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https://github.com/saymrwulf/pytorch.git
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24087d07ca
Summary: Pull Request resolved: https://github.com/pytorch/pytorch/pull/57745 Reviewed By: bdhirsh Differential Revision: D28318164 Pulled By: mruberry fbshipit-source-id: b8e3cb9d7ab33f30c8653ec39f932a8af8bd2a50
101 lines
3.6 KiB
Python
101 lines
3.6 KiB
Python
import io
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import os
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import shutil
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import sys
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import tempfile
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import torch
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import torch.nn as nn
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from torch.onnx import OperatorExportTypes
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from torch.autograd import Variable
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# Make the helper files in test/ importable
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pytorch_test_dir = os.path.dirname(os.path.dirname(os.path.realpath(__file__)))
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sys.path.append(pytorch_test_dir)
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from torch.testing._internal.jit_utils import JitTestCase
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from torch.testing._internal.common_utils import skipIfNoLapack
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if __name__ == '__main__':
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raise RuntimeError("This test file is not meant to be run directly, use:\n\n"
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"\tpython test/test_jit.py TESTNAME\n\n"
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"instead.")
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# Smoke tests for export methods
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class TestExportModes(JitTestCase):
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class MyModel(nn.Module):
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def __init__(self):
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super(TestExportModes.MyModel, self).__init__()
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def forward(self, x):
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return x.transpose(0, 1)
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def test_protobuf(self):
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torch_model = TestExportModes.MyModel()
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fake_input = Variable(torch.randn(1, 1, 224, 224), requires_grad=True)
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f = io.BytesIO()
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torch.onnx._export(torch_model, (fake_input), f, verbose=False,
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export_type=torch.onnx.ExportTypes.PROTOBUF_FILE)
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def test_zipfile(self):
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torch_model = TestExportModes.MyModel()
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fake_input = Variable(torch.randn(1, 1, 224, 224), requires_grad=True)
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f = io.BytesIO()
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torch.onnx._export(torch_model, (fake_input), f, verbose=False,
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export_type=torch.onnx.ExportTypes.ZIP_ARCHIVE)
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def test_compressed_zipfile(self):
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torch_model = TestExportModes.MyModel()
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fake_input = Variable(torch.randn(1, 1, 224, 224), requires_grad=True)
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f = io.BytesIO()
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torch.onnx._export(torch_model, (fake_input), f, verbose=False,
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export_type=torch.onnx.ExportTypes.COMPRESSED_ZIP_ARCHIVE)
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def test_directory(self):
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torch_model = TestExportModes.MyModel()
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fake_input = Variable(torch.randn(1, 1, 224, 224), requires_grad=True)
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d = tempfile.mkdtemp()
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torch.onnx._export(torch_model, (fake_input), d, verbose=False,
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export_type=torch.onnx.ExportTypes.DIRECTORY)
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shutil.rmtree(d)
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def test_onnx_multiple_return(self):
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@torch.jit.script
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def foo(a):
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return (a, a)
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f = io.BytesIO()
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x = torch.ones(3)
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torch.onnx._export(foo, (x,), f, example_outputs=(x, x))
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@skipIfNoLapack
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def test_aten_fallback(self):
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class ModelWithAtenNotONNXOp(nn.Module):
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def forward(self, x, y):
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abcd = x + y
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defg = torch.linalg.qr(abcd)
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return defg
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x = torch.rand(3, 4)
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y = torch.rand(3, 4)
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f = io.BytesIO()
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torch.onnx.export_to_pretty_string(
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ModelWithAtenNotONNXOp(), (x, y), f,
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add_node_names=False,
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do_constant_folding=False,
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operator_export_type=OperatorExportTypes.ONNX_ATEN_FALLBACK)
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# torch.fmod is using to test ONNX_ATEN.
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# If you plan to remove fmod from aten, or found this test failed.
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# please contact @Rui.
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def test_onnx_aten(self):
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class ModelWithAtenFmod(nn.Module):
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def forward(self, x, y):
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return torch.fmod(x, y)
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f = io.BytesIO()
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x = torch.randn(3, 4, dtype=torch.float32)
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y = torch.randn(3, 4, dtype=torch.float32)
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torch.onnx.export_to_pretty_string(
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ModelWithAtenFmod(), (x, y), f,
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add_node_names=False,
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do_constant_folding=False,
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operator_export_type=OperatorExportTypes.ONNX_ATEN)
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