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#include <cstring>
#include <string>
#include <vector>
#include "boost/scoped_ptr.hpp"
#include "google/protobuf/text_format.h"
#include "gtest/gtest.h"
#include "caffe/blob.hpp"
#include "caffe/common.hpp"
#include "caffe/layer.hpp"
#include "caffe/util/db.hpp"
#include "caffe/util/io.hpp"
#include "caffe/util/upgrade_proto.hpp"
#include "caffe/test/test_caffe_main.hpp"
namespace caffe {
class PaddingLayerUpgradeTest : public ::testing::Test {
protected:
void RunPaddingUpgradeTest(
const string& input_param_string, const string& output_param_string) {
// Test that UpgradeV0PaddingLayers called on the proto specified by
// input_param_string results in the proto specified by
// output_param_string.
NetParameter input_param;
CHECK(google::protobuf::TextFormat::ParseFromString(
input_param_string, &input_param));
NetParameter expected_output_param;
CHECK(google::protobuf::TextFormat::ParseFromString(
output_param_string, &expected_output_param));
NetParameter actual_output_param;
UpgradeV0PaddingLayers(input_param, &actual_output_param);
EXPECT_EQ(expected_output_param.DebugString(),
actual_output_param.DebugString());
// Also test idempotence.
NetParameter double_pad_upgrade_param;
UpgradeV0PaddingLayers(actual_output_param, &double_pad_upgrade_param);
EXPECT_EQ(actual_output_param.DebugString(),
double_pad_upgrade_param.DebugString());
}
};
TEST_F(PaddingLayerUpgradeTest, TestSimple) {
const string& input_proto =
"name: 'CaffeNet' "
"layers { "
" layer { "
" name: 'data' "
" type: 'data' "
" source: '/home/jiayq/Data/ILSVRC12/train-leveldb' "
" meanfile: '/home/jiayq/Data/ILSVRC12/image_mean.binaryproto' "
" batchsize: 256 "
" cropsize: 227 "
" mirror: true "
" } "
" top: 'data' "
" top: 'label' "
"} "
"layers { "
" layer { "
" name: 'pad1' "
" type: 'padding' "
" pad: 2 "
" } "
" bottom: 'data' "
" top: 'pad1' "
"} "
"layers { "
" layer { "
" name: 'conv1' "
" type: 'conv' "
" num_output: 96 "
" kernelsize: 11 "
" stride: 4 "
" weight_filler { "
" type: 'gaussian' "
" std: 0.01 "
" } "
" bias_filler { "
" type: 'constant' "
" value: 0. "
" } "
" blobs_lr: 1. "
" blobs_lr: 2. "
" weight_decay: 1. "
" weight_decay: 0. "
" } "
" bottom: 'pad1' "
" top: 'conv1' "
"} "
"layers { "
" layer { "
" name: 'fc8' "
" type: 'innerproduct' "
" num_output: 1000 "
" weight_filler { "
" type: 'gaussian' "
" std: 0.01 "
" } "
" bias_filler { "
" type: 'constant' "
" value: 0 "
" } "
" blobs_lr: 1. "
" blobs_lr: 2. "
" weight_decay: 1. "
" weight_decay: 0. "
" } "
" bottom: 'conv1' "
" top: 'fc8' "
"} "
"layers { "
" layer { "
" name: 'loss' "
" type: 'softmax_loss' "
" } "
" bottom: 'fc8' "
" bottom: 'label' "
"} ";
const string& expected_output_proto =
"name: 'CaffeNet' "
"layers { "
" layer { "
" name: 'data' "
" type: 'data' "
" source: '/home/jiayq/Data/ILSVRC12/train-leveldb' "
" meanfile: '/home/jiayq/Data/ILSVRC12/image_mean.binaryproto' "
" batchsize: 256 "
" cropsize: 227 "
" mirror: true "
" } "
" top: 'data' "
" top: 'label' "
"} "
"layers { "
" layer { "
" name: 'conv1' "
" type: 'conv' "
" num_output: 96 "
" kernelsize: 11 "
" stride: 4 "
" pad: 2 "
" weight_filler { "
" type: 'gaussian' "
" std: 0.01 "
" } "
" bias_filler { "
" type: 'constant' "
" value: 0. "
" } "
" blobs_lr: 1. "
" blobs_lr: 2. "
" weight_decay: 1. "
" weight_decay: 0. "
" } "
" bottom: 'data' "
" top: 'conv1' "
"} "
"layers { "
" layer { "
" name: 'fc8' "
" type: 'innerproduct' "
" num_output: 1000 "
" weight_filler { "
" type: 'gaussian' "
" std: 0.01 "
" } "
" bias_filler { "
" type: 'constant' "
" value: 0 "
" } "
" blobs_lr: 1. "
" blobs_lr: 2. "
" weight_decay: 1. "
" weight_decay: 0. "
" } "
" bottom: 'conv1' "
" top: 'fc8' "
"} "
"layers { "
" layer { "
" name: 'loss' "
" type: 'softmax_loss' "
" } "
" bottom: 'fc8' "
" bottom: 'label' "
"} ";
this->RunPaddingUpgradeTest(input_proto, expected_output_proto);
}
TEST_F(PaddingLayerUpgradeTest, TestTwoTops) {
const string& input_proto =
"name: 'CaffeNet' "
"layers { "
" layer { "
" name: 'data' "
" type: 'data' "
" source: '/home/jiayq/Data/ILSVRC12/train-leveldb' "
" meanfile: '/home/jiayq/Data/ILSVRC12/image_mean.binaryproto' "
" batchsize: 256 "
" cropsize: 227 "
" mirror: true "
" } "
" top: 'data' "
" top: 'label' "
"} "
"layers { "
" layer { "
" name: 'pad1' "
" type: 'padding' "
" pad: 2 "
" } "
" bottom: 'data' "
" top: 'pad1' "
"} "
"layers { "
" layer { "
" name: 'conv1' "
" type: 'conv' "
" num_output: 96 "
" kernelsize: 11 "
" stride: 4 "
" weight_filler { "
" type: 'gaussian' "
" std: 0.01 "
" } "
" bias_filler { "
" type: 'constant' "
" value: 0. "
" } "
" blobs_lr: 1. "
" blobs_lr: 2. "
" weight_decay: 1. "
" weight_decay: 0. "
" } "
" bottom: 'pad1' "
" top: 'conv1' "
"} "
"layers { "
" layer { "
" name: 'fc8' "
" type: 'innerproduct' "
" num_output: 1000 "
" weight_filler { "
" type: 'gaussian' "
" std: 0.01 "
" } "
" bias_filler { "
" type: 'constant' "
" value: 0 "
" } "
" blobs_lr: 1. "
" blobs_lr: 2. "
" weight_decay: 1. "
" weight_decay: 0. "
" } "
" bottom: 'conv1' "
" top: 'fc8' "
"} "
"layers { "
" layer { "
" name: 'conv2' "
" type: 'conv' "
" num_output: 96 "
" kernelsize: 11 "
" stride: 4 "
" weight_filler { "
" type: 'gaussian' "
" std: 0.01 "
" } "
" bias_filler { "
" type: 'constant' "
" value: 0. "
" } "
" blobs_lr: 1. "
" blobs_lr: 2. "
" weight_decay: 1. "
" weight_decay: 0. "
" } "
" bottom: 'pad1' "
" top: 'conv2' "
"} "
"layers { "
" layer { "
" name: 'loss' "
" type: 'softmax_loss' "
" } "
" bottom: 'fc8' "
" bottom: 'label' "
"} ";
const string& expected_output_proto =
"name: 'CaffeNet' "
"layers { "
" layer { "
" name: 'data' "
" type: 'data' "
" source: '/home/jiayq/Data/ILSVRC12/train-leveldb' "
" meanfile: '/home/jiayq/Data/ILSVRC12/image_mean.binaryproto' "
" batchsize: 256 "
" cropsize: 227 "
" mirror: true "
" } "
" top: 'data' "
" top: 'label' "
"} "
"layers { "
" layer { "
" name: 'conv1' "
" type: 'conv' "
" num_output: 96 "
" kernelsize: 11 "
" stride: 4 "
" pad: 2 "
" weight_filler { "
" type: 'gaussian' "
" std: 0.01 "
" } "
" bias_filler { "
" type: 'constant' "
" value: 0. "
" } "
" blobs_lr: 1. "
" blobs_lr: 2. "
" weight_decay: 1. "
" weight_decay: 0. "
" } "
" bottom: 'data' "
" top: 'conv1' "
"} "
"layers { "
" layer { "
" name: 'fc8' "
" type: 'innerproduct' "
" num_output: 1000 "
" weight_filler { "
" type: 'gaussian' "
" std: 0.01 "
" } "
" bias_filler { "
" type: 'constant' "
" value: 0 "
" } "
" blobs_lr: 1. "
" blobs_lr: 2. "
" weight_decay: 1. "
" weight_decay: 0. "
" } "
" bottom: 'conv1' "
" top: 'fc8' "
"} "
"layers { "
" layer { "
" name: 'conv2' "
" type: 'conv' "
" num_output: 96 "
" kernelsize: 11 "
" stride: 4 "
" pad: 2 "
" weight_filler { "
" type: 'gaussian' "
" std: 0.01 "
" } "
" bias_filler { "
" type: 'constant' "
" value: 0. "
" } "
" blobs_lr: 1. "
" blobs_lr: 2. "
" weight_decay: 1. "
" weight_decay: 0. "
" } "
" bottom: 'data' "
" top: 'conv2' "
"} "
"layers { "
" layer { "
" name: 'loss' "
" type: 'softmax_loss' "
" } "
" bottom: 'fc8' "
" bottom: 'label' "
"} ";
this->RunPaddingUpgradeTest(input_proto, expected_output_proto);
}
TEST_F(PaddingLayerUpgradeTest, TestImageNet) {
const string& input_proto =
"name: 'CaffeNet' "
"layers { "
" layer { "
" name: 'data' "
" type: 'data' "
" source: '/home/jiayq/Data/ILSVRC12/train-leveldb' "
" meanfile: '/home/jiayq/Data/ILSVRC12/image_mean.binaryproto' "
" batchsize: 256 "
" cropsize: 227 "
" mirror: true "
" } "
" top: 'data' "
" top: 'label' "
"} "
"layers { "
" layer { "
" name: 'conv1' "
" type: 'conv' "
" num_output: 96 "
" kernelsize: 11 "
" stride: 4 "
" weight_filler { "
" type: 'gaussian' "
" std: 0.01 "
" } "
" bias_filler { "
" type: 'constant' "
" value: 0. "
" } "
" blobs_lr: 1. "
" blobs_lr: 2. "
" weight_decay: 1. "
" weight_decay: 0. "
" } "
" bottom: 'data' "
" top: 'conv1' "
"} "
"layers { "
" layer { "
" name: 'relu1' "
" type: 'relu' "
" } "
" bottom: 'conv1' "
" top: 'conv1' "
"} "
"layers { "
" layer { "
" name: 'pool1' "
" type: 'pool' "
" pool: MAX "
" kernelsize: 3 "
" stride: 2 "
" } "
" bottom: 'conv1' "
" top: 'pool1' "
"} "
"layers { "
" layer { "
" name: 'norm1' "
" type: 'lrn' "
" local_size: 5 "
" alpha: 0.0001 "
" beta: 0.75 "
" } "
" bottom: 'pool1' "
" top: 'norm1' "
"} "
"layers { "
" layer { "
" name: 'pad2' "
" type: 'padding' "
" pad: 2 "
" } "
" bottom: 'norm1' "
" top: 'pad2' "
"} "
"layers { "
" layer { "
" name: 'conv2' "
" type: 'conv' "
" num_output: 256 "
" group: 2 "
" kernelsize: 5 "
" weight_filler { "
" type: 'gaussian' "
" std: 0.01 "
" } "
" bias_filler { "
" type: 'constant' "
" value: 1. "
" } "
" blobs_lr: 1. "
" blobs_lr: 2. "
" weight_decay: 1. "
" weight_decay: 0. "
" } "
" bottom: 'pad2' "
" top: 'conv2' "
"} "
"layers { "
" layer { "
" name: 'relu2' "
" type: 'relu' "
" } "
" bottom: 'conv2' "
" top: 'conv2' "
"} "
"layers { "
" layer { "
" name: 'pool2' "
" type: 'pool' "
" pool: MAX "
" kernelsize: 3 "
" stride: 2 "
" } "
" bottom: 'conv2' "
" top: 'pool2' "
"} "
"layers { "
" layer { "
" name: 'norm2' "
" type: 'lrn' "
" local_size: 5 "
" alpha: 0.0001 "
" beta: 0.75 "
" } "
" bottom: 'pool2' "
" top: 'norm2' "
"} "
"layers { "
" layer { "
" name: 'pad3' "
" type: 'padding' "
" pad: 1 "
" } "
" bottom: 'norm2' "
" top: 'pad3' "
"} "
"layers { "
" layer { "
" name: 'conv3' "
" type: 'conv' "
" num_output: 384 "
" kernelsize: 3 "
" weight_filler { "
" type: 'gaussian' "
" std: 0.01 "
" } "
" bias_filler { "
" type: 'constant' "
" value: 0. "
" } "
" blobs_lr: 1. "
" blobs_lr: 2. "
" weight_decay: 1. "
" weight_decay: 0. "
" } "
" bottom: 'pad3' "
" top: 'conv3' "
"} "
"layers { "
" layer { "
" name: 'relu3' "
" type: 'relu' "
" } "
" bottom: 'conv3' "
" top: 'conv3' "
"} "
"layers { "
" layer { "
" name: 'pad4' "
" type: 'padding' "
" pad: 1 "
" } "
" bottom: 'conv3' "
" top: 'pad4' "
"} "
"layers { "
" layer { "
" name: 'conv4' "
" type: 'conv' "
" num_output: 384 "
" group: 2 "
" kernelsize: 3 "
" weight_filler { "
" type: 'gaussian' "
" std: 0.01 "
" } "
" bias_filler { "
" type: 'constant' "
" value: 1. "
" } "
" blobs_lr: 1. "
" blobs_lr: 2. "
" weight_decay: 1. "
" weight_decay: 0. "
" } "
" bottom: 'pad4' "
" top: 'conv4' "
"} "
"layers { "
" layer { "
" name: 'relu4' "
" type: 'relu' "
" } "
" bottom: 'conv4' "
" top: 'conv4' "
"} "
"layers { "
" layer { "
" name: 'pad5' "
" type: 'padding' "
" pad: 1 "
" } "
" bottom: 'conv4' "
" top: 'pad5' "
"} "
"layers { "
" layer { "
" name: 'conv5' "
" type: 'conv' "
" num_output: 256 "
" group: 2 "
" kernelsize: 3 "
" weight_filler { "
" type: 'gaussian' "
" std: 0.01 "
" } "
" bias_filler { "
" type: 'constant' "
" value: 1. "
" } "
" blobs_lr: 1. "
" blobs_lr: 2. "
" weight_decay: 1. "
" weight_decay: 0. "
" } "
" bottom: 'pad5' "
" top: 'conv5' "
"} "
"layers { "
" layer { "
" name: 'relu5' "
" type: 'relu' "
" } "
" bottom: 'conv5' "
" top: 'conv5' "
"} "
"layers { "
" layer { "
" name: 'pool5' "
" type: 'pool' "
" kernelsize: 3 "
" pool: MAX "
" stride: 2 "
" } "
" bottom: 'conv5' "
" top: 'pool5' "
"} "
"layers { "
" layer { "
" name: 'fc6' "
" type: 'innerproduct' "
" num_output: 4096 "
" weight_filler { "
" type: 'gaussian' "
" std: 0.005 "
" } "
" bias_filler { "
" type: 'constant' "
" value: 1. "
" } "
" blobs_lr: 1. "
" blobs_lr: 2. "
" weight_decay: 1. "
" weight_decay: 0. "
" } "
" bottom: 'pool5' "
" top: 'fc6' "
"} "
"layers { "
" layer { "
" name: 'relu6' "
" type: 'relu' "
" } "
" bottom: 'fc6' "
" top: 'fc6' "
"} "
"layers { "
" layer { "
" name: 'drop6' "
" type: 'dropout' "
" dropout_ratio: 0.5 "
" } "
" bottom: 'fc6' "
" top: 'fc6' "
"} "
"layers { "
" layer { "
" name: 'fc7' "
" type: 'innerproduct' "
" num_output: 4096 "
" weight_filler { "
" type: 'gaussian' "
" std: 0.005 "
" } "
" bias_filler { "
" type: 'constant' "
" value: 1. "
" } "
" blobs_lr: 1. "
" blobs_lr: 2. "
" weight_decay: 1. "
" weight_decay: 0. "
" } "
" bottom: 'fc6' "
" top: 'fc7' "
"} "
"layers { "
" layer { "
" name: 'relu7' "
" type: 'relu' "
" } "
" bottom: 'fc7' "
" top: 'fc7' "
"} "
"layers { "
" layer { "
" name: 'drop7' "
" type: 'dropout' "
" dropout_ratio: 0.5 "
" } "
" bottom: 'fc7' "
" top: 'fc7' "
"} "
"layers { "
" layer { "
" name: 'fc8' "
" type: 'innerproduct' "
" num_output: 1000 "
" weight_filler { "
" type: 'gaussian' "
" std: 0.01 "
" } "
" bias_filler { "
" type: 'constant' "
" value: 0 "
" } "
" blobs_lr: 1. "
" blobs_lr: 2. "
" weight_decay: 1. "
" weight_decay: 0. "
" } "
" bottom: 'fc7' "
" top: 'fc8' "
"} "
"layers { "
" layer { "
" name: 'loss' "
" type: 'softmax_loss' "
" } "
" bottom: 'fc8' "
" bottom: 'label' "
"} ";
const string& expected_output_proto =
"name: 'CaffeNet' "
"layers { "
" layer { "
" name: 'data' "
" type: 'data' "
" source: '/home/jiayq/Data/ILSVRC12/train-leveldb' "
" meanfile: '/home/jiayq/Data/ILSVRC12/image_mean.binaryproto' "
" batchsize: 256 "
" cropsize: 227 "
" mirror: true "
" } "
" top: 'data' "
" top: 'label' "
"} "
"layers { "
" layer { "
" name: 'conv1' "
" type: 'conv' "
" num_output: 96 "
" kernelsize: 11 "
" stride: 4 "
" weight_filler { "
" type: 'gaussian' "
" std: 0.01 "
" } "
" bias_filler { "
" type: 'constant' "
" value: 0. "
" } "
" blobs_lr: 1. "
" blobs_lr: 2. "
" weight_decay: 1. "
" weight_decay: 0. "
" } "
" bottom: 'data' "
" top: 'conv1' "
"} "
"layers { "
" layer { "
" name: 'relu1' "
" type: 'relu' "
" } "
" bottom: 'conv1' "
" top: 'conv1' "
"} "
"layers { "
" layer { "
" name: 'pool1' "
" type: 'pool' "
" pool: MAX "
" kernelsize: 3 "
" stride: 2 "
" } "
" bottom: 'conv1' "
" top: 'pool1' "
"} "
"layers { "
" layer { "
" name: 'norm1' "
" type: 'lrn' "
" local_size: 5 "
" alpha: 0.0001 "
" beta: 0.75 "
" } "
" bottom: 'pool1' "
" top: 'norm1' "
"} "
"layers { "
" layer { "
" name: 'conv2' "
" type: 'conv' "
" num_output: 256 "
" group: 2 "
" kernelsize: 5 "
" pad: 2 "
" weight_filler { "
" type: 'gaussian' "
" std: 0.01 "
" } "
" bias_filler { "
" type: 'constant' "
" value: 1. "
" } "
" blobs_lr: 1. "
" blobs_lr: 2. "
" weight_decay: 1. "
" weight_decay: 0. "
" } "
" bottom: 'norm1' "
" top: 'conv2' "
"} "
"layers { "
" layer { "
" name: 'relu2' "
" type: 'relu' "
" } "
" bottom: 'conv2' "
" top: 'conv2' "
"} "
"layers { "
" layer { "
" name: 'pool2' "
" type: 'pool' "
" pool: MAX "
" kernelsize: 3 "
" stride: 2 "
" } "
" bottom: 'conv2' "
" top: 'pool2' "
"} "
"layers { "
" layer { "
" name: 'norm2' "
" type: 'lrn' "
" local_size: 5 "
" alpha: 0.0001 "
" beta: 0.75 "
" } "
" bottom: 'pool2' "
" top: 'norm2' "
"} "
"layers { "
" layer { "
" name: 'conv3' "
" type: 'conv' "
" num_output: 384 "
" kernelsize: 3 "
" pad: 1 "
" weight_filler { "
" type: 'gaussian' "
" std: 0.01 "
" } "
" bias_filler { "
" type: 'constant' "
" value: 0. "
" } "
" blobs_lr: 1. "
" blobs_lr: 2. "
" weight_decay: 1. "
" weight_decay: 0. "
" } "
" bottom: 'norm2' "
" top: 'conv3' "
"} "
"layers { "
" layer { "
" name: 'relu3' "
" type: 'relu' "
" } "
" bottom: 'conv3' "
" top: 'conv3' "
"} "
"layers { "
" layer { "
" name: 'conv4' "
" type: 'conv' "
" num_output: 384 "
" group: 2 "
" kernelsize: 3 "
" pad: 1 "
" weight_filler { "
" type: 'gaussian' "
" std: 0.01 "
" } "
" bias_filler { "
" type: 'constant' "
" value: 1. "
" } "
" blobs_lr: 1. "
" blobs_lr: 2. "
" weight_decay: 1. "
" weight_decay: 0. "
" } "
" bottom: 'conv3' "
" top: 'conv4' "
"} "
"layers { "
" layer { "
" name: 'relu4' "
" type: 'relu' "
" } "
" bottom: 'conv4' "
" top: 'conv4' "
"} "
"layers { "
" layer { "
" name: 'conv5' "
" type: 'conv' "
" num_output: 256 "
" group: 2 "
" kernelsize: 3 "
" pad: 1 "
" weight_filler { "
" type: 'gaussian' "
" std: 0.01 "
" } "
" bias_filler { "
" type: 'constant' "
" value: 1. "
" } "
" blobs_lr: 1. "
" blobs_lr: 2. "
" weight_decay: 1. "
" weight_decay: 0. "
" } "
" bottom: 'conv4' "
" top: 'conv5' "
"} "
"layers { "
" layer { "
" name: 'relu5' "
" type: 'relu' "
" } "
" bottom: 'conv5' "
" top: 'conv5' "
"} "
"layers { "
" layer { "
" name: 'pool5' "
" type: 'pool' "
" kernelsize: 3 "
" pool: MAX "
" stride: 2 "
" } "
" bottom: 'conv5' "
" top: 'pool5' "
"} "
"layers { "
" layer { "
" name: 'fc6' "
" type: 'innerproduct' "
" num_output: 4096 "
" weight_filler { "
" type: 'gaussian' "
" std: 0.005 "
" } "
" bias_filler { "
" type: 'constant' "
" value: 1. "
" } "
" blobs_lr: 1. "
" blobs_lr: 2. "
" weight_decay: 1. "
" weight_decay: 0. "
" } "
" bottom: 'pool5' "