MNN/test/expr/MatrixBandTest.cpp

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- build: - unify schema building in core and converter; - add more build script for android; - add linux build script for python; - ops impl: - add floor mod support in binary; - use eltwise impl in add/max/sub/mul binary for optimization; - remove fake double support in cast; - fix 5d support for concat; - add adjX and adjY support for batch matmul; - optimize conv2d back prop filter; - add pad mode support for conv3d; - fix bug in conv2d & conv depthwise with very small feature map; - optimize binary without broacast; - add data types support for gather; - add gather ND support; - use uint8 data type in gather v2; - add transpose support for matmul; - add matrix band part; - add dim != 4 support for padding, reshape & tensor convert; - add pad type support for pool3d; - make ops based on TensorFlow Lite quantization optional; - add all & any support for reduction; - use type in parameter as output type in reduction; - add int support for unary; - add variable weight support for conv2d; - fix conv2d depthwise weights initialization; - fix type support for transpose; - fix grad outputs count for reduce grad and reshape grad; - fix priorbox & detection output; - fix metal softmax error; - python: - add runSessionWithCallBackInfo interface; - add max nodes limit (1400) for visualization tool; - fix save error in python3; - align default dim; - convert: - add extra design for optimization; - add more post converting optimizers; - add caffe v1 weights blob support; - add cast, unary, conv transpose support for onnx model; - optimize batchnorm, conv with variable weights, prelu, reshape, slice, upsample for onnx model; - add cos/sin/atan/tan support for unary for tensorflow model; - add any/all support for reduction for tensorflow model; - add elu, conv3d, pool3d support for tensorflow model; - optimize argmax, batchnorm, concat, batch to space, conv with variable weights, prelu, slice for tensorflow model; - others: - fix size computer lock; - fix thread pool deadlock; - add express & parameters in express; - rewrite blitter chooser without static map; - add tests for expr;
2019-10-29 13:37:26 +08:00
//
// MatrixBandTest.cpp
// MNNTests
//
// Created by MNN on 2019/09/17.
// Copyright © 2018, Alibaba Group Holding Limited
//
/*
Test Case From https://www.tensorflow.org/api_docs/cc/class/tensorflow/ops/matrix-band-part
*/
#include "ExprCreator.hpp"
#include "MNNTestSuite.h"
#include "MNN_generated.h"
using namespace MNN::Express;
class MatrixBandTest : public MNNTestCase {
public:
virtual bool run() {
std::unique_ptr<MNN::OpT> MatrixBandOp(new MNN::OpT);
MatrixBandOp->type = MNN::OpType_MatrixBandPart;
auto matrix = _Input({4, 4}, NHWC, halide_type_of<float>());
auto lower = _Input({}, NHWC, halide_type_of<int32_t>());
auto upper = _Input({}, NHWC, halide_type_of<int32_t>());
auto y = Variable::create(Expr::create(MatrixBandOp.get(), {matrix, lower, upper}));
std::vector<float> values = {
0.0f, 1.0f, 2.0f, 3.0f,
-1.0f, 0.0f, 1.0f, 2.0f,
-2.0f, -1.0f, 0.0f, 1.0f,
-3.0f, -2.0f, -1.0f, 0.0f
};
::memcpy(matrix->writeMap<float>(), values.data(), values.size() * sizeof(float));
{
lower->writeMap<int>()[0] = 1;
upper->writeMap<int>()[0] = -1;
{
auto yPtr = y->readMap<float>();
for (int h=0; h<4; ++h) {
for(int w=0; w<4; ++w) {
auto computed = yPtr[4*h+w];
auto expected = 0.0f;
if (h-w <= 1) {
expected = values[4*h+w];
}
if (computed != expected) {
FUNC_PRINT(1);
return false;
}
}
}
}
}
{
lower->writeMap<int>()[0] = 2;
upper->writeMap<int>()[0] = 1;
{
auto yPtr = y->readMap<float>();
for (int h=0; h<4; ++h) {
for(int w=0; w<4; ++w) {
auto computed = yPtr[4*h+w];
auto expected = 0.0f;
if ((h-w) <= 2 && (w-h) <= 1) {
expected = values[4*h+w];
}
if (computed != expected) {
FUNC_PRINT(1);
return false;
}
}
}
}
}
{
matrix->resize({3, 5, 5});
auto matrixPtr = matrix->writeMap<float>();
for (int i=0; i<matrix->getInfo()->size; ++i) {
matrixPtr[i] = (float)i;
}
lower->writeMap<int>()[0] = 2;
upper->writeMap<int>()[0] = 1;
auto yPtr = y->readMap<float>();
for (int z=0; z<3; ++z) {
for (int h=0; h<5; ++h) {
for(int w=0; w<5; ++w) {
auto index = w + 5*h + 5*5*z;
auto computed = yPtr[index];
auto expected = 0.0f;
if ((h-w) <= 2 && (w-h) <= 1) {
expected = (float)(index);
}
if (computed != expected) {
FUNC_PRINT(1);
return false;
}
}
}
}
}
return true;
}
};
MNNTestSuiteRegister(MatrixBandTest, "expr/MatrixBand");