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1d1faaabef
Added int32, int64 support and type inference to dnn #24411 **Added a type inference to dnn similar to the shape inference, added int32 and int64 support.** - Added getTypes method for layers that calculates layer outputs types and internals types from inputs types (Similar to getMemoryShapes). By default outputs and internals types = input[0] type - Added type inference pipeline similar to shape inference pipeline. LayersShapes struct (that is used in shape inference pipeline) now contains both shapes and types - All layers output blobs are now allocated using the calculated types from the type inference. - Inputs and constants with int32 and int64 types are not automatically converted into float32 now. - Added int32 and int64 support for all the layers with indexing and for all the layers required in tests. Added int32 and int64 support for CUDA: - Added host<->device data moving for int32 and int64 - Added int32 and int64 support for several layers (just slightly modified CUDA C++ templates) Passed all the accuracy tests on CPU, OCL, OCL_FP16, CUDA, CUDA_FP16. (except RAFT model) **CURRENT PROBLEMS**: - ONNX parser always converts int64 constants and layers attributes to int32, so some models with int64 constants doesn't work (e.g. RAFT). The solution is to disable int64->int32 conversion and fix attributes reading in a lot of ONNX layers parsers (https://github.com/opencv/opencv/issues/25102) - I didn't add type inference and int support to VULCAN, so it doesn't work at all now. - Some layers don't support int yet, so some unknown models may not work. **CURRENT WORKAROUNDS**: - CPU arg_layer indides are implemented in int32 followed by a int32->int64 conversion (the master branch has the same workaround with int32->float conversion) - CPU and OCL pooling_layer indices are implemented in float followed by a float->int64 conversion - CPU gather_layer indices are implemented in int32, so int64 indices are converted to int32 (the master branch has the same workaround with float->int32 conversion) **DISABLED TESTS**: - RAFT model **REMOVED TESTS**: - Greater_input_dtype_int64 (because it doesn't fit ONNX rules, the whole test is just comparing float tensor with int constant) **TODO IN NEXT PULL REQUESTS**: - Add int64 support for ONNX parser - Add int support for more layers - Add int support for OCL (currently int layers just run on CPU) - Add int tests - Add int support for other backends
57 lines
1.6 KiB
C++
57 lines
1.6 KiB
C++
// This file is part of OpenCV project.
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// It is subject to the license terms in the LICENSE file found in the top-level directory
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// of this distribution and at http://opencv.org/license.html.
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//
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// Copyright (C) 2017, Intel Corporation, all rights reserved.
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// Third party copyrights are property of their respective owners.
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#include "test_precomp.hpp"
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namespace opencv_test { namespace {
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typedef testing::TestWithParam<tuple<Backend, Target> > Test_int64_sum;
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TEST_P(Test_int64_sum, basic)
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{
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Backend backend = get<0>(GetParam());
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Target target = get<1>(GetParam());
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int64_t a_value = 1000000000000000ll;
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int64_t b_value = 1;
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int64_t result_value = 1000000000000001ll;
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EXPECT_NE(int64_t(float(a_value) + float(b_value)), result_value);
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Mat a(3, 5, CV_64SC1, cv::Scalar_<int64_t>(a_value));
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Mat b = Mat::ones(3, 5, CV_64S);
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Net net;
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LayerParams lp;
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lp.type = "NaryEltwise";
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lp.name = "testLayer";
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lp.set("operation", "sum");
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int id = net.addLayerToPrev(lp.name, lp.type, lp);
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net.connect(0, 1, id, 1);
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vector<String> inpNames(2);
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inpNames[0] = "a";
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inpNames[1] = "b";
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net.setInputsNames(inpNames);
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net.setInput(a, inpNames[0]);
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net.setInput(b, inpNames[1]);
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net.setPreferableBackend(backend);
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net.setPreferableTarget(target);
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Mat re;
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re = net.forward();
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EXPECT_EQ(re.depth(), CV_64S);
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auto ptr_re = (int64_t *) re.data;
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for (int i = 0; i < re.total(); i++)
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ASSERT_EQ(result_value, ptr_re[i]);
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}
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INSTANTIATE_TEST_CASE_P(/*nothing*/, Test_int64_sum,
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dnnBackendsAndTargets()
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);
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}} // namespace
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