Skip to content
New issue

Have a question about this project? Sign up for a free GitHub account to open an issue and contact its maintainers and the community.

By clicking “Sign up for GitHub”, you agree to our terms of service and privacy statement. We’ll occasionally send you account related emails.

Already on GitHub? Sign in to your account

mapped pool1d #536

Merged
merged 1 commit into from
Dec 30, 2022
Merged
Show file tree
Hide file tree
Changes from all commits
Commits
File filter

Filter by extension

Filter by extension

Conversations
Failed to load comments.
Loading
Jump to
Jump to file
Failed to load files.
Loading
Diff view
Diff view
1 change: 1 addition & 0 deletions include/tim/vx/ops.h
Original file line number Diff line number Diff line change
Expand Up @@ -63,6 +63,7 @@
#include "tim/vx/ops/onehot.h"
#include "tim/vx/ops/pad.h"
#include "tim/vx/ops/pad_v2.h"
#include "tim/vx/ops/pool1d.h"
#include "tim/vx/ops/pool2d.h"
#include "tim/vx/ops/reduce.h"
#include "tim/vx/ops/relational_operations.h"
Expand Down
110 changes: 110 additions & 0 deletions include/tim/vx/ops/pool1d.h
Original file line number Diff line number Diff line change
@@ -0,0 +1,110 @@
/****************************************************************************
*
* Copyright (c) 2022 Vivante Corporation
*
* Permission is hereby granted, free of charge, to any person obtaining a
* copy of this software and associated documentation files (the "Software"),
* to deal in the Software without restriction, including without limitation
* the rights to use, copy, modify, merge, publish, distribute, sublicense,
* and/or sell copies of the Software, and to permit persons to whom the
* Software is furnished to do so, subject to the following conditions:
*
* The above copyright notice and this permission notice shall be included in
* all copies or substantial portions of the Software.
*
* THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
* IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
* FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
* AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
* LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING
* FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER
* DEALINGS IN THE SOFTWARE.
*
*****************************************************************************/
#ifndef TIM_VX_OPS_POOL1D_H_
#define TIM_VX_OPS_POOL1D_H_

#include <array>

#include "tim/vx/builtin_op.h"
#include "tim/vx/types.h"

namespace tim {
namespace vx {
namespace ops {

/**
* ## Pool1d
*
* ### Classic Pool1d
*
* Performs an 1-D pooling operation.
*
* - type : MAX, AVG, L2 or AVG_ANDROID.
* - padding : AUTO, VALID or SAME.
* - pad : Specify the number of pad values for left, right.
* - ksize : filter size.
* - stride : stride along each spatial axis.
* - round_type : CEILING or FLOOR.
*
* ### Global Pool1d
*
* - type : MAX, AVG, L2 or AVG_ANDROID.
* - input_size : input size(only [W])
* - round_type : CEILING or FLOOR.
*
* ### Adaptive Pool1d
*
* Same as torch.nn.AdaptiveXXXPool1d.
*
* - type : MAX, AVG, L2 or AVG_ANDROID.
* - input_size : input size(only [W])
* - output_size : output size(only [W])
* - round_type : CEILING or FLOOR.
*
*/

class Pool1d : public BuiltinOp {
public:
/* for Classic Pool1d, pool does not support auto-completion of pad value,
you need to specify pad size explicitly, it is recommended to use the second api.*/
Pool1d(Graph* graph, PoolType type, PadType padding,
uint32_t ksize,
uint32_t stride,
RoundType round_type = RoundType::FLOOR,
DataLayout layout = DataLayout::WCN);
Pool1d(Graph* graph, PoolType type, const std::array<uint32_t, 2>& pad,
uint32_t ksize,
uint32_t stride,
RoundType round_type = RoundType::FLOOR,
DataLayout layout = DataLayout::WCN);

// for Global Pool1d
Pool1d(Graph* graph, PoolType type, uint32_t input_size,
RoundType round_type = RoundType::FLOOR,
DataLayout layout = DataLayout::WCN);

// for Adaptive Pool1d
Pool1d(Graph* graph, PoolType type, uint32_t input_size,
uint32_t output_size,
RoundType round_type = RoundType::FLOOR,
DataLayout layout = DataLayout::WCN);

std::shared_ptr<Operation> Clone(
std::shared_ptr<Graph>& graph) const override;
void Init();

protected:
const PoolType type_;
const PadType padding_;
uint32_t ksize_;
uint32_t stride_;
const RoundType round_type_;
const std::array<uint32_t, 2> pad_;
};

} // namespace ops
} // namespace vx
} // namespace tim

#endif /* TIM_VX_OPS_POOL1D_H_ */
228 changes: 228 additions & 0 deletions src/tim/vx/ops/avg_pool_test.cc
Original file line number Diff line number Diff line change
Expand Up @@ -24,10 +24,238 @@
#include "tim/vx/context.h"
#include "tim/vx/graph.h"
#include "tim/vx/ops/pool2d.h"
#include "tim/vx/ops/pool1d.h"
#include <iostream>
#include "gtest/gtest.h"
#include "test_utils.h"

TEST(AVG, shape_32_3_1_fp32_kernel_2_stride_1) {
auto ctx = tim::vx::Context::Create();
auto graph = ctx->CreateGraph();

tim::vx::ShapeType in_shape({32, 3, 1});
tim::vx::ShapeType out_shape({31, 3, 1});
tim::vx::TensorSpec input_spec(tim::vx::DataType::FLOAT32,
in_shape, tim::vx::TensorAttribute::INPUT);
tim::vx::TensorSpec output_spec(tim::vx::DataType::FLOAT32,
out_shape, tim::vx::TensorAttribute::OUTPUT);

auto input_tensor = graph->CreateTensor(input_spec);
auto output_tensor = graph->CreateTensor(output_spec);

std::vector<float> in_data = {
1.764052391052246,
0.40015721321105957,
0.978738009929657,
2.2408931255340576,
1.8675580024719238,
-0.9772778749465942,
0.9500884413719177,
-0.15135720372200012,
-0.10321885347366333,
0.4105985164642334,
0.14404356479644775,
1.4542734622955322,
0.7610377073287964,
0.12167501449584961,
0.44386324286460876,
0.3336743414402008,
1.4940791130065918,
-0.2051582634449005,
0.3130677044391632,
-0.8540957570075989,
-2.5529897212982178,
0.653618574142456,
0.8644362092018127,
-0.7421650290489197,
2.269754648208618,
-1.4543657302856445,
0.04575851559638977,
-0.18718385696411133,
1.5327792167663574,
1.4693588018417358,
0.154947429895401,
0.37816253304481506,

-0.8877857327461243,
-1.980796456336975,
-0.34791216254234314,
0.15634897351264954,
1.2302906513214111,
1.202379822731018,
-0.38732680678367615,
-0.302302747964859,
-1.0485529899597168,
-1.420017957687378,
-1.7062702178955078,
1.950775384902954,
-0.5096521973609924,
-0.4380742907524109,
-1.2527953386306763,
0.7774903774261475,
-1.6138978004455566,
-0.21274028718471527,
-0.8954665660858154,
0.38690251111984253,
-0.5108051300048828,
-1.18063223361969,
-0.02818222902715206,
0.4283318817615509,
0.06651721894741058,
0.30247190594673157,
-0.6343221068382263,
-0.3627411723136902,
-0.6724604368209839,
-0.35955315828323364,
-0.8131462931632996,
-1.7262825965881348,

0.17742614448070526,
-0.4017809331417084,
-1.630198359489441,
0.46278226375579834,
-0.9072983860969543,
0.05194539576768875,
0.7290905714035034,
0.12898291647434235,
1.1394007205963135,
-1.234825849533081,
0.4023416340351105,
-0.6848101019859314,
-0.8707971572875977,
-0.5788496732711792,
-0.3115525245666504,
0.056165341287851334,
-1.1651498079299927,
0.9008265137672424,
0.4656624495983124,
-1.5362436771392822,
1.4882521629333496,
1.895889163017273,
1.1787796020507812,
-0.1799248307943344,
-1.0707526206970215,
1.0544517040252686,
-0.4031769335269928,
1.222445011138916,
0.2082749754190445,
0.9766390323638916,
0.3563663959503174,
0.7065731883049011
};
std::vector<float> golden = {
1.0821048021316528,
0.6894476413726807,
1.6098155975341797,
2.054225444793701,
0.4451400637626648,
-0.013594716787338257,
0.3993656039237976,
-0.12728802859783173,
0.15368983149528503,
0.2773210406303406,
0.79915851354599,
1.1076555252075195,
0.441356360912323,
0.2827691435813904,
0.3887687921524048,
0.9138767123222351,
0.6444604396820068,
0.05395472049713135,
-0.27051401138305664,
-1.703542709350586,
-0.9496855735778809,
0.759027361869812,
0.06113559007644653,
0.7637947797775269,
0.4076944589614868,
-0.7043036222457886,
-0.07071267068386078,
0.672797679901123,
1.5010690689086914,
0.8121531009674072,
0.26655498147010803,

-1.434291124343872,
-1.1643543243408203,
-0.0957815945148468,
0.6933197975158691,
1.2163352966308594,
0.40752649307250977,
-0.3448147773742676,
-0.6754278540611267,
-1.2342854738235474,
-1.5631440877914429,
0.12225258350372314,
0.7205616235733032,
-0.47386324405670166,
-0.8454347848892212,
-0.2376524806022644,
-0.4182037115097046,
-0.9133190512657166,
-0.554103434085846,
-0.25428202748298645,
-0.06195130944252014,
-0.8457186818122864,
-0.6044072508811951,
0.20007482171058655,
0.24742454290390015,
0.18449455499649048,
-0.16592510044574738,
-0.49853163957595825,
-0.5176007747650146,
-0.5160068273544312,
-0.5863497257232666,
-1.2697144746780396,

-0.11217739433050156,
-1.0159896612167358,
-0.5837080478668213,
-0.222258061170578,
-0.4276764988899231,
0.3905179798603058,
0.4290367364883423,
0.6341918110847473,
-0.04771256446838379,
-0.4162421226501465,
-0.14123423397541046,
-0.7778036594390869,
-0.7248234152793884,
-0.4452010989189148,
-0.12769359350204468,
-0.5544922351837158,
-0.13216164708137512,
0.6832444667816162,
-0.5352905988693237,
-0.02399575710296631,
1.692070722579956,
1.5373344421386719,
0.4994273781776428,
-0.6253387331962585,
-0.008150458335876465,
0.3256374001502991,
0.4096340537071228,
0.7153599858283997,
0.5924569964408875,
0.6665027141571045,
0.5314698219299316
};

EXPECT_TRUE(input_tensor->CopyDataToTensor(in_data.data(), in_data.size()*4));
uint32_t ksize = 2;
uint32_t stride = 1;
auto op = graph->CreateOperation<tim::vx::ops::Pool1d>(tim::vx::PoolType::AVG,
tim::vx::PadType::VALID, ksize, stride);
(*op).BindInputs({input_tensor}).BindOutputs({output_tensor});

EXPECT_TRUE(graph->Compile());
EXPECT_TRUE(graph->Run());

std::vector<float> output(golden.size());
EXPECT_TRUE(output_tensor->CopyDataFromTensor(output.data()));
EXPECT_EQ(golden, output);
}

TEST(AVG, shape_3_3_1_2_fp32_kernel_2_stride_1) {
auto ctx = tim::vx::Context::Create();
auto graph = ctx->CreateGraph();
Expand Down
Loading