#include #include #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' " " 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' " "} "; this->RunPaddingUpgradeTest(input_proto, expected_output_proto); } class NetUpgradeTest : public ::testing::Test { protected: void RunV0UpgradeTest( const string& input_param_string, const string& output_param_string) { // Test that UpgradeV0Net called on the NetParameter proto specified by // input_param_string results in the NetParameter 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; UpgradeV0Net(input_param, &actual_output_param); EXPECT_EQ(expected_output_param.DebugString(), actual_output_param.DebugString()); } void RunV1UpgradeTest( const string& input_param_string, const string& output_param_string) { // Test that UpgradeV0Net called on the NetParameter proto specified by // input_param_string results in the NetParameter 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; UpgradeV1Net(input_param, &actual_output_param); EXPECT_EQ(expected_output_param.DebugString(), actual_output_param.DebugString()); } }; TEST_F(NetUpgradeTest, TestSimple) { const string& v0_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_v1_proto = "name: 'CaffeNet' " "layers { " " name: 'data' " " type: DATA " " data_param { " " source: '/home/jiayq/Data/ILSVRC12/train-leveldb' " " batch_size: 256 " " } " " transform_param { " " crop_size: 227 " " mirror: true " " mean_file: '/home/jiayq/Data/ILSVRC12/image_mean.binaryproto' " " } " " top: 'data' " " top: 'label' " "} " "layers { " " name: 'conv1' " " type: CONVOLUTION " " convolution_param { " " num_output: 96 " " kernel_size: 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 { " " name: 'fc8' " " type: INNER_PRODUCT " " inner_product_param { " " 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 { " " name: 'loss' " " type: SOFTMAX_LOSS " " bottom: 'fc8' " " bottom: 'label' " "} "; this->RunV0UpgradeTest(v0_proto, expected_v1_proto); const string& expected_v2_proto = "name: 'CaffeNet' " "layer { " " name: 'data' " " type: 'Data' " " data_param { " " source: '/home/jiayq/Data/ILSVRC12/train-leveldb' " " batch_size: 256 " " } " " transform_param { " " crop_size: 227 " " mirror: true " " mean_file: '/home/jiayq/Data/ILSVRC12/image_mean.binaryproto' " " } " " top: 'data' " " top: 'label' " "} " "layer { " " name: 'conv1' " " type: 'Convolution' " " convolution_param { " " num_output: 96 " " kernel_size: 11 " " stride: 4 " " pad: 2 " " weight_filler { " " type: 'gaussian' " " std: 0.01 " " } " " bias_filler { " " type: 'constant' " " value: 0. " " } " " } " " param { " " lr_mult: 1 " " decay_mult: 1 " " } " " param { " " lr_mult: 2 " " decay_mult: 0 " " } " " bottom: 'data' " " top: 'conv1' " "} " "layer { " " name: 'fc8' " " type: 'InnerProduct' " " inner_product_param { " " num_output: 1000 " " weight_filler { " " type: 'gaussian' " " std: 0.01 " " } " " bias_filler { " " type: 'constant' " " value: 0 " " } " " } " " param { " " lr_mult: 1 " " decay_mult: 1 " " } " " param { " " lr_mult: 2 " " decay_mult: 0 " " } " " bottom: 'conv1' " " top: 'fc8' " "} " "layer { " " name: 'loss' " " type: 'SoftmaxWithLoss' " " bottom: 'fc8' " " bottom: 'label' " "} "; this->RunV1UpgradeTest(expected_v1_proto, expected_v2_proto); } // Test any layer or parameter upgrades not covered by other tests. TEST_F(NetUpgradeTest, TestAllParams) { const string& input_proto = "name: 'CaffeNet' " "input: 'input_data' " "input_dim: 64 " "input_dim: 3 " "input_dim: 32 " "input_dim: 32 " "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 " " scale: 0.25 " " rand_skip: 73 " " } " " top: 'data' " " top: 'label' " "} " "layers { " " layer { " " name: 'images' " " type: 'images' " " source: '/home/jiayq/Data/ILSVRC12/train-images' " " meanfile: '/home/jiayq/Data/ILSVRC12/image_mean.binaryproto' " " batchsize: 256 " " cropsize: 227 " " mirror: true " " scale: 0.25 " " rand_skip: 73 " " shuffle_images: true " " new_height: 40 " " new_width: 30 " " } " " top: 'images_data' " " top: 'images_label' " "} " "layers { " " layer { " " name: 'window_data' " " type: 'window_data' " " source: '/home/jiayq/Data/ILSVRC12/train-leveldb' " " meanfile: '/home/jiayq/Data/ILSVRC12/image_mean.binaryproto' " " batchsize: 256 " " cropsize: 227 " " mirror: true " " det_fg_threshold: 0.25 " " det_bg_threshold: 0.75 " " det_fg_fraction: 0.5 " " det_context_pad: 16 " " det_crop_mode: 'square' " " } " " top: 'window_data' " " top: 'window_label' " "} " "layers { " " layer { " " name: 'hdf5data' " " type: 'hdf5_data' " " source: '/my/hdf5/data' " " batchsize: 256 " " } " " top: 'hdf5data' " "} " "layers { " " layer { " " name: 'conv1' " " type: 'conv' " " num_output: 96 " " biasterm: false " " pad: 4 " " kernelsize: 11 " " stride: 4 " " weight_filler { " " type: 'gaussian' " " std: 0.01 " " } " " bias_filler { " " type: 'constant' " " value: 3. " " } " " blobs_lr: 1. " " blobs_lr: 2. " " weight_decay: 1. " " weight_decay: 0. " " } " " bottom: 'data' " " top: 'conv1' " "} " "layers { " " layer { " " name: 'pool1ave' " " type: 'pool' " " pool: AVE " " kernelsize: 3 " " stride: 2 " " } " " bottom: 'conv1' " " top: 'pool1ave' " "} " "layers { " " layer { " " name: 'pool1stoch' " " type: 'pool' " " pool: STOCHASTIC " " kernelsize: 4 " " stride: 5 " " } " " bottom: 'conv1' " " top: 'pool1stoch' " "} " "layers { " " layer { " " name: 'concat' " " type: 'concat' " " concat_dim: 2 " " } " " bottom: 'pool1ave' " " bottom: 'pool1stoch' " " top: 'pool1concat' " "} " "layers { " " layer { " " name: 'norm1' " " type: 'lrn' " " local_size: 5 " " alpha: 0.0001 " " beta: 0.75 " " } " " bottom: 'pool1concat' " " top: 'norm1' " "} " "layers { " " layer { " " name: 'fc6' " " type: 'innerproduct' " " num_output: 4096 " " biasterm: false " " 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: 'norm1' " " top: 'fc6' " "} " "layers { " " layer { " " name: 'relu6' " " type: 'relu' " " } " " bottom: 'fc6' " " top: 'fc6' " "} " "layers { " " layer { " " name: 'drop6' " " type: 'dropout' " " dropout_ratio: 0.2 " " } " " bottom: 'fc6' " " top: 'fc6' " "} " "layers { " " layer { " " name: 'loss' " " type: 'infogain_loss' " " source: '/my/infogain/matrix' " " } " " bottom: 'fc6' " " bottom: 'label' " "} " "layers { " " layer { " " name: 'accuracy' " " type: 'accuracy' " " } " "} " "layers { " " layer { " " name: 'bnll' " " type: 'bnll' " " } " "} " "layers { " " layer { " " name: 'euclidean_loss' " " type: 'euclidean_loss' " " } " "} " "layers { " " layer { " " name: 'flatten' " " type: 'flatten' " " } " "} " "layers { " " layer { " " name: 'hdf5_output' " " type: 'hdf5_output' " " hdf5_output_param { " " file_name: '/my/hdf5/output/file' " " } " " } " "} " "layers { " " layer { " " name: 'im2col' " " type: 'im2col' " " } " "} " "layers { " " layer { " " name: 'images' " " type: 'images' " " } " "} " "layers { " " layer { " " name: 'multinomial_logistic_loss' " " type: 'multinomial_logistic_loss' " " } " "} " "layers { " " layer { " " name: 'sigmoid' " " type: 'sigmoid' " " } " "} " "layers { " " layer { " " name: 'softmax' " " type: 'softmax' " " } " "} " "layers { " " layer { " " name: 'split' " " type: 'split' " " } " "} " "layers { " " layer { " " name: 'tanh' " " type: 'tanh' " " } " "} "; const string& expected_output_proto = "name: 'CaffeNet' " "input: 'input_data' " "input_dim: 64 " "input_dim: 3 " "input_dim: 32 " "input_dim: 32 " "layers { " " name: 'data' " " type: DATA " " data_param { " " source: '/home/jiayq/Data/ILSVRC12/train-leveldb' " " batch_size: 256 " " rand_skip: 73 " " } " " transform_param { " " crop_size: 227 " " mirror: true " " scale: 0.25 " " mean_file: '/home/jiayq/Data/ILSVRC12/image_mean.binaryproto' " " } " " top: 'data' " " top: 'label' " "} " "layers { " " name: 'images' " " type: IMAGE_DATA " " image_data_param { " " source: '/home/jiayq/Data/ILSVRC12/train-images' " " batch_size: 256 " " rand_skip: 73 " " shuffle: true " " new_height: 40 " " new_width: 30 " " } " " transform_param {" " mean_file: '/home/jiayq/Data/ILSVRC12/image_mean.binaryproto' " " crop_size: 227 " " mirror: true " " scale: 0.25 " " } " " top: 'images_data' " " top: 'images_label' " "} " "layers { " " name: 'window_data' " " type: WINDOW_DATA " " window_data_param { " " source: '/home/jiayq/Data/ILSVRC12/train-leveldb' " " batch_size: 256 " " fg_threshold: 0.25 " " bg_threshold: 0.75 " " fg_fraction: 0.5 " " context_pad: 16 " " crop_mode: 'square' " " } " " transform_param { " " mirror: true " " crop_size: 227 " " mean_file: '/home/jiayq/Data/ILSVRC12/image_mean.binaryproto' " " }" " top: 'window_data' " " top: 'window_label' " "} " "layers { " " name: 'hdf5data' " " type: HDF5_DATA " " hdf5_data_param { " " source: '/my/hdf5/data' " " batch_size: 256 " " } " " top: 'hdf5data' " "} " "layers { " " name: 'conv1' " " type: CONVOLUTION " " convolution_param { " " num_output: 96 " " bias_term: false " " pad: 4 " " kernel_size: 11 " " stride: 4 " " weight_filler { " " type: 'gaussian' " " std: 0.01 " " } " " bias_filler { " " type: 'constant' " " value: 3. " " } " " } " " blobs_lr: 1. " " blobs_lr: 2. " " weight_decay: 1. " " weight_decay: 0. " " bottom: 'data' " " top: 'conv1' " "} " "layers { " " name: 'pool1ave' " " type: POOLING " " pooling_param { " " pool: AVE " " kernel_size: 3 " " stride: 2 " " } " " bottom: 'conv1' " " top: 'pool1ave' " "} " "layers { " " name: 'pool1stoch' " " type: POOLING " " pooling_param { " " pool: STOCHASTIC " " kernel_size: 4 " " stride: 5 " " } " " bottom: 'conv1' " " top: 'pool1stoch' " "} " "layers { " " name: 'concat' " " type: CONCAT " " concat_param { " " concat_dim: 2 " " } " " bottom: 'pool1ave' " " bottom: 'pool1stoch' " " top: 'pool1concat' " "} " "layers { " " name: 'norm1' " " type: LRN " " lrn_param { " " local_size: 5 " " alpha: 0.0001 " " beta: 0.75 " " } " " bottom: 'pool1concat' " " top: 'norm1' " "} " "layers { " " name: 'fc6' " " type: INNER_PRODUCT " " inner_product_param { " " num_output: 4096 " " bias_term: false " " 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: 'norm1' " " top: 'fc6' " "} " "layers { " " name: 'relu6' " " type: RELU " " bottom: 'fc6' " " top: 'fc6' " "} " "layers { " " name: 'drop6' " " type: DROPOUT " " dropout_param { " " dropout_ratio: 0.2 " " } " " bottom: 'fc6' " " top: 'fc6' " "} " "layers { " " name: 'loss' " " type: INFOGAIN_LOSS " " infogain_loss_param { " " source: '/my/infogain/matrix' " " } " " bottom: 'fc6' " " bottom: 'label' " "} " "layers { " " name: 'accuracy' " " type: ACCURACY " "} " "layers { " " name: 'bnll' " " type: BNLL " "} " "layers { " " name: 'euclidean_loss' " " type: EUCLIDEAN_LOSS " "} " "layers { " " name: 'flatten' " " type: FLATTEN " "} " "layers { " " name: 'hdf5_output' " " type: HDF5_OUTPUT " " hdf5_output_param { " " file_name: '/my/hdf5/output/file' " " } " "} " "layers { " " name: 'im2col' " " type: IM2COL " "} " "layers { " " name: 'images' " " type: IMAGE_DATA " "} " "layers { " " name: 'multinomial_logistic_loss' " " type: MULTINOMIAL_LOGISTIC_LOSS " "} " "layers { " " name: 'sigmoid' " " type: SIGMOID " "} " "layers { " " name: 'softmax' " " type: SOFTMAX " "} " "layers { " " name: 'split' " " type: SPLIT " "} " "layers { " " name: 'tanh' " " type: TANH " "} "; this->RunV0UpgradeTest(input_proto, expected_output_proto); } TEST_F(NetUpgradeTest, TestImageNet) { const string& v0_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_v1_proto = "name: 'CaffeNet' " "layers { " " name: 'data' " " type: DATA " " data_param { " " source: '/home/jiayq/Data/ILSVRC12/train-leveldb' " " batch_size: 256 " " } " " transform_param { " " crop_size: 227 " " mirror: true " " mean_file: '/home/jiayq/Data/ILSVRC12/image_mean.binaryproto' " " } " " top: 'data' " " top: 'label' " "} " "layers { " " name: 'conv1' " " type: CONVOLUTION " " convolution_param { " " num_output: 96 " " kernel_size: 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 { " " name: 'relu1' " " type: RELU " " bottom: 'conv1' " " top: 'conv1' " "} " "layers { " " name: 'pool1' " " type: POOLING " " pooling_param { " " pool: MAX " " kernel_size: 3 " " stride: 2 " " } " " bottom: 'conv1' " " top: 'pool1' " "} " "layers { " " name: 'norm1' " " type: LRN " " lrn_param { " " local_size: 5 " " alpha: 0.0001 " " beta: 0.75 " " } " " bottom: 'pool1' " " top: 'norm1' " "} " "layers { " " name: 'conv2' " " type: CONVOLUTION " " convolution_param { " " num_output: 256 " " group: 2 " " kernel_size: 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 { " " name: 'relu2' " " type: RELU " " bottom: 'conv2' " " top: 'conv2' " "} " "layers { " " name: 'pool2' " " type: POOLING " " pooling_param { " " pool: MAX " " kernel_size: 3 " " stride: 2 " " } " " bottom: 'conv2' " " top: 'pool2' " "} " "layers { " " name: 'norm2' " " type: LRN " " lrn_param { " " local_size: 5 " " alpha: 0.0001 " " beta: 0.75 " " } " " bottom: 'pool2' " " top: 'norm2' " "} " "layers { " " name: 'conv3' " " type: CONVOLUTION " " convolution_param { " " num_output: 384 " " kernel_size: 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 { " " name: 'relu3' " " type: RELU " " bottom: 'conv3' " " top: 'conv3' " "} " "layers { " " name: 'conv4' " " type: CONVOLUTION " " convolution_param { " " num_output: 384 " " group: 2 " " kernel_size: 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 { " " name: 'relu4' " " type: RELU " " bottom: 'conv4' " " top: 'conv4' " "} " "layers { " " name: 'conv5' " " type: CONVOLUTION " " convolution_param { " " num_output: 256 " " group: 2 " " kernel_size: 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 { " " name: 'relu5' " " type: RELU " " bottom: 'conv5' " " top: 'conv5' " "} " "layers { " " name: 'pool5' " " type: POOLING " " pooling_param { " " kernel_size: 3 " " pool: MAX " " stride: 2 " " } " " bottom: 'conv5' " " top: 'pool5' " "} " "layers { " " name: 'fc6' " " type: INNER_PRODUCT " " inner_product_param { " " 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 { " " name: 'relu6' " " type: RELU " " bottom: 'fc6' " " top: 'fc6' " "} " "layers { " " name: 'drop6' " " type: DROPOUT " " dropout_param { " " dropout_ratio: 0.5 " " } " " bottom: 'fc6' " " top: 'fc6' " "} " "layers { " " name: 'fc7' " " type: INNER_PRODUCT " " inner_product_param { " " 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 { " " name: 'relu7' " " type: RELU " " bottom: 'fc7' " " top: 'fc7' " "} " "layers { " " name: 'drop7' " " type: DROPOUT " " dropout_param { " " dropout_ratio: 0.5 " " } " " bottom: 'fc7' " " top: 'fc7' " "} " "layers { " " name: 'fc8' " " type: INNER_PRODUCT " " inner_product_param { " " 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 { " " name: 'loss' " " type: SOFTMAX_LOSS " " bottom: 'fc8' " " bottom: 'label' " "} "; this->RunV0UpgradeTest(v0_proto, expected_v1_proto); const string& expected_v2_proto = "name: 'CaffeNet' " "layer { " " name: 'data' " " type: 'Data' " " data_param { " " source: '/home/jiayq/Data/ILSVRC12/train-leveldb' " " batch_size: 256 " " } " " transform_param { " " crop_size: 227 " " mirror: true " " mean_file: '/home/jiayq/Data/ILSVRC12/image_mean.binaryproto' " " } " " top: 'data' " " top: 'label' " "} " "layer { " " name: 'conv1' " " type: 'Convolution' " " convolution_param { " " num_output: 96 " " kernel_size: 11 " " stride: 4 " " weight_filler { " " type: 'gaussian' " " std: 0.01 " " } " " bias_filler { " " type: 'constant' " " value: 0. " " } " " } " " param { " " lr_mult: 1 " " decay_mult: 1 " " } " " param { " " lr_mult: 2 " " decay_mult: 0 " " } " " bottom: 'data' " " top: 'conv1' " "} " "layer { " " name: 'relu1' " " type: 'ReLU' " " bottom: 'conv1' " " top: 'conv1' " "} " "layer { " " name: 'pool1' " " type: 'Pooling' " " pooling_param { " " pool: MAX " " kernel_size: 3 " " stride: 2 " " } " " bottom: 'conv1' " " top: 'pool1' " "} " "layer { " " name: 'norm1' " " type: 'LRN' " " lrn_param { " " local_size: 5 " " alpha: 0.0001 " " beta: 0.75 " " } " " bottom: 'pool1' " " top: 'norm1' " "} " "layer { " " name: 'conv2' " " type: 'Convolution' " " convolution_param { " " num_output: 256 " " group: 2 " " kernel_size: 5 " " pad: 2 " " weight_filler { " " type: 'gaussian' " " std: 0.01 " " } " " bias_filler { " " type: 'constant' " " value: 1. " " } " " } " " param { " " lr_mult: 1 " " decay_mult: 1 " " } " " param { " " lr_mult: 2 " " decay_mult: 0 " " } " " bottom: 'norm1' " " top: 'conv2' " "} " "layer { " " name: 'relu2' " " type: 'ReLU' " " bottom: 'conv2' " " top: 'conv2' " "} " "layer { " " name: 'pool2' " " type: 'Pooling' " " pooling_param { " " pool: MAX " " kernel_size: 3 " " stride: 2 " " } " " bottom: 'conv2' " " top: 'pool2' " "} " "layer { " " name: 'norm2' " " type: 'LRN' " " lrn_param { " " local_size: 5 " " alpha: 0.0001 " " beta: 0.75 " " } " " bottom: 'pool2' " " top: 'norm2' " "} " "layer { " " name: 'conv3' " " type: 'Convolution' " " convolution_param { " " num_output: 384 " " kernel_size: 3 " " pad: 1 " " weight_filler { " " type: 'gaussian' " " std: 0.01 " " } " " bias_filler { " " type: 'constant' " " value: 0. " " } " " } " " param { " " lr_mult: 1 " " decay_mult: 1 " " } " " param { " " lr_mult: 2 " " decay_mult: 0 " " } " " bottom: 'norm2' " " top: 'conv3' " "} " "layer { " " name: 'relu3' " " type: 'ReLU' " " bottom: 'conv3' " " top: 'conv3' " "} " "layer { " " name: 'conv4' " " type: 'Convolution' " " convolution_param { " " num_output: 384 " " group: 2 " " kernel_size: 3 " " pad: 1 " " weight_filler { " " type: 'gaussian' " " std: 0.01 " " } " " bias_filler { " " type: 'constant' " " value: 1. " " } " " } " " param { " " lr_mult: 1 " " decay_mult: 1 " " } " " param { " " lr_mult: 2 " " decay_mult: 0 " " } " " bottom: 'conv3' " " top: 'conv4' " "} " "layer { " " name: 'relu4' " " type: 'ReLU' " " bottom: 'conv4' " " top: 'conv4' " "} " "layer { " " name: 'conv5' " " type: 'Convolution' " " convolution_param { " " num_output: 256 " " group: 2 " " kernel_size: 3 " " pad: 1 " " weight_filler { " " type: 'gaussian' " " std: 0.01 " " } " " bias_filler { " " type: 'constant' " " value: 1. " " } " " } " " param { " " lr_mult: 1 " " decay_mult: 1 " " } " " param { " " lr_mult: 2 " " decay_mult: 0 " " } " " bottom: 'conv4' " " top: 'conv5' " "} " "layer { " " name: 'relu5' " " type: 'ReLU' " " bottom: 'conv5' " " top: 'conv5' " "} " "layer { " " name: 'pool5' " " type: 'Pooling' " " pooling_param { " " kernel_size: 3 " " pool: MAX " " stride: 2 " " } " " bottom: 'conv5' " " top: 'pool5' " "} " "layer { " " name: 'fc6' " " type: 'InnerProduct' " " inner_product_param { " " num_output: 4096 " " weight_filler { " " type: 'gaussian' " " std: 0.005 " " } " " bias_filler { " " type: 'constant' " " value: 1. " " } " " } " " param { " " lr_mult: 1 " " decay_mult: 1 " " } " " param { " " lr_mult: 2 " " decay_mult: 0 " " } " " bottom: 'pool5' " " top: 'fc6' " "} " "layer { " " name: 'relu6' " " type: 'ReLU' " " bottom: 'fc6' " " top: 'fc6' " "} " "layer { " " name: 'drop6' " " type: 'Dropout' " " dropout_param { " " dropout_ratio: 0.5 " " } " " bottom: 'fc6' " " top: 'fc6' " "} " "layer { " " name: 'fc7' " " type: 'InnerProduct' " " inner_product_param { " " num_output: 4096 " " weight_filler { " " type: 'gaussian' " " std: 0.005 " " } " " bias_filler { " " type: 'constant' " " value: 1. " " } " " } " " param { " " lr_mult: 1 " " decay_mult: 1 " " } " " param { " " lr_mult: 2 " " decay_mult: 0 " " } " " bottom: 'fc6' " " top: 'fc7' " "} " "layer { " " name: 'relu7' " " type: 'ReLU' " " bottom: 'fc7' " " top: 'fc7' " "} " "layer { " " name: 'drop7' " " type: 'Dropout' " " dropout_param { " " dropout_ratio: 0.5 " " } " " bottom: 'fc7' " " top: 'fc7' " "} " "layer { " " name: 'fc8' " " type: 'InnerProduct' " " inner_product_param { " " num_output: 1000 " " weight_filler { " " type: 'gaussian' " " std: 0.01 " " } " " bias_filler { " " type: 'constant' " " value: 0 " " } " " } " " param { " " lr_mult: 1 " " decay_mult: 1 " " } " " param { " " lr_mult: 2 " " decay_mult: 0 " " } " " bottom: 'fc7' " " top: 'fc8' " "} " "layer { " " name: 'loss' " " type: 'SoftmaxWithLoss' " " bottom: 'fc8' " " bottom: 'label' " "} "; this->RunV1UpgradeTest(expected_v1_proto, expected_v2_proto); } // NOLINT(readability/fn_size) TEST_F(NetUpgradeTest, TestUpgradeV1LayerType) { LayerParameter layer_param; shared_ptr > layer; for (int i = 0; i < V1LayerParameter_LayerType_LayerType_ARRAYSIZE; ++i) { ASSERT_TRUE(V1LayerParameter_LayerType_IsValid(i)); V1LayerParameter_LayerType v1_type = V1LayerParameter_LayerType(i); string v2_layer_type(UpgradeV1LayerType(v1_type)); if (v2_layer_type == "") { EXPECT_EQ(V1LayerParameter_LayerType_NONE, v1_type); continue; // Empty string isn't actually a valid layer type. } layer_param.set_type(v2_layer_type); // Data layers expect a DB if (v2_layer_type == "Data") { #ifdef USE_LEVELDB string tmp; MakeTempDir(&tmp); boost::scoped_ptr db(db::GetDB(DataParameter_DB_LEVELDB)); db->Open(tmp, db::NEW); db->Close(); layer_param.mutable_data_param()->set_source(tmp); #else continue; #endif // USE_LEVELDB } #ifndef USE_OPENCV if (v2_layer_type == "ImageData" || v2_layer_type == "WindowData") { continue; } #endif // !USE_OPENCV layer = LayerRegistry::CreateLayer(layer_param); EXPECT_EQ(v2_layer_type, layer->type()); } } class SolverTypeUpgradeTest : public ::testing::Test { protected: void RunSolverTypeUpgradeTest( const string& input_param_string, const string& output_param_string) { // Test upgrading old solver_type field (enum) to new type field (string) SolverParameter input_param; CHECK(google::protobuf::TextFormat::ParseFromString( input_param_string, &input_param)); SolverParameter expected_output_param; CHECK(google::protobuf::TextFormat::ParseFromString( output_param_string, &expected_output_param)); SolverParameter actual_output_param = input_param; UpgradeSolverType(&actual_output_param); EXPECT_EQ(expected_output_param.DebugString(), actual_output_param.DebugString()); } }; TEST_F(SolverTypeUpgradeTest, TestSimple) { const char* old_type_vec[6] = { "SGD", "ADAGRAD", "NESTEROV", "RMSPROP", "ADADELTA", "ADAM" }; const char* new_type_vec[6] = { "SGD", "AdaGrad", "Nesterov", "RMSProp", "AdaDelta", "Adam" }; for (int i = 0; i < 6; ++i) { const string& input_proto = "net: 'examples/mnist/lenet_train_test.prototxt' " "weights: 'examples/mnist/lenet_train_test1.caffemodel' " "weights: 'examples/mnist/lenet_train_test2.caffemodel' " "test_iter: 100 " "test_interval: 500 " "base_lr: 0.01 " "momentum: 0.0 " "weight_decay: 0.0005 " "lr_policy: 'inv' " "gamma: 0.0001 " "power: 0.75 " "display: 100 " "max_iter: 10000 " "snapshot: 5000 " "snapshot_prefix: 'examples/mnist/lenet_rmsprop' " "solver_mode: GPU " "solver_type: " + std::string(old_type_vec[i]) + " "; const string& expected_output_proto = "net: 'examples/mnist/lenet_train_test.prototxt' " "weights: 'examples/mnist/lenet_train_test1.caffemodel' " "weights: 'examples/mnist/lenet_train_test2.caffemodel' " "test_iter: 100 " "test_interval: 500 " "base_lr: 0.01 " "momentum: 0.0 " "weight_decay: 0.0005 " "lr_policy: 'inv' " "gamma: 0.0001 " "power: 0.75 " "display: 100 " "max_iter: 10000 " "snapshot: 5000 " "snapshot_prefix: 'examples/mnist/lenet_rmsprop' " "solver_mode: GPU " "type: '" + std::string(new_type_vec[i]) + "' "; this->RunSolverTypeUpgradeTest(input_proto, expected_output_proto); } } } // NOLINT(readability/fn_size) // namespace caffe