未验证 提交 2997b937 编写于 作者: M MaxwellDing 提交者: GitHub

refactor(*): reduce Wsign-compare warning (#3391)

refactor(*): reduce Wsign-compare warning
上级 3d2a99e7
......@@ -82,7 +82,7 @@ Tensor* LightPredictor::GetInputByName(const std::string& name) {
if (element == input_names_.end()) {
LOG(ERROR) << "Model do not have input named with: [" << name
<< "], model's inputs include:";
for (int i = 0; i < input_names_.size(); i++) {
for (size_t i = 0; i < input_names_.size(); i++) {
LOG(ERROR) << "[" << input_names_[i] << "]";
}
return nullptr;
......@@ -114,7 +114,7 @@ void LightPredictor::PrepareFeedFetch() {
auto current_block = cpp_program_desc_.GetBlock<cpp::BlockDesc>(0);
std::vector<cpp::OpDesc*> feeds;
std::vector<cpp::OpDesc*> fetchs;
for (int i = 0; i < current_block->OpsSize(); i++) {
for (size_t i = 0; i < current_block->OpsSize(); i++) {
auto op = current_block->GetOp<cpp::OpDesc>(i);
if (op->Type() == "feed") {
feeds.push_back(op);
......@@ -124,11 +124,11 @@ void LightPredictor::PrepareFeedFetch() {
}
input_names_.resize(feeds.size());
output_names_.resize(fetchs.size());
for (int i = 0; i < feeds.size(); i++) {
for (size_t i = 0; i < feeds.size(); i++) {
input_names_[feeds[i]->GetAttr<int>("col")] =
feeds[i]->Output("Out").front();
}
for (int i = 0; i < fetchs.size(); i++) {
for (size_t i = 0; i < fetchs.size(); i++) {
output_names_[fetchs[i]->GetAttr<int>("col")] =
fetchs[i]->Input("X").front();
}
......
......@@ -37,11 +37,11 @@ TEST(LightAPI, load) {
const std::vector<std::string> inputs = predictor.GetInputNames();
LOG(INFO) << "input size: " << inputs.size();
for (int i = 0; i < inputs.size(); i++) {
for (size_t i = 0; i < inputs.size(); i++) {
LOG(INFO) << "inputnames: " << inputs[i];
}
const std::vector<std::string> outputs = predictor.GetOutputNames();
for (int i = 0; i < outputs.size(); i++) {
for (size_t i = 0; i < outputs.size(); i++) {
LOG(INFO) << "outputnames: " << outputs[i];
}
......
......@@ -293,13 +293,13 @@ int main(int argc, char** argv) {
std::vector<std::string> str_input_shapes = split_string(FLAGS_input_shape);
std::vector<std::vector<int64_t>> input_shapes;
for (int i = 0; i < str_input_shapes.size(); ++i) {
for (size_t i = 0; i < str_input_shapes.size(); ++i) {
input_shapes.push_back(get_shape(str_input_shapes[i]));
}
std::vector<std::string> str_input_shapes_0 =
split_string(FLAGS_input_shape_0);
std::vector<std::vector<int64_t>> input_shapes_0;
for (int i = 0; i < str_input_shapes_0.size(); ++i) {
for (size_t i = 0; i < str_input_shapes_0.size(); ++i) {
input_shapes_0.push_back(get_shape(str_input_shapes_0[i]));
}
......
......@@ -204,7 +204,7 @@ int main(int argc, char** argv) {
LOG(INFO) << "input shapes: " << FLAGS_input_shape;
std::vector<std::string> str_input_shapes = split_string(FLAGS_input_shape);
std::vector<std::vector<int64_t>> input_shapes;
for (int i = 0; i < str_input_shapes.size(); ++i) {
for (size_t i = 0; i < str_input_shapes.size(); ++i) {
LOG(INFO) << "input shape: " << str_input_shapes[i];
input_shapes.push_back(get_shape(str_input_shapes[i]));
}
......
......@@ -310,7 +310,7 @@ int main(int argc, char** argv) {
LOG(INFO) << "input shapes: " << FLAGS_input_shape;
std::vector<std::string> str_input_shapes = split_string(FLAGS_input_shape);
std::vector<std::vector<int64_t>> input_shapes;
for (int i = 0; i < str_input_shapes.size(); ++i) {
for (size_t i = 0; i < str_input_shapes.size(); ++i) {
LOG(INFO) << "input shape: " << str_input_shapes[i];
input_shapes.push_back(get_shape(str_input_shapes[i]));
}
......
......@@ -114,7 +114,7 @@ void detect_object(const float* dout,
}
std::string name = FLAGS_out_txt + "_accu.txt";
FILE* fp = fopen(name.c_str(), "w");
for (int i = 0; i < objects.size(); ++i) {
for (size_t i = 0; i < objects.size(); ++i) {
Object object = objects.at(i);
if (object.prob > thresh && object.x > 0 && object.y > 0 &&
object.width > 0 && object.height > 0) {
......@@ -324,7 +324,7 @@ int main(int argc, char** argv) {
LOG(INFO) << "input shapes: " << FLAGS_input_shape;
std::vector<std::string> str_input_shapes = split_string(FLAGS_input_shape);
std::vector<std::vector<int64_t>> input_shapes;
for (int i = 0; i < str_input_shapes.size(); ++i) {
for (size_t i = 0; i < str_input_shapes.size(); ++i) {
LOG(INFO) << "input shape: " << str_input_shapes[i];
input_shapes.push_back(get_shape(str_input_shapes[i]));
}
......
......@@ -36,11 +36,11 @@ TEST(CxxApi, run) {
auto inputs = predictor->GetInputNames();
LOG(INFO) << "input size: " << inputs.size();
for (int i = 0; i < inputs.size(); i++) {
for (size_t i = 0; i < inputs.size(); i++) {
LOG(INFO) << "inputnames: " << inputs[i];
}
auto outputs = predictor->GetOutputNames();
for (int i = 0; i < outputs.size(); i++) {
for (size_t i = 0; i < outputs.size(); i++) {
LOG(INFO) << "outputnames: " << outputs[i];
}
auto input_tensor = predictor->GetInputByName(inputs[0]);
......
......@@ -38,7 +38,7 @@ TEST(CXXApi, test_lite_googlenet) {
input_tensor->Resize(input_shape);
auto* data = input_tensor->mutable_data<float>();
int input_num = 1;
for (int i = 0; i < input_shape.size(); ++i) {
for (size_t i = 0; i < input_shape.size(); ++i) {
input_num *= input_shape[i];
}
for (int i = 0; i < input_num; i++) {
......@@ -69,7 +69,7 @@ TEST(CXXApi, test_lite_googlenet) {
for (size_t i = 0; i < results.size(); ++i) {
EXPECT_NEAR(out->data<float>()[i * 51], results[i], 1e-5);
}
ASSERT_EQ(out->shape().size(), 2);
ASSERT_EQ(out->shape().size(), 2u);
ASSERT_EQ(out->shape()[0], 1);
ASSERT_EQ(out->shape()[1], 1000);
}
......
......@@ -38,7 +38,7 @@ TEST(InceptionV4, test_inceptionv4_lite_x86) {
input_tensor->Resize(input_shape);
auto* data = input_tensor->mutable_data<float>();
int input_num = 1;
for (int i = 0; i < input_shape.size(); ++i) {
for (size_t i = 0; i < input_shape.size(); ++i) {
input_num *= input_shape[i];
}
for (int i = 0; i < input_num; i++) {
......@@ -69,13 +69,13 @@ TEST(InceptionV4, test_inceptionv4_lite_x86) {
0.0010612885, 0.00089107914, 0.0010112736, 0.00097655767}));
auto out = predictor->GetOutput(0);
ASSERT_EQ(out->shape().size(), 2);
ASSERT_EQ(out->shape().size(), 2u);
ASSERT_EQ(out->shape()[0], 1);
ASSERT_EQ(out->shape()[1], 1000);
int step = 50;
for (int i = 0; i < results.size(); ++i) {
for (int j = 0; j < results[i].size(); ++j) {
for (size_t i = 0; i < results.size(); ++i) {
for (size_t j = 0; j < results[i].size(); ++j) {
EXPECT_NEAR(out->data<float>()[j * step + (out->shape()[1] * i)],
results[i][j],
1e-6);
......
......@@ -38,7 +38,7 @@ TEST(Mobilenet_v1, test_mobilenetv1_lite_x86) {
input_tensor->Resize(input_shape);
auto* data = input_tensor->mutable_data<float>();
int input_num = 1;
for (int i = 0; i < input_shape.size(); ++i) {
for (size_t i = 0; i < input_shape.size(); ++i) {
input_num *= input_shape[i];
}
for (int i = 0; i < input_num; i++) {
......@@ -68,13 +68,13 @@ TEST(Mobilenet_v1, test_mobilenetv1_lite_x86) {
0.0048292773, 0.0013995157, 0.0018453331, 0.0002428986,
0.00020211363, 0.00013668182, 0.0005855956, 0.00025901722}));
auto out = predictor->GetOutput(0);
ASSERT_EQ(out->shape().size(), 2);
ASSERT_EQ(out->shape().size(), 2u);
ASSERT_EQ(out->shape()[0], 1);
ASSERT_EQ(out->shape()[1], 1000);
int step = 50;
for (int i = 0; i < results.size(); ++i) {
for (int j = 0; j < results[i].size(); ++j) {
for (size_t i = 0; i < results.size(); ++i) {
for (size_t j = 0; j < results[i].size(); ++j) {
EXPECT_NEAR(out->data<float>()[j * step + (out->shape()[1] * i)],
results[i][j],
1e-6);
......
......@@ -39,7 +39,7 @@ TEST(Mobilenet_v2, test_mobilenetv2_lite_x86) {
input_tensor->Resize(input_shape);
auto* data = input_tensor->mutable_data<float>();
int input_num = 1;
for (int i = 0; i < input_shape.size(); ++i) {
for (size_t i = 0; i < input_shape.size(); ++i) {
input_num *= input_shape[i];
}
for (int i = 0; i < input_num; i++) {
......@@ -69,13 +69,13 @@ TEST(Mobilenet_v2, test_mobilenetv2_lite_x86) {
0.0070957416, 0.0016094646, 0.0018807327, 0.00010506048,
6.823785e-05, 0.00012269315, 0.0007806194, 0.00022354358}));
auto out = predictor->GetOutput(0);
ASSERT_EQ(out->shape().size(), 2);
ASSERT_EQ(out->shape().size(), 2u);
ASSERT_EQ(out->shape()[0], 1);
ASSERT_EQ(out->shape()[1], 1000);
int step = 50;
for (int i = 0; i < results.size(); ++i) {
for (int j = 0; j < results[i].size(); ++j) {
for (size_t i = 0; i < results.size(); ++i) {
for (size_t j = 0; j < results[i].size(); ++j) {
EXPECT_NEAR(out->data<float>()[j * step + (out->shape()[1] * i)],
results[i][j],
1e-6);
......
......@@ -38,7 +38,7 @@ TEST(Resnet50, test_resnet50_lite_x86) {
input_tensor->Resize(input_shape);
auto* data = input_tensor->mutable_data<float>();
int input_num = 1;
for (int i = 0; i < input_shape.size(); ++i) {
for (size_t i = 0; i < input_shape.size(); ++i) {
input_num *= input_shape[i];
}
for (int i = 0; i < input_num; i++) {
......@@ -69,13 +69,13 @@ TEST(Resnet50, test_resnet50_lite_x86) {
0.006387163, 0.0037145028, 0.0012812682, 0.00045948103,
0.00013535398, 0.0002483765, 0.00076759676, 0.0002773295}));
auto out = predictor->GetOutput(0);
ASSERT_EQ(out->shape().size(), 2);
ASSERT_EQ(out->shape().size(), 2u);
ASSERT_EQ(out->shape()[0], 1);
ASSERT_EQ(out->shape()[1], 1000);
int step = 50;
for (int i = 0; i < results.size(); ++i) {
for (int j = 0; j < results[i].size(); ++j) {
for (size_t i = 0; i < results.size(); ++i) {
for (size_t j = 0; j < results[i].size(); ++j) {
EXPECT_NEAR(out->data<float>()[j * step + (out->shape()[1] * i)],
results[i][j],
1e-6);
......
......@@ -232,8 +232,8 @@ void TestModel(const std::vector<Place>& valid_places,
for (int i = 0; i < outs->numel(); ++i) {
LOG(INFO) << o_data[i];
}
for (int i = 0; i < lod.size(); ++i) {
for (int j = 0; j < lod[i].size(); ++j) {
for (size_t i = 0; i < lod.size(); ++i) {
for (size_t j = 0; j < lod[i].size(); ++j) {
LOG(INFO) << lod[i][j];
}
}
......
......@@ -40,7 +40,7 @@ void MatMulJitCode::genCode() {
for (size_t g = 0; g < groups.size(); ++g) {
size_t x_offset = 0;
size_t wgt_offset_tmp = 0;
for (int i = 0; i < g; ++i) {
for (size_t i = 0; i < g; ++i) {
wgt_offset_tmp += groups[i] * block_len;
}
for (int k = 0; k < k_; ++k) {
......
......@@ -265,7 +265,7 @@ class BeamSearchFunctor<TARGET(kX86), T> {
// size_t num_seqs = scores->NumElements(lod_level);
size_t num_seqs = scores->lod()[lod_level].size() - 1;
size_t seq_width = 1;
for (int i = 1; i < scores->dims().size(); i++) {
for (size_t i = 1; i < scores->dims().size(); i++) {
seq_width *= scores->dims()[i];
}
......
......@@ -23,7 +23,7 @@ namespace math {
MatDescriptor CreateMatrixDescriptor(const lite::DDimLite &tensor_dim,
int num_flatten_cols,
bool trans) {
PADDLE_ENFORCE_GT(tensor_dim.size(), 1);
PADDLE_ENFORCE_GT(tensor_dim.size(), 1u);
MatDescriptor retv;
if (num_flatten_cols > 1) {
auto flatten_dim = tensor_dim.Flatten2D(num_flatten_cols);
......
......@@ -46,9 +46,9 @@ class MaxSeqPoolFunctor {
auto in_dims = input.dims();
auto out_dims = output->dims();
auto idx_dims = index->dims();
PADDLE_ENFORCE_GT(in_dims.size(), 1);
PADDLE_ENFORCE_GT(out_dims.size(), 1);
for (int64_t i = 1; i < in_dims.size(); ++i) {
PADDLE_ENFORCE_GT(in_dims.size(), 1u);
PADDLE_ENFORCE_GT(out_dims.size(), 1u);
for (size_t i = 1; i < in_dims.size(); ++i) {
PADDLE_ENFORCE_EQ(in_dims[i], out_dims[i]);
}
PADDLE_ENFORCE_EQ(idx_dims, out_dims);
......@@ -95,9 +95,9 @@ class MaxSeqPoolFunctor<T, true> {
lite::Tensor* index) {
auto in_dims = input.dims();
auto out_dims = output->dims();
PADDLE_ENFORCE_GT(in_dims.size(), 1);
PADDLE_ENFORCE_GT(out_dims.size(), 1);
for (int64_t i = 1; i < in_dims.size(); ++i) {
PADDLE_ENFORCE_GT(in_dims.size(), 1u);
PADDLE_ENFORCE_GT(out_dims.size(), 1u);
for (size_t i = 1; i < in_dims.size(); ++i) {
PADDLE_ENFORCE_EQ(in_dims[i], out_dims[i]);
}
......@@ -138,7 +138,7 @@ class MaxSeqPoolGradFunctor {
auto idx_dims = index.dims();
PADDLE_ENFORCE_GT(og_dims.size(), 1);
PADDLE_ENFORCE_GT(ig_dims.size(), 1);
for (int64_t i = 1; i < og_dims.size(); ++i) {
for (size_t i = 1; i < og_dims.size(); ++i) {
PADDLE_ENFORCE_EQ(og_dims[i], ig_dims[i]);
}
PADDLE_ENFORCE_EQ(idx_dims, og_dims);
......
......@@ -107,7 +107,7 @@ void TestCase::PrepareInputsForInstruction() {
CHECK(!shared_tensor_array->empty())
<< "shared_tensor_array is empty yet";
target_tensor_array->resize(shared_tensor_array->size());
for (int i = 0; i < shared_tensor_array->size(); i++) {
for (size_t i = 0; i < shared_tensor_array->size(); i++) {
target_tensor_array->at(i).Resize(
shared_tensor_array->at(i).dims());
TargetCopy(param_type->type->target(),
......@@ -219,7 +219,7 @@ bool TestCase::CheckPrecision(const std::string& var_name,
auto b_tensor_array =
base_scope_->FindVar(var_name)->GetMutable<std::vector<Tensor>>();
CHECK_EQ(a_tensor_array->size(), b_tensor_array->size());
for (int i = 0; i < a_tensor_array->size(); i++) {
for (size_t i = 0; i < a_tensor_array->size(); i++) {
Tensor* a_tensor = &(a_tensor_array->at(i));
Tensor* b_tensor = &(b_tensor_array->at(i));
if (a_tensor->dims().size() == 0 && b_tensor->dims().size() == 0) {
......
......@@ -166,7 +166,7 @@ class TestCase {
// TODO(Superjomn) Move this method to utils or DDim?
bool ShapeEquals(const DDim& a, const DDim& b) {
if (a.size() != b.size()) return false;
for (int i = 0; i < a.size(); i++) {
for (size_t i = 0; i < a.size(); i++) {
if (a[i] != b[i]) return false;
}
return true;
......
......@@ -947,7 +947,7 @@ void DeviceInfo::RequestPowerNoBindMode(int thread_num) {
active_ids_ = core_ids_;
} else {
active_ids_.resize(thread_num);
for (int i = 0; i < thread_num; ++i) {
for (uint32_t i = 0; i < thread_num; ++i) {
if (i < big_core_ids_.size()) {
active_ids_[i] = big_core_ids_[i];
} else {
......
......@@ -57,7 +57,7 @@ void KernelBase::ParseKernelType(const std::string &kernel_type,
std::string *alias,
Place *place) {
auto parts = Split(kernel_type, "/");
CHECK_EQ(parts.size(), 5);
CHECK_EQ(parts.size(), 5u);
*op_type = parts[0];
*alias = parts[1];
......
......@@ -163,23 +163,23 @@ void ConvBNFuser::InsertNewNode(SSAGraph* graph, const key2nodes_t& matched) {
int c_size = conv_weight_t->dims()[1] * conv_weight_t->dims()[2] *
conv_weight_t->dims()[3];
int hw = conv_weight_t->dims()[2] * conv_weight_t->dims()[3];
for (unsigned int k = 0; k < conv_weight_t->dims()[0]; ++k) {
for (unsigned int i = 0; i < h; ++i) {
for (int k = 0; k < conv_weight_t->dims()[0]; ++k) {
for (int i = 0; i < h; ++i) {
weight_scale[i] *= fabsf(alpha_data[i]);
if (alpha_data[i] < 0.f) {
auto ptr_row = conv_weight_d + k * c_size + i * hw;
for (unsigned int j = 0; j < hw; ++j) {
for (int j = 0; j < hw; ++j) {
ptr_row[j] *= -1;
}
}
}
}
} else {
for (unsigned int i = 0; i < h; ++i) {
for (int i = 0; i < h; ++i) {
weight_scale[i] *= fabsf(alpha_data[i]);
if (alpha_data[i] < 0.f) {
auto ptr_row = conv_weight_d + i * w;
for (unsigned int j = 0; j < w; ++j) {
for (int j = 0; j < w; ++j) {
ptr_row[j] *= -1;
}
}
......@@ -203,17 +203,17 @@ void ConvBNFuser::InsertNewNode(SSAGraph* graph, const key2nodes_t& matched) {
int c_size = conv_weight_t->dims()[1] * conv_weight_t->dims()[2] *
conv_weight_t->dims()[3];
int hw = conv_weight_t->dims()[2] * conv_weight_t->dims()[3];
for (unsigned int k = 0; k < conv_weight_t->dims()[0]; ++k) {
for (unsigned int i = 0; i < h; ++i) {
for (int k = 0; k < conv_weight_t->dims()[0]; ++k) {
for (int i = 0; i < h; ++i) {
auto ptr_row = conv_weight_d + k * c_size + i * hw;
for (unsigned int j = 0; j < hw; ++j) {
for (int j = 0; j < hw; ++j) {
ptr_row[j] *= alpha_data[i];
}
}
}
} else {
for (unsigned int i = 0; i < h; ++i) { // n: conv2d output channels
for (unsigned int j = 0; j < w; ++j) { // w: conv2d input channels
for (int i = 0; i < h; ++i) { // n: conv2d output channels
for (int j = 0; j < w; ++j) { // w: conv2d input channels
conv_weight_d[i * w + j] *= alpha_data[i];
}
}
......
......@@ -260,7 +260,7 @@ void ChannelWiseDequantOpFuser::InsertNewNode(SSAGraph* graph,
auto channel_scale_tensor =
scope->FindVar(channel_scale_name)->GetMutable<lite::Tensor>();
auto* channel_scale_data = channel_scale_tensor->data<float>();
for (int i = 0; i < channel_scale_tensor->data_size(); i++) {
for (size_t i = 0; i < channel_scale_tensor->data_size(); i++) {
weight_scale.push_back(channel_scale_data[i] / range);
}
......
......@@ -292,7 +292,7 @@ void MLUPostprocessPass::GetSubgraphOpArgType(Node* inst_node,
// get subgraph op's type info
size_t kernel_size = inst_node->AsStmt().kernels().size();
CHECK_GT(kernel_size, 0);
CHECK_GT(kernel_size, 0u);
VLOG(4) << "subgraph kernel size: " << kernel_size;
for (size_t i = 0; i < kernel_size; ++i) {
......@@ -450,7 +450,7 @@ bool MLUPostprocessPass::IsFirstConvInSubgraph(Node* arg_node, Node* inst) {
auto* block_desc =
static_cast<operators::SubgraphOp*>(inst->AsStmt().op().get())
->GetSubBlock();
for (int op_idx = 0; op_idx < block_desc->OpsSize(); op_idx++) {
for (size_t op_idx = 0; op_idx < block_desc->OpsSize(); op_idx++) {
auto op_desc = block_desc->GetOp<cpp::OpDesc>(op_idx);
CHECK(op_desc);
if (op_desc->Type() == "conv2d") {
......
......@@ -47,8 +47,8 @@ std::string SubgraphVisualizer::operator()() {
"turquoise4", "snow3", "sienna4", "salmon2",
};
std::unordered_map<Node *, int> subgraph_indices;
for (int i = 0; i < subgraphs_.size(); i++) {
for (int j = 0; j < subgraphs_[i].size(); j++) {
for (size_t i = 0; i < subgraphs_.size(); i++) {
for (size_t j = 0; j < subgraphs_[i].size(); j++) {
subgraph_indices[subgraphs_[i][j]] = i;
}
}
......@@ -538,7 +538,8 @@ void SubgraphFuser::ReplaceNodesWithSubgraphs(SSAGraph *graph,
std::vector<std::vector<Node *>> subgraphs =
SubgraphDetector(graph, teller)();
SubgraphVisualizer(graph, subgraphs)();
for (int subgraph_idx = 0; subgraph_idx < subgraphs.size(); subgraph_idx++) {
for (size_t subgraph_idx = 0; subgraph_idx < subgraphs.size();
subgraph_idx++) {
if (subgraphs[subgraph_idx].size() >= min_subgraph_size) {
InsertNewNode(graph, subgraph_idx, subgraphs[subgraph_idx]);
}
......
......@@ -36,8 +36,8 @@ std::vector<std::string> AddFCDesc(
const std::shared_ptr<Scope>& scope,
const std::vector<std::string>& input_var_names,
const std::vector<int64_t>& wshape) {
CHECK_EQ(input_var_names.size(), 1);
CHECK_EQ(wshape.size(), 2);
CHECK_EQ(input_var_names.size(), 1u);
CHECK_EQ(wshape.size(), 2u);
static int id = 0;
std::string prefix = "fc_" + paddle::lite::to_string(id);
auto* op_desc = block_desc->AddOp<cpp::OpDesc>();
......@@ -169,8 +169,8 @@ TEST(Subgraph, detect_simple_model) {
};
std::vector<std::vector<mir::Node*>> subgraphs =
mir::SubgraphDetector(graph.get(), teller)();
ASSERT_EQ(subgraphs.size(), 1);
ASSERT_EQ(graph->nodes().size(), 9);
ASSERT_EQ(subgraphs.size(), 1u);
ASSERT_EQ(graph->nodes().size(), 9u);
mir::SubgraphVisualizer(graph.get(), subgraphs)();
}
......@@ -221,7 +221,7 @@ TEST(Subgraph, detect_custom_model) {
std::vector<std::vector<mir::Node*>> subgraphs =
mir::SubgraphDetector(graph.get(), teller)();
mir::SubgraphVisualizer(graph.get(), subgraphs)();
ASSERT_EQ(subgraphs.size(), 1);
ASSERT_EQ(subgraphs.size(), 1u);
}
} // namespace lite
......
......@@ -39,7 +39,7 @@ std::vector<std::vector<int64_t>> ShapeParsing(std::string text) {
std::vector<std::vector<int64_t>> shapes;
std::vector<std::string> shape_strings = Split(text, ":");
shapes.resize(shape_strings.size());
for (int i = 0; i < shape_strings.size(); i++) {
for (size_t i = 0; i < shape_strings.size(); i++) {
std::vector<std::string> shape_nums = Split(shape_strings[i], ",");
for (auto shape_num : shape_nums) {
shapes[i].push_back(atoi(shape_num.c_str()));
......@@ -66,7 +66,7 @@ void FillInputTensors(
for (int j = 0; j < input_tensor_size; j++) { \
input_tensor_data[j] = static_cast<type>(value); \
}
for (int i = 0; i < input_tensor_shape.size(); i++) {
for (size_t i = 0; i < input_tensor_shape.size(); i++) {
auto input_tensor = predictor->GetInput(i);
input_tensor->Resize(input_tensor_shape[i]);
auto input_tensor_size = ShapeProduction(input_tensor->shape());
......@@ -95,7 +95,7 @@ void CheckOutputTensors(
<< " abs_diff: " << abs_diff << " rel_diff: " << rel_diff; \
EXPECT_LT(rel_diff, 0.1); \
}
for (int i = 0; i < output_tensor_type.size(); i++) {
for (size_t i = 0; i < output_tensor_type.size(); i++) {
auto tar_output_tensor = tar_predictor->GetOutput(i);
auto ref_output_tensor = ref_predictor->GetOutput(i);
auto tar_output_tensor_size = ShapeProduction(tar_output_tensor->shape());
......
......@@ -41,7 +41,7 @@ bool OpLite::InferShapeWithCache() {
iter++) {
// combined dims value into new_hash value.
auto &element_dims = (*iter)->dims();
for (int i = 0; i < element_dims.size(); i++) {
for (size_t i = 0; i < element_dims.size(); i++) {
new_hash =
lite::hash_combine(new_hash, static_cast<int>(element_dims[i]));
}
......@@ -49,7 +49,7 @@ bool OpLite::InferShapeWithCache() {
auto &emement_lods = (*iter)->lod();
for (auto lod_iter = emement_lods.begin(); lod_iter != emement_lods.end();
lod_iter++) {
for (int i = 0; i < lod_iter->size(); i++) {
for (size_t i = 0; i < lod_iter->size(); i++) {
new_hash =
lite::hash_combine(new_hash, static_cast<int>(lod_iter->at(i)));
}
......@@ -60,7 +60,7 @@ bool OpLite::InferShapeWithCache() {
// if current hash value is consistent with io_shape_lod_hash_,
// previous outputs shape and lod are reused.
auto *current_outputs = param_.output_tensor_ptrs();
for (int i = 0; i < current_outputs->size(); i++) {
for (size_t i = 0; i < current_outputs->size(); i++) {
current_outputs->at(i)->Resize(last_output_shapes[i]);
current_outputs->at(i)->set_lod(last_output_lods[i]);
}
......@@ -69,7 +69,7 @@ bool OpLite::InferShapeWithCache() {
io_shape_lod_hash_ = new_hash;
this->InferShapeImpl();
auto *current_outputs = param_.output_tensor_ptrs();
for (int i = 0; i < current_outputs->size(); i++) {
for (size_t i = 0; i < current_outputs->size(); i++) {
last_output_shapes[i] = current_outputs->at(i)->dims();
last_output_lods[i] = current_outputs->at(i)->lod();
}
......
......@@ -72,7 +72,7 @@ void RuntimeProgram::UpdateVarsOfProgram(cpp::ProgramDesc* desc) {
std::unordered_map<std::string, cpp::VarDesc> origin_var_maps;
auto& main_block = *desc->GetBlock<cpp::BlockDesc>(0);
auto var_size = main_block.VarsSize();
for (int i = 0; i < var_size; i++) {
for (size_t i = 0; i < var_size; i++) {
auto v = main_block.GetVar<cpp::VarDesc>(i);
auto name = v->Name();
origin_var_maps.emplace(name, *v);
......
......@@ -100,7 +100,7 @@ void *TensorLite::mutable_data(TargetType target, size_t memory_size) {
void TensorLite::ResetBuffer(std::shared_ptr<Buffer> buffer,
size_t memory_size) {
CHECK_EQ(offset_, 0)
CHECK_EQ(offset_, 0u)
<< "Only the offset is supported to zero when the Buffer is reset.";
if (buffer_) {
CHECK_LE(memory_size_, buffer->space())
......
......@@ -44,40 +44,40 @@ void act_ref(const std::shared_ptr<operators::ActivationOp> op) {
// "sigmoid","relu","tanh","relu_clipped","leaky_relu","softsign","hard_sigmoid"
if (op_type == "sigmoid") {
for (size_t i = 0; i < out->numel(); i++) {
for (int i = 0; i < out->numel(); i++) {
out_data[i] = 1.f / (1.f + std::exp(-x_data[i]));
}
} else if (op_type == "relu") {
for (size_t i = 0; i < out->numel(); i++) {
for (int i = 0; i < out->numel(); i++) {
out_data[i] = std::max(0.f, x_data[i]);
}
} else if (op_type == "tanh") {
for (size_t i = 0; i < out->numel(); i++) {
for (int i = 0; i < out->numel(); i++) {
out_data[i] = (std::exp(x_data[i]) - std::exp(-x_data[i])) /
(std::exp(x_data[i]) + std::exp(-x_data[i]));
}
} else if (op_type == "relu_clipped") {
auto relu_clipped_coef = op_info->GetAttr<float>("Relu_clipped_coef");
for (size_t i = 0; i < out->numel(); i++) {
for (int i = 0; i < out->numel(); i++) {
out_data[i] = std::min(std::max(0.f, x_data[i]), relu_clipped_coef);
}
} else if (op_type == "relu6") {
for (size_t i = 0; i < out->numel(); i++) {
for (int i = 0; i < out->numel(); i++) {
out_data[i] = std::min(std::max(0.f, x_data[i]), 6.f);
}
} else if (op_type == "leaky_relu") {
auto alpha = op_info->GetAttr<float>("alpha");
for (size_t i = 0; i < out->numel(); i++) {
for (int i = 0; i < out->numel(); i++) {
out_data[i] = std::max(x_data[i], x_data[i] * alpha);
}
} else if (op_type == "softsign") {
for (size_t i = 0; i < out->numel(); i++) {
for (int i = 0; i < out->numel(); i++) {
out_data[i] = x_data[i] / (1 + std::abs(x_data[i]));
}
} else if (op_type == "hard_sigmoid") {
auto slope = op_info->GetAttr<float>("slope");
auto offset = op_info->GetAttr<float>("offset");
for (size_t i = 0; i < out->numel(); i++) {
for (int i = 0; i < out->numel(); i++) {
out_data[i] = std::min(1.f, slope * x_data[i] + offset);
out_data[i] = std::max(0.f, out_data[i]);
}
......
......@@ -37,7 +37,7 @@ void concat_ref(const std::shared_ptr<operators::ConcatOpLite> op) {
scope->FindVar(op_info->Output("Out").front())->GetMutable<Tensor>();
int axis = op_info->GetAttr<int>("axis");
std::vector<lite::Tensor*> inputs_concat(inputs.size());
for (int j = 0; j < inputs.size(); ++j) {
for (size_t j = 0; j < inputs.size(); ++j) {
inputs_concat[j] = inputs[j];
}
size_t num = inputs.size();
......@@ -48,7 +48,7 @@ void concat_ref(const std::shared_ptr<operators::ConcatOpLite> op) {
}
int out_rows = rows, out_cols = 0;
std::vector<int64_t> inputs_cols(inputs.size());
for (int i = 0; i < num; ++i) {
for (size_t i = 0; i < num; ++i) {
int t_cols = inputs[i]->numel() / rows;
out_cols += t_cols;
inputs_cols[i] = t_cols;
......@@ -56,7 +56,7 @@ void concat_ref(const std::shared_ptr<operators::ConcatOpLite> op) {
for (int k = 0; k < out_rows; ++k) {
float* dst_ptr = out->mutable_data<float>() + k * out_cols;
int col_idx = 0;
for (int j = 0; j < num; ++j) {
for (size_t j = 0; j < num; ++j) {
int col_len = inputs_cols[j];
const float* src_prt = inputs[j]->data<float>() + k * col_len;
std::memcpy(dst_ptr + col_idx, src_prt, sizeof(float) * col_len);
......
......@@ -43,20 +43,20 @@ int ConvConverter(void* ctx, OpLite* op, KernelBase* kernel) {
const auto output_shape = output->dims().Vectorize();
const auto bs = input_dims[0];
const auto oc = filter_dims[0];
CHECK_EQ(input_dims.size(), 4);
CHECK_EQ(filter_dims.size(), 4);
CHECK_EQ(input_dims.size(), 4u);
CHECK_EQ(filter_dims.size(), 4u);
const auto strides = op_info->GetAttr<std::vector<int>>("strides");
auto dilations = op_info->GetAttr<std::vector<int>>("dilations");
auto paddings = op_info->GetAttr<std::vector<int>>("paddings");
CHECK_EQ(strides.size(), 2L);
CHECK_EQ(dilations.size(), 2L);
if (paddings.size() == 2L) {
CHECK_EQ(strides.size(), 2u);
CHECK_EQ(dilations.size(), 2u);
if (paddings.size() == 2u) {
for (size_t i = 0; i < strides.size(); ++i) {
int copy_pad = *(paddings.begin() + 2 * i);
paddings.insert(paddings.begin() + 2 * i + 1, copy_pad);
}
}
CHECK_EQ(paddings.size(), 4L)
CHECK_EQ(paddings.size(), 4u)
<< "Paddings size should be the same or twice as the input size.";
const std::string padding_algorithm =
......
......@@ -173,10 +173,10 @@ void test_conv(int bs,
Tensor input_int;
input_int.Resize(input_shape);
FillTensor<int8_t, int8_t>(&input_int, -127, 127);
for (int i = 0; i < input->data_size(); i++) {
for (size_t i = 0; i < input->data_size(); i++) {
input->mutable_data<float>()[i] = input_int.data<int8_t>()[i] * input_scale;
}
for (int i = 0; i < filter->data_size(); i++) {
for (size_t i = 0; i < filter->data_size(); i++) {
filter->mutable_data<float>()[i] =
filter_int->data<int8_t>()[i] * filter_scale;
}
......
......@@ -97,11 +97,11 @@ void test_fc(const std::vector<int64_t>& input_shape,
Tensor input_int;
input_int.Resize(input_shape);
FillTensor<int8_t, int8_t>(&input_int, -127, 127);
for (int i = 0; i < input->data_size(); i++) {
for (size_t i = 0; i < input->data_size(); i++) {
input->mutable_data<float>()[i] = input_int.data<int8_t>()[i] * input_scale;
}
for (int i = 0; i < w->data_size(); i++) {
for (size_t i = 0; i < w->data_size(); i++) {
w->mutable_data<float>()[i] = w_int->data<int8_t>()[i] * w_scale;
}
......
......@@ -36,7 +36,7 @@ int InterpolateConverter(void* ctx, OpLite* op, KernelBase* kernel) {
auto x = scope->FindVar(x_var_name)->GetMutable<Tensor>();
auto out = scope->FindVar(out_var_name)->GetMutable<Tensor>();
auto x_dims = x->dims();
CHECK_EQ(x_dims.size(), 4);
CHECK_EQ(x_dims.size(), 4u);
auto scale = op_info->GetAttr<float>("scale");
auto out_w = op_info->GetAttr<int>("out_w");
auto out_h = op_info->GetAttr<int>("out_h");
......
......@@ -85,7 +85,7 @@ void BilinearInterpRef(const lite::Tensor* x,
int channel_size = x_dims[1];
auto x_h = x_dims[2];
auto x_w = x_dims[3];
CHECK_EQ(x_dims.size(), 4);
CHECK_EQ(x_dims.size(), 4u);
auto out_dims = out->dims();
int out_h = out_dims[2];
......
......@@ -59,10 +59,10 @@ void dequant(float* dst,
size_t size,
size_t size_in,
std::vector<float> scales) {
for (int out = 0; out < size_o; ++out) {
for (int s = 0; s < size; ++s) {
for (size_t out = 0; out < size_o; ++out) {
for (size_t s = 0; s < size; ++s) {
auto scale = scales[s];
for (int in = 0; in < size_in; ++in) {
for (size_t in = 0; in < size_in; ++in) {
int idx = in + s * size_in + out * size_in * size;
dst[idx] = static_cast<float>(src[idx]) * scale;
}
......
......@@ -30,7 +30,7 @@ int Engine::BuildOriginProgram() {
// TODO(hong19860320) The block_desc need to be divided into subgraphs during
// the exection time. But only see them as a subgraph now.
origin_program_.clear();
for (int op_idx = 0; op_idx < block_desc_->OpsSize(); op_idx++) {
for (size_t op_idx = 0; op_idx < block_desc_->OpsSize(); op_idx++) {
auto op_desc = block_desc_->GetOp<cpp::OpDesc>(op_idx);
CHECK(op_desc);
std::string op_type = op_desc->Type();
......@@ -46,7 +46,7 @@ int Engine::BuildOriginProgram() {
VLOG(3) << "Found the attr '" << kKernelTypeAttr << "': " << kernel_type
<< " for " << op_type;
auto kernels = op->CreateKernels({place});
CHECK_GT(kernels.size(), 0) << "No kernels found for " << op_type;
CHECK_GT(kernels.size(), 0u) << "No kernels found for " << op_type;
auto it = std::find_if(
kernels.begin(), kernels.end(), [&](std::unique_ptr<KernelBase>& it) {
return it->alias() == alias;
......@@ -96,7 +96,7 @@ int Engine::Build() {
}
bool Engine::InputShapeChanged() {
for (int i = 0; i < origin_itensors_.size(); i++) {
for (size_t i = 0; i < origin_itensors_.size(); i++) {
if (origin_itensors_[i]->dims() != origin_idims_[i]) {
return true;
}
......
......@@ -64,14 +64,14 @@ inline void get_mid_dims(const lite::DDim &x_dims,
for (int i = 0; i < axis; ++i) {
(*pre) *= x_dims[i];
}
for (int i = 0; i < y_dims.size(); ++i) {
for (size_t i = 0; i < y_dims.size(); ++i) {
if (x_dims[i + axis] != y_dims[i]) {
// only support single y_dims[i] = 1 now.
PADDLE_ENFORCE_EQ(
*mid_flag, 0, "Broadcast support y_dims with single 1.");
PADDLE_ENFORCE_EQ(y_dims[i], 1, "Broadcast dimension mismatch.");
// m*n*k m*1*k
for (int j = 0; j < i; ++j) {
for (size_t j = 0; j < i; ++j) {
(*pre) *= y_dims[j];
}
*n = std::max(x_dims[i + axis], y_dims[i]);
......@@ -82,11 +82,11 @@ inline void get_mid_dims(const lite::DDim &x_dims,
(*n) *= y_dims[i];
}
if (*mid_flag) {
for (int i = mid + 1; i < x_dims.size(); ++i) {
for (size_t i = mid + 1; i < x_dims.size(); ++i) {
(*post) *= x_dims[i];
}
} else {
for (int i = axis + y_dims.size(); i < x_dims.size(); ++i) {
for (size_t i = axis + y_dims.size(); i < x_dims.size(); ++i) {
(*post) *= x_dims[i];
}
}
......@@ -95,13 +95,13 @@ inline void get_mid_dims(const lite::DDim &x_dims,
(*pre) *= x_dims[i];
}
for (int i = 0; i < y_dims.size(); ++i) {
for (size_t i = 0; i < y_dims.size(); ++i) {
PADDLE_ENFORCE_EQ(
x_dims[i + axis], y_dims[i], "Broadcast dimension mismatch.");
(*n) *= y_dims[i];
}
for (int i = axis + y_dims.size(); i < x_dims.size(); ++i) {
for (size_t i = axis + y_dims.size(); i < x_dims.size(); ++i) {
(*post) *= x_dims[i];
}
}
......@@ -116,7 +116,7 @@ inline lite::DDim trim_trailing_singular_dims(const lite::DDim &dims) {
std::vector<int64_t> trim_dims;
trim_dims.resize(actual_dims_size);
for (int i = 0; i < actual_dims_size; ++i) {
for (size_t i = 0; i < actual_dims_size; ++i) {
trim_dims[i] = dims[i];
}
if (trim_dims.size() == 0) {
......
......@@ -71,7 +71,7 @@ TEST(fill_constant_batch_size_like_x86, run_test) {
std::vector<float> ref_results{
3.5, 3.5, 3.5, 3.5, 3.5, 3.5, 3.5, 3.5, 3.5, 3.5};
for (int i = 0; i < ref_results.size(); i++) {
for (size_t i = 0; i < ref_results.size(); i++) {
EXPECT_NEAR(out_data[i], ref_results[i], 1e-3);
}
}
......
......@@ -56,7 +56,7 @@ void CPUGather(const lite::Tensor* src,
// slice size
int slice_size = 1;
for (int i = 1; i < src_dims.size(); ++i) slice_size *= src_dims[i];
for (size_t i = 1; i < src_dims.size(); ++i) slice_size *= src_dims[i];
const size_t slice_bytes = slice_size * sizeof(T);
for (int64_t i = 0; i < index_size; ++i) {
......
......@@ -108,7 +108,7 @@ TEST(layer_norm_x86, run_test) {
for (int i = 0; i < begin_norm_axis; ++i) {
pre *= x_shape[i];
}
for (int i = begin_norm_axis; i < x_shape.size(); ++i) {
for (size_t i = begin_norm_axis; i < x_shape.size(); ++i) {
post *= x_shape[i];
}
std::vector<int64_t> scale_shape({post});
......
......@@ -66,8 +66,8 @@ class SequenceExpandAsCompute
auto *out = param.out;
auto &y_lod = y->lod();
CHECK_EQ(y_lod.size(), 1);
CHECK_GT(y_lod[0].size(), 1);
CHECK_EQ(y_lod.size(), 1u);
CHECK_GT(y_lod[0].size(), 1u);
out->template mutable_data<T, T>();
......
......@@ -30,7 +30,7 @@ static void sequence_reverse_ref(const lite::Tensor* x, lite::Tensor* y) {
auto seq_offset = x->lod()[x->lod().size() - 1];
int width = x->numel() / x->dims()[0];
auto* y_data = y->mutable_data<float>();
for (int i = 0; i < seq_offset.size() - 1; ++i) {
for (size_t i = 0; i < seq_offset.size() - 1; ++i) {
auto start_pos = seq_offset[i];
auto end_pos = seq_offset[i + 1];
for (auto pos = start_pos; pos < end_pos; ++pos) {
......
......@@ -31,7 +31,7 @@ class ShapeCompute : public KernelLite<TARGET(kX86), PRECISION(kFloat)> {
// auto& context = context_->As<X86Context>();
auto out_data = param.Out->template mutable_data<int32_t>();
auto in_dims = param.X->dims();
for (int i = 0; i < in_dims.size(); ++i) {
for (size_t i = 0; i < in_dims.size(); ++i) {
out_data[i] = in_dims[i];
}
}
......
......@@ -118,7 +118,7 @@ void slice_compute(const lite::Tensor* in,
out_dims[decrease_axis[i]] = 0;
}
for (int i = 0; i < out_dims.size(); ++i) {
for (size_t i = 0; i < out_dims.size(); ++i) {
if (out_dims[i] != 0) {
new_out_shape.push_back(out_dims[i]);
}
......
......@@ -34,10 +34,10 @@ static void slice_ref(const float* input,
std::vector<int> real_starts(in_dims.size(), 0);
std::vector<int> real_ends(in_dims.size(), 0);
std::vector<int> real_step(in_dims.size(), 0);
for (int i = 0; i < in_dims.size(); i++) {
for (size_t i = 0; i < in_dims.size(); i++) {
real_ends[i] = in_dims[i];
}
for (int i = 0; i < axes.size(); i++) {
for (size_t i = 0; i < axes.size(); i++) {
int dim_value = in_dims[axes[i]];
if (dim_value > 0) {
int start = starts[i] < 0 ? (starts[i] + dim_value) : starts[i];
......@@ -52,11 +52,11 @@ static void slice_ref(const float* input,
}
const int LEN = in_dims.size();
int dst_step[LEN];
for (int i = 0; i < in_dims.size(); ++i) {
for (size_t i = 0; i < in_dims.size(); ++i) {
dst_step[i] = 1;
}
int src_step[LEN];
for (int i = 0; i < in_dims.size(); ++i) {
for (size_t i = 0; i < in_dims.size(); ++i) {
src_step[i] = 1;
}
int out_num = out_dims[in_dims.size() - 1];
......@@ -69,7 +69,7 @@ static void slice_ref(const float* input,
for (int dst_id = 0; dst_id < out_num; dst_id++) {
int src_id = 0;
int index_id = dst_id;
for (int j = 0; j < out_dims.size(); j++) {
for (size_t j = 0; j < out_dims.size(); j++) {
int cur_id = index_id / dst_step[j];
index_id = index_id % dst_step[j];
src_id += (cur_id + real_starts[j]) * src_step[j];
......@@ -409,7 +409,7 @@ void test_tensor_case3(lite::Tensor x, lite::Tensor out) {
lite::Tensor starts_tensor, ends_tensor;
starts_tensor.Resize(DDim({3}));
ends_tensor.Resize(DDim({3}));
for (int i = 0; i < starts.size(); ++i) {
for (size_t i = 0; i < starts.size(); ++i) {
starts_tensor.mutable_data<int>()[i] = starts[i];
ends_tensor.mutable_data<int>()[i] = ends[i];
}
......
......@@ -47,7 +47,7 @@ class StackCompute : public KernelLite<TARGET(kX86), PRECISION(kFloat)> {
int pre = 1, post = 1;
auto dim = x[0]->dims();
for (int i = 0; i < axis; ++i) pre *= dim[i];
for (int i = axis; i < dim.size(); ++i) post *= dim[i];
for (size_t i = axis; i < dim.size(); ++i) post *= dim[i];
auto x_data_arr = x_datas.data();
......
......@@ -44,7 +44,7 @@ class VarConv2DCompute : public KernelLite<TARGET(kX86), PRECISION(kFloat)> {
// 2-D lod info.
// const auto& offset_x = in_col->lod()[0];
// const auto& offset_y = in_row->lod()[0];
CHECK_EQ(param.X->lod().size(), 3) << "input lod size should be 3!";
CHECK_EQ(param.X->lod().size(), 3u) << "input lod size should be 3!";
const auto& offset_y = param.X->lod()[1];
const auto& offset_x = param.X->lod()[2];
......
......@@ -107,7 +107,7 @@ TEST(ModelParser, LoadParamNaive) {
ASSERT_EQ(bg_lod, tensor.lod());
ASSERT_EQ(tensor.data_size(), size);
auto* data = tensor.data<float>();
for (int i = 0; i < size; ++i) {
for (size_t i = 0; i < size; ++i) {
EXPECT_NEAR(bg_data[i], data[i], 1e-6);
}
}
......
......@@ -35,7 +35,8 @@ bool ElementwiseOp::InferShapeImpl() const {
auto out_lod = param_.Out->mutable_lod();
*out_lod = param_.X->lod();
} else {
int max_dim = (x_dim.size() > y_dim.size() ? x_dim.size() : y_dim.size());
size_t max_dim =
(x_dim.size() > y_dim.size() ? x_dim.size() : y_dim.size());
int axis = param_.axis;
axis = (axis == -1 ? std::abs(static_cast<int>(x_dim.size() - y_dim.size()))
: axis);
......@@ -48,12 +49,12 @@ bool ElementwiseOp::InferShapeImpl() const {
y_dims_array[i] = 1;
}
if (axis + y_dim.size() < max_dim) {
for (int i = axis + y_dim.size(); i < max_dim; ++i) {
for (size_t i = axis + y_dim.size(); i < max_dim; ++i) {
y_dims_array[i] = 1;
}
}
x_dims_array = x_dim.Vectorize();
for (int i = 0; i < y_dim.size(); ++i) {
for (size_t i = 0; i < y_dim.size(); ++i) {
y_dims_array[i + axis] = y_dim[i];
}
} else {
......@@ -61,16 +62,16 @@ bool ElementwiseOp::InferShapeImpl() const {
x_dims_array[i] = 1;
}
if (axis + x_dim.size() < max_dim) {
for (int i = axis + x_dim.size(); i < max_dim; ++i) {
for (size_t i = axis + x_dim.size(); i < max_dim; ++i) {
x_dims_array[i] = 1;
}
}
y_dims_array = y_dim.Vectorize();
for (int i = 0; i < x_dim.size(); ++i) {
for (size_t i = 0; i < x_dim.size(); ++i) {
x_dims_array[i + axis] = x_dim[i];
}
}
for (int i = 0; i < max_dim; i++) {
for (size_t i = 0; i < max_dim; i++) {
if (x_dims_array[i] == -1 || y_dims_array[i] == -1) {
out_dims_array[i] = -1;
} else {
......
......@@ -27,7 +27,7 @@ bool ExpandOpLite::CheckShape() const {
CHECK_EQ(expand_size, x_dims_size)
<< "The number of expand_times size must be qual to the rank of "
"Input(X).";
CHECK_LE(param_.X->dims().size(), 6)
CHECK_LE(param_.X->dims().size(), 6u)
<< "The rank of Input(X) must not be greater than 6.";
return true;
}
......
......@@ -22,7 +22,7 @@ namespace operators {
bool FillConstantBatchSizeLikeOp::CheckShape() const {
CHECK(param_.out);
CHECK(param_.input);
CHECK_GT(param_.shape.size(), 0);
CHECK_GT(param_.shape.size(), 0u);
CHECK_GE(param_.input_dim_idx, 0);
CHECK_GE(param_.output_dim_idx, 0);
return true;
......
......@@ -34,7 +34,7 @@ bool FillConstantOp::InferShapeImpl() const {
out_shape.push_back(shape_tensor_data[i]);
}
} else if (!shape_tensor_list.empty()) {
for (int i = 0; i < shape_tensor_list.size(); i++) {
for (size_t i = 0; i < shape_tensor_list.size(); i++) {
out_shape.push_back(shape_tensor_list[i]->data<int>()[0]);
}
} else if (!param_.shape.empty()) {
......
......@@ -32,7 +32,7 @@ bool FlattenOp::InferShapeImpl() const {
*out_lod = param_.x->lod();
int64_t outer = 1, inner = 1;
for (int i = 0; i < x_dims.size(); ++i) {
for (size_t i = 0; i < x_dims.size(); ++i) {
if (i < axis_) {
outer *= x_dims[i];
} else {
......
......@@ -48,14 +48,14 @@ bool InterpolateOp::InferShapeImpl() const {
auto OutSize = param_.OutSize;
auto Scale = param_.Scale;
if (!SizeTensor.empty()) {
CHECK_EQ(SizeTensor.size(), 2)
CHECK_EQ(SizeTensor.size(), 2u)
<< "Input(SizeTensor)'size of Op(interpolate) must be 2. "
"Attr(out_shape)'s length must be 2 for 4-D input tensor.";
out_h = SizeTensor[0]->data<int>()[0];
out_w = SizeTensor[1]->data<int>()[0];
} else if (OutSize) {
auto OutSize_dims = OutSize->dims();
CHECK_EQ(OutSize_dims.size(), 1) << "Input(OutSize)'s dims size must be 1";
CHECK_EQ(OutSize_dims.size(), 1u) << "Input(OutSize)'s dims size must be 1";
CHECK_EQ(OutSize_dims[0], 2) << "OutSize's dim[0] must be 2";
auto OutSize_data = OutSize->data<int>();
out_h = OutSize_data[0];
......
......@@ -105,7 +105,7 @@ inline void UpdatePadding(std::vector<int> *paddings,
const std::vector<int> &ksize) {
// when padding_algorithm is "VALID" or "SAME"
if (padding_algorithm == "SAME") {
for (int i = 0; i < strides.size(); ++i) {
for (size_t i = 0; i < strides.size(); ++i) {
int out_size = (data_dims[i + 2] + strides[i] - 1) / strides[i];
int pad_sum =
std::max((out_size - 1) * strides[i] + ksize[i] - data_dims[i + 2],
......
......@@ -29,7 +29,7 @@ bool ReduceMeanOp::CheckShape() const {
auto x_dims = param_.X->dims();
int x_rank = x_dims.size();
if (dims.size() != 0) {
for (int i = 0; i < dims.size(); i++) {
for (size_t i = 0; i < dims.size(); i++) {
if (dims[i] < 0) {
dims[i] = x_rank + dims[i];
}
......@@ -46,7 +46,7 @@ bool ReduceMeanOp::InferShapeImpl() const {
bool keep_dim = param_.keep_dim;
auto x_rank = x_dims.size();
if (dims.size() != 0) {
for (int i = 0; i < dims.size(); i++) {
for (size_t i = 0; i < dims.size(); i++) {
if (dims[i] < 0) {
dims[i] = x_rank + dims[i];
}
......@@ -65,7 +65,7 @@ bool ReduceMeanOp::InferShapeImpl() const {
out_dims.push_back(1);
}
} else {
for (int i = 0; i < x_dims.size(); i++) {
for (size_t i = 0; i < x_dims.size(); i++) {
out_dims.push_back(x_dims[i]);
}
if (keep_dim) {
......
......@@ -70,7 +70,7 @@ bool ReshapeOp::AttachImpl(const cpp::OpDesc &opdesc, lite::Scope *scope) {
param_.shape_tensor_vct.push_back(var->GetMutable<lite::Tensor>());
}
}
CHECK_GT(param_.shape_tensor_vct.size(), 0)
CHECK_GT(param_.shape_tensor_vct.size(), 0u)
<< "ShapeError: When `shape` in ReshapeOp is a list or tuple "
"which contains Tensor, the shape's size can't be zero. "
"But received shape's size is "
......@@ -145,7 +145,7 @@ std::vector<DDim::value_type> ValidateShape(const std::vector<int> &shape,
<< "Only one input dimension of Attr(shape) can be unknown.";
unk_dim_idx = i;
} else if (shape[i] == copy_dim_val) {
CHECK_LT(static_cast<int>(i), input_dims.size())
CHECK_LT(i, input_dims.size())
<< "The index of dimension to copy from input shape must be less "
"than the size of input shape.";
} else {
......
......@@ -41,11 +41,11 @@ bool SearchFcOpLite::CheckShape() const {
CHECK_OR_FALSE(param_.Out);
auto x_dims = param_.X->dims();
CHECK_EQ(x_dims.size(), 2) << "The rank of X(Input) should be 2.";
CHECK_EQ(x_dims.size(), 2u) << "The rank of X(Input) should be 2.";
auto w_dims = param_.W->dims();
CHECK_EQ(w_dims.size(), 2) << "W should be 2-D tensor.";
CHECK_EQ(w_dims.size(), 2u) << "W should be 2-D tensor.";
auto b_dims = param_.b->dims();
CHECK_EQ(b_dims.size(), 1) << "b should be 1-D tensor.";
CHECK_EQ(b_dims.size(), 1u) << "b should be 1-D tensor.";
CHECK_EQ(w_dims[1], x_dims[1]) << "wrong shape: w_dims[1] != x_dims[1]";
return true;
}
......
......@@ -22,7 +22,7 @@ namespace operators {
bool SliceOp::CheckShape() const {
CHECK_OR_FALSE(param_.X);
CHECK_OR_FALSE(param_.Out);
CHECK_LT(param_.X->dims().size(), 7)
CHECK_LT(param_.X->dims().size(), 7u)
<< "The rank of input X should be less than 7";
return true;
}
......@@ -67,7 +67,7 @@ bool SliceOp::InferShapeImpl() const {
}
out_dims[decrease_axis[i]] = 0;
}
for (int i = 0; i < out_dims.size(); ++i) {
for (size_t i = 0; i < out_dims.size(); ++i) {
if (out_dims[i] != 0) {
new_out_shape.push_back(out_dims[i]);
}
......@@ -108,7 +108,7 @@ bool SliceOp::AttachImpl(const cpp::OpDesc &opdesc, lite::Scope *scope) {
// The priority: StartsTensor > StartsTensorList > attr(starts).
// The priority: EndsTensor > EndsTensorList > attr(ends).
int starts_size, ends_size;
size_t starts_size, ends_size;
if (opdesc.HasAttr("starts")) {
param_.starts = opdesc.GetAttr<std::vector<int>>("starts");
}
......@@ -129,7 +129,7 @@ bool SliceOp::AttachImpl(const cpp::OpDesc &opdesc, lite::Scope *scope) {
param_.StartsTensorList.push_back(
scope->FindVar(var)->GetMutable<lite::Tensor>());
}
CHECK_GT(param_.StartsTensorList.size(), 0)
CHECK_GT(param_.StartsTensorList.size(), 0u)
<< "StartsTensorList size can't be zero";
starts_size = param_.StartsTensorList.size();
}
......@@ -141,7 +141,7 @@ bool SliceOp::AttachImpl(const cpp::OpDesc &opdesc, lite::Scope *scope) {
param_.EndsTensorList.push_back(
scope->FindVar(var)->GetMutable<lite::Tensor>());
}
CHECK_GT(param_.EndsTensorList.size(), 0)
CHECK_GT(param_.EndsTensorList.size(), 0u)
<< "EndsTensorList size can't be zero";
ends_size = param_.EndsTensorList.size();
}
......
......@@ -67,7 +67,7 @@ bool SplitOp::InferShapeImpl() const {
axis = param_.axis_tensor->data<int>()[0];
}
for (int j = 0; j < outs_dims.size(); ++j) {
for (size_t j = 0; j < outs_dims.size(); ++j) {
outs[j]->Resize(outs_dims[j]);
}
......
......@@ -28,7 +28,7 @@ static DDim GetOutputShape(const std::vector<int> &squeeze_dims,
// Determines number of dimensions of output tensor after squeeze.
// Mark and count the dimensions need to be squeezed
if (num_squeeze_dims == 0) {
for (int idx = 0; idx < in_dims.size(); ++idx) {
for (size_t idx = 0; idx < in_dims.size(); ++idx) {
if (in_dims[idx] == 1) {
should_squeeze[idx] = true;
++cnt_squeezed_dims;
......@@ -57,7 +57,7 @@ static DDim GetOutputShape(const std::vector<int> &squeeze_dims,
// Make output dimensions
std::vector<int64_t> output_shape(in_dims.size() - cnt_squeezed_dims, 0);
for (int in_idx = 0, out_idx = 0; in_idx < in_dims.size(); ++in_idx) {
for (size_t in_idx = 0, out_idx = 0; in_idx < in_dims.size(); ++in_idx) {
if (!should_squeeze[in_idx]) {
output_shape[out_idx++] = in_dims[in_idx];
}
......
......@@ -75,7 +75,7 @@ bool UnsqueezeOp::InferShapeImpl() const {
final_axes = std::vector<int>(axes_tensor_data,
axes_tensor_data + axes_tensor->numel());
} else if (!axes_tensor_vct.empty()) {
for (int i = 0; i < axes_tensor_vct.size(); i++) {
for (size_t i = 0; i < axes_tensor_vct.size(); i++) {
final_axes.push_back(axes_tensor_vct[i]->data<int>()[0]);
}
} else {
......
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