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TypeError for Pytorch Model #94

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hongshanli23 opened this issue Jun 6, 2022 · 4 comments
Open

TypeError for Pytorch Model #94

hongshanli23 opened this issue Jun 6, 2022 · 4 comments

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@hongshanli23
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Hello, I am trying to use hiddenlayer to draw a pytorch model, I got some error coming out of onnx

---------------------------------------------------------------------------
TypeError                                 Traceback (most recent call last)
/home/ubuntu/mstar/scripts/rlfh/visualization.ipynb Cell 4' in <cell line: 13>()
      [6](vscode-notebook-cell://ssh-remote%2Bmistd/home/ubuntu/mstar/scripts/rlfh/visualization.ipynb#ch0000003vscode-remote?line=5) #model = torchvision.models.vgg16()
      [8](vscode-notebook-cell://ssh-remote%2Bmistd/home/ubuntu/mstar/scripts/rlfh/visualization.ipynb#ch0000003vscode-remote?line=7) model = torch.nn.Sequential(
      [9](vscode-notebook-cell://ssh-remote%2Bmistd/home/ubuntu/mstar/scripts/rlfh/visualization.ipynb#ch0000003vscode-remote?line=8)     nn.Linear(10, 10),
     [10](vscode-notebook-cell://ssh-remote%2Bmistd/home/ubuntu/mstar/scripts/rlfh/visualization.ipynb#ch0000003vscode-remote?line=9)     nn.Linear(10, 2)
     [11](vscode-notebook-cell://ssh-remote%2Bmistd/home/ubuntu/mstar/scripts/rlfh/visualization.ipynb#ch0000003vscode-remote?line=10) )
---> [13](vscode-notebook-cell://ssh-remote%2Bmistd/home/ubuntu/mstar/scripts/rlfh/visualization.ipynb#ch0000003vscode-remote?line=12) hl.build_graph(model, torch.zeros([1, 10]))

File ~/py38/lib/python3.8/site-packages/hiddenlayer/graph.py:143, in build_graph(model, args, input_names, transforms, framework_transforms)
    [141](file:///home/ubuntu/py38/lib/python3.8/site-packages/hiddenlayer/graph.py?line=140)     from .pytorch_builder import import_graph, FRAMEWORK_TRANSFORMS
    [142](file:///home/ubuntu/py38/lib/python3.8/site-packages/hiddenlayer/graph.py?line=141)     assert args is not None, "Argument args must be provided for Pytorch models."
--> [143](file:///home/ubuntu/py38/lib/python3.8/site-packages/hiddenlayer/graph.py?line=142)     import_graph(g, model, args)
    [144](file:///home/ubuntu/py38/lib/python3.8/site-packages/hiddenlayer/graph.py?line=143) elif framework == "tensorflow":
    [145](file:///home/ubuntu/py38/lib/python3.8/site-packages/hiddenlayer/graph.py?line=144)     from .tf_builder import import_graph, FRAMEWORK_TRANSFORMS

File ~/py38/lib/python3.8/site-packages/hiddenlayer/pytorch_builder.py:71, in import_graph(hl_graph, model, args, input_names, verbose)
     [66](file:///home/ubuntu/py38/lib/python3.8/site-packages/hiddenlayer/pytorch_builder.py?line=65) def import_graph(hl_graph, model, args, input_names=None, verbose=False):
     [67](file:///home/ubuntu/py38/lib/python3.8/site-packages/hiddenlayer/pytorch_builder.py?line=66)     # TODO: add input names to graph
     [68](file:///home/ubuntu/py38/lib/python3.8/site-packages/hiddenlayer/pytorch_builder.py?line=67) 
     [69](file:///home/ubuntu/py38/lib/python3.8/site-packages/hiddenlayer/pytorch_builder.py?line=68)     # Run the Pytorch graph to get a trace and generate a graph from it
     [70](file:///home/ubuntu/py38/lib/python3.8/site-packages/hiddenlayer/pytorch_builder.py?line=69)     trace, out = torch.jit._get_trace_graph(model, args)
---> [71](file:///home/ubuntu/py38/lib/python3.8/site-packages/hiddenlayer/pytorch_builder.py?line=70)     torch_graph = torch.onnx._optimize_trace(trace, torch.onnx.OperatorExportTypes.ONNX)
     [73](file:///home/ubuntu/py38/lib/python3.8/site-packages/hiddenlayer/pytorch_builder.py?line=72)     # Dump list of nodes (DEBUG only)
     [74](file:///home/ubuntu/py38/lib/python3.8/site-packages/hiddenlayer/pytorch_builder.py?line=73)     if verbose:

File ~/py38/lib/python3.8/site-packages/torch/onnx/__init__.py:394, in _optimize_trace(graph, operator_export_type)
    [391](file:///home/ubuntu/py38/lib/python3.8/site-packages/torch/onnx/__init__.py?line=390) def _optimize_trace(graph, operator_export_type):
    [392](file:///home/ubuntu/py38/lib/python3.8/site-packages/torch/onnx/__init__.py?line=391)     from torch.onnx import utils
--> [394](file:///home/ubuntu/py38/lib/python3.8/site-packages/torch/onnx/__init__.py?line=393)     return utils._optimize_graph(graph, operator_export_type)

File ~/py38/lib/python3.8/site-packages/torch/onnx/utils.py:276, in _optimize_graph(graph, operator_export_type, _disable_torch_constant_prop, fixed_batch_size, params_dict, dynamic_axes, input_names, module)
    [274](file:///home/ubuntu/py38/lib/python3.8/site-packages/torch/onnx/utils.py?line=273) symbolic_helper._quantized_ops.clear()
    [275](file:///home/ubuntu/py38/lib/python3.8/site-packages/torch/onnx/utils.py?line=274) # Unpack quantized weights for conv and linear ops and insert into graph.
--> [276](file:///home/ubuntu/py38/lib/python3.8/site-packages/torch/onnx/utils.py?line=275) _C._jit_pass_onnx_unpack_quantized_weights(
    [277](file:///home/ubuntu/py38/lib/python3.8/site-packages/torch/onnx/utils.py?line=276)     graph, params_dict, symbolic_helper.is_caffe2_aten_fallback()
    [278](file:///home/ubuntu/py38/lib/python3.8/site-packages/torch/onnx/utils.py?line=277) )
    [279](file:///home/ubuntu/py38/lib/python3.8/site-packages/torch/onnx/utils.py?line=278) if symbolic_helper.is_caffe2_aten_fallback():
    [280](file:///home/ubuntu/py38/lib/python3.8/site-packages/torch/onnx/utils.py?line=279)     # Insert permutes before and after each conv op to ensure correct order.
    [281](file:///home/ubuntu/py38/lib/python3.8/site-packages/torch/onnx/utils.py?line=280)     _C._jit_pass_onnx_quantization_insert_permutes(graph, params_dict)

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(%0 : Float(1, 10, strides=[10, 1], requires_grad=0, device=cpu),
      %1 : Float(10, 10, strides=[10, 1], requires_grad=1, device=cpu),
      %2 : Float(10, strides=[1], requires_grad=1, device=cpu),
      %3 : Float(2, 10, strides=[10, 1], requires_grad=1, device=cpu),
      %4 : Float(2, strides=[1], requires_grad=1, device=cpu)):
  %15 : Float(1, 10, strides=[10, 1], requires_grad=1, device=cpu) = aten::linear(%0, %1, %2) # /home/ubuntu/py38/lib/python3.8/site-packages/torch/nn/modules/linear.py:114:0
  %16 : Float(1, 2, strides=[2, 1], requires_grad=1, device=cpu) = aten::linear(%15, %3, %4) # /home/ubuntu/py38/lib/python3.8/site-packages/torch/nn/modules/linear.py:114:0
  return (%16)
, None, False

runtime:

ubuntu 20.04, python 3.8, torch 1.13.0 (experimental), hiddenlayer 0.3

script to reproduce the error:

import hiddenlayer as hl
import torch
import torch.nn as nn
import torchvision.models

#model = torchvision.models.vgg16()

model = torch.nn.Sequential(
    nn.Linear(10, 10),
    nn.Linear(10, 2)
)

hl.build_graph(model, torch.zeros([1, 10]))
@julien-blanchon
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julien-blanchon commented Jun 23, 2022 •
edited
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Same, It may be torch 1.13.0

julien-blanchon added a commit to julien-blanchon/hiddenlayer that referenced this issue Jun 23, 2022
@julien-blanchon
Fix TypeError for Pytorch Model waleedka#94
8e14b20
@julien-blanchon julien-blanchon mentioned this issue Jun 23, 2022
Closed
@simone-viozzi
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simone-viozzi commented Aug 10, 2022 •
edited
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torch 1.12.1+cu116
hiddenlayer 0.3

same error with code:

# VGG16 with BatchNorm
model = torchvision.models.vgg16()

# Build HiddenLayer graph
# Jupyter Notebook renders it automatically
hl.build_graph(model, torch.zeros([1, 3, 224, 224]))
TypeError                                 Traceback (most recent call last)
/home/simone/workspace/cloe_ai/deep-learning-sandbox/pytorch/hiddenlayer_tests.ipynb Cell 3 in <cell line: 6>()
      [2](vscode-notebook-cell:/home/simone/workspace/cloe_ai/deep-learning-sandbox/pytorch/hiddenlayer_tests.ipynb#ch0000002?line=1) model = torchvision.models.vgg16()
      [4](vscode-notebook-cell:/home/simone/workspace/cloe_ai/deep-learning-sandbox/pytorch/hiddenlayer_tests.ipynb#ch0000002?line=3) # Build HiddenLayer graph
      [5](vscode-notebook-cell:/home/simone/workspace/cloe_ai/deep-learning-sandbox/pytorch/hiddenlayer_tests.ipynb#ch0000002?line=4) # Jupyter Notebook renders it automatically
----> [6](vscode-notebook-cell:/home/simone/workspace/cloe_ai/deep-learning-sandbox/pytorch/hiddenlayer_tests.ipynb#ch0000002?line=5) hl.build_graph(model, torch.zeros([1, 3, 224, 224]))

File ~/workspace/cloe_ai/deep-learning-sandbox/.direnv/python-3.8.12/lib/python3.8/site-packages/hiddenlayer/graph.py:143, in build_graph(model, args, input_names, transforms, framework_transforms)
    141     from .pytorch_builder import import_graph, FRAMEWORK_TRANSFORMS
    142     assert args is not None, "Argument args must be provided for Pytorch models."
--> 143     import_graph(g, model, args)
    144 elif framework == "tensorflow":
    145     from .tf_builder import import_graph, FRAMEWORK_TRANSFORMS

File ~/workspace/cloe_ai/deep-learning-sandbox/.direnv/python-3.8.12/lib/python3.8/site-packages/hiddenlayer/pytorch_builder.py:71, in import_graph(hl_graph, model, args, input_names, verbose)
     66 def import_graph(hl_graph, model, args, input_names=None, verbose=False):
     67     # TODO: add input names to graph
     68 
     69     # Run the Pytorch graph to get a trace and generate a graph from it
     70     trace, out = torch.jit._get_trace_graph(model, args)
---> 71     torch_graph = torch.onnx._optimize_trace(trace, torch.onnx.OperatorExportTypes.ONNX)
     73     # Dump list of nodes (DEBUG only)
     74     if verbose:

File ~/workspace/cloe_ai/deep-learning-sandbox/.direnv/python-3.8.12/lib/python3.8/site-packages/torch/onnx/__init__.py:394, in _optimize_trace(graph, operator_export_type)
    391 def _optimize_trace(graph, operator_export_type):
    392     from torch.onnx import utils
--> 394     return utils._optimize_graph(graph, operator_export_type)

File ~/workspace/cloe_ai/deep-learning-sandbox/.direnv/python-3.8.12/lib/python3.8/site-packages/torch/onnx/utils.py:277, in _optimize_graph(graph, operator_export_type, _disable_torch_constant_prop, fixed_batch_size, params_dict, dynamic_axes, input_names, module)
    275 symbolic_helper._quantized_ops.clear()
    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():
    281     # Insert permutes before and after each conv op to ensure correct order.
    282     _C._jit_pass_onnx_quantization_insert_permutes(graph, params_dict)

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::Constant[value=[1, 1]]()
  %460 : int[] = prim::Constant[value=[1, 1]]()
  %461 : int[] = prim::Constant[value=[1, 1]]()
  %108 : bool = prim::Constant[value=0]() # /home/simone/workspace/cloe_ai/deep-learning-sandbox/.direnv/python-3.8.12/lib/python3.8/site-packages/torch/nn/modules/conv.py:453:0
  %462 : int[] = prim::Constant[value=[0, 0]]()
  %112 : int = prim::Constant[value=1]() # /home/simone/workspace/cloe_ai/deep-learning-sandbox/.direnv/python-3.8.12/lib/python3.8/site-packages/torch/nn/modules/conv.py:453:0
  %113 : bool = prim::Constant[value=0]() # /home/simone/workspace/cloe_ai/deep-learning-sandbox/.direnv/python-3.8.12/lib/python3.8/site-packages/torch/nn/modules/conv.py:453:0
  %114 : bool = prim::Constant[value=0]() # /home/simone/workspace/cloe_ai/deep-learning-sandbox/.direnv/python-3.8.12/lib/python3.8/site-packages/torch/nn/modules/conv.py:453:0
  %115 : bool = prim::Constant[value=1]() # /home/simone/workspace/cloe_ai/deep-learning-sandbox/.direnv/python-3.8.12/lib/python3.8/site-packages/torch/nn/modules/conv.py:453:0
  %116 : bool = prim::Constant[value=1]() # /home/simone/workspace/cloe_ai/deep-learning-sandbox/.direnv/python-3.8.12/lib/python3.8/site-packages/torch/nn/modules/conv.py:453:0
  %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) # /home/simone/workspace/cloe_ai/deep-learning-sandbox/.direnv/python-3.8.12/lib/python3.8/site-packages/torch/nn/modules/conv.py:453:0
  %532 : Float(1, 64, 224, 224, strides=[3211264, 50176, 224, 1], requires_grad=1, device=cpu) = aten::relu(%input.3) # /home/simone/workspace/cloe_ai/deep-learning-sandbox/.direnv/python-3.8.12/lib/python3.8/site-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]() # /home/simone/workspace/cloe_ai/deep-learning-sandbox/.direnv/python-3.8.12/lib/python3.8/site-packages/torch/nn/modules/conv.py:453:0
  %466 : int[] = prim::Constant[value=[0, 0]]()
  %132 : int = prim::Constant[value=1]() # /home/simone/workspace/cloe_ai/deep-learning-sandbox/.direnv/python-3.8.12/lib/python3.8/site-packages/torch/nn/modules/conv.py:453:0
  %133 : bool = prim::Constant[value=0]() # /home/simone/workspace/cloe_ai/deep-learning-sandbox/.direnv/python-3.8.12/lib/python3.8/site-packages/torch/nn/modules/conv.py:453:0
  %134 : bool = prim::Constant[value=0]() # /home/simone/workspace/cloe_ai/deep-learning-sandbox/.direnv/python-3.8.12/lib/python3.8/site-packages/torch/nn/modules/conv.py:453:0
  %135 : bool = prim::Constant[value=1]() # /home/simone/workspace/cloe_ai/deep-learning-sandbox/.direnv/python-3.8.12/lib/python3.8/site-packages/torch/nn/modules/conv.py:453:0
  %136 : bool = prim::Constant[value=1]() # /home/simone/workspace/cloe_ai/deep-learning-sandbox/.direnv/python-3.8.12/lib/python3.8/site-packages/torch/nn/modules/conv.py:453:0
  %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) # /home/simone/workspace/cloe_ai/deep-learning-sandbox/.direnv/python-3.8.12/lib/python3.8/site-packages/torch/nn/modules/conv.py:453:0
  %533 : Float(1, 64, 224, 224, strides=[3211264, 50176, 224, 1], requires_grad=1, device=cpu) = aten::relu(%input.7) # /home/simone/workspace/cloe_ai/deep-learning-sandbox/.direnv/python-3.8.12/lib/python3.8/site-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]() # /home/simone/workspace/cloe_ai/deep-learning-sandbox/.direnv/python-3.8.12/lib/python3.8/site-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) # /home/simone/workspace/cloe_ai/deep-learning-sandbox/.direnv/python-3.8.12/lib/python3.8/site-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]() # /home/simone/workspace/cloe_ai/deep-learning-sandbox/.direnv/python-3.8.12/lib/python3.8/site-packages/torch/nn/modules/conv.py:453:0
  %474 : int[] = prim::Constant[value=[0, 0]]()
  %166 : int = prim::Constant[value=1]() # /home/simone/workspace/cloe_ai/deep-learning-sandbox/.direnv/python-3.8.12/lib/python3.8/site-packages/torch/nn/modules/conv.py:453:0
  %167 : bool = prim::Constant[value=0]() # /home/simone/workspace/cloe_ai/deep-learning-sandbox/.direnv/python-3.8.12/lib/python3.8/site-packages/torch/nn/modules/conv.py:453:0
  %168 : bool = prim::Constant[value=0]() # /home/simone/workspace/cloe_ai/deep-learning-sandbox/.direnv/python-3.8.12/lib/python3.8/site-packages/torch/nn/modules/conv.py:453:0
  %169 : bool = prim::Constant[value=1]() # /home/simone/workspace/cloe_ai/deep-learning-sandbox/.direnv/python-3.8.12/lib/python3.8/site-packages/torch/nn/modules/conv.py:453:0
  %170 : bool = prim::Constant[value=1]() # /home/simone/workspace/cloe_ai/deep-learning-sandbox/.direnv/python-3.8.12/lib/python3.8/site-packages/torch/nn/modules/conv.py:453:0
  %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) # /home/simone/workspace/cloe_ai/deep-learning-sandbox/.direnv/python-3.8.12/lib/python3.8/site-packages/torch/nn/modules/conv.py:453:0
  %534 : Float(1, 128, 112, 112, strides=[1605632, 12544, 112, 1], requires_grad=1, device=cpu) = aten::relu(%input.11) # /home/simone/workspace/cloe_ai/deep-learning-sandbox/.direnv/python-3.8.12/lib/python3.8/site-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]() # /home/simone/workspace/cloe_ai/deep-learning-sandbox/.direnv/python-3.8.12/lib/python3.8/site-packages/torch/nn/modules/conv.py:453:0
  %478 : int[] = prim::Constant[value=[0, 0]]()
  %186 : int = prim::Constant[value=1]() # /home/simone/workspace/cloe_ai/deep-learning-sandbox/.direnv/python-3.8.12/lib/python3.8/site-packages/torch/nn/modules/conv.py:453:0
  %187 : bool = prim::Constant[value=0]() # /home/simone/workspace/cloe_ai/deep-learning-sandbox/.direnv/python-3.8.12/lib/python3.8/site-packages/torch/nn/modules/conv.py:453:0
  %188 : bool = prim::Constant[value=0]() # /home/simone/workspace/cloe_ai/deep-learning-sandbox/.direnv/python-3.8.12/lib/python3.8/site-packages/torch/nn/modules/conv.py:453:0
  %189 : bool = prim::Constant[value=1]() # /home/simone/workspace/cloe_ai/deep-learning-sandbox/.direnv/python-3.8.12/lib/python3.8/site-packages/torch/nn/modules/conv.py:453:0
  %190 : bool = prim::Constant[value=1]() # /home/simone/workspace/cloe_ai/deep-learning-sandbox/.direnv/python-3.8.12/lib/python3.8/site-packages/torch/nn/modules/conv.py:453:0
  %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) # /home/simone/workspace/cloe_ai/deep-learning-sandbox/.direnv/python-3.8.12/lib/python3.8/site-packages/torch/nn/modules/conv.py:453:0
  %535 : Float(1, 128, 112, 112, strides=[1605632, 12544, 112, 1], requires_grad=1, device=cpu) = aten::relu(%input.15) # /home/simone/workspace/cloe_ai/deep-learning-sandbox/.direnv/python-3.8.12/lib/python3.8/site-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]() # /home/simone/workspace/cloe_ai/deep-learning-sandbox/.direnv/python-3.8.12/lib/python3.8/site-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) # /home/simone/workspace/cloe_ai/deep-learning-sandbox/.direnv/python-3.8.12/lib/python3.8/site-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]() # /home/simone/workspace/cloe_ai/deep-learning-sandbox/.direnv/python-3.8.12/lib/python3.8/site-packages/torch/nn/modules/conv.py:453:0
  %486 : int[] = prim::Constant[value=[0, 0]]()
  %220 : int = prim::Constant[value=1]() # /home/simone/workspace/cloe_ai/deep-learning-sandbox/.direnv/python-3.8.12/lib/python3.8/site-packages/torch/nn/modules/conv.py:453:0
  %221 : bool = prim::Constant[value=0]() # /home/simone/workspace/cloe_ai/deep-learning-sandbox/.direnv/python-3.8.12/lib/python3.8/site-packages/torch/nn/modules/conv.py:453:0
  %222 : bool = prim::Constant[value=0]() # /home/simone/workspace/cloe_ai/deep-learning-sandbox/.direnv/python-3.8.12/lib/python3.8/site-packages/torch/nn/modules/conv.py:453:0
  %223 : bool = prim::Constant[value=1]() # /home/simone/workspace/cloe_ai/deep-learning-sandbox/.direnv/python-3.8.12/lib/python3.8/site-packages/torch/nn/modules/conv.py:453:0
  %224 : bool = prim::Constant[value=1]() # /home/simone/workspace/cloe_ai/deep-learning-sandbox/.direnv/python-3.8.12/lib/python3.8/site-packages/torch/nn/modules/conv.py:453:0
  %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) # /home/simone/workspace/cloe_ai/deep-learning-sandbox/.direnv/python-3.8.12/lib/python3.8/site-packages/torch/nn/modules/conv.py:453:0
  %536 : Float(1, 256, 56, 56, strides=[802816, 3136, 56, 1], requires_grad=1, device=cpu) = aten::relu(%input.19) # /home/simone/workspace/cloe_ai/deep-learning-sandbox/.direnv/python-3.8.12/lib/python3.8/site-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]() # /home/simone/workspace/cloe_ai/deep-learning-sandbox/.direnv/python-3.8.12/lib/python3.8/site-packages/torch/nn/modules/conv.py:453:0
  %490 : int[] = prim::Constant[value=[0, 0]]()
  %240 : int = prim::Constant[value=1]() # /home/simone/workspace/cloe_ai/deep-learning-sandbox/.direnv/python-3.8.12/lib/python3.8/site-packages/torch/nn/modules/conv.py:453:0
  %241 : bool = prim::Constant[value=0]() # /home/simone/workspace/cloe_ai/deep-learning-sandbox/.direnv/python-3.8.12/lib/python3.8/site-packages/torch/nn/modules/conv.py:453:0
  %242 : bool = prim::Constant[value=0]() # /home/simone/workspace/cloe_ai/deep-learning-sandbox/.direnv/python-3.8.12/lib/python3.8/site-packages/torch/nn/modules/conv.py:453:0
  %243 : bool = prim::Constant[value=1]() # /home/simone/workspace/cloe_ai/deep-learning-sandbox/.direnv/python-3.8.12/lib/python3.8/site-packages/torch/nn/modules/conv.py:453:0
  %244 : bool = prim::Constant[value=1]() # /home/simone/workspace/cloe_ai/deep-learning-sandbox/.direnv/python-3.8.12/lib/python3.8/site-packages/torch/nn/modules/conv.py:453:0
  %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) # /home/simone/workspace/cloe_ai/deep-learning-sandbox/.direnv/python-3.8.12/lib/python3.8/site-packages/torch/nn/modules/conv.py:453:0
  %537 : Float(1, 256, 56, 56, strides=[802816, 3136, 56, 1], requires_grad=1, device=cpu) = aten::relu(%input.23) # /home/simone/workspace/cloe_ai/deep-learning-sandbox/.direnv/python-3.8.12/lib/python3.8/site-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]() # /home/simone/workspace/cloe_ai/deep-learning-sandbox/.direnv/python-3.8.12/lib/python3.8/site-packages/torch/nn/modules/conv.py:453:0
  %494 : int[] = prim::Constant[value=[0, 0]]()
  %260 : int = prim::Constant[value=1]() # /home/simone/workspace/cloe_ai/deep-learning-sandbox/.direnv/python-3.8.12/lib/python3.8/site-packages/torch/nn/modules/conv.py:453:0
  %261 : bool = prim::Constant[value=0]() # /home/simone/workspace/cloe_ai/deep-learning-sandbox/.direnv/python-3.8.12/lib/python3.8/site-packages/torch/nn/modules/conv.py:453:0
  %262 : bool = prim::Constant[value=0]() # /home/simone/workspace/cloe_ai/deep-learning-sandbox/.direnv/python-3.8.12/lib/python3.8/site-packages/torch/nn/modules/conv.py:453:0
  %263 : bool = prim::Constant[value=1]() # /home/simone/workspace/cloe_ai/deep-learning-sandbox/.direnv/python-3.8.12/lib/python3.8/site-packages/torch/nn/modules/conv.py:453:0
  %264 : bool = prim::Constant[value=1]() # /home/simone/workspace/cloe_ai/deep-learning-sandbox/.direnv/python-3.8.12/lib/python3.8/site-packages/torch/nn/modules/conv.py:453:0
  %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) # /home/simone/workspace/cloe_ai/deep-learning-sandbox/.direnv/python-3.8.12/lib/python3.8/site-packages/torch/nn/modules/conv.py:453:0
  %538 : Float(1, 256, 56, 56, strides=[802816, 3136, 56, 1], requires_grad=1, device=cpu) = aten::relu(%input.27) # /home/simone/workspace/cloe_ai/deep-learning-sandbox/.direnv/python-3.8.12/lib/python3.8/site-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]() # /home/simone/workspace/cloe_ai/deep-learning-sandbox/.direnv/python-3.8.12/lib/python3.8/site-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) # /home/simone/workspace/cloe_ai/deep-learning-sandbox/.direnv/python-3.8.12/lib/python3.8/site-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]() # /home/simone/workspace/cloe_ai/deep-learning-sandbox/.direnv/python-3.8.12/lib/python3.8/site-packages/torch/nn/modules/conv.py:453:0
  %502 : int[] = prim::Constant[value=[0, 0]]()
  %294 : int = prim::Constant[value=1]() # /home/simone/workspace/cloe_ai/deep-learning-sandbox/.direnv/python-3.8.12/lib/python3.8/site-packages/torch/nn/modules/conv.py:453:0
  %295 : bool = prim::Constant[value=0]() # /home/simone/workspace/cloe_ai/deep-learning-sandbox/.direnv/python-3.8.12/lib/python3.8/site-packages/torch/nn/modules/conv.py:453:0
  %296 : bool = prim::Constant[value=0]() # /home/simone/workspace/cloe_ai/deep-learning-sandbox/.direnv/python-3.8.12/lib/python3.8/site-packages/torch/nn/modules/conv.py:453:0
  %297 : bool = prim::Constant[value=1]() # /home/simone/workspace/cloe_ai/deep-learning-sandbox/.direnv/python-3.8.12/lib/python3.8/site-packages/torch/nn/modules/conv.py:453:0
  %298 : bool = prim::Constant[value=1]() # /home/simone/workspace/cloe_ai/deep-learning-sandbox/.direnv/python-3.8.12/lib/python3.8/site-packages/torch/nn/modules/conv.py:453:0
  %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) # /home/simone/workspace/cloe_ai/deep-learning-sandbox/.direnv/python-3.8.12/lib/python3.8/site-packages/torch/nn/modules/conv.py:453:0
  %539 : Float(1, 512, 28, 28, strides=[401408, 784, 28, 1], requires_grad=1, device=cpu) = aten::relu(%input.31) # /home/simone/workspace/cloe_ai/deep-learning-sandbox/.direnv/python-3.8.12/lib/python3.8/site-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]() # /home/simone/workspace/cloe_ai/deep-learning-sandbox/.direnv/python-3.8.12/lib/python3.8/site-packages/torch/nn/modules/conv.py:453:0
  %506 : int[] = prim::Constant[value=[0, 0]]()
  %314 : int = prim::Constant[value=1]() # /home/simone/workspace/cloe_ai/deep-learning-sandbox/.direnv/python-3.8.12/lib/python3.8/site-packages/torch/nn/modules/conv.py:453:0
  %315 : bool = prim::Constant[value=0]() # /home/simone/workspace/cloe_ai/deep-learning-sandbox/.direnv/python-3.8.12/lib/python3.8/site-packages/torch/nn/modules/conv.py:453:0
  %316 : bool = prim::Constant[value=0]() # /home/simone/workspace/cloe_ai/deep-learning-sandbox/.direnv/python-3.8.12/lib/python3.8/site-packages/torch/nn/modules/conv.py:453:0
  %317 : bool = prim::Constant[value=1]() # /home/simone/workspace/cloe_ai/deep-learning-sandbox/.direnv/python-3.8.12/lib/python3.8/site-packages/torch/nn/modules/conv.py:453:0
  %318 : bool = prim::Constant[value=1]() # /home/simone/workspace/cloe_ai/deep-learning-sandbox/.direnv/python-3.8.12/lib/python3.8/site-packages/torch/nn/modules/conv.py:453:0
  %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) # /home/simone/workspace/cloe_ai/deep-learning-sandbox/.direnv/python-3.8.12/lib/python3.8/site-packages/torch/nn/modules/conv.py:453:0
  %540 : Float(1, 512, 28, 28, strides=[401408, 784, 28, 1], requires_grad=1, device=cpu) = aten::relu(%input.35) # /home/simone/workspace/cloe_ai/deep-learning-sandbox/.direnv/python-3.8.12/lib/python3.8/site-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]() # /home/simone/workspace/cloe_ai/deep-learning-sandbox/.direnv/python-3.8.12/lib/python3.8/site-packages/torch/nn/modules/conv.py:453:0
  %510 : int[] = prim::Constant[value=[0, 0]]()
  %334 : int = prim::Constant[value=1]() # /home/simone/workspace/cloe_ai/deep-learning-sandbox/.direnv/python-3.8.12/lib/python3.8/site-packages/torch/nn/modules/conv.py:453:0
  %335 : bool = prim::Constant[value=0]() # /home/simone/workspace/cloe_ai/deep-learning-sandbox/.direnv/python-3.8.12/lib/python3.8/site-packages/torch/nn/modules/conv.py:453:0
  %336 : bool = prim::Constant[value=0]() # /home/simone/workspace/cloe_ai/deep-learning-sandbox/.direnv/python-3.8.12/lib/python3.8/site-packages/torch/nn/modules/conv.py:453:0
  %337 : bool = prim::Constant[value=1]() # /home/simone/workspace/cloe_ai/deep-learning-sandbox/.direnv/python-3.8.12/lib/python3.8/site-packages/torch/nn/modules/conv.py:453:0
  %338 : bool = prim::Constant[value=1]() # /home/simone/workspace/cloe_ai/deep-learning-sandbox/.direnv/python-3.8.12/lib/python3.8/site-packages/torch/nn/modules/conv.py:453:0
  %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) # /home/simone/workspace/cloe_ai/deep-learning-sandbox/.direnv/python-3.8.12/lib/python3.8/site-packages/torch/nn/modules/conv.py:453:0
  %541 : Float(1, 512, 28, 28, strides=[401408, 784, 28, 1], requires_grad=1, device=cpu) = aten::relu(%input.39) # /home/simone/workspace/cloe_ai/deep-learning-sandbox/.direnv/python-3.8.12/lib/python3.8/site-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]() # /home/simone/workspace/cloe_ai/deep-learning-sandbox/.direnv/python-3.8.12/lib/python3.8/site-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) # /home/simone/workspace/cloe_ai/deep-learning-sandbox/.direnv/python-3.8.12/lib/python3.8/site-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]() # /home/simone/workspace/cloe_ai/deep-learning-sandbox/.direnv/python-3.8.12/lib/python3.8/site-packages/torch/nn/modules/conv.py:453:0
  %518 : int[] = prim::Constant[value=[0, 0]]()
  %368 : int = prim::Constant[value=1]() # /home/simone/workspace/cloe_ai/deep-learning-sandbox/.direnv/python-3.8.12/lib/python3.8/site-packages/torch/nn/modules/conv.py:453:0
  %369 : bool = prim::Constant[value=0]() # /home/simone/workspace/cloe_ai/deep-learning-sandbox/.direnv/python-3.8.12/lib/python3.8/site-packages/torch/nn/modules/conv.py:453:0
  %370 : bool = prim::Constant[value=0]() # /home/simone/workspace/cloe_ai/deep-learning-sandbox/.direnv/python-3.8.12/lib/python3.8/site-packages/torch/nn/modules/conv.py:453:0
  %371 : bool = prim::Constant[value=1]() # /home/simone/workspace/cloe_ai/deep-learning-sandbox/.direnv/python-3.8.12/lib/python3.8/site-packages/torch/nn/modules/conv.py:453:0
  %372 : bool = prim::Constant[value=1]() # /home/simone/workspace/cloe_ai/deep-learning-sandbox/.direnv/python-3.8.12/lib/python3.8/site-packages/torch/nn/modules/conv.py:453:0
  %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) # /home/simone/workspace/cloe_ai/deep-learning-sandbox/.direnv/python-3.8.12/lib/python3.8/site-packages/torch/nn/modules/conv.py:453:0
  %542 : Float(1, 512, 14, 14, strides=[100352, 196, 14, 1], requires_grad=1, device=cpu) = aten::relu(%input.43) # /home/simone/workspace/cloe_ai/deep-learning-sandbox/.direnv/python-3.8.12/lib/python3.8/site-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]() # /home/simone/workspace/cloe_ai/deep-learning-sandbox/.direnv/python-3.8.12/lib/python3.8/site-packages/torch/nn/modules/conv.py:453:0
  %522 : int[] = prim::Constant[value=[0, 0]]()
  %388 : int = prim::Constant[value=1]() # /home/simone/workspace/cloe_ai/deep-learning-sandbox/.direnv/python-3.8.12/lib/python3.8/site-packages/torch/nn/modules/conv.py:453:0
  %389 : bool = prim::Constant[value=0]() # /home/simone/workspace/cloe_ai/deep-learning-sandbox/.direnv/python-3.8.12/lib/python3.8/site-packages/torch/nn/modules/conv.py:453:0
  %390 : bool = prim::Constant[value=0]() # /home/simone/workspace/cloe_ai/deep-learning-sandbox/.direnv/python-3.8.12/lib/python3.8/site-packages/torch/nn/modules/conv.py:453:0
  %391 : bool = prim::Constant[value=1]() # /home/simone/workspace/cloe_ai/deep-learning-sandbox/.direnv/python-3.8.12/lib/python3.8/site-packages/torch/nn/modules/conv.py:453:0
  %392 : bool = prim::Constant[value=1]() # /home/simone/workspace/cloe_ai/deep-learning-sandbox/.direnv/python-3.8.12/lib/python3.8/site-packages/torch/nn/modules/conv.py:453:0
  %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) # /home/simone/workspace/cloe_ai/deep-learning-sandbox/.direnv/python-3.8.12/lib/python3.8/site-packages/torch/nn/modules/conv.py:453:0
  %543 : Float(1, 512, 14, 14, strides=[100352, 196, 14, 1], requires_grad=1, device=cpu) = aten::relu(%input.47) # /home/simone/workspace/cloe_ai/deep-learning-sandbox/.direnv/python-3.8.12/lib/python3.8/site-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]() # /home/simone/workspace/cloe_ai/deep-learning-sandbox/.direnv/python-3.8.12/lib/python3.8/site-packages/torch/nn/modules/conv.py:453:0
  %526 : int[] = prim::Constant[value=[0, 0]]()
  %408 : int = prim::Constant[value=1]() # /home/simone/workspace/cloe_ai/deep-learning-sandbox/.direnv/python-3.8.12/lib/python3.8/site-packages/torch/nn/modules/conv.py:453:0
  %409 : bool = prim::Constant[value=0]() # /home/simone/workspace/cloe_ai/deep-learning-sandbox/.direnv/python-3.8.12/lib/python3.8/site-packages/torch/nn/modules/conv.py:453:0
  %410 : bool = prim::Constant[value=0]() # /home/simone/workspace/cloe_ai/deep-learning-sandbox/.direnv/python-3.8.12/lib/python3.8/site-packages/torch/nn/modules/conv.py:453:0
  %411 : bool = prim::Constant[value=1]() # /home/simone/workspace/cloe_ai/deep-learning-sandbox/.direnv/python-3.8.12/lib/python3.8/site-packages/torch/nn/modules/conv.py:453:0
  %412 : bool = prim::Constant[value=1]() # /home/simone/workspace/cloe_ai/deep-learning-sandbox/.direnv/python-3.8.12/lib/python3.8/site-packages/torch/nn/modules/conv.py:453:0
  %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) # /home/simone/workspace/cloe_ai/deep-learning-sandbox/.direnv/python-3.8.12/lib/python3.8/site-packages/torch/nn/modules/conv.py:453:0
  %544 : Float(1, 512, 14, 14, strides=[100352, 196, 14, 1], requires_grad=1, device=cpu) = aten::relu(%input.51) # /home/simone/workspace/cloe_ai/deep-learning-sandbox/.direnv/python-3.8.12/lib/python3.8/site-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]() # /home/simone/workspace/cloe_ai/deep-learning-sandbox/.direnv/python-3.8.12/lib/python3.8/site-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) # /home/simone/workspace/cloe_ai/deep-learning-sandbox/.direnv/python-3.8.12/lib/python3.8/site-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) # /home/simone/workspace/cloe_ai/deep-learning-sandbox/.direnv/python-3.8.12/lib/python3.8/site-packages/torch/nn/functional.py:1214:0
  %445 : int = prim::Constant[value=1]() # /home/simone/workspace/cloe_ai/deep-learning-sandbox/.direnv/python-3.8.12/lib/python3.8/site-packages/torchvision/models/vgg.py:68:0
  %446 : int = prim::Constant[value=-1]() # /home/simone/workspace/cloe_ai/deep-learning-sandbox/.direnv/python-3.8.12/lib/python3.8/site-packages/torchvision/models/vgg.py:68:0
  %447 : Float(1, 25088, strides=[25088, 1], requires_grad=1, device=cpu) = aten::flatten(%444, %445, %446) # /home/simone/workspace/cloe_ai/deep-learning-sandbox/.direnv/python-3.8.12/lib/python3.8/site-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) # /home/simone/workspace/cloe_ai/deep-learning-sandbox/.direnv/python-3.8.12/lib/python3.8/site-packages/torch/nn/modules/linear.py:114:0
  %545 : Float(1, 4096, strides=[4096, 1], requires_grad=1, device=cpu) = aten::relu(%input.55) # /home/simone/workspace/cloe_ai/deep-learning-sandbox/.direnv/python-3.8.12/lib/python3.8/site-packages/torch/nn/functional.py:1455:0
  %450 : float = prim::Constant[value=0.5]() # /home/simone/workspace/cloe_ai/deep-learning-sandbox/.direnv/python-3.8.12/lib/python3.8/site-packages/torch/nn/functional.py:1252:0
  %451 : bool = prim::Constant[value=1]() # /home/simone/workspace/cloe_ai/deep-learning-sandbox/.direnv/python-3.8.12/lib/python3.8/site-packages/torch/nn/functional.py:1252:0
  %452 : Float(1, 4096, strides=[4096, 1], requires_grad=1, device=cpu) = aten::dropout(%545, %450, %451) # /home/simone/workspace/cloe_ai/deep-learning-sandbox/.direnv/python-3.8.12/lib/python3.8/site-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) # /home/simone/workspace/cloe_ai/deep-learning-sandbox/.direnv/python-3.8.12/lib/python3.8/site-packages/torch/nn/modules/linear.py:114:0
  %546 : Float(1, 4096, strides=[4096, 1], requires_grad=1, device=cpu) = aten::relu(%input.59) # /home/simone/workspace/cloe_ai/deep-learning-sandbox/.direnv/python-3.8.12/lib/python3.8/site-packages/torch/nn/functional.py:1455:0
  %455 : float = prim::Constant[value=0.5]() # /home/simone/workspace/cloe_ai/deep-learning-sandbox/.direnv/python-3.8.12/lib/python3.8/site-packages/torch/nn/functional.py:1252:0
  %456 : bool = prim::Constant[value=1]() # /home/simone/workspace/cloe_ai/deep-learning-sandbox/.direnv/python-3.8.12/lib/python3.8/site-packages/torch/nn/functional.py:1252:0
  %457 : Float(1, 4096, strides=[4096, 1], requires_grad=1, device=cpu) = aten::dropout(%546, %455, %456) # /home/simone/workspace/cloe_ai/deep-learning-sandbox/.direnv/python-3.8.12/lib/python3.8/site-packages/torch/nn/functional.py:1252:0
  %458 : Float(1, 1000, strides=[1000, 1], requires_grad=1, device=cpu) = aten::linear(%457, %31, %32) # /home/simone/workspace/cloe_ai/deep-learning-sandbox/.direnv/python-3.8.12/lib/python3.8/site-packages/torch/nn/modules/linear.py:114:0
  return (%458)
, None, False

@SamWangGibhub
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I'm meeting a similar problem. How to fix it?
Thanks.

@hitbuyi
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hitbuyi commented Oct 18, 2022

same error , what's the remedy?

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Fix TypeError for Pytorch Model #94 julien-blanchon/hiddenlayer
5 participants
@hitbuyi @julien-blanchon @simone-viozzi @hongshanli23 @SamWangGibhub