mirror of
https://github.com/zebrajr/pytorch.git
synced 2025-12-07 12:21:27 +01:00
4cb534f92e
Summary:
This is an automatic change generated by the following script:
```
#!/usr/bin/env python3
from subprocess import check_output, check_call
import os
def get_compiled_files_list():
import json
with open("build/compile_commands.json") as f:
data = json.load(f)
files = [os.path.relpath(node['file']) for node in data]
for idx, fname in enumerate(files):
if fname.startswith('build/') and fname.endswith('.DEFAULT.cpp'):
files[idx] = fname[len('build/'):-len('.DEFAULT.cpp')]
return files
def run_clang_tidy(fname):
check_call(["python3", "tools/clang_tidy.py", "-c", "build", "-x", fname,"-s"])
changes = check_output(["git", "ls-files", "-m"])
if len(changes) == 0:
return
check_call(["git", "commit","--all", "-m", f"NOLINT stubs for {fname}"])
def main():
git_files = check_output(["git", "ls-files"]).decode("ascii").split("\n")
compiled_files = get_compiled_files_list()
for idx, fname in enumerate(git_files):
if fname not in compiled_files:
continue
if fname.startswith("caffe2/contrib/aten/"):
continue
print(f"[{idx}/{len(git_files)}] Processing {fname}")
run_clang_tidy(fname)
if __name__ == "__main__":
main()
```
Pull Request resolved: https://github.com/pytorch/pytorch/pull/56892
Reviewed By: H-Huang
Differential Revision: D27991944
Pulled By: malfet
fbshipit-source-id: 5415e1eb2c1b34319a4f03024bfaa087007d7179
166 lines
6.4 KiB
C++
166 lines
6.4 KiB
C++
#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 {};
|
|
|
|
// NOLINTNEXTLINE(cppcoreguidelines-avoid-non-const-global-variables)
|
|
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());
|
|
}
|
|
|
|
// NOLINTNEXTLINE(cppcoreguidelines-avoid-non-const-global-variables)
|
|
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());
|
|
}
|
|
|
|
// NOLINTNEXTLINE(cppcoreguidelines-avoid-non-const-global-variables)
|
|
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"));
|
|
}
|
|
|
|
// NOLINTNEXTLINE(cppcoreguidelines-avoid-non-const-global-variables)
|
|
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);
|
|
}
|
|
|
|
// NOLINTNEXTLINE(cppcoreguidelines-avoid-non-const-global-variables)
|
|
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>());
|
|
}
|
|
|
|
// NOLINTNEXTLINE(cppcoreguidelines-avoid-non-const-global-variables)
|
|
TEST_F(ParameterDictTest, SimpleUpdate) {
|
|
ParameterDict dict;
|
|
ParameterDict wrongDict;
|
|
ParameterDict rightDict;
|
|
dict->insert("A", torch::tensor({1.0}));
|
|
// NOLINTNEXTLINE(cppcoreguidelines-avoid-magic-numbers)
|
|
dict->insert("B", torch::tensor({2.0}));
|
|
// NOLINTNEXTLINE(cppcoreguidelines-avoid-magic-numbers)
|
|
dict->insert("C", torch::tensor({3.0}));
|
|
// NOLINTNEXTLINE(cppcoreguidelines-avoid-magic-numbers)
|
|
wrongDict->insert("A", torch::tensor({5.0}));
|
|
// NOLINTNEXTLINE(cppcoreguidelines-avoid-magic-numbers)
|
|
wrongDict->insert("D", torch::tensor({5.0}));
|
|
ASSERT_THROWS_WITH(dict->update(*wrongDict), "Parameter 'D' is not defined");
|
|
// NOLINTNEXTLINE(cppcoreguidelines-avoid-magic-numbers)
|
|
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>());
|
|
}
|
|
|
|
// NOLINTNEXTLINE(cppcoreguidelines-avoid-non-const-global-variables)
|
|
TEST_F(ParameterDictTest, Keys) {
|
|
torch::OrderedDict<std::string, torch::Tensor> params = {
|
|
{"a", torch::tensor({1.0})},
|
|
// NOLINTNEXTLINE(cppcoreguidelines-avoid-magic-numbers)
|
|
{"b", torch::tensor({2.0})},
|
|
// NOLINTNEXTLINE(cppcoreguidelines-avoid-magic-numbers)
|
|
{"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);
|
|
}
|
|
|
|
// NOLINTNEXTLINE(cppcoreguidelines-avoid-non-const-global-variables)
|
|
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 = 0; i < values.size(); i += 1) {
|
|
ASSERT_TRUE(torch::all(torch::eq(values[i], true_values[i])).item<bool>());
|
|
}
|
|
}
|
|
|
|
// NOLINTNEXTLINE(cppcoreguidelines-avoid-non-const-global-variables)
|
|
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());
|
|
}
|
|
|
|
// NOLINTNEXTLINE(cppcoreguidelines-avoid-non-const-global-variables)
|
|
TEST_F(ParameterDictTest, PrettyPrintParameterDict) {
|
|
torch::OrderedDict<std::string, torch::Tensor> params = {
|
|
{"a", torch::tensor({1.0})},
|
|
// NOLINTNEXTLINE(cppcoreguidelines-avoid-magic-numbers)
|
|
{"b", torch::tensor({2.0, 1.0})},
|
|
// NOLINTNEXTLINE(cppcoreguidelines-avoid-magic-numbers)
|
|
{"c", torch::tensor({{3.0}, {2.1}})},
|
|
// NOLINTNEXTLINE(cppcoreguidelines-avoid-magic-numbers)
|
|
{"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"
|
|
")");
|
|
}
|