fix bug in conv2d transpose

mindspore/lite/src/runtime/kernel/opencl/kernel/concat.cc

@@ -168,8 +168,9 @@ int ConcatOpenCLKernel::ConvertWeightToTensor() {
     if (in_tensor->IsConst()) {
       std::vector<char> weight(in_shape.Image2DSize, 0);
       bool src_is_fp16 = in_tensor->data_type() == kNumberTypeFloat16;
-      PackNHWCToNHWC4(in_tensor->data_c(), weight.data(), src_is_fp16, fp16_enable, in_shape);
-      size_t dtype = fp16_enable ? CL_HALF_FLOAT : CL_FLOAT;
+      PackNHWCToNHWC4(in_tensor->data_c(), weight.data(), src_is_fp16,
+                      fp16_enable && in_tensor->data_type() != kNumberTypeInt32, in_shape);
+      size_t dtype = fp16_enable && in_tensor->data_type() != kNumberTypeInt32 ? CL_HALF_FLOAT : CL_FLOAT;
       ImageSize img_size{in_shape.width, in_shape.height, dtype};
       auto weight_ptr_ = allocator->Malloc(img_size, weight.data());
       weight_ptrs_.push_back(weight_ptr_);
@@ -206,7 +207,7 @@ int ConcatOpenCLKernel::Prepare() {
   std::string source = concat_source;
   std::string program_name = "Concat";
   ocl_runtime_->LoadSource(program_name, source);
-  ocl_runtime_->BuildKernel(kernel_, program_name, kernel_name);
+  ocl_runtime_->BuildKernel(kernel_, program_name, kernel_name, {}, out_tensors_[0]->data_type());
   MS_LOG(DEBUG) << kernel_name << " Init Done!";
   SetConstArgs();
   SetGlobalLocal();

mindspore/lite/src/runtime/kernel/opencl/kernel/conv2d_transpose.cc

@@ -42,10 +42,6 @@ int Conv2dTransposeOpenCLKernel::CheckSpecs() {
     return RET_ERROR;
   }
   ConvParameter *param = reinterpret_cast<ConvParameter *>(op_parameter_);
-  if (param->pad_l_ != param->pad_r_ || param->pad_u_ != param->pad_d_) {
-    MS_LOG(ERROR) << "only support symmetric padding";
-    return RET_ERROR;
-  }
   if (param->act_type_ != ActType_No && param->act_type_ != ActType_Relu && param->act_type_ != ActType_Relu6) {
     MS_LOG(ERROR) << "Unsupported activation type " << param->act_type_;
     return RET_ERROR;
@@ -102,8 +98,8 @@ void Conv2dTransposeOpenCLKernel::SetConstArgs() {
   int co = out_tensors_[0]->shape()[3];
   int kh = param->kernel_h_;
   int kw = param->kernel_w_;
-  int pad_h = param->pad_l_;
-  int pad_w = param->pad_u_;
+  int pad_h = param->pad_u_;
+  int pad_w = param->pad_l_;
   int stride_h = param->stride_h_;
   int stride_w = param->stride_w_;
   int oh = out_tensors_[0]->shape()[1];

mindspore/lite/src/runtime/kernel/opencl/kernel/fusion_eltwise.h

@@ -155,7 +155,7 @@ class FusionEltwiseOpenCLKernel : public OpenCLKernel {
 
   ~FusionEltwiseOpenCLKernel() override {
     if (op_parameter_ != nullptr) {
-      delete op_parameter_;
+      delete reinterpret_cast<FusionEltwiseParameter *>(op_parameter_);
       op_parameter_ = nullptr;
     }
   }

mindspore/lite/src/runtime/kernel/opencl/kernel/gather.cc

@@ -107,7 +107,7 @@ int GatherOpenCLKernel::Prepare() {
 #else
   std::string program_name = "gather";
   ocl_runtime_->LoadSource(program_name, gather_source);
-  ocl_runtime_->BuildKernel(kernel_, program_name, kernel_name);
+  ocl_runtime_->BuildKernel(kernel_, program_name, kernel_name, {}, out_tensors_[0]->data_type());
 #endif
   if (in_tensors_.at(1)->IsConst()) {
     intensor1_is_tensor = false;

mindspore/lite/src/runtime/kernel/opencl/kernel/reshape.cc

@@ -79,7 +79,7 @@ int ReshapeOpenCLKernel::Prepare() {
   std::string source = reshape_source;
   std::string program_name = "reshape";
   ocl_runtime_->LoadSource(program_name, source);
-  ocl_runtime_->BuildKernel(kernel_, program_name, kernel_name);
+  ocl_runtime_->BuildKernel(kernel_, program_name, kernel_name, {}, out_tensors_[0]->data_type());
 #endif
 
   SetGlobalLocal();

mindspore/lite/src/runtime/kernel/opencl/opencl_subgraph.cc

@@ -172,7 +172,7 @@ int OpenCLSubGraph::GenToFormatOp(const std::vector<lite::Tensor *> &in_tensors,
       return RET_ERROR;
     }
     static int index = 0;
-    in_convert_op->set_name("ToFormat_" + std::to_string(index));
+    in_convert_op->set_name("ToFormat_" + std::to_string(index++));
 
     ReplaceOutTensorAndKernelToConvert(in_tensor, in_kernels.at(i), new_tensor, in_convert_op, mem_type);
 
@@ -318,6 +318,14 @@ bool OpenCLSubGraph::IsSubGraphInferShapeDone() {
 }
 
 int OpenCLSubGraph::Prepare() {
+  for (const auto tensor : in_tensors_) {
+    MS_ASSERT(tensor);
+    tensor->set_allocator(allocator_);
+  }
+  for (const auto tensor : out_tensors_) {
+    MS_ASSERT(tensor);
+    tensor->set_allocator(allocator_);
+  }
   executor_ = new (std::nothrow) lite::opencl::OpenCLExecutor();
   if (executor_ == nullptr) {
     MS_LOG(ERROR) << "Create OpenCLExecutor fail";

mindspore/lite/src/runtime/opencl/opencl_runtime.cc

@@ -363,9 +363,9 @@ bool OpenCLRuntime::SetFp16Enable(bool enable) {
 }
 
 int OpenCLRuntime::BuildKernel(cl::Kernel &kernel, const std::string &program_name, const std::string &kernel_name,
-                               const std::vector<std::string> &build_options_ext) {
+                               const std::vector<std::string> &build_options_ext, TypeId data_type) {
   std::string build_option = default_build_option_;
-  if (fp16_enable_) {
+  if (fp16_enable_ && data_type != kNumberTypeInt32) {
     build_option +=
       " -DFP16_ENABLE=1 -DFLT=half -DFLT4=half4 -DFLT16=half16 -DAS_FLT4=as_half4 -DAS_UINT4=as_ushort4 -DUINT4=ushort4"
       " -DWRITE_IMAGE=write_imageh -DREAD_IMAGE=read_imageh -DTO_FLT=convert_half -DTO_FLT4=convert_half4";

mindspore/lite/src/runtime/opencl/opencl_runtime.h

@@ -25,6 +25,7 @@ j* you may not use this file except in compliance with the License.
 #include <string>
 #include <utility>
 #include <type_traits>
+#include "dtype/type_id.h"
 #include "src/common/log_adapter.h"
 #include "src/runtime/opencl/opencl_wrapper.h"
 #include "src/runtime/opencl/opencl_allocator.h"
@@ -118,7 +119,7 @@ class OpenCLRuntime {
   std::vector<unsigned char> GetProgramBinary(const cl::Program &program);
   bool LoadSource(const std::string &program_name, const std::string &source);
   int BuildKernel(cl::Kernel &kernel, const std::string &program_name, const std::string &kernel_name,
-                  const std::vector<std::string> &build_options_ext = {});
+                  const std::vector<std::string> &build_options_ext = {}, TypeId data_type = kNumberTypeFloat32);
   int RunKernel(const cl::Kernel &kernel, const cl::NDRange &global, const cl::NDRange &local,
                 cl::CommandQueue *command_queue = nullptr, cl::Event *event = nullptr);
   int ReadOrWriteImage(void *buffer, void *data, bool is_read);

mindspore/lite/test/ut/src/runtime/kernel/opencl/conv2d_transpose_tests.cc

@@ -22,7 +22,7 @@ class TestOpenCL_Conv2dTranspose : public CommonTest {};
 
 namespace {
 // PrimitiveType_DeConv2D: src/ops/populate/deconv2d_populate.cc
-OpParameter *CreateParameter(int n, int h, int w, int ci, int co, int kh, int kw, int pad,
+OpParameter *CreateParameter(int n, int h, int w, int ci, int co, int kh, int kw, std::vector<int> pad, int oh, int ow,
                              std::vector<int> *input_shape, std::vector<int> *weight_shape,
                              std::vector<int> *bias_shape, std::vector<int> *output_shape) {
   auto *param = test::CreateParameter<ConvParameter>(schema::PrimitiveType_DeConv2D);
@@ -30,16 +30,15 @@ OpParameter *CreateParameter(int n, int h, int w, int ci, int co, int kh, int kw
   param->kernel_w_ = kw;
   param->stride_h_ = 2;
   param->stride_w_ = 2;
-  param->pad_u_ = pad;
-  param->pad_d_ = pad;
-  param->pad_l_ = pad;
-  param->pad_r_ = pad;
+  MS_ASSERT(pad.size() == 4);
+  param->pad_u_ = pad[0];
+  param->pad_d_ = pad[1];
+  param->pad_l_ = pad[2];
+  param->pad_r_ = pad[3];
   param->dilation_h_ = 1;
   param->dilation_w_ = 1;
   param->act_type_ = ActType_No;
 
-  int oh = 2 * h - 1 + 2 * (kh - 1 - pad) - kh + 1;
-  int ow = 2 * w - 1 + 2 * (kw - 1 - pad) - kw + 1;
   *input_shape = {n, h, w, ci};
   *weight_shape = {co, kh, kw, ci};
   *bias_shape = {co};
@@ -52,11 +51,13 @@ TEST_F(TestOpenCL_Conv2dTranspose, test0) {
   int n = 1;
   int h = 2;
   int w = 2;
+  int oh = 4;
+  int ow = 4;
   int ci = 2;
   int co = 1;
   int kh = 2;
   int kw = 2;
-  int pad = 0;
+  std::vector<int> pad = {0, 0, 0, 0};
   float input_data[] = {0, 1, 2, 3, 4, 5, 6, 7};
   float weight_data[] = {1, 2, 3, 4, 5, 6, 7, 8};
   float bias_data[] = {0.5};
@@ -65,7 +66,36 @@ TEST_F(TestOpenCL_Conv2dTranspose, test0) {
   for (auto fp16_enable : {false, true}) {
     std::vector<int> input_shape, weight_shape, bias_shape, output_shape;
     auto *param =
-      CreateParameter(n, h, w, ci, co, kh, kw, pad, &input_shape, &weight_shape, &bias_shape, &output_shape);
+      CreateParameter(n, h, w, ci, co, kh, kw, pad, oh, ow, &input_shape, &weight_shape, &bias_shape, &output_shape);
+    TestMain({{input_shape, input_data, VAR},
+              {weight_shape, weight_data, CONST_TENSOR},
+              {bias_shape, bias_data, CONST_TENSOR}},
+             {output_shape, output_data}, param, fp16_enable);
+  }
+}
+
+TEST_F(TestOpenCL_Conv2dTranspose, test1) {
+  int n = 1;
+  int h = 3;
+  int w = 3;
+  int oh = 6;
+  int ow = 6;
+  int ci = 2;
+  int co = 1;
+  int kh = 2;
+  int kw = 2;
+  std::vector<int> pad = {0, 1, 0, 1};
+  float input_data[] = {0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17};
+  float weight_data[] = {0, 2, 4, 6, 1, 3, 5, 7};
+  float bias_data[] = {0.5};
+  float output_data[] = {1.5,  3.5,  3.5,  13.5, 5.5,  23.5, 5.5,   7.5,   23.5,  33.5,  41.5,  59.5,
+                         7.5,  33.5, 9.5,  43.5, 11.5, 53.5, 59.5,  85.5,  77.5,  111.5, 95.5,  137.5,
+                         13.5, 63.5, 15.5, 73.5, 17.5, 83.5, 113.5, 163.5, 131.5, 189.5, 149.5, 215.5};
+
+  for (auto fp16_enable : {false, true}) {
+    std::vector<int> input_shape, weight_shape, bias_shape, output_shape;
+    auto *param =
+      CreateParameter(n, h, w, ci, co, kh, kw, pad, oh, ow, &input_shape, &weight_shape, &bias_shape, &output_shape);
     TestMain({{input_shape, input_data, VAR},
               {weight_shape, weight_data, CONST_TENSOR},
               {bias_shape, bias_data, CONST_TENSOR}},