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
|
#include <gtest/gtest.h>
#include <torch/torch.h>
#include <algorithm>
#include <memory>
#include <vector>
#include <test/cpp/api/support.h>
using namespace torch::nn;
using namespace torch::test;
struct ParameterDictTest : torch::test::SeedingFixture {};
TEST_F(ParameterDictTest, ConstructFromTensor) {
ParameterDict dict;
torch::Tensor ta = torch::randn({1, 2}, torch::requires_grad(true));
torch::Tensor tb = torch::randn({1, 2}, torch::requires_grad(false));
torch::Tensor tc = torch::randn({1, 2});
ASSERT_TRUE(ta.requires_grad());
ASSERT_FALSE(tb.requires_grad());
dict->insert("A", ta);
dict->insert("B", tb);
dict->insert("C", tc);
ASSERT_EQ(dict->size(), 3);
ASSERT_TRUE(torch::all(torch::eq(dict["A"], ta)).item<bool>());
ASSERT_TRUE(dict["A"].requires_grad());
ASSERT_TRUE(torch::all(torch::eq(dict["B"], tb)).item<bool>());
ASSERT_FALSE(dict["B"].requires_grad());
}
TEST_F(ParameterDictTest, ConstructFromOrderedDict) {
torch::Tensor ta = torch::randn({1, 2}, torch::requires_grad(true));
torch::Tensor tb = torch::randn({1, 2}, torch::requires_grad(false));
torch::Tensor tc = torch::randn({1, 2});
torch::OrderedDict<std::string, torch::Tensor> params = {
{"A", ta}, {"B", tb}, {"C", tc}};
auto dict = torch::nn::ParameterDict(params);
ASSERT_EQ(dict->size(), 3);
ASSERT_TRUE(torch::all(torch::eq(dict["A"], ta)).item<bool>());
ASSERT_TRUE(dict["A"].requires_grad());
ASSERT_TRUE(torch::all(torch::eq(dict["B"], tb)).item<bool>());
ASSERT_FALSE(dict["B"].requires_grad());
}
TEST_F(ParameterDictTest, InsertAndContains) {
ParameterDict dict;
dict->insert("A", torch::tensor({1.0}));
ASSERT_EQ(dict->size(), 1);
ASSERT_TRUE(dict->contains("A"));
ASSERT_FALSE(dict->contains("C"));
}
TEST_F(ParameterDictTest, InsertAndClear) {
ParameterDict dict;
dict->insert("A", torch::tensor({1.0}));
ASSERT_EQ(dict->size(), 1);
dict->clear();
ASSERT_EQ(dict->size(), 0);
}
TEST_F(ParameterDictTest, InsertAndPop) {
ParameterDict dict;
dict->insert("A", torch::tensor({1.0}));
ASSERT_EQ(dict->size(), 1);
ASSERT_THROWS_WITH(dict->pop("B"), "Parameter 'B' is not defined");
torch::Tensor p = dict->pop("A");
ASSERT_EQ(dict->size(), 0);
ASSERT_TRUE(torch::eq(p, torch::tensor({1.0})).item<bool>());
}
TEST_F(ParameterDictTest, SimpleUpdate) {
ParameterDict dict;
ParameterDict wrongDict;
ParameterDict rightDict;
dict->insert("A", torch::tensor({1.0}));
dict->insert("B", torch::tensor({2.0}));
dict->insert("C", torch::tensor({3.0}));
wrongDict->insert("A", torch::tensor({5.0}));
wrongDict->insert("D", torch::tensor({5.0}));
ASSERT_THROWS_WITH(dict->update(*wrongDict), "Parameter 'D' is not defined");
rightDict->insert("A", torch::tensor({5.0}));
dict->update(*rightDict);
ASSERT_EQ(dict->size(), 3);
ASSERT_TRUE(torch::eq(dict["A"], torch::tensor({5.0})).item<bool>());
}
TEST_F(ParameterDictTest, Keys) {
torch::OrderedDict<std::string, torch::Tensor> params = {
{"a", torch::tensor({1.0})},
{"b", torch::tensor({2.0})},
{"c", torch::tensor({1.0, 2.0})}};
auto dict = torch::nn::ParameterDict(params);
std::vector<std::string> keys = dict->keys();
std::vector<std::string> true_keys{"a", "b", "c"};
ASSERT_EQ(keys, true_keys);
}
TEST_F(ParameterDictTest, Values) {
torch::Tensor ta = torch::randn({1, 2}, torch::requires_grad(true));
torch::Tensor tb = torch::randn({1, 2}, torch::requires_grad(false));
torch::Tensor tc = torch::randn({1, 2});
torch::OrderedDict<std::string, torch::Tensor> params = {
{"a", ta}, {"b", tb}, {"c", tc}};
auto dict = torch::nn::ParameterDict(params);
std::vector<torch::Tensor> values = dict->values();
std::vector<torch::Tensor> true_values{ta, tb, tc};
for (auto i = 0U; i < values.size(); i += 1) {
ASSERT_TRUE(torch::all(torch::eq(values[i], true_values[i])).item<bool>());
}
}
TEST_F(ParameterDictTest, Get) {
ParameterDict dict;
torch::Tensor ta = torch::randn({1, 2}, torch::requires_grad(true));
torch::Tensor tb = torch::randn({1, 2}, torch::requires_grad(false));
torch::Tensor tc = torch::randn({1, 2});
ASSERT_TRUE(ta.requires_grad());
ASSERT_FALSE(tb.requires_grad());
dict->insert("A", ta);
dict->insert("B", tb);
dict->insert("C", tc);
ASSERT_EQ(dict->size(), 3);
ASSERT_TRUE(torch::all(torch::eq(dict->get("A"), ta)).item<bool>());
ASSERT_TRUE(dict->get("A").requires_grad());
ASSERT_TRUE(torch::all(torch::eq(dict->get("B"), tb)).item<bool>());
ASSERT_FALSE(dict->get("B").requires_grad());
}
TEST_F(ParameterDictTest, PrettyPrintParameterDict) {
torch::OrderedDict<std::string, torch::Tensor> params = {
{"a", torch::tensor({1.0})},
{"b", torch::tensor({2.0, 1.0})},
{"c", torch::tensor({{3.0}, {2.1}})},
{"d", torch::tensor({{3.0, 1.3}, {1.2, 2.1}})}};
auto dict = torch::nn::ParameterDict(params);
ASSERT_EQ(
c10::str(dict),
"torch::nn::ParameterDict(\n"
"(a): Parameter containing: [Float of size [1]]\n"
"(b): Parameter containing: [Float of size [2]]\n"
"(c): Parameter containing: [Float of size [2, 1]]\n"
"(d): Parameter containing: [Float of size [2, 2]]\n"
")");
}
|
