latest version of onnx or torch fails pytest: pip install onnx onnxruntime --upgrade produces Successfully installed onnx-1.10.2 onnxruntime-1.9.0

which fails the pipeline

================================================================================================================================== test session starts ===================================================================================================================================
platform linux -- Python 3.9.7, pytest-6.2.5, py-1.11.0, pluggy-1.0.0
rootdir: <me>/Documents/travail/programs/onnx-pytorch
plugins: dash-2.0.0
collected 88 items                                                                                                                                                                                                                                                                       

onnx_pytorch/tests/test_base.py .F.................F..................s.................................................                                                                                                                                                           [100%]

======================================================================================================================================== FAILURES ========================================================================================================================================
_________________________________________________________________________________________________________________ TestBase.test_conv_batchnorm_maxpool_flatten_add_relu __________________________________________________________________________________________________________________

self = <onnx_pytorch.tests.test_base.TestBase object at 0x7fce8a666880>

    def test_conv_batchnorm_maxpool_flatten_add_relu(self):
      reset_model(13)
      nps = [np.random.randn(1, 3, 224, 224).astype(np.float32)]
      inputs = Input(*nps)
      conv_node = Conv(inputs[0],
                       np.random.randn(32, 3, 3, 3).astype(np.float32),
                       np.random.randn(32).astype(np.float32))
      bn_node = BatchNormalization(
          conv_node,
          np.ones(32,).astype(np.float32),
          np.zeros(32,).astype(np.float32),
          np.random.randn(32).astype(np.float32),
          np.abs(np.random.randn(32).astype(np.float32)),
      )
      max_pool_node = MaxPool(bn_node,
                              kernel_shape=(3, 3),
                              strides=(2, 2),
                              pads=(0, 0, 1, 1))
      flatten_node = Flatten(max_pool_node, axis=1)
      add_node = Add(flatten_node, np.random.randn(1).astype(np.float32))
      relu_node = Relu(add_node)
      Output(relu_node)
>     self._run(list(zip(inputs, nps)))

onnx_pytorch/tests/test_base.py:103: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self = <onnx_pytorch.tests.test_base.TestBase object at 0x7fce8a666880>
inputs_np = [('_t_Input_0', array([[[[ 1.0018734 , -0.62048906,  1.2765806 , ...,  0.25725722,
          -1.1847678 ,  1.8534303 ]...     [-0.86980325, -0.2758593 ,  0.05530448, ...,  0.2182875 ,
           0.33060816,  0.6260562 ]]]], dtype=float32))]

    def _run(self, inputs_np):
      inputs_np_dict = {k: v for k, v in inputs_np if k != ""}
      model = onnx.ModelProto()
      model.CopyFrom(omm.model)
      sess_options = onnxruntime.SessionOptions()
      session = onnxruntime.InferenceSession(model.SerializeToString(),
                                             sess_options)
      ort_outputs = session.run(None, inputs_np_dict)
      model.graph.ClearField("value_info")
      initializers = {i.name: i for i in model.graph.initializer}
      for i in model.graph.input:
        if i.name in initializers:
          continue
        for idx, d in enumerate(i.type.tensor_type.shape.dim):
          if d.dim_param != "":
            d.ClearField("dim_param")
          d.dim_value = inputs_np_dict[i.name].shape[idx]
      try:
        model = SymbolicShapeInference.infer_shapes(model, 2**31 - 1, True, True,
                                                    1)
      except:
        logging.warning("Shape infer by onnxruntime failed.")
      with TemporaryDirectory() as tmpdir:
        clear_op_code_generator()
        model_code_generator = code_gen.get_model_code_generator(
            model,
            output_dir=tmpdir,
            tensor_inplace=True,
            simplify_names=True,
            shape_infer=False)
        model_code_generator.run()
        spec = importlib.util.spec_from_file_location(
            "model", os.path.join(tmpdir, "model.py"))
        mod = importlib.util.module_from_spec(spec)
        spec.loader.exec_module(mod)
        pt_outputs = mod.test_run_model(
            [torch.from_numpy(v) for k, v in inputs_np if k != ""])
        if type(pt_outputs) == torch.Tensor:
          pt_outputs = [pt_outputs.detach().numpy()]
        elif type(pt_outputs) in (list, tuple):
          pt_outputs = [o.detach().numpy() for o in pt_outputs]
        for l, r in zip(ort_outputs, pt_outputs):
>         assert np.allclose(l, r, atol=1e-4, rtol=1e-4, equal_nan=True)
E         assert False
E          +  where False = <function allclose at 0x7fcee3f60550>(array([[1.3416731 , 0.8318468 , 0.6191998 , ..., 1.1701062 , 0.6089205 ,\n        0.57694536]], dtype=float32), array([[10.049213 ,  6.957016 ,  5.667273 , ..., 10.965231 ,  7.2742968,\n         7.0639963]], dtype=float32), atol=0.0001, rtol=0.0001, equal_nan=True)
E          +    where <function allclose at 0x7fcee3f60550> = np.allclose

onnx_pytorch/tests/test_base.py:67: AssertionError
---------------------------------------------------------------------------------------------------------------------------------- Captured stdout call ----------------------------------------------------------------------------------------------------------------------------------
# Autogenerated by onnx-pytorch.

import glob
import os
import math

import numpy as np
import torch
import torch.nn as nn
import torch.nn.functional as F
import torchvision


class Model(nn.Module):
  def __init__(self):
    super(Model, self).__init__()
    self._vars = nn.ParameterDict()
    self._regularizer_params = []
    for b in glob.glob(
        os.path.join(os.path.dirname(__file__), "variables", "*.npy")):
      v = torch.from_numpy(np.load(b))
      requires_grad = v.dtype.is_floating_point or v.dtype.is_complex
      self._vars[os.path.basename(b)[:-4]] = nn.Parameter(v, requires_grad=requires_grad)
    self.n_Conv_0 = nn.Conv2d(**{'groups': 1, 'dilation': 1, 'out_channels': 32, 'padding': 0, 'kernel_size': (3, 3), 'stride': 1, 'in_channels': 3, 'bias': True})
    self.n_Conv_0.weight.data = self._vars["t_0"]
    self.n_Conv_0.bias.data = self._vars["t_1"]
    self.n_BatchNormalization_0 = nn.BatchNorm2d(**{'num_features': 32, 'eps': 9.999999747378752e-06, 'momentum': 0.8999999761581421})
    self.n_BatchNormalization_0.weight.data = self._vars["t_2"]
    self.n_BatchNormalization_0.bias.data = self._vars["t_3"]
    self.n_BatchNormalization_0.running_mean.data = self._vars["t_4"]
    self.n_BatchNormalization_0.running_var.data = self._vars["t_5"]
    self.n_MaxPool_0 = nn.MaxPool2d(**{'dilation': 1, 'kernel_size': [3, 3], 'ceil_mode': False, 'stride': [2, 2], 'return_indices': True})
    self.n_Flatten_0 = nn.Flatten(**{'start_dim': 1})

  def forward(self, *inputs):
    t_7, = inputs
    t_8 = self.n_Conv_0(t_7)
    t_9 = self.n_BatchNormalization_0(t_8)
    t_9 = F.pad(t_9, [0, 1, 0, 1], value=float('-inf'))
    t_14, t_15 = self.n_MaxPool_0(t_9)
    t_16 = self.n_Flatten_0(t_14)
    t_17 = torch.add(t_16, self._vars["t_6"])
    t_18 = F.relu(t_17)
    return t_18

  def compatible_auto_pad(self, input, kernel_spatial_shape, nn_mod, auto_pad=None, **kwargs):
    input_spatial_shape = input.shape[2:]
    d = len(input_spatial_shape)
    strides = nn_mod.stride
    dilations = nn_mod.dilation
    output_spatial_shape = [math.ceil(float(l) / float(r)) for l, r in zip(input.shape[2:], strides)]
    pt_padding = [0] * 2 * d
    pad_shape = [0] * d
    for i in range(d):
      pad_shape[i] = (output_spatial_shape[i] - 1) * strides[i] + ((kernel_spatial_shape[i] - 1) * dilations[i] + 1) - input_spatial_shape[i]
      mean = pad_shape[i] // 2
      if auto_pad == b"SAME_UPPER":
        l, r = pad_shape[i] - mean, mean
      else:
        l, r = mean, pad_shape[i] - mean
      pt_padding.insert(0, r)
      pt_padding.insert(0, l)
    return F.pad(input, pt_padding)

@torch.no_grad()
def test_run_model(inputs=[torch.from_numpy(np.random.randn(*[1, 3, 224, 224]).astype(np.float32))]):
  model = Model()
  model.eval()
  rs = model(*inputs)
  print(rs)
  return rs

tensor([[10.04921341,  6.95701599,  5.66727304,  ..., 10.96523094,
          7.27429676,  7.06399632]])
----------------------------------------------------------------------------------------------------------------------------------- Captured log call ------------------------------------------------------------------------------------------------------------------------------------
WARNING  root:__init__.py:41 Cannot get default value for dilations of MaxPool.
WARNING  root:__init__.py:41 Cannot get default value for kernel_shape of MaxPool.
WARNING  root:__init__.py:41 Cannot get default value for pads of MaxPool.
WARNING  root:__init__.py:41 Cannot get default value for strides of MaxPool.
WARNING  root:MaxPool.py:47 MaxPool with asymmetric padding will get incorrect indices.
___________________________________________________________________________________________________________________________ TestBase.test_batch_normalization ____________________________________________________________________________________________________________________________

self = <onnx_pytorch.tests.test_base.TestBase object at 0x7fce88ce44c0>

    def test_batch_normalization(self):
      reset_model(13)
      nps = [np.random.randn(1, 32, 3, 3).astype(np.float32)]
      inputs = Input(*nps)
      Output(BatchNormalization(
          inputs[0],
          np.ones(32,).astype(np.float32),
          np.zeros(32,).astype(np.float32),
          np.random.randn(32).astype(np.float32),
          np.abs(np.random.randn(32).astype(np.float32)),
      ),
             output_num=1)
>     self._run(list(zip(inputs, nps)))

onnx_pytorch/tests/test_base.py:239: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self = <onnx_pytorch.tests.test_base.TestBase object at 0x7fce88ce44c0>
inputs_np = [('_t_Input_0', array([[[[ 6.35267049e-02,  5.02886951e-01, -6.22651100e-01],
         [ 1.44260633e+00,  1.56048670e-...51401734e-01,  5.14413416e-01],
         [-1.90268409e+00, -7.60383308e-02,  2.99409509e-01]]]],
      dtype=float32))]

    def _run(self, inputs_np):
      inputs_np_dict = {k: v for k, v in inputs_np if k != ""}
      model = onnx.ModelProto()
      model.CopyFrom(omm.model)
      sess_options = onnxruntime.SessionOptions()
      session = onnxruntime.InferenceSession(model.SerializeToString(),
                                             sess_options)
      ort_outputs = session.run(None, inputs_np_dict)
      model.graph.ClearField("value_info")
      initializers = {i.name: i for i in model.graph.initializer}
      for i in model.graph.input:
        if i.name in initializers:
          continue
        for idx, d in enumerate(i.type.tensor_type.shape.dim):
          if d.dim_param != "":
            d.ClearField("dim_param")
          d.dim_value = inputs_np_dict[i.name].shape[idx]
      try:
        model = SymbolicShapeInference.infer_shapes(model, 2**31 - 1, True, True,
                                                    1)
      except:
        logging.warning("Shape infer by onnxruntime failed.")
      with TemporaryDirectory() as tmpdir:
        clear_op_code_generator()
        model_code_generator = code_gen.get_model_code_generator(
            model,
            output_dir=tmpdir,
            tensor_inplace=True,
            simplify_names=True,
            shape_infer=False)
        model_code_generator.run()
        spec = importlib.util.spec_from_file_location(
            "model", os.path.join(tmpdir, "model.py"))
        mod = importlib.util.module_from_spec(spec)
        spec.loader.exec_module(mod)
        pt_outputs = mod.test_run_model(
            [torch.from_numpy(v) for k, v in inputs_np if k != ""])
        if type(pt_outputs) == torch.Tensor:
          pt_outputs = [pt_outputs.detach().numpy()]
        elif type(pt_outputs) in (list, tuple):
          pt_outputs = [o.detach().numpy() for o in pt_outputs]
        for l, r in zip(ort_outputs, pt_outputs):
>         assert np.allclose(l, r, atol=1e-4, rtol=1e-4, equal_nan=True)
E         assert False
E          +  where False = <function allclose at 0x7fcee3f60550>(array([[[[-0.13030988,  0.44412366, -1.0274405 ],\n         [ 1.6727427 , -0.00934371, -0.14003941],\n         [ 1.48930...,\n         [ 0.7121257 , -0.5435372 ,  0.5330533 ],\n         [-1.9084809 , -0.06336791,  0.31587568]]]], dtype=float32), array([[[[ 1.03302915e-02,  4.43110734e-01, -6.65571392e-01],\n         [ 1.36875701e+00,  1.01466656e-01,  3.00002005e...8.79306126e+00,  1.40610695e+01],\n         [ 2.11407280e+00,  1.11426420e+01,  1.29983692e+01]]]],\n      dtype=float32), atol=0.0001, rtol=0.0001, equal_nan=True)
E          +    where <function allclose at 0x7fcee3f60550> = np.allclose

onnx_pytorch/tests/test_base.py:67: AssertionError
---------------------------------------------------------------------------------------------------------------------------------- Captured stdout call ----------------------------------------------------------------------------------------------------------------------------------
# Autogenerated by onnx-pytorch.

import glob
import os
import math

import numpy as np
import torch
import torch.nn as nn
import torch.nn.functional as F
import torchvision


class Model(nn.Module):
  def __init__(self):
    super(Model, self).__init__()
    self._vars = nn.ParameterDict()
    self._regularizer_params = []
    for b in glob.glob(
        os.path.join(os.path.dirname(__file__), "variables", "*.npy")):
      v = torch.from_numpy(np.load(b))
      requires_grad = v.dtype.is_floating_point or v.dtype.is_complex
      self._vars[os.path.basename(b)[:-4]] = nn.Parameter(v, requires_grad=requires_grad)
    self.n_BatchNormalization_0 = nn.BatchNorm2d(**{'num_features': 32, 'eps': 9.999999747378752e-06, 'momentum': 0.8999999761581421})
    self.n_BatchNormalization_0.weight.data = self._vars["t_0"]
    self.n_BatchNormalization_0.bias.data = self._vars["t_1"]
    self.n_BatchNormalization_0.running_mean.data = self._vars["t_2"]
    self.n_BatchNormalization_0.running_var.data = self._vars["t_3"]

  def forward(self, *inputs):
    t_4, = inputs
    t_5 = self.n_BatchNormalization_0(t_4)
    return t_5

  
@torch.no_grad()
def test_run_model(inputs=[torch.from_numpy(np.random.randn(*[1, 32, 3, 3]).astype(np.float32))]):
  model = Model()
  model.eval()
  rs = model(*inputs)
  print(rs)
  return rs

tensor([[[[ 1.03302915e-02,  4.43110734e-01, -6.65571392e-01],
          [ 1.36875701e+00,  1.01466656e-01,  3.00002005e-03],
          [ 1.23055291e+00, -6.36751056e-01, -8.78339052e-01]],

         [[-4.64856595e-01,  1.01388752e+00,  2.45039845e+00],
          [-1.51369238e+00, -7.56639481e-01, -1.26973033e+00],
          [ 3.04206324e+00, -1.07024908e+00,  1.22984998e-01]],

         [[-2.69752383e-01, -9.64242399e-01, -2.14787436e+00],
          [-3.66215348e-01, -7.90006399e-01, -1.19138491e+00],
          [-6.34383440e-01,  4.39469069e-01, -1.50392938e+00]],

         [[ 5.44885218e-01,  1.98177516e+00,  2.14701653e+00],
          [ 2.57987189e+00,  6.98854351e+00,  5.21536064e+00],
          [-1.14435458e+00,  1.33780324e+00,  3.80742407e+00]],

         [[-1.26968300e+00, -4.35954601e-01,  5.31747639e-01],
          [-2.33643723e+00, -2.31319714e+00, -1.69136405e+00],
          [-1.01814747e+00, -1.30057871e+00,  1.37861446e-01]],

         [[-7.35616326e-01, -1.18806839e+00, -1.10327315e+00],
          [-1.21497869e+00,  2.44642749e-01, -1.08295512e+00],
          [-7.17091501e-01, -2.20478797e+00, -1.50086403e+00]],

         [[-3.56589526e-01, -1.32543945e+00, -3.12406365e-02],
          [-7.59021521e-01,  8.00770998e-01, -1.86119422e-01],
          [-2.47674465e-01,  3.34041089e-01,  4.68768179e-01]],

         [[-3.02949500e+00, -9.34190691e-01, -6.01976514e-01],
          [-1.39591777e+00,  9.02901888e-01, -1.70761660e-01],
          [-7.49238193e-01, -8.39863300e-01, -1.61441386e+00]],

         [[ 5.27461350e-01, -1.29779911e+00, -1.84558618e+00],
          [-1.37622201e+00, -2.75002476e-02, -4.80182886e-01],
          [-1.48854208e+00, -2.23460600e-01, -1.37674761e+00]],

         [[ 8.06057811e-01,  8.74002814e-01, -1.36947542e-01],
          [ 1.77069342e+00,  1.01755619e+00,  3.84808660e-01],
          [ 6.74725831e-01,  3.76408148e+00,  2.22828791e-01]],

         [[ 3.71400404e+00,  2.69624019e+00,  1.77703583e+00],
          [ 2.33299780e+00,  2.48477370e-01,  3.29037476e+00],
          [ 1.03505504e+00,  2.66409278e+00,  3.81201744e+00]],

         [[ 1.02166690e-01, -1.42813325e-01, -4.73593771e-01],
          [-2.43843883e-01,  4.17272627e-01,  8.99561644e-01],
          [-7.05574870e-01,  2.67669708e-01,  5.22894859e-01]],

         [[-1.17352533e+00, -5.71887255e-01, -3.19737315e-01],
          [-1.18356705e+00, -2.85988569e+00, -7.28449404e-01],
          [-1.39273572e+00, -1.43941092e+00, -4.75017697e-01]],

         [[-9.16496933e-01, -1.37783527e+00,  1.75405681e+00],
          [-2.10685277e+00, -1.30036724e+00,  2.50304151e+00],
          [ 3.88478422e+00,  8.30973566e-01,  3.44308519e+00]],

         [[-1.08552837e+00, -1.35483885e+00,  9.10718501e-01],
          [ 7.22618103e-01, -3.82872492e-01,  3.09645385e-01],
          [ 1.25192356e+00,  1.48433483e+00, -7.20467627e-01]],

         [[ 2.90476012e+00,  2.38905120e+00,  3.20962930e+00],
          [ 4.72063154e-01,  1.03854692e+00,  1.42332995e+00],
          [-2.65931457e-01,  2.61525941e+00,  1.36843193e+00]],

         [[ 2.29905200e+00,  7.33413887e+00, -2.16392994e+01],
          [-9.26441479e+00, -4.63282776e+00,  8.38395882e+00],
          [-6.14768124e+00, -1.39623775e+01, -5.33043909e+00]],

         [[-1.18203115e+00,  7.83545434e-01, -1.33013463e+00],
          [ 1.55748868e+00,  2.99707323e-01, -1.74411178e-01],
          [-3.15904379e-01, -1.27137268e+00,  2.87169278e-01]],

         [[ 2.82064867e+00, -3.11068088e-01, -7.12420881e-01],
          [ 1.99217871e-01,  8.75358164e-01,  5.74787557e-01],
          [ 1.21458745e+00,  1.32562840e+00,  1.46251321e-01]],

         [[-2.08626246e+00, -1.01060474e+00, -1.84688258e+00],
          [-1.30853727e-01, -7.70996749e-01,  7.53721535e-01],
          [ 1.19904697e+00, -1.62641481e-01, -8.22388411e-01]],

         [[ 1.33589315e+00,  3.14021409e-01,  2.48438573e+00],
          [-2.21844530e+00,  5.82929230e+00,  2.27573776e+00],
          [ 5.50253439e+00,  2.19331694e+00,  4.72958851e+00]],

         [[-1.88447189e+00, -9.36176181e-01, -1.94018316e+00],
          [-1.43561804e+00, -4.47861242e+00, -3.19850969e+00],
          [-9.75790977e-01, -2.53019547e+00, -2.31218606e-01]],

         [[ 1.56031847e+00, -8.49840164e-01,  2.18206739e+00],
          [ 1.86757004e+00, -9.00376320e-01, -3.14888433e-02],
          [-2.60793537e-01,  3.81440073e-01,  1.87343729e+00]],

         [[-2.49012423e+00, -1.80255661e+01, -1.39246368e+01],
          [-7.12090111e+00, -1.14031210e+01, -3.02313328e+00],
          [-5.08311844e+00, -7.04758024e+00, -8.73173904e+00]],

         [[-3.17438930e-01, -5.40359974e-01, -8.29769790e-01],
          [-2.39079952e+00, -7.72985220e-01, -1.00527453e+00],
          [-4.49523091e-01, -1.43823814e+00, -8.15485835e-01]],

         [[-1.75956070e+00, -3.46495295e+00, -5.70724130e-01],
          [-1.35396278e+00, -1.52985775e+00, -9.15392518e-01],
          [ 1.32145539e-01, -1.15701056e+00, -3.28669786e+00]],

         [[ 9.83868241e-01,  1.86329472e+00,  3.16185784e+00],
          [ 3.53541660e+00,  3.46067637e-01, -4.36942726e-01],
          [ 8.96343887e-01,  1.15589023e+00,  1.66808695e-01]],

         [[ 1.45385325e+00, -2.57331681e+00,  2.47062397e+00],
          [ 5.09636497e+00, -4.55582333e+00,  6.47839642e+00],
          [ 6.10593510e+00,  8.07678998e-01,  2.03531766e+00]],

         [[-7.87889004e+00,  2.15410185e+00, -1.72434068e+00],
          [-4.13584518e+00, -5.07564878e+00, -7.04525948e+00],
          [-4.00902462e+00,  6.43981886e+00,  4.90088892e+00]],

         [[-8.97298872e-01, -6.58248663e-01,  3.97185832e-01],
          [ 1.26078165e+00, -5.88805914e-01, -1.58723903e+00],
          [ 1.83342293e-01,  5.42823195e-01, -8.95587146e-01]],

         [[-2.58091998e+00,  1.56836367e+00,  4.73235160e-01],
          [ 6.95867360e-01,  3.10397220e+00,  8.56488526e-01],
          [-5.79270065e-01, -1.23413563e+00,  2.25809479e+00]],

         [[ 1.47533607e+01,  5.50610733e+00,  1.87684441e+01],
          [ 1.49373131e+01,  8.79306126e+00,  1.40610695e+01],
          [ 2.11407280e+00,  1.11426420e+01,  1.29983692e+01]]]])
==================================================================================================================================== warnings summary ====================================================================================================================================
../../../../anaconda3/envs/onnx-pytorch/lib/python3.9/site-packages/onnx/mapping.py:27
  <me>/anaconda3/envs/onnx-pytorch/lib/python3.9/site-packages/onnx/mapping.py:27: DeprecationWarning: `np.object` is a deprecated alias for the builtin `object`. To silence this warning, use `object` by itself. Doing this will not modify any behavior and is safe. 
  Deprecated in NumPy 1.20; for more details and guidance: https://numpy.org/devdocs/release/1.20.0-notes.html#deprecations
    int(TensorProto.STRING): np.dtype(np.object)

onnx_pytorch/tests/test_base.py: 186 warnings
  <me>/anaconda3/envs/onnx-pytorch/lib/python3.9/site-packages/onnx/numpy_helper.py:93: DeprecationWarning: `np.object` is a deprecated alias for the builtin `object`. To silence this warning, use `object` by itself. Doing this will not modify any behavior and is safe. 
  Deprecated in NumPy 1.20; for more details and guidance: https://numpy.org/devdocs/release/1.20.0-notes.html#deprecations
    if arr.dtype == np.object:

onnx_pytorch/tests/test_base.py::TestBase::test_conv_batchnorm_maxpool_flatten_add_relu
  <me>/anaconda3/envs/onnx-pytorch/lib/python3.9/site-packages/onnx/helper.py:365: DeprecationWarning: Using or importing the ABCs from 'collections' instead of from 'collections.abc' is deprecated since Python 3.3, and in 3.10 it will stop working
    is_iterable = isinstance(value, collections.Iterable)

onnx_pytorch/tests/test_base.py::TestBase::test_and
onnx_pytorch/tests/test_base.py::TestBase::test_and
  /tmp/tmpdcjl7rk5/model.py:33: DeprecationWarning: `np.bool` is a deprecated alias for the builtin `bool`. To silence this warning, use `bool` by itself. Doing this will not modify any behavior and is safe. If you specifically wanted the numpy scalar type, use `np.bool_` here.
  Deprecated in NumPy 1.20; for more details and guidance: https://numpy.org/devdocs/release/1.20.0-notes.html#deprecations

onnx_pytorch/tests/test_base.py::TestBase::test_non_zero
  /tmp/tmpxjta2pa8/model.py:33: DeprecationWarning: `np.bool` is a deprecated alias for the builtin `bool`. To silence this warning, use `bool` by itself. Doing this will not modify any behavior and is safe. If you specifically wanted the numpy scalar type, use `np.bool_` here.
  Deprecated in NumPy 1.20; for more details and guidance: https://numpy.org/devdocs/release/1.20.0-notes.html#deprecations

onnx_pytorch/tests/test_base.py::TestBase::test_resize_downsample_sizes_linear_pytorch_half_pixel
onnx_pytorch/tests/test_base.py::TestBase::test_resize_pt_bilinear
  <me>/anaconda3/envs/onnx-pytorch/lib/python3.9/site-packages/torch/nn/functional.py:3454: UserWarning: Default upsampling behavior when mode=bilinear is changed to align_corners=False since 0.4.0. Please specify align_corners=True if the old behavior is desired. See the documentation of nn.Upsample for details.
    warnings.warn(

-- Docs: https://docs.pytest.org/en/stable/warnings.html
================================================================================================================================ short test summary info =================================================================================================================================
FAILED onnx_pytorch/tests/test_base.py::TestBase::test_conv_batchnorm_maxpool_flatten_add_relu - assert False
FAILED onnx_pytorch/tests/test_base.py::TestBase::test_batch_normalization - assert False
================================================================================================================= 2 failed, 85 passed, 1 skipped, 193 warnings in 1.50s ==================================================================================================================

This is ipython code (at colab) which makes an error.

Code

!pip install tensorflow==2.6.4 onnx==1.12.0 onnx-pytorch git+https://github.com/onnx/tensorflow-onnx

import tensorflow as tf
import onnx

from onnx_pytorch import code_gen

with tf.device("/cpu:0"):
    tf_model = tf.keras.Sequential()
    tf_model.add(tf.keras.layers.Input((123,)))
    tf_model.add(tf.keras.layers.LayerNormalization())
    tf.keras.models.save_model(
        tf_model,
        "model.tf",
        overwrite=True,
        include_optimizer=False,
        save_format=None,
        signatures=None,
        options=None,
        save_traces=True
    )
!python -m tf2onnx.convert --saved-model model.tf --output model.onnx --opset 11 --verbose
code_gen.gen("model.onnx", "./")

Error Message

---------------------------------------------------------------------------
NotImplementedError                       Traceback (most recent call last)
[<ipython-input-8-b7c6a94144c8>](https://localhost:8080/#) in <module>()
     21     )
     22 get_ipython().system('python -m tf2onnx.convert --saved-model model.tf --output model.onnx --opset 11 --verbose')
---> 23 code_gen.gen("model.onnx", "./")

1 frames
[/usr/local/lib/python3.7/dist-packages/onnx_pytorch/code_gen.py](https://localhost:8080/#) in gen(onnx_model, output_dir, overwrite, tensor_inplace, simplify_names, continue_on_error, embedding_conf_file, shape_infer)
    289       onnx_model, output_dir, overwrite, tensor_inplace, simplify_names,
    290       continue_on_error, embedding_conf_file, shape_infer)
--> 291   model_code_generator.run()
    292 
    293 

[/usr/local/lib/python3.7/dist-packages/onnx_pytorch/code_gen.py](https://localhost:8080/#) in run(self)
    245         else:
    246           raise NotImplementedError(
--> 247               f"OpCodeGenerator is unimplemented for {n.op_type}.")
    248       else:
    249         try:

NotImplementedError: OpCodeGenerator is unimplemented for ReduceSumSquare.

onnxruntime==1.9.0

(onnx-pytorch) <me>:<me>/onnx-pytorch$ pytest onnx_pytorch/tests/test_base.py 
=============================================================================================== test session starts ===============================================================================================
platform linux -- Python 3.9.7, pytest-6.2.5, py-1.11.0, pluggy-1.0.0
rootdir: <me>//onnx-pytorch
plugins: dash-2.0.0
collected 88 items                                                                                                                                                                                                

onnx_pytorch/tests/test_base.py .F.................F..................s...........................s.....................                                                                                    [100%]

==================================================================================================== FAILURES =====================================================================================================
______________________________________________________________________________ TestBase.test_conv_batchnorm_maxpool_flatten_add_relu ______________________________________________________________________________

self = <onnx_pytorch.tests.test_base.TestBase object at 0x7f7aa0349d90>

    def test_conv_batchnorm_maxpool_flatten_add_relu(self):
      reset_model(13)
      nps = [np.random.randn(1, 3, 224, 224).astype(np.float32)]
      inputs = Input(*nps)
      conv_node = Conv(inputs[0],
                       np.random.randn(32, 3, 3, 3).astype(np.float32),
                       np.random.randn(32).astype(np.float32))
      bn_node = BatchNormalization(
          conv_node,
          np.ones(32,).astype(np.float32),
          np.zeros(32,).astype(np.float32),
          np.random.randn(32).astype(np.float32),
          np.abs(np.random.randn(32).astype(np.float32)),
      )
      max_pool_node = MaxPool(bn_node,
                              kernel_shape=(3, 3),
                              strides=(2, 2),
                              pads=(0, 0, 1, 1))
      flatten_node = Flatten(max_pool_node, axis=1)
      add_node = Add(flatten_node, np.random.randn(1).astype(np.float32))
      relu_node = Relu(add_node)
      Output(relu_node)
>     self._run(list(zip(inputs, nps)))

onnx_pytorch/tests/test_base.py:103: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _

self = <onnx_pytorch.tests.test_base.TestBase object at 0x7f7aa0349d90>
inputs_np = [('_t_Input_0', array([[[[ 0.08681966,  0.31802994, -0.46221298, ...,  0.86617213,
          -0.37778926, -0.6164783 ]...     [-0.22646298, -0.44820276, -0.9840031 , ...,  0.5185814 ,
           1.3545119 , -0.98803467]]]], dtype=float32))]

    def _run(self, inputs_np):
      inputs_np_dict = {k: v for k, v in inputs_np if k != ""}
      model = onnx.ModelProto()
      model.CopyFrom(omm.model)
      sess_options = onnxruntime.SessionOptions()
      session = onnxruntime.InferenceSession(model.SerializeToString(),
                                             sess_options)
      ort_outputs = session.run(None, inputs_np_dict)
      model.graph.ClearField("value_info")
      initializers = {i.name: i for i in model.graph.initializer}
      for i in model.graph.input:
        if i.name in initializers:
          continue
        for idx, d in enumerate(i.type.tensor_type.shape.dim):
          if d.dim_param != "":
            d.ClearField("dim_param")
          d.dim_value = inputs_np_dict[i.name].shape[idx]
      try:
        model = SymbolicShapeInference.infer_shapes(model, 2**31 - 1, True, True,
                                                    1)
      except:
        logging.warning("Shape infer by onnxruntime failed.")
      with TemporaryDirectory() as tmpdir:
        clear_op_code_generator()
        model_code_generator = code_gen.get_model_code_generator(
            model,
            output_dir=tmpdir,
            tensor_inplace=True,
            simplify_names=True,
            shape_infer=False)
        model_code_generator.run()
        spec = importlib.util.spec_from_file_location(
            "model", os.path.join(tmpdir, "model.py"))
        mod = importlib.util.module_from_spec(spec)
        spec.loader.exec_module(mod)
        pt_outputs = mod.test_run_model(
            [torch.from_numpy(v) for k, v in inputs_np if k != ""])
        if type(pt_outputs) == torch.Tensor:
          pt_outputs = [pt_outputs.detach().numpy()]
        elif type(pt_outputs) in (list, tuple):
          pt_outputs = [o.detach().numpy() for o in pt_outputs]
        for l, r in zip(ort_outputs, pt_outputs):
>         assert np.allclose(l, r, atol=1e-4, rtol=1e-4, equal_nan=True)
E         assert False
E          +  where False = <function allclose at 0x7f7b043f61f0>(array([[1.2242965 , 0.41702545, 0.28294265, ..., 0.12723899, 0.12723899,\n        0.        ]], dtype=float32), array([[5.1290994, 2.8178134, 2.4339228, ..., 7.237103 , 7.237103 ,\n        0.       ]], dtype=float32), atol=0.0001, rtol=0.0001, equal_nan=True)
E          +    where <function allclose at 0x7f7b043f61f0> = np.allclose

onnx_pytorch/tests/test_base.py:67: AssertionError
---------------------------------------------------------------------------------------------- Captured stdout call -----------------------------------------------------------------------------------------------
# Autogenerated by onnx-pytorch.

import glob
import os
import math

import numpy as np
import torch
import torch.nn as nn
import torch.nn.functional as F
import torchvision


class Model(nn.Module):
  def __init__(self):
    super(Model, self).__init__()
    self._vars = nn.ParameterDict()
    self._regularizer_params = []
    for b in glob.glob(
        os.path.join(os.path.dirname(__file__), "variables", "*.npy")):
      v = torch.from_numpy(np.load(b))
      requires_grad = v.dtype.is_floating_point or v.dtype.is_complex
      self._vars[os.path.basename(b)[:-4]] = nn.Parameter(v, requires_grad=requires_grad)
    self.n_Conv_0 = nn.Conv2d(**{'groups': 1, 'dilation': 1, 'out_channels': 32, 'padding': 0, 'kernel_size': (3, 3), 'stride': 1, 'in_channels': 3, 'bias': True})
    self.n_Conv_0.weight.data = self._vars["t_0"]
    self.n_Conv_0.bias.data = self._vars["t_1"]
    self.n_BatchNormalization_0 = nn.BatchNorm2d(**{'num_features': 32, 'eps': 9.999999747378752e-06, 'momentum': 0.8999999761581421})
    self.n_BatchNormalization_0.weight.data = self._vars["t_2"]
    self.n_BatchNormalization_0.bias.data = self._vars["t_3"]
    self.n_BatchNormalization_0.running_mean.data = self._vars["t_4"]
    self.n_BatchNormalization_0.running_var.data = self._vars["t_5"]
    self.n_MaxPool_0 = nn.MaxPool2d(**{'dilation': 1, 'kernel_size': [3, 3], 'ceil_mode': False, 'stride': [2, 2], 'return_indices': True})
    self.n_Flatten_0 = nn.Flatten(**{'start_dim': 1})

  def forward(self, *inputs):
    t_7, = inputs
    t_8 = self.n_Conv_0(t_7)
    t_9 = self.n_BatchNormalization_0(t_8)
    t_9 = F.pad(t_9, [0, 1, 0, 1], value=float('-inf'))
    t_14, t_15 = self.n_MaxPool_0(t_9)
    t_16 = self.n_Flatten_0(t_14)
    t_17 = torch.add(t_16, self._vars["t_6"])
    t_18 = F.relu(t_17)
    return t_18

  def compatible_auto_pad(self, input, kernel_spatial_shape, nn_mod, auto_pad=None, **kwargs):
    input_spatial_shape = input.shape[2:]
    d = len(input_spatial_shape)
    strides = nn_mod.stride
    dilations = nn_mod.dilation
    output_spatial_shape = [math.ceil(float(l) / float(r)) for l, r in zip(input.shape[2:], strides)]
    pt_padding = [0] * 2 * d
    pad_shape = [0] * d
    for i in range(d):
      pad_shape[i] = (output_spatial_shape[i] - 1) * strides[i] + ((kernel_spatial_shape[i] - 1) * dilations[i] + 1) - input_spatial_shape[i]
      mean = pad_shape[i] // 2
      if auto_pad == b"SAME_UPPER":
        l, r = pad_shape[i] - mean, mean
      else:
        l, r = mean, pad_shape[i] - mean
      pt_padding.insert(0, r)
      pt_padding.insert(0, l)
    return F.pad(input, pt_padding)

@torch.no_grad()
def test_run_model(inputs=[torch.from_numpy(np.random.randn(*[1, 3, 224, 224]).astype(np.float32))]):
  model = Model()
  model.eval()
  rs = model(*inputs)
  print(rs)
  return rs

tensor([[5.12909937, 2.81781340, 2.43392277,  ..., 7.23710299, 7.23710299,
         0.00000000]])
------------------------------------------------------------------------------------------------ Captured log call ------------------------------------------------------------------------------------------------
WARNING  root:__init__.py:41 Cannot get default value for dilations of MaxPool.
WARNING  root:__init__.py:41 Cannot get default value for kernel_shape of MaxPool.
WARNING  root:__init__.py:41 Cannot get default value for pads of MaxPool.
WARNING  root:__init__.py:41 Cannot get default value for strides of MaxPool.
WARNING  root:MaxPool.py:47 MaxPool with asymmetric padding will get incorrect indices.
________________________________________________________________________________________ TestBase.test_batch_normalization ________________________________________________________________________________________

self = <onnx_pytorch.tests.test_base.TestBase object at 0x7f7a9eacfa00>

    def test_batch_normalization(self):
      reset_model(13)
      nps = [np.random.randn(1, 32, 3, 3).astype(np.float32)]
      inputs = Input(*nps)
      Output(BatchNormalization(
          inputs[0],
          np.ones(32,).astype(np.float32),
          np.zeros(32,).astype(np.float32),
          np.random.randn(32).astype(np.float32),
          np.abs(np.random.randn(32).astype(np.float32)),
      ),
             output_num=1)
>     self._run(list(zip(inputs, nps)))

onnx_pytorch/tests/test_base.py:239: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _

self = <onnx_pytorch.tests.test_base.TestBase object at 0x7f7a9eacfa00>
inputs_np = [('_t_Input_0', array([[[[ 0.7745172 , -1.4926829 , -1.6556902 ],
         [-0.7622266 ,  0.04088752,  0.83572936],
  ...         [ 0.5896988 , -0.8963601 ,  0.9315137 ],
         [-1.5789044 , -0.9300383 , -0.8664075 ]]]], dtype=float32))]

    def _run(self, inputs_np):
      inputs_np_dict = {k: v for k, v in inputs_np if k != ""}
      model = onnx.ModelProto()
      model.CopyFrom(omm.model)
      sess_options = onnxruntime.SessionOptions()
      session = onnxruntime.InferenceSession(model.SerializeToString(),
                                             sess_options)
      ort_outputs = session.run(None, inputs_np_dict)
      model.graph.ClearField("value_info")
      initializers = {i.name: i for i in model.graph.initializer}
      for i in model.graph.input:
        if i.name in initializers:
          continue
        for idx, d in enumerate(i.type.tensor_type.shape.dim):
          if d.dim_param != "":
            d.ClearField("dim_param")
          d.dim_value = inputs_np_dict[i.name].shape[idx]
      try:
        model = SymbolicShapeInference.infer_shapes(model, 2**31 - 1, True, True,
                                                    1)
      except:
        logging.warning("Shape infer by onnxruntime failed.")
      with TemporaryDirectory() as tmpdir:
        clear_op_code_generator()
        model_code_generator = code_gen.get_model_code_generator(
            model,
            output_dir=tmpdir,
            tensor_inplace=True,
            simplify_names=True,
            shape_infer=False)
        model_code_generator.run()
        spec = importlib.util.spec_from_file_location(
            "model", os.path.join(tmpdir, "model.py"))
        mod = importlib.util.module_from_spec(spec)
        spec.loader.exec_module(mod)
        pt_outputs = mod.test_run_model(
            [torch.from_numpy(v) for k, v in inputs_np if k != ""])
        if type(pt_outputs) == torch.Tensor:
          pt_outputs = [pt_outputs.detach().numpy()]
        elif type(pt_outputs) in (list, tuple):
          pt_outputs = [o.detach().numpy() for o in pt_outputs]
        for l, r in zip(ort_outputs, pt_outputs):
>         assert np.allclose(l, r, atol=1e-4, rtol=1e-4, equal_nan=True)
E         assert False
E          +  where False = <function allclose at 0x7f7b043f61f0>(array([[[[ 9.91475940e-01, -1.39311564e+00, -1.56456316e+00],\n         [-6.24837637e-01,  2.19860300e-01,  1.05585766e...7.59569287e-01,  1.25005341e+00],\n         [-1.50998020e+00, -7.96596169e-01, -7.26638436e-01]]]],\n      dtype=float32), array([[[[ 2.11514905e-02, -1.92307127e+00, -2.06285715e+00],\n         [-1.29667318e+00, -6.07967854e-01,  7.36436024e...8.19936633e-01,  1.26697469e+00],\n         [-1.59920776e+00, -8.58387530e-01, -7.85739303e-01]]]],\n      dtype=float32), atol=0.0001, rtol=0.0001, equal_nan=True)
E          +    where <function allclose at 0x7f7b043f61f0> = np.allclose

onnx_pytorch/tests/test_base.py:67: AssertionError
---------------------------------------------------------------------------------------------- Captured stdout call -----------------------------------------------------------------------------------------------
# Autogenerated by onnx-pytorch.

import glob
import os
import math

import numpy as np
import torch
import torch.nn as nn
import torch.nn.functional as F
import torchvision


class Model(nn.Module):
  def __init__(self):
    super(Model, self).__init__()
    self._vars = nn.ParameterDict()
    self._regularizer_params = []
    for b in glob.glob(
        os.path.join(os.path.dirname(__file__), "variables", "*.npy")):
      v = torch.from_numpy(np.load(b))
      requires_grad = v.dtype.is_floating_point or v.dtype.is_complex
      self._vars[os.path.basename(b)[:-4]] = nn.Parameter(v, requires_grad=requires_grad)
    self.n_BatchNormalization_0 = nn.BatchNorm2d(**{'num_features': 32, 'eps': 9.999999747378752e-06, 'momentum': 0.8999999761581421})
    self.n_BatchNormalization_0.weight.data = self._vars["t_0"]
    self.n_BatchNormalization_0.bias.data = self._vars["t_1"]
    self.n_BatchNormalization_0.running_mean.data = self._vars["t_2"]
    self.n_BatchNormalization_0.running_var.data = self._vars["t_3"]

  def forward(self, *inputs):
    t_4, = inputs
    t_5 = self.n_BatchNormalization_0(t_4)
    return t_5

  
@torch.no_grad()
def test_run_model(inputs=[torch.from_numpy(np.random.randn(*[1, 32, 3, 3]).astype(np.float32))]):
  model = Model()
  model.eval()
  rs = model(*inputs)
  print(rs)
  return rs

tensor([[[[ 2.11514905e-02, -1.92307127e+00, -2.06285715e+00],
          [-1.29667318e+00, -6.07967854e-01,  7.36436024e-02],
          [-1.24425519e+00, -4.32142057e-03, -4.06830050e-02]],

         [[ 4.27835196e-01, -4.02293563e-01,  1.25209391e+00],
          [-1.35146415e+00, -2.52955347e-01,  1.47779858e+00],
          [-6.49659276e-01,  4.79720533e-01,  2.22885060e+00]],

         [[-2.09176064e+00, -1.05400944e+00, -2.06602669e+00],
          [-1.94747806e+00, -2.88019228e+00, -2.62886310e+00],
          [-3.44989538e+00, -2.75009131e+00, -2.39562416e+00]],

         [[ 1.11013091e+00,  1.28344691e+00, -6.32941604e-01],
          [ 7.57854998e-01, -2.10156515e-01,  1.47328424e+00],
          [-2.59426326e-01, -2.84430325e-01,  9.00919676e-01]],

         [[ 4.08791155e-01,  2.89755702e-01,  6.62197396e-02],
          [-1.76871634e+00, -5.03794849e-01, -4.27903265e-01],
          [ 9.95307684e-01, -4.92222719e-02, -1.14720094e+00]],

         [[-1.45369780e+00,  2.33676344e-01, -1.03255248e+00],
          [ 1.32926130e+00,  2.23724812e-01, -2.06382227e+00],
          [-7.27365375e-01, -3.29207569e-01, -1.84505939e+00]],

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================================================================================================ warnings summary =================================================================================================
../../../../anaconda3/envs/onnx-pytorch/lib/python3.9/site-packages/onnx/mapping.py:27
  <me>/anaconda3/envs/onnx-pytorch/lib/python3.9/site-packages/onnx/mapping.py:27: DeprecationWarning: `np.object` is a deprecated alias for the builtin `object`. To silence this warning, use `object` by itself. Doing this will not modify any behavior and is safe. 
  Deprecated in NumPy 1.20; for more details and guidance: https://numpy.org/devdocs/release/1.20.0-notes.html#deprecations
    int(TensorProto.STRING): np.dtype(np.object)

onnx_pytorch/tests/test_base.py: 182 warnings
  <me>/anaconda3/envs/onnx-pytorch/lib/python3.9/site-packages/onnx/numpy_helper.py:93: DeprecationWarning: `np.object` is a deprecated alias for the builtin `object`. To silence this warning, use `object` by itself. Doing this will not modify any behavior and is safe. 
  Deprecated in NumPy 1.20; for more details and guidance: https://numpy.org/devdocs/release/1.20.0-notes.html#deprecations
    if arr.dtype == np.object:

onnx_pytorch/tests/test_base.py::TestBase::test_conv_batchnorm_maxpool_flatten_add_relu
  <me>/anaconda3/envs/onnx-pytorch/lib/python3.9/site-packages/onnx/helper.py:365: DeprecationWarning: Using or importing the ABCs from 'collections' instead of from 'collections.abc' is deprecated since Python 3.3, and in 3.10 it will stop working
    is_iterable = isinstance(value, collections.Iterable)

onnx_pytorch/tests/test_base.py::TestBase::test_and
onnx_pytorch/tests/test_base.py::TestBase::test_and
  /tmp/tmpms_osm8m/model.py:33: DeprecationWarning: `np.bool` is a deprecated alias for the builtin `bool`. To silence this warning, use `bool` by itself. Doing this will not modify any behavior and is safe. If you specifically wanted the numpy scalar type, use `np.bool_` here.
  Deprecated in NumPy 1.20; for more details and guidance: https://numpy.org/devdocs/release/1.20.0-notes.html#deprecations

onnx_pytorch/tests/test_base.py::TestBase::test_non_zero
  /tmp/tmpjqh2vsx2/model.py:33: DeprecationWarning: `np.bool` is a deprecated alias for the builtin `bool`. To silence this warning, use `bool` by itself. Doing this will not modify any behavior and is safe. If you specifically wanted the numpy scalar type, use `np.bool_` here.
  Deprecated in NumPy 1.20; for more details and guidance: https://numpy.org/devdocs/release/1.20.0-notes.html#deprecations

onnx_pytorch/tests/test_base.py::TestBase::test_resize_pt_bilinear
  <me>/anaconda3/envs/onnx-pytorch/lib/python3.9/site-packages/torch/nn/functional.py:3631: UserWarning: Default upsampling behavior when mode=bilinear is changed to align_corners=False since 0.4.0. Please specify align_corners=True if the old behavior is desired. See the documentation of nn.Upsample for details.
    warnings.warn(

-- Docs: https://docs.pytest.org/en/stable/warnings.html
============================================================================================= short test summary info =============================================================================================
FAILED onnx_pytorch/tests/test_base.py::TestBase::test_conv_batchnorm_maxpool_flatten_add_relu - assert False
FAILED onnx_pytorch/tests/test_base.py::TestBase::test_batch_normalization - assert False
============================================================================== 2 failed, 84 passed, 2 skipped, 188 warnings in 1.47s ==============================================================================

I've converted a BiT model (https://tfhub.dev/google/bit/m-r101x1/1) from TF to ONNX, and then used this package to convert to Pytorch.

The result works out-of-the-box in the CPU, I get the same outputs as the TF model. But when I try it in the GPU, I get some fatal errors saying that some ops are using tensors in different devices. Looking into the generated code, I see a lot of calls like these in forward(): t_323 = torch.tensor(t_321.shape)

These are being created in the CPU, so operations with these tensors (when the input is in the GPU) result in error. I can fix it manually by changing all such calls to: torch.tensor(..., device=inputs[0].device), and then everything works well: the results are the same as TF, and the performance is also the same.

https://github.com/fumihwh/onnx-pytorch/blob/29cd1dafb47e4e4bc598c700c44f53815e7b8c9a/README.md?plain=1#L19

the command line block should be

git clone https://github.com/fumihwh/onnx-pytorch.git
cd onnx-pytorch
pip install -r requirements.txt
pip install -e .

https://github.com/fumihwh/onnx-pytorch/blob/29cd1dafb47e4e4bc598c700c44f53815e7b8c9a/README.md?plain=1#L87

I would suggest changing the following line

inputs = {"data": inp}

to this one, in the README

inputs = {session.get_inputs()[0].name: inp}

This allows to adapt to a way larger variety of model, without hardcoding the input name.

Hi, I tried this tool for the first time

I did it the following way：

1. pip install onnx_pytorch

2. from onnx_pytorch import code_gen

3. code_gen.gen('resnet18-v2-7.onnx', './')

But, there is an error about: DecodeError: Unexpected end-group tag.

How to deal it?

When trying to convert a Yolov4 ONNX model with onnx-pytorch I get the following error. Seems to be an unimplemented OpCode for Softplus.

WARNING:root:Cannot get default value for dilations of Conv. WARNING:root:Cannot get default value for kernel_shape of Conv. WARNING:root:Cannot get default value for pads of Conv. WARNING:root:Cannot get default value for strides of Conv. Traceback (most recent call last): File "/usr/lib/python3.8/runpy.py", line 194, in _run_module_as_main return _run_code(code, main_globals, None, File "/usr/lib/python3.8/runpy.py", line 87, in _run_code exec(code, run_globals) File "/someenv/lib/python3.8/site-packages/onnx_pytorch/code_gen.py", line 378, in main() File "/someenv/python3.8/site-packages/onnx_pytorch/code_gen.py", line 368, in main gen(onnx_model=args.onnx_model_path, File "/someenv/python3.8/site-packages/onnx_pytorch/code_gen.py", line 291, in gen model_code_generator.run() File "/someenv/python3.8/site-packages/onnx_pytorch/code_gen.py", line 246, in run raise NotImplementedError( NotImplementedError: OpCodeGenerator is unimplemented for Softplus.

Installed version:

pip show onnx_pytorch Name: onnx-pytorch Version: 0.1.4 Summary: Convert ONNX to PyTorch code. Home-page: https://github.com/fumihwh/onnx-pytorch Author: fumihwh Author-email: [email protected] License: Apache 2.0 Location: /someenv/lib/python3.8/site-packages Requires: torchvision, setuptools, torch, PyYAML, tqdm, onnxruntime, onnx, sympy, pytest, numpy Required-by:

/usr/local/lib/python3.7/dist-packages/onnx_pytorch/code_gen.py in run(self) 245 else: 246 raise NotImplementedError( --> 247 f"OpCodeGenerator is unimplemented for {n.op_type}.") 248 else: 249 try:

NotImplementedError: OpCodeGenerator is unimplemented for DequantizeLinear.

What is the problem？