add test which includes upgraded params

diff --git a/src/caffe/test/test_upgrade_proto.cpp b/src/caffe/test/test_upgrade_proto.cpp
index 3d64fe6..d6ca6d0 100644 (file)
--- a/src/caffe/test/test_upgrade_proto.cpp
+++ b/src/caffe/test/test_upgrade_proto.cpp
@@ -1263,6 +1263,292 @@ TYPED_TEST(V0UpgradeTest, TestSimple) {
   this->RunV0UpgradeTest(input_proto, expected_output_proto);
 }
 
+TYPED_TEST(V0UpgradeTest, TestAllParams) {
+  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 "
+      "    scale: 0.25 "
+      "    rand_skip: 73 "
+      "  } "
+      "  top: 'data' "
+      "  top: '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' "
+      "} ";
+  const string& expected_output_proto =
+      "name: 'CaffeNet' "
+      "layers { "
+      "  name: 'data' "
+      "  type: 'data' "
+      "  data_param { "
+      "    source: '/home/jiayq/Data/ILSVRC12/train-leveldb' "
+      "    mean_file: '/home/jiayq/Data/ILSVRC12/image_mean.binaryproto' "
+      "    batch_size: 256 "
+      "    crop_size: 227 "
+      "    mirror: true "
+      "    scale: 0.25 "
+      "    rand_skip: 73 "
+      "  } "
+      "  top: 'data' "
+      "  top: 'label' "
+      "} "
+      "layers { "
+      "  name: 'hdf5data' "
+      "  type: 'hdf5_data' "
+      "  hdf5_data_param { "
+      "    source: '/my/hdf5/data' "
+      "    batch_size: 256 "
+      "  } "
+      "  top: 'hdf5data' "
+      "} "
+      "layers { "
+      "  name: 'conv1' "
+      "  type: 'conv' "
+      "  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: 'pool' "
+      "  pooling_param { "
+      "    pool: AVE "
+      "    kernel_size: 3 "
+      "    stride: 2 "
+      "  } "
+      "  bottom: 'conv1' "
+      "  top: 'pool1ave' "
+      "} "
+      "layers { "
+      "  name: 'pool1stoch' "
+      "  type: 'pool' "
+      "  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: 'innerproduct' "
+      "  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' "
+      "} ";
+  this->RunV0UpgradeTest(input_proto, expected_output_proto);
+}
+
 TYPED_TEST(V0UpgradeTest, TestImageNet) {
   const string& input_proto =
       "name: 'CaffeNet' "
@@ -1949,5 +2235,4 @@ TYPED_TEST(V0UpgradeTest, TestImageNet) {
   this->RunV0UpgradeTest(input_proto, expected_output_proto);
 }
 
-
 }  // namespace caffe

diff --git a/src/caffe/util/upgrade_proto.cpp b/src/caffe/util/upgrade_proto.cpp
index cc7ed51..94546ac 100644 (file)
--- a/src/caffe/util/upgrade_proto.cpp
+++ b/src/caffe/util/upgrade_proto.cpp
@@ -295,6 +295,9 @@ bool UpgradeV0LayerConnection(const V0LayerConnection& v0_layer_connection,
       if (type == "data") {
         layer_param->mutable_data_param()->set_batch_size(
             v0_layer_param.batchsize());
+      } else if (type == "hdf5_data") {
+        layer_param->mutable_hdf5_data_param()->set_batch_size(
+            v0_layer_param.batchsize());
       } else {
         LOG(ERROR) << "Unknown parameter batchsize for layer type " << type;
         is_fully_compatible = false;