Spaces:
Sleeping
Sleeping
File size: 9,831 Bytes
dc2106c |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 185 186 187 188 189 190 191 192 193 194 195 196 197 198 199 200 201 202 203 204 205 206 207 208 209 210 211 212 213 214 215 216 217 218 219 220 221 222 223 224 225 226 227 228 229 230 231 232 |
# Copyright (c) ONNX Project Contributors
# SPDX-License-Identifier: Apache-2.0
import io
import os
import pathlib
import tempfile
import unittest
import google.protobuf.message
import google.protobuf.text_format
import parameterized
import onnx
from onnx import serialization
def _simple_model() -> onnx.ModelProto:
model = onnx.ModelProto()
model.ir_version = onnx.IR_VERSION
model.producer_name = "onnx-test"
model.graph.name = "test"
return model
def _simple_tensor() -> onnx.TensorProto:
tensor = onnx.helper.make_tensor(
name="test-tensor",
data_type=onnx.TensorProto.FLOAT,
dims=(2, 3, 4),
vals=[x + 0.5 for x in range(24)],
)
return tensor
@parameterized.parameterized_class(
[
{"format": "protobuf"},
{"format": "textproto"},
{"format": "json"},
{"format": "onnxtxt"},
]
)
class TestIO(unittest.TestCase):
format: str
def test_load_model_when_input_is_bytes(self) -> None:
proto = _simple_model()
proto_string = serialization.registry.get(self.format).serialize_proto(proto)
loaded_proto = onnx.load_model_from_string(proto_string, format=self.format)
self.assertEqual(proto, loaded_proto)
def test_save_and_load_model_when_input_has_read_function(self) -> None:
proto = _simple_model()
# When the proto is a bytes representation provided to `save_model`,
# it should always be a serialized binary protobuf representation. Aka. format="protobuf"
# The saved file format is specified by the `format` argument.
proto_string = serialization.registry.get("protobuf").serialize_proto(proto)
f = io.BytesIO()
onnx.save_model(proto_string, f, format=self.format)
loaded_proto = onnx.load_model(io.BytesIO(f.getvalue()), format=self.format)
self.assertEqual(proto, loaded_proto)
def test_save_and_load_model_when_input_is_file_name(self) -> None:
proto = _simple_model()
with tempfile.TemporaryDirectory() as temp_dir:
model_path = os.path.join(temp_dir, "model.onnx")
onnx.save_model(proto, model_path, format=self.format)
loaded_proto = onnx.load_model(model_path, format=self.format)
self.assertEqual(proto, loaded_proto)
def test_save_and_load_model_when_input_is_pathlike(self) -> None:
proto = _simple_model()
with tempfile.TemporaryDirectory() as temp_dir:
model_path = pathlib.Path(temp_dir, "model.onnx")
onnx.save_model(proto, model_path, format=self.format)
loaded_proto = onnx.load_model(model_path, format=self.format)
self.assertEqual(proto, loaded_proto)
@parameterized.parameterized_class(
[
{"format": "protobuf"},
{"format": "textproto"},
{"format": "json"},
# The onnxtxt format does not support saving/loading tensors yet
]
)
class TestIOTensor(unittest.TestCase):
"""Test loading and saving of TensorProto."""
format: str
def test_load_tensor_when_input_is_bytes(self) -> None:
proto = _simple_tensor()
proto_string = serialization.registry.get(self.format).serialize_proto(proto)
loaded_proto = onnx.load_tensor_from_string(proto_string, format=self.format)
self.assertEqual(proto, loaded_proto)
def test_save_and_load_tensor_when_input_has_read_function(self) -> None:
# Test if input has a read function
proto = _simple_tensor()
f = io.BytesIO()
onnx.save_tensor(proto, f, format=self.format)
loaded_proto = onnx.load_tensor(io.BytesIO(f.getvalue()), format=self.format)
self.assertEqual(proto, loaded_proto)
def test_save_and_load_tensor_when_input_is_file_name(self) -> None:
# Test if input is a file name
proto = _simple_tensor()
with tempfile.TemporaryDirectory() as temp_dir:
model_path = os.path.join(temp_dir, "model.onnx")
onnx.save_tensor(proto, model_path, format=self.format)
loaded_proto = onnx.load_tensor(model_path, format=self.format)
self.assertEqual(proto, loaded_proto)
def test_save_and_load_tensor_when_input_is_pathlike(self) -> None:
# Test if input is a file name
proto = _simple_tensor()
with tempfile.TemporaryDirectory() as temp_dir:
model_path = pathlib.Path(temp_dir, "model.onnx")
onnx.save_tensor(proto, model_path, format=self.format)
loaded_proto = onnx.load_tensor(model_path, format=self.format)
self.assertEqual(proto, loaded_proto)
class TestSaveAndLoadFileExtensions(unittest.TestCase):
def test_save_model_picks_correct_format_from_extension(self) -> None:
proto = _simple_model()
with tempfile.TemporaryDirectory() as temp_dir:
model_path = os.path.join(temp_dir, "model.textproto")
# No format is specified, so the extension should be used to determine the format
onnx.save_model(proto, model_path)
loaded_proto = onnx.load_model(model_path, format="textproto")
self.assertEqual(proto, loaded_proto)
def test_load_model_picks_correct_format_from_extension(self) -> None:
proto = _simple_model()
with tempfile.TemporaryDirectory() as temp_dir:
model_path = os.path.join(temp_dir, "model.textproto")
onnx.save_model(proto, model_path, format="textproto")
# No format is specified, so the extension should be used to determine the format
loaded_proto = onnx.load_model(model_path)
self.assertEqual(proto, loaded_proto)
def test_save_model_uses_format_when_it_is_specified(self) -> None:
proto = _simple_model()
with tempfile.TemporaryDirectory() as temp_dir:
model_path = os.path.join(temp_dir, "model.textproto")
# `format` is specified. It should take precedence over the extension
onnx.save_model(proto, model_path, format="protobuf")
loaded_proto = onnx.load_model(model_path, format="protobuf")
self.assertEqual(proto, loaded_proto)
with self.assertRaises(google.protobuf.text_format.ParseError):
# Loading it as textproto (by file extension) should fail
onnx.load_model(model_path)
def test_load_model_uses_format_when_it_is_specified(self) -> None:
proto = _simple_model()
with tempfile.TemporaryDirectory() as temp_dir:
model_path = os.path.join(temp_dir, "model.protobuf")
onnx.save_model(proto, model_path)
with self.assertRaises(google.protobuf.text_format.ParseError):
# `format` is specified. It should take precedence over the extension
# Loading it as textproto should fail
onnx.load_model(model_path, format="textproto")
loaded_proto = onnx.load_model(model_path, format="protobuf")
self.assertEqual(proto, loaded_proto)
def test_load_and_save_model_to_path_without_specifying_extension_succeeds(
self,
) -> None:
proto = _simple_model()
with tempfile.TemporaryDirectory() as temp_dir:
# No extension is specified
model_path = os.path.join(temp_dir, "model")
onnx.save_model(proto, model_path, format="textproto")
with self.assertRaises(google.protobuf.message.DecodeError):
# `format` is not specified. load_model should assume protobuf
# and fail to load it
onnx.load_model(model_path)
loaded_proto = onnx.load_model(model_path, format="textproto")
self.assertEqual(proto, loaded_proto)
def test_load_and_save_model_without_specifying_extension_or_format_defaults_to_protobuf(
self,
) -> None:
proto = _simple_model()
with tempfile.TemporaryDirectory() as temp_dir:
# No extension is specified
model_path = os.path.join(temp_dir, "model")
onnx.save_model(proto, model_path)
with self.assertRaises(google.protobuf.text_format.ParseError):
# The model is saved as protobuf, so loading it as textproto should fail
onnx.load_model(model_path, format="textproto")
loaded_proto = onnx.load_model(model_path)
self.assertEqual(proto, loaded_proto)
loaded_proto_as_explicitly_protobuf = onnx.load_model(
model_path, format="protobuf"
)
self.assertEqual(proto, loaded_proto_as_explicitly_protobuf)
class TestBasicFunctions(unittest.TestCase):
def test_protos_exist(self) -> None:
# The proto classes should exist
_ = onnx.AttributeProto
_ = onnx.NodeProto
_ = onnx.GraphProto
_ = onnx.ModelProto
def test_version_exists(self) -> None:
model = onnx.ModelProto()
# When we create it, graph should not have a version string.
self.assertFalse(model.HasField("ir_version"))
# We should touch the version so it is annotated with the current
# ir version of the running ONNX
model.ir_version = onnx.IR_VERSION
model_string = model.SerializeToString()
model.ParseFromString(model_string)
self.assertTrue(model.HasField("ir_version"))
# Check if the version is correct.
self.assertEqual(model.ir_version, onnx.IR_VERSION)
if __name__ == "__main__":
unittest.main()
|