Merge pull request #24411 from alexlyulkov:al/dnn-type-inference

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

modules/dnn/test/test_int.cpp

@@ -0,0 +1,56 @@
+// This file is part of OpenCV project.
+// It is subject to the license terms in the LICENSE file found in the top-level directory
+// of this distribution and at http://opencv.org/license.html.
+//
+// Copyright (C) 2017, Intel Corporation, all rights reserved.
+// Third party copyrights are property of their respective owners.
+
+#include "test_precomp.hpp"
+
+namespace opencv_test { namespace {
+
+typedef testing::TestWithParam<tuple<Backend, Target> > Test_int64_sum;
+TEST_P(Test_int64_sum, basic)
+{
+    Backend backend = get<0>(GetParam());
+    Target target = get<1>(GetParam());
+
+    int64_t a_value = 1000000000000000ll;
+    int64_t b_value = 1;
+    int64_t result_value = 1000000000000001ll;
+    EXPECT_NE(int64_t(float(a_value) + float(b_value)), result_value);
+
+    Mat a(3, 5, CV_64SC1, cv::Scalar_<int64_t>(a_value));
+    Mat b = Mat::ones(3, 5, CV_64S);
+
+    Net net;
+    LayerParams lp;
+    lp.type = "NaryEltwise";
+    lp.name = "testLayer";
+    lp.set("operation", "sum");
+    int id = net.addLayerToPrev(lp.name, lp.type, lp);
+    net.connect(0, 1, id, 1);
+
+    vector<String> inpNames(2);
+    inpNames[0] = "a";
+    inpNames[1] = "b";
+    net.setInputsNames(inpNames);
+    net.setInput(a, inpNames[0]);
+    net.setInput(b, inpNames[1]);
+
+    net.setPreferableBackend(backend);
+    net.setPreferableTarget(target);
+
+    Mat re;
+    re = net.forward();
+    EXPECT_EQ(re.depth(), CV_64S);
+    auto ptr_re = (int64_t *) re.data;
+    for (int i = 0; i < re.total(); i++)
+        ASSERT_EQ(result_value, ptr_re[i]);
+}
+
+INSTANTIATE_TEST_CASE_P(/*nothing*/, Test_int64_sum,
+    dnnBackendsAndTargets()
+);
+
+}} // namespace