diff --git a/unit_tests/operator/Test_ConvImpl.cpp b/unit_tests/operator/Test_ConvImpl.cpp
index 7b14546351e1eb61af1063a5b98aa16fcebdd029..6a49a7995e4827294466e10b1347e81a79478f6a 100644
--- a/unit_tests/operator/Test_ConvImpl.cpp
+++ b/unit_tests/operator/Test_ConvImpl.cpp
@@ -1986,39 +1986,17 @@ TEST_CASE("[cpu/operator] Conv(backward)", "[Conv][CPU]") {
auto input = std::make_shared<Tensor>(
Array3D<float, batchSize, inChannels, inDataSize>(
- {{{{1.000000,
- 1.000000,
- 1.000000,
- 1.000000,
- 1.000000,
- 1.000000,
- 1.000000,
- 1.000000,
- 1.000000,
- 1.000000,
- 1.000000,
- 1.000000},
- {1.000000,
- 1.000000,
- 1.000000,
- 1.000000,
- 1.000000,
- 1.000000,
- 1.000000,
- 1.000000,
- 1.000000,
- 1.000000,
- 1.000000,
- 1.000000}}}}));
+ {{{{1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1.},
+ {1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1.}}}}));
auto weights = std::make_shared<Tensor>(
Array3D<float, outChannels, inChannels, kernelSize>(
- {{{{0.100000, 0.100000, 0.100000, 0.100000},
- {0.100000, 0.100000, 0.100000, 0.100000}},
- {{0.100000, 0.100000, 0.100000, 0.100000},
- {0.100000, 0.100000, 0.100000, 0.100000}},
- {{0.100000, 0.100000, 0.100000, 0.100000},
- {0.100000, 0.100000, 0.100000, 0.100000}}}
+ {{{{0.1, 0.1, 0.1, 0.1},
+ {0.1, 0.1, 0.1, 0.1}},
+ {{0.1, 0.1, 0.1, 0.1},
+ {0.1, 0.1, 0.1, 0.1}},
+ {{0.1, 0.1, 0.1, 0.1},
+ {0.1, 0.1, 0.1, 0.1}}}
}));
@@ -2054,30 +2032,8 @@ TEST_CASE("[cpu/operator] Conv(backward)", "[Conv][CPU]") {
SECTION("Input Grad") {
auto expectedInputGrad = std::make_shared<Tensor>(
Array3D<float, batchSize, inChannels, inDataSize>(
- {{{{0.3000,
- 0.6000,
- 0.9000,
- 1.2000,
- 1.2000,
- 1.2000,
- 1.2000,
- 1.2000,
- 1.2000,
- 0.9000,
- 0.6000,
- 0.3000},
- {0.3000,
- 0.6000,
- 0.9000,
- 1.2000,
- 1.2000,
- 1.2000,
- 1.2000,
- 1.2000,
- 1.2000,
- 0.9000,
- 0.6000,
- 0.3000}}}}));
+ {{{{0.3, 0.6, 0.9, 1.2, 1.2, 1.2, 1.2, 1.2, 1.2, 0.9, 0.6, 0.3},
+ {0.3, 0.6, 0.9, 1.2, 1.2, 1.2, 1.2, 1.2, 1.2, 0.9, 0.6, 0.3}}}}));
CHECK(approxEq<float, float>(*op->getInput(0)->grad(),
*expectedInputGrad));
}
@@ -2128,9 +2084,9 @@ TEST_CASE("[cpu/operator] Conv(backward)", "[Conv][CPU]") {
{1., 1., 1., 1., 1., 1., 1., 1.}}}}));
auto weights = std::make_shared<Tensor>(
Array3D<float, outChannels, inChannels, kernelSize>(
- {{{{0.1000, 0.1000},
- {0.1000, 0.1000},
- {0.1000, 0.1000}}}}));
+ {{{{0.1, 0.1},
+ {0.1, 0.1},
+ {0.1, 0.1}}}}));
auto biases = std::make_shared<Tensor>(
Array1D<float, outChannels>({0.060000}));
@@ -2164,55 +2120,13 @@ TEST_CASE("[cpu/operator] Conv(backward)", "[Conv][CPU]") {
SECTION("Input Grad") {
auto expectedInputGrad = std::make_shared<Tensor>(
Array3D<float, batchSize, inChannels, inDataSize>(
- {{{{0.1000,
- 0.1000,
- 0.0000,
- 0.1000,
- 0.1000,
- 0.0000,
- 0.1000,
- 0.1000},
- {0.1000,
- 0.1000,
- 0.0000,
- 0.1000,
- 0.1000,
- 0.0000,
- 0.1000,
- 0.1000},
- {0.1000,
- 0.1000,
- 0.0000,
- 0.1000,
- 0.1000,
- 0.0000,
- 0.1000,
- 0.1000}},
-
- {{0.1000,
- 0.1000,
- 0.0000,
- 0.1000,
- 0.1000,
- 0.0000,
- 0.1000,
- 0.1000},
- {0.1000,
- 0.1000,
- 0.0000,
- 0.1000,
- 0.1000,
- 0.0000,
- 0.1000,
- 0.1000},
- {0.1000,
- 0.1000,
- 0.0000,
- 0.1000,
- 0.1000,
- 0.0000,
- 0.1000,
- 0.1000}}}}));
+ {{{{0.1, 0.1, 0.0, 0.1, 0.1, 0.0, 0.1, 0.1},
+ {0.1, 0.1, 0.0, 0.1, 0.1, 0.0, 0.1, 0.1},
+ {0.1, 0.1, 0.0, 0.1, 0.1, 0.0, 0.1, 0.1}},
+
+ {{0.1, 0.1, 0.0, 0.1, 0.1, 0.0, 0.1, 0.1},
+ {0.1, 0.1, 0.0, 0.1, 0.1, 0.0, 0.1, 0.1},
+ {0.1, 0.1, 0.0, 0.1, 0.1, 0.0, 0.1, 0.1}}}}));
CHECK(approxEq<float, float>(*op->getInput(0)->grad(),
*expectedInputGrad));
}
@@ -2256,12 +2170,12 @@ TEST_CASE("[cpu/operator] Conv(backward)", "[Conv][CPU]") {
{1., 1., 1., 1., 1., 1., 1., 1.}}}}));
auto weights = std::make_shared<Tensor>(
Array3D<float, outChannels, inChannels, kernelSize>(
- {{{{0.1000, 0.1000},
- {0.1000, 0.1000},
- {0.1000, 0.1000}}}}));
+ {{{{0.1, 0.1},
+ {0.1, 0.1},
+ {0.1, 0.1}}}}));
auto biases = std::make_shared<Tensor>(
- Array1D<float, outChannels>({0.060000}));
+ Array1D<float, outChannels>({0.06}));
auto op = setupTestConv<DIM>(
batchSize,
@@ -2292,55 +2206,13 @@ TEST_CASE("[cpu/operator] Conv(backward)", "[Conv][CPU]") {
SECTION("Input Grad") {
auto expectedInputGrad = std::make_shared<Tensor>(
Array3D<float, batchSize, inChannels, inDataSize>(
- {{{{0.1000,
- 0.1000,
- 0.2000,
- 0.2000,
- 0.2000,
- 0.2000,
- 0.1000,
- 0.1000},
- {0.1000,
- 0.1000,
- 0.2000,
- 0.2000,
- 0.2000,
- 0.2000,
- 0.1000,
- 0.1000},
- {0.1000,
- 0.1000,
- 0.2000,
- 0.2000,
- 0.2000,
- 0.2000,
- 0.1000,
- 0.1000}},
-
- {{0.1000,
- 0.1000,
- 0.2000,
- 0.2000,
- 0.2000,
- 0.2000,
- 0.1000,
- 0.1000},
- {0.1000,
- 0.1000,
- 0.2000,
- 0.2000,
- 0.2000,
- 0.2000,
- 0.1000,
- 0.1000},
- {0.1000,
- 0.1000,
- 0.2000,
- 0.2000,
- 0.2000,
- 0.2000,
- 0.1000,
- 0.1000}}}}));
+ {{{{0.1, 0.1, 0.2, 0.2, 0.2, 0.2, 0.1, 0.1},
+ {0.1, 0.1, 0.2, 0.2, 0.2, 0.2, 0.1, 0.1},
+ {0.1, 0.1, 0.2, 0.2, 0.2, 0.2, 0.1, 0.1}},
+
+ {{0.1, 0.1, 0.2, 0.2, 0.2, 0.2, 0.1, 0.1},
+ {0.1, 0.1, 0.2, 0.2, 0.2, 0.2, 0.1, 0.1},
+ {0.1, 0.1, 0.2, 0.2, 0.2, 0.2, 0.1, 0.1}}}}));
CHECK(approxEq<float, float>(*op->getInput(0)->grad(),
*expectedInputGrad));
}
@@ -2380,28 +2252,28 @@ TEST_CASE("[cpu/operator] Conv(backward)", "[Conv][CPU]") {
{1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1.}}}}));
auto weights = std::make_shared<Tensor>(
Array3D<float, outChannels, inChannels, kernelSize>(
- {{{{0.1000, 0.1000, 0.1000},
- {0.1000, 0.1000, 0.1000},
- {0.1000, 0.1000, 0.1000},
- {0.1000, 0.1000, 0.1000}},
-
- {{0.1000, 0.1000, 0.1000},
- {0.1000, 0.1000, 0.1000},
- {0.1000, 0.1000, 0.1000},
- {0.1000, 0.1000, 0.1000}},
-
- {{0.1000, 0.1000, 0.1000},
- {0.1000, 0.1000, 0.1000},
- {0.1000, 0.1000, 0.1000},
- {0.1000, 0.1000, 0.1000}},
-
- {{0.1000, 0.1000, 0.1000},
- {0.1000, 0.1000, 0.1000},
- {0.1000, 0.1000, 0.1000},
- {0.1000, 0.1000, 0.1000}}}}));
+ {{{{0.1, 0.1, 0.1},
+ {0.1, 0.1, 0.1},
+ {0.1, 0.1, 0.1},
+ {0.1, 0.1, 0.1}},
+
+ {{0.1, 0.1, 0.1},
+ {0.1, 0.1, 0.1},
+ {0.1, 0.1, 0.1},
+ {0.1, 0.1, 0.1}},
+
+ {{0.1, 0.1, 0.1},
+ {0.1, 0.1, 0.1},
+ {0.1, 0.1, 0.1},
+ {0.1, 0.1, 0.1}},
+
+ {{0.1, 0.1, 0.1},
+ {0.1, 0.1, 0.1},
+ {0.1, 0.1, 0.1},
+ {0.1, 0.1, 0.1}}}}));
auto biases = std::make_shared<Tensor>(Array1D<float, outChannels>(
- {{0.0100, 0.0100, 0.0100, 0.0100}}));
+ {{0.01, 0.01, 0.01, 0.01}}));
auto op = setupTestConv<DIM>(
batchSize,
@@ -2432,58 +2304,10 @@ TEST_CASE("[cpu/operator] Conv(backward)", "[Conv][CPU]") {
SECTION("Input Grad") {
auto expectedInputGrad = std::make_shared<Tensor>(
Array3D<float, batchSize, inChannels, inDataSize>(
- {{{{0.4000,
- 0.0000,
- 0.0000,
- 0.4000,
- 0.4000,
- 0.0000,
- 0.4000,
- 0.4000,
- 0.0000,
- 0.0000,
- 0.4000,
- 0.0000,
- 0.0000},
- {0.4000,
- 0.0000,
- 0.0000,
- 0.4000,
- 0.4000,
- 0.0000,
- 0.4000,
- 0.4000,
- 0.0000,
- 0.0000,
- 0.4000,
- 0.0000,
- 0.0000},
- {0.4000,
- 0.0000,
- 0.0000,
- 0.4000,
- 0.4000,
- 0.0000,
- 0.4000,
- 0.4000,
- 0.0000,
- 0.0000,
- 0.4000,
- 0.0000,
- 0.0000},
- {0.4000,
- 0.0000,
- 0.0000,
- 0.4000,
- 0.4000,
- 0.0000,
- 0.4000,
- 0.4000,
- 0.0000,
- 0.0000,
- 0.4000,
- 0.0000,
- 0.0000}}}}));
+ {{{{0.4,0.,0.,0.4,0.4,0.,0.4,0.4,0.,0.,0.4,0.,0.},
+ {0.4,0.,0.,0.4,0.4,0.,0.4,0.4,0.,0.,0.4,0.,0.},
+ {0.4,0.,0.,0.4,0.4,0.,0.4,0.4,0.,0.,0.4,0.,0.},
+ {0.4,0.,0.,0.4,0.4,0.,0.4,0.4,0.,0.,0.4,0.,0.}}}}));
CHECK(approxEq<float, float>(*op->getInput(0)->grad(),
*expectedInputGrad));
}
@@ -2543,9 +2367,9 @@ TEST_CASE("[cpu/operator] Conv(backward)", "[Conv][CPU]") {
{9., 10., 11., 12., 13., 14., 15., 16.}}}}));
auto weights = std::make_shared<Tensor>(
Array3D<float, outChannels, inChannels, kernelSize>(
- {{{{0.1000, 0.2000, 0.3000}, {0.4000, 0.5000, 0.6000}},
+ {{{{0.1, 0.2, 0.3}, {0.4, 0.5, 0.6}},
- {{0.7000, 0.8000, 0.9000}, {1.0000, 1.1000, 1.2000}}}}));
+ {{0.7, 0.8, 0.9}, {1.0, 1.1, 1.2}}}}));
auto biases = std::make_shared<Tensor>(
Array1D<float, outChannels>({{0.0100, 0.0200}}));
@@ -2576,22 +2400,8 @@ TEST_CASE("[cpu/operator] Conv(backward)", "[Conv][CPU]") {
SECTION("Input Grad") {
auto expectedInputGrad = std::make_shared<Tensor>(
Array3D<float, batchSize, inChannels, inDataSize>(
- {{{{2.2000,
- 0.0000,
- 5.6000,
- 0.0000,
- 6.6000,
- 0.0000,
- 4.2000,
- 0.0000},
- {3.4000,
- 0.0000,
- 8.6000,
- 0.0000,
- 9.6000,
- 0.0000,
- 6.0000,
- 0.0000}}}}));
+ {{{{2.2,0.,5.6,0.,6.6,0.,4.2,0.},
+ {3.4,0.,8.6,0.,9.6,0.,6.,0.}}}}));
CHECK(approxEq<float, float>(*op->getInput(0)->grad(),
*expectedInputGrad));
}
@@ -3140,18 +2950,14 @@ TEST_CASE("[cpu/operator] Conv(backward)", "[Conv][CPU]") {
inChannels,
inDataSize[0],
inDataSize[1],
- inDataSize[2]>({{{{{{0.100000, 0.200000},
- {0.300000, 0.400000}},
-
- {{0.500000, 0.600000},
- {0.700000, 0.800000}}},
+ inDataSize[2]>(
+ {{{{{{0.100000, 0.200000}, {0.300000, 0.400000}},
+ {{0.500000, 0.600000}, {0.700000, 0.800000}}},
- {{{0.900000, 1.000000},
- {1.100000, 1.200000}},
+ {{{0.900000, 1.000000}, {1.100000, 1.200000}},
- {{1.300000, 1.400000},
- {1.500000, 1.600000}}}}}}));
+ {{1.300000, 1.400000}, {1.500000, 1.600000}}}}}}));
CHECK(approxEq<float, float>(*op->getInput(0)->grad(),
*expectedInputGrad));
}
@@ -3162,18 +2968,15 @@ TEST_CASE("[cpu/operator] Conv(backward)", "[Conv][CPU]") {
inChannels,
kernelSize[0],
kernelSize[1],
- kernelSize[2]>({{{{{{ 1.000000, 2.000000},
- { 3.000000, 4.000000}},
-
- {{ 5.000000, 6.000000},
- { 7.000000, 8.000000}}},
+ kernelSize[2]>(
+ {{{{{{1.000000, 2.000000}, {3.000000, 4.000000}},
+ {{5.000000, 6.000000}, {7.000000, 8.000000}}},
- {{{ 9.000000, 10.000000},
- {11.000000, 12.000000}},
+ {{{9.000000, 10.000000}, {11.000000, 12.000000}},
- {{13.000000, 14.000000},
- {15.000000, 16.000000}}}}}}));
+ {{13.000000, 14.000000},
+ {15.000000, 16.000000}}}}}}));
CHECK(approxEq<float, float>(*op->getInput(1)->grad(),
*expectedWeightsGrad));
}