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Test(tests/core/converters) added 5 tests for test_interpolate
Signed-off-by: Abhiram Iyer <[email protected]> Signed-off-by: Abhiram Iyer <[email protected]>
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‎tests/core/converters/BUILD

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@@ -51,6 +51,10 @@ converter_test(
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name = "test_unary"
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)
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converter_test(
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name = "test_interpolate"
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)
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test_suite(
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name = "test_converters",
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tests = [
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":test_shuffle",
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":test_softmax",
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":test_unary",
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":test_interpolate",
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]
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)
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‎tests/core/converters/test_interpolate.cpp

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#include <string>
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#include "gtest/gtest.h"
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#include "torch/csrc/jit/ir/irparser.h"
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#include "tests/util/util.h"
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#include "core/compiler.h"
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TEST(Converters, ATenUpsampleNearest1dConvertsCorrectly) {
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const auto graph = R"IR(
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graph(%0 : Tensor):
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%2 : int = prim::Constant[value=10]()
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%3 : int[] = prim::ListConstruct(%2)
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%4 : None = prim::Constant()
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%5 : Tensor = aten::upsample_nearest1d(%0, %3, %4)
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return (%5))IR";
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auto g = std::make_shared<torch::jit::Graph>();
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torch::jit::parseIR(graph, &*g);
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// Input Tensor needs to be 3D for TensorRT upsample_nearest1d
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auto in = at::randint(1, 10, {10, 2, 2}, {at::kCUDA});
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auto jit_in = at::clone(in);
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auto params = trtorch::core::conversion::get_named_params(g->inputs(), {});
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auto jit_results = trtorch::tests::util::RunGraph(g, params, {jit_in});
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auto trt_in = at::clone(in);
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params = trtorch::core::conversion::get_named_params(g->inputs(), {});
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auto trt_results = trtorch::tests::util::RunGraphEngine(g, params, {trt_in});
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auto trt = trt_results[0].reshape(jit_results[0].sizes());
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ASSERT_TRUE(trtorch::tests::util::almostEqual(jit_results[0], trt, 2e-6));
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}
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TEST(Converters, ATenUpsampleNearest2dConvertsCorrectly1dOutputSize) {
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const auto graph = R"IR(
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graph(%0 : Tensor):
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%2 : int = prim::Constant[value=10]()
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%3 : int[] = prim::ListConstruct(%2, %2)
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%4 : None = prim::Constant()
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%5 : Tensor = aten::upsample_nearest2d(%0, %3, %4, %4)
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return (%5))IR";
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auto g = std::make_shared<torch::jit::Graph>();
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torch::jit::parseIR(graph, &*g);
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// Input Tensor needs to be 4D for TensorRT upsample_nearest2d
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auto in = at::randint(1, 10, {10, 2, 2, 2}, {at::kCUDA});
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auto jit_in = at::clone(in);
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auto params = trtorch::core::conversion::get_named_params(g->inputs(), {});
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auto jit_results = trtorch::tests::util::RunGraph(g, params, {jit_in});
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auto trt_in = at::clone(in);
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params = trtorch::core::conversion::get_named_params(g->inputs(), {});
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auto trt_results = trtorch::tests::util::RunGraphEngine(g, params, {trt_in});
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auto trt = trt_results[0].reshape(jit_results[0].sizes());
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ASSERT_TRUE(trtorch::tests::util::almostEqual(jit_results[0], trt, 2e-6));
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}
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TEST(Converters, ATenUpsampleNearest2dConvertsCorrectly2dOutputSize) {
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const auto graph = R"IR(
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graph(%0 : Tensor):
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%2 : int = prim::Constant[value=10]()
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%3 : int[] = prim::ListConstruct(%2, %2)
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%4 : None = prim::Constant()
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%5 : Tensor = aten::upsample_nearest2d(%0, %3, %4, %4)
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return (%5))IR";
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auto g = std::make_shared<torch::jit::Graph>();
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torch::jit::parseIR(graph, &*g);
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// Input Tensor needs to be 4D for TensorRT upsample_nearest2d
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auto in = at::randint(1, 10, {10, 2, 2, 2}, {at::kCUDA});
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auto jit_in = at::clone(in);
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auto params = trtorch::core::conversion::get_named_params(g->inputs(), {});
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auto jit_results = trtorch::tests::util::RunGraph(g, params, {jit_in});
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auto trt_in = at::clone(in);
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params = trtorch::core::conversion::get_named_params(g->inputs(), {});
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auto trt_results = trtorch::tests::util::RunGraphEngine(g, params, {trt_in});
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auto trt = trt_results[0].reshape(jit_results[0].sizes());
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ASSERT_TRUE(trtorch::tests::util::almostEqual(jit_results[0], trt, 2e-6));
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}
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TEST(Converters, ATenUpsampleNearest3dConvertsCorrectly1dOutputSize) {
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const auto graph = R"IR(
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graph(%0 : Tensor):
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%2 : int = prim::Constant[value=10]()
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%3 : int[] = prim::ListConstruct(%2, %2, %2)
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%4 : None = prim::Constant()
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%5 : Tensor = aten::upsample_nearest3d(%0, %3, %4, %4, %4)
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return (%5))IR";
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auto g = std::make_shared<torch::jit::Graph>();
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torch::jit::parseIR(graph, &*g);
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// Input Tensor needs to be 5D for TensorRT upsample_nearest3d
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auto in = at::randint(1, 10, {10, 2, 2, 2, 2}, {at::kCUDA});
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auto jit_in = at::clone(in);
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auto params = trtorch::core::conversion::get_named_params(g->inputs(), {});
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auto jit_results = trtorch::tests::util::RunGraph(g, params, {jit_in});
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auto trt_in = at::clone(in);
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params = trtorch::core::conversion::get_named_params(g->inputs(), {});
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auto trt_results = trtorch::tests::util::RunGraphEngine(g, params, {trt_in});
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auto trt = trt_results[0].reshape(jit_results[0].sizes());
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ASSERT_TRUE(trtorch::tests::util::almostEqual(jit_results[0], trt, 2e-6));
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}
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TEST(Converters, ATenUpsampleNearest3dConvertsCorrectly3dOutputSize) {
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const auto graph = R"IR(
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graph(%0 : Tensor):
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%2 : int = prim::Constant[value=10]()
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%3 : int[] = prim::ListConstruct(%2, %2, %2)
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%4 : None = prim::Constant()
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%5 : Tensor = aten::upsample_nearest3d(%0, %3, %4, %4, %4)
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return (%5))IR";
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auto g = std::make_shared<torch::jit::Graph>();
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torch::jit::parseIR(graph, &*g);
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// Input Tensor needs to be 5D for TensorRT upsample_nearest3d
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auto in = at::randint(1, 10, {10, 2, 2, 2, 2}, {at::kCUDA});
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auto jit_in = at::clone(in);
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auto params = trtorch::core::conversion::get_named_params(g->inputs(), {});
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auto jit_results = trtorch::tests::util::RunGraph(g, params, {jit_in});
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auto trt_in = at::clone(in);
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params = trtorch::core::conversion::get_named_params(g->inputs(), {});
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auto trt_results = trtorch::tests::util::RunGraphEngine(g, params, {trt_in});
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auto trt = trt_results[0].reshape(jit_results[0].sizes());
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ASSERT_TRUE(trtorch::tests::util::almostEqual(jit_results[0], trt, 2e-6));
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}

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