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test_utility_funs.py
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from __future__ import absolute_import, division, print_function, unicode_literals
from test_pytorch_common import TestCase, run_tests
import torch
import torch.onnx
from torch.onnx import utils
from torch.onnx.symbolic_helper import _set_opset_version
import onnx
import io
import copy
class TestUtilityFuns(TestCase):
opset_version = 9
def test_is_in_onnx_export(self):
test_self = self
class MyModule(torch.nn.Module):
def forward(self, x):
test_self.assertTrue(torch.onnx.is_in_onnx_export())
raise ValueError
return x + 1
x = torch.randn(3, 4)
f = io.BytesIO()
try:
torch.onnx.export(MyModule(), x, f, opset_version=self.opset_version)
except ValueError:
self.assertFalse(torch.onnx.is_in_onnx_export())
def test_validate_dynamic_axes_invalid_input_output_name(self):
import warnings
with warnings.catch_warnings(record=True) as w:
warnings.simplefilter("always")
utils._validate_dynamic_axes({'input1': {}, 'output': {},
'invalid_name1': {}, 'invalid_name2': {}},
None, ['input1', 'input2'], ['output'])
messages = [str(warning.message) for warning in w]
assert "Provided key invalid_name1 for dynamic axes is not a valid input/output name" in messages
assert "Provided key invalid_name2 for dynamic axes is not a valid input/output name" in messages
assert len(messages) == 2
def test_constant_fold_transpose(self):
class TransposeModule(torch.nn.Module):
def forward(self, x):
a = torch.tensor([[1., 2., 3.], [4., 5., 6.]])
b = torch.transpose(a, 1, 0)
return b + x
_set_opset_version(self.opset_version)
x = torch.ones(3, 2)
graph, _, __ = utils._model_to_graph(TransposeModule(), (x, ),
do_constant_folding=True,
_disable_torch_constant_prop=True)
for node in graph.nodes():
assert node.kind() != "onnx::Transpose"
assert node.kind() != "onnx::Cast"
assert node.kind() != "onnx::Constant"
assert len(list(graph.nodes())) == 1
def test_constant_fold_slice(self):
class NarrowModule(torch.nn.Module):
def forward(self, x):
a = torch.tensor([[1., 2., 3.], [4., 5., 6.]])
b = torch.narrow(a, 0, 0, 1)
return b + x
_set_opset_version(self.opset_version)
x = torch.ones(1, 3)
graph, _, __ = utils._model_to_graph(NarrowModule(), (x, ),
do_constant_folding=True,
_disable_torch_constant_prop=True)
for node in graph.nodes():
assert node.kind() != "onnx::Slice"
assert node.kind() != "onnx::Cast"
assert node.kind() != "onnx::Constant"
assert len(list(graph.nodes())) == 1
def test_constant_fold_slice_index_exceeds_dim(self):
class SliceIndexExceedsDimModule(torch.nn.Module):
def forward(self, x):
a = torch.tensor([[1., 2., 3.], [4., 5., 6.]])
b = a[1:10] # index exceeds dimension
return b + x
_set_opset_version(self.opset_version)
x = torch.ones(1, 3)
graph, _, __ = utils._model_to_graph(SliceIndexExceedsDimModule(), (x, ),
do_constant_folding=True,
_disable_torch_constant_prop=True)
for node in graph.nodes():
assert node.kind() != "onnx::Slice"
assert node.kind() != "onnx::Cast"
assert node.kind() != "onnx::Constant"
assert len(list(graph.nodes())) == 1
def test_constant_fold_slice_negative_index(self):
class SliceNegativeIndexModule(torch.nn.Module):
def forward(self, x):
a = torch.tensor([[1., 2., 3.], [4., 5., 6.]])
b = a[0:-1] # index relative to the end
return b + x
_set_opset_version(self.opset_version)
x = torch.ones(1, 3)
graph, _, __ = utils._model_to_graph(SliceNegativeIndexModule(), (x, ),
do_constant_folding=True,
_disable_torch_constant_prop=True)
for node in graph.nodes():
assert node.kind() != "onnx::Slice"
assert node.kind() != "onnx::Cast"
assert node.kind() != "onnx::Constant"
assert len(list(graph.nodes())) == 1
def test_constant_fold_unsqueeze(self):
class UnsqueezeModule(torch.nn.Module):
def forward(self, x):
a = torch.tensor([[1., 2., 3.], [4., 5., 6.]])
b = torch.unsqueeze(a, 0)
return b + x
_set_opset_version(self.opset_version)
x = torch.ones(1, 2, 3)
graph, _, __ = utils._model_to_graph(UnsqueezeModule(), (x, ),
do_constant_folding=True,
_disable_torch_constant_prop=True)
for node in graph.nodes():
assert node.kind() != "onnx::Unsqueeeze"
assert node.kind() != "onnx::Cast"
assert node.kind() != "onnx::Constant"
assert len(list(graph.nodes())) == 1
def test_constant_fold_concat(self):
class ConcatModule(torch.nn.Module):
def forward(self, x):
a = torch.tensor([[1., 2., 3.]])
b = torch.tensor([[4., 5., 6.]])
c = torch.cat((a, b), 0)
return b + c
_set_opset_version(self.opset_version)
x = torch.ones(2, 3)
graph, _, __ = utils._model_to_graph(ConcatModule(), (x, ),
do_constant_folding=True,
_disable_torch_constant_prop=True)
for node in graph.nodes():
assert node.kind() != "onnx::Concat"
assert node.kind() != "onnx::Cast"
assert node.kind() != "onnx::Constant"
assert len(list(graph.nodes())) == 1
def test_constant_fold_lstm(self):
class GruNet(torch.nn.Module):
def __init__(self):
super(GruNet, self).__init__()
self.mygru = torch.nn.GRU(7, 3, 1, bidirectional=False)
def forward(self, input, initial_state):
return self.mygru(input, initial_state)
_set_opset_version(self.opset_version)
input = torch.randn(5, 3, 7)
h0 = torch.randn(1, 3, 3)
graph, _, __ = utils._model_to_graph(GruNet(), (input, h0),
do_constant_folding=True)
for node in graph.nodes():
assert node.kind() != "onnx::Slice"
assert node.kind() != "onnx::Concat"
assert node.kind() != "onnx::Unsqueeze"
assert len(list(graph.nodes())) == 3
def test_constant_fold_transpose_matmul(self):
class MatMulNet(torch.nn.Module):
def __init__(self):
super(MatMulNet, self).__init__()
self.B = torch.nn.Parameter(torch.ones(5, 3))
def forward(self, A):
return torch.matmul(A, torch.transpose(self.B, -1, -2))
_set_opset_version(self.opset_version)
A = torch.randn(2, 3)
graph, _, __ = utils._model_to_graph(MatMulNet(), (A),
do_constant_folding=True)
for node in graph.nodes():
assert node.kind() != "onnx::Transpose"
assert len(list(graph.nodes())) == 1
def test_strip_doc_string(self):
class MyModule(torch.nn.Module):
def forward(self, input):
return torch.exp(input)
x = torch.randn(3, 4)
def is_model_stripped(f, strip_doc_string=None):
if strip_doc_string is None:
torch.onnx.export(MyModule(), x, f, opset_version=self.opset_version)
else:
torch.onnx.export(MyModule(), x, f, strip_doc_string=strip_doc_string,
opset_version=self.opset_version)
model = onnx.load(io.BytesIO(f.getvalue()))
model_strip = copy.copy(model)
onnx.helper.strip_doc_string(model_strip)
return model == model_strip
# test strip_doc_string=True (default)
self.assertTrue(is_model_stripped(io.BytesIO()))
# test strip_doc_string=False
self.assertFalse(is_model_stripped(io.BytesIO(), False))
# NB: remove this test once DataParallel can be correctly handled
def test_error_on_data_parallel(self):
model = torch.nn.DataParallel(torch.nn.ReflectionPad2d((1, 2, 3, 4)))
x = torch.randn(1, 2, 3, 4)
f = io.BytesIO()
with self.assertRaisesRegex(ValueError,
'torch.nn.DataParallel is not supported by ONNX '
'exporter, please use \'attribute\' module to '
'unwrap model from torch.nn.DataParallel. Try '):
torch.onnx.export(model, x, f, opset_version=self.opset_version)
# opset 10 tests
TestUtilityFuns_opset10 = type(str("TestUtilityFuns_opset10"),
(TestCase,),
dict(TestUtilityFuns.__dict__, opset_version=10))
if __name__ == '__main__':
run_tests()