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e71b526e7e
pytorch/test/cpp/api/parameterlist.cpp
Nikita Shulga 4cb534f92e Make PyTorch code-base clang-tidy compliant (#56892) 
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
2021-04-28 14:10:25 -07:00

171 lines
6.3 KiB
C++
Raw Blame History

#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 ParameterListTest : torch::test::SeedingFixture {};
// NOLINTNEXTLINE(cppcoreguidelines-avoid-non-const-global-variables)
TEST_F(ParameterListTest, ConstructsFromSharedPointer) {
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());
ParameterList list(ta, tb, tc);
ASSERT_EQ(list->size(), 3);
}
// NOLINTNEXTLINE(cppcoreguidelines-avoid-non-const-global-variables)
TEST_F(ParameterListTest, isEmpty) {
torch::Tensor ta = torch::randn({1, 2}, torch::requires_grad(true));
ParameterList list;
ASSERT_TRUE(list->is_empty());
list->append(ta);
ASSERT_FALSE(list->is_empty());
ASSERT_EQ(list->size(), 1);
}
// NOLINTNEXTLINE(cppcoreguidelines-avoid-non-const-global-variables)
TEST_F(ParameterListTest, PushBackAddsAnElement) {
ParameterList list;
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::Tensor td = torch::randn({1, 2, 3});
ASSERT_EQ(list->size(), 0);
ASSERT_TRUE(list->is_empty());
list->append(ta);
ASSERT_EQ(list->size(), 1);
list->append(tb);
ASSERT_EQ(list->size(), 2);
list->append(tc);
ASSERT_EQ(list->size(), 3);
list->append(td);
ASSERT_EQ(list->size(), 4);
}
// NOLINTNEXTLINE(cppcoreguidelines-avoid-non-const-global-variables)
TEST_F(ParameterListTest, ForEachLoop) {
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::Tensor td = torch::randn({1, 2, 3});
ParameterList list(ta, tb, tc, td);
std::vector<torch::Tensor> params = {ta, tb, tc, td};
ASSERT_EQ(list->size(), 4);
int idx = 0;
for (const auto& pair : *list) {
ASSERT_TRUE(
torch::all(torch::eq(pair.value(), params[idx++])).item<bool>());
}
}
// NOLINTNEXTLINE(cppcoreguidelines-avoid-non-const-global-variables)
TEST_F(ParameterListTest, AccessWithAt) {
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::Tensor td = torch::randn({1, 2, 3});
std::vector<torch::Tensor> params = {ta, tb, tc, td};
ParameterList list;
for (auto& param : params) {
list->append(param);
}
ASSERT_EQ(list->size(), 4);
// returns the correct module for a given index
for (size_t i = 0; i < params.size(); ++i) {
ASSERT_TRUE(torch::all(torch::eq(list->at(i), params[i])).item<bool>());
}
for (size_t i = 0; i < params.size(); ++i) {
ASSERT_TRUE(torch::all(torch::eq(list[i], params[i])).item<bool>());
}
// throws for a bad index
ASSERT_THROWS_WITH(list->at(params.size() + 100), "Index out of range");
ASSERT_THROWS_WITH(list->at(params.size() + 1), "Index out of range");
ASSERT_THROWS_WITH(list[params.size() + 1], "Index out of range");
}
// NOLINTNEXTLINE(cppcoreguidelines-avoid-non-const-global-variables)
TEST_F(ParameterListTest, ExtendPushesParametersFromOtherParameterList) {
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::Tensor td = torch::randn({1, 2, 3});
torch::Tensor te = torch::randn({1, 2});
torch::Tensor tf = torch::randn({1, 2, 3});
ParameterList a(ta, tb);
ParameterList b(tc, td);
a->extend(*b);
ASSERT_EQ(a->size(), 4);
ASSERT_TRUE(torch::all(torch::eq(a[0], ta)).item<bool>());
ASSERT_TRUE(torch::all(torch::eq(a[1], tb)).item<bool>());
ASSERT_TRUE(torch::all(torch::eq(a[2], tc)).item<bool>());
ASSERT_TRUE(torch::all(torch::eq(a[3], td)).item<bool>());
ASSERT_EQ(b->size(), 2);
ASSERT_TRUE(torch::all(torch::eq(b[0], tc)).item<bool>());
ASSERT_TRUE(torch::all(torch::eq(b[1], td)).item<bool>());
std::vector<torch::Tensor> c = {te, tf};
b->extend(c);
ASSERT_EQ(b->size(), 4);
ASSERT_TRUE(torch::all(torch::eq(b[0], tc)).item<bool>());
ASSERT_TRUE(torch::all(torch::eq(b[1], td)).item<bool>());
ASSERT_TRUE(torch::all(torch::eq(b[2], te)).item<bool>());
ASSERT_TRUE(torch::all(torch::eq(b[3], tf)).item<bool>());
}
// NOLINTNEXTLINE(cppcoreguidelines-avoid-non-const-global-variables)
TEST_F(ParameterListTest, PrettyPrintParameterList) {
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});
ParameterList list(ta, tb, tc);
ASSERT_EQ(
c10::str(list),
"torch::nn::ParameterList(\n"
"(0): Parameter containing: [Float of size [1, 2]]\n"
"(1): Parameter containing: [Float of size [1, 2]]\n"
"(2): Parameter containing: [Float of size [1, 2]]\n"
")");
}
// NOLINTNEXTLINE(cppcoreguidelines-avoid-non-const-global-variables)
TEST_F(ParameterListTest, IncrementAdd) {
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::Tensor td = torch::randn({1, 2, 3});
torch::Tensor te = torch::randn({1, 2});
torch::Tensor tf = torch::randn({1, 2, 3});
ParameterList listA(ta, tb, tc);
ParameterList listB(td, te, tf);
std::vector<torch::Tensor> tensors{ta, tb, tc, td, te, tf};
int idx = 0;
*listA += *listB;
ASSERT_TRUE(torch::all(torch::eq(listA[0], ta)).item<bool>());
ASSERT_TRUE(torch::all(torch::eq(listA[1], tb)).item<bool>());
ASSERT_TRUE(torch::all(torch::eq(listA[2], tc)).item<bool>());
ASSERT_TRUE(torch::all(torch::eq(listA[3], td)).item<bool>());
ASSERT_TRUE(torch::all(torch::eq(listA[4], te)).item<bool>());
ASSERT_TRUE(torch::all(torch::eq(listA[5], tf)).item<bool>());
for (const auto& P : listA->named_parameters(false))
ASSERT_TRUE(torch::all(torch::eq(P.value(), tensors[idx++])).item<bool>());
ASSERT_EQ(idx, 6);
}
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