A code generator from ONNX to PyTorch code

Overview

onnx-pytorch

Build Status

Generating pytorch code from ONNX. Currently support onnx==1.9.0 and torch==1.8.1.

Installation

  • From PyPI
pip install onnx-pytorch
  • From source
git clone https://github.com/fumihwh/onnx-pytorch.git
pip install -r requirements.txt
pip install -e .

Usage

from onnx_pytorch import code_gen
code_gen.gen("/path/to/onnx_model", "/path/to/output_dir")

A model.py file and variables folder will be created under output_dir.

Tutorial

  • Download resnet18 onnx model

wget https://github.com/onnx/models/raw/master/vision/classification/resnet/model/resnet18-v2-7.onnx

  • Use onnx-pytorch to generate pytorch code and variables.
from onnx_pytorch import code_gen
code_gen.gen("resnet18-v2-7.onnx", "./")
  • Test result
import numpy as np
import onnx
import onnxruntime
import torch
torch.set_printoptions(8)

from model import Model

model = Model()
model.eval()
inp = np.random.randn(1, 3, 224, 224).astype(np.float32)
with torch.no_grad():
  torch_outputs = model(torch.from_numpy(inp))

onnx_model = onnx.load("resnet18-v2-7.onnx")
sess_options = onnxruntime.SessionOptions()
session = onnxruntime.InferenceSession(onnx_model.SerializeToString(),
                                       sess_options)
inputs = {"data": inp}
ort_outputs = session.run(None, inputs)

print(
    "Comparison result:",
    np.allclose(torch_outputs.detach().numpy(),
                ort_outputs[0],
                atol=1e-5,
                rtol=1e-5))
Comments
  • latest version of onnx or torch fails pytest

    latest version of onnx or torch fails pytest

    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 ==================================================================================================================
    
    opened by helion-du-mas-des-bourboux-thales 3
  • Function `code_gen.gen` failed with layer `LayerNormalization`. However, `BatchNormalization` succeeds.

    Function `code_gen.gen` failed with layer `LayerNormalization`. However, `BatchNormalization` succeeds.

    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.
    
    opened by klae01 2
  • latest onnxruntime fails test

    latest onnxruntime fails test

    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]],
    
             [[-7.30695367e-01, -9.48697507e-01,  1.02768219e+00],
              [-3.11210537e+00, -2.19822788e+00,  1.94993824e-01],
              [-5.17953396e-01,  9.80266273e-01,  1.58678629e-02]],
    
             [[-5.50329685e-01, -2.20515108e+00,  5.57632744e-01],
              [-4.76857811e-01,  1.53507262e-01, -1.43097568e+00],
              [ 4.82103467e-01, -1.68012989e+00,  3.24517749e-02]],
    
             [[-5.33442855e-01,  5.51209152e-01,  9.62817371e-01],
              [ 2.40877175e+00,  1.32837451e+00,  1.65606558e+00],
              [-4.13032651e-01,  3.72783518e+00,  3.40976954e-01]],
    
             [[ 6.73895895e-01, -2.66826779e-01,  2.70163131e+00],
              [ 1.51779735e+00,  1.03770292e+00,  3.58062625e-01],
              [ 3.07913351e+00,  1.82803762e+00,  1.80789387e+00]],
    
             [[-5.71182489e-01, -9.17714715e-01, -1.13700569e+00],
              [-1.86594054e-01, -3.26027721e-01, -7.83864677e-01],
              [-8.37005913e-01, -1.44201532e-01, -1.28018081e+00]],
    
             [[-2.11968374e+00,  4.36148047e-01, -2.25281045e-01],
              [-2.65030837e+00, -2.46051192e+00, -7.95132637e-01],
              [-2.29407355e-01, -2.05399799e+00, -3.97852802e+00]],
    
             [[ 1.99362409e+00, -2.22769213e+00,  3.03191710e+00],
              [ 6.41038036e+00,  7.57672191e-01,  2.30211586e-01],
              [ 4.41129446e+00,  5.71550274e+00,  2.88953924e+00]],
    
             [[-1.67502999e+00,  4.71590012e-01,  4.20928180e-01],
              [ 1.42629158e+00,  2.22070456e+00, -2.48521614e+00],
              [-2.90164924e+00, -1.70486748e+00,  3.05718213e-01]],
    
             [[ 1.31291842e+00,  1.51544333e+00,  9.34356451e-01],
              [ 2.45068908e+00,  9.35024202e-01,  1.16957915e+00],
              [ 1.73736286e+00,  1.44560516e+00,  1.79951024e+00]],
    
             [[-1.78257480e-01, -1.50668001e+00, -3.93693089e-01],
              [ 9.00940716e-01,  1.75067687e+00,  1.56921744e-01],
              [-1.68945998e-01, -7.10348845e-01,  2.69243687e-01]],
    
             [[-1.44925761e+00, -8.86168003e-01, -2.19026709e+00],
              [-5.69859803e-01,  6.73547387e-01, -1.53828010e-01],
              [-3.62083554e+00, -1.68905407e-02, -1.03936875e+00]],
    
             [[-2.79535174e+00, -3.87425613e+00,  4.66894388e+00],
              [-3.84637070e+00, -1.71726680e+00, -3.25723600e+00],
              [-6.84032822e+00, -1.06125496e-01,  2.27101946e+00]],
    
             [[ 9.65043604e-01, -3.17505288e+00,  1.14182040e-01],
              [-2.67569017e+00,  1.84636426e+00, -7.68563211e-01],
              [-2.11804008e+00, -2.63963199e+00, -2.71025586e+00]],
    
             [[-4.97454464e-01, -1.84077692e+00, -1.13075355e-03],
              [-2.12281924e-02,  1.43575883e+00, -9.79906857e-01],
              [-1.43173182e+00, -1.10443759e+00, -1.83555901e+00]],
    
             [[ 6.83952451e-01,  3.86664987e+00,  6.27903759e-01],
              [ 6.22224391e-01,  3.38052392e+00,  2.65812469e+00],
              [ 1.35363007e+00, -1.32484972e+00,  2.16152740e+00]],
    
             [[-2.97609538e-01, -5.97289562e-01, -5.53929061e-02],
              [-9.01254416e-01, -1.31918341e-01, -1.91106975e+00],
              [ 1.30615933e-02, -1.13118947e+00, -1.71910405e+00]],
    
             [[-3.56180477e+00,  1.03958499e+00, -2.59528255e+00],
              [-3.63754392e-01,  1.45368779e+00,  6.28106117e-01],
              [-1.52019906e+00,  2.27045107e+00, -2.04589820e+00]],
    
             [[ 2.96379948e+00,  1.40205872e+00,  6.10626042e-01],
              [ 9.29273069e-01, -2.59484500e-01,  1.29350579e+00],
              [-2.03710818e+00,  2.09723279e-01,  3.75842363e-01]],
    
             [[ 1.15190208e+00, -1.79379475e+00, -1.03870857e+00],
              [-2.49877191e+00,  5.20503461e-01, -1.32148862e+00],
              [ 1.14259291e+00, -1.22499466e+00, -1.77996016e+00]],
    
             [[ 5.53968525e+00,  2.88090467e+00,  1.01117289e+00],
              [ 5.58917379e+00,  6.44941425e+00,  4.39829063e+00],
              [ 5.66234684e+00,  6.48445272e+00,  7.14439631e+00]],
    
             [[ 2.75992036e-01,  2.69333333e-01,  2.09721066e-02],
              [-3.83876115e-01, -8.62384975e-01, -9.11671594e-02],
              [ 6.93263173e-01,  1.74463049e-01,  4.79215592e-01]],
    
             [[-1.01199875e+01, -7.20881653e+00, -5.04845047e+00],
              [-6.25630283e+00, -1.05240383e+01, -2.73052502e+00],
              [-7.76849747e+00, -2.49891591e+00, -8.07278156e+00]],
    
             [[ 1.54215002e+00,  1.09585929e+00,  1.14009336e-01],
              [ 1.12563217e+00,  2.39603353e+00,  1.73558319e+00],
              [-3.81684572e-01,  5.00159383e-01,  1.24173117e+00]],
    
             [[-1.65010154e-01, -5.65712094e-01,  3.59763801e-02],
              [-3.90798420e-01, -1.16110936e-01, -1.36400402e-01],
              [-1.34565961e+00,  4.39721853e-01,  8.28600407e-01]],
    
             [[-4.84672832e+00, -6.60604596e-01,  1.73845172e-01],
              [-5.31565666e-01, -1.43216908e-01,  3.46095473e-01],
              [-2.08822680e+00, -1.05168688e+00, -1.98360145e-01]],
    
             [[ 1.07395852e+00,  1.13209188e+00, -5.66867292e-01],
              [ 8.76719356e-01, -8.19936633e-01,  1.26697469e+00],
              [-1.59920776e+00, -8.58387530e-01, -7.85739303e-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: 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 ==============================================================================
    
    
    opened by helion-du-mas-des-bourboux-thales 2
  • Tensors in the converted model are being placed in the wrong device

    Tensors in the converted model are being placed in the wrong device

    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.

    opened by jorgemcgomes 2
  • change directory is missing

    change directory is missing

    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 .
    
    opened by londumas 1
  • input name in onnxruntime is hardcoded in README

    input name in onnxruntime is hardcoded in README

    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.

    opened by londumas 1
  • DecodeError: Unexpected end-group tag.

    DecodeError: Unexpected end-group tag.

    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?

    opened by xiaopengaia 1
  • OpCodeGenerator is unimplemented for Softplus

    OpCodeGenerator is unimplemented for Softplus

    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:

    opened by juhan 1
  • NotImplementedError: OpCodeGenerator is unimplemented for DequantizeLinear.

    NotImplementedError: OpCodeGenerator is unimplemented for DequantizeLinear.

    opened by LiuFeiOne 1
Releases(v0.1.5)
  • v0.1.5(Aug 3, 2022)

    What's Changed

    • create python publish action by @fumihwh in https://github.com/fumihwh/onnx-pytorch/pull/42

    Full Changelog: https://github.com/fumihwh/onnx-pytorch/compare/v0.1.4...v0.1.5

    Source code(tar.gz)
    Source code(zip)
  • v0.1.4(Nov 23, 2021)

    What's Changed

    • Add some ops by @fumihwh in https://github.com/fumihwh/onnx-pytorch/pull/13
    • Bump up to 0.1.3 by @fumihwh in https://github.com/fumihwh/onnx-pytorch/pull/14
    • Add ops and model test cases by @fumihwh in https://github.com/fumihwh/onnx-pytorch/pull/15
    • Support frcnn by @fumihwh in https://github.com/fumihwh/onnx-pytorch/pull/16
    • Support mask rcnn, ssd and style transfer models by @fumihwh in https://github.com/fumihwh/onnx-pytorch/pull/17
    • refactor: Small readability improvements by @rogier-stegeman in https://github.com/fumihwh/onnx-pytorch/pull/4
    • Fix CI by @fumihwh in https://github.com/fumihwh/onnx-pytorch/pull/25
    • Some nit by @fumihwh in https://github.com/fumihwh/onnx-pytorch/pull/24
    • add OP Elu/Sub/Tanh by @maimaixiong in https://github.com/fumihwh/onnx-pytorch/pull/19
    • Adds device information when creating new tensors by @jorgemcgomes in https://github.com/fumihwh/onnx-pytorch/pull/29
    • Ci by @fumihwh in https://github.com/fumihwh/onnx-pytorch/pull/40
    • add version by @helion-du-mas-des-bourboux-thales in https://github.com/fumihwh/onnx-pytorch/pull/33
    • more general tutorial by @helion-du-mas-des-bourboux-thales in https://github.com/fumihwh/onnx-pytorch/pull/37
    • Fix dependencies by @helion-du-mas-des-bourboux-thales in https://github.com/fumihwh/onnx-pytorch/pull/35
    • Release 0.1.4 by @fumihwh in https://github.com/fumihwh/onnx-pytorch/pull/41

    New Contributors

    • @rogier-stegeman made their first contribution in https://github.com/fumihwh/onnx-pytorch/pull/4
    • @maimaixiong made their first contribution in https://github.com/fumihwh/onnx-pytorch/pull/19
    • @jorgemcgomes made their first contribution in https://github.com/fumihwh/onnx-pytorch/pull/29
    • @helion-du-mas-des-bourboux-thales made their first contribution in https://github.com/fumihwh/onnx-pytorch/pull/33

    Full Changelog: https://github.com/fumihwh/onnx-pytorch/compare/v0.1.3...v0.1.4

    Source code(tar.gz)
    Source code(zip)
  • v0.1.3(Nov 18, 2021)

    What's Changed

    • Develop by @fumihwh in https://github.com/fumihwh/onnx-pytorch/pull/1
    • Add tutorial and fix some bugs by @fumihwh in https://github.com/fumihwh/onnx-pytorch/pull/2
    • Bump up to 0.1.2 by @fumihwh in https://github.com/fumihwh/onnx-pytorch/pull/3
    • Introduce new features and some bug fix by @fumihwh in https://github.com/fumihwh/onnx-pytorch/pull/5
    • Ci by @fumihwh in https://github.com/fumihwh/onnx-pytorch/pull/6
    • Add some ops by @fumihwh in https://github.com/fumihwh/onnx-pytorch/pull/7
    • Improve ci by @fumihwh in https://github.com/fumihwh/onnx-pytorch/pull/8
    • Add some ops by @fumihwh in https://github.com/fumihwh/onnx-pytorch/pull/9
    • Fix ops and use ParameterDict by @fumihwh in https://github.com/fumihwh/onnx-pytorch/pull/10
    • Ci by @fumihwh in https://github.com/fumihwh/onnx-pytorch/pull/11

    Full Changelog: https://github.com/fumihwh/onnx-pytorch/compare/v0.1.2...v0.1.3

    Source code(tar.gz)
    Source code(zip)
Owner
Wenhao Hu
Wenhao Hu
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