-// Copyright (C) 2018 Intel Corporation
+// Copyright (C) 2018-2019 Intel Corporation
// SPDX-License-Identifier: Apache-2.0
//
using namespace ShapeInfer;
class BuiltInShapeInferPoolImplTest
- : public BuiltInShapeInferTestWithParam<std::tuple<InOutShapes, kernel, stride, pad, pool_type, exclude_pad, auto_pad, NewInOutShapes, padrb>> {
+ : public BuiltInShapeInferTestWithParam<std::tuple<InOutShapes, kernel, stride, pad, pool_type, exclude_pad, auto_pad, NewInOutShapes, pad_end>> {
protected:
void SetUp() override {
BuiltInShapeInferCommon::SetUp();
exclude_pad = std::get<5>(params);
auto_pad = std::get<6>(params);
newInOutShapes = std::get<7>(params);
- padrb = std::get<8>(params);
+ pad_end = std::get<8>(params);
}
std::map<std::string, std::string> getMapParams() {
- std::map<std::string, std::string> params{
- {"kernel-x", std::to_string(kernel.x)},
- {"kernel-y", std::to_string(kernel.y)},
- {"stride-x", std::to_string(stride.x)},
- {"stride-y", std::to_string(stride.y)},
- {"pad-x", std::to_string(pad.x)},
- {"pad-y", std::to_string(pad.y)},
- {"pool-method", pool_type},
- {"exclude-pad", exclude_pad ? "false" : "true"},
- };
- if (!auto_pad.empty()) params["auto_pad"] = auto_pad;
- if (padrb.x) params["pad-r"] = std::to_string(padrb.x);
- if (padrb.y) params["pad-b"] = std::to_string(padrb.y);
- return params;
- }
-
- std::map<std::string, std::string> getMapParams_IRv3() {
std::map<std::string, std::string> params = {
{"kernel", kernel.toSeparetedRow(",")},
{"strides", stride.toSeparetedRow(",")},
{"exclude-pad", exclude_pad ? "false" : "true"}
};
if (!auto_pad.empty()) params["auto_pad"] = auto_pad;
- if (padrb.x != 0 && padrb.y != 0) {
- params["pads_end"] = padrb.toSeparetedRow(",");
- }
+ if (!pad_end.empty()) params["pads_end"] = pad_end.toSeparetedRow(",");
return params;
}
protected:
std::string type = "Pooling";
- testing::InOutData inOutShapes;
- testing::InOutData newInOutShapes;
+ testing::InOutShapes inOutShapes;
+ testing::InOutShapes newInOutShapes;
param_size kernel;
param_size stride;
param_size pad;
std::string pool_type;
bool exclude_pad;
std::string auto_pad;
- param_size padrb;
+ param_size pad_end;
};
TEST_P(BuiltInShapeInferPoolImplTest, body) {
auto impl = getShapeInferImpl(type);
ASSERT_NE(nullptr, impl);
- ASSERT_NO_THROW(sts = impl->inferShapes(inOutShapes.inDims, getMapParams(), blobs, outShapes, &resp));
+ ASSERT_NO_THROW(sts = impl->inferShapes(getBlobs(inOutShapes.inDims), getMapParams(), blobs, outShapes, &resp));
ASSERT_EQ(int(OK), sts) << resp.msg;
ASSERT_EQ(inOutShapes.outDims, outShapes);
}
TEST_P(BuiltInShapeInferPoolImplTest, reshaper) {
auto layerParams = getMapParams();
- auto cnnNetworkImplPtr = buildSingleLayerNetwork<2>(type, inOutShapes, &layerParams, "pooling_data");
+ auto cnnNetworkImplPtr = buildSingleLayerNetwork<4>(type, inOutShapes, &layerParams, "pooling_data");
auto reshaper = std::make_shared<Reshaper>(*cnnNetworkImplPtr);
auto inputShapes = setInputShapes(*cnnNetworkImplPtr, newInOutShapes.inDims);
reshaper->run(inputShapes);
TEST_P(BuiltInShapeInferPoolImplTest, batch) {
auto layerParams = getMapParams();
- auto cnnNetworkImplPtr = buildSingleLayerNetwork<2>(type, inOutShapes, &layerParams, "pooling_data");
- auto reshaper = std::make_shared<Reshaper>(*cnnNetworkImplPtr);
- sts = cnnNetworkImplPtr->setBatchSize(BATCH, &resp);
- ASSERT_EQ((int)OK, sts) << resp.msg;
- inOutShapes.inDims[0][0] = inOutShapes.outDims[0][0] = BATCH;
- checkNetworkInOut(*cnnNetworkImplPtr, inOutShapes);
-}
-
-TEST_P(BuiltInShapeInferPoolImplTest, body_IRv3) {
- auto impl = getShapeInferImpl(type);
- ASSERT_NE(nullptr, impl);
- ASSERT_NO_THROW(sts = impl->inferShapes(inOutShapes.inDims, getMapParams_IRv3(), blobs, outShapes, &resp));
- ASSERT_EQ(int(OK), sts) << resp.msg;
- ASSERT_EQ(inOutShapes.outDims, outShapes);
-}
-
-TEST_P(BuiltInShapeInferPoolImplTest, reshaper_IRv3) {
- auto layerParams = getMapParams_IRv3();
- auto cnnNetworkImplPtr = buildSingleLayerNetwork<3>(type, inOutShapes, &layerParams, "pooling_data");
- auto reshaper = std::make_shared<Reshaper>(*cnnNetworkImplPtr);
- auto inputShapes = setInputShapes(*cnnNetworkImplPtr, newInOutShapes.inDims);
- reshaper->run(inputShapes);
- checkNetworkInOut(*cnnNetworkImplPtr, newInOutShapes);
-}
-
-TEST_P(BuiltInShapeInferPoolImplTest, batch_IRv3) {
- auto layerParams = getMapParams_IRv3();
- auto cnnNetworkImplPtr = buildSingleLayerNetwork<3>(type, inOutShapes, &layerParams, "pooling_data");
+ auto cnnNetworkImplPtr = buildSingleLayerNetwork<4>(type, inOutShapes, &layerParams, "pooling_data");
auto reshaper = std::make_shared<Reshaper>(*cnnNetworkImplPtr);
sts = cnnNetworkImplPtr->setBatchSize(BATCH, &resp);
ASSERT_EQ((int)OK, sts) << resp.msg;
{{4, 3, 229, 115}}}), kernel({4, 2}), stride({2, 1}),
pad({2, 1}), pool_type("max"), exclude_pad(true), auto_pad(""),
NewInOutShapes({{{1, 3, 228, 228}},
- {{1, 3, 229, 115}}}), padrb({0, 0})),
+ {{1, 3, 229, 115}}}), pad_end()),
// fixate pad + right/bottom
::testing::make_tuple(InOutShapes({{{4, 3, 228, 228}},
{{4, 3, 229, 115}}}), kernel({4, 2}), stride({2, 1}),
pad({2, 1}), pool_type("max"), exclude_pad(true), auto_pad(""),
NewInOutShapes({{{1, 3, 228, 228}},
- {{1, 3, 229, 115}}}), padrb({3, 2})),
+ {{1, 3, 229, 115}}}), pad_end({3, 2})),
// valid + empty paddings
::testing::make_tuple(InOutShapes({{{4, 3, 228, 228}},
{{4, 3, 227, 113}}}), kernel({4, 2}), stride({2, 1}),
pad({0, 0}), pool_type("max"), exclude_pad(true), auto_pad("valid"),
NewInOutShapes({{{1, 3, 228, 228}},
- {{1, 3, 227, 113}}}), padrb({0, 0})),
+ {{1, 3, 227, 113}}}), pad_end()),
// valid + fixated paddings (shouldn't affect)
::testing::make_tuple(InOutShapes({{{4, 3, 228, 228}},
{{4, 3, 227, 113}}}), kernel({4, 2}), stride({2, 1}),
pad({2, 4}), pool_type("max"), exclude_pad(true), auto_pad("valid"),
NewInOutShapes({{{1, 3, 228, 228}},
- {{1, 3, 227, 113}}}), padrb({2, 1})),
+ {{1, 3, 227, 113}}}), pad_end({2, 1})),
// same_upper + empty paddings
::testing::make_tuple(InOutShapes({{{4, 3, 227, 227}},
{{4, 3, 227, 114}}}), kernel({4, 2}), stride({2, 1}),
pad({0, 0}), pool_type("max"), exclude_pad(true), auto_pad("same_upper"),
NewInOutShapes({{{1, 3, 227, 227}},
- {{1, 3, 227, 114}}}), padrb({0, 0})),
+ {{1, 3, 227, 114}}}), pad_end()),
// same_upper + fixated paddings (shouldn't affect)
::testing::make_tuple(InOutShapes({{{4, 3, 227, 227}},
{{4, 3, 227, 114}}}), kernel({4, 2}), stride({2, 1}),
pad({2, 4}), pool_type("max"), exclude_pad(true), auto_pad("same_upper"),
NewInOutShapes({{{1, 3, 227, 227}},
- {{1, 3, 227, 114}}}), padrb({0, 0})),
+ {{1, 3, 227, 114}}}), pad_end({0, 0})),
// same_lower + empty paddings
::testing::make_tuple(InOutShapes({{{4, 3, 227, 227}},
{{4, 3, 227, 113}}}), kernel({4, 2}), stride({2, 1}),
pad({0, 0}), pool_type("max"), exclude_pad(true), auto_pad("same_lower"),
NewInOutShapes({{{1, 3, 227, 227}},
- {{1, 3, 227, 113}}}), padrb({0, 0})),
+ {{1, 3, 227, 113}}}), pad_end({0, 0})),
// same_lower + fixated paddings (shouldn't affect)
::testing::make_tuple(InOutShapes({{{4, 3, 227, 227}},
{{4, 3, 227, 113}}}), kernel({4, 2}), stride({2, 1}),
pad({2, 4}), pool_type("max"), exclude_pad(true), auto_pad("same_lower"),
NewInOutShapes({{{1, 3, 227, 227}},
- {{1, 3, 227, 113}}}), padrb({0, 0}))
+ {{1, 3, 227, 113}}}), pad_end({0, 0})),
+ // 5D tensors
+ // fixate pad
+ ::testing::make_tuple(InOutShapes({{{4, 3, 16, 128, 130}},
+ {{4, 3, 17, 129, 66}}}), kernel({4, 2, 2}), stride({2, 1, 1}),
+ pad({2, 1, 1}), pool_type("max"), exclude_pad(true), auto_pad(""),
+ NewInOutShapes({{{1, 3, 16, 128, 130}},
+ {{1, 3, 17, 129, 66}}}), pad_end()),
+ // valid + empty paddings
+ ::testing::make_tuple(InOutShapes({{{4, 3, 16, 128, 130}},
+ {{4, 3, 15, 127, 64}}}), kernel({4, 2, 2}), stride({2, 1, 1}),
+ pad({0, 0, 0}), pool_type("max"), exclude_pad(true), auto_pad("valid"),
+ NewInOutShapes({{{1, 3, 16, 128, 130}},
+ {{1, 3, 15, 127, 64}}}), pad_end()),
+ // same_upper + empty paddings
+ ::testing::make_tuple(InOutShapes({{{4, 3, 16, 128, 130}},
+ {{4, 3, 16, 128, 65}}}), kernel({4, 2, 2}), stride({2, 1, 1}),
+ pad({0, 0, 0}), pool_type("max"), exclude_pad(true), auto_pad("same_upper"),
+ NewInOutShapes({{{1, 3, 16, 128, 130}},
+ {{1, 3, 16, 128, 65}}}), pad_end())
)
);
projects / platform / upstream / dldt.git / blobdiff