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bcf7036be5
Fixes https://github.com/microsoft/onnx-converters-private/issues/132 @kit1980 and @malfet agreed in disabling ONNX tests for Caffe2 builds. With this change, exporting models with `operator+export_type=ONNX_ATEN_FALLBACK` will properly test non-caffe2 builds, which is the only scenario for aten fallback after caffe2 deprecation Pull Request resolved: https://github.com/pytorch/pytorch/pull/90475 Approved by: https://github.com/kit1980, https://github.com/BowenBao
73 lines
2.7 KiB
Python
73 lines
2.7 KiB
Python
# Owner(s): ["module: onnx"]
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# Some standard imports
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import unittest
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import numpy as np
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import pytorch_test_common
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import torch.nn.init as init
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import torch.onnx
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from caffe2.python.core import workspace
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from caffe2.python.model_helper import ModelHelper
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from pytorch_helper import PyTorchModule
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from torch import nn
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from torch.testing._internal import common_utils
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from torch.testing._internal.common_utils import skipIfNoLapack
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class TestCaffe2Backend(pytorch_test_common.ExportTestCase):
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@skipIfNoLapack
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@unittest.skip("test broken because Lapack was always missing.")
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def test_helper(self):
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class SuperResolutionNet(nn.Module):
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def __init__(self, upscale_factor, inplace=False):
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super().__init__()
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self.relu = nn.ReLU(inplace=inplace)
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self.conv1 = nn.Conv2d(1, 64, (5, 5), (1, 1), (2, 2))
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self.conv2 = nn.Conv2d(64, 64, (3, 3), (1, 1), (1, 1))
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self.conv3 = nn.Conv2d(64, 32, (3, 3), (1, 1), (1, 1))
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self.conv4 = nn.Conv2d(32, upscale_factor**2, (3, 3), (1, 1), (1, 1))
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self.pixel_shuffle = nn.PixelShuffle(upscale_factor)
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self._initialize_weights()
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def forward(self, x):
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x = self.relu(self.conv1(x))
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x = self.relu(self.conv2(x))
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x = self.relu(self.conv3(x))
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x = self.pixel_shuffle(self.conv4(x))
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return x
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def _initialize_weights(self):
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init.orthogonal(self.conv1.weight, init.calculate_gain("relu"))
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init.orthogonal(self.conv2.weight, init.calculate_gain("relu"))
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init.orthogonal(self.conv3.weight, init.calculate_gain("relu"))
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init.orthogonal(self.conv4.weight)
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torch_model = SuperResolutionNet(upscale_factor=3)
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fake_input = torch.randn(1, 1, 224, 224, requires_grad=True)
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# use ModelHelper to create a C2 net
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helper = ModelHelper(name="test_model")
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start = helper.Sigmoid(["the_input"])
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# Embed the ONNX-converted pytorch net inside it
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(toutput,) = PyTorchModule(helper, torch_model, (fake_input,), [start])
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output = helper.Sigmoid(toutput)
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workspace.RunNetOnce(helper.InitProto())
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workspace.FeedBlob("the_input", fake_input.data.numpy())
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# print([ k for k in workspace.blobs ])
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workspace.RunNetOnce(helper.Proto())
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c2_out = workspace.FetchBlob(str(output))
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torch_out = torch.sigmoid(torch_model(torch.sigmoid(fake_input)))
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np.testing.assert_almost_equal(torch_out.data.cpu().numpy(), c2_out, decimal=3)
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if __name__ == "__main__":
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common_utils.run_tests()
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