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173f224570
Summary: Pull Request resolved: https://github.com/pytorch/pytorch/pull/18598 ghimport-source-id: c74597e5e7437e94a43c163cee0639b20d0d0c6a Stack from [ghstack](https://github.com/ezyang/ghstack): * **#18598 Turn on F401: Unused import warning.** This was requested by someone at Facebook; this lint is turned on for Facebook by default. "Sure, why not." I had to noqa a number of imports in __init__. Hypothetically we're supposed to use __all__ in this case, but I was too lazy to fix it. Left for future work. Be careful! flake8-2 and flake8-3 behave differently with respect to import resolution for # type: comments. flake8-3 will report an import unused; flake8-2 will not. For now, I just noqa'd all these sites. All the changes were done by hand. Signed-off-by: Edward Z. Yang <ezyang@fb.com> Differential Revision: D14687478 fbshipit-source-id: 30d532381e914091aadfa0d2a5a89404819663e3
69 lines
2.5 KiB
Python
69 lines
2.5 KiB
Python
# Some standard imports
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import numpy as np
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from torch import nn
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import torch.onnx
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import torch.nn.init as init
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from caffe2.python.model_helper import ModelHelper
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from pytorch_helper import PyTorchModule
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import unittest
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from caffe2.python.core import workspace
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from test_pytorch_common import skipIfNoLapack
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class TestCaffe2Backend(unittest.TestCase):
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@skipIfNoLapack
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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(SuperResolutionNet, self).__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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unittest.main()
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