Merge pull request #1768 from luotao1/pad

fix PadOp bug on Gpu

Author: luotao1

paddle/function/Function.h

@@ -38,7 +38,7 @@ class FuncConfig {
  if (err) {
  *err = Error(e.what());
  } else {
- LOG(FATAL) << "Cannot get key " << key << "with error " << e.what();
+ LOG(FATAL) << "Cannot get key " << key << " with error " << e.what();
  }
  return T();
  }

paddle/function/PadOpGpu.cu

@@ -44,9 +44,9 @@ void Pad<DEVICE_TYPE_GPU>(real* outputs,
  size_t nth = num * inC * inH * inW;
  int blockSize = 1024;
  int gridSize = (nth + 1024 - 1) / 1024;
- int cstart = pad.channelStart, cend = pad.channelEnd;
- int hstart = pad.heightStart, hend = pad.heightEnd;
- int wstart = pad.widthStart, wend = pad.widthEnd;
+ int cstart = pad.channel[0], cend = pad.channel[1];
+ int hstart = pad.height[0], hend = pad.height[1];
+ int wstart = pad.width[0], wend = pad.width[1];
  int outC = inC + cstart + cend;
  int outH = inH + hstart + hend;
  int outW = inW + wstart + wend;
@@ -83,9 +83,9 @@ void PadGrad<DEVICE_TYPE_GPU>(real* inGrad,
  int nth = num * inC * inH * inW;
  int blockSize = 1024;
  int gridSize = (nth + 1024 - 1) / 1024;
- int cstart = pad.channelStart, cend = pad.channelEnd;
- int hstart = pad.heightStart, hend = pad.heightEnd;
- int wstart = pad.widthStart, wend = pad.widthEnd;
+ int cstart = pad.channel[0], cend = pad.channel[1];
+ int hstart = pad.height[0], hend = pad.height[1];
+ int wstart = pad.width[0], wend = pad.width[1];
  int outC = inC + cstart + cend;
  int outH = inH + hstart + hend;
  int outW = inW + wstart + wend;

paddle/function/PadOpTest.cpp

@@ -24,48 +24,22 @@ TEST(Pad, real) {
  for (size_t imgSizeW : {5, 32, 96}) {
  VLOG(3) << " numSamples=" << numSamples << " channels=" << channels
  << " imgSizeH=" << imgSizeH << " imgSizeW=" << imgSizeW;
-
- FunctionCompare compare("Pad",
- FuncConfig()
- .set("cstart", 2)
- .set("cend", 3)
- .set("hstart", 1)
- .set("hend", 2)
- .set("wstart", 3)
- .set("wend", 2));
- TensorShape inDims{numSamples, channels, imgSizeH, imgSizeW};
- TensorShape outDims{
- numSamples, channels + 5, imgSizeH + 3, imgSizeW + 5};
- compare.addInputs(BufferArg(VALUE_TYPE_FLOAT, inDims));
- compare.addOutputs(BufferArg(VALUE_TYPE_FLOAT, outDims, ASSIGN_TO));
- compare.run();
- }
- }
- }
- }
-}
-
-TEST(PadGrad, real) {
- for (size_t numSamples : {5, 32}) {
- for (size_t channels : {1, 5, 32}) {
- for (size_t imgSizeH : {5, 33, 100}) {
- for (size_t imgSizeW : {5, 32, 96}) {
- VLOG(3) << " numSamples=" << numSamples << " channels=" << channels
- << " imgSizeH=" << imgSizeH << " imgSizeW=" << imgSizeW;
- FunctionCompare compare("PadGrad",
- FuncConfig()
- .set("cstart", 2)
- .set("cend", 3)
- .set("hstart", 1)
- .set("hend", 2)
- .set("wstart", 3)
- .set("wend", 2));
- TensorShape inDims{numSamples, channels, imgSizeH, imgSizeW};
- TensorShape outDims{
- numSamples, channels + 5, imgSizeH + 3, imgSizeW + 5};
- compare.addInputs(BufferArg(VALUE_TYPE_FLOAT, outDims));
- compare.addOutputs(BufferArg(VALUE_TYPE_FLOAT, inDims, ASSIGN_TO));
- compare.run();
+ for (bool test_grad : {false, true}) {
+ FunctionCompare compare(
+ test_grad ? "PadGrad" : "Pad",
+ FuncConfig()
+ .set<std::vector<uint32_t>>("channel", {2, 3})
+ .set<std::vector<uint32_t>>("height", {1, 2})
+ .set<std::vector<uint32_t>>("width", {3, 2}));
+ TensorShape inDims{numSamples, channels, imgSizeH, imgSizeW};
+ TensorShape outDims{
+ numSamples, channels + 5, imgSizeH + 3, imgSizeW + 5};
+ compare.addInputs(
+ BufferArg(VALUE_TYPE_FLOAT, test_grad ? outDims : inDims));
+ compare.addOutputs(BufferArg(
+ VALUE_TYPE_FLOAT, test_grad ? inDims : outDims, ASSIGN_TO));
+ compare.run();
+ }
  }
  }
  }