add v1 to v2 upgrade tests

diff --git a/src/caffe/test/test_upgrade_proto.cpp b/src/caffe/test/test_upgrade_proto.cpp
index 52e7f1f..3a135a6 100644 (file)
--- a/src/caffe/test/test_upgrade_proto.cpp
+++ b/src/caffe/test/test_upgrade_proto.cpp
@@ -1083,7 +1083,7 @@ TEST_F(PaddingLayerUpgradeTest, TestImageNet) {
   this->RunPaddingUpgradeTest(input_proto, expected_output_proto);
 }
 
-class V0UpgradeTest : public ::testing::Test {
+class NetUpgradeTest : public ::testing::Test {
  protected:
   void RunV0UpgradeTest(
       const string& input_param_string, const string& output_param_string) {
@@ -1101,10 +1101,27 @@ class V0UpgradeTest : public ::testing::Test {
     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(V0UpgradeTest, TestSimple) {
-  const string& input_proto =
+TEST_F(NetUpgradeTest, TestSimple) {
+  const string& v0_proto =
       "name: 'CaffeNet' "
       "layers { "
       "  layer { "
@@ -1180,7 +1197,7 @@ TEST_F(V0UpgradeTest, TestSimple) {
       "  bottom: 'fc8' "
       "  bottom: 'label' "
       "} ";
-  const string& expected_output_proto =
+  const string& expected_v1_proto =
       "name: 'CaffeNet' "
       "layers { "
       "  name: 'data' "
@@ -1248,11 +1265,81 @@ TEST_F(V0UpgradeTest, TestSimple) {
       "  bottom: 'fc8' "
       "  bottom: 'label' "
       "} ";
-  this->RunV0UpgradeTest(input_proto, expected_output_proto);
+  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. "
+      "    } "
+      "  } "
+      "  blobs_lr: 1. "
+      "  blobs_lr: 2. "
+      "  weight_decay: 1. "
+      "  weight_decay: 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 "
+      "    } "
+      "  } "
+      "  blobs_lr: 1. "
+      "  blobs_lr: 2. "
+      "  weight_decay: 1. "
+      "  weight_decay: 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(V0UpgradeTest, TestAllParams) {
+TEST_F(NetUpgradeTest, TestAllParams) {
   const string& input_proto =
       "name: 'CaffeNet' "
       "input: 'input_data' "
@@ -1752,8 +1839,8 @@ TEST_F(V0UpgradeTest, TestAllParams) {
   this->RunV0UpgradeTest(input_proto, expected_output_proto);
 }
 
-TEST_F(V0UpgradeTest, TestImageNet) {
-  const string& input_proto =
+TEST_F(NetUpgradeTest, TestImageNet) {
+  const string& v0_proto =
       "name: 'CaffeNet' "
       "layers { "
       "  layer { "
@@ -2118,7 +2205,7 @@ TEST_F(V0UpgradeTest, TestImageNet) {
       "  bottom: 'fc8' "
       "  bottom: 'label' "
       "} ";
-  const string& expected_output_proto =
+  const string& expected_v1_proto =
       "name: 'CaffeNet' "
       "layers { "
       "  name: 'data' "
@@ -2437,7 +2524,328 @@ TEST_F(V0UpgradeTest, TestImageNet) {
       "  bottom: 'fc8' "
       "  bottom: 'label' "
       "} ";
-  this->RunV0UpgradeTest(input_proto, expected_output_proto);
-}
+  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. "
+      "    } "
+      "  } "
+      "  blobs_lr: 1. "
+      "  blobs_lr: 2. "
+      "  weight_decay: 1. "
+      "  weight_decay: 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. "
+      "    } "
+      "  } "
+      "  blobs_lr: 1. "
+      "  blobs_lr: 2. "
+      "  weight_decay: 1. "
+      "  weight_decay: 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. "
+      "    } "
+      "  } "
+      "  blobs_lr: 1. "
+      "  blobs_lr: 2. "
+      "  weight_decay: 1. "
+      "  weight_decay: 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. "
+      "    } "
+      "  } "
+      "  blobs_lr: 1. "
+      "  blobs_lr: 2. "
+      "  weight_decay: 1. "
+      "  weight_decay: 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. "
+      "    } "
+      "  } "
+      "  blobs_lr: 1. "
+      "  blobs_lr: 2. "
+      "  weight_decay: 1. "
+      "  weight_decay: 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. "
+      "    } "
+      "  } "
+      "  blobs_lr: 1. "
+      "  blobs_lr: 2. "
+      "  weight_decay: 1. "
+      "  weight_decay: 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. "
+      "    } "
+      "  } "
+      "  blobs_lr: 1. "
+      "  blobs_lr: 2. "
+      "  weight_decay: 1. "
+      "  weight_decay: 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 "
+      "    } "
+      "  } "
+      "  blobs_lr: 1. "
+      "  blobs_lr: 2. "
+      "  weight_decay: 1. "
+      "  weight_decay: 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)
 
-}  // namespace caffe
+}  // NOLINT(readability/fn_size)  // namespace caffe