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
|
#import <XCTest/XCTest.h>
#include <torch/csrc/jit/mobile/import.h>
#include <torch/csrc/jit/mobile/module.h>
#include <torch/script.h>
@interface TestAppTests : XCTestCase
@end
@implementation TestAppTests {
}
- (void)testCoreML {
NSString* modelPath = [[NSBundle bundleForClass:[self class]] pathForResource:@"model_coreml"
ofType:@"ptl"];
auto module = torch::jit::_load_for_mobile(modelPath.UTF8String);
c10::InferenceMode mode;
auto input = torch::ones({1, 3, 224, 224}, at::kFloat);
auto outputTensor = module.forward({input}).toTensor();
XCTAssertTrue(outputTensor.numel() == 1000);
}
- (void)testModel:(NSString*)modelName {
NSString* modelPath = [[NSBundle bundleForClass:[self class]] pathForResource:modelName
ofType:@"ptl"];
XCTAssertNotNil(modelPath, @"Model not found. See https://github.com/pytorch/pytorch/tree/master/test/mobile/model_test#diagnose-failed-test.");
[self runModel:modelPath];
// model generated on the fly
NSString* onTheFlyModelName = [NSString stringWithFormat:@"%@", modelName];
NSString* onTheFlyModelPath = [[NSBundle bundleForClass:[self class]] pathForResource:onTheFlyModelName
ofType:@"ptl"];
XCTAssertNotNil(onTheFlyModelPath, @"On-the-fly model not found. Follow https://github.com/pytorch/pytorch/tree/master/test/mobile/model_test#diagnose-failed-test to generate them and run the setup.rb script again.");
[self runModel:onTheFlyModelPath];
}
- (void)runModel:(NSString*)modelPath {
c10::InferenceMode mode;
auto module = torch::jit::_load_for_mobile(modelPath.UTF8String);
auto has_bundled_input = module.find_method("get_all_bundled_inputs");
if (has_bundled_input) {
c10::IValue bundled_inputs = module.run_method("get_all_bundled_inputs");
c10::List<at::IValue> all_inputs = bundled_inputs.toList();
std::vector<std::vector<at::IValue>> inputs;
for (at::IValue input : all_inputs) {
inputs.push_back(input.toTupleRef().elements());
}
// run with the first bundled input
XCTAssertNoThrow(module.forward(inputs[0]));
} else {
XCTAssertNoThrow(module.forward({}));
}
}
// TODO remove this once updated test script
- (void)testLiteInterpreter {
XCTAssertTrue(true);
}
- (void)testMobileNetV2 {
[self testModel:@"mobilenet_v2"];
}
- (void)testPointwiseOps {
[self testModel:@"pointwise_ops"];
}
- (void)testReductionOps {
[self testModel:@"reduction_ops"];
}
- (void)testComparisonOps {
[self testModel:@"comparison_ops"];
}
- (void)testOtherMathOps {
[self testModel:@"other_math_ops"];
}
- (void)testSpectralOps {
[self testModel:@"spectral_ops"];
}
- (void)testBlasLapackOps {
[self testModel:@"blas_lapack_ops"];
}
- (void)testSamplingOps {
[self testModel:@"sampling_ops"];
}
- (void)testTensorOps {
[self testModel:@"tensor_general_ops"];
}
- (void)testTensorCreationOps {
[self testModel:@"tensor_creation_ops"];
}
- (void)testTensorIndexingOps {
[self testModel:@"tensor_indexing_ops"];
}
- (void)testTensorTypingOps {
[self testModel:@"tensor_typing_ops"];
}
- (void)testTensorViewOps {
[self testModel:@"tensor_view_ops"];
}
- (void)testConvolutionOps {
[self testModel:@"convolution_ops"];
}
- (void)testPoolingOps {
[self testModel:@"pooling_ops"];
}
- (void)testPaddingOps {
[self testModel:@"padding_ops"];
}
- (void)testActivationOps {
[self testModel:@"activation_ops"];
}
- (void)testNormalizationOps {
[self testModel:@"normalization_ops"];
}
- (void)testRecurrentOps {
[self testModel:@"recurrent_ops"];
}
- (void)testTransformerOps {
[self testModel:@"transformer_ops"];
}
- (void)testLinearOps {
[self testModel:@"linear_ops"];
}
- (void)testDropoutOps {
[self testModel:@"dropout_ops"];
}
- (void)testSparseOps {
[self testModel:@"sparse_ops"];
}
- (void)testDistanceFunctionOps {
[self testModel:@"distance_function_ops"];
}
- (void)testLossFunctionOps {
[self testModel:@"loss_function_ops"];
}
- (void)testVisionFunctionOps {
[self testModel:@"vision_function_ops"];
}
- (void)testShuffleOps {
[self testModel:@"shuffle_ops"];
}
- (void)testNNUtilsOps {
[self testModel:@"nn_utils_ops"];
}
- (void)testQuantOps {
[self testModel:@"general_quant_ops"];
}
- (void)testDynamicQuantOps {
[self testModel:@"dynamic_quant_ops"];
}
- (void)testStaticQuantOps {
[self testModel:@"static_quant_ops"];
}
- (void)testFusedQuantOps {
[self testModel:@"fused_quant_ops"];
}
- (void)testTorchScriptBuiltinQuantOps {
[self testModel:@"torchscript_builtin_ops"];
}
- (void)testTorchScriptCollectionQuantOps {
[self testModel:@"torchscript_collection_ops"];
}
@end
|
