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Lightweight, Portable, Flexible Distributed/Mobile Deep Learning with Dynamic, Mutation-aware Dataflow Dep Scheduler; for Python, R, Julia, Scala, Go, Javascript and more

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Add back cpp-package (#20131) This updates and adds back the cpp-package removed in https://github.com/apache/incubator-mxnet/commit/97d4ba5a133f93ff6075dcde3ef842b23d498a12
2021-05-24 13:44:39 -07:00
/*
* 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 <map>
#include <string>
#include <fstream>
#include <vector>
#include "utils.h"
#include "mxnet-cpp/MxNetCpp.h"
using namespace mxnet::cpp;
Symbol ConvFactoryBN(Symbol data, int num_filter,
Shape kernel, Shape stride, Shape pad,
const std::string & name,
const std::string & suffix = "") {
Symbol conv_w("conv_" + name + suffix + "_w"), conv_b("conv_" + name + suffix + "_b");
Symbol conv = Convolution("conv_" + name + suffix, data,
conv_w, conv_b, kernel,
num_filter, stride, Shape(1, 1), pad);
std::string name_suffix = name + suffix;
Symbol gamma(name_suffix + "_gamma");
Symbol beta(name_suffix + "_beta");
Symbol mmean(name_suffix + "_mmean");
Symbol mvar(name_suffix + "_mvar");
Symbol bn = BatchNorm("bn_" + name + suffix, conv, gamma, beta, mmean, mvar);
return Activation("relu_" + name + suffix, bn, "relu");
}
Symbol InceptionFactoryA(Symbol data, int num_1x1, int num_3x3red,
int num_3x3, int num_d3x3red, int num_d3x3,
PoolingPoolType pool, int proj,
const std::string & name) {
Symbol c1x1 = ConvFactoryBN(data, num_1x1, Shape(1, 1), Shape(1, 1),
Shape(0, 0), name + "1x1");
Symbol c3x3r = ConvFactoryBN(data, num_3x3red, Shape(1, 1), Shape(1, 1),
Shape(0, 0), name + "_3x3r");
Symbol c3x3 = ConvFactoryBN(c3x3r, num_3x3, Shape(3, 3), Shape(1, 1),
Shape(1, 1), name + "_3x3");
Symbol cd3x3r = ConvFactoryBN(data, num_d3x3red, Shape(1, 1), Shape(1, 1),
Shape(0, 0), name + "_double_3x3", "_reduce");
Symbol cd3x3 = ConvFactoryBN(cd3x3r, num_d3x3, Shape(3, 3), Shape(1, 1),
Shape(1, 1), name + "_double_3x3_0");
cd3x3 = ConvFactoryBN(data = cd3x3, num_d3x3, Shape(3, 3), Shape(1, 1),
Shape(1, 1), name + "_double_3x3_1");
Symbol pooling = Pooling(name + "_pool", data,
Shape(3, 3), pool, false, false,
PoolingPoolingConvention::kValid,
Shape(1, 1), Shape(1, 1));
Symbol cproj = ConvFactoryBN(pooling, proj, Shape(1, 1), Shape(1, 1),
Shape(0, 0), name + "_proj");
std::vector<Symbol> lst;
lst.push_back(c1x1);
lst.push_back(c3x3);
lst.push_back(cd3x3);
lst.push_back(cproj);
return Concat("ch_concat_" + name + "_chconcat", lst, lst.size());
}
Symbol InceptionFactoryB(Symbol data, int num_3x3red, int num_3x3,
int num_d3x3red, int num_d3x3, const std::string & name) {
Symbol c3x3r = ConvFactoryBN(data, num_3x3red, Shape(1, 1),
Shape(1, 1), Shape(0, 0),
name + "_3x3", "_reduce");
Symbol c3x3 = ConvFactoryBN(c3x3r, num_3x3, Shape(3, 3), Shape(2, 2),
Shape(1, 1), name + "_3x3");
Symbol cd3x3r = ConvFactoryBN(data, num_d3x3red, Shape(1, 1), Shape(1, 1),
Shape(0, 0), name + "_double_3x3", "_reduce");
Symbol cd3x3 = ConvFactoryBN(cd3x3r, num_d3x3, Shape(3, 3), Shape(1, 1),
Shape(1, 1), name + "_double_3x3_0");
cd3x3 = ConvFactoryBN(cd3x3, num_d3x3, Shape(3, 3), Shape(2, 2),
Shape(1, 1), name + "_double_3x3_1");
Symbol pooling = Pooling("max_pool_" + name + "_pool", data,
Shape(3, 3), PoolingPoolType::kMax,
false, false, PoolingPoolingConvention::kValid,
Shape(2, 2), Shape(1, 1));
std::vector<Symbol> lst;
lst.push_back(c3x3);
lst.push_back(cd3x3);
lst.push_back(pooling);
return Concat("ch_concat_" + name + "_chconcat", lst, lst.size());
}
Symbol InceptionSymbol(int num_classes) {
// data and label
Symbol data = Symbol::Variable("data");
Symbol data_label = Symbol::Variable("data_label");
// stage 1
Symbol conv1 = ConvFactoryBN(data, 64, Shape(7, 7), Shape(2, 2), Shape(3, 3), "conv1");
Symbol pool1 = Pooling("pool1", conv1, Shape(3, 3), PoolingPoolType::kMax,
false, false, PoolingPoolingConvention::kValid, Shape(2, 2));
// stage 2
Symbol conv2red = ConvFactoryBN(pool1, 64, Shape(1, 1), Shape(1, 1), Shape(0, 0), "conv2red");
Symbol conv2 = ConvFactoryBN(conv2red, 192, Shape(3, 3), Shape(1, 1), Shape(1, 1), "conv2");
Symbol pool2 = Pooling("pool2", conv2, Shape(3, 3), PoolingPoolType::kMax,
false, false, PoolingPoolingConvention::kValid, Shape(2, 2));
// stage 3
Symbol in3a = InceptionFactoryA(pool2, 64, 64, 64, 64, 96, PoolingPoolType::kAvg, 32, "3a");
Symbol in3b = InceptionFactoryA(in3a, 64, 64, 96, 64, 96, PoolingPoolType::kAvg, 64, "3b");
Symbol in3c = InceptionFactoryB(in3b, 128, 160, 64, 96, "3c");
// stage 4
Symbol in4a = InceptionFactoryA(in3c, 224, 64, 96, 96, 128, PoolingPoolType::kAvg, 128, "4a");
Symbol in4b = InceptionFactoryA(in4a, 192, 96, 128, 96, 128, PoolingPoolType::kAvg, 128, "4b");
Symbol in4c = InceptionFactoryA(in4b, 160, 128, 160, 128, 160, PoolingPoolType::kAvg, 128, "4c");
Symbol in4d = InceptionFactoryA(in4c, 96, 128, 192, 160, 192, PoolingPoolType::kAvg, 128, "4d");
Symbol in4e = InceptionFactoryB(in4d, 128, 192, 192, 256, "4e");
// stage 5
Symbol in5a = InceptionFactoryA(in4e, 352, 192, 320, 160, 224, PoolingPoolType::kAvg, 128, "5a");
Symbol in5b = InceptionFactoryA(in5a, 352, 192, 320, 192, 224, PoolingPoolType::kMax, 128, "5b");
// average pooling
Symbol avg = Pooling("global_pool", in5b, Shape(7, 7), PoolingPoolType::kAvg);
// classifier
Symbol flatten = Flatten("flatten", avg);
Symbol conv1_w("conv1_w"), conv1_b("conv1_b");
Symbol fc1 = FullyConnected("fc1", flatten, conv1_w, conv1_b, num_classes);
return SoftmaxOutput("softmax", fc1, data_label);
}
NDArray ResizeInput(NDArray data, const Shape new_shape) {
NDArray pic = data.Reshape(Shape(0, 1, 28, 28));
NDArray pic_1channel;
Operator("_contrib_BilinearResize2D")
.SetParam("height", new_shape[2])
.SetParam("width", new_shape[3])
(pic).Invoke(pic_1channel);
NDArray output;
Operator("tile")
.SetParam("reps", Shape(1, 3, 1, 1))
(pic_1channel).Invoke(output);
return output;
}
int main(int argc, char const *argv[]) {
int batch_size = 40;
int max_epoch = argc > 1 ? strtol(argv[1], nullptr, 10) : 100;
float learning_rate = 1e-2;
float weight_decay = 1e-4;
/*context*/
auto ctx = Context::cpu();
int num_gpu;
MXGetGPUCount(&num_gpu);
#if MXNET_USE_CUDA
if (num_gpu > 0) {
ctx = Context::gpu();
}
#endif
TRY
auto inception_bn_net = InceptionSymbol(10);
std::map<std::string, NDArray> args_map;
std::map<std::string, NDArray> aux_map;
const Shape data_shape = Shape(batch_size, 3, 224, 224),
label_shape = Shape(batch_size);
args_map["data"] = NDArray(data_shape, ctx);
args_map["data_label"] = NDArray(label_shape, ctx);
inception_bn_net.InferArgsMap(ctx, &args_map, args_map);
std::vector<std::string> data_files = { "./data/mnist_data/train-images-idx3-ubyte",
"./data/mnist_data/train-labels-idx1-ubyte",
"./data/mnist_data/t10k-images-idx3-ubyte",
"./data/mnist_data/t10k-labels-idx1-ubyte"
};
auto train_iter = MXDataIter("MNISTIter");
if (!setDataIter(&train_iter, "Train", data_files, batch_size)) {
return 1;
}
auto val_iter = MXDataIter("MNISTIter");
if (!setDataIter(&val_iter, "Label", data_files, batch_size)) {
return 1;
}
// initialize parameters
auto initializer = Uniform(0.07);
for (auto& arg : args_map) {
initializer(arg.first, &arg.second);
}
Optimizer* opt = OptimizerRegistry::Find("sgd");
opt->SetParam("momentum", 0.9)
->SetParam("rescale_grad", 1.0 / batch_size)
->SetParam("clip_gradient", 10)
->SetParam("lr", learning_rate)
->SetParam("wd", weight_decay);
auto *exec = inception_bn_net.SimpleBind(ctx, args_map);
auto arg_names = inception_bn_net.ListArguments();
// Create metrics
Accuracy train_acc, val_acc;
for (int iter = 0; iter < max_epoch; ++iter) {
LG << "Epoch: " << iter;
train_iter.Reset();
train_acc.Reset();
while (train_iter.Next()) {
auto data_batch = train_iter.GetDataBatch();
ResizeInput(data_batch.data, data_shape).CopyTo(&args_map["data"]);
data_batch.label.CopyTo(&args_map["data_label"]);
NDArray::WaitAll();
exec->Forward(true);
exec->Backward();
// Update parameters
for (size_t i = 0; i < arg_names.size(); ++i) {
if (arg_names[i] == "data" || arg_names[i] == "data_label") continue;
opt->Update(i, exec->arg_arrays[i], exec->grad_arrays[i]);
}
NDArray::WaitAll();
train_acc.Update(data_batch.label, exec->outputs[0]);
}
val_iter.Reset();
val_acc.Reset();
while (val_iter.Next()) {
auto data_batch = val_iter.GetDataBatch();
ResizeInput(data_batch.data, data_shape).CopyTo(&args_map["data"]);
data_batch.label.CopyTo(&args_map["data_label"]);
NDArray::WaitAll();
exec->Forward(false);
NDArray::WaitAll();
val_acc.Update(data_batch.label, exec->outputs[0]);
}
LG << "Train Accuracy: " << train_acc.Get();
LG << "Validation Accuracy: " << val_acc.Get();
}
delete exec;
delete opt;
MXNotifyShutdown();
CATCH
return 0;
}
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