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Posted to commits@singa.apache.org by ka...@apache.org on 2017/01/24 08:52:24 UTC
[1/2] incubator-singa git commit: SINGA-295 - Add an example of image
classification using GoogleNet
Repository: incubator-singa
Updated Branches:
refs/heads/master 8101f0066 -> d190fa89a
http://git-wip-us.apache.org/repos/asf/incubator-singa/blob/d190fa89/examples/imagenet/ilsvrc12.h
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diff --git a/examples/imagenet/ilsvrc12.h b/examples/imagenet/ilsvrc12.h
deleted file mode 100644
index 74fffbb..0000000
--- a/examples/imagenet/ilsvrc12.h
+++ /dev/null
@@ -1,376 +0,0 @@
-/************************************************************
-*
-* Licensed to the Apache Software Foundation (ASF) under one
-* or more contributor license agreements. See the NOTICE file
-* distributed with this work for additional information
-* regarding copyright ownership. The ASF licenses this file
-* to you under the Apache License, Version 2.0 (the
-* "License"); you may not use this file except in compliance
-* with the License. You may obtain a copy of the License at
-*
-* http://www.apache.org/licenses/LICENSE-2.0
-*
-* Unless required by applicable law or agreed to in writing,
-* software distributed under the License is distributed on an
-* "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY
-* KIND, either express or implied. See the License for the
-* specific language governing permissions and limitations
-* under the License.
-*
-*************************************************************/
-#include "singa/singa_config.h"
-#ifdef USE_OPENCV
-#ifndef SINGA_EXAMPLES_IMAGENET_ILSVRC12_H_
-#define SINGA_EXAMPLES_IMAGENET_ILSVRC12_H_
-#include <omp.h>
-#include <cstdint>
-#include <fstream>
-#include <iostream>
-#include <opencv2/highgui/highgui.hpp>
-#include <opencv2/imgproc/imgproc.hpp>
-#include <string>
-#include <thread>
-#include <vector>
-#include "singa/core/tensor.h"
-#include "singa/io/decoder.h"
-#include "singa/io/encoder.h"
-#include "singa/io/reader.h"
-#include "singa/io/transformer.h"
-#include "singa/io/writer.h"
-#include "singa/proto/io.pb.h"
-#include "singa/utils/timer.h"
-
-using std::string;
-using namespace singa::io;
-namespace singa {
-/// For reading ILSVRC2012 image data as tensors.
-class ILSVRC {
- public:
- /// Setup encoder, decoder
- ILSVRC();
- ~ILSVRC() {
- if (encoder != nullptr) delete encoder;
- if (decoder != nullptr) delete decoder;
- if (transformer != nullptr) delete transformer;
- if (reader != nullptr) {
- reader->Close();
- delete reader;
- }
- if (writer != nullptr) {
- writer->Close();
- delete writer;
- }
- }
- /// Create binary files for training data
- /// train_image_list: list file of training images
- /// train_image_folder: folder where stores original training images
- /// train_bin_folder: folder to store binary files
- /// train_file_size: number of images that are contain in one binary file
- void CreateTrainData(string train_image_list, string train_image_folder,
- string train_bin_folder, size_t train_file_size);
- /// Create binary files for test data
- /// train_image_list: list file of test images
- /// train_image_folder: folder where saves original test images
- /// train_bin_folder: folder to save binary files
- void CreateTestData(string test_image_list, string test_image_folder,
- string test_bin_folder);
- /// Load data from a binary file, return <images, labels> pair
- /// suppose the data will be loaded file by file.
- /// flag: kTrain or kTest
- /// file: binary file which stores the images
- /// read_size: number of images to be loaded
- /// offset: offset in the file
- /// n_read: number of images which are read
- size_t LoadData(int flag, string file, size_t read_size, Tensor *x, Tensor *y,
- size_t *n_read, int nthreads);
- /// A wrapper method to spawn a thread to execute LoadData() method.
- std::thread AsyncLoadData(int flag, string file, size_t read_size, Tensor *x,
- Tensor *y, size_t *n_read, int nthreads);
-
- void DecodeTransform(int flag, int thid, int nthreads,
- vector<string *> images, Tensor *x, Tensor *y);
- /// A wrapper method to spawn a thread to execute Decodetransform() method.
- std::thread AsyncDecodeTransform(int flag, int thid, int nthreads,
- vector<string *> images, Tensor *x,
- Tensor *y);
-
- /// Read mean from path
- void ReadMean(string path);
-
- protected:
- /// Read one image at path, resize the image
- Tensor ReadImage(string path);
- /// Write buff to the file in kCreate/kAppend mode
- void Write(string outfile, singa::io::Mode mode);
- void WriteMean(Tensor &mean, string path);
-
- private:
- /// size for resizing
- const size_t kImageSize = 256;
- const size_t kImageNBytes = 3 * kImageSize * kImageSize;
- /// size for cropping
- const size_t kCropSize = 227;
- Tensor mean;
- string last_read_file = "";
-
- JPGEncoder *encoder = nullptr;
- JPGDecoder *decoder = nullptr;
- ImageTransformer *transformer = nullptr;
- BinFileReader *reader = nullptr;
- BinFileWriter *writer = nullptr;
-};
-
-ILSVRC::ILSVRC() {
- EncoderConf en_conf;
- en_conf.set_image_dim_order("CHW");
- encoder = new JPGEncoder();
- encoder->Setup(en_conf);
-
- DecoderConf de_conf;
- de_conf.set_image_dim_order("CHW");
- decoder = new JPGDecoder();
- decoder->Setup(de_conf);
-
- TransformerConf trans_conf;
- trans_conf.add_crop_shape(kCropSize);
- trans_conf.add_crop_shape(kCropSize);
- trans_conf.set_image_dim_order("CHW");
- trans_conf.set_horizontal_mirror(true);
- transformer = new ImageTransformer();
- transformer->Setup(trans_conf);
-}
-
-Tensor ILSVRC::ReadImage(string path) {
- cv::Mat mat = cv::imread(path, CV_LOAD_IMAGE_COLOR);
- CHECK(mat.data != NULL) << "OpenCV load image fail: " << path;
- cv::Size size(kImageSize, kImageSize);
- cv::Mat resized;
- cv::resize(mat, resized, size);
- CHECK_EQ((size_t)resized.size().height, kImageSize);
- CHECK_EQ((size_t)resized.size().width, kImageSize);
- // dimension_order: CHW
- Shape shape{(size_t)resized.channels(), (size_t)resized.rows,
- (size_t)resized.cols};
- Tensor image(shape, singa::kUChar);
- unsigned char *data = new unsigned char[kImageNBytes];
- for (int i = 0; i < resized.rows; i++)
- for (int j = 0; j < resized.cols; j++)
- for (int k = 0; k < resized.channels(); k++)
- data[k * kImageSize * kImageSize + i * kImageSize + j] =
- resized.at<cv::Vec3b>(i, j)[k];
- image.CopyDataFromHostPtr<unsigned char>(data, kImageNBytes);
- delete[] data;
-
- return image;
-}
-
-void ILSVRC::WriteMean(Tensor &mean, string path) {
- Tensor mean_lb(Shape{1}, kInt);
- std::vector<Tensor> input;
- input.push_back(mean);
- input.push_back(mean_lb);
- BinFileWriter bfwriter;
- bfwriter.Open(path, kCreate);
- bfwriter.Write(path, encoder->Encode(input));
- bfwriter.Flush();
- bfwriter.Close();
-}
-
-void ILSVRC::CreateTrainData(string image_list, string input_folder,
- string output_folder, size_t file_size = 12800) {
- std::vector<std::pair<string, int>> file_list;
- size_t *sum = new size_t[kImageNBytes];
- for (size_t i = 0; i < kImageNBytes; i++) sum[i] = 0u;
- string image_file_name;
- int label;
- string outfile;
- std::ifstream image_list_file(image_list.c_str(), std::ios::in);
- while (image_list_file >> image_file_name >> label)
- file_list.push_back(std::make_pair(image_file_name, label));
- LOG(INFO) << "Data Shuffling";
- std::shuffle(file_list.begin(), file_list.end(),
- std::default_random_engine());
- LOG(INFO) << "Total number of training images is " << file_list.size();
- size_t num_train_images = file_list.size();
- if (file_size == 0) file_size = num_train_images;
- for (size_t imageid = 0; imageid < num_train_images; imageid++) {
- string path = input_folder + "/" + file_list[imageid].first;
- Tensor image = ReadImage(path);
- auto image_data = image.data<unsigned char>();
- for (size_t i = 0; i < kImageNBytes; i++)
- sum[i] += static_cast<size_t>(image_data[i]);
- label = file_list[imageid].second;
- Tensor lb(Shape{1}, kInt);
- lb.CopyDataFromHostPtr<int>(&label, 1);
- std::vector<Tensor> input;
- input.push_back(image);
- input.push_back(lb);
- string encoded_str = encoder->Encode(input);
- if (writer == nullptr) {
- writer = new BinFileWriter();
- outfile = output_folder + "/train" +
- std::to_string(imageid / file_size + 1) + ".bin";
- writer->Open(outfile, kCreate);
- }
- writer->Write(path, encoded_str);
- if ((imageid + 1) % file_size == 0) {
- writer->Flush();
- writer->Close();
- LOG(INFO) << "Write " << file_size << " images into " << outfile;
- delete writer;
- writer = nullptr;
- }
- }
- if (writer != nullptr) {
- writer->Flush();
- writer->Close();
- LOG(INFO) << "Write " << num_train_images % file_size << " images into "
- << outfile;
- delete writer;
- writer = nullptr;
- }
- size_t num_file =
- num_train_images / file_size + ((num_train_images % file_size) ? 1 : 0);
- LOG(INFO) << "Write " << num_train_images << " images into " << num_file
- << " binary files";
- Tensor mean = Tensor(Shape{3, kImageSize, kImageSize}, kUChar);
- unsigned char *mean_data = new unsigned char[kImageNBytes];
- for (size_t i = 0; i < kImageNBytes; i++)
- mean_data[i] = static_cast<unsigned char>(sum[i] / num_train_images);
- mean.CopyDataFromHostPtr<unsigned char>(mean_data, kImageNBytes);
- string mean_path = output_folder + "/mean.bin";
- WriteMean(mean, mean_path);
- delete[] mean_data;
- delete[] sum;
-}
-
-void ILSVRC::CreateTestData(string image_list, string input_folder,
- string output_folder) {
- std::vector<std::pair<string, int>> file_list;
- string image_file_name;
- string outfile = output_folder + "/test.bin";
- int label;
- std::ifstream image_list_file(image_list.c_str(), std::ios::in);
- while (image_list_file >> image_file_name >> label)
- file_list.push_back(std::make_pair(image_file_name, label));
- LOG(INFO) << "Total number of test images is " << file_list.size();
- size_t num_test_images = file_list.size();
- for (size_t imageid = 0; imageid < num_test_images; imageid++) {
- string path = input_folder + "/" + file_list[imageid].first;
- Tensor image = ReadImage(path);
- label = file_list[imageid].second;
- Tensor lb(Shape{1}, singa::kInt);
- lb.CopyDataFromHostPtr<int>(&label, 1);
- std::vector<Tensor> input;
- input.push_back(image);
- input.push_back(lb);
- string encoded_str = encoder->Encode(input);
- if (writer == nullptr) {
- writer = new BinFileWriter();
- writer->Open(outfile, kCreate);
- }
- writer->Write(path, encoded_str);
- }
- if (writer != nullptr) {
- writer->Flush();
- writer->Close();
- delete writer;
- writer = nullptr;
- }
- LOG(INFO) << "Write " << num_test_images << " images into " << outfile;
-}
-
-void ILSVRC::ReadMean(string path) {
- BinFileReader bfreader;
- string key, value;
- bfreader.Open(path);
- bfreader.Read(&key, &value);
- auto ret = decoder->Decode(value);
- bfreader.Close();
- mean = ret[0];
-}
-
-std::thread ILSVRC::AsyncLoadData(int flag, string file, size_t read_size,
- Tensor *x, Tensor *y, size_t *n_read,
- int nthreads) {
- return std::thread(
- [=]() { LoadData(flag, file, read_size, x, y, n_read, nthreads); });
-}
-
-size_t ILSVRC::LoadData(int flag, string file, size_t read_size, Tensor *x,
- Tensor *y, size_t *n_read, int nthreads) {
- x->Reshape(Shape{read_size, 3, kCropSize, kCropSize});
- y->AsType(kInt);
- y->Reshape(Shape{read_size});
- if (file != last_read_file) {
- if (reader != nullptr) {
- reader->Close();
- delete reader;
- reader = nullptr;
- }
- reader = new BinFileReader();
- reader->Open(file, 100 << 20);
- last_read_file = file;
- } else if (reader == nullptr) {
- reader = new BinFileReader();
- reader->Open(file, 100 << 20);
- }
- vector<string *> images;
- for (size_t i = 0; i < read_size; i++) {
- string image_path;
- string *image = new string();
- bool ret = reader->Read(&image_path, image);
- if (ret == false) {
- reader->Close();
- delete reader;
- reader = nullptr;
- break;
- }
- images.push_back(image);
- }
- int nimg = images.size();
- *n_read = nimg;
-
- vector<std::thread> threads;
- for (int i = 1; i < nthreads; i++) {
- threads.push_back(AsyncDecodeTransform(flag, i, nthreads, images, x, y));
- }
- DecodeTransform(flag, 0, nthreads, images, x, y);
- for (size_t i = 0; i < threads.size(); i++) threads[i].join();
- for (int k = 0; k < nimg; k++) delete images.at(k);
- return nimg;
-}
-
-std::thread ILSVRC::AsyncDecodeTransform(int flag, int thid, int nthreads,
- vector<string *> images, Tensor *x,
- Tensor *y) {
- return std::thread(
- [=]() { DecodeTransform(flag, thid, nthreads, images, x, y); });
-}
-
-void ILSVRC::DecodeTransform(int flag, int thid, int nthreads,
- vector<string *> images, Tensor *x, Tensor *y) {
- int nimg = images.size();
- int start = nimg / nthreads * thid;
- int end = start + nimg / nthreads;
- for (int k = start; k < end; k++) {
- std::vector<Tensor> pair = decoder->Decode(*images.at(k));
- auto tmp_image = pair[0] - mean;
- Tensor aug_image = transformer->Apply(flag, tmp_image);
- CopyDataToFrom(x, aug_image, aug_image.Size(), k * aug_image.Size());
- CopyDataToFrom(y, pair[1], 1, k);
- }
- if (thid == 0) {
- for (int k = nimg / nthreads * nthreads; k < nimg; k++) {
- std::vector<Tensor> pair = decoder->Decode(*images.at(k));
- auto tmp_image = pair[0] - mean;
- Tensor aug_image = transformer->Apply(flag, tmp_image);
- CopyDataToFrom(x, aug_image, aug_image.Size(), k * aug_image.Size());
- CopyDataToFrom(y, pair[1], 1, k);
- }
- }
-}
-} // namespace singa
-
-#endif // SINGA_EXAMPLES_IMAGENET_ILSVRC12_H_
-#endif // USE_OPENCV
http://git-wip-us.apache.org/repos/asf/incubator-singa/blob/d190fa89/examples/imagenet/run.sh
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diff --git a/examples/imagenet/run.sh b/examples/imagenet/run.sh
deleted file mode 100755
index 6277d23..0000000
--- a/examples/imagenet/run.sh
+++ /dev/null
@@ -1,21 +0,0 @@
-#!/usr/bin/env sh
-#
-# Licensed to the Apache Software Foundation (ASF) under one
-# or more contributor license agreements. See the NOTICE file
-# distributed with this work for additional information
-# regarding copyright ownership. The ASF licenses this file
-# to you under the Apache License, Version 2.0 (the
-# "License"); you may not use this file except in compliance
-# with the License. You may obtain a copy of the License at
-#
-# http://www.apache.org/licenses/LICENSE-2.0
-#
-# Unless required by applicable law or agreed to in writing, software
-# distributed under the License is distributed on an "AS IS" BASIS,
-# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-# See the License for the specific language governing permissions and
-# limitations under the License.
-#
-
-../../build/bin/imagenet -epoch 90 -lr 0.01 -batchsize 256 -filesize 1280 -ntrain 1281167 -ntest 50000 \
- -data "imagenet_data" -pfreq 100 -nthreads 12
http://git-wip-us.apache.org/repos/asf/incubator-singa/blob/d190fa89/include/singa/core/tensor.h
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diff --git a/include/singa/core/tensor.h b/include/singa/core/tensor.h
index 28d1619..c89fa83 100644
--- a/include/singa/core/tensor.h
+++ b/include/singa/core/tensor.h
@@ -458,6 +458,8 @@ void SoftmaxCrossEntropyBwd(const Tensor &t, Tensor *p);
Tensor CopyRows(const Tensor &in, const size_t start, const size_t end);
/// Alias of CopyRows
Tensor SliceRows(const Tensor &in, const size_t start, const size_t end);
+/// Slice the input tensor along the give axis to generate a new tensor
+Tensor SliceOn(const Tensor &in, const size_t start, const size_t end, int axis);
/// Return a tensor consisting of columns ([start, end)) from 'in'. It copies
/// the values from 'in'. 'in' is a 2D Tensor.
Tensor CopyColumns(const Tensor &in, const size_t start, const size_t end);
@@ -466,6 +468,8 @@ Tensor SliceColumns(const Tensor &in, const size_t start, const size_t end);
/// Return a tensor which is vertically stacked from tensors in 'in'. Each
/// tensor in 'in' is a 2D tensor. Values are copied, no memory sharing.
Tensor ConcatenateRows(const vector<Tensor> &in);
+/// Return a tensor concatenated of the input tensors along the give axis.
+Tensor ConcatOn(const vector<Tensor> &in, int axis);
/// Alias name for function ConcatenateRows
Tensor ConcatRows(const vector<Tensor> &in);
/// Return a tensor which is horizontally stacked from tensors in 'in'. Each
http://git-wip-us.apache.org/repos/asf/incubator-singa/blob/d190fa89/python/setup.py.in
----------------------------------------------------------------------
diff --git a/python/setup.py.in b/python/setup.py.in
index 503ac8a..30be063 100644
--- a/python/setup.py.in
+++ b/python/setup.py.in
@@ -61,7 +61,10 @@ setup(
install_requires=[
'numpy>=1.11.0',
'protobuf>=2.5.0,<3',
- 'unittest-xml-reporting'
+ 'unittest-xml-reporting',
+ 'flask>=0.10.1',
+ 'flask_cors>=3.0.2',
+ 'pillow>=2.3.0'
],
#List additional groups of dependencies here (e.g. development
http://git-wip-us.apache.org/repos/asf/incubator-singa/blob/d190fa89/python/singa/net.py
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diff --git a/python/singa/net.py b/python/singa/net.py
index d6e313e..027e78c 100644
--- a/python/singa/net.py
+++ b/python/singa/net.py
@@ -409,7 +409,12 @@ class FeedForwardNet(object):
params = pickle.load(fd)
for (specs, val) in zip(self.param_specs(),
self.param_values()):
- val.copy_from_numpy(params[specs.name])
+ try:
+ val.copy_from_numpy(params[specs.name])
+ except AssertionError as err:
+ print 'Error from copying values for param: %s' % specs.name
+ print 'shape of param vs checkpoint', val.shape, params[specs.name].shape
+ raise err
else:
print 'NOTE: If your model was saved using pickle, '\
'then set use_pickle=True for loading it'
http://git-wip-us.apache.org/repos/asf/incubator-singa/blob/d190fa89/python/singa/tensor.py
----------------------------------------------------------------------
diff --git a/python/singa/tensor.py b/python/singa/tensor.py
index 12d7c53..7dee9f5 100644
--- a/python/singa/tensor.py
+++ b/python/singa/tensor.py
@@ -578,9 +578,9 @@ def to_numpy(t):
'''
th = to_host(t)
if th.dtype == core_pb2.kFloat32:
- np_array = ret.singa_tensor.GetFloatValue(int(th.size()))
+ np_array = th.singa_tensor.GetFloatValue(int(th.size()))
elif th.dtype == core_pb2.kInt:
- np_array = ret.singa_tensor.GetIntValue(int(th.size()))
+ np_array = th.singa_tensor.GetIntValue(int(th.size()))
else:
print 'Not implemented yet for ', th.dtype
return np_array.reshape(th.shape)
http://git-wip-us.apache.org/repos/asf/incubator-singa/blob/d190fa89/src/core/tensor/tensor.cc
----------------------------------------------------------------------
diff --git a/src/core/tensor/tensor.cc b/src/core/tensor/tensor.cc
index d40fd88..ed4da96 100644
--- a/src/core/tensor/tensor.cc
+++ b/src/core/tensor/tensor.cc
@@ -742,6 +742,42 @@ void DivColumn(const Tensor &v, Tensor *M) {
MultColumn(inv, M);
}
+Tensor ConcatOn(const vector<Tensor> &in, int axis) {
+ vector<Tensor> tmp;
+ Shape out_shape = in[0].shape();
+ size_t dim = in[0].shape().size();
+ CHECK_GE(dim, 2u) << " Only work for tensor of dim >=2 ";
+ size_t size = in[0].Size() / in[0].shape(axis);
+ size_t new_size = 0u;
+ for (const auto& t: in) {
+ CHECK_EQ(dim, t.shape().size()) << "All tensors should have the same dim";
+ CHECK_EQ(size, t.Size() / t.shape(axis)) << "The size of all axis should "
+ <<" be the same except the concatenated axis";
+ new_size += t.shape(axis);
+ }
+ out_shape[axis] = new_size;
+ if (axis == 0) {
+ size_t nrow = 0;
+ for (const auto& t: in) {
+ nrow += t.shape(0);
+ tmp.push_back(Reshape(t, {t.shape(0), t.Size() / t.shape(0)}));
+ }
+ auto ret = ConcatenateRows(tmp);
+ ret.Reshape(out_shape);
+ return ret;
+ } else {
+ for (const auto& t: in) {
+ size_t nrow = 1;
+ for (int i = 0; i < axis; i++)
+ nrow *= t.shape(i);
+ tmp.push_back(Reshape(t, {nrow, t.Size() / nrow}));
+ }
+ auto ret = ConcatenateColumns(tmp);
+ ret.Reshape(out_shape);
+ return ret;
+ }
+}
+
Tensor ConcatenateRows(const vector<Tensor> &in) {
size_t nrow = 0, ncol = 0;
CHECK(in.size());
@@ -805,6 +841,27 @@ Tensor CopyRows(const Tensor &in, const size_t start, const size_t end) {
return out;
}
+
+Tensor SliceOn(const Tensor&in, const size_t start, const size_t end, int axis) {
+ Shape out_shape = in.shape();
+ out_shape[axis] = end - start;
+ if (axis == 0) {
+ auto ret = SliceRows(Reshape(in, {in.shape(0), in.Size() / in.shape(0)}),
+ start, end);
+ ret.Reshape(out_shape);
+ return ret;
+ } else {
+ size_t nrow = 1;
+ for (int i = 0; i < axis; i++)
+ nrow *= in.shape(i);
+ auto suffix = in.Size() / nrow / in.shape(axis);
+ auto ret = SliceColumns(Reshape(in, {nrow, in.Size() / nrow}),
+ start * suffix, end * suffix);
+ ret.Reshape(out_shape);
+ return ret;
+ }
+}
+
Tensor SliceRows(const Tensor &in, const size_t start, const size_t end) {
return CopyRows(in, start, end);
}
http://git-wip-us.apache.org/repos/asf/incubator-singa/blob/d190fa89/src/io/network/endpoint.cc
----------------------------------------------------------------------
diff --git a/src/io/network/endpoint.cc b/src/io/network/endpoint.cc
index e61acdb..f88edbf 100644
--- a/src/io/network/endpoint.cc
+++ b/src/io/network/endpoint.cc
@@ -227,6 +227,9 @@ void NetworkThread::doWork() {
LOG(FATAL) << "Bind Error: " << strerror(errno);
}
+ // TODO(wangwei) remove the hardcode setting, which would result erros if
+ // there are more than 10 connections
+ // reported by yaochang
if (listen(socket_fd_, 10)) {
LOG(FATAL) << "Listen Error: " << strerror(errno);
}
http://git-wip-us.apache.org/repos/asf/incubator-singa/blob/d190fa89/src/model/layer/concat.cc
----------------------------------------------------------------------
diff --git a/src/model/layer/concat.cc b/src/model/layer/concat.cc
index 88c2409..a94b68e 100644
--- a/src/model/layer/concat.cc
+++ b/src/model/layer/concat.cc
@@ -30,14 +30,26 @@ void Concat::Setup(const vector<Shape>& in_shapes, const LayerConf& conf) {
out_sample_shape_.clear();
slice_point_.clear();
axis_ = conf.concat_conf().axis();
- if (axis_ == 0)
- out_sample_shape_.push_back(in_shapes[0][0]);
- else {
+ CHECK_GE(axis_, 0);
+
+ if (axis_ == 0) {
+ out_sample_shape_ = in_shapes[0];
+ size_t fea_size = Product(in_shapes[0]);
+ for (auto& s: in_shapes) {
+ CHECK_EQ(Product(s), fea_size) << "Feature length of all source samples "
+ << "must be the same";
+ }
+ } else {
+ out_sample_shape_ = in_shapes[0];
+ size_t fea_size = Product(in_shapes[0]) / in_shapes[0][axis_ - 1];
size_t l = 0;
for (auto& s: in_shapes) {
- l += s[0];
+ CHECK_GE(s.size(), axis_);
+ l += s[axis_ - 1];
+ CHECK_EQ(fea_size, Product(s) / s[axis_ - 1])
+ << "Feature length for all axis except axis_ must be the same";
}
- out_sample_shape_.push_back(l);
+ out_sample_shape_[axis_ - 1] = l;
}
}
@@ -52,10 +64,7 @@ const vector<Tensor> Concat::Forward(int flag, const vector<Tensor>& inputs) {
if (inputs.size() == 1u) {
outputs = inputs;
} else {
- if(axis_ == 0)
- outputs.push_back(ConcatRows(inputs));
- else
- outputs.push_back(ConcatColumns(inputs));
+ outputs.push_back(ConcatOn(inputs, axis_));
}
return outputs;
}
@@ -66,10 +75,7 @@ const std::pair<vector<Tensor>, vector<Tensor>> Concat::Backward(
CHECK_EQ(grads.size(), 1u) << "Concat layer only have one output tensor.";
size_t last_offset = 0u;
for (auto p : slice_point_) {
- if (axis_ == 0)
- input_grad.push_back(SliceRows(grads.at(0), last_offset, p));
- else
- input_grad.push_back(SliceColumns(grads.at(0), last_offset, p));
+ input_grad.push_back(SliceOn(grads.at(0), last_offset, p, axis_));
last_offset = p;
}
return std::make_pair(input_grad, param_grad);
http://git-wip-us.apache.org/repos/asf/incubator-singa/blob/d190fa89/src/model/layer/concat.h
----------------------------------------------------------------------
diff --git a/src/model/layer/concat.h b/src/model/layer/concat.h
index 4e9a967..a759167 100644
--- a/src/model/layer/concat.h
+++ b/src/model/layer/concat.h
@@ -23,6 +23,9 @@
#include "singa/model/layer.h"
namespace singa {
+/**
+ * Cocnatenate tensors from the source layers along the give axis.
+ */
class Concat : public Layer {
public:
/// \copydoc Layer::layer_type()
@@ -43,7 +46,7 @@ class Concat : public Layer {
const vector<Tensor>& grad) override;
protected:
- /// 0 for concat rows; 1 for concat cols
+ /// >= 0
int axis_ = 0;
/// slice_point_[i] the end offset of the i-th source tensor on the concat
/// axis of the result tensor
http://git-wip-us.apache.org/repos/asf/incubator-singa/blob/d190fa89/src/model/layer/pooling.cc
----------------------------------------------------------------------
diff --git a/src/model/layer/pooling.cc b/src/model/layer/pooling.cc
index 75dc133..a8f3d3d 100644
--- a/src/model/layer/pooling.cc
+++ b/src/model/layer/pooling.cc
@@ -65,6 +65,8 @@ void Pooling::Setup(const Shape& in_sample, const LayerConf& conf) {
width_ = in_sample.at(2);
pooled_height_ = 1;
if (pool_conf.ceil()) {
+ // TODO(wangwei): caffe also ensures the last pooling window starts strictly
+ // within the original area
if (stride_h_ > 0)
pooled_height_ = static_cast<int>(ceil(static_cast<float>(
height_ + 2 * pad_h_ - kernel_h_) / stride_h_)) + 1;
http://git-wip-us.apache.org/repos/asf/incubator-singa/blob/d190fa89/src/model/layer/slice.cc
----------------------------------------------------------------------
diff --git a/src/model/layer/slice.cc b/src/model/layer/slice.cc
index 66c05ee..8a3e4bf 100644
--- a/src/model/layer/slice.cc
+++ b/src/model/layer/slice.cc
@@ -30,22 +30,26 @@ void Slice::Setup(const Shape& in_sample, const LayerConf& conf) {
out_sample_shapes_.clear();
slice_point_.clear();
axis_ = conf.slice_conf().axis();
+ CHECK_GE(axis_, 0u);
int offset = 0;
// #slice point = # out tensors - 1
for (size_t p : conf.slice_conf().slice_point()) {
slice_point_.push_back(p);
- if (axis_ == 1) {
- out_sample_shapes_.push_back({p - offset});
- offset = p;
- } else {
+ if (axis_ == 0) {
out_sample_shapes_.push_back(in_sample);
+ } else {
+ auto s = in_sample;
+ s[axis_ - 1] = p - offset;
+ out_sample_shapes_.push_back(s);
+ offset = p;
}
}
- slice_point_.push_back(in_sample[0]);
- if (axis_ == 1) {
- out_sample_shapes_.push_back({in_sample[0] - offset});
- } else {
+ if (axis_ == 0) {
out_sample_shapes_.push_back(in_sample);
+ } else {
+ auto s = in_sample;
+ s[axis_ - 1] = in_sample[axis_ - 1] - offset;
+ out_sample_shapes_.push_back(s);
}
}
@@ -54,12 +58,11 @@ const vector<Tensor> Slice::Forward(int flag, const vector<Tensor>& inputs) {
CHECK_EQ(inputs.size(), 1u) << "Split layer only have one input tensor.";
size_t offset = 0;
for (auto& s : slice_point_) {
- if (axis_ == 0)
- outputs.push_back(SliceRows(inputs.at(0), offset, s));
- else
- outputs.push_back(SliceColumns(inputs.at(0), offset, s));
+ outputs.push_back(SliceOn(inputs.at(0), offset, s, axis_));
offset = s;
}
+ outputs.push_back(SliceOn(inputs.at(0), offset, inputs.at(0).shape(axis_),
+ axis_));
return outputs;
}
@@ -67,10 +70,7 @@ const std::pair<vector<Tensor>, vector<Tensor>> Slice::Backward(
int flag, const vector<Tensor>& grads) {
vector<Tensor> input_grad, param_grad;
CHECK_EQ(grads.size(), out_sample_shapes_.size());
- if (axis_ == 0)
- input_grad.push_back(ConcatRows(grads));
- else
- input_grad.push_back(ConcatColumns(grads));
+ input_grad.push_back(ConcatOn(grads, axis_));
return std::make_pair(input_grad, param_grad);
}
http://git-wip-us.apache.org/repos/asf/incubator-singa/blob/d190fa89/src/model/layer/slice.h
----------------------------------------------------------------------
diff --git a/src/model/layer/slice.h b/src/model/layer/slice.h
index 7ed61fc..536fe64 100644
--- a/src/model/layer/slice.h
+++ b/src/model/layer/slice.h
@@ -23,6 +23,10 @@
#include "singa/model/layer.h"
namespace singa {
+/**
+ * Slice the tensor from the source layer along the give axis and according to
+ * the give slicep points.
+ */
class Slice : public Layer {
public:
/// \copydoc Layer::layer_type()
@@ -44,7 +48,7 @@ class Slice : public Layer {
const vector<Tensor>& grad) override;
protected:
- /// 0 for slice rows; 1 for slice cols
+ /// >= 0
int axis_ = 0;
/// out_sample_shapes_[i] is the shape of the i-th output tensor
vector<Shape> out_sample_shapes_;
http://git-wip-us.apache.org/repos/asf/incubator-singa/blob/d190fa89/test/CMakeLists.txt
----------------------------------------------------------------------
diff --git a/test/CMakeLists.txt b/test/CMakeLists.txt
index 79e481c..a056631 100644
--- a/test/CMakeLists.txt
+++ b/test/CMakeLists.txt
@@ -21,8 +21,8 @@ INCLUDE_DIRECTORIES(${CMAKE_BINARY_DIR}/include)
IF(ENABLE_DIST)
ADD_EXECUTABLE(test_ep "singa/test_ep.cc")
- ADD_DEPENDENCIES(test_ep singa_io)
- TARGET_LINK_LIBRARIES(test_ep singa_utils singa_io protobuf ${SINGA_LINKER_LIBS})
+ ADD_DEPENDENCIES(test_ep singa)
+ TARGET_LINK_LIBRARIES(test_ep singa protobuf ${SINGA_LINKER_LIBS})
ENDIF()
ADD_LIBRARY(gtest STATIC EXCLUDE_FROM_ALL "gtest/gtest.h" "gtest/gtest-all.cc")
[2/2] incubator-singa git commit: SINGA-295 - Add an example of image
classification using GoogleNet
Posted by ka...@apache.org.
SINGA-295 - Add an example of image classification using GoogleNet
Add googlenet; update concat and slice.
Fix the bug from padding layers due to difference of rounding strategies between caffe and cudnn
Move the alexnet files into examples/imagenet/alexnet
Update the prediction file and the readme file according to rafiki's format.
Users can submit mutliple queries via curl.
fixed a bug in tensor.py to_numpy()
test on cpu
update setup config file to install dependent libs (flask, pillow) when
installing pysinga
Project: http://git-wip-us.apache.org/repos/asf/incubator-singa/repo
Commit: http://git-wip-us.apache.org/repos/asf/incubator-singa/commit/d190fa89
Tree: http://git-wip-us.apache.org/repos/asf/incubator-singa/tree/d190fa89
Diff: http://git-wip-us.apache.org/repos/asf/incubator-singa/diff/d190fa89
Branch: refs/heads/master
Commit: d190fa89aabedb97e1e7af7a7222cadfa5187452
Parents: 8101f00
Author: Wei Wang <wa...@gmail.com>
Authored: Mon Jan 16 20:37:31 2017 +0800
Committer: Wei Wang <wa...@gmail.com>
Committed: Mon Jan 23 19:47:11 2017 +0800
----------------------------------------------------------------------
examples/CMakeLists.txt | 8 +-
examples/imagenet/CMakeLists.txt | 34 ---
examples/imagenet/README.md | 58 ----
examples/imagenet/alexnet.cc | 402 --------------------------
examples/imagenet/alexnet/CMakeLists.txt | 34 +++
examples/imagenet/alexnet/README.md | 58 ++++
examples/imagenet/alexnet/alexnet.cc | 402 ++++++++++++++++++++++++++
examples/imagenet/alexnet/create_data.sh | 21 ++
examples/imagenet/alexnet/ilsvrc12.cc | 70 +++++
examples/imagenet/alexnet/ilsvrc12.h | 376 ++++++++++++++++++++++++
examples/imagenet/alexnet/run.sh | 21 ++
examples/imagenet/create_data.sh | 21 --
examples/imagenet/googlenet/README.md | 66 +++++
examples/imagenet/googlenet/serve.py | 240 +++++++++++++++
examples/imagenet/ilsvrc12.cc | 70 -----
examples/imagenet/ilsvrc12.h | 376 ------------------------
examples/imagenet/run.sh | 21 --
include/singa/core/tensor.h | 4 +
python/setup.py.in | 5 +-
python/singa/net.py | 7 +-
python/singa/tensor.py | 4 +-
src/core/tensor/tensor.cc | 57 ++++
src/io/network/endpoint.cc | 3 +
src/model/layer/concat.cc | 32 +-
src/model/layer/concat.h | 5 +-
src/model/layer/pooling.cc | 2 +
src/model/layer/slice.cc | 32 +-
src/model/layer/slice.h | 6 +-
test/CMakeLists.txt | 4 +-
29 files changed, 1416 insertions(+), 1023 deletions(-)
----------------------------------------------------------------------
http://git-wip-us.apache.org/repos/asf/incubator-singa/blob/d190fa89/examples/CMakeLists.txt
----------------------------------------------------------------------
diff --git a/examples/CMakeLists.txt b/examples/CMakeLists.txt
index 0bb6c2f..f372692 100644
--- a/examples/CMakeLists.txt
+++ b/examples/CMakeLists.txt
@@ -6,15 +6,15 @@
# to you under the Apache License, Version 2.0 (the
# "License"); you may not use this file except in compliance
# with the License. You may obtain a copy of the License at
-#
+#
# http://www.apache.org/licenses/LICENSE-2.0
-#
+#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
-#
+#
ADD_SUBDIRECTORY(cifar10)
-ADD_SUBDIRECTORY(imagenet)
+ADD_SUBDIRECTORY(imagenet/alexnet)
http://git-wip-us.apache.org/repos/asf/incubator-singa/blob/d190fa89/examples/imagenet/CMakeLists.txt
----------------------------------------------------------------------
diff --git a/examples/imagenet/CMakeLists.txt b/examples/imagenet/CMakeLists.txt
deleted file mode 100644
index fbb7235..0000000
--- a/examples/imagenet/CMakeLists.txt
+++ /dev/null
@@ -1,34 +0,0 @@
-#
-# Licensed to the Apache Software Foundation (ASF) under one
-# or more contributor license agreements. See the NOTICE file
-# distributed with this work for additional information
-# regarding copyright ownership. The ASF licenses this file
-# to you under the Apache License, Version 2.0 (the
-# "License"); you may not use this file except in compliance
-# with the License. You may obtain a copy of the License at
-#
-# http://www.apache.org/licenses/LICENSE-2.0
-#
-# Unless required by applicable law or agreed to in writing, software
-# distributed under the License is distributed on an "AS IS" BASIS,
-# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-# See the License for the specific language governing permissions and
-# limitations under the License.
-#
-
-INCLUDE_DIRECTORIES(${CMAKE_CURRENT_SOURCE_DIR})
-INCLUDE_DIRECTORIES(${CMAKE_BINARY_DIR}/include)
-
-IF(USE_CUDNN)
- IF(USE_OPENCV)
- SET(CMAKE_CXX_FLAGS "${CMAKE_CXX_FLAGS} -fopenmp ")
- ADD_EXECUTABLE(imagenet alexnet.cc)
- ADD_DEPENDENCIES(imagenet singa)
- TARGET_LINK_LIBRARIES(imagenet singa protobuf ${SINGA_LIBKER_LIBS})
-
- ADD_EXECUTABLE(createdata ilsvrc12.cc)
- ADD_DEPENDENCIES(createdata singa)
- TARGET_LINK_LIBRARIES(createdata singa protobuf ${SINGA_LIBKER_LIBS})
- #SET_TARGET_PROPERTIES(createdata PROPERTIES LINK_FLAGS "${LINK_FLAGS}")
- ENDIF(USE_OPENCV)
-ENDIF(USE_CUDNN)
http://git-wip-us.apache.org/repos/asf/incubator-singa/blob/d190fa89/examples/imagenet/README.md
----------------------------------------------------------------------
diff --git a/examples/imagenet/README.md b/examples/imagenet/README.md
deleted file mode 100644
index be6797c..0000000
--- a/examples/imagenet/README.md
+++ /dev/null
@@ -1,58 +0,0 @@
-# Train AlexNet over ImageNet
-
-Convolution neural network (CNN) is a type of feed-forward neural
-network widely used for image and video classification. In this example, we will
-use a [deep CNN model](http://papers.nips.cc/paper/4824-imagenet-classification-with-deep-convolutional-neural-networks)
-to do image classification against the ImageNet dataset.
-
-## Instructions
-
-### Compile SINGA
-
-Please compile SINGA with CUDA, CUDNN and OpenCV. You can manually turn on the
-options in CMakeLists.txt or run `ccmake ..` in build/ folder.
-
-We have tested CUDNN V4 and V5 (V5 requires CUDA 7.5)
-
-### Data download
-* Please refer to step1-3 on [Instructions to create ImageNet 2012 data](https://github.com/amd/OpenCL-caffe/wiki/Instructions-to-create-ImageNet-2012-data)
- to download and decompress the data.
-* You can download the training and validation list by
- [get_ilsvrc_aux.sh](https://github.com/BVLC/caffe/blob/master/data/ilsvrc12/get_ilsvrc_aux.sh)
- or from [Imagenet](http://www.image-net.org/download-images).
-
-### Data preprocessing
-* Assuming you have downloaded the data and the list.
- Now we should transform the data into binary files. You can run:
-
- sh create_data.sh
-
- The script will generate a test file(`test.bin`), a mean file(`mean.bin`) and
- several training files(`trainX.bin`) in the specified output folder.
-* You can also change the parameters in `create_data.sh`.
- + `-trainlist <file>`: the file of training list;
- + `-trainfolder <folder>`: the folder of training images;
- + `-testlist <file>`: the file of test list;
- + `-testfolder <floder>`: the folder of test images;
- + `-outdata <folder>`: the folder to save output files, including mean, training and test files.
- The script will generate these files in the specified folder;
- + `-filesize <int>`: number of training images that stores in each binary file.
-
-### Training
-* After preparing data, you can run the following command to train the Alexnet model.
-
- sh run.sh
-
-* You may change the parameters in `run.sh`.
- + `-epoch <int>`: number of epoch to be trained, default is 90;
- + `-lr <float>`: base learning rate, the learning rate will decrease each 20 epochs,
- more specifically, `lr = lr * exp(0.1 * (epoch / 20))`;
- + `-batchsize <int>`: batchsize, it should be changed regarding to your memory;
- + `-filesize <int>`: number of training images that stores in each binary file, it is the
- same as the `filesize` in data preprocessing;
- + `-ntrain <int>`: number of training images;
- + `-ntest <int>`: number of test images;
- + `-data <folder>`: the folder which stores the binary files, it is exactly the output
- folder in data preprocessing step;
- + `-pfreq <int>`: the frequency(in batch) of printing current model status(loss and accuracy);
- + `-nthreads <int>`: the number of threads to load data which feed to the model.
http://git-wip-us.apache.org/repos/asf/incubator-singa/blob/d190fa89/examples/imagenet/alexnet.cc
----------------------------------------------------------------------
diff --git a/examples/imagenet/alexnet.cc b/examples/imagenet/alexnet.cc
deleted file mode 100644
index 4ac1130..0000000
--- a/examples/imagenet/alexnet.cc
+++ /dev/null
@@ -1,402 +0,0 @@
-/************************************************************
-*
-* Licensed to the Apache Software Foundation (ASF) under one
-* or more contributor license agreements. See the NOTICE file
-* distributed with this work for additional information
-* regarding copyright ownership. The ASF licenses this file
-* to you under the Apache License, Version 2.0 (the
-* "License"); you may not use this file except in compliance
-* with the License. You may obtain a copy of the License at
-*
-* http://www.apache.org/licenses/LICENSE-2.0
-*
-* Unless required by applicable law or agreed to in writing,
-* software distributed under the License is distributed on an
-* "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY
-* KIND, either express or implied. See the License for the
-* specific language governing permissions and limitations
-* under the License.
-*
-*************************************************************/
-
-#include "singa/singa_config.h"
-#ifdef USE_OPENCV
-#include <cmath>
-#include "./ilsvrc12.h"
-#include "singa/io/snapshot.h"
-#include "singa/model/feed_forward_net.h"
-#include "singa/model/initializer.h"
-#include "singa/model/metric.h"
-#include "singa/model/optimizer.h"
-#include "singa/utils/channel.h"
-#include "singa/utils/string.h"
-#include "singa/utils/timer.h"
-namespace singa {
-
-// currently supports 'cudnn' and 'singacpp'
-const std::string engine = "cudnn";
-LayerConf GenConvConf(string name, int nb_filter, int kernel, int stride,
- int pad, float std, float bias = .0f) {
- LayerConf conf;
- conf.set_name(name);
- conf.set_type(engine + "_convolution");
- ConvolutionConf *conv = conf.mutable_convolution_conf();
- conv->set_num_output(nb_filter);
- conv->add_kernel_size(kernel);
- conv->add_stride(stride);
- conv->add_pad(pad);
- conv->set_bias_term(true);
-
- ParamSpec *wspec = conf.add_param();
- wspec->set_name(name + "_weight");
- auto wfill = wspec->mutable_filler();
- wfill->set_type("Gaussian");
- wfill->set_std(std);
-
- ParamSpec *bspec = conf.add_param();
- bspec->set_name(name + "_bias");
- bspec->set_lr_mult(2);
- bspec->set_decay_mult(0);
- auto bfill = bspec->mutable_filler();
- bfill->set_value(bias);
- return conf;
-}
-
-LayerConf GenPoolingConf(string name, bool max_pool, int kernel, int stride,
- int pad) {
- LayerConf conf;
- conf.set_name(name);
- conf.set_type(engine + "_pooling");
- PoolingConf *pool = conf.mutable_pooling_conf();
- pool->set_kernel_size(kernel);
- pool->set_stride(stride);
- pool->set_pad(pad);
- if (!max_pool) pool->set_pool(PoolingConf_PoolMethod_AVE);
- return conf;
-}
-
-LayerConf GenReLUConf(string name) {
- LayerConf conf;
- conf.set_name(name);
- conf.set_type(engine + "_relu");
- return conf;
-}
-
-LayerConf GenDenseConf(string name, int num_output, float std, float wd,
- float bias = .0f) {
- LayerConf conf;
- conf.set_name(name);
- conf.set_type("singa_dense");
- DenseConf *dense = conf.mutable_dense_conf();
- dense->set_num_output(num_output);
-
- ParamSpec *wspec = conf.add_param();
- wspec->set_name(name + "_weight");
- wspec->set_decay_mult(wd);
- auto wfill = wspec->mutable_filler();
- wfill->set_type("Gaussian");
- wfill->set_std(std);
-
- ParamSpec *bspec = conf.add_param();
- bspec->set_name(name + "_bias");
- bspec->set_lr_mult(2);
- bspec->set_decay_mult(0);
- auto bfill = bspec->mutable_filler();
- bfill->set_value(bias);
-
- return conf;
-}
-
-LayerConf GenLRNConf(string name) {
- LayerConf conf;
- conf.set_name(name);
- conf.set_type(engine + "_lrn");
- LRNConf *lrn = conf.mutable_lrn_conf();
- lrn->set_local_size(5);
- lrn->set_alpha(1e-04);
- lrn->set_beta(0.75);
- return conf;
-}
-
-LayerConf GenFlattenConf(string name) {
- LayerConf conf;
- conf.set_name(name);
- conf.set_type("singa_flatten");
- return conf;
-}
-
-LayerConf GenDropoutConf(string name, float dropout_ratio) {
- LayerConf conf;
- conf.set_name(name);
- conf.set_type(engine + "_dropout");
- DropoutConf *dropout = conf.mutable_dropout_conf();
- dropout->set_dropout_ratio(dropout_ratio);
- return conf;
-}
-
-FeedForwardNet CreateNet() {
- FeedForwardNet net;
- Shape s{3, 227, 227};
-
- net.Add(GenConvConf("conv1", 96, 11, 4, 0, 0.01), &s);
- net.Add(GenReLUConf("relu1"));
- net.Add(GenPoolingConf("pool1", true, 3, 2, 0));
- net.Add(GenLRNConf("lrn1"));
- net.Add(GenConvConf("conv2", 256, 5, 1, 2, 0.01, 1.0));
- net.Add(GenReLUConf("relu2"));
- net.Add(GenPoolingConf("pool2", true, 3, 2, 0));
- net.Add(GenLRNConf("lrn2"));
- net.Add(GenConvConf("conv3", 384, 3, 1, 1, 0.01));
- net.Add(GenReLUConf("relu3"));
- net.Add(GenConvConf("conv4", 384, 3, 1, 1, 0.01, 1.0));
- net.Add(GenReLUConf("relu4"));
- net.Add(GenConvConf("conv5", 256, 3, 1, 1, 0.01, 1.0));
- net.Add(GenReLUConf("relu5"));
- net.Add(GenPoolingConf("pool5", true, 3, 2, 0));
- net.Add(GenFlattenConf("flat"));
- net.Add(GenDenseConf("ip6", 4096, 0.005, 1, 1.0));
- net.Add(GenReLUConf("relu6"));
- net.Add(GenDropoutConf("drop6", 0.5));
- net.Add(GenDenseConf("ip7", 4096, 0.005, 1, 1.0));
- net.Add(GenReLUConf("relu7"));
- net.Add(GenDropoutConf("drop7", 0.5));
- net.Add(GenDenseConf("ip8", 1000, 0.01, 1));
-
- return net;
-}
-
-void TrainOneEpoch(FeedForwardNet &net, ILSVRC &data,
- std::shared_ptr<Device> device, int epoch, string bin_folder,
- size_t num_train_files, size_t batchsize, float lr,
- Channel *train_ch, size_t pfreq, int nthreads) {
- float loss = 0.0f, metric = 0.0f;
- float load_time = 0.0f, train_time = 0.0f;
- size_t b = 0;
- size_t n_read;
- Timer timer, ttr;
- Tensor prefetch_x, prefetch_y;
- string binfile = bin_folder + "/train1.bin";
- timer.Tick();
- data.LoadData(kTrain, binfile, batchsize, &prefetch_x, &prefetch_y, &n_read,
- nthreads);
- load_time += timer.Elapsed();
- CHECK_EQ(n_read, batchsize);
- Tensor train_x(prefetch_x.shape(), device);
- Tensor train_y(prefetch_y.shape(), device, kInt);
- std::thread th;
- for (size_t fno = 1; fno <= num_train_files; fno++) {
- binfile = bin_folder + "/train" + std::to_string(fno) + ".bin";
- while (true) {
- if (th.joinable()) {
- th.join();
- load_time += timer.Elapsed();
- // LOG(INFO) << "num of samples: " << n_read;
- if (n_read < batchsize) {
- if (n_read > 0) {
- LOG(WARNING) << "Pls set batchsize to make num_total_samples "
- << "% batchsize == 0. Otherwise, the last " << n_read
- << " samples would not be used";
- }
- break;
- }
- }
- if (n_read == batchsize) {
- train_x.CopyData(prefetch_x);
- train_y.CopyData(prefetch_y);
- }
- timer.Tick();
- th = data.AsyncLoadData(kTrain, binfile, batchsize, &prefetch_x,
- &prefetch_y, &n_read, nthreads);
- if (n_read < batchsize) continue;
- CHECK_EQ(train_x.shape(0), train_y.shape(0));
- ttr.Tick();
- auto ret = net.TrainOnBatch(epoch, train_x, train_y);
- train_time += ttr.Elapsed();
- loss += ret.first;
- metric += ret.second;
- b++;
- }
- if (b % pfreq == 0) {
- train_ch->Send(
- "Epoch " + std::to_string(epoch) + ", training loss = " +
- std::to_string(loss / b) + ", accuracy = " +
- std::to_string(metric / b) + ", lr = " + std::to_string(lr) +
- ", time of loading " + std::to_string(batchsize) + " images = " +
- std::to_string(load_time / b) +
- " ms, time of training (batchsize = " + std::to_string(batchsize) +
- ") = " + std::to_string(train_time / b) + " ms.");
- loss = 0.0f;
- metric = 0.0f;
- load_time = 0.0f;
- train_time = 0.0f;
- b = 0;
- }
- }
-}
-
-void TestOneEpoch(FeedForwardNet &net, ILSVRC &data,
- std::shared_ptr<Device> device, int epoch, string bin_folder,
- size_t num_test_images, size_t batchsize, Channel *val_ch,
- int nthreads) {
- float loss = 0.0f, metric = 0.0f;
- float load_time = 0.0f, eval_time = 0.0f;
- size_t n_read;
- string binfile = bin_folder + "/test.bin";
- Timer timer, tte;
- Tensor prefetch_x, prefetch_y;
- timer.Tick();
- data.LoadData(kEval, binfile, batchsize, &prefetch_x, &prefetch_y, &n_read,
- nthreads);
- load_time += timer.Elapsed();
- Tensor test_x(prefetch_x.shape(), device);
- Tensor test_y(prefetch_y.shape(), device, kInt);
- int remain = (int)num_test_images - n_read;
- CHECK_EQ(n_read, batchsize);
- std::thread th;
- while (true) {
- if (th.joinable()) {
- th.join();
- load_time += timer.Elapsed();
- remain -= n_read;
- if (remain < 0) break;
- if (n_read < batchsize) break;
- }
- test_x.CopyData(prefetch_x);
- test_y.CopyData(prefetch_y);
- timer.Tick();
- th = data.AsyncLoadData(kEval, binfile, batchsize, &prefetch_x, &prefetch_y,
- &n_read, nthreads);
-
- CHECK_EQ(test_x.shape(0), test_y.shape(0));
- tte.Tick();
- auto ret = net.EvaluateOnBatch(test_x, test_y);
- eval_time += tte.Elapsed();
- ret.first.ToHost();
- ret.second.ToHost();
- loss += Sum(ret.first);
- metric += Sum(ret.second);
- }
- loss /= num_test_images;
- metric /= num_test_images;
- val_ch->Send("Epoch " + std::to_string(epoch) + ", val loss = " +
- std::to_string(loss) + ", accuracy = " + std::to_string(metric) +
- ", time of loading " + std::to_string(num_test_images) +
- " images = " + std::to_string(load_time) +
- " ms, time of evaluating " + std::to_string(num_test_images) +
- " images = " + std::to_string(eval_time) + " ms.");
-}
-
-void Checkpoint(FeedForwardNet &net, string prefix) {
- Snapshot snapshot(prefix, Snapshot::kWrite, 200);
- auto names = net.GetParamNames();
- auto values = net.GetParamValues();
- for (size_t k = 0; k < names.size(); k++) {
- values.at(k).ToHost();
- snapshot.Write(names.at(k), values.at(k));
- }
- LOG(INFO) << "Write snapshot into " << prefix;
-}
-
-void Train(int num_epoch, float lr, size_t batchsize, size_t train_file_size,
- string bin_folder, size_t num_train_images, size_t num_test_images,
- size_t pfreq, int nthreads) {
- ILSVRC data;
- data.ReadMean(bin_folder + "/mean.bin");
- auto net = CreateNet();
- auto cuda = std::make_shared<CudaGPU>(0);
- net.ToDevice(cuda);
- SGD sgd;
- OptimizerConf opt_conf;
- opt_conf.set_momentum(0.9);
- auto reg = opt_conf.mutable_regularizer();
- reg->set_coefficient(0.0005);
- sgd.Setup(opt_conf);
- sgd.SetLearningRateGenerator(
- [lr](int epoch) { return lr * std::pow(0.1, epoch / 20); });
-
- SoftmaxCrossEntropy loss;
- Accuracy acc;
- net.Compile(true, &sgd, &loss, &acc);
-
- Channel *train_ch = GetChannel("train_perf");
- train_ch->EnableDestStderr(true);
- Channel *val_ch = GetChannel("val_perf");
- val_ch->EnableDestStderr(true);
- size_t num_train_files = num_train_images / train_file_size +
- (num_train_images % train_file_size ? 1 : 0);
- for (int epoch = 0; epoch < num_epoch; epoch++) {
- float epoch_lr = sgd.GetLearningRate(epoch);
- TrainOneEpoch(net, data, cuda, epoch, bin_folder, num_train_files,
- batchsize, epoch_lr, train_ch, pfreq, nthreads);
- if (epoch % 10 == 0 && epoch > 0) {
- string prefix = "snapshot_epoch" + std::to_string(epoch);
- Checkpoint(net, prefix);
- }
- TestOneEpoch(net, data, cuda, epoch, bin_folder, num_test_images, batchsize,
- val_ch, nthreads);
- }
-}
-}
-
-int main(int argc, char **argv) {
- singa::InitChannel(nullptr);
- int pos = singa::ArgPos(argc, argv, "-h");
- if (pos != -1) {
- std::cout << "Usage:\n"
- << "\t-epoch <int>: number of epoch to be trained, default is 90;\n"
- << "\t-lr <float>: base learning rate;\n"
- << "\t-batchsize <int>: batchsize, it should be changed regarding "
- "to your memory;\n"
- << "\t-filesize <int>: number of training images that stores in "
- "each binary file;\n"
- << "\t-ntrain <int>: number of training images;\n"
- << "\t-ntest <int>: number of test images;\n"
- << "\t-data <folder>: the folder which stores the binary files;\n"
- << "\t-pfreq <int>: the frequency(in batch) of printing current "
- "model status(loss and accuracy);\n"
- << "\t-nthreads <int>`: the number of threads to load data which "
- "feed to the model.\n";
- return 0;
- }
- pos = singa::ArgPos(argc, argv, "-epoch");
- int nEpoch = 90;
- if (pos != -1) nEpoch = atoi(argv[pos + 1]);
-
- pos = singa::ArgPos(argc, argv, "-lr");
- float lr = 0.01;
- if (pos != -1) lr = atof(argv[pos + 1]);
-
- pos = singa::ArgPos(argc, argv, "-batchsize");
- int batchsize = 256;
- if (pos != -1) batchsize = atof(argv[pos + 1]);
-
- pos = singa::ArgPos(argc, argv, "-filesize");
- size_t train_file_size = 1280;
- if (pos != -1) train_file_size = atoi(argv[pos + 1]);
-
- pos = singa::ArgPos(argc, argv, "-ntrain");
- size_t num_train_images = 1281167;
- if (pos != -1) num_train_images = atoi(argv[pos + 1]);
-
- pos = singa::ArgPos(argc, argv, "-ntest");
- size_t num_test_images = 50000;
- if (pos != -1) num_test_images = atoi(argv[pos + 1]);
-
- pos = singa::ArgPos(argc, argv, "-data");
- string bin_folder = "imagenet_data";
- if (pos != -1) bin_folder = argv[pos + 1];
-
- pos = singa::ArgPos(argc, argv, "-pfreq");
- size_t pfreq = 100;
- if (pos != -1) pfreq = atoi(argv[pos + 1]);
-
- pos = singa::ArgPos(argc, argv, "-nthreads");
- int nthreads = 12;
- if (pos != -1) nthreads = atoi(argv[pos + 1]);
-
- LOG(INFO) << "Start training";
- singa::Train(nEpoch, lr, batchsize, train_file_size, bin_folder,
- num_train_images, num_test_images, pfreq, nthreads);
- LOG(INFO) << "End training";
-}
-#endif
http://git-wip-us.apache.org/repos/asf/incubator-singa/blob/d190fa89/examples/imagenet/alexnet/CMakeLists.txt
----------------------------------------------------------------------
diff --git a/examples/imagenet/alexnet/CMakeLists.txt b/examples/imagenet/alexnet/CMakeLists.txt
new file mode 100644
index 0000000..fbb7235
--- /dev/null
+++ b/examples/imagenet/alexnet/CMakeLists.txt
@@ -0,0 +1,34 @@
+#
+# Licensed to the Apache Software Foundation (ASF) under one
+# or more contributor license agreements. See the NOTICE file
+# distributed with this work for additional information
+# regarding copyright ownership. The ASF licenses this file
+# to you under the Apache License, Version 2.0 (the
+# "License"); you may not use this file except in compliance
+# with the License. You may obtain a copy of the License at
+#
+# http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+#
+
+INCLUDE_DIRECTORIES(${CMAKE_CURRENT_SOURCE_DIR})
+INCLUDE_DIRECTORIES(${CMAKE_BINARY_DIR}/include)
+
+IF(USE_CUDNN)
+ IF(USE_OPENCV)
+ SET(CMAKE_CXX_FLAGS "${CMAKE_CXX_FLAGS} -fopenmp ")
+ ADD_EXECUTABLE(imagenet alexnet.cc)
+ ADD_DEPENDENCIES(imagenet singa)
+ TARGET_LINK_LIBRARIES(imagenet singa protobuf ${SINGA_LIBKER_LIBS})
+
+ ADD_EXECUTABLE(createdata ilsvrc12.cc)
+ ADD_DEPENDENCIES(createdata singa)
+ TARGET_LINK_LIBRARIES(createdata singa protobuf ${SINGA_LIBKER_LIBS})
+ #SET_TARGET_PROPERTIES(createdata PROPERTIES LINK_FLAGS "${LINK_FLAGS}")
+ ENDIF(USE_OPENCV)
+ENDIF(USE_CUDNN)
http://git-wip-us.apache.org/repos/asf/incubator-singa/blob/d190fa89/examples/imagenet/alexnet/README.md
----------------------------------------------------------------------
diff --git a/examples/imagenet/alexnet/README.md b/examples/imagenet/alexnet/README.md
new file mode 100644
index 0000000..be6797c
--- /dev/null
+++ b/examples/imagenet/alexnet/README.md
@@ -0,0 +1,58 @@
+# Train AlexNet over ImageNet
+
+Convolution neural network (CNN) is a type of feed-forward neural
+network widely used for image and video classification. In this example, we will
+use a [deep CNN model](http://papers.nips.cc/paper/4824-imagenet-classification-with-deep-convolutional-neural-networks)
+to do image classification against the ImageNet dataset.
+
+## Instructions
+
+### Compile SINGA
+
+Please compile SINGA with CUDA, CUDNN and OpenCV. You can manually turn on the
+options in CMakeLists.txt or run `ccmake ..` in build/ folder.
+
+We have tested CUDNN V4 and V5 (V5 requires CUDA 7.5)
+
+### Data download
+* Please refer to step1-3 on [Instructions to create ImageNet 2012 data](https://github.com/amd/OpenCL-caffe/wiki/Instructions-to-create-ImageNet-2012-data)
+ to download and decompress the data.
+* You can download the training and validation list by
+ [get_ilsvrc_aux.sh](https://github.com/BVLC/caffe/blob/master/data/ilsvrc12/get_ilsvrc_aux.sh)
+ or from [Imagenet](http://www.image-net.org/download-images).
+
+### Data preprocessing
+* Assuming you have downloaded the data and the list.
+ Now we should transform the data into binary files. You can run:
+
+ sh create_data.sh
+
+ The script will generate a test file(`test.bin`), a mean file(`mean.bin`) and
+ several training files(`trainX.bin`) in the specified output folder.
+* You can also change the parameters in `create_data.sh`.
+ + `-trainlist <file>`: the file of training list;
+ + `-trainfolder <folder>`: the folder of training images;
+ + `-testlist <file>`: the file of test list;
+ + `-testfolder <floder>`: the folder of test images;
+ + `-outdata <folder>`: the folder to save output files, including mean, training and test files.
+ The script will generate these files in the specified folder;
+ + `-filesize <int>`: number of training images that stores in each binary file.
+
+### Training
+* After preparing data, you can run the following command to train the Alexnet model.
+
+ sh run.sh
+
+* You may change the parameters in `run.sh`.
+ + `-epoch <int>`: number of epoch to be trained, default is 90;
+ + `-lr <float>`: base learning rate, the learning rate will decrease each 20 epochs,
+ more specifically, `lr = lr * exp(0.1 * (epoch / 20))`;
+ + `-batchsize <int>`: batchsize, it should be changed regarding to your memory;
+ + `-filesize <int>`: number of training images that stores in each binary file, it is the
+ same as the `filesize` in data preprocessing;
+ + `-ntrain <int>`: number of training images;
+ + `-ntest <int>`: number of test images;
+ + `-data <folder>`: the folder which stores the binary files, it is exactly the output
+ folder in data preprocessing step;
+ + `-pfreq <int>`: the frequency(in batch) of printing current model status(loss and accuracy);
+ + `-nthreads <int>`: the number of threads to load data which feed to the model.
http://git-wip-us.apache.org/repos/asf/incubator-singa/blob/d190fa89/examples/imagenet/alexnet/alexnet.cc
----------------------------------------------------------------------
diff --git a/examples/imagenet/alexnet/alexnet.cc b/examples/imagenet/alexnet/alexnet.cc
new file mode 100644
index 0000000..4ac1130
--- /dev/null
+++ b/examples/imagenet/alexnet/alexnet.cc
@@ -0,0 +1,402 @@
+/************************************************************
+*
+* Licensed to the Apache Software Foundation (ASF) under one
+* or more contributor license agreements. See the NOTICE file
+* distributed with this work for additional information
+* regarding copyright ownership. The ASF licenses this file
+* to you under the Apache License, Version 2.0 (the
+* "License"); you may not use this file except in compliance
+* with the License. You may obtain a copy of the License at
+*
+* http://www.apache.org/licenses/LICENSE-2.0
+*
+* Unless required by applicable law or agreed to in writing,
+* software distributed under the License is distributed on an
+* "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY
+* KIND, either express or implied. See the License for the
+* specific language governing permissions and limitations
+* under the License.
+*
+*************************************************************/
+
+#include "singa/singa_config.h"
+#ifdef USE_OPENCV
+#include <cmath>
+#include "./ilsvrc12.h"
+#include "singa/io/snapshot.h"
+#include "singa/model/feed_forward_net.h"
+#include "singa/model/initializer.h"
+#include "singa/model/metric.h"
+#include "singa/model/optimizer.h"
+#include "singa/utils/channel.h"
+#include "singa/utils/string.h"
+#include "singa/utils/timer.h"
+namespace singa {
+
+// currently supports 'cudnn' and 'singacpp'
+const std::string engine = "cudnn";
+LayerConf GenConvConf(string name, int nb_filter, int kernel, int stride,
+ int pad, float std, float bias = .0f) {
+ LayerConf conf;
+ conf.set_name(name);
+ conf.set_type(engine + "_convolution");
+ ConvolutionConf *conv = conf.mutable_convolution_conf();
+ conv->set_num_output(nb_filter);
+ conv->add_kernel_size(kernel);
+ conv->add_stride(stride);
+ conv->add_pad(pad);
+ conv->set_bias_term(true);
+
+ ParamSpec *wspec = conf.add_param();
+ wspec->set_name(name + "_weight");
+ auto wfill = wspec->mutable_filler();
+ wfill->set_type("Gaussian");
+ wfill->set_std(std);
+
+ ParamSpec *bspec = conf.add_param();
+ bspec->set_name(name + "_bias");
+ bspec->set_lr_mult(2);
+ bspec->set_decay_mult(0);
+ auto bfill = bspec->mutable_filler();
+ bfill->set_value(bias);
+ return conf;
+}
+
+LayerConf GenPoolingConf(string name, bool max_pool, int kernel, int stride,
+ int pad) {
+ LayerConf conf;
+ conf.set_name(name);
+ conf.set_type(engine + "_pooling");
+ PoolingConf *pool = conf.mutable_pooling_conf();
+ pool->set_kernel_size(kernel);
+ pool->set_stride(stride);
+ pool->set_pad(pad);
+ if (!max_pool) pool->set_pool(PoolingConf_PoolMethod_AVE);
+ return conf;
+}
+
+LayerConf GenReLUConf(string name) {
+ LayerConf conf;
+ conf.set_name(name);
+ conf.set_type(engine + "_relu");
+ return conf;
+}
+
+LayerConf GenDenseConf(string name, int num_output, float std, float wd,
+ float bias = .0f) {
+ LayerConf conf;
+ conf.set_name(name);
+ conf.set_type("singa_dense");
+ DenseConf *dense = conf.mutable_dense_conf();
+ dense->set_num_output(num_output);
+
+ ParamSpec *wspec = conf.add_param();
+ wspec->set_name(name + "_weight");
+ wspec->set_decay_mult(wd);
+ auto wfill = wspec->mutable_filler();
+ wfill->set_type("Gaussian");
+ wfill->set_std(std);
+
+ ParamSpec *bspec = conf.add_param();
+ bspec->set_name(name + "_bias");
+ bspec->set_lr_mult(2);
+ bspec->set_decay_mult(0);
+ auto bfill = bspec->mutable_filler();
+ bfill->set_value(bias);
+
+ return conf;
+}
+
+LayerConf GenLRNConf(string name) {
+ LayerConf conf;
+ conf.set_name(name);
+ conf.set_type(engine + "_lrn");
+ LRNConf *lrn = conf.mutable_lrn_conf();
+ lrn->set_local_size(5);
+ lrn->set_alpha(1e-04);
+ lrn->set_beta(0.75);
+ return conf;
+}
+
+LayerConf GenFlattenConf(string name) {
+ LayerConf conf;
+ conf.set_name(name);
+ conf.set_type("singa_flatten");
+ return conf;
+}
+
+LayerConf GenDropoutConf(string name, float dropout_ratio) {
+ LayerConf conf;
+ conf.set_name(name);
+ conf.set_type(engine + "_dropout");
+ DropoutConf *dropout = conf.mutable_dropout_conf();
+ dropout->set_dropout_ratio(dropout_ratio);
+ return conf;
+}
+
+FeedForwardNet CreateNet() {
+ FeedForwardNet net;
+ Shape s{3, 227, 227};
+
+ net.Add(GenConvConf("conv1", 96, 11, 4, 0, 0.01), &s);
+ net.Add(GenReLUConf("relu1"));
+ net.Add(GenPoolingConf("pool1", true, 3, 2, 0));
+ net.Add(GenLRNConf("lrn1"));
+ net.Add(GenConvConf("conv2", 256, 5, 1, 2, 0.01, 1.0));
+ net.Add(GenReLUConf("relu2"));
+ net.Add(GenPoolingConf("pool2", true, 3, 2, 0));
+ net.Add(GenLRNConf("lrn2"));
+ net.Add(GenConvConf("conv3", 384, 3, 1, 1, 0.01));
+ net.Add(GenReLUConf("relu3"));
+ net.Add(GenConvConf("conv4", 384, 3, 1, 1, 0.01, 1.0));
+ net.Add(GenReLUConf("relu4"));
+ net.Add(GenConvConf("conv5", 256, 3, 1, 1, 0.01, 1.0));
+ net.Add(GenReLUConf("relu5"));
+ net.Add(GenPoolingConf("pool5", true, 3, 2, 0));
+ net.Add(GenFlattenConf("flat"));
+ net.Add(GenDenseConf("ip6", 4096, 0.005, 1, 1.0));
+ net.Add(GenReLUConf("relu6"));
+ net.Add(GenDropoutConf("drop6", 0.5));
+ net.Add(GenDenseConf("ip7", 4096, 0.005, 1, 1.0));
+ net.Add(GenReLUConf("relu7"));
+ net.Add(GenDropoutConf("drop7", 0.5));
+ net.Add(GenDenseConf("ip8", 1000, 0.01, 1));
+
+ return net;
+}
+
+void TrainOneEpoch(FeedForwardNet &net, ILSVRC &data,
+ std::shared_ptr<Device> device, int epoch, string bin_folder,
+ size_t num_train_files, size_t batchsize, float lr,
+ Channel *train_ch, size_t pfreq, int nthreads) {
+ float loss = 0.0f, metric = 0.0f;
+ float load_time = 0.0f, train_time = 0.0f;
+ size_t b = 0;
+ size_t n_read;
+ Timer timer, ttr;
+ Tensor prefetch_x, prefetch_y;
+ string binfile = bin_folder + "/train1.bin";
+ timer.Tick();
+ data.LoadData(kTrain, binfile, batchsize, &prefetch_x, &prefetch_y, &n_read,
+ nthreads);
+ load_time += timer.Elapsed();
+ CHECK_EQ(n_read, batchsize);
+ Tensor train_x(prefetch_x.shape(), device);
+ Tensor train_y(prefetch_y.shape(), device, kInt);
+ std::thread th;
+ for (size_t fno = 1; fno <= num_train_files; fno++) {
+ binfile = bin_folder + "/train" + std::to_string(fno) + ".bin";
+ while (true) {
+ if (th.joinable()) {
+ th.join();
+ load_time += timer.Elapsed();
+ // LOG(INFO) << "num of samples: " << n_read;
+ if (n_read < batchsize) {
+ if (n_read > 0) {
+ LOG(WARNING) << "Pls set batchsize to make num_total_samples "
+ << "% batchsize == 0. Otherwise, the last " << n_read
+ << " samples would not be used";
+ }
+ break;
+ }
+ }
+ if (n_read == batchsize) {
+ train_x.CopyData(prefetch_x);
+ train_y.CopyData(prefetch_y);
+ }
+ timer.Tick();
+ th = data.AsyncLoadData(kTrain, binfile, batchsize, &prefetch_x,
+ &prefetch_y, &n_read, nthreads);
+ if (n_read < batchsize) continue;
+ CHECK_EQ(train_x.shape(0), train_y.shape(0));
+ ttr.Tick();
+ auto ret = net.TrainOnBatch(epoch, train_x, train_y);
+ train_time += ttr.Elapsed();
+ loss += ret.first;
+ metric += ret.second;
+ b++;
+ }
+ if (b % pfreq == 0) {
+ train_ch->Send(
+ "Epoch " + std::to_string(epoch) + ", training loss = " +
+ std::to_string(loss / b) + ", accuracy = " +
+ std::to_string(metric / b) + ", lr = " + std::to_string(lr) +
+ ", time of loading " + std::to_string(batchsize) + " images = " +
+ std::to_string(load_time / b) +
+ " ms, time of training (batchsize = " + std::to_string(batchsize) +
+ ") = " + std::to_string(train_time / b) + " ms.");
+ loss = 0.0f;
+ metric = 0.0f;
+ load_time = 0.0f;
+ train_time = 0.0f;
+ b = 0;
+ }
+ }
+}
+
+void TestOneEpoch(FeedForwardNet &net, ILSVRC &data,
+ std::shared_ptr<Device> device, int epoch, string bin_folder,
+ size_t num_test_images, size_t batchsize, Channel *val_ch,
+ int nthreads) {
+ float loss = 0.0f, metric = 0.0f;
+ float load_time = 0.0f, eval_time = 0.0f;
+ size_t n_read;
+ string binfile = bin_folder + "/test.bin";
+ Timer timer, tte;
+ Tensor prefetch_x, prefetch_y;
+ timer.Tick();
+ data.LoadData(kEval, binfile, batchsize, &prefetch_x, &prefetch_y, &n_read,
+ nthreads);
+ load_time += timer.Elapsed();
+ Tensor test_x(prefetch_x.shape(), device);
+ Tensor test_y(prefetch_y.shape(), device, kInt);
+ int remain = (int)num_test_images - n_read;
+ CHECK_EQ(n_read, batchsize);
+ std::thread th;
+ while (true) {
+ if (th.joinable()) {
+ th.join();
+ load_time += timer.Elapsed();
+ remain -= n_read;
+ if (remain < 0) break;
+ if (n_read < batchsize) break;
+ }
+ test_x.CopyData(prefetch_x);
+ test_y.CopyData(prefetch_y);
+ timer.Tick();
+ th = data.AsyncLoadData(kEval, binfile, batchsize, &prefetch_x, &prefetch_y,
+ &n_read, nthreads);
+
+ CHECK_EQ(test_x.shape(0), test_y.shape(0));
+ tte.Tick();
+ auto ret = net.EvaluateOnBatch(test_x, test_y);
+ eval_time += tte.Elapsed();
+ ret.first.ToHost();
+ ret.second.ToHost();
+ loss += Sum(ret.first);
+ metric += Sum(ret.second);
+ }
+ loss /= num_test_images;
+ metric /= num_test_images;
+ val_ch->Send("Epoch " + std::to_string(epoch) + ", val loss = " +
+ std::to_string(loss) + ", accuracy = " + std::to_string(metric) +
+ ", time of loading " + std::to_string(num_test_images) +
+ " images = " + std::to_string(load_time) +
+ " ms, time of evaluating " + std::to_string(num_test_images) +
+ " images = " + std::to_string(eval_time) + " ms.");
+}
+
+void Checkpoint(FeedForwardNet &net, string prefix) {
+ Snapshot snapshot(prefix, Snapshot::kWrite, 200);
+ auto names = net.GetParamNames();
+ auto values = net.GetParamValues();
+ for (size_t k = 0; k < names.size(); k++) {
+ values.at(k).ToHost();
+ snapshot.Write(names.at(k), values.at(k));
+ }
+ LOG(INFO) << "Write snapshot into " << prefix;
+}
+
+void Train(int num_epoch, float lr, size_t batchsize, size_t train_file_size,
+ string bin_folder, size_t num_train_images, size_t num_test_images,
+ size_t pfreq, int nthreads) {
+ ILSVRC data;
+ data.ReadMean(bin_folder + "/mean.bin");
+ auto net = CreateNet();
+ auto cuda = std::make_shared<CudaGPU>(0);
+ net.ToDevice(cuda);
+ SGD sgd;
+ OptimizerConf opt_conf;
+ opt_conf.set_momentum(0.9);
+ auto reg = opt_conf.mutable_regularizer();
+ reg->set_coefficient(0.0005);
+ sgd.Setup(opt_conf);
+ sgd.SetLearningRateGenerator(
+ [lr](int epoch) { return lr * std::pow(0.1, epoch / 20); });
+
+ SoftmaxCrossEntropy loss;
+ Accuracy acc;
+ net.Compile(true, &sgd, &loss, &acc);
+
+ Channel *train_ch = GetChannel("train_perf");
+ train_ch->EnableDestStderr(true);
+ Channel *val_ch = GetChannel("val_perf");
+ val_ch->EnableDestStderr(true);
+ size_t num_train_files = num_train_images / train_file_size +
+ (num_train_images % train_file_size ? 1 : 0);
+ for (int epoch = 0; epoch < num_epoch; epoch++) {
+ float epoch_lr = sgd.GetLearningRate(epoch);
+ TrainOneEpoch(net, data, cuda, epoch, bin_folder, num_train_files,
+ batchsize, epoch_lr, train_ch, pfreq, nthreads);
+ if (epoch % 10 == 0 && epoch > 0) {
+ string prefix = "snapshot_epoch" + std::to_string(epoch);
+ Checkpoint(net, prefix);
+ }
+ TestOneEpoch(net, data, cuda, epoch, bin_folder, num_test_images, batchsize,
+ val_ch, nthreads);
+ }
+}
+}
+
+int main(int argc, char **argv) {
+ singa::InitChannel(nullptr);
+ int pos = singa::ArgPos(argc, argv, "-h");
+ if (pos != -1) {
+ std::cout << "Usage:\n"
+ << "\t-epoch <int>: number of epoch to be trained, default is 90;\n"
+ << "\t-lr <float>: base learning rate;\n"
+ << "\t-batchsize <int>: batchsize, it should be changed regarding "
+ "to your memory;\n"
+ << "\t-filesize <int>: number of training images that stores in "
+ "each binary file;\n"
+ << "\t-ntrain <int>: number of training images;\n"
+ << "\t-ntest <int>: number of test images;\n"
+ << "\t-data <folder>: the folder which stores the binary files;\n"
+ << "\t-pfreq <int>: the frequency(in batch) of printing current "
+ "model status(loss and accuracy);\n"
+ << "\t-nthreads <int>`: the number of threads to load data which "
+ "feed to the model.\n";
+ return 0;
+ }
+ pos = singa::ArgPos(argc, argv, "-epoch");
+ int nEpoch = 90;
+ if (pos != -1) nEpoch = atoi(argv[pos + 1]);
+
+ pos = singa::ArgPos(argc, argv, "-lr");
+ float lr = 0.01;
+ if (pos != -1) lr = atof(argv[pos + 1]);
+
+ pos = singa::ArgPos(argc, argv, "-batchsize");
+ int batchsize = 256;
+ if (pos != -1) batchsize = atof(argv[pos + 1]);
+
+ pos = singa::ArgPos(argc, argv, "-filesize");
+ size_t train_file_size = 1280;
+ if (pos != -1) train_file_size = atoi(argv[pos + 1]);
+
+ pos = singa::ArgPos(argc, argv, "-ntrain");
+ size_t num_train_images = 1281167;
+ if (pos != -1) num_train_images = atoi(argv[pos + 1]);
+
+ pos = singa::ArgPos(argc, argv, "-ntest");
+ size_t num_test_images = 50000;
+ if (pos != -1) num_test_images = atoi(argv[pos + 1]);
+
+ pos = singa::ArgPos(argc, argv, "-data");
+ string bin_folder = "imagenet_data";
+ if (pos != -1) bin_folder = argv[pos + 1];
+
+ pos = singa::ArgPos(argc, argv, "-pfreq");
+ size_t pfreq = 100;
+ if (pos != -1) pfreq = atoi(argv[pos + 1]);
+
+ pos = singa::ArgPos(argc, argv, "-nthreads");
+ int nthreads = 12;
+ if (pos != -1) nthreads = atoi(argv[pos + 1]);
+
+ LOG(INFO) << "Start training";
+ singa::Train(nEpoch, lr, batchsize, train_file_size, bin_folder,
+ num_train_images, num_test_images, pfreq, nthreads);
+ LOG(INFO) << "End training";
+}
+#endif
http://git-wip-us.apache.org/repos/asf/incubator-singa/blob/d190fa89/examples/imagenet/alexnet/create_data.sh
----------------------------------------------------------------------
diff --git a/examples/imagenet/alexnet/create_data.sh b/examples/imagenet/alexnet/create_data.sh
new file mode 100755
index 0000000..4c2c034
--- /dev/null
+++ b/examples/imagenet/alexnet/create_data.sh
@@ -0,0 +1,21 @@
+#!/usr/bin/env sh
+#
+# Licensed to the Apache Software Foundation (ASF) under one
+# or more contributor license agreements. See the NOTICE file
+# distributed with this work for additional information
+# regarding copyright ownership. The ASF licenses this file
+# to you under the Apache License, Version 2.0 (the
+# "License"); you may not use this file except in compliance
+# with the License. You may obtain a copy of the License at
+#
+# http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+#
+
+../../build/bin/createdata -trainlist "imagenet/label/train.txt" -trainfolder "imagenet/ILSVRC2012_img_train" \
+ -testlist "imagenet/label/val.txt" -testfolder "imagenet/ILSVRC2012_img_val" -outdata "imagenet_data" -filesize 1280
http://git-wip-us.apache.org/repos/asf/incubator-singa/blob/d190fa89/examples/imagenet/alexnet/ilsvrc12.cc
----------------------------------------------------------------------
diff --git a/examples/imagenet/alexnet/ilsvrc12.cc b/examples/imagenet/alexnet/ilsvrc12.cc
new file mode 100644
index 0000000..c9e6d2f
--- /dev/null
+++ b/examples/imagenet/alexnet/ilsvrc12.cc
@@ -0,0 +1,70 @@
+/************************************************************
+*
+* Licensed to the Apache Software Foundation (ASF) under one
+* or more contributor license agreements. See the NOTICE file
+* distributed with this work for additional information
+* regarding copyright ownership. The ASF licenses this file
+* to you under the Apache License, Version 2.0 (the
+* "License"); you may not use this file except in compliance
+* with the License. You may obtain a copy of the License at
+*
+* http://www.apache.org/licenses/LICENSE-2.0
+*
+* Unless required by applicable law or agreed to in writing,
+* software distributed under the License is distributed on an
+* "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY
+* KIND, either express or implied. See the License for the
+* specific language governing permissions and limitations
+* under the License.
+*
+*************************************************************/
+#include "singa/singa_config.h"
+#ifdef USE_OPENCV
+#include "ilsvrc12.h"
+#include "singa/utils/channel.h"
+#include "singa/utils/string.h"
+int main(int argc, char **argv) {
+ int pos = singa::ArgPos(argc, argv, "-h");
+ if (pos != -1) {
+ std::cout << "Usage:\n"
+ << "\t-trainlist <file>: the file of training list;\n"
+ << "\t-trainfolder <folder>: the folder of training images;\n"
+ << "\t-testlist <file>: the file of test list;\n"
+ << "\t-testfolder <floder>: the folder of test images;\n"
+ << "\t-outdata <folder>: the folder to save output files;\n"
+ << "\t-filesize <int>: number of training images that stores in "
+ "each binary file.\n";
+ return 0;
+ }
+ pos = singa::ArgPos(argc, argv, "-trainlist");
+ string train_image_list = "imagenet/label/train.txt";
+ if (pos != -1) train_image_list = argv[pos + 1];
+
+ pos = singa::ArgPos(argc, argv, "-trainfolder");
+ string train_image_folder = "imagenet/ILSVRC2012_img_train";
+ if (pos != -1) train_image_folder = argv[pos + 1];
+
+ pos = singa::ArgPos(argc, argv, "-testlist");
+ string test_image_list = "imagenet/label/val.txt";
+ if (pos != -1) test_image_list = argv[pos + 1];
+
+ pos = singa::ArgPos(argc, argv, "-testfolder");
+ string test_image_folder = "imagenet/ILSVRC2012_img_val";
+ if (pos != -1) test_image_folder = argv[pos + 1];
+
+ pos = singa::ArgPos(argc, argv, "-outdata");
+ string bin_folder = "imagenet_data";
+ if (pos != -1) bin_folder = argv[pos + 1];
+
+ pos = singa::ArgPos(argc, argv, "-filesize");
+ size_t train_file_size = 1280;
+ if (pos != -1) train_file_size = atoi(argv[pos + 1]);
+ singa::ILSVRC data;
+ LOG(INFO) << "Creating training and test data...";
+ data.CreateTrainData(train_image_list, train_image_folder, bin_folder,
+ train_file_size);
+ data.CreateTestData(test_image_list, test_image_folder, bin_folder);
+ LOG(INFO) << "Data created!";
+ return 0;
+}
+#endif // USE_OPENCV
http://git-wip-us.apache.org/repos/asf/incubator-singa/blob/d190fa89/examples/imagenet/alexnet/ilsvrc12.h
----------------------------------------------------------------------
diff --git a/examples/imagenet/alexnet/ilsvrc12.h b/examples/imagenet/alexnet/ilsvrc12.h
new file mode 100644
index 0000000..74fffbb
--- /dev/null
+++ b/examples/imagenet/alexnet/ilsvrc12.h
@@ -0,0 +1,376 @@
+/************************************************************
+*
+* Licensed to the Apache Software Foundation (ASF) under one
+* or more contributor license agreements. See the NOTICE file
+* distributed with this work for additional information
+* regarding copyright ownership. The ASF licenses this file
+* to you under the Apache License, Version 2.0 (the
+* "License"); you may not use this file except in compliance
+* with the License. You may obtain a copy of the License at
+*
+* http://www.apache.org/licenses/LICENSE-2.0
+*
+* Unless required by applicable law or agreed to in writing,
+* software distributed under the License is distributed on an
+* "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY
+* KIND, either express or implied. See the License for the
+* specific language governing permissions and limitations
+* under the License.
+*
+*************************************************************/
+#include "singa/singa_config.h"
+#ifdef USE_OPENCV
+#ifndef SINGA_EXAMPLES_IMAGENET_ILSVRC12_H_
+#define SINGA_EXAMPLES_IMAGENET_ILSVRC12_H_
+#include <omp.h>
+#include <cstdint>
+#include <fstream>
+#include <iostream>
+#include <opencv2/highgui/highgui.hpp>
+#include <opencv2/imgproc/imgproc.hpp>
+#include <string>
+#include <thread>
+#include <vector>
+#include "singa/core/tensor.h"
+#include "singa/io/decoder.h"
+#include "singa/io/encoder.h"
+#include "singa/io/reader.h"
+#include "singa/io/transformer.h"
+#include "singa/io/writer.h"
+#include "singa/proto/io.pb.h"
+#include "singa/utils/timer.h"
+
+using std::string;
+using namespace singa::io;
+namespace singa {
+/// For reading ILSVRC2012 image data as tensors.
+class ILSVRC {
+ public:
+ /// Setup encoder, decoder
+ ILSVRC();
+ ~ILSVRC() {
+ if (encoder != nullptr) delete encoder;
+ if (decoder != nullptr) delete decoder;
+ if (transformer != nullptr) delete transformer;
+ if (reader != nullptr) {
+ reader->Close();
+ delete reader;
+ }
+ if (writer != nullptr) {
+ writer->Close();
+ delete writer;
+ }
+ }
+ /// Create binary files for training data
+ /// train_image_list: list file of training images
+ /// train_image_folder: folder where stores original training images
+ /// train_bin_folder: folder to store binary files
+ /// train_file_size: number of images that are contain in one binary file
+ void CreateTrainData(string train_image_list, string train_image_folder,
+ string train_bin_folder, size_t train_file_size);
+ /// Create binary files for test data
+ /// train_image_list: list file of test images
+ /// train_image_folder: folder where saves original test images
+ /// train_bin_folder: folder to save binary files
+ void CreateTestData(string test_image_list, string test_image_folder,
+ string test_bin_folder);
+ /// Load data from a binary file, return <images, labels> pair
+ /// suppose the data will be loaded file by file.
+ /// flag: kTrain or kTest
+ /// file: binary file which stores the images
+ /// read_size: number of images to be loaded
+ /// offset: offset in the file
+ /// n_read: number of images which are read
+ size_t LoadData(int flag, string file, size_t read_size, Tensor *x, Tensor *y,
+ size_t *n_read, int nthreads);
+ /// A wrapper method to spawn a thread to execute LoadData() method.
+ std::thread AsyncLoadData(int flag, string file, size_t read_size, Tensor *x,
+ Tensor *y, size_t *n_read, int nthreads);
+
+ void DecodeTransform(int flag, int thid, int nthreads,
+ vector<string *> images, Tensor *x, Tensor *y);
+ /// A wrapper method to spawn a thread to execute Decodetransform() method.
+ std::thread AsyncDecodeTransform(int flag, int thid, int nthreads,
+ vector<string *> images, Tensor *x,
+ Tensor *y);
+
+ /// Read mean from path
+ void ReadMean(string path);
+
+ protected:
+ /// Read one image at path, resize the image
+ Tensor ReadImage(string path);
+ /// Write buff to the file in kCreate/kAppend mode
+ void Write(string outfile, singa::io::Mode mode);
+ void WriteMean(Tensor &mean, string path);
+
+ private:
+ /// size for resizing
+ const size_t kImageSize = 256;
+ const size_t kImageNBytes = 3 * kImageSize * kImageSize;
+ /// size for cropping
+ const size_t kCropSize = 227;
+ Tensor mean;
+ string last_read_file = "";
+
+ JPGEncoder *encoder = nullptr;
+ JPGDecoder *decoder = nullptr;
+ ImageTransformer *transformer = nullptr;
+ BinFileReader *reader = nullptr;
+ BinFileWriter *writer = nullptr;
+};
+
+ILSVRC::ILSVRC() {
+ EncoderConf en_conf;
+ en_conf.set_image_dim_order("CHW");
+ encoder = new JPGEncoder();
+ encoder->Setup(en_conf);
+
+ DecoderConf de_conf;
+ de_conf.set_image_dim_order("CHW");
+ decoder = new JPGDecoder();
+ decoder->Setup(de_conf);
+
+ TransformerConf trans_conf;
+ trans_conf.add_crop_shape(kCropSize);
+ trans_conf.add_crop_shape(kCropSize);
+ trans_conf.set_image_dim_order("CHW");
+ trans_conf.set_horizontal_mirror(true);
+ transformer = new ImageTransformer();
+ transformer->Setup(trans_conf);
+}
+
+Tensor ILSVRC::ReadImage(string path) {
+ cv::Mat mat = cv::imread(path, CV_LOAD_IMAGE_COLOR);
+ CHECK(mat.data != NULL) << "OpenCV load image fail: " << path;
+ cv::Size size(kImageSize, kImageSize);
+ cv::Mat resized;
+ cv::resize(mat, resized, size);
+ CHECK_EQ((size_t)resized.size().height, kImageSize);
+ CHECK_EQ((size_t)resized.size().width, kImageSize);
+ // dimension_order: CHW
+ Shape shape{(size_t)resized.channels(), (size_t)resized.rows,
+ (size_t)resized.cols};
+ Tensor image(shape, singa::kUChar);
+ unsigned char *data = new unsigned char[kImageNBytes];
+ for (int i = 0; i < resized.rows; i++)
+ for (int j = 0; j < resized.cols; j++)
+ for (int k = 0; k < resized.channels(); k++)
+ data[k * kImageSize * kImageSize + i * kImageSize + j] =
+ resized.at<cv::Vec3b>(i, j)[k];
+ image.CopyDataFromHostPtr<unsigned char>(data, kImageNBytes);
+ delete[] data;
+
+ return image;
+}
+
+void ILSVRC::WriteMean(Tensor &mean, string path) {
+ Tensor mean_lb(Shape{1}, kInt);
+ std::vector<Tensor> input;
+ input.push_back(mean);
+ input.push_back(mean_lb);
+ BinFileWriter bfwriter;
+ bfwriter.Open(path, kCreate);
+ bfwriter.Write(path, encoder->Encode(input));
+ bfwriter.Flush();
+ bfwriter.Close();
+}
+
+void ILSVRC::CreateTrainData(string image_list, string input_folder,
+ string output_folder, size_t file_size = 12800) {
+ std::vector<std::pair<string, int>> file_list;
+ size_t *sum = new size_t[kImageNBytes];
+ for (size_t i = 0; i < kImageNBytes; i++) sum[i] = 0u;
+ string image_file_name;
+ int label;
+ string outfile;
+ std::ifstream image_list_file(image_list.c_str(), std::ios::in);
+ while (image_list_file >> image_file_name >> label)
+ file_list.push_back(std::make_pair(image_file_name, label));
+ LOG(INFO) << "Data Shuffling";
+ std::shuffle(file_list.begin(), file_list.end(),
+ std::default_random_engine());
+ LOG(INFO) << "Total number of training images is " << file_list.size();
+ size_t num_train_images = file_list.size();
+ if (file_size == 0) file_size = num_train_images;
+ for (size_t imageid = 0; imageid < num_train_images; imageid++) {
+ string path = input_folder + "/" + file_list[imageid].first;
+ Tensor image = ReadImage(path);
+ auto image_data = image.data<unsigned char>();
+ for (size_t i = 0; i < kImageNBytes; i++)
+ sum[i] += static_cast<size_t>(image_data[i]);
+ label = file_list[imageid].second;
+ Tensor lb(Shape{1}, kInt);
+ lb.CopyDataFromHostPtr<int>(&label, 1);
+ std::vector<Tensor> input;
+ input.push_back(image);
+ input.push_back(lb);
+ string encoded_str = encoder->Encode(input);
+ if (writer == nullptr) {
+ writer = new BinFileWriter();
+ outfile = output_folder + "/train" +
+ std::to_string(imageid / file_size + 1) + ".bin";
+ writer->Open(outfile, kCreate);
+ }
+ writer->Write(path, encoded_str);
+ if ((imageid + 1) % file_size == 0) {
+ writer->Flush();
+ writer->Close();
+ LOG(INFO) << "Write " << file_size << " images into " << outfile;
+ delete writer;
+ writer = nullptr;
+ }
+ }
+ if (writer != nullptr) {
+ writer->Flush();
+ writer->Close();
+ LOG(INFO) << "Write " << num_train_images % file_size << " images into "
+ << outfile;
+ delete writer;
+ writer = nullptr;
+ }
+ size_t num_file =
+ num_train_images / file_size + ((num_train_images % file_size) ? 1 : 0);
+ LOG(INFO) << "Write " << num_train_images << " images into " << num_file
+ << " binary files";
+ Tensor mean = Tensor(Shape{3, kImageSize, kImageSize}, kUChar);
+ unsigned char *mean_data = new unsigned char[kImageNBytes];
+ for (size_t i = 0; i < kImageNBytes; i++)
+ mean_data[i] = static_cast<unsigned char>(sum[i] / num_train_images);
+ mean.CopyDataFromHostPtr<unsigned char>(mean_data, kImageNBytes);
+ string mean_path = output_folder + "/mean.bin";
+ WriteMean(mean, mean_path);
+ delete[] mean_data;
+ delete[] sum;
+}
+
+void ILSVRC::CreateTestData(string image_list, string input_folder,
+ string output_folder) {
+ std::vector<std::pair<string, int>> file_list;
+ string image_file_name;
+ string outfile = output_folder + "/test.bin";
+ int label;
+ std::ifstream image_list_file(image_list.c_str(), std::ios::in);
+ while (image_list_file >> image_file_name >> label)
+ file_list.push_back(std::make_pair(image_file_name, label));
+ LOG(INFO) << "Total number of test images is " << file_list.size();
+ size_t num_test_images = file_list.size();
+ for (size_t imageid = 0; imageid < num_test_images; imageid++) {
+ string path = input_folder + "/" + file_list[imageid].first;
+ Tensor image = ReadImage(path);
+ label = file_list[imageid].second;
+ Tensor lb(Shape{1}, singa::kInt);
+ lb.CopyDataFromHostPtr<int>(&label, 1);
+ std::vector<Tensor> input;
+ input.push_back(image);
+ input.push_back(lb);
+ string encoded_str = encoder->Encode(input);
+ if (writer == nullptr) {
+ writer = new BinFileWriter();
+ writer->Open(outfile, kCreate);
+ }
+ writer->Write(path, encoded_str);
+ }
+ if (writer != nullptr) {
+ writer->Flush();
+ writer->Close();
+ delete writer;
+ writer = nullptr;
+ }
+ LOG(INFO) << "Write " << num_test_images << " images into " << outfile;
+}
+
+void ILSVRC::ReadMean(string path) {
+ BinFileReader bfreader;
+ string key, value;
+ bfreader.Open(path);
+ bfreader.Read(&key, &value);
+ auto ret = decoder->Decode(value);
+ bfreader.Close();
+ mean = ret[0];
+}
+
+std::thread ILSVRC::AsyncLoadData(int flag, string file, size_t read_size,
+ Tensor *x, Tensor *y, size_t *n_read,
+ int nthreads) {
+ return std::thread(
+ [=]() { LoadData(flag, file, read_size, x, y, n_read, nthreads); });
+}
+
+size_t ILSVRC::LoadData(int flag, string file, size_t read_size, Tensor *x,
+ Tensor *y, size_t *n_read, int nthreads) {
+ x->Reshape(Shape{read_size, 3, kCropSize, kCropSize});
+ y->AsType(kInt);
+ y->Reshape(Shape{read_size});
+ if (file != last_read_file) {
+ if (reader != nullptr) {
+ reader->Close();
+ delete reader;
+ reader = nullptr;
+ }
+ reader = new BinFileReader();
+ reader->Open(file, 100 << 20);
+ last_read_file = file;
+ } else if (reader == nullptr) {
+ reader = new BinFileReader();
+ reader->Open(file, 100 << 20);
+ }
+ vector<string *> images;
+ for (size_t i = 0; i < read_size; i++) {
+ string image_path;
+ string *image = new string();
+ bool ret = reader->Read(&image_path, image);
+ if (ret == false) {
+ reader->Close();
+ delete reader;
+ reader = nullptr;
+ break;
+ }
+ images.push_back(image);
+ }
+ int nimg = images.size();
+ *n_read = nimg;
+
+ vector<std::thread> threads;
+ for (int i = 1; i < nthreads; i++) {
+ threads.push_back(AsyncDecodeTransform(flag, i, nthreads, images, x, y));
+ }
+ DecodeTransform(flag, 0, nthreads, images, x, y);
+ for (size_t i = 0; i < threads.size(); i++) threads[i].join();
+ for (int k = 0; k < nimg; k++) delete images.at(k);
+ return nimg;
+}
+
+std::thread ILSVRC::AsyncDecodeTransform(int flag, int thid, int nthreads,
+ vector<string *> images, Tensor *x,
+ Tensor *y) {
+ return std::thread(
+ [=]() { DecodeTransform(flag, thid, nthreads, images, x, y); });
+}
+
+void ILSVRC::DecodeTransform(int flag, int thid, int nthreads,
+ vector<string *> images, Tensor *x, Tensor *y) {
+ int nimg = images.size();
+ int start = nimg / nthreads * thid;
+ int end = start + nimg / nthreads;
+ for (int k = start; k < end; k++) {
+ std::vector<Tensor> pair = decoder->Decode(*images.at(k));
+ auto tmp_image = pair[0] - mean;
+ Tensor aug_image = transformer->Apply(flag, tmp_image);
+ CopyDataToFrom(x, aug_image, aug_image.Size(), k * aug_image.Size());
+ CopyDataToFrom(y, pair[1], 1, k);
+ }
+ if (thid == 0) {
+ for (int k = nimg / nthreads * nthreads; k < nimg; k++) {
+ std::vector<Tensor> pair = decoder->Decode(*images.at(k));
+ auto tmp_image = pair[0] - mean;
+ Tensor aug_image = transformer->Apply(flag, tmp_image);
+ CopyDataToFrom(x, aug_image, aug_image.Size(), k * aug_image.Size());
+ CopyDataToFrom(y, pair[1], 1, k);
+ }
+ }
+}
+} // namespace singa
+
+#endif // SINGA_EXAMPLES_IMAGENET_ILSVRC12_H_
+#endif // USE_OPENCV
http://git-wip-us.apache.org/repos/asf/incubator-singa/blob/d190fa89/examples/imagenet/alexnet/run.sh
----------------------------------------------------------------------
diff --git a/examples/imagenet/alexnet/run.sh b/examples/imagenet/alexnet/run.sh
new file mode 100755
index 0000000..6277d23
--- /dev/null
+++ b/examples/imagenet/alexnet/run.sh
@@ -0,0 +1,21 @@
+#!/usr/bin/env sh
+#
+# Licensed to the Apache Software Foundation (ASF) under one
+# or more contributor license agreements. See the NOTICE file
+# distributed with this work for additional information
+# regarding copyright ownership. The ASF licenses this file
+# to you under the Apache License, Version 2.0 (the
+# "License"); you may not use this file except in compliance
+# with the License. You may obtain a copy of the License at
+#
+# http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+#
+
+../../build/bin/imagenet -epoch 90 -lr 0.01 -batchsize 256 -filesize 1280 -ntrain 1281167 -ntest 50000 \
+ -data "imagenet_data" -pfreq 100 -nthreads 12
http://git-wip-us.apache.org/repos/asf/incubator-singa/blob/d190fa89/examples/imagenet/create_data.sh
----------------------------------------------------------------------
diff --git a/examples/imagenet/create_data.sh b/examples/imagenet/create_data.sh
deleted file mode 100755
index 4c2c034..0000000
--- a/examples/imagenet/create_data.sh
+++ /dev/null
@@ -1,21 +0,0 @@
-#!/usr/bin/env sh
-#
-# Licensed to the Apache Software Foundation (ASF) under one
-# or more contributor license agreements. See the NOTICE file
-# distributed with this work for additional information
-# regarding copyright ownership. The ASF licenses this file
-# to you under the Apache License, Version 2.0 (the
-# "License"); you may not use this file except in compliance
-# with the License. You may obtain a copy of the License at
-#
-# http://www.apache.org/licenses/LICENSE-2.0
-#
-# Unless required by applicable law or agreed to in writing, software
-# distributed under the License is distributed on an "AS IS" BASIS,
-# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-# See the License for the specific language governing permissions and
-# limitations under the License.
-#
-
-../../build/bin/createdata -trainlist "imagenet/label/train.txt" -trainfolder "imagenet/ILSVRC2012_img_train" \
- -testlist "imagenet/label/val.txt" -testfolder "imagenet/ILSVRC2012_img_val" -outdata "imagenet_data" -filesize 1280
http://git-wip-us.apache.org/repos/asf/incubator-singa/blob/d190fa89/examples/imagenet/googlenet/README.md
----------------------------------------------------------------------
diff --git a/examples/imagenet/googlenet/README.md b/examples/imagenet/googlenet/README.md
new file mode 100644
index 0000000..e597fc6
--- /dev/null
+++ b/examples/imagenet/googlenet/README.md
@@ -0,0 +1,66 @@
+---
+name: GoogleNet on ImageNet
+SINGA version: 1.0.1
+SINGA commit: 8c990f7da2de220e8a012c6a8ecc897dc7532744
+parameter_url: https://s3-ap-southeast-1.amazonaws.com/dlfile/bvlc_googlenet.tar.gz
+parameter_sha1: 0a88e8948b1abca3badfd8d090d6be03f8d7655d
+license: unrestricted https://github.com/BVLC/caffe/tree/master/models/bvlc_googlenet
+---
+
+# Image Classification using GoogleNet
+
+
+In this example, we convert GoogleNet trained on Caffe to SINGA for image classification.
+
+## Instructions
+
+* Download the parameter checkpoint file into this folder
+
+ $ wget https://s3-ap-southeast-1.amazonaws.com/dlfile/bvlc_googlenet.tar.gz
+ $ tar xvf bvlc_googlenet.tar.gz
+
+* Run the program
+
+ # use cpu
+ $ python serve.py -C &
+ # use gpu
+ $ python serve.py &
+
+* Submit images for classification
+
+ $ curl -i -F image=@image1.jpg http://localhost:9999/api
+ $ curl -i -F image=@image2.jpg http://localhost:9999/api
+ $ curl -i -F image=@image3.jpg http://localhost:9999/api
+
+image1.jpg, image2.jpg and image3.jpg should be downloaded before executing the above commands.
+
+## Details
+
+We first extract the parameter values from [Caffe's checkpoint file](http://dl.caffe.berkeleyvision.org/bvlc_googlenet.caffemodel) into a pickle version
+After downloading the checkpoint file into `caffe_root/python` folder, run the following script
+
+ # to be executed within caffe_root/python folder
+ import caffe
+ import numpy as np
+ import cPickle as pickle
+
+ model_def = '../models/bvlc_googlenet/deploy.prototxt'
+ weight = 'bvlc_googlenet.caffemodel' # must be downloaded at first
+ net = caffe.Net(model_def, weight, caffe.TEST)
+
+ params = {}
+ for layer_name in net.params.keys():
+ weights=np.copy(net.params[layer_name][0].data)
+ bias=np.copy(net.params[layer_name][1].data)
+ params[layer_name+'_weight']=weights
+ params[layer_name+'_bias']=bias
+ print layer_name, weights.shape, bias.shape
+
+ with open('bvlc_googlenet.pickle', 'wb') as fd:
+ pickle.dump(params, fd)
+
+Then we construct the GoogleNet using SINGA's FeedForwardNet structure.
+Note that we added a EndPadding layer to resolve the issue from discrepancy
+of the rounding strategy of the pooling layer between Caffe (ceil) and cuDNN (floor).
+Only the MaxPooling layers outside inception blocks have this problem.
+Refer to [this](http://joelouismarino.github.io/blog_posts/blog_googlenet_keras.html) for more detials.
http://git-wip-us.apache.org/repos/asf/incubator-singa/blob/d190fa89/examples/imagenet/googlenet/serve.py
----------------------------------------------------------------------
diff --git a/examples/imagenet/googlenet/serve.py b/examples/imagenet/googlenet/serve.py
new file mode 100644
index 0000000..57e005d
--- /dev/null
+++ b/examples/imagenet/googlenet/serve.py
@@ -0,0 +1,240 @@
+# Licensed to the Apache Software Foundation (ASF) under one
+# or more contributor license agreements. See the NOTICE file
+# distributed with this work for additional information
+# regarding copyright ownership. The ASF licenses this file
+# to you under the Apache License, Version 2.0 (the
+# "License"); you may not use this file except in compliance
+# with the License. You may obtain a copy of the License at
+#
+# http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+# =============================================================================
+''' This model is created following Caffe implementation of GoogleNet
+https://github.com/BVLC/caffe/blob/master/models/bvlc_googlenet/
+'''
+import os
+import sys
+import time
+import numpy as np
+import threading
+import traceback
+from argparse import ArgumentParser
+from scipy.misc import imread, imresize
+import numpy as np
+
+from singa.layer import Layer, Conv2D, Activation, MaxPooling2D, AvgPooling2D,\
+ Split, Concat, LRN, Dropout, Flatten, Dense
+from singa import layer
+from singa import net as ffnet
+from singa import device
+from singa import tensor
+from rafiki.agent import Agent, MsgType
+
+
+def add_to_tuple(x):
+ '''return a tuple with the last two values incremented by 1'''
+ if len(x) == 3:
+ return (x[0], x[1] + 1, x[2] + 1)
+ else:
+ return (x[0], x[1], x[2] + 1, x[3] + 1)
+
+class EndPadding(Layer):
+ '''Pad the end of the spatial axis with 1 row and 1 column of zeros.
+
+ This layer is inserted before the pooling layers outside the inception
+ block. We need such a layer because Caffe (ceil) and cuDNN (floor) have
+ different rounding strategies for the pooling layer.
+ http://joelouismarino.github.io/blog_posts/blog_googlenet_keras.html
+ '''
+ def __init__(self, name, input_sample_shape=None):
+ super(EndPadding, self).__init__(name)
+ if input_sample_shape is not None:
+ assert len(input_sample_shape) == 3, 'input must has 4 dims'
+ self.output_sample_shape = add_to_tuple(input_sample_shape)
+
+ def get_output_sample_shape(self):
+ return self.output_sample_shape
+
+ def setup(self, input_sample_shape):
+ assert len(input_sample_shape) == 3, 'input must has 4 dims'
+ self.output_sample_shape = add_to_tuple(input_sample_shape)
+ self.has_setup = True
+
+ def forward(self, flag, x):
+ '''pad zeros'''
+ tmp = tensor.to_numpy(x)
+ shape = add_to_tuple(x.shape)
+ ret = np.zeros(shape)
+ ret[:,:,:-1, :-1] = tmp
+ y = tensor.from_numpy(ret)
+ y.to_device(x.device)
+ return y
+
+ def backward(self, falg, dy):
+ '''remove paddings'''
+ tmp = tensor.to_numpy(dy)
+ dx = tensor.from_numpy(tmp[:,:,:-1,:-1])
+ dx.to_device(dy.device)
+ return dx, []
+
+# b_specs = {'init': 'constant', 'value': 0, 'lr_mult': 2, 'decay_mult': 0}
+
+def conv(net, src, name, num, kernel, stride=1, pad=0, suffix=''):
+ net.add(Conv2D('%s/%s' % (name, suffix), num, kernel, stride, pad=pad), src)
+ return net.add(Activation('%s/relue_%s' % (name, suffix)))
+
+def pool(net, src, name, kernel, stride):
+ net.add(EndPadding('%s/pad' % name), src)
+ ret = net.add(MaxPooling2D('%s' % name, 3, 2, pad=0))
+ return ret
+
+def inception(net, src, name, nb1x1, nb3x3r, nb3x3, nb5x5r, nb5x5, nbproj):
+ split = net.add(Split('%s/split' % name, 4), src)
+
+ c1x1 = conv(net, split, name, nb1x1, 1, suffix='1x1')
+
+ c3x3r = conv(net, split, name, nb3x3r, 1, suffix='3x3_reduce')
+ c3x3 = conv(net, c3x3r, name, nb3x3, 3, pad=1, suffix='3x3')
+
+ c5x5r = conv(net, split, name, nb5x5r, 1, suffix='5x5_reduce')
+ c5x5 = conv(net, c5x5r, name, nb5x5, 5, pad=2, suffix='5x5')
+
+ pool = net.add(MaxPooling2D('%s/pool' % name, 3, 1, pad=1), split)
+ cproj = conv(net, pool, name, nbproj, 1, suffix='pool_proj')
+
+ return net.add(Concat('%s/output' % name, 1), [c1x1, c3x3, c5x5, cproj])
+
+
+def create_net(shape, weight_path='bvlc_googlenet.pickle'):
+ net = ffnet.FeedForwardNet()
+ net.add(Conv2D('conv1/7x7_s2', 64, 7, 2, pad=3, input_sample_shape=shape))
+ c1 = net.add(Activation('conv1/relu_7x7'))
+ pool1 = pool(net, c1, 'pool1/3x3_s2', 3, 2)
+ norm1 = net.add(LRN('pool1/norm1', 5, 0.0001, 0.75))
+ c3x3r = conv(net, norm1 , 'conv2', 64, 1, suffix='3x3_reduce')
+ c3x3 = conv(net, c3x3r, 'conv2', 192, 3, pad=1, suffix='3x3')
+ norm2 = net.add(LRN('conv2/norm2', 5, 0.0001, 0.75))
+ pool2 = pool(net, norm2, 'pool2/3x3_s2', 3, 2)
+
+ i3a=inception(net, pool2, 'inception_3a', 64, 96, 128, 16, 32, 32)
+ i3b=inception(net, i3a, 'inception_3b', 128, 128, 192, 32, 96, 64)
+ pool3=pool(net, i3b, 'pool3/3x3_s2', 3, 2)
+ i4a=inception(net, pool3, 'inception_4a', 192, 96, 208, 16, 48, 64)
+ i4b=inception(net, i4a, 'inception_4b', 160, 112, 224, 24, 64, 64)
+ i4c=inception(net, i4b, 'inception_4c', 128, 128, 256, 24, 64, 64)
+ i4d=inception(net, i4c, 'inception_4d', 112, 144, 288, 32, 64, 64)
+ i4e=inception(net, i4d, 'inception_4e', 256, 160, 320, 32, 128, 128)
+ pool4=pool(net, i4e,'pool4/3x3_s2', 3, 2)
+ i5a=inception(net, pool4, 'inception_5a', 256, 160, 320, 32, 128, 128)
+ i5b=inception(net, i5a, 'inception_5b', 384, 192, 384, 48, 128, 128)
+ pool5=net.add(AvgPooling2D('pool5/7x7_s1', 7, 1, pad=0))
+ drop5=net.add(Dropout('drop', 0.4))
+ flat=net.add(Flatten('flat'))
+ dense=net.add(Dense('loss3/classifier', 1000))
+ # prob=net.add(Softmax('softmax'))
+
+ net.load(weight_path, use_pickle=True)
+ print 'total num of params %d' % (len(net.param_names()))
+ # SINGA and Caffe have different layout for the weight matrix of the dense
+ # layer
+ for key, val in zip(net.param_names(), net.param_values()):
+ # print key
+ if key == 'loss3/classifier_weight':
+ tmp = tensor.to_numpy(val)
+ tmp = tmp.reshape(tmp.shape[::-1])
+ val.copy_from_numpy(np.transpose(tmp))
+ return net
+
+
+def serve(agent, use_cpu, parameter_file, topk=5):
+ if use_cpu:
+ print 'running with cpu'
+ dev = device.get_default_device()
+ layer.engine = 'singacpp'
+ else:
+ print "runing with gpu"
+ dev = device.create_cuda_gpu()
+ agent = agent
+
+ print 'Start intialization............'
+ net = create_net((3, 224, 224), parameter_file)
+ net.to_device(dev)
+ print 'End intialization............'
+
+ labels = np.loadtxt('synset_words.txt', str, delimiter='\t ')
+ while True:
+ key, val = agent.pull()
+ if key is None:
+ time.sleep(0.1)
+ continue
+ msg_type = MsgType.parse(key)
+ if msg_type.is_request():
+ try:
+ response = ""
+ img = imread(val['image'], mode='RGB').astype(np.float32)
+ height,width = img.shape[:2]
+ img[:, :, 0] -= 123.68
+ img[:, :, 1] -= 116.779
+ img[:, :, 2] -= 103.939
+ img[:,:,[0,1,2]] = img[:,:,[2,1,0]]
+ img = img.transpose((2, 0, 1))
+ img = img[:,(height-224)//2:(height+224)//2,(width-224)//2:(width+224)//2]
+ images = np.expand_dims(img, axis=0)
+
+ x = tensor.from_numpy(images.astype(np.float32))
+ x.to_device(dev)
+ y = net.predict(x)
+ prob = np.average(tensor.to_numpy(y), 0)
+ # sort and reverse
+ idx = np.argsort(-prob)[0:topk]
+ for i in idx:
+ response += "%s:%s<br/>" % (labels[i], prob[i])
+ except:
+ traceback.print_exc()
+ response = "Sorry, system error during prediction."
+ agent.push(MsgType.kResponse, response)
+ elif MsgType.kCommandStop.equal(msg_type):
+ print 'get stop command'
+ agent.push(MsgType.kStatus, "success")
+ break
+ else:
+ print 'get unsupported message %s' % str(msg_type)
+ agent.push(MsgType.kStatus, "Unknown command")
+ break
+ # while loop
+ print "server stop"
+
+
+def main():
+ try:
+ # Setup argument parser
+ parser = ArgumentParser(description="GooleNet for image classification")
+ parser.add_argument("-p", "--port", default=9999, help="listen port")
+ parser.add_argument("-C", "--use_cpu", action="store_true")
+ parser.add_argument("--parameter_file", default="bvlc_googlenet.pickle",
+ help="relative path")
+
+ # Process arguments
+ args = parser.parse_args()
+ port = args.port
+
+ # start to train
+ agent = Agent(port)
+ serve(agent, args.use_cpu, args.parameter_file)
+ agent.stop()
+
+ except SystemExit:
+ return
+ except:
+ traceback.print_exc()
+ sys.stderr.write(" for help use --help \n\n")
+ return 2
+
+
+if __name__ == '__main__':
+ main()
http://git-wip-us.apache.org/repos/asf/incubator-singa/blob/d190fa89/examples/imagenet/ilsvrc12.cc
----------------------------------------------------------------------
diff --git a/examples/imagenet/ilsvrc12.cc b/examples/imagenet/ilsvrc12.cc
deleted file mode 100644
index c9e6d2f..0000000
--- a/examples/imagenet/ilsvrc12.cc
+++ /dev/null
@@ -1,70 +0,0 @@
-/************************************************************
-*
-* Licensed to the Apache Software Foundation (ASF) under one
-* or more contributor license agreements. See the NOTICE file
-* distributed with this work for additional information
-* regarding copyright ownership. The ASF licenses this file
-* to you under the Apache License, Version 2.0 (the
-* "License"); you may not use this file except in compliance
-* with the License. You may obtain a copy of the License at
-*
-* http://www.apache.org/licenses/LICENSE-2.0
-*
-* Unless required by applicable law or agreed to in writing,
-* software distributed under the License is distributed on an
-* "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY
-* KIND, either express or implied. See the License for the
-* specific language governing permissions and limitations
-* under the License.
-*
-*************************************************************/
-#include "singa/singa_config.h"
-#ifdef USE_OPENCV
-#include "ilsvrc12.h"
-#include "singa/utils/channel.h"
-#include "singa/utils/string.h"
-int main(int argc, char **argv) {
- int pos = singa::ArgPos(argc, argv, "-h");
- if (pos != -1) {
- std::cout << "Usage:\n"
- << "\t-trainlist <file>: the file of training list;\n"
- << "\t-trainfolder <folder>: the folder of training images;\n"
- << "\t-testlist <file>: the file of test list;\n"
- << "\t-testfolder <floder>: the folder of test images;\n"
- << "\t-outdata <folder>: the folder to save output files;\n"
- << "\t-filesize <int>: number of training images that stores in "
- "each binary file.\n";
- return 0;
- }
- pos = singa::ArgPos(argc, argv, "-trainlist");
- string train_image_list = "imagenet/label/train.txt";
- if (pos != -1) train_image_list = argv[pos + 1];
-
- pos = singa::ArgPos(argc, argv, "-trainfolder");
- string train_image_folder = "imagenet/ILSVRC2012_img_train";
- if (pos != -1) train_image_folder = argv[pos + 1];
-
- pos = singa::ArgPos(argc, argv, "-testlist");
- string test_image_list = "imagenet/label/val.txt";
- if (pos != -1) test_image_list = argv[pos + 1];
-
- pos = singa::ArgPos(argc, argv, "-testfolder");
- string test_image_folder = "imagenet/ILSVRC2012_img_val";
- if (pos != -1) test_image_folder = argv[pos + 1];
-
- pos = singa::ArgPos(argc, argv, "-outdata");
- string bin_folder = "imagenet_data";
- if (pos != -1) bin_folder = argv[pos + 1];
-
- pos = singa::ArgPos(argc, argv, "-filesize");
- size_t train_file_size = 1280;
- if (pos != -1) train_file_size = atoi(argv[pos + 1]);
- singa::ILSVRC data;
- LOG(INFO) << "Creating training and test data...";
- data.CreateTrainData(train_image_list, train_image_folder, bin_folder,
- train_file_size);
- data.CreateTestData(test_image_list, test_image_folder, bin_folder);
- LOG(INFO) << "Data created!";
- return 0;
-}
-#endif // USE_OPENCV