0 %170 : bool = prim::Constant[value=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::Constant[value=[1, 1]]() %476 : int[] = prim::Constant[value=[1, 1]]() %477 : int[] = prim::Constant[value=[1, 1]]() %182 : bool = prim::Constant[value=0]() # /usr/local/lib/python3.7/dist-packages/torch/nn/modules/conv.py:454:0 %478 : int[] = prim::Constant[value=[0, 0]]() %186 : int = prim::Constant[value=1]() # /usr/local/lib/python3.7/dist-packages/torch/nn/modules/conv.py:454:0 %187 : bool = prim::Constant[value=0]() # /usr/local/lib/python3.7/dist-packages/torch/nn/modules/conv.py:454:0 %188 : bool = prim::Constant[value=0]() # /usr/local/lib/python3.7/dist-packages/torch/nn/modules/conv.py:454:0 %189 : bool = prim::Constant[value=1]() # /usr/local/lib/python3.7/dist-packages/torch/nn/modules/conv.py:454:0 %190 : bool = prim::Constant[value=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::Constant[value=[2, 2]]() %480 : int[] = prim::Constant[value=[2, 2]]() %481 : int[] = prim::Constant[value=[0, 0]]() %482 : int[] = prim::Constant[value=[1, 1]]() %205 : bool = prim::Constant[value=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::Constant[value=[1, 1]]() %484 : int[] = prim::Constant[value=[1, 1]]() %485 : int[] = prim::Constant[value=[1, 1]]() %216 : bool = prim::Constant[value=0]() # /usr/local/lib/python3.7/dist-packages/torch/nn/modules/conv.py:454:0 %486 : int[] = prim::Constant[value=[0, 0]]() %220 : int = prim::Constant[value=1]() # /usr/local/lib/python3.7/dist-packages/torch/nn/modules/conv.py:454:0 %221 : bool = prim::Constant[value=0]() # /usr/local/lib/python3.7/dist-packages/torch/nn/modules/conv.py:454:0 %222 : bool = prim::Constant[value=0]() # /usr/local/lib/python3.7/dist-packages/torch/nn/modules/conv.py:454:0 %223 : bool = prim::Constant[value=1]() # /usr/local/lib/python3.7/dist-packages/torch/nn/modules/conv.py:454:0 %224 : bool = prim::Constant[value=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::Constant[value=[1, 1]]() %488 : int[] = prim::Constant[value=[1, 1]]() %489 : int[] = prim::Constant[value=[1, 1]]() %236 : bool = prim::Constant[value=0]() # /usr/local/lib/python3.7/dist-packages/torch/nn/modules/conv.py:454:0 %490 : int[] = prim::Constant[value=[0, 0]]() %240 : int = prim::Constant[value=1]() # /usr/local/lib/python3.7/dist-packages/torch/nn/modules/conv.py:454:0 %241 : bool = prim::Constant[value=0]() # /usr/local/lib/python3.7/dist-packages/torch/nn/modules/conv.py:454:0 %242 : bool = prim::Constant[value=0]() # /usr/local/lib/python3.7/dist-packages/torch/nn/modules/conv.py:454:0 %243 : bool = prim::Constant[value=1]() # /usr/local/lib/python3.7/dist-packages/torch/nn/modules/conv.py:454:0 %244 : bool = prim::Constant[value=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::Constant[value=[1, 1]]() %492 : int[] = prim::Constant[value=[1, 1]]() %493 : int[] = prim::Constant[value=[1, 1]]() %256 : bool = prim::Constant[value=0]() # /usr/local/lib/python3.7/dist-packages/torch/nn/modules/conv.py:454:0 %494 : int[] = prim::Constant[value=[0, 0]]() %260 : int = prim::Constant[value=1]() # /usr/local/lib/python3.7/dist-packages/torch/nn/modules/conv.py:454:0 %261 : bool = prim::Constant[value=0]() # /usr/local/lib/python3.7/dist-packages/torch/nn/modules/conv.py:454:0 %262 : bool = prim::Constant[value=0]() # /usr/local/lib/python3.7/dist-packages/torch/nn/modules/conv.py:454:0 %263 : bool = prim::Constant[value=1]() # /usr/local/lib/python3.7/dist-packages/torch/nn/modules/conv.py:454:0 %264 : bool = prim::Constant[value=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::Constant[value=[2, 2]]() %496 : int[] = prim::Constant[value=[2, 2]]() %497 : int[] = prim::Constant[value=[0, 0]]() %498 : int[] = prim::Constant[value=[1, 1]]() %279 : bool = prim::Constant[value=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::Constant[value=[1, 1]]() %500 : int[] = prim::Constant[value=[1, 1]]() %501 : int[] = prim::Constant[value=[1, 1]]() %290 : bool = prim::Constant[value=0]() # /usr/local/lib/python3.7/dist-packages/torch/nn/modules/conv.py:454:0 %502 : int[] = prim::Constant[value=[0, 0]]() %294 : int = prim::Constant[value=1]() # /usr/local/lib/python3.7/dist-packages/torch/nn/modules/conv.py:454:0 %295 : bool = prim::Constant[value=0]() # /usr/local/lib/python3.7/dist-packages/torch/nn/modules/conv.py:454:0 %296 : bool = prim::Constant[value=0]() # /usr/local/lib/python3.7/dist-packages/torch/nn/modules/conv.py:454:0 %297 : bool = prim::Constant[value=1]() # /usr/local/lib/python3.7/dist-packages/torch/nn/modules/conv.py:454:0 %298 : bool = prim::Constant[value=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::Constant[value=[1, 1]]() %504 : int[] = prim::Constant[value=[1, 1]]() %505 : int[] = prim::Constant[value=[1, 1]]() %310 : bool = prim::Constant[value=0]() # /usr/local/lib/python3.7/dist-packages/torch/nn/modules/conv.py:454:0 %506 : int[] = prim::Constant[value=[0, 0]]() %314 : int = prim::Constant[value=1]() # /usr/local/lib/python3.7/dist-packages/torch/nn/modules/conv.py:454:0 %315 : bool = prim::Constant[value=0]() # /usr/local/lib/python3.7/dist-packages/torch/nn/modules/conv.py:454:0 %316 : bool = prim::Constant[value=0]() # /usr/local/lib/python3.7/dist-packages/torch/nn/modules/conv.py:454:0 %317 : bool = prim::Constant[value=1]() # /usr/local/lib/python3.7/dist-packages/torch/nn/modules/conv.py:454:0 %318 : bool = prim::Constant[value=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::Constant[value=[1, 1]]() %508 : int[] = prim::Constant[value=[1, 1]]() %509 : int[] = prim::Constant[value=[1, 1]]() %330 : bool = prim::Constant[value=0]() # /usr/local/lib/python3.7/dist-packages/torch/nn/modules/conv.py:454:0 %510 : int[] = prim::Constant[value=[0, 0]]() %334 : int = prim::Constant[value=1]() # /usr/local/lib/python3.7/dist-packages/torch/nn/modules/conv.py:454:0 %335 : bool = prim::Constant[value=0]() # /usr/local/lib/python3.7/dist-packages/torch/nn/modules/conv.py:454:0 %336 : bool = prim::Constant[value=0]() # /usr/local/lib/python3.7/dist-packages/torch/nn/modules/conv.py:454:0 %337 : bool = prim::Constant[value=1]() # /usr/local/lib/python3.7/dist-packages/torch/nn/modules/conv.py:454:0 %338 : bool = prim::Constant[value=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::Constant[value=[2, 2]]() %512 : int[] = prim::Constant[value=[2, 2]]() %513 : int[] = prim::Constant[value=[0, 0]]() %514 : int[] = prim::Constant[value=[1, 1]]() %353 : bool = prim::Constant[value=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::Constant[value=[1, 1]]() %516 : int[] = prim::Constant[value=[1, 1]]() %517 : int[] = prim::Constant[value=[1, 1]]() %364 : bool = prim::Constant[value=0]() # /usr/local/lib/python3.7/dist-packages/torch/nn/modules/conv.py:454:0 %518 : int[] = prim::Constant[value=[0, 0]]() %368 : int = prim::Constant[value=1]() # /usr/local/lib/python3.7/dist-packages/torch/nn/modules/conv.py:454:0 %369 : bool = prim::Constant[value=0]() # /usr/local/lib/python3.7/dist-packages/torch/nn/modules/conv.py:454:0 %370 : bool = prim::Constant[value=0]() # /usr/local/lib/python3.7/dist-packages/torch/nn/modules/conv.py:454:0 %371 : bool = prim::Constant[value=1]() # /usr/local/lib/python3.7/dist-packages/torch/nn/modules/conv.py:454:0 %372 : bool = prim::Constant[value=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::Constant[value=[1, 1]]() %520 : int[] = prim::Constant[value=[1, 1]]() %521 : int[] = prim::Constant[value=[1, 1]]() %384 : bool = prim::Constant[value=0]() # /usr/local/lib/python3.7/dist-packages/torch/nn/modules/conv.py:454:0 %522 : int[] = prim::Constant[value=[0, 0]]() %388 : int = prim::Constant[value=1]() # /usr/local/lib/python3.7/dist-packages/torch/nn/modules/conv.py:454:0 %389 : bool = prim::Constant[value=0]() # /usr/local/lib/python3.7/dist-packages/torch/nn/modules/conv.py:454:0 %390 : bool = prim::Constant[value=0]() # /usr/local/lib/python3.7/dist-packages/torch/nn/modules/conv.py:454:0 %391 : bool = prim::Constant[value=1]() # /usr/local/lib/python3.7/dist-packages/torch/nn/modules/conv.py:454:0 %392 : bool = prim::Constant[value=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::Constant[value=[1, 1]]() %524 : int[] = prim::Constant[value=[1, 1]]() %525 : int[] = prim::Constant[value=[1, 1]]() %404 : bool = prim::Constant[value=0]() # /usr/local/lib/python3.7/dist-packages/torch/nn/modules/conv.py:454:0 %526 : int[] = prim::Constant[value=[0, 0]]() %408 : int = prim::Constant[value=1]() # /usr/local/lib/python3.7/dist-packages/torch/nn/modules/conv.py:454:0 %409 : bool = prim::Constant[value=0]() # /usr/local/lib/python3.7/dist-packages/torch/nn/modules/conv.py:454:0 %410 : bool = prim::Constant[value=0]() # /usr/local/lib/python3.7/dist-packages/torch/nn/modules/conv.py:454:0 %411 : bool = prim::Constant[value=1]() # /usr/local/lib/python3.7/dist-packages/torch/nn/modules/conv.py:454:0 %412 : bool = prim::Constant[value=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::Constant[value=[2, 2]]() %528 : int[] = prim::Constant[value=[2, 2]]() %529 : int[] = prim::Constant[value=[0, 0]]() %530 : int[] = prim::Constant[value=[1, 1]]() %427 : bool = prim::Constant[value=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::Constant[value=[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::Constant[value=1]() # /usr/local/lib/python3.7/dist-packages/torchvision/models/vgg.py:68:0 %446 : int = prim::Constant[value=-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::Constant[value=0.5]() # /usr/local/lib/python3.7/dist-packages/torch/nn/functional.py:1252:0 %451 : bool = prim::Constant[value=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::Constant[value=0.5]() # /usr/local/lib/python3.7/dist-packages/torch/nn/functional.py:1252:0 %456 : bool = prim::Constant[value=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