From 17c24094ac283ecc7c0fb6ac5f9c13981b9579be Mon Sep 17 00:00:00 2001
From: chuanqiw <chuanqi.wang@intel.com>
Date: Thu, 6 Sep 2018 22:55:17 +0800
Subject: [PATCH 01/26] modify concat scale unify method and update inceptionv3
 int8 prototxt. Change-Id: Ib705f750b277244a905349628ca95de8bbb2049d

---
 .../int8/inceptionv3_int8.prototxt            | 34966 ++++++++--------
 .../int8/inceptionv3_int8_acc.prototxt        | 21589 ++++++++++
 scripts/calibrator.py                         |    14 +-
 3 files changed, 39082 insertions(+), 17487 deletions(-)
 create mode 100644 models/intel_optimized_models/int8/inceptionv3_int8_acc.prototxt

diff --git a/models/intel_optimized_models/int8/inceptionv3_int8.prototxt b/models/intel_optimized_models/int8/inceptionv3_int8.prototxt
index ec2e3b753..ce4bbee23 100644
--- a/models/intel_optimized_models/int8/inceptionv3_int8.prototxt
+++ b/models/intel_optimized_models/int8/inceptionv3_int8.prototxt
@@ -1,4 +1,5 @@
 name: "InceptionV3"
+
 layer {
   name: "data"
   type: "DummyData"
@@ -7,15 +8,16 @@ layer {
     data_filler {
       type: "constant"
       value: 0.01
-    }   
+    }
     shape {
       dim: 64
       dim: 3
       dim: 299
-      dim: 299 
-    }   
+      dim: 299
+    }
   }
 }
+
 layer {
   name: "conv_conv2d"
   type: "Convolution"
@@ -33,7 +35,7 @@ layer {
     stride: 2
     weight_filler {
       type: "gaussian"
-      std: 0.00999999977648
+      std: 0.01
     }
   }
 }
@@ -68,47 +70,47 @@ layer {
     stride: 1
     weight_filler {
       type: "gaussian"
-      std: 0.00999999977648
+      std: 0.01
     }
   }
   quantization_param {
     bw_layer_in: 8
     bw_layer_out: 8
     bw_params: 8
-    scale_in: 64.0800018311
-    scale_out: 45.8899993896
-    scale_params: 252.449996948
-    scale_params: 167.039993286
-    scale_params: 1207.32995605
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-    scale_params: 212.5
-    scale_params: 198.770004272
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-    scale_params: 238.470001221
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-    scale_params: 655.109985352
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-    scale_params: 172.479995728
-    scale_params: 124.379997253
+    scale_in: 71.6240293266
+    scale_out: 46.751291122
+    scale_params: 252.45444554
+    scale_params: 167.035860665
+    scale_params: 1207.3269412
+    scale_params: 1072.40737768
+    scale_params: 1075.72182979
+    scale_params: 91.3528991245
+    scale_params: 1389.04496976
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+    scale_params: 1210.28619856
+    scale_params: 2963.05843668
+    scale_params: 212.500907018
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+    scale_params: 1247.43279478
+    scale_params: 235.291787881
+    scale_params: 110.0454524
+    scale_params: 191.262698213
+    scale_params: 104.211152374
+    scale_params: 128.839975815
+    scale_params: 61.5235986337
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+    scale_params: 238.469986023
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+    scale_params: 655.110746804
+    scale_params: 7.87616182794e+20
+    scale_params: 172.483722652
+    scale_params: 124.383030159
   }
 }
 layer {
@@ -142,79 +144,79 @@ layer {
     stride: 1
     weight_filler {
       type: "gaussian"
-      std: 0.00999999977648
+      std: 0.01
     }
   }
   quantization_param {
     bw_layer_in: 8
     bw_layer_out: 8
     bw_params: 8
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 }
 layer {
@@ -238,9 +240,9 @@ layer {
   top: "pool"
   pooling_param {
     pool: MAX
-    pad: 0
     kernel_size: 3
     stride: 2
+    pad: 0
   }
 }
 layer {
@@ -260,95 +262,95 @@ layer {
     stride: 1
     weight_filler {
       type: "gaussian"
-      std: 0.00999999977648
+      std: 0.01
     }
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@@ -382,207 +384,207 @@ layer {
     stride: 1
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-      std: 0.00999999977648
+      std: 0.01
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   pooling_param {
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     kernel_size: 3
     stride: 1
+    pad: 1
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 }
 layer {
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   pooling_param {
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-    pad: 1
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     stride: 1
+    pad: 1
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 }
 layer {
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 }
 layer {
@@ -21508,9 +21510,9 @@ layer {
   top: "global_pool"
   pooling_param {
     pool: AVE
-    pad: 0
     kernel_size: 8
     stride: 1
+    pad: 0
   }
 }
 layer {
@@ -21519,7 +21521,7 @@ layer {
   bottom: "global_pool"
   top: "global_pool"
   dropout_param {
-    dropout_ratio: 0.800000011921
+    dropout_ratio: 0.8
   }
 }
 layer {
diff --git a/models/intel_optimized_models/int8/inceptionv3_int8_acc.prototxt b/models/intel_optimized_models/int8/inceptionv3_int8_acc.prototxt
new file mode 100644
index 000000000..ddc0a2949
--- /dev/null
+++ b/models/intel_optimized_models/int8/inceptionv3_int8_acc.prototxt
@@ -0,0 +1,21589 @@
+name: "InceptionV3"
+layer {
+  name: "data"
+  type: "Data"
+  top: "data"
+  top: "label"
+  include {
+    phase: TEST
+  }
+  transform_param {
+    mirror: false
+    crop_size: 299
+    mean_value: 104.0
+    mean_value: 117.0
+    mean_value: 123.0
+    random_resize_param {
+      min_size: 320
+      max_size: 320
+      resize_param {
+        interp_mode: CUBIC
+      }
+    }
+  }
+  data_param {
+    source: "/lustre/dataset/lmdb/imagenet/lmdb_raw_compressed/ilsvrc12_val_lmdb"
+    batch_size: 50
+    backend: LMDB
+    shuffle: false
+  }
+}
+layer {
+  name: "conv_conv2d"
+  type: "Convolution"
+  bottom: "data"
+  top: "conv_conv2d"
+  param {
+    lr_mult: 1.0
+    decay_mult: 1.0
+  }
+  convolution_param {
+    num_output: 32
+    bias_term: false
+    pad: 0
+    kernel_size: 3
+    stride: 2
+    weight_filler {
+      type: "gaussian"
+      std: 0.01
+    }
+  }
+}
+layer {
+  name: "conv_batchnorm"
+  type: "BatchNorm"
+  bottom: "conv_conv2d"
+  top: "conv_conv2d_bn"
+  batch_norm_param {
+  }
+}
+layer {
+  name: "conv_relu"
+  type: "ReLU"
+  bottom: "conv_conv2d_bn"
+  top: "conv_conv2d_relu"
+}
+layer {
+  name: "conv_1_1_conv2d"
+  type: "Convolution"
+  bottom: "conv_conv2d_relu"
+  top: "conv_1_1_conv2d"
+  param {
+    lr_mult: 1.0
+    decay_mult: 1.0
+  }
+  convolution_param {
+    num_output: 32
+    bias_term: false
+    pad: 0
+    kernel_size: 3
+    stride: 1
+    weight_filler {
+      type: "gaussian"
+      std: 0.01
+    }
+  }
+  quantization_param {
+    bw_layer_in: 8
+    bw_layer_out: 8
+    bw_params: 8
+    scale_in: 71.6240293266
+    scale_out: 46.751291122
+    scale_params: 252.45444554
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+    scale_params: 7.87616182794e+20
+    scale_params: 172.483722652
+    scale_params: 124.383030159
+  }
+}
+layer {
+  name: "conv_1_1_batchnorm"
+  type: "BatchNorm"
+  bottom: "conv_1_1_conv2d"
+  top: "conv_1_1_conv2d_bn"
+  batch_norm_param {
+  }
+}
+layer {
+  name: "conv_1_1_relu"
+  type: "ReLU"
+  bottom: "conv_1_1_conv2d_bn"
+  top: "conv_1_1_conv2d_relu"
+}
+layer {
+  name: "conv_2_2_conv2d"
+  type: "Convolution"
+  bottom: "conv_1_1_conv2d_relu"
+  top: "conv_2_2_conv2d"
+  param {
+    lr_mult: 1.0
+    decay_mult: 1.0
+  }
+  convolution_param {
+    num_output: 64
+    bias_term: false
+    pad: 1
+    kernel_size: 3
+    stride: 1
+    weight_filler {
+      type: "gaussian"
+      std: 0.01
+    }
+  }
+  quantization_param {
+    bw_layer_in: 8
+    bw_layer_out: 8
+    bw_params: 8
+    scale_in: 46.751291122
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+  bottom: "mixed_tower_conv_conv2d_bn"
+  top: "mixed_tower_conv_conv2d_relu"
+}
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+  bottom: "mixed_tower_conv_conv2d_relu"
+  top: "mixed_tower_conv_1_conv2d"
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+  top: "mixed_tower_conv_1_conv2d_bn"
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+}
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+  bottom: "mixed_tower_conv_1_conv2d_bn"
+  top: "mixed_tower_conv_1_conv2d_relu"
+}
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+  top: "mixed_tower_1_conv_conv2d"
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+  top: "mixed_tower_1_conv_conv2d_bn"
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+}
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+  bottom: "mixed_tower_1_conv_conv2d_bn"
+  top: "mixed_tower_1_conv_conv2d_relu"
+}
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+  bottom: "mixed_tower_1_conv_conv2d_relu"
+  top: "mixed_tower_1_conv_1_conv2d"
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+  top: "mixed_tower_1_conv_1_conv2d_bn"
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+}
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+  top: "mixed_tower_1_conv_1_conv2d_relu"
+}
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+  type: "Convolution"
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+  top: "mixed_tower_1_conv_2_conv2d"
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+  type: "BatchNorm"
+  bottom: "mixed_tower_1_conv_2_conv2d"
+  top: "mixed_tower_1_conv_2_conv2d_bn"
+  batch_norm_param {
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+}
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+  type: "ReLU"
+  bottom: "mixed_tower_1_conv_2_conv2d_bn"
+  top: "mixed_tower_1_conv_2_conv2d_relu"
+}
+layer {
+  name: "AVE_pool_mixed_pool"
+  type: "Pooling"
+  bottom: "pool1"
+  top: "AVE_pool_mixed_pool"
+  pooling_param {
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+    stride: 1
+    pad: 1
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+}
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+  type: "Convolution"
+  bottom: "AVE_pool_mixed_pool"
+  top: "mixed_tower_2_conv_conv2d"
+  param {
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+}
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+  type: "BatchNorm"
+  bottom: "mixed_tower_2_conv_conv2d"
+  top: "mixed_tower_2_conv_conv2d_bn"
+  batch_norm_param {
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+}
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+  type: "ReLU"
+  bottom: "mixed_tower_2_conv_conv2d_bn"
+  top: "mixed_tower_2_conv_conv2d_relu"
+}
+layer {
+  name: "ch_concat_mixed_chconcat"
+  type: "Concat"
+  bottom: "mixed_conv_conv2d_relu"
+  bottom: "mixed_tower_conv_1_conv2d_relu"
+  bottom: "mixed_tower_1_conv_2_conv2d_relu"
+  bottom: "mixed_tower_2_conv_conv2d_relu"
+  top: "ch_concat_mixed_chconcat"
+  concat_param {
+    axis: 1
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+}
+layer {
+  name: "mixed_1_conv_conv2d"
+  type: "Convolution"
+  bottom: "ch_concat_mixed_chconcat"
+  top: "mixed_1_conv_conv2d"
+  param {
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+}
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+  type: "BatchNorm"
+  bottom: "mixed_1_tower_1_conv_2_conv2d"
+  top: "mixed_1_tower_1_conv_2_conv2d_bn"
+  batch_norm_param {
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+}
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+  name: "mixed_1_tower_1_conv_2_relu"
+  type: "ReLU"
+  bottom: "mixed_1_tower_1_conv_2_conv2d_bn"
+  top: "mixed_1_tower_1_conv_2_conv2d_relu"
+}
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+  name: "AVE_pool_mixed_1_pool"
+  type: "Pooling"
+  bottom: "ch_concat_mixed_chconcat"
+  top: "AVE_pool_mixed_1_pool"
+  pooling_param {
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+    stride: 1
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+}
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+  type: "Convolution"
+  bottom: "AVE_pool_mixed_1_pool"
+  top: "mixed_1_tower_2_conv_conv2d"
+  param {
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+  bottom: "mixed_1_tower_2_conv_conv2d"
+  top: "mixed_1_tower_2_conv_conv2d_bn"
+  batch_norm_param {
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+}
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+  type: "ReLU"
+  bottom: "mixed_1_tower_2_conv_conv2d_bn"
+  top: "mixed_1_tower_2_conv_conv2d_relu"
+}
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+  type: "Concat"
+  bottom: "mixed_1_conv_conv2d_relu"
+  bottom: "mixed_1_tower_conv_1_conv2d_relu"
+  bottom: "mixed_1_tower_1_conv_2_conv2d_relu"
+  bottom: "mixed_1_tower_2_conv_conv2d_relu"
+  top: "ch_concat_mixed_1_chconcat"
+  concat_param {
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+}
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+  bottom: "ch_concat_mixed_1_chconcat"
+  top: "mixed_2_conv_conv2d"
+  param {
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+  type: "BatchNorm"
+  bottom: "mixed_2_conv_conv2d"
+  top: "mixed_2_conv_conv2d_bn"
+  batch_norm_param {
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+}
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+  type: "ReLU"
+  bottom: "mixed_2_conv_conv2d_bn"
+  top: "mixed_2_conv_conv2d_relu"
+}
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+  bottom: "ch_concat_mixed_1_chconcat"
+  top: "mixed_2_tower_conv_conv2d"
+  param {
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+  top: "mixed_2_tower_conv_conv2d_bn"
+  batch_norm_param {
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+}
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+  type: "ReLU"
+  bottom: "mixed_2_tower_conv_conv2d_bn"
+  top: "mixed_2_tower_conv_conv2d_relu"
+}
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+  bottom: "mixed_2_tower_conv_conv2d_relu"
+  top: "mixed_2_tower_conv_1_conv2d"
+  param {
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+}
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+  bottom: "mixed_2_tower_conv_1_conv2d"
+  top: "mixed_2_tower_conv_1_conv2d_bn"
+  batch_norm_param {
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+}
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+  type: "ReLU"
+  bottom: "mixed_2_tower_conv_1_conv2d_bn"
+  top: "mixed_2_tower_conv_1_conv2d_relu"
+}
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+  type: "Convolution"
+  bottom: "ch_concat_mixed_1_chconcat"
+  top: "mixed_2_tower_1_conv_conv2d"
+  param {
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+  bottom: "mixed_2_tower_1_conv_conv2d"
+  top: "mixed_2_tower_1_conv_conv2d_bn"
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+}
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+  type: "ReLU"
+  bottom: "mixed_2_tower_1_conv_conv2d_bn"
+  top: "mixed_2_tower_1_conv_conv2d_relu"
+}
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+  type: "Convolution"
+  bottom: "mixed_2_tower_1_conv_conv2d_relu"
+  top: "mixed_2_tower_1_conv_1_conv2d"
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+}
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+  top: "AVE_pool_mixed_2_pool"
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+}
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+  top: "mixed_3_tower_conv_1_conv2d_relu"
+}
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+  top: "mixed_3_tower_conv_2_conv2d"
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+  type: "BatchNorm"
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+  top: "mixed_3_tower_conv_2_conv2d_bn"
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+}
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+  type: "ReLU"
+  bottom: "mixed_3_tower_conv_2_conv2d_bn"
+  top: "mixed_3_tower_conv_2_conv2d_relu"
+}
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+  type: "Pooling"
+  bottom: "ch_concat_mixed_2_chconcat"
+  top: "max_pool_mixed_3_pool"
+  pooling_param {
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+}
+layer {
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+  type: "Concat"
+  bottom: "max_pool_mixed_3_pool"
+  bottom: "mixed_3_conv_conv2d_relu"
+  bottom: "mixed_3_tower_conv_2_conv2d_relu"
+  top: "ch_concat_mixed_3_chconcat"
+  concat_param {
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+}
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+  type: "Convolution"
+  bottom: "ch_concat_mixed_3_chconcat"
+  top: "mixed_4_conv_conv2d"
+  param {
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+  bottom: "mixed_4_tower_2_conv_conv2d_relu"
+  top: "ch_concat_mixed_4_chconcat"
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+  top: "mixed_5_conv_conv2d_relu"
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+  type: "ReLU"
+  bottom: "mixed_5_tower_conv_conv2d_bn"
+  top: "mixed_5_tower_conv_conv2d_relu"
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+  type: "Convolution"
+  bottom: "mixed_5_tower_conv_conv2d_relu"
+  top: "mixed_5_tower_conv_1_conv2d"
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+}
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+}
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+  type: "ReLU"
+  bottom: "mixed_5_tower_1_conv_3_conv2d_bn"
+  top: "mixed_5_tower_1_conv_3_conv2d_relu"
+}
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+  top: "mixed_5_tower_1_conv_4_conv2d"
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+}
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+  top: "mixed_5_tower_1_conv_4_conv2d_relu"
+}
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+  top: "AVE_pool_mixed_5_pool"
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+}
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+  bottom: "AVE_pool_mixed_5_pool"
+  top: "mixed_5_tower_2_conv_conv2d"
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+}
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+  type: "ReLU"
+  bottom: "mixed_5_tower_2_conv_conv2d_bn"
+  top: "mixed_5_tower_2_conv_conv2d_relu"
+}
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+  bottom: "mixed_5_tower_conv_2_conv2d_relu"
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+  bottom: "mixed_5_tower_2_conv_conv2d_relu"
+  top: "ch_concat_mixed_5_chconcat"
+  concat_param {
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+}
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+  bottom: "ch_concat_mixed_5_chconcat"
+  top: "mixed_6_conv_conv2d"
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+  top: "AVE_pool_mixed_6_pool"
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+}
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+  bottom: "mixed_6_tower_conv_2_conv2d_relu"
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+  top: "ch_concat_mixed_6_chconcat"
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+}
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+}
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+  bottom: "mixed_7_tower_1_conv_4_conv2d_bn"
+  top: "mixed_7_tower_1_conv_4_conv2d_relu"
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+  top: "mixed_7_tower_2_conv_conv2d_relu"
+}
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+  bottom: "mixed_7_tower_conv_2_conv2d_relu"
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+  bottom: "mixed_7_tower_2_conv_conv2d_relu"
+  top: "ch_concat_mixed_7_chconcat"
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+}
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+  top: "mixed_8_tower_conv_conv2d"
+  param {
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+  top: "mixed_8_tower_conv_conv2d_relu"
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+  name: "mixed_8_tower_1_conv_3_batchnorm"
+  type: "BatchNorm"
+  bottom: "mixed_8_tower_1_conv_3_conv2d"
+  top: "mixed_8_tower_1_conv_3_conv2d_bn"
+  batch_norm_param {
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+}
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+  name: "mixed_8_tower_1_conv_3_relu"
+  type: "ReLU"
+  bottom: "mixed_8_tower_1_conv_3_conv2d_bn"
+  top: "mixed_8_tower_1_conv_3_conv2d_relu"
+}
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+  name: "MAX_pool_mixed_8_pool"
+  type: "Pooling"
+  bottom: "ch_concat_mixed_7_chconcat"
+  top: "MAX_pool_mixed_8_pool"
+  pooling_param {
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+}
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+  type: "Concat"
+  bottom: "mixed_8_tower_conv_1_conv2d_relu"
+  bottom: "mixed_8_tower_1_conv_3_conv2d_relu"
+  bottom: "MAX_pool_mixed_8_pool"
+  top: "ch_concat_mixed_8_chconcat"
+  concat_param {
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+}
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+  type: "Convolution"
+  bottom: "ch_concat_mixed_8_chconcat"
+  top: "mixed_9_conv_conv2d"
+  param {
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+  top: "mixed_9_conv_conv2d_bn"
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+  bottom: "mixed_9_conv_conv2d_bn"
+  top: "mixed_9_conv_conv2d_relu"
+}
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+  bottom: "ch_concat_mixed_8_chconcat"
+  top: "mixed_9_tower_conv_conv2d"
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+  top: "mixed_9_tower_conv_conv2d_relu"
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+  type: "ReLU"
+  bottom: "mixed_9_tower_mixed_conv_1_conv2d_bn"
+  top: "mixed_9_tower_mixed_conv_1_conv2d_relu"
+}
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+  type: "Convolution"
+  bottom: "ch_concat_mixed_8_chconcat"
+  top: "mixed_9_tower_1_conv_conv2d"
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+}
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+  top: "mixed_9_tower_1_conv_conv2d_relu"
+}
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+  bottom: "mixed_9_tower_1_conv_conv2d_relu"
+  top: "mixed_9_tower_1_conv_1_conv2d"
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+}
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+  type: "BatchNorm"
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+  top: "mixed_9_tower_1_mixed_conv_1_conv2d_bn"
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+}
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+  type: "ReLU"
+  bottom: "mixed_9_tower_1_mixed_conv_1_conv2d_bn"
+  top: "mixed_9_tower_1_mixed_conv_1_conv2d_relu"
+}
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+  bottom: "mixed_10_conv_conv2d_bn"
+  top: "mixed_10_conv_conv2d_relu"
+}
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+  bottom: "ch_concat_mixed_9_chconcat"
+  top: "mixed_10_tower_conv_conv2d"
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+}
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+  top: "mixed_10_tower_mixed_conv_1_conv2d_relu"
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+    scale_params: 90.927366379
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+    scale_params: 93.186063847
+    scale_params: 105.523917065
+    scale_params: 89.104122664
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+    scale_params: 80.3298884871
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+    scale_params: 90.4744124678
+    scale_params: 96.8453708581
+    scale_params: 106.095692877
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+    scale_params: 93.825478356
+    scale_params: 120.874452402
+    scale_params: 70.2311391545
+    scale_params: 98.0804891903
+    scale_params: 102.297435509
+    scale_params: 95.9166484274
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+    scale_params: 89.517950297
+    scale_params: 110.211982309
+    scale_params: 106.999732539
+    scale_params: 92.5529897385
+    scale_params: 101.896035847
+    scale_params: 82.5854694043
+    scale_params: 82.0237911778
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+    scale_params: 73.0674288215
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+    scale_params: 110.680183804
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+    scale_params: 102.642986639
+    scale_params: 81.7648801119
+    scale_params: 84.0282862961
+    scale_params: 105.712025772
+    scale_params: 105.859535498
+    scale_params: 112.307796331
+    scale_params: 115.906526539
+    scale_params: 85.8816586803
+  }
+}
+layer {
+  name: "mixed_10_tower_1_mixed_conv_1_batchnorm"
+  type: "BatchNorm"
+  bottom: "mixed_10_tower_1_mixed_conv_1_conv2d"
+  top: "mixed_10_tower_1_mixed_conv_1_conv2d_bn"
+  batch_norm_param {
+  }
+}
+layer {
+  name: "mixed_10_tower_1_mixed_conv_1_relu"
+  type: "ReLU"
+  bottom: "mixed_10_tower_1_mixed_conv_1_conv2d_bn"
+  top: "mixed_10_tower_1_mixed_conv_1_conv2d_relu"
+}
+layer {
+  name: "MAX_pool_mixed_10_pool"
+  type: "Pooling"
+  bottom: "ch_concat_mixed_9_chconcat"
+  top: "MAX_pool_mixed_10_pool"
+  pooling_param {
+    pool: MAX
+    kernel_size: 3
+    stride: 1
+    pad: 1
+  }
+}
+layer {
+  name: "mixed_10_tower_2_conv_conv2d"
+  type: "Convolution"
+  bottom: "MAX_pool_mixed_10_pool"
+  top: "mixed_10_tower_2_conv_conv2d"
+  param {
+    lr_mult: 1.0
+    decay_mult: 1.0
+  }
+  convolution_param {
+    num_output: 192
+    bias_term: false
+    pad: 0
+    kernel_size: 1
+    stride: 1
+    weight_filler {
+      type: "gaussian"
+      std: 0.01
+    }
+  }
+  quantization_param {
+    bw_layer_in: 8
+    bw_layer_out: 8
+    bw_params: 8
+    scale_in: 168.412537114
+    scale_out: 18.4830402873
+    scale_params: 117.076452171
+    scale_params: 109.38132524
+    scale_params: 126.485560138
+    scale_params: 139.175996717
+    scale_params: 146.788217903
+    scale_params: 117.146420984
+    scale_params: 148.351520816
+    scale_params: 125.551606463
+    scale_params: 120.896440514
+    scale_params: 81.6685536965
+    scale_params: 109.432008758
+    scale_params: 135.386090775
+    scale_params: 141.227823448
+    scale_params: 93.7501537567
+    scale_params: 146.569366235
+    scale_params: 105.036389303
+    scale_params: 70.0361777002
+    scale_params: 108.307459076
+    scale_params: 94.6549094197
+    scale_params: 109.828892391
+    scale_params: 102.363369408
+    scale_params: 128.029734391
+    scale_params: 138.029739887
+    scale_params: 100.09099304
+    scale_params: 109.255208156
+    scale_params: 168.4898205
+    scale_params: 150.684789616
+    scale_params: 175.781745186
+    scale_params: 143.984454294
+    scale_params: 138.375980699
+    scale_params: 138.64648539
+    scale_params: 187.492915865
+    scale_params: 104.455047109
+    scale_params: 120.023002528
+    scale_params: 126.165754075
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+    scale_params: 129.690456772
+    scale_params: 139.228452572
+    scale_params: 128.45203211
+    scale_params: 91.2505137884
+    scale_params: 85.0394696573
+    scale_params: 118.49440497
+    scale_params: 129.981160933
+    scale_params: 145.927072572
+    scale_params: 158.514772473
+    scale_params: 123.184750783
+    scale_params: 117.115899923
+    scale_params: 71.314448879
+    scale_params: 127.39450252
+    scale_params: 154.45501235
+    scale_params: 98.7179644564
+    scale_params: 138.888936966
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+    scale_params: 134.759584618
+    scale_params: 117.905255525
+    scale_params: 74.3244709103
+    scale_params: 119.271782779
+    scale_params: 132.289230185
+    scale_params: 103.931971226
+    scale_params: 135.629729601
+    scale_params: 114.300485074
+    scale_params: 148.122518302
+    scale_params: 148.706364756
+    scale_params: 132.560244801
+    scale_params: 153.083277138
+    scale_params: 141.534307236
+    scale_params: 155.420569183
+    scale_params: 134.893717594
+    scale_params: 51.6420816812
+    scale_params: 134.739192014
+    scale_params: 134.652390156
+    scale_params: 122.135072602
+    scale_params: 141.416096843
+    scale_params: 92.0481408554
+    scale_params: 73.3419755923
+    scale_params: 131.001188764
+    scale_params: 146.251645009
+    scale_params: 122.26412437
+    scale_params: 110.726726826
+    scale_params: 157.140179207
+    scale_params: 121.429084535
+    scale_params: 137.715991792
+    scale_params: 128.216701759
+    scale_params: 112.282087248
+    scale_params: 154.860366041
+    scale_params: 87.3773199391
+    scale_params: 133.997566832
+    scale_params: 117.152127739
+    scale_params: 161.835633916
+    scale_params: 148.282977935
+    scale_params: 126.987677578
+    scale_params: 134.816500093
+    scale_params: 94.3145513361
+    scale_params: 124.591640509
+    scale_params: 135.866180601
+    scale_params: 156.966707791
+    scale_params: 141.977308887
+    scale_params: 91.5486595533
+    scale_params: 149.900734734
+    scale_params: 135.672642965
+    scale_params: 92.2778174532
+    scale_params: 133.557587549
+    scale_params: 134.561102505
+    scale_params: 159.859648649
+    scale_params: 78.7038236345
+    scale_params: 96.6767049453
+    scale_params: 146.737996163
+    scale_params: 115.863050255
+    scale_params: 141.238495663
+    scale_params: 96.7147246967
+    scale_params: 105.375881315
+    scale_params: 145.192329136
+    scale_params: 136.910056719
+    scale_params: 89.1284169834
+    scale_params: 157.655283882
+    scale_params: 110.407843591
+    scale_params: 144.804796628
+    scale_params: 132.330671781
+    scale_params: 119.187077558
+    scale_params: 141.267215676
+    scale_params: 87.3857914992
+    scale_params: 137.261208896
+    scale_params: 122.693277865
+    scale_params: 161.999123518
+    scale_params: 73.7566291284
+    scale_params: 96.0848195462
+    scale_params: 117.138460794
+    scale_params: 148.854062749
+    scale_params: 143.275684461
+    scale_params: 140.924183389
+    scale_params: 87.8938165707
+    scale_params: 127.999438671
+    scale_params: 82.4688687251
+    scale_params: 120.147478202
+    scale_params: 137.083061907
+    scale_params: 79.0554027288
+    scale_params: 136.265129816
+    scale_params: 128.649354434
+    scale_params: 146.440948744
+    scale_params: 87.5277801167
+    scale_params: 162.975184355
+    scale_params: 150.784867436
+    scale_params: 91.0905694123
+    scale_params: 152.635938989
+    scale_params: 114.428324219
+    scale_params: 121.882274445
+    scale_params: 153.840686598
+    scale_params: 129.760869229
+    scale_params: 170.070251006
+    scale_params: 81.2849054231
+    scale_params: 126.380662127
+    scale_params: 140.651123687
+    scale_params: 145.235568081
+    scale_params: 72.0536897282
+    scale_params: 126.395926109
+    scale_params: 134.747517041
+    scale_params: 75.4978631623
+    scale_params: 148.285289541
+    scale_params: 148.049434062
+    scale_params: 160.49081796
+    scale_params: 58.4234355058
+    scale_params: 134.790675401
+    scale_params: 120.347073881
+    scale_params: 93.2576678781
+    scale_params: 120.714808995
+    scale_params: 65.4427151456
+    scale_params: 156.856030127
+    scale_params: 120.480124995
+    scale_params: 128.491406293
+    scale_params: 134.650322378
+    scale_params: 140.149582243
+    scale_params: 118.829531919
+    scale_params: 84.4938100452
+    scale_params: 106.987053034
+    scale_params: 121.697976771
+    scale_params: 133.026730257
+    scale_params: 109.648404269
+    scale_params: 147.231896136
+    scale_params: 92.0838402533
+    scale_params: 78.7592031589
+    scale_params: 179.912165435
+    scale_params: 151.535921273
+    scale_params: 130.144464501
+    scale_params: 147.793914019
+    scale_params: 120.227041597
+    scale_params: 147.953651837
+    scale_params: 137.34146555
+    scale_params: 121.021965532
+    scale_params: 120.089268999
+    scale_params: 151.548175986
+    scale_params: 140.497186702
+    scale_params: 158.829674157
+  }
+}
+layer {
+  name: "mixed_10_tower_2_conv_batchnorm"
+  type: "BatchNorm"
+  bottom: "mixed_10_tower_2_conv_conv2d"
+  top: "mixed_10_tower_2_conv_conv2d_bn"
+  batch_norm_param {
+  }
+}
+layer {
+  name: "mixed_10_tower_2_conv_relu"
+  type: "ReLU"
+  bottom: "mixed_10_tower_2_conv_conv2d_bn"
+  top: "mixed_10_tower_2_conv_conv2d_relu"
+}
+layer {
+  name: "ch_concat_mixed_10_chconcat"
+  type: "Concat"
+  bottom: "mixed_10_conv_conv2d_relu"
+  bottom: "mixed_10_tower_mixed_conv_conv2d_relu"
+  bottom: "mixed_10_tower_mixed_conv_1_conv2d_relu"
+  bottom: "mixed_10_tower_1_mixed_conv_conv2d_relu"
+  bottom: "mixed_10_tower_1_mixed_conv_1_conv2d_relu"
+  bottom: "mixed_10_tower_2_conv_conv2d_relu"
+  top: "ch_concat_mixed_10_chconcat"
+  concat_param {
+    axis: 1
+  }
+}
+layer {
+  name: "global_pool"
+  type: "Pooling"
+  bottom: "ch_concat_mixed_10_chconcat"
+  top: "global_pool"
+  pooling_param {
+    pool: AVE
+    kernel_size: 8
+    stride: 1
+    pad: 0
+  }
+}
+layer {
+  name: "drop"
+  type: "Dropout"
+  bottom: "global_pool"
+  top: "global_pool"
+  dropout_param {
+    dropout_ratio: 0.8
+  }
+}
+layer {
+  name: "flatten"
+  type: "Flatten"
+  bottom: "global_pool"
+  top: "flatten"
+}
+layer {
+  name: "fc1"
+  type: "InnerProduct"
+  bottom: "flatten"
+  top: "fc1"
+  param {
+    lr_mult: 1.0
+    decay_mult: 1.0
+  }
+  param {
+    lr_mult: 2.0
+    decay_mult: 0.0
+  }
+  inner_product_param {
+    num_output: 1000
+    weight_filler {
+      type: "xavier"
+    }
+    bias_filler {
+      type: "constant"
+      value: 0.0
+    }
+  }
+}
+layer {
+  name: "acc/top-1"
+  type: "Accuracy"
+  bottom: "fc1"
+  bottom: "label"
+  top: "acc/top-1"
+  include {
+    phase: TEST
+  }
+}
+layer {
+  name: "acc/top-5"
+  type: "Accuracy"
+  bottom: "fc1"
+  bottom: "label"
+  top: "acc/top-5"
+  include {
+    phase: TEST
+  }
+  accuracy_param {
+    top_k: 5
+  }
+}
diff --git a/scripts/calibrator.py b/scripts/calibrator.py
index b14b69bf4..73bc72053 100644
--- a/scripts/calibrator.py
+++ b/scripts/calibrator.py
@@ -88,7 +88,7 @@ def get_input_layers(l, net, end):
     return top_layers
 
 
-def get_input_convolutions(l, net, end, interesting_layers):
+def get_input_convolutions(l, net, end, interesting_layers, uninteresting_layers=[]):
     all_input_layers = []
     input_layers = get_input_layers(l, net, end)
     while True:
@@ -106,9 +106,12 @@ def get_input_convolutions(l, net, end, interesting_layers):
                     all_input_layers.append(lp)
                 continue
 
-            new_input_layers = get_input_layers(net.layer[lp[0]], net, lp[0])
-            input_layers.remove(lp)
-            input_layers.extend(new_input_layers)
+            if lp[2] not in uninteresting_layers:
+                new_input_layers = get_input_layers(net.layer[lp[0]], net, lp[0])
+                input_layers.remove(lp)
+                input_layers.extend(new_input_layers)
+            else:
+                input_layers.remove(lp)
 
     return all_input_layers
 
@@ -259,7 +262,8 @@ def transform_convolutions(model_path, compiled_model_path, top_blobs_map, botto
             for (l, index) in concat_layers:
                 index_in_compiled_net = find_index_by_name(l.name, compiled_concat_layers)
                 assert(index_in_compiled_net >= 0)
-                conv_inputs = get_input_convolutions(l, compiled_net, index_in_compiled_net, ["Convolution"])
+                conv_inputs = get_input_convolutions(l, compiled_net, index_in_compiled_net, ["Convolution"], ["Concat"])
+                # TODO: support resonable cross-levels concat scale unify
                 min_concat_scale = sys.float_info.max
                 concat_input_indexes = []
                 for conv_input in conv_inputs:

From 70df2d22344f4d3261f63763d245972843e7cae7 Mon Sep 17 00:00:00 2001
From: chuanqiw <chuanqi.wang@intel.com>
Date: Mon, 10 Sep 2018 13:46:51 +0800
Subject: [PATCH 02/26] modify rfcn int8 prototxt for performance and accuracy
 test.

---
 .../ResNet-101/rfcn_end2end/test_agnostic_int8.prototxt       | 4 ++--
 1 file changed, 2 insertions(+), 2 deletions(-)

diff --git a/models/intel_optimized_models/rfcn/pascal_voc/ResNet-101/rfcn_end2end/test_agnostic_int8.prototxt b/models/intel_optimized_models/rfcn/pascal_voc/ResNet-101/rfcn_end2end/test_agnostic_int8.prototxt
index 7a2428e88..ae89a5e86 100755
--- a/models/intel_optimized_models/rfcn/pascal_voc/ResNet-101/rfcn_end2end/test_agnostic_int8.prototxt
+++ b/models/intel_optimized_models/rfcn/pascal_voc/ResNet-101/rfcn_end2end/test_agnostic_int8.prototxt
@@ -4,8 +4,8 @@ input: "im_info"
 input_shape {
   dim: 1
   dim: 3
-  dim: 224
-  dim: 224
+  dim: 600
+  dim: 800
 }
 input_shape {
   dim: 1

From b3f22d1d11e517e98e242c1f941ac8dd623c7ed3 Mon Sep 17 00:00:00 2001
From: "Zhang, Guoming" <guoming.zhang@intel.com>
Date: Thu, 20 Sep 2018 22:58:01 +0800
Subject: [PATCH 03/26] update scripts for winograd prototxt generation

---
 examples/pycaffe/tune_model.py | 68 +++++++++-------------------------
 examples/pycaffe/utils.py      |  2 +
 2 files changed, 19 insertions(+), 51 deletions(-)

diff --git a/examples/pycaffe/tune_model.py b/examples/pycaffe/tune_model.py
index ae8c87592..2add04726 100644
--- a/examples/pycaffe/tune_model.py
+++ b/examples/pycaffe/tune_model.py
@@ -1,12 +1,14 @@
 import os
 import sys
 import argparse
+
+sys.path.insert(0, "python")
+import caffe
 from caffe.proto import caffe_pb2
 import google.protobuf.text_format as txtf
 import copy
 import utils
 
-
 def genOptimalModel(net, mkldnn_direct_time_map, mkldnn_winograd_time_map, optimal_model):
     for index in range(0, len(net.layer)):
         l = net.layer[index]
@@ -17,37 +19,21 @@ def genOptimalModel(net, mkldnn_direct_time_map, mkldnn_winograd_time_map, optim
             stride = 1
             if len(l.convolution_param.stride) != 0:
                 stride = l.convolution_param.stride[0]
-            if mkldnn_winograd_time_map[l.name] < mkldnn_direct_time_map[l.name] and kernel_size == 3 and stride == 1 and l.convolution_param.num_output % 16 == 0:
+            if kernel_size == 3 and stride == 1 and l.convolution_param.num_output % 16 ==0:
                 l.convolution_param.conv_algorithm = "winograd"
             else:
                 l.convolution_param.conv_algorithm = "direct"
-
+        
     with open(optimal_model, "w") as f:
         f.write(txtf.MessageToString(net, float_format=".17g"))
 
-
-def tuneModelDefinition(model_path, iteration, is_test_phase, core_num, socket_num):
-    working_dir = sys.path[0]
-    caffe_path = os.path.join(working_dir, "..", "..",
-                              "build", "tools", "caffe")
+def tuneModelDefinition(model_path, iteration):
+    working_dir = sys.argv[0]
+    
+    caffe_path = os.path.join(os.path.dirname(os.path.abspath(working_dir)), "../..",  "build", "tools", "caffe")
     if not os.path.exists(caffe_path):
         print "Caffe binary does not exist; please build Caffe binary first."
         sys.exit(1)
-    core_num_per_socket = int([i for i in os.popen('lscpu').readlines(
-    ) if i.startswith('Core(s) per socket:')][0].strip().split(':')[-1].strip())
-    core_num_per_socket *= socket_num
-    if is_test_phase:
-        caffe_path += ' time -phase TEST -forward_only'
-        if core_num != 0:
-            core_num = core_num_per_socket if core_num > core_num_per_socket else core_num
-            env_prefix = 'OMP_NUM_THREADS={} KMP_HW_SUBSET=1t KMP_AFFINITY=compact,granularity=fine numactl -C 0-{} -m 0 '.format(
-                core_num, core_num)
-        elif socket_num == 1:
-            env_prefix = 'OMP_NUM_THREADS={} KMP_HW_SUBSET=1t KMP_AFFINITY=compact,granularity=fine numactl -C 0-{} -m 0 '.format(
-                core_num_per_socket, core_num_per_socket)
-        else:
-            env_prefix = 'numactl -l '
-        caffe_path = env_prefix + caffe_path
 
     base_model_name = os.path.basename(model_path)
     model_dir = os.path.dirname(model_path)
@@ -83,30 +69,21 @@ def tuneModelDefinition(model_path, iteration, is_test_phase, core_num, socket_n
     mkldnn_winograd_log = "mkldnn_winograd.log"
     mkldnn_direct_log_path = os.path.join(model_dir, mkldnn_direct_log)
     mkldnn_winograd_log_path = os.path.join(model_dir, mkldnn_winograd_log)
-    mkldnn_direct_command = caffe_path + " -model " + direct_model_path + \
-        " -engine MKLDNN -iterations " + \
-        str(iteration) + " >& " + mkldnn_direct_log_path
+    mkldnn_direct_command = caffe_path + " time -model " + direct_model_path + " -engine MKLDNN -iterations " + str(iteration) + " >& " + mkldnn_direct_log_path
     os.system(mkldnn_direct_command)
-    mkldnn_winograd_command = caffe_path + " -model " + winograd_model_path + \
-        " -engine MKLDNN -iterations " + \
-        str(iteration) + " >& " + mkldnn_winograd_log_path
+    mkldnn_winograd_command = caffe_path + " time -model " + winograd_model_path + " -engine MKLDNN -iterations " + str(iteration) + " >& " + mkldnn_winograd_log_path
     os.system(mkldnn_winograd_command)
 
     (model_str, mkldnn_direct_time_lines) = utils.parseLog(mkldnn_direct_log_path)
-    mkldnn_direct_layer_time_map = utils.parseTimeLines(
-        mkldnn_direct_time_lines)
-    (model_str, mkldnn_winograd_time_lines) = utils.parseLog(
-        mkldnn_winograd_log_path)
-    mkldnn_winograd_layer_time_map = utils.parseTimeLines(
-        mkldnn_winograd_time_lines)
+    mkldnn_direct_layer_time_map = utils.parseTimeLines(mkldnn_direct_time_lines)
+    (model_str, mkldnn_winograd_time_lines) = utils.parseLog(mkldnn_winograd_log_path)
+    mkldnn_winograd_layer_time_map = utils.parseTimeLines(mkldnn_winograd_time_lines)
 
     hybrid_model_name = base_model_name.split(".")[0] + "_hybrid.prototxt"
     hybrid_model_path = os.path.join(model_dir, hybrid_model_name)
-    genOptimalModel(base_net, mkldnn_direct_layer_time_map,
-                    mkldnn_winograd_layer_time_map, hybrid_model_path)
+    genOptimalModel(base_net, mkldnn_direct_layer_time_map, mkldnn_winograd_layer_time_map, hybrid_model_path)
     print '{} has been generated.'.format(hybrid_model_path)
 
-
 if __name__ == '__main__':
     parser = argparse.ArgumentParser()
 
@@ -116,17 +93,7 @@ def tuneModelDefinition(model_path, iteration, is_test_phase, core_num, socket_n
     parser.add_argument('-i', '--iteration', action='store', dest='iterations', type=int, default=10,
                         help='require iterations number to run the model')
 
-    parser.add_argument('-t', '--phase', action='store', dest='is_test_phase', type=bool, default=False,
-                        help='Train or Test phase')
-
-    parser.add_argument('-c', '--core_num', action='store', dest='core_num', type=int, default=0,
-                        help='core number for inference')
-
-    parser.add_argument('-s', '--socket', action='store', dest='socket_num', type=int, default=2,
-                        help='socket number for inference')
-
-    parser.add_argument('-v', '--version', action='version',
-                        version='%(prog)s 1.0')
+    parser.add_argument('-v', '--version', action='version', version='%(prog)s 1.0')
 
     params = parser.parse_args()
 
@@ -135,5 +102,4 @@ def tuneModelDefinition(model_path, iteration, is_test_phase, core_num, socket_n
         print "[ERROR] Please specify the model definition file with -m"
         exit(1)
 
-    tuneModelDefinition(params.model, params.iterations,
-                        params.is_test_phase, params.core_num,  params.socket_num)
+    tuneModelDefinition(params.model, params.iterations)
diff --git a/examples/pycaffe/utils.py b/examples/pycaffe/utils.py
index 91c32bba3..bcee8e36c 100755
--- a/examples/pycaffe/utils.py
+++ b/examples/pycaffe/utils.py
@@ -1,5 +1,7 @@
 import os
 import sys
+sys.path.insert(0,"python")
+import caffe
 from caffe.proto import caffe_pb2
 import google.protobuf.text_format as txtf
 

From 1756d401230aecddcefd3648d9eb08c980ca8b85 Mon Sep 17 00:00:00 2001
From: Daisy Deng <daisy.deng@intel.com>
Date: Sun, 30 Sep 2018 19:56:54 +0800
Subject: [PATCH 04/26] remove unused prototxt

---
 .../int8/inceptionv3_int8_mc.prototxt         | 21554 -----------
 .../int8/resnet50_int8_mc.prototxt            | 29412 ----------------
 .../int8/resnet50_sparse_int8.prototxt        |  3068 --
 .../int8/ssd_int8_mc.prototxt                 | 10740 ------
 4 files changed, 64774 deletions(-)
 delete mode 100644 models/intel_optimized_models/int8/inceptionv3_int8_mc.prototxt
 delete mode 100644 models/intel_optimized_models/int8/resnet50_int8_mc.prototxt
 delete mode 100644 models/intel_optimized_models/int8/resnet50_sparse_int8.prototxt
 delete mode 100644 models/intel_optimized_models/int8/ssd_int8_mc.prototxt

diff --git a/models/intel_optimized_models/int8/inceptionv3_int8_mc.prototxt b/models/intel_optimized_models/int8/inceptionv3_int8_mc.prototxt
deleted file mode 100644
index ec2e3b753..000000000
--- a/models/intel_optimized_models/int8/inceptionv3_int8_mc.prototxt
+++ /dev/null
@@ -1,21554 +0,0 @@
-name: "InceptionV3"
-layer {
-  name: "data"
-  type: "DummyData"
-  top: "data"
-  dummy_data_param {
-    data_filler {
-      type: "constant"
-      value: 0.01
-    }   
-    shape {
-      dim: 64
-      dim: 3
-      dim: 299
-      dim: 299 
-    }   
-  }
-}
-layer {
-  name: "conv_conv2d"
-  type: "Convolution"
-  bottom: "data"
-  top: "conv_conv2d"
-  param {
-    lr_mult: 1.0
-    decay_mult: 1.0
-  }
-  convolution_param {
-    num_output: 32
-    bias_term: false
-    pad: 0
-    kernel_size: 3
-    stride: 2
-    weight_filler {
-      type: "gaussian"
-      std: 0.00999999977648
-    }
-  }
-}
-layer {
-  name: "conv_batchnorm"
-  type: "BatchNorm"
-  bottom: "conv_conv2d"
-  top: "conv_conv2d_bn"
-  batch_norm_param {
-  }
-}
-layer {
-  name: "conv_relu"
-  type: "ReLU"
-  bottom: "conv_conv2d_bn"
-  top: "conv_conv2d_relu"
-}
-layer {
-  name: "conv_1_1_conv2d"
-  type: "Convolution"
-  bottom: "conv_conv2d_relu"
-  top: "conv_1_1_conv2d"
-  param {
-    lr_mult: 1.0
-    decay_mult: 1.0
-  }
-  convolution_param {
-    num_output: 32
-    bias_term: false
-    pad: 0
-    kernel_size: 3
-    stride: 1
-    weight_filler {
-      type: "gaussian"
-      std: 0.00999999977648
-    }
-  }
-  quantization_param {
-    bw_layer_in: 8
-    bw_layer_out: 8
-    bw_params: 8
-    scale_in: 64.0800018311
-    scale_out: 45.8899993896
-    scale_params: 252.449996948
-    scale_params: 167.039993286
-    scale_params: 1207.32995605
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-    scale_params: 7.87616172589e+20
-    scale_params: 172.479995728
-    scale_params: 124.379997253
-  }
-}
-layer {
-  name: "conv_1_1_batchnorm"
-  type: "BatchNorm"
-  bottom: "conv_1_1_conv2d"
-  top: "conv_1_1_conv2d_bn"
-  batch_norm_param {
-  }
-}
-layer {
-  name: "conv_1_1_relu"
-  type: "ReLU"
-  bottom: "conv_1_1_conv2d_bn"
-  top: "conv_1_1_conv2d_relu"
-}
-layer {
-  name: "conv_2_2_conv2d"
-  type: "Convolution"
-  bottom: "conv_1_1_conv2d_relu"
-  top: "conv_2_2_conv2d"
-  param {
-    lr_mult: 1.0
-    decay_mult: 1.0
-  }
-  convolution_param {
-    num_output: 64
-    bias_term: false
-    pad: 1
-    kernel_size: 3
-    stride: 1
-    weight_filler {
-      type: "gaussian"
-      std: 0.00999999977648
-    }
-  }
-  quantization_param {
-    bw_layer_in: 8
-    bw_layer_out: 8
-    bw_params: 8
-    scale_in: 45.8899993896
-    scale_out: 60.1300010681
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-}
-layer {
-  name: "conv_2_2_batchnorm"
-  type: "BatchNorm"
-  bottom: "conv_2_2_conv2d"
-  top: "conv_2_2_conv2d_bn"
-  batch_norm_param {
-  }
-}
-layer {
-  name: "conv_2_2_relu"
-  type: "ReLU"
-  bottom: "conv_2_2_conv2d_bn"
-  top: "conv_2_2_conv2d_relu"
-}
-layer {
-  name: "pool"
-  type: "Pooling"
-  bottom: "conv_2_2_conv2d_relu"
-  top: "pool"
-  pooling_param {
-    pool: MAX
-    pad: 0
-    kernel_size: 3
-    stride: 2
-  }
-}
-layer {
-  name: "conv_3_3_conv2d"
-  type: "Convolution"
-  bottom: "pool"
-  top: "conv_3_3_conv2d"
-  param {
-    lr_mult: 1.0
-    decay_mult: 1.0
-  }
-  convolution_param {
-    num_output: 80
-    bias_term: false
-    pad: 0
-    kernel_size: 1
-    stride: 1
-    weight_filler {
-      type: "gaussian"
-      std: 0.00999999977648
-    }
-  }
-  quantization_param {
-    bw_layer_in: 8
-    bw_layer_out: 8
-    bw_params: 8
-    scale_in: 60.1300010681
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-}
-layer {
-  name: "conv_3_3_batchnorm"
-  type: "BatchNorm"
-  bottom: "conv_3_3_conv2d"
-  top: "conv_3_3_conv2d_bn"
-  batch_norm_param {
-  }
-}
-layer {
-  name: "conv_3_3_relu"
-  type: "ReLU"
-  bottom: "conv_3_3_conv2d_bn"
-  top: "conv_3_3_conv2d_relu"
-}
-layer {
-  name: "conv_4_4_conv2d"
-  type: "Convolution"
-  bottom: "conv_3_3_conv2d_relu"
-  top: "conv_4_4_conv2d"
-  param {
-    lr_mult: 1.0
-    decay_mult: 1.0
-  }
-  convolution_param {
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-    bias_term: false
-    pad: 0
-    kernel_size: 3
-    stride: 1
-    weight_filler {
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-      std: 0.00999999977648
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-  quantization_param {
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-  top: "mixed_10_tower_1_mixed_conv_1_conv2d_bn"
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-  top: "mixed_10_tower_2_conv_conv2d"
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-  type: "BatchNorm"
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-  top: "mixed_10_tower_2_conv_conv2d_bn"
-  batch_norm_param {
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-layer {
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-  type: "ReLU"
-  bottom: "mixed_10_tower_2_conv_conv2d_bn"
-  top: "mixed_10_tower_2_conv_conv2d_relu"
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-layer {
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-  type: "Concat"
-  bottom: "mixed_10_conv_conv2d_relu"
-  bottom: "mixed_10_tower_mixed_conv_conv2d_relu"
-  bottom: "mixed_10_tower_mixed_conv_1_conv2d_relu"
-  bottom: "mixed_10_tower_1_mixed_conv_conv2d_relu"
-  bottom: "mixed_10_tower_1_mixed_conv_1_conv2d_relu"
-  bottom: "mixed_10_tower_2_conv_conv2d_relu"
-  top: "ch_concat_mixed_10_chconcat"
-  concat_param {
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-layer {
-  name: "global_pool"
-  type: "Pooling"
-  bottom: "ch_concat_mixed_10_chconcat"
-  top: "global_pool"
-  pooling_param {
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-    pad: 0
-    kernel_size: 8
-    stride: 1
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-layer {
-  name: "drop"
-  type: "Dropout"
-  bottom: "global_pool"
-  top: "global_pool"
-  dropout_param {
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-layer {
-  name: "flatten"
-  type: "Flatten"
-  bottom: "global_pool"
-  top: "flatten"
-}
-layer {
-  name: "fc1"
-  type: "InnerProduct"
-  bottom: "flatten"
-  top: "fc1"
-  param {
-    lr_mult: 1.0
-    decay_mult: 1.0
-  }
-  param {
-    lr_mult: 2.0
-    decay_mult: 0.0
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-  inner_product_param {
-    num_output: 1000
-    weight_filler {
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-    bias_filler {
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diff --git a/models/intel_optimized_models/int8/resnet50_int8_mc.prototxt b/models/intel_optimized_models/int8/resnet50_int8_mc.prototxt
deleted file mode 100644
index b95dccb11..000000000
--- a/models/intel_optimized_models/int8/resnet50_int8_mc.prototxt
+++ /dev/null
@@ -1,29412 +0,0 @@
-name: "ResNet-50"
-layer {
-  name: "data"
-  type: "DummyData"
-  top: "data"
-  dummy_data_param {
-    data_filler {
-      type: "constant"
-      value: 0.01 
-    }    
-    shape {
-      dim: 64
-      dim: 3
-      dim: 224
-      dim: 224  
-    }    
-  }
-}
-layer {
-  name: "conv1"
-  type: "Convolution"
-  bottom: "data"
-  top: "conv1"
-  convolution_param {
-    num_output: 64
-    pad: 3
-    kernel_size: 7
-    stride: 2
-    weight_filler {
-      type: "xavier"
-    }
-    bias_filler {
-      type: "constant"
-      value: 0.20000000298
-    }
-  }
-}
-layer {
-  name: "bn_conv1"
-  type: "BatchNorm"
-  bottom: "conv1"
-  top: "conv1"
-  batch_norm_param {
-  }
-}
-layer {
-  name: "scale_conv1"
-  type: "Scale"
-  bottom: "conv1"
-  top: "conv1"
-  scale_param {
-    bias_term: true
-  }
-}
-layer {
-  name: "conv1_relu"
-  type: "ReLU"
-  bottom: "conv1"
-  top: "conv1"
-  relu_param {
-  }
-}
-layer {
-  name: "pool1"
-  type: "Pooling"
-  bottom: "conv1"
-  top: "pool1"
-  pooling_param {
-    pool: MAX
-    kernel_size: 3
-    stride: 2
-  }
-}
-layer {
-  name: "res2a_branch1"
-  type: "Convolution"
-  bottom: "pool1"
-  top: "res2a_branch1"
-  convolution_param {
-    num_output: 256
-    bias_term: false
-    pad: 0
-    kernel_size: 1
-    stride: 1
-    weight_filler {
-      type: "xavier"
-    }
-    bias_filler {
-      type: "constant"
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-    scale_params: 129.449996948
-    scale_params: 136.910003662
-  }
-}
-layer {
-  name: "bn5c_branch2c"
-  type: "BatchNorm"
-  bottom: "res5c_branch2c"
-  top: "res5c_branch2c"
-  batch_norm_param {
-  }
-}
-layer {
-  name: "scale5c_branch2c"
-  type: "Scale"
-  bottom: "res5c_branch2c"
-  top: "res5c_branch2c"
-  scale_param {
-    bias_term: true
-  }
-}
-layer {
-  name: "res5c"
-  type: "Eltwise"
-  bottom: "res5b"
-  bottom: "res5c_branch2c"
-  top: "res5c"
-  eltwise_param {
-  }
-}
-layer {
-  name: "res5c_relu"
-  type: "ReLU"
-  bottom: "res5c"
-  top: "res5c"
-  relu_param {
-  }
-}
-layer {
-  name: "pool5"
-  type: "Pooling"
-  bottom: "res5c"
-  top: "pool5"
-  pooling_param {
-    pool: AVE
-    kernel_size: 7
-    stride: 1
-  }
-}
-layer {
-  name: "fc1000"
-  type: "InnerProduct"
-  bottom: "pool5"
-  top: "fc1000"
-  inner_product_param {
-    num_output: 1000
-    weight_filler {
-      type: "xavier"
-    }
-    bias_filler {
-      type: "constant"
-      value: 0.0
-    }
-  }
-}
diff --git a/models/intel_optimized_models/int8/resnet50_sparse_int8.prototxt b/models/intel_optimized_models/int8/resnet50_sparse_int8.prototxt
deleted file mode 100644
index 22eb796ff..000000000
--- a/models/intel_optimized_models/int8/resnet50_sparse_int8.prototxt
+++ /dev/null
@@ -1,3068 +0,0 @@
-# For INT8 reference
-name: "ResNet-50"
-layer {
-  name: "data"
-  type: "DummyData"
-  top: "data"
-  dummy_data_param {
-    data_filler {
-      type: "constant"
-      value: 0.01
-    }    
-    shape {
-      dim: 64
-      dim: 3
-      dim: 224
-      dim: 224
-    }    
-  }
-}
-layer {
-  name: "data"
-  type: "DummyData"
-  top: "label"
-  dummy_data_param {
-    data_filler {
-      type: "constant"
-    }    
-    shape {
-      dim: 64
-    }    
-  }
-}
-layer {
-  name: "conv1"
-  type: "Convolution"
-  bottom: "data"
-  top: "conv1"
-  convolution_param {
-    num_output: 64
-    pad: 3
-    kernel_size: 7
-    stride: 2
-    weight_filler {
-      type: "xavier"
-    }
-    bias_filler {
-      type: "constant"
-      value: 0.20000000298
-    }
-  }
-}
-layer {
-  name: "bn_conv1"
-  type: "BatchNorm"
-  bottom: "conv1"
-  top: "conv1"
-  batch_norm_param {
-  }
-}
-layer {
-  name: "scale_conv1"
-  type: "Scale"
-  bottom: "conv1"
-  top: "conv1"
-  scale_param {
-    bias_term: true
-  }
-}
-layer {
-  name: "conv1_relu"
-  type: "ReLU"
-  bottom: "conv1"
-  top: "conv1"
-  relu_param {
-  }
-}
-layer {
-  name: "pool1"
-  type: "Pooling"
-  bottom: "conv1"
-  top: "pool1"
-  pooling_param {
-    pool: MAX
-    kernel_size: 3
-    stride: 2
-  }
-}
-layer {
-  name: "res2a_branch1"
-  type: "Convolution"
-  bottom: "pool1"
-  top: "res2a_branch1"
-  convolution_param {
-    num_output: 256
-    bias_term: false
-    pad: 0
-    kernel_size: 1
-    stride: 1
-    weight_filler {
-      type: "xavier"
-    }
-    bias_filler {
-      type: "constant"
-      value: 0.20000000298
-    }
-  }
-  quantization_param {
-    precision: DYNAMIC_FIXED_POINT
-    bw_layer_in: 8
-    bw_layer_out: 8
-    bw_params: 8
-    scale_in: 8.38000011444
-    scale_out: 6.03000020981
-    scale_params: 75.9199981689
-  }
-}
-layer {
-  name: "bn2a_branch1"
-  type: "BatchNorm"
-  bottom: "res2a_branch1"
-  top: "res2a_branch1"
-  batch_norm_param {
-  }
-}
-layer {
-  name: "scale2a_branch1"
-  type: "Scale"
-  bottom: "res2a_branch1"
-  top: "res2a_branch1"
-  scale_param {
-    bias_term: true
-  }
-}
-layer {
-  name: "res2a_branch2a"
-  type: "Convolution"
-  bottom: "pool1"
-  top: "res2a_branch2a"
-  convolution_param {
-    num_output: 64
-    bias_term: false
-    pad: 0
-    kernel_size: 1
-    stride: 1
-    weight_filler {
-      type: "xavier"
-    }
-    bias_filler {
-      type: "constant"
-      value: 0.20000000298
-    }
-  }
-  quantization_param {
-    precision: DYNAMIC_FIXED_POINT
-    bw_layer_in: 8
-    bw_layer_out: 8
-    bw_params: 8
-    scale_in: 8.38000011444
-    scale_out: 17.6599998474
-    scale_params: 151.089996338
-  }
-}
-layer {
-  name: "bn2a_branch2a"
-  type: "BatchNorm"
-  bottom: "res2a_branch2a"
-  top: "res2a_branch2a"
-  batch_norm_param {
-  }
-}
-layer {
-  name: "scale2a_branch2a"
-  type: "Scale"
-  bottom: "res2a_branch2a"
-  top: "res2a_branch2a"
-  scale_param {
-    bias_term: true
-  }
-}
-layer {
-  name: "res2a_branch2a_relu"
-  type: "ReLU"
-  bottom: "res2a_branch2a"
-  top: "res2a_branch2a"
-  relu_param {
-  }
-}
-layer {
-  name: "res2a_branch2b"
-  type: "Convolution"
-  bottom: "res2a_branch2a"
-  top: "res2a_branch2b"
-  convolution_param {
-    num_output: 64
-    bias_term: false
-    pad: 1
-    kernel_size: 3
-    stride: 1
-    weight_filler {
-      type: "xavier"
-    }
-    bias_filler {
-      type: "constant"
-      value: 0.20000000298
-    }
-  }
-  quantization_param {
-    precision: DYNAMIC_FIXED_POINT
-    bw_layer_in: 8
-    bw_layer_out: 8
-    bw_params: 8
-    scale_in: 17.6599998474
-    scale_out: 19.9799995422
-    scale_params: 416.320007324
-  }
-}
-layer {
-  name: "bn2a_branch2b"
-  type: "BatchNorm"
-  bottom: "res2a_branch2b"
-  top: "res2a_branch2b"
-  batch_norm_param {
-  }
-}
-layer {
-  name: "scale2a_branch2b"
-  type: "Scale"
-  bottom: "res2a_branch2b"
-  top: "res2a_branch2b"
-  scale_param {
-    bias_term: true
-  }
-}
-layer {
-  name: "res2a_branch2b_relu"
-  type: "ReLU"
-  bottom: "res2a_branch2b"
-  top: "res2a_branch2b"
-  relu_param {
-  }
-}
-layer {
-  name: "res2a_branch2c"
-  type: "Convolution"
-  bottom: "res2a_branch2b"
-  top: "res2a_branch2c"
-  convolution_param {
-    num_output: 256
-    bias_term: false
-    pad: 0
-    kernel_size: 1
-    stride: 1
-    weight_filler {
-      type: "xavier"
-    }
-    bias_filler {
-      type: "constant"
-      value: 0.20000000298
-    }
-  }
-  quantization_param {
-    precision: DYNAMIC_FIXED_POINT
-    bw_layer_in: 8
-    bw_layer_out: 8
-    bw_params: 8
-    scale_in: 19.9799995422
-    scale_out: 12.1099996567
-    scale_params: 72.1699981689
-  }
-}
-layer {
-  name: "bn2a_branch2c"
-  type: "BatchNorm"
-  bottom: "res2a_branch2c"
-  top: "res2a_branch2c"
-  batch_norm_param {
-  }
-}
-layer {
-  name: "scale2a_branch2c"
-  type: "Scale"
-  bottom: "res2a_branch2c"
-  top: "res2a_branch2c"
-  scale_param {
-    bias_term: true
-  }
-}
-layer {
-  name: "res2a"
-  type: "Eltwise"
-  bottom: "res2a_branch1"
-  bottom: "res2a_branch2c"
-  top: "res2a"
-  eltwise_param {
-  }
-}
-layer {
-  name: "res2a_relu"
-  type: "ReLU"
-  bottom: "res2a"
-  top: "res2a"
-  relu_param {
-  }
-}
-layer {
-  name: "res2b_branch2a"
-  type: "Convolution"
-  bottom: "res2a"
-  top: "res2b_branch2a"
-  convolution_param {
-    num_output: 64
-    bias_term: false
-    pad: 0
-    kernel_size: 1
-    stride: 1
-    weight_filler {
-      type: "xavier"
-    }
-    bias_filler {
-      type: "constant"
-      value: 0.20000000298
-    }
-  }
-  quantization_param {
-    precision: DYNAMIC_FIXED_POINT
-    bw_layer_in: 8
-    bw_layer_out: 8
-    bw_params: 8
-    scale_in: 12.1099996567
-    scale_out: 17.9099998474
-    scale_params: 192.330001831
-  }
-}
-layer {
-  name: "bn2b_branch2a"
-  type: "BatchNorm"
-  bottom: "res2b_branch2a"
-  top: "res2b_branch2a"
-  batch_norm_param {
-  }
-}
-layer {
-  name: "scale2b_branch2a"
-  type: "Scale"
-  bottom: "res2b_branch2a"
-  top: "res2b_branch2a"
-  scale_param {
-    bias_term: true
-  }
-}
-layer {
-  name: "res2b_branch2a_relu"
-  type: "ReLU"
-  bottom: "res2b_branch2a"
-  top: "res2b_branch2a"
-  relu_param {
-  }
-}
-layer {
-  name: "res2b_branch2b"
-  type: "Convolution"
-  bottom: "res2b_branch2a"
-  top: "res2b_branch2b"
-  convolution_param {
-    num_output: 64
-    bias_term: false
-    pad: 1
-    kernel_size: 3
-    stride: 1
-    weight_filler {
-      type: "xavier"
-    }
-    bias_filler {
-      type: "constant"
-      value: 0.20000000298
-    }
-  }
-  quantization_param {
-    precision: DYNAMIC_FIXED_POINT
-    bw_layer_in: 8
-    bw_layer_out: 8
-    bw_params: 8
-    scale_in: 17.9099998474
-    scale_out: 19.5799999237
-    scale_params: 210.529998779
-  }
-}
-layer {
-  name: "bn2b_branch2b"
-  type: "BatchNorm"
-  bottom: "res2b_branch2b"
-  top: "res2b_branch2b"
-  batch_norm_param {
-  }
-}
-layer {
-  name: "scale2b_branch2b"
-  type: "Scale"
-  bottom: "res2b_branch2b"
-  top: "res2b_branch2b"
-  scale_param {
-    bias_term: true
-  }
-}
-layer {
-  name: "res2b_branch2b_relu"
-  type: "ReLU"
-  bottom: "res2b_branch2b"
-  top: "res2b_branch2b"
-  relu_param {
-  }
-}
-layer {
-  name: "res2b_branch2c"
-  type: "Convolution"
-  bottom: "res2b_branch2b"
-  top: "res2b_branch2c"
-  convolution_param {
-    num_output: 256
-    bias_term: false
-    pad: 0
-    kernel_size: 1
-    stride: 1
-    weight_filler {
-      type: "xavier"
-    }
-    bias_filler {
-      type: "constant"
-      value: 0.20000000298
-    }
-  }
-  quantization_param {
-    precision: DYNAMIC_FIXED_POINT
-    bw_layer_in: 8
-    bw_layer_out: 8
-    bw_params: 8
-    scale_in: 19.5799999237
-    scale_out: 12.1099996567
-    scale_params: 88.4599990845
-  }
-}
-layer {
-  name: "bn2b_branch2c"
-  type: "BatchNorm"
-  bottom: "res2b_branch2c"
-  top: "res2b_branch2c"
-  batch_norm_param {
-  }
-}
-layer {
-  name: "scale2b_branch2c"
-  type: "Scale"
-  bottom: "res2b_branch2c"
-  top: "res2b_branch2c"
-  scale_param {
-    bias_term: true
-  }
-}
-layer {
-  name: "res2b"
-  type: "Eltwise"
-  bottom: "res2a"
-  bottom: "res2b_branch2c"
-  top: "res2b"
-  eltwise_param {
-  }
-}
-layer {
-  name: "res2b_relu"
-  type: "ReLU"
-  bottom: "res2b"
-  top: "res2b"
-  relu_param {
-  }
-}
-layer {
-  name: "res2b_p"
-  type: "Pooling"
-  bottom: "res2b"
-  top: "res2b_p"
-  pooling_param {
-    pool: MAX
-    kernel_size: 1
-    stride: 2
-  }
-}
-layer {
-  name: "res2c_branch2a"
-  type: "Convolution"
-  bottom: "res2b"
-  top: "res2c_branch2a"
-  convolution_param {
-    num_output: 64
-    bias_term: false
-    pad: 0
-    kernel_size: 1
-    stride: 1
-    weight_filler {
-      type: "xavier"
-    }
-    bias_filler {
-      type: "constant"
-      value: 0.20000000298
-    }
-  }
-  quantization_param {
-    precision: DYNAMIC_FIXED_POINT
-    bw_layer_in: 8
-    bw_layer_out: 8
-    bw_params: 8
-    scale_in: 12.1099996567
-    scale_out: 18.6000003815
-    scale_params: 360.359985352
-  }
-}
-layer {
-  name: "bn2c_branch2a"
-  type: "BatchNorm"
-  bottom: "res2c_branch2a"
-  top: "res2c_branch2a"
-  batch_norm_param {
-  }
-}
-layer {
-  name: "scale2c_branch2a"
-  type: "Scale"
-  bottom: "res2c_branch2a"
-  top: "res2c_branch2a"
-  scale_param {
-    bias_term: true
-  }
-}
-layer {
-  name: "res2c_branch2a_relu"
-  type: "ReLU"
-  bottom: "res2c_branch2a"
-  top: "res2c_branch2a"
-  relu_param {
-  }
-}
-layer {
-  name: "res2c_branch2b"
-  type: "Convolution"
-  bottom: "res2c_branch2a"
-  top: "res2c_branch2b"
-  convolution_param {
-    num_output: 64
-    bias_term: false
-    pad: 1
-    kernel_size: 3
-    stride: 2
-    weight_filler {
-      type: "xavier"
-    }
-    bias_filler {
-      type: "constant"
-      value: 0.20000000298
-    }
-  }
-  quantization_param {
-    precision: DYNAMIC_FIXED_POINT
-    bw_layer_in: 8
-    bw_layer_out: 8
-    bw_params: 8
-    scale_in: 18.6000003815
-    scale_out: 16.9200000763
-    scale_params: 308.029998779
-  }
-}
-layer {
-  name: "bn2c_branch2b"
-  type: "BatchNorm"
-  bottom: "res2c_branch2b"
-  top: "res2c_branch2b"
-  batch_norm_param {
-  }
-}
-layer {
-  name: "scale2c_branch2b"
-  type: "Scale"
-  bottom: "res2c_branch2b"
-  top: "res2c_branch2b"
-  scale_param {
-    bias_term: true
-  }
-}
-layer {
-  name: "res2c_branch2b_relu"
-  type: "ReLU"
-  bottom: "res2c_branch2b"
-  top: "res2c_branch2b"
-  relu_param {
-  }
-}
-layer {
-  name: "res2c_branch2c"
-  type: "Convolution"
-  bottom: "res2c_branch2b"
-  top: "res2c_branch2c"
-  convolution_param {
-    num_output: 256
-    bias_term: false
-    pad: 0
-    kernel_size: 1
-    stride: 1
-    weight_filler {
-      type: "xavier"
-    }
-    bias_filler {
-      type: "constant"
-      value: 0.20000000298
-    }
-  }
-  quantization_param {
-    precision: DYNAMIC_FIXED_POINT
-    bw_layer_in: 8
-    bw_layer_out: 8
-    bw_params: 8
-    scale_in: 16.9200000763
-    scale_out: 11.5200004578
-    scale_params: 103.910003662
-  }
-}
-layer {
-  name: "bn2c_branch2c"
-  type: "BatchNorm"
-  bottom: "res2c_branch2c"
-  top: "res2c_branch2c"
-  batch_norm_param {
-  }
-}
-layer {
-  name: "scale2c_branch2c"
-  type: "Scale"
-  bottom: "res2c_branch2c"
-  top: "res2c_branch2c"
-  scale_param {
-    bias_term: true
-  }
-}
-layer {
-  name: "res2c"
-  type: "Eltwise"
-  bottom: "res2b_p"
-  bottom: "res2c_branch2c"
-  top: "res2c"
-  eltwise_param {
-  }
-}
-layer {
-  name: "res2c_relu"
-  type: "ReLU"
-  bottom: "res2c"
-  top: "res2c"
-  relu_param {
-  }
-}
-layer {
-  name: "res3a_branch1"
-  type: "Convolution"
-  bottom: "res2c"
-  top: "res3a_branch1"
-  convolution_param {
-    num_output: 512
-    bias_term: false
-    pad: 0
-    kernel_size: 1
-    stride: 1
-    weight_filler {
-      type: "xavier"
-    }
-    bias_filler {
-      type: "constant"
-      value: 0.20000000298
-    }
-  }
-  quantization_param {
-    precision: DYNAMIC_FIXED_POINT
-    bw_layer_in: 8
-    bw_layer_out: 8
-    bw_params: 8
-    scale_in: 11.5200004578
-    scale_out: 4.65000009537
-    scale_params: 135.169998169
-  }
-}
-layer {
-  name: "bn3a_branch1"
-  type: "BatchNorm"
-  bottom: "res3a_branch1"
-  top: "res3a_branch1"
-  batch_norm_param {
-  }
-}
-layer {
-  name: "scale3a_branch1"
-  type: "Scale"
-  bottom: "res3a_branch1"
-  top: "res3a_branch1"
-  scale_param {
-    bias_term: true
-  }
-}
-layer {
-  name: "res3a_branch2a"
-  type: "Convolution"
-  bottom: "res2c"
-  top: "res3a_branch2a"
-  convolution_param {
-    num_output: 128
-    bias_term: false
-    pad: 0
-    kernel_size: 1
-    stride: 1
-    weight_filler {
-      type: "xavier"
-    }
-    bias_filler {
-      type: "constant"
-      value: 0.20000000298
-    }
-  }
-  quantization_param {
-    precision: DYNAMIC_FIXED_POINT
-    bw_layer_in: 8
-    bw_layer_out: 8
-    bw_params: 8
-    scale_in: 11.5200004578
-    scale_out: 19.0499992371
-    scale_params: 277.709991455
-  }
-}
-layer {
-  name: "bn3a_branch2a"
-  type: "BatchNorm"
-  bottom: "res3a_branch2a"
-  top: "res3a_branch2a"
-  batch_norm_param {
-  }
-}
-layer {
-  name: "scale3a_branch2a"
-  type: "Scale"
-  bottom: "res3a_branch2a"
-  top: "res3a_branch2a"
-  scale_param {
-    bias_term: true
-  }
-}
-layer {
-  name: "res3a_branch2a_relu"
-  type: "ReLU"
-  bottom: "res3a_branch2a"
-  top: "res3a_branch2a"
-  relu_param {
-  }
-}
-layer {
-  name: "res3a_branch2b"
-  type: "Convolution"
-  bottom: "res3a_branch2a"
-  top: "res3a_branch2b"
-  convolution_param {
-    num_output: 128
-    bias_term: false
-    pad: 1
-    kernel_size: 3
-    stride: 1
-    weight_filler {
-      type: "xavier"
-    }
-    bias_filler {
-      type: "constant"
-      value: 0.20000000298
-    }
-  }
-  quantization_param {
-    precision: DYNAMIC_FIXED_POINT
-    bw_layer_in: 8
-    bw_layer_out: 8
-    bw_params: 8
-    scale_in: 19.0499992371
-    scale_out: 25.5400009155
-    scale_params: 320.339996338
-  }
-}
-layer {
-  name: "bn3a_branch2b"
-  type: "BatchNorm"
-  bottom: "res3a_branch2b"
-  top: "res3a_branch2b"
-  batch_norm_param {
-  }
-}
-layer {
-  name: "scale3a_branch2b"
-  type: "Scale"
-  bottom: "res3a_branch2b"
-  top: "res3a_branch2b"
-  scale_param {
-    bias_term: true
-  }
-}
-layer {
-  name: "res3a_branch2b_relu"
-  type: "ReLU"
-  bottom: "res3a_branch2b"
-  top: "res3a_branch2b"
-  relu_param {
-  }
-}
-layer {
-  name: "res3a_branch2c"
-  type: "Convolution"
-  bottom: "res3a_branch2b"
-  top: "res3a_branch2c"
-  convolution_param {
-    num_output: 512
-    bias_term: false
-    pad: 0
-    kernel_size: 1
-    stride: 1
-    weight_filler {
-      type: "xavier"
-    }
-    bias_filler {
-      type: "constant"
-      value: 0.20000000298
-    }
-  }
-  quantization_param {
-    precision: DYNAMIC_FIXED_POINT
-    bw_layer_in: 8
-    bw_layer_out: 8
-    bw_params: 8
-    scale_in: 25.5400009155
-    scale_out: 9.32999992371
-    scale_params: 65.7200012207
-  }
-}
-layer {
-  name: "bn3a_branch2c"
-  type: "BatchNorm"
-  bottom: "res3a_branch2c"
-  top: "res3a_branch2c"
-  batch_norm_param {
-  }
-}
-layer {
-  name: "scale3a_branch2c"
-  type: "Scale"
-  bottom: "res3a_branch2c"
-  top: "res3a_branch2c"
-  scale_param {
-    bias_term: true
-  }
-}
-layer {
-  name: "res3a"
-  type: "Eltwise"
-  bottom: "res3a_branch1"
-  bottom: "res3a_branch2c"
-  top: "res3a"
-  eltwise_param {
-  }
-}
-layer {
-  name: "res3a_relu"
-  type: "ReLU"
-  bottom: "res3a"
-  top: "res3a"
-  relu_param {
-  }
-}
-layer {
-  name: "res3b_branch2a"
-  type: "Convolution"
-  bottom: "res3a"
-  top: "res3b_branch2a"
-  convolution_param {
-    num_output: 128
-    bias_term: false
-    pad: 0
-    kernel_size: 1
-    stride: 1
-    weight_filler {
-      type: "xavier"
-    }
-    bias_filler {
-      type: "constant"
-      value: 0.20000000298
-    }
-  }
-  quantization_param {
-    precision: DYNAMIC_FIXED_POINT
-    bw_layer_in: 8
-    bw_layer_out: 8
-    bw_params: 8
-    scale_in: 9.32999992371
-    scale_out: 24.1900005341
-    scale_params: 493.920013428
-  }
-}
-layer {
-  name: "bn3b_branch2a"
-  type: "BatchNorm"
-  bottom: "res3b_branch2a"
-  top: "res3b_branch2a"
-  batch_norm_param {
-  }
-}
-layer {
-  name: "scale3b_branch2a"
-  type: "Scale"
-  bottom: "res3b_branch2a"
-  top: "res3b_branch2a"
-  scale_param {
-    bias_term: true
-  }
-}
-layer {
-  name: "res3b_branch2a_relu"
-  type: "ReLU"
-  bottom: "res3b_branch2a"
-  top: "res3b_branch2a"
-  relu_param {
-  }
-}
-layer {
-  name: "res3b_branch2b"
-  type: "Convolution"
-  bottom: "res3b_branch2a"
-  top: "res3b_branch2b"
-  convolution_param {
-    num_output: 128
-    bias_term: false
-    pad: 1
-    kernel_size: 3
-    stride: 1
-    weight_filler {
-      type: "xavier"
-    }
-    bias_filler {
-      type: "constant"
-      value: 0.20000000298
-    }
-  }
-  quantization_param {
-    precision: DYNAMIC_FIXED_POINT
-    bw_layer_in: 8
-    bw_layer_out: 8
-    bw_params: 8
-    scale_in: 24.1900005341
-    scale_out: 20.4899997711
-    scale_params: 308.959991455
-  }
-}
-layer {
-  name: "bn3b_branch2b"
-  type: "BatchNorm"
-  bottom: "res3b_branch2b"
-  top: "res3b_branch2b"
-  batch_norm_param {
-  }
-}
-layer {
-  name: "scale3b_branch2b"
-  type: "Scale"
-  bottom: "res3b_branch2b"
-  top: "res3b_branch2b"
-  scale_param {
-    bias_term: true
-  }
-}
-layer {
-  name: "res3b_branch2b_relu"
-  type: "ReLU"
-  bottom: "res3b_branch2b"
-  top: "res3b_branch2b"
-  relu_param {
-  }
-}
-layer {
-  name: "res3b_branch2c"
-  type: "Convolution"
-  bottom: "res3b_branch2b"
-  top: "res3b_branch2c"
-  convolution_param {
-    num_output: 512
-    bias_term: false
-    pad: 0
-    kernel_size: 1
-    stride: 1
-    weight_filler {
-      type: "xavier"
-    }
-    bias_filler {
-      type: "constant"
-      value: 0.20000000298
-    }
-  }
-  quantization_param {
-    precision: DYNAMIC_FIXED_POINT
-    bw_layer_in: 8
-    bw_layer_out: 8
-    bw_params: 8
-    scale_in: 20.4899997711
-    scale_out: 9.32999992371
-    scale_params: 55.6899986267
-  }
-}
-layer {
-  name: "bn3b_branch2c"
-  type: "BatchNorm"
-  bottom: "res3b_branch2c"
-  top: "res3b_branch2c"
-  batch_norm_param {
-  }
-}
-layer {
-  name: "scale3b_branch2c"
-  type: "Scale"
-  bottom: "res3b_branch2c"
-  top: "res3b_branch2c"
-  scale_param {
-    bias_term: true
-  }
-}
-layer {
-  name: "res3b"
-  type: "Eltwise"
-  bottom: "res3a"
-  bottom: "res3b_branch2c"
-  top: "res3b"
-  eltwise_param {
-  }
-}
-layer {
-  name: "res3b_relu"
-  type: "ReLU"
-  bottom: "res3b"
-  top: "res3b"
-  relu_param {
-  }
-}
-layer {
-  name: "res3c_branch2a"
-  type: "Convolution"
-  bottom: "res3b"
-  top: "res3c_branch2a"
-  convolution_param {
-    num_output: 128
-    bias_term: false
-    pad: 0
-    kernel_size: 1
-    stride: 1
-    weight_filler {
-      type: "xavier"
-    }
-    bias_filler {
-      type: "constant"
-      value: 0.20000000298
-    }
-  }
-  quantization_param {
-    precision: DYNAMIC_FIXED_POINT
-    bw_layer_in: 8
-    bw_layer_out: 8
-    bw_params: 8
-    scale_in: 9.32999992371
-    scale_out: 9.35999965668
-    scale_params: 142.929992676
-  }
-}
-layer {
-  name: "bn3c_branch2a"
-  type: "BatchNorm"
-  bottom: "res3c_branch2a"
-  top: "res3c_branch2a"
-  batch_norm_param {
-  }
-}
-layer {
-  name: "scale3c_branch2a"
-  type: "Scale"
-  bottom: "res3c_branch2a"
-  top: "res3c_branch2a"
-  scale_param {
-    bias_term: true
-  }
-}
-layer {
-  name: "res3c_branch2a_relu"
-  type: "ReLU"
-  bottom: "res3c_branch2a"
-  top: "res3c_branch2a"
-  relu_param {
-  }
-}
-layer {
-  name: "res3c_branch2b"
-  type: "Convolution"
-  bottom: "res3c_branch2a"
-  top: "res3c_branch2b"
-  convolution_param {
-    num_output: 128
-    bias_term: false
-    pad: 1
-    kernel_size: 3
-    stride: 1
-    weight_filler {
-      type: "xavier"
-    }
-    bias_filler {
-      type: "constant"
-      value: 0.20000000298
-    }
-  }
-  quantization_param {
-    precision: DYNAMIC_FIXED_POINT
-    bw_layer_in: 8
-    bw_layer_out: 8
-    bw_params: 8
-    scale_in: 9.35999965668
-    scale_out: 3.70000004768
-    scale_params: 88.1699981689
-  }
-}
-layer {
-  name: "bn3c_branch2b"
-  type: "BatchNorm"
-  bottom: "res3c_branch2b"
-  top: "res3c_branch2b"
-  batch_norm_param {
-  }
-}
-layer {
-  name: "scale3c_branch2b"
-  type: "Scale"
-  bottom: "res3c_branch2b"
-  top: "res3c_branch2b"
-  scale_param {
-    bias_term: true
-  }
-}
-layer {
-  name: "res3c_branch2b_relu"
-  type: "ReLU"
-  bottom: "res3c_branch2b"
-  top: "res3c_branch2b"
-  relu_param {
-  }
-}
-layer {
-  name: "res3c_branch2c"
-  type: "Convolution"
-  bottom: "res3c_branch2b"
-  top: "res3c_branch2c"
-  convolution_param {
-    num_output: 512
-    bias_term: false
-    pad: 0
-    kernel_size: 1
-    stride: 1
-    weight_filler {
-      type: "xavier"
-    }
-    bias_filler {
-      type: "constant"
-      value: 0.20000000298
-    }
-  }
-  quantization_param {
-    precision: DYNAMIC_FIXED_POINT
-    bw_layer_in: 8
-    bw_layer_out: 8
-    bw_params: 8
-    scale_in: 3.70000004768
-    scale_out: 9.32999992371
-    scale_params: 66.2300033569
-  }
-}
-layer {
-  name: "bn3c_branch2c"
-  type: "BatchNorm"
-  bottom: "res3c_branch2c"
-  top: "res3c_branch2c"
-  batch_norm_param {
-  }
-}
-layer {
-  name: "scale3c_branch2c"
-  type: "Scale"
-  bottom: "res3c_branch2c"
-  top: "res3c_branch2c"
-  scale_param {
-    bias_term: true
-  }
-}
-layer {
-  name: "res3c"
-  type: "Eltwise"
-  bottom: "res3b"
-  bottom: "res3c_branch2c"
-  top: "res3c"
-  eltwise_param {
-  }
-}
-layer {
-  name: "res3c_relu"
-  type: "ReLU"
-  bottom: "res3c"
-  top: "res3c"
-  relu_param {
-  }
-}
-layer {
-  name: "res3c_p"
-  type: "Pooling"
-  bottom: "res3c"
-  top: "res3c_p"
-  pooling_param {
-    pool: MAX
-    kernel_size: 1
-    stride: 2
-  }
-}
-layer {
-  name: "res3d_branch2a"
-  type: "Convolution"
-  bottom: "res3c"
-  top: "res3d_branch2a"
-  convolution_param {
-    num_output: 128
-    bias_term: false
-    pad: 0
-    kernel_size: 1
-    stride: 1
-    weight_filler {
-      type: "xavier"
-    }
-    bias_filler {
-      type: "constant"
-      value: 0.20000000298
-    }
-  }
-  quantization_param {
-    precision: DYNAMIC_FIXED_POINT
-    bw_layer_in: 8
-    bw_layer_out: 8
-    bw_params: 8
-    scale_in: 9.32999992371
-    scale_out: 7.23000001907
-    scale_params: 240.589996338
-  }
-}
-layer {
-  name: "bn3d_branch2a"
-  type: "BatchNorm"
-  bottom: "res3d_branch2a"
-  top: "res3d_branch2a"
-  batch_norm_param {
-  }
-}
-layer {
-  name: "scale3d_branch2a"
-  type: "Scale"
-  bottom: "res3d_branch2a"
-  top: "res3d_branch2a"
-  scale_param {
-    bias_term: true
-  }
-}
-layer {
-  name: "res3d_branch2a_relu"
-  type: "ReLU"
-  bottom: "res3d_branch2a"
-  top: "res3d_branch2a"
-  relu_param {
-  }
-}
-layer {
-  name: "res3d_branch2b"
-  type: "Convolution"
-  bottom: "res3d_branch2a"
-  top: "res3d_branch2b"
-  convolution_param {
-    num_output: 128
-    bias_term: false
-    pad: 1
-    kernel_size: 3
-    stride: 2
-    weight_filler {
-      type: "xavier"
-    }
-    bias_filler {
-      type: "constant"
-      value: 0.20000000298
-    }
-  }
-  quantization_param {
-    precision: DYNAMIC_FIXED_POINT
-    bw_layer_in: 8
-    bw_layer_out: 8
-    bw_params: 8
-    scale_in: 7.23000001907
-    scale_out: 14.6999998093
-    scale_params: 314.239990234
-  }
-}
-layer {
-  name: "bn3d_branch2b"
-  type: "BatchNorm"
-  bottom: "res3d_branch2b"
-  top: "res3d_branch2b"
-  batch_norm_param {
-  }
-}
-layer {
-  name: "scale3d_branch2b"
-  type: "Scale"
-  bottom: "res3d_branch2b"
-  top: "res3d_branch2b"
-  scale_param {
-    bias_term: true
-  }
-}
-layer {
-  name: "res3d_branch2b_relu"
-  type: "ReLU"
-  bottom: "res3d_branch2b"
-  top: "res3d_branch2b"
-  relu_param {
-  }
-}
-layer {
-  name: "res3d_branch2c"
-  type: "Convolution"
-  bottom: "res3d_branch2b"
-  top: "res3d_branch2c"
-  convolution_param {
-    num_output: 512
-    bias_term: false
-    pad: 0
-    kernel_size: 1
-    stride: 1
-    weight_filler {
-      type: "xavier"
-    }
-    bias_filler {
-      type: "constant"
-      value: 0.20000000298
-    }
-  }
-  quantization_param {
-    precision: DYNAMIC_FIXED_POINT
-    bw_layer_in: 8
-    bw_layer_out: 8
-    bw_params: 8
-    scale_in: 14.6999998093
-    scale_out: 9.82999992371
-    scale_params: 113.980003357
-  }
-}
-layer {
-  name: "bn3d_branch2c"
-  type: "BatchNorm"
-  bottom: "res3d_branch2c"
-  top: "res3d_branch2c"
-  batch_norm_param {
-  }
-}
-layer {
-  name: "scale3d_branch2c"
-  type: "Scale"
-  bottom: "res3d_branch2c"
-  top: "res3d_branch2c"
-  scale_param {
-    bias_term: true
-  }
-}
-layer {
-  name: "res3d"
-  type: "Eltwise"
-  bottom: "res3c_p"
-  bottom: "res3d_branch2c"
-  top: "res3d"
-  eltwise_param {
-  }
-}
-layer {
-  name: "res3d_relu"
-  type: "ReLU"
-  bottom: "res3d"
-  top: "res3d"
-  relu_param {
-  }
-}
-layer {
-  name: "res4a_branch1"
-  type: "Convolution"
-  bottom: "res3d"
-  top: "res4a_branch1"
-  convolution_param {
-    num_output: 1024
-    bias_term: false
-    pad: 0
-    kernel_size: 1
-    stride: 1
-    weight_filler {
-      type: "xavier"
-    }
-    bias_filler {
-      type: "constant"
-      value: 0.20000000298
-    }
-  }
-  quantization_param {
-    precision: DYNAMIC_FIXED_POINT
-    bw_layer_in: 8
-    bw_layer_out: 8
-    bw_params: 8
-    scale_in: 9.82999992371
-    scale_out: 4.28000020981
-    scale_params: 213.380004883
-  }
-}
-layer {
-  name: "bn4a_branch1"
-  type: "BatchNorm"
-  bottom: "res4a_branch1"
-  top: "res4a_branch1"
-  batch_norm_param {
-  }
-}
-layer {
-  name: "scale4a_branch1"
-  type: "Scale"
-  bottom: "res4a_branch1"
-  top: "res4a_branch1"
-  scale_param {
-    bias_term: true
-  }
-}
-layer {
-  name: "res4a_branch2a"
-  type: "Convolution"
-  bottom: "res3d"
-  top: "res4a_branch2a"
-  convolution_param {
-    num_output: 256
-    bias_term: false
-    pad: 0
-    kernel_size: 1
-    stride: 1
-    weight_filler {
-      type: "xavier"
-    }
-    bias_filler {
-      type: "constant"
-      value: 0.20000000298
-    }
-  }
-  quantization_param {
-    precision: DYNAMIC_FIXED_POINT
-    bw_layer_in: 8
-    bw_layer_out: 8
-    bw_params: 8
-    scale_in: 9.82999992371
-    scale_out: 12.3900003433
-    scale_params: 254.339996338
-  }
-}
-layer {
-  name: "bn4a_branch2a"
-  type: "BatchNorm"
-  bottom: "res4a_branch2a"
-  top: "res4a_branch2a"
-  batch_norm_param {
-  }
-}
-layer {
-  name: "scale4a_branch2a"
-  type: "Scale"
-  bottom: "res4a_branch2a"
-  top: "res4a_branch2a"
-  scale_param {
-    bias_term: true
-  }
-}
-layer {
-  name: "res4a_branch2a_relu"
-  type: "ReLU"
-  bottom: "res4a_branch2a"
-  top: "res4a_branch2a"
-  relu_param {
-  }
-}
-layer {
-  name: "res4a_branch2b"
-  type: "Convolution"
-  bottom: "res4a_branch2a"
-  top: "res4a_branch2b"
-  convolution_param {
-    num_output: 256
-    bias_term: false
-    pad: 1
-    kernel_size: 3
-    stride: 1
-    weight_filler {
-      type: "xavier"
-    }
-    bias_filler {
-      type: "constant"
-      value: 0.20000000298
-    }
-  }
-  quantization_param {
-    precision: DYNAMIC_FIXED_POINT
-    bw_layer_in: 8
-    bw_layer_out: 8
-    bw_params: 8
-    scale_in: 12.3900003433
-    scale_out: 14.8900003433
-    scale_params: 473.709991455
-  }
-}
-layer {
-  name: "bn4a_branch2b"
-  type: "BatchNorm"
-  bottom: "res4a_branch2b"
-  top: "res4a_branch2b"
-  batch_norm_param {
-  }
-}
-layer {
-  name: "scale4a_branch2b"
-  type: "Scale"
-  bottom: "res4a_branch2b"
-  top: "res4a_branch2b"
-  scale_param {
-    bias_term: true
-  }
-}
-layer {
-  name: "res4a_branch2b_relu"
-  type: "ReLU"
-  bottom: "res4a_branch2b"
-  top: "res4a_branch2b"
-  relu_param {
-  }
-}
-layer {
-  name: "res4a_branch2c"
-  type: "Convolution"
-  bottom: "res4a_branch2b"
-  top: "res4a_branch2c"
-  convolution_param {
-    num_output: 1024
-    bias_term: false
-    pad: 0
-    kernel_size: 1
-    stride: 1
-    weight_filler {
-      type: "xavier"
-    }
-    bias_filler {
-      type: "constant"
-      value: 0.20000000298
-    }
-  }
-  quantization_param {
-    precision: DYNAMIC_FIXED_POINT
-    bw_layer_in: 8
-    bw_layer_out: 8
-    bw_params: 8
-    scale_in: 14.8900003433
-    scale_out: 8.60000038147
-    scale_params: 75.6200027466
-  }
-}
-layer {
-  name: "bn4a_branch2c"
-  type: "BatchNorm"
-  bottom: "res4a_branch2c"
-  top: "res4a_branch2c"
-  batch_norm_param {
-  }
-}
-layer {
-  name: "scale4a_branch2c"
-  type: "Scale"
-  bottom: "res4a_branch2c"
-  top: "res4a_branch2c"
-  scale_param {
-    bias_term: true
-  }
-}
-layer {
-  name: "res4a"
-  type: "Eltwise"
-  bottom: "res4a_branch1"
-  bottom: "res4a_branch2c"
-  top: "res4a"
-  eltwise_param {
-  }
-}
-layer {
-  name: "res4a_relu"
-  type: "ReLU"
-  bottom: "res4a"
-  top: "res4a"
-  relu_param {
-  }
-}
-layer {
-  name: "res4b_branch2a"
-  type: "Convolution"
-  bottom: "res4a"
-  top: "res4b_branch2a"
-  convolution_param {
-    num_output: 256
-    bias_term: false
-    pad: 0
-    kernel_size: 1
-    stride: 1
-    weight_filler {
-      type: "xavier"
-    }
-    bias_filler {
-      type: "constant"
-      value: 0.20000000298
-    }
-  }
-  quantization_param {
-    precision: DYNAMIC_FIXED_POINT
-    bw_layer_in: 8
-    bw_layer_out: 8
-    bw_params: 8
-    scale_in: 8.60000038147
-    scale_out: 12.0799999237
-    scale_params: 325.369995117
-  }
-}
-layer {
-  name: "bn4b_branch2a"
-  type: "BatchNorm"
-  bottom: "res4b_branch2a"
-  top: "res4b_branch2a"
-  batch_norm_param {
-  }
-}
-layer {
-  name: "scale4b_branch2a"
-  type: "Scale"
-  bottom: "res4b_branch2a"
-  top: "res4b_branch2a"
-  scale_param {
-    bias_term: true
-  }
-}
-layer {
-  name: "res4b_branch2a_relu"
-  type: "ReLU"
-  bottom: "res4b_branch2a"
-  top: "res4b_branch2a"
-  relu_param {
-  }
-}
-layer {
-  name: "res4b_branch2b"
-  type: "Convolution"
-  bottom: "res4b_branch2a"
-  top: "res4b_branch2b"
-  convolution_param {
-    num_output: 256
-    bias_term: false
-    pad: 1
-    kernel_size: 3
-    stride: 1
-    weight_filler {
-      type: "xavier"
-    }
-    bias_filler {
-      type: "constant"
-      value: 0.20000000298
-    }
-  }
-  quantization_param {
-    precision: DYNAMIC_FIXED_POINT
-    bw_layer_in: 8
-    bw_layer_out: 8
-    bw_params: 8
-    scale_in: 12.0799999237
-    scale_out: 6.30999994278
-    scale_params: 131.820007324
-  }
-}
-layer {
-  name: "bn4b_branch2b"
-  type: "BatchNorm"
-  bottom: "res4b_branch2b"
-  top: "res4b_branch2b"
-  batch_norm_param {
-  }
-}
-layer {
-  name: "scale4b_branch2b"
-  type: "Scale"
-  bottom: "res4b_branch2b"
-  top: "res4b_branch2b"
-  scale_param {
-    bias_term: true
-  }
-}
-layer {
-  name: "res4b_branch2b_relu"
-  type: "ReLU"
-  bottom: "res4b_branch2b"
-  top: "res4b_branch2b"
-  relu_param {
-  }
-}
-layer {
-  name: "res4b_branch2c"
-  type: "Convolution"
-  bottom: "res4b_branch2b"
-  top: "res4b_branch2c"
-  convolution_param {
-    num_output: 1024
-    bias_term: false
-    pad: 0
-    kernel_size: 1
-    stride: 1
-    weight_filler {
-      type: "xavier"
-    }
-    bias_filler {
-      type: "constant"
-      value: 0.20000000298
-    }
-  }
-  quantization_param {
-    precision: DYNAMIC_FIXED_POINT
-    bw_layer_in: 8
-    bw_layer_out: 8
-    bw_params: 8
-    scale_in: 6.30999994278
-    scale_out: 8.60000038147
-    scale_params: 67.4400024414
-  }
-}
-layer {
-  name: "bn4b_branch2c"
-  type: "BatchNorm"
-  bottom: "res4b_branch2c"
-  top: "res4b_branch2c"
-  batch_norm_param {
-  }
-}
-layer {
-  name: "scale4b_branch2c"
-  type: "Scale"
-  bottom: "res4b_branch2c"
-  top: "res4b_branch2c"
-  scale_param {
-    bias_term: true
-  }
-}
-layer {
-  name: "res4b"
-  type: "Eltwise"
-  bottom: "res4a"
-  bottom: "res4b_branch2c"
-  top: "res4b"
-  eltwise_param {
-  }
-}
-layer {
-  name: "res4b_relu"
-  type: "ReLU"
-  bottom: "res4b"
-  top: "res4b"
-  relu_param {
-  }
-}
-layer {
-  name: "res4c_branch2a"
-  type: "Convolution"
-  bottom: "res4b"
-  top: "res4c_branch2a"
-  convolution_param {
-    num_output: 256
-    bias_term: false
-    pad: 0
-    kernel_size: 1
-    stride: 1
-    weight_filler {
-      type: "xavier"
-    }
-    bias_filler {
-      type: "constant"
-      value: 0.20000000298
-    }
-  }
-  quantization_param {
-    precision: DYNAMIC_FIXED_POINT
-    bw_layer_in: 8
-    bw_layer_out: 8
-    bw_params: 8
-    scale_in: 8.60000038147
-    scale_out: 11.4099998474
-    scale_params: 369.480010986
-  }
-}
-layer {
-  name: "bn4c_branch2a"
-  type: "BatchNorm"
-  bottom: "res4c_branch2a"
-  top: "res4c_branch2a"
-  batch_norm_param {
-  }
-}
-layer {
-  name: "scale4c_branch2a"
-  type: "Scale"
-  bottom: "res4c_branch2a"
-  top: "res4c_branch2a"
-  scale_param {
-    bias_term: true
-  }
-}
-layer {
-  name: "res4c_branch2a_relu"
-  type: "ReLU"
-  bottom: "res4c_branch2a"
-  top: "res4c_branch2a"
-  relu_param {
-  }
-}
-layer {
-  name: "res4c_branch2b"
-  type: "Convolution"
-  bottom: "res4c_branch2a"
-  top: "res4c_branch2b"
-  convolution_param {
-    num_output: 256
-    bias_term: false
-    pad: 1
-    kernel_size: 3
-    stride: 1
-    weight_filler {
-      type: "xavier"
-    }
-    bias_filler {
-      type: "constant"
-      value: 0.20000000298
-    }
-  }
-  quantization_param {
-    precision: DYNAMIC_FIXED_POINT
-    bw_layer_in: 8
-    bw_layer_out: 8
-    bw_params: 8
-    scale_in: 11.4099998474
-    scale_out: 6.90999984741
-    scale_params: 139.050003052
-  }
-}
-layer {
-  name: "bn4c_branch2b"
-  type: "BatchNorm"
-  bottom: "res4c_branch2b"
-  top: "res4c_branch2b"
-  batch_norm_param {
-  }
-}
-layer {
-  name: "scale4c_branch2b"
-  type: "Scale"
-  bottom: "res4c_branch2b"
-  top: "res4c_branch2b"
-  scale_param {
-    bias_term: true
-  }
-}
-layer {
-  name: "res4c_branch2b_relu"
-  type: "ReLU"
-  bottom: "res4c_branch2b"
-  top: "res4c_branch2b"
-  relu_param {
-  }
-}
-layer {
-  name: "res4c_branch2c"
-  type: "Convolution"
-  bottom: "res4c_branch2b"
-  top: "res4c_branch2c"
-  convolution_param {
-    num_output: 1024
-    bias_term: false
-    pad: 0
-    kernel_size: 1
-    stride: 1
-    weight_filler {
-      type: "xavier"
-    }
-    bias_filler {
-      type: "constant"
-      value: 0.20000000298
-    }
-  }
-  quantization_param {
-    precision: DYNAMIC_FIXED_POINT
-    bw_layer_in: 8
-    bw_layer_out: 8
-    bw_params: 8
-    scale_in: 6.90999984741
-    scale_out: 8.60000038147
-    scale_params: 76.9700012207
-  }
-}
-layer {
-  name: "bn4c_branch2c"
-  type: "BatchNorm"
-  bottom: "res4c_branch2c"
-  top: "res4c_branch2c"
-  batch_norm_param {
-  }
-}
-layer {
-  name: "scale4c_branch2c"
-  type: "Scale"
-  bottom: "res4c_branch2c"
-  top: "res4c_branch2c"
-  scale_param {
-    bias_term: true
-  }
-}
-layer {
-  name: "res4c"
-  type: "Eltwise"
-  bottom: "res4b"
-  bottom: "res4c_branch2c"
-  top: "res4c"
-  eltwise_param {
-  }
-}
-layer {
-  name: "res4c_relu"
-  type: "ReLU"
-  bottom: "res4c"
-  top: "res4c"
-  relu_param {
-  }
-}
-layer {
-  name: "res4d_branch2a"
-  type: "Convolution"
-  bottom: "res4c"
-  top: "res4d_branch2a"
-  convolution_param {
-    num_output: 256
-    bias_term: false
-    pad: 0
-    kernel_size: 1
-    stride: 1
-    weight_filler {
-      type: "xavier"
-    }
-    bias_filler {
-      type: "constant"
-      value: 0.20000000298
-    }
-  }
-  quantization_param {
-    precision: DYNAMIC_FIXED_POINT
-    bw_layer_in: 8
-    bw_layer_out: 8
-    bw_params: 8
-    scale_in: 8.60000038147
-    scale_out: 10.7299995422
-    scale_params: 312.220001221
-  }
-}
-layer {
-  name: "bn4d_branch2a"
-  type: "BatchNorm"
-  bottom: "res4d_branch2a"
-  top: "res4d_branch2a"
-  batch_norm_param {
-  }
-}
-layer {
-  name: "scale4d_branch2a"
-  type: "Scale"
-  bottom: "res4d_branch2a"
-  top: "res4d_branch2a"
-  scale_param {
-    bias_term: true
-  }
-}
-layer {
-  name: "res4d_branch2a_relu"
-  type: "ReLU"
-  bottom: "res4d_branch2a"
-  top: "res4d_branch2a"
-  relu_param {
-  }
-}
-layer {
-  name: "res4d_branch2b"
-  type: "Convolution"
-  bottom: "res4d_branch2a"
-  top: "res4d_branch2b"
-  convolution_param {
-    num_output: 256
-    bias_term: false
-    pad: 1
-    kernel_size: 3
-    stride: 1
-    weight_filler {
-      type: "xavier"
-    }
-    bias_filler {
-      type: "constant"
-      value: 0.20000000298
-    }
-  }
-  quantization_param {
-    precision: DYNAMIC_FIXED_POINT
-    bw_layer_in: 8
-    bw_layer_out: 8
-    bw_params: 8
-    scale_in: 10.7299995422
-    scale_out: 5.40000009537
-    scale_params: 201.61000061
-  }
-}
-layer {
-  name: "bn4d_branch2b"
-  type: "BatchNorm"
-  bottom: "res4d_branch2b"
-  top: "res4d_branch2b"
-  batch_norm_param {
-  }
-}
-layer {
-  name: "scale4d_branch2b"
-  type: "Scale"
-  bottom: "res4d_branch2b"
-  top: "res4d_branch2b"
-  scale_param {
-    bias_term: true
-  }
-}
-layer {
-  name: "res4d_branch2b_relu"
-  type: "ReLU"
-  bottom: "res4d_branch2b"
-  top: "res4d_branch2b"
-  relu_param {
-  }
-}
-layer {
-  name: "res4d_branch2c"
-  type: "Convolution"
-  bottom: "res4d_branch2b"
-  top: "res4d_branch2c"
-  convolution_param {
-    num_output: 1024
-    bias_term: false
-    pad: 0
-    kernel_size: 1
-    stride: 1
-    weight_filler {
-      type: "xavier"
-    }
-    bias_filler {
-      type: "constant"
-      value: 0.20000000298
-    }
-  }
-  quantization_param {
-    precision: DYNAMIC_FIXED_POINT
-    bw_layer_in: 8
-    bw_layer_out: 8
-    bw_params: 8
-    scale_in: 5.40000009537
-    scale_out: 8.60000038147
-    scale_params: 77.1500015259
-  }
-}
-layer {
-  name: "bn4d_branch2c"
-  type: "BatchNorm"
-  bottom: "res4d_branch2c"
-  top: "res4d_branch2c"
-  batch_norm_param {
-  }
-}
-layer {
-  name: "scale4d_branch2c"
-  type: "Scale"
-  bottom: "res4d_branch2c"
-  top: "res4d_branch2c"
-  scale_param {
-    bias_term: true
-  }
-}
-layer {
-  name: "res4d"
-  type: "Eltwise"
-  bottom: "res4c"
-  bottom: "res4d_branch2c"
-  top: "res4d"
-  eltwise_param {
-  }
-}
-layer {
-  name: "res4d_relu"
-  type: "ReLU"
-  bottom: "res4d"
-  top: "res4d"
-  relu_param {
-  }
-}
-layer {
-  name: "res4e_branch2a"
-  type: "Convolution"
-  bottom: "res4d"
-  top: "res4e_branch2a"
-  convolution_param {
-    num_output: 256
-    bias_term: false
-    pad: 0
-    kernel_size: 1
-    stride: 1
-    weight_filler {
-      type: "xavier"
-    }
-    bias_filler {
-      type: "constant"
-      value: 0.20000000298
-    }
-  }
-  quantization_param {
-    precision: DYNAMIC_FIXED_POINT
-    bw_layer_in: 8
-    bw_layer_out: 8
-    bw_params: 8
-    scale_in: 8.60000038147
-    scale_out: 11.75
-    scale_params: 293.720001221
-  }
-}
-layer {
-  name: "bn4e_branch2a"
-  type: "BatchNorm"
-  bottom: "res4e_branch2a"
-  top: "res4e_branch2a"
-  batch_norm_param {
-  }
-}
-layer {
-  name: "scale4e_branch2a"
-  type: "Scale"
-  bottom: "res4e_branch2a"
-  top: "res4e_branch2a"
-  scale_param {
-    bias_term: true
-  }
-}
-layer {
-  name: "res4e_branch2a_relu"
-  type: "ReLU"
-  bottom: "res4e_branch2a"
-  top: "res4e_branch2a"
-  relu_param {
-  }
-}
-layer {
-  name: "res4e_branch2b"
-  type: "Convolution"
-  bottom: "res4e_branch2a"
-  top: "res4e_branch2b"
-  convolution_param {
-    num_output: 256
-    bias_term: false
-    pad: 1
-    kernel_size: 3
-    stride: 1
-    weight_filler {
-      type: "xavier"
-    }
-    bias_filler {
-      type: "constant"
-      value: 0.20000000298
-    }
-  }
-  quantization_param {
-    precision: DYNAMIC_FIXED_POINT
-    bw_layer_in: 8
-    bw_layer_out: 8
-    bw_params: 8
-    scale_in: 11.75
-    scale_out: 6.51999998093
-    scale_params: 185.36000061
-  }
-}
-layer {
-  name: "bn4e_branch2b"
-  type: "BatchNorm"
-  bottom: "res4e_branch2b"
-  top: "res4e_branch2b"
-  batch_norm_param {
-  }
-}
-layer {
-  name: "scale4e_branch2b"
-  type: "Scale"
-  bottom: "res4e_branch2b"
-  top: "res4e_branch2b"
-  scale_param {
-    bias_term: true
-  }
-}
-layer {
-  name: "res4e_branch2b_relu"
-  type: "ReLU"
-  bottom: "res4e_branch2b"
-  top: "res4e_branch2b"
-  relu_param {
-  }
-}
-layer {
-  name: "res4e_branch2c"
-  type: "Convolution"
-  bottom: "res4e_branch2b"
-  top: "res4e_branch2c"
-  convolution_param {
-    num_output: 1024
-    bias_term: false
-    pad: 0
-    kernel_size: 1
-    stride: 1
-    weight_filler {
-      type: "xavier"
-    }
-    bias_filler {
-      type: "constant"
-      value: 0.20000000298
-    }
-  }
-  quantization_param {
-    precision: DYNAMIC_FIXED_POINT
-    bw_layer_in: 8
-    bw_layer_out: 8
-    bw_params: 8
-    scale_in: 6.51999998093
-    scale_out: 8.60000038147
-    scale_params: 98.8199996948
-  }
-}
-layer {
-  name: "bn4e_branch2c"
-  type: "BatchNorm"
-  bottom: "res4e_branch2c"
-  top: "res4e_branch2c"
-  batch_norm_param {
-  }
-}
-layer {
-  name: "scale4e_branch2c"
-  type: "Scale"
-  bottom: "res4e_branch2c"
-  top: "res4e_branch2c"
-  scale_param {
-    bias_term: true
-  }
-}
-layer {
-  name: "res4e"
-  type: "Eltwise"
-  bottom: "res4d"
-  bottom: "res4e_branch2c"
-  top: "res4e"
-  eltwise_param {
-  }
-}
-layer {
-  name: "res4e_relu"
-  type: "ReLU"
-  bottom: "res4e"
-  top: "res4e"
-  relu_param {
-  }
-}
-layer {
-  name: "res4e_p"
-  type: "Pooling"
-  bottom: "res4e"
-  top: "res4e_p"
-  pooling_param {
-    pool: MAX
-    kernel_size: 1
-    stride: 2
-  }
-}
-layer {
-  name: "res4f_branch2a"
-  type: "Convolution"
-  bottom: "res4e"
-  top: "res4f_branch2a"
-  convolution_param {
-    num_output: 256
-    bias_term: false
-    pad: 0
-    kernel_size: 1
-    stride: 1
-    weight_filler {
-      type: "xavier"
-    }
-    bias_filler {
-      type: "constant"
-      value: 0.20000000298
-    }
-  }
-  quantization_param {
-    precision: DYNAMIC_FIXED_POINT
-    bw_layer_in: 8
-    bw_layer_out: 8
-    bw_params: 8
-    scale_in: 8.60000038147
-    scale_out: 16.2900009155
-    scale_params: 300.049987793
-  }
-}
-layer {
-  name: "bn4f_branch2a"
-  type: "BatchNorm"
-  bottom: "res4f_branch2a"
-  top: "res4f_branch2a"
-  batch_norm_param {
-  }
-}
-layer {
-  name: "scale4f_branch2a"
-  type: "Scale"
-  bottom: "res4f_branch2a"
-  top: "res4f_branch2a"
-  scale_param {
-    bias_term: true
-  }
-}
-layer {
-  name: "res4f_branch2a_relu"
-  type: "ReLU"
-  bottom: "res4f_branch2a"
-  top: "res4f_branch2a"
-  relu_param {
-  }
-}
-layer {
-  name: "res4f_branch2b"
-  type: "Convolution"
-  bottom: "res4f_branch2a"
-  top: "res4f_branch2b"
-  convolution_param {
-    num_output: 256
-    bias_term: false
-    pad: 1
-    kernel_size: 3
-    stride: 2
-    weight_filler {
-      type: "xavier"
-    }
-    bias_filler {
-      type: "constant"
-      value: 0.20000000298
-    }
-  }
-  quantization_param {
-    precision: DYNAMIC_FIXED_POINT
-    bw_layer_in: 8
-    bw_layer_out: 8
-    bw_params: 8
-    scale_in: 16.2900009155
-    scale_out: 16.6200008392
-    scale_params: 240.550003052
-  }
-}
-layer {
-  name: "bn4f_branch2b"
-  type: "BatchNorm"
-  bottom: "res4f_branch2b"
-  top: "res4f_branch2b"
-  batch_norm_param {
-  }
-}
-layer {
-  name: "scale4f_branch2b"
-  type: "Scale"
-  bottom: "res4f_branch2b"
-  top: "res4f_branch2b"
-  scale_param {
-    bias_term: true
-  }
-}
-layer {
-  name: "res4f_branch2b_relu"
-  type: "ReLU"
-  bottom: "res4f_branch2b"
-  top: "res4f_branch2b"
-  relu_param {
-  }
-}
-layer {
-  name: "res4f_branch2c"
-  type: "Convolution"
-  bottom: "res4f_branch2b"
-  top: "res4f_branch2c"
-  convolution_param {
-    num_output: 1024
-    bias_term: false
-    pad: 0
-    kernel_size: 1
-    stride: 1
-    weight_filler {
-      type: "xavier"
-    }
-    bias_filler {
-      type: "constant"
-      value: 0.20000000298
-    }
-  }
-  quantization_param {
-    precision: DYNAMIC_FIXED_POINT
-    bw_layer_in: 8
-    bw_layer_out: 8
-    bw_params: 8
-    scale_in: 16.6200008392
-    scale_out: 8.5
-    scale_params: 93.3000030518
-  }
-}
-layer {
-  name: "bn4f_branch2c"
-  type: "BatchNorm"
-  bottom: "res4f_branch2c"
-  top: "res4f_branch2c"
-  batch_norm_param {
-  }
-}
-layer {
-  name: "scale4f_branch2c"
-  type: "Scale"
-  bottom: "res4f_branch2c"
-  top: "res4f_branch2c"
-  scale_param {
-    bias_term: true
-  }
-}
-layer {
-  name: "res4f"
-  type: "Eltwise"
-  bottom: "res4e_p"
-  bottom: "res4f_branch2c"
-  top: "res4f"
-  eltwise_param {
-  }
-}
-layer {
-  name: "res4f_relu"
-  type: "ReLU"
-  bottom: "res4f"
-  top: "res4f"
-  relu_param {
-  }
-}
-layer {
-  name: "res5a_branch1"
-  type: "Convolution"
-  bottom: "res4f"
-  top: "res5a_branch1"
-  convolution_param {
-    num_output: 2048
-    bias_term: false
-    pad: 0
-    kernel_size: 1
-    stride: 1
-    weight_filler {
-      type: "xavier"
-    }
-    bias_filler {
-      type: "constant"
-      value: 0.20000000298
-    }
-  }
-  quantization_param {
-    precision: DYNAMIC_FIXED_POINT
-    bw_layer_in: 8
-    bw_layer_out: 8
-    bw_params: 8
-    scale_in: 8.5
-    scale_out: 0.939999997616
-    scale_params: 45.0
-  }
-}
-layer {
-  name: "bn5a_branch1"
-  type: "BatchNorm"
-  bottom: "res5a_branch1"
-  top: "res5a_branch1"
-  batch_norm_param {
-  }
-}
-layer {
-  name: "scale5a_branch1"
-  type: "Scale"
-  bottom: "res5a_branch1"
-  top: "res5a_branch1"
-  scale_param {
-    bias_term: true
-  }
-}
-layer {
-  name: "res5a_branch2a"
-  type: "Convolution"
-  bottom: "res4f"
-  top: "res5a_branch2a"
-  convolution_param {
-    num_output: 512
-    bias_term: false
-    pad: 0
-    kernel_size: 1
-    stride: 1
-    weight_filler {
-      type: "xavier"
-    }
-    bias_filler {
-      type: "constant"
-      value: 0.20000000298
-    }
-  }
-  quantization_param {
-    precision: DYNAMIC_FIXED_POINT
-    bw_layer_in: 8
-    bw_layer_out: 8
-    bw_params: 8
-    scale_in: 8.5
-    scale_out: 21.5699996948
-    scale_params: 375.869995117
-  }
-}
-layer {
-  name: "bn5a_branch2a"
-  type: "BatchNorm"
-  bottom: "res5a_branch2a"
-  top: "res5a_branch2a"
-  batch_norm_param {
-  }
-}
-layer {
-  name: "scale5a_branch2a"
-  type: "Scale"
-  bottom: "res5a_branch2a"
-  top: "res5a_branch2a"
-  scale_param {
-    bias_term: true
-  }
-}
-layer {
-  name: "res5a_branch2a_relu"
-  type: "ReLU"
-  bottom: "res5a_branch2a"
-  top: "res5a_branch2a"
-  relu_param {
-  }
-}
-layer {
-  name: "res5a_branch2b"
-  type: "Convolution"
-  bottom: "res5a_branch2a"
-  top: "res5a_branch2b"
-  convolution_param {
-    num_output: 512
-    bias_term: false
-    pad: 1
-    kernel_size: 3
-    stride: 1
-    weight_filler {
-      type: "xavier"
-    }
-    bias_filler {
-      type: "constant"
-      value: 0.20000000298
-    }
-  }
-  quantization_param {
-    precision: DYNAMIC_FIXED_POINT
-    bw_layer_in: 8
-    bw_layer_out: 8
-    bw_params: 8
-    scale_in: 21.5699996948
-    scale_out: 25.2000007629
-    scale_params: 228.160003662
-  }
-}
-layer {
-  name: "bn5a_branch2b"
-  type: "BatchNorm"
-  bottom: "res5a_branch2b"
-  top: "res5a_branch2b"
-  batch_norm_param {
-  }
-}
-layer {
-  name: "scale5a_branch2b"
-  type: "Scale"
-  bottom: "res5a_branch2b"
-  top: "res5a_branch2b"
-  scale_param {
-    bias_term: true
-  }
-}
-layer {
-  name: "res5a_branch2b_relu"
-  type: "ReLU"
-  bottom: "res5a_branch2b"
-  top: "res5a_branch2b"
-  relu_param {
-  }
-}
-layer {
-  name: "res5a_branch2c"
-  type: "Convolution"
-  bottom: "res5a_branch2b"
-  top: "res5a_branch2c"
-  convolution_param {
-    num_output: 2048
-    bias_term: false
-    pad: 0
-    kernel_size: 1
-    stride: 1
-    weight_filler {
-      type: "xavier"
-    }
-    bias_filler {
-      type: "constant"
-      value: 0.20000000298
-    }
-  }
-  quantization_param {
-    precision: DYNAMIC_FIXED_POINT
-    bw_layer_in: 8
-    bw_layer_out: 8
-    bw_params: 8
-    scale_in: 25.2000007629
-    scale_out: 1.87999999523
-    scale_params: 27.1299991608
-  }
-}
-layer {
-  name: "bn5a_branch2c"
-  type: "BatchNorm"
-  bottom: "res5a_branch2c"
-  top: "res5a_branch2c"
-  batch_norm_param {
-  }
-}
-layer {
-  name: "scale5a_branch2c"
-  type: "Scale"
-  bottom: "res5a_branch2c"
-  top: "res5a_branch2c"
-  scale_param {
-    bias_term: true
-  }
-}
-layer {
-  name: "res5a"
-  type: "Eltwise"
-  bottom: "res5a_branch1"
-  bottom: "res5a_branch2c"
-  top: "res5a"
-  eltwise_param {
-  }
-}
-layer {
-  name: "res5a_relu"
-  type: "ReLU"
-  bottom: "res5a"
-  top: "res5a"
-  relu_param {
-  }
-}
-layer {
-  name: "res5b_branch2a"
-  type: "Convolution"
-  bottom: "res5a"
-  top: "res5b_branch2a"
-  convolution_param {
-    num_output: 512
-    bias_term: false
-    pad: 0
-    kernel_size: 1
-    stride: 1
-    weight_filler {
-      type: "xavier"
-    }
-    bias_filler {
-      type: "constant"
-      value: 0.20000000298
-    }
-  }
-  quantization_param {
-    precision: DYNAMIC_FIXED_POINT
-    bw_layer_in: 8
-    bw_layer_out: 8
-    bw_params: 8
-    scale_in: 1.87999999523
-    scale_out: 25.4400005341
-    scale_params: 999.760009766
-  }
-}
-layer {
-  name: "bn5b_branch2a"
-  type: "BatchNorm"
-  bottom: "res5b_branch2a"
-  top: "res5b_branch2a"
-  batch_norm_param {
-  }
-}
-layer {
-  name: "scale5b_branch2a"
-  type: "Scale"
-  bottom: "res5b_branch2a"
-  top: "res5b_branch2a"
-  scale_param {
-    bias_term: true
-  }
-}
-layer {
-  name: "res5b_branch2a_relu"
-  type: "ReLU"
-  bottom: "res5b_branch2a"
-  top: "res5b_branch2a"
-  relu_param {
-  }
-}
-layer {
-  name: "res5b_branch2b"
-  type: "Convolution"
-  bottom: "res5b_branch2a"
-  top: "res5b_branch2b"
-  convolution_param {
-    num_output: 512
-    bias_term: false
-    pad: 1
-    kernel_size: 3
-    stride: 1
-    weight_filler {
-      type: "xavier"
-    }
-    bias_filler {
-      type: "constant"
-      value: 0.20000000298
-    }
-  }
-  quantization_param {
-    precision: DYNAMIC_FIXED_POINT
-    bw_layer_in: 8
-    bw_layer_out: 8
-    bw_params: 8
-    scale_in: 25.4400005341
-    scale_out: 26.6200008392
-    scale_params: 153.630004883
-  }
-}
-layer {
-  name: "bn5b_branch2b"
-  type: "BatchNorm"
-  bottom: "res5b_branch2b"
-  top: "res5b_branch2b"
-  batch_norm_param {
-  }
-}
-layer {
-  name: "scale5b_branch2b"
-  type: "Scale"
-  bottom: "res5b_branch2b"
-  top: "res5b_branch2b"
-  scale_param {
-    bias_term: true
-  }
-}
-layer {
-  name: "res5b_branch2b_relu"
-  type: "ReLU"
-  bottom: "res5b_branch2b"
-  top: "res5b_branch2b"
-  relu_param {
-  }
-}
-layer {
-  name: "res5b_branch2c"
-  type: "Convolution"
-  bottom: "res5b_branch2b"
-  top: "res5b_branch2c"
-  convolution_param {
-    num_output: 2048
-    bias_term: false
-    pad: 0
-    kernel_size: 1
-    stride: 1
-    weight_filler {
-      type: "xavier"
-    }
-    bias_filler {
-      type: "constant"
-      value: 0.20000000298
-    }
-  }
-  quantization_param {
-    precision: DYNAMIC_FIXED_POINT
-    bw_layer_in: 8
-    bw_layer_out: 8
-    bw_params: 8
-    scale_in: 26.6200008392
-    scale_out: 1.87999999523
-    scale_params: 35.3400001526
-  }
-}
-layer {
-  name: "bn5b_branch2c"
-  type: "BatchNorm"
-  bottom: "res5b_branch2c"
-  top: "res5b_branch2c"
-  batch_norm_param {
-  }
-}
-layer {
-  name: "scale5b_branch2c"
-  type: "Scale"
-  bottom: "res5b_branch2c"
-  top: "res5b_branch2c"
-  scale_param {
-    bias_term: true
-  }
-}
-layer {
-  name: "res5b"
-  type: "Eltwise"
-  bottom: "res5a"
-  bottom: "res5b_branch2c"
-  top: "res5b"
-  eltwise_param {
-  }
-}
-layer {
-  name: "res5b_relu"
-  type: "ReLU"
-  bottom: "res5b"
-  top: "res5b"
-  relu_param {
-  }
-}
-layer {
-  name: "res5c_branch2a"
-  type: "Convolution"
-  bottom: "res5b"
-  top: "res5c_branch2a"
-  convolution_param {
-    num_output: 512
-    bias_term: false
-    pad: 0
-    kernel_size: 1
-    stride: 1
-    weight_filler {
-      type: "xavier"
-    }
-    bias_filler {
-      type: "constant"
-      value: 0.20000000298
-    }
-  }
-  quantization_param {
-    precision: DYNAMIC_FIXED_POINT
-    bw_layer_in: 8
-    bw_layer_out: 8
-    bw_params: 8
-    scale_in: 1.87999999523
-    scale_out: 21.8099994659
-    scale_params: 1013.09997559
-  }
-}
-layer {
-  name: "bn5c_branch2a"
-  type: "BatchNorm"
-  bottom: "res5c_branch2a"
-  top: "res5c_branch2a"
-  batch_norm_param {
-  }
-}
-layer {
-  name: "scale5c_branch2a"
-  type: "Scale"
-  bottom: "res5c_branch2a"
-  top: "res5c_branch2a"
-  scale_param {
-    bias_term: true
-  }
-}
-layer {
-  name: "res5c_branch2a_relu"
-  type: "ReLU"
-  bottom: "res5c_branch2a"
-  top: "res5c_branch2a"
-  relu_param {
-  }
-}
-layer {
-  name: "res5c_branch2b"
-  type: "Convolution"
-  bottom: "res5c_branch2a"
-  top: "res5c_branch2b"
-  convolution_param {
-    num_output: 512
-    bias_term: false
-    pad: 1
-    kernel_size: 3
-    stride: 1
-    weight_filler {
-      type: "xavier"
-    }
-    bias_filler {
-      type: "constant"
-      value: 0.20000000298
-    }
-  }
-  quantization_param {
-    precision: DYNAMIC_FIXED_POINT
-    bw_layer_in: 8
-    bw_layer_out: 8
-    bw_params: 8
-    scale_in: 21.8099994659
-    scale_out: 14.7100000381
-    scale_params: 321.25
-  }
-}
-layer {
-  name: "bn5c_branch2b"
-  type: "BatchNorm"
-  bottom: "res5c_branch2b"
-  top: "res5c_branch2b"
-  batch_norm_param {
-  }
-}
-layer {
-  name: "scale5c_branch2b"
-  type: "Scale"
-  bottom: "res5c_branch2b"
-  top: "res5c_branch2b"
-  scale_param {
-    bias_term: true
-  }
-}
-layer {
-  name: "res5c_branch2b_relu"
-  type: "ReLU"
-  bottom: "res5c_branch2b"
-  top: "res5c_branch2b"
-  relu_param {
-  }
-}
-layer {
-  name: "res5c_branch2c"
-  type: "Convolution"
-  bottom: "res5c_branch2b"
-  top: "res5c_branch2c"
-  convolution_param {
-    num_output: 2048
-    bias_term: false
-    pad: 0
-    kernel_size: 1
-    stride: 1
-    weight_filler {
-      type: "xavier"
-    }
-    bias_filler {
-      type: "constant"
-      value: 0.20000000298
-    }
-  }
-  quantization_param {
-    precision: DYNAMIC_FIXED_POINT
-    bw_layer_in: 8
-    bw_layer_out: 8
-    bw_params: 8
-    scale_in: 14.7100000381
-    scale_out: 1.87999999523
-    scale_params: 23.2399997711
-  }
-}
-layer {
-  name: "bn5c_branch2c"
-  type: "BatchNorm"
-  bottom: "res5c_branch2c"
-  top: "res5c_branch2c"
-  batch_norm_param {
-  }
-}
-layer {
-  name: "scale5c_branch2c"
-  type: "Scale"
-  bottom: "res5c_branch2c"
-  top: "res5c_branch2c"
-  scale_param {
-    bias_term: true
-  }
-}
-layer {
-  name: "res5c"
-  type: "Eltwise"
-  bottom: "res5b"
-  bottom: "res5c_branch2c"
-  top: "res5c"
-  eltwise_param {
-  }
-}
-layer {
-  name: "res5c_relu"
-  type: "ReLU"
-  bottom: "res5c"
-  top: "res5c"
-  relu_param {
-  }
-}
-layer {
-  name: "pool5"
-  type: "Pooling"
-  bottom: "res5c"
-  top: "pool5"
-  pooling_param {
-    pool: AVE
-    kernel_size: 7
-    stride: 1
-  }
-}
-layer {
-  name: "fc1000"
-  type: "InnerProduct"
-  bottom: "pool5"
-  top: "fc1000"
-  inner_product_param {
-    num_output: 1000
-    weight_filler {
-      type: "xavier"
-    }
-    bias_filler {
-      type: "constant"
-      value: 0.0
-    }
-  }
-}
-
diff --git a/models/intel_optimized_models/int8/ssd_int8_mc.prototxt b/models/intel_optimized_models/int8/ssd_int8_mc.prototxt
deleted file mode 100644
index 04512b1e8..000000000
--- a/models/intel_optimized_models/int8/ssd_int8_mc.prototxt
+++ /dev/null
@@ -1,10740 +0,0 @@
-name: "VGG_VOC0712_SSD_300x300_test"
-layer {
-  name: "data"
-  type: "DummyData"
-  top: "data"
-  dummy_data_param {
-    data_filler {
-      type: "constant"
-      value: 0.01
-    }
-    shape {
-      dim: 32
-      dim: 3
-      dim: 300
-      dim: 300
-    }
-  }
-}
-layer {
-  name: "data"
-  type: "DummyData"
-  top: "label"
-  dummy_data_param {
-    data_filler {
-      type: "constant"
-      value: 1
-    }
-    shape {
-      dim: 1
-      dim: 1
-      dim: 1
-      dim: 8
-    }
-  }
-}
-layer {
-  name: "conv1_1"
-  type: "Convolution"
-  bottom: "data"
-  top: "conv1_1"
-  param {
-    lr_mult: 1.0
-    decay_mult: 1.0
-  }
-  param {
-    lr_mult: 2.0
-    decay_mult: 0.0
-  }
-  convolution_param {
-    num_output: 64
-    pad: 1
-    kernel_size: 3
-    weight_filler {
-      type: "xavier"
-    }
-    bias_filler {
-      type: "constant"
-      value: 0.0
-    }
-  }
-}
-layer {
-  name: "relu1_1"
-  type: "ReLU"
-  bottom: "conv1_1"
-  top: "conv1_1"
-}
-layer {
-  name: "conv1_2"
-  type: "Convolution"
-  bottom: "conv1_1"
-  top: "conv1_2"
-  param {
-    lr_mult: 1.0
-    decay_mult: 1.0
-  }
-  param {
-    lr_mult: 2.0
-    decay_mult: 0.0
-  }
-  convolution_param {
-    num_output: 64
-    pad: 1
-    kernel_size: 3
-    weight_filler {
-      type: "xavier"
-    }
-    bias_filler {
-      type: "constant"
-      value: 0.0
-    }
-  }
-  quantization_param {
-    bw_layer_in: 8
-    bw_layer_out: 8
-    bw_params: 8
-    scale_in: 0.319999992847
-    scale_out: 0.0799999982119
-    scale_params: 619.210021973
-    scale_params: 919.309997559
-    scale_params: 750.010009766
-    scale_params: 683.83001709
-    scale_params: 705.219970703
-    scale_params: 671.940002441
-    scale_params: 878.900024414
-    scale_params: 1156.5
-    scale_params: 1258.94995117
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-}
-layer {
-  name: "conv8_2_mbox_priorbox"
-  type: "PriorBox"
-  bottom: "conv8_2"
-  bottom: "data"
-  top: "conv8_2_mbox_priorbox"
-  prior_box_param {
-    min_size: 213.0
-    max_size: 264.0
-    aspect_ratio: 2.0
-    flip: true
-    clip: false
-    variance: 0.10000000149
-    variance: 0.10000000149
-    variance: 0.20000000298
-    variance: 0.20000000298
-    step: 100.0
-    offset: 0.5
-  }
-}
-layer {
-  name: "conv9_2_mbox_loc"
-  type: "Convolution"
-  bottom: "conv9_2"
-  top: "conv9_2_mbox_loc"
-  param {
-    lr_mult: 1.0
-    decay_mult: 1.0
-  }
-  param {
-    lr_mult: 2.0
-    decay_mult: 0.0
-  }
-  convolution_param {
-    num_output: 16
-    pad: 1
-    kernel_size: 3
-    stride: 1
-    weight_filler {
-      type: "xavier"
-    }
-    bias_filler {
-      type: "constant"
-      value: 0.0
-    }
-  }
-  quantization_param {
-    bw_layer_in: 8
-    bw_layer_out: 8
-    bw_params: 8
-    scale_in: 10.6499996185
-    scale_out: 35.0600013733
-    scale_params: 1395.75
-    scale_params: 1995.0300293
-    scale_params: 1638.64001465
-    scale_params: 1182.09997559
-    scale_params: 1228.89001465
-    scale_params: 1826.86999512
-    scale_params: 1477.43005371
-    scale_params: 1117.32995605
-    scale_params: 2557.14990234
-    scale_params: 1970.0300293
-    scale_params: 1542.5
-    scale_params: 1937.68005371
-    scale_params: 1574.31994629
-    scale_params: 2650.35009766
-    scale_params: 1466.65002441
-    scale_params: 1949.35998535
-  }
-}
-layer {
-  name: "conv9_2_mbox_loc_perm"
-  type: "Permute"
-  bottom: "conv9_2_mbox_loc"
-  top: "conv9_2_mbox_loc_perm"
-  permute_param {
-    order: 0
-    order: 2
-    order: 3
-    order: 1
-  }
-}
-layer {
-  name: "conv9_2_mbox_loc_flat"
-  type: "Flatten"
-  bottom: "conv9_2_mbox_loc_perm"
-  top: "conv9_2_mbox_loc_flat"
-  flatten_param {
-    axis: 1
-  }
-}
-layer {
-  name: "conv9_2_mbox_conf"
-  type: "Convolution"
-  bottom: "conv9_2"
-  top: "conv9_2_mbox_conf"
-  param {
-    lr_mult: 1.0
-    decay_mult: 1.0
-  }
-  param {
-    lr_mult: 2.0
-    decay_mult: 0.0
-  }
-  convolution_param {
-    num_output: 84
-    pad: 1
-    kernel_size: 3
-    stride: 1
-    weight_filler {
-      type: "xavier"
-    }
-    bias_filler {
-      type: "constant"
-      value: 0.0
-    }
-  }
-  quantization_param {
-    bw_layer_in: 8
-    bw_layer_out: 8
-    bw_params: 8
-    scale_in: 10.6499996185
-    scale_out: 5.90000009537
-    scale_params: 806.66998291
-    scale_params: 1770.57995605
-    scale_params: 1947.86999512
-    scale_params: 1485.20996094
-    scale_params: 1861.61999512
-    scale_params: 1462.15002441
-    scale_params: 1776.93994141
-    scale_params: 1505.81994629
-    scale_params: 1463.84997559
-    scale_params: 1618.56005859
-    scale_params: 1565.9699707
-    scale_params: 1643.83996582
-    scale_params: 1647.56005859
-    scale_params: 1546.43994141
-    scale_params: 1373.51000977
-    scale_params: 1154.81005859
-    scale_params: 1440.40002441
-    scale_params: 1355.79003906
-    scale_params: 1445.5
-    scale_params: 1603.31005859
-    scale_params: 1621.93994141
-    scale_params: 718.020019531
-    scale_params: 1865.56994629
-    scale_params: 1657.57995605
-    scale_params: 1622.45996094
-    scale_params: 1719.45996094
-    scale_params: 1736.31005859
-    scale_params: 1847.75
-    scale_params: 1735.90002441
-    scale_params: 1387.80004883
-    scale_params: 1509.61999512
-    scale_params: 1506.32995605
-    scale_params: 1723.02001953
-    scale_params: 1294.05004883
-    scale_params: 1520.64001465
-    scale_params: 1277.75
-    scale_params: 1078.31994629
-    scale_params: 1777.9699707
-    scale_params: 1413.48999023
-    scale_params: 1276.61999512
-    scale_params: 1662.91003418
-    scale_params: 1637.61999512
-    scale_params: 810.549987793
-    scale_params: 1936.89001465
-    scale_params: 1878.16003418
-    scale_params: 1562.52001953
-    scale_params: 1950.86999512
-    scale_params: 2141.04003906
-    scale_params: 1921.17004395
-    scale_params: 1561.09997559
-    scale_params: 1465.67004395
-    scale_params: 1473.29003906
-    scale_params: 1713.57995605
-    scale_params: 1946.72998047
-    scale_params: 1372.68994141
-    scale_params: 1971.61999512
-    scale_params: 1599.29003906
-    scale_params: 1638.35998535
-    scale_params: 1858.81994629
-    scale_params: 1588.88000488
-    scale_params: 1305.41003418
-    scale_params: 1585.4699707
-    scale_params: 1794.26000977
-    scale_params: 539.520019531
-    scale_params: 2117.44995117
-    scale_params: 1980.93994141
-    scale_params: 1773.5300293
-    scale_params: 2036.69995117
-    scale_params: 1646.82995605
-    scale_params: 2085.48999023
-    scale_params: 1450.42004395
-    scale_params: 1434.81994629
-    scale_params: 1885.29003906
-    scale_params: 1420.23999023
-    scale_params: 1644.43994141
-    scale_params: 1563.40002441
-    scale_params: 1598.38000488
-    scale_params: 1405.4699707
-    scale_params: 1065.10998535
-    scale_params: 1888.45996094
-    scale_params: 1524.33996582
-    scale_params: 1435.15002441
-    scale_params: 1506.07995605
-    scale_params: 1646.11999512
-  }
-}
-layer {
-  name: "conv9_2_mbox_conf_perm"
-  type: "Permute"
-  bottom: "conv9_2_mbox_conf"
-  top: "conv9_2_mbox_conf_perm"
-  permute_param {
-    order: 0
-    order: 2
-    order: 3
-    order: 1
-  }
-}
-layer {
-  name: "conv9_2_mbox_conf_flat"
-  type: "Flatten"
-  bottom: "conv9_2_mbox_conf_perm"
-  top: "conv9_2_mbox_conf_flat"
-  flatten_param {
-    axis: 1
-  }
-}
-layer {
-  name: "conv9_2_mbox_priorbox"
-  type: "PriorBox"
-  bottom: "conv9_2"
-  bottom: "data"
-  top: "conv9_2_mbox_priorbox"
-  prior_box_param {
-    min_size: 264.0
-    max_size: 315.0
-    aspect_ratio: 2.0
-    flip: true
-    clip: false
-    variance: 0.10000000149
-    variance: 0.10000000149
-    variance: 0.20000000298
-    variance: 0.20000000298
-    step: 300.0
-    offset: 0.5
-  }
-}
-layer {
-  name: "mbox_loc"
-  type: "Concat"
-  bottom: "conv4_3_norm_mbox_loc_flat"
-  bottom: "fc7_mbox_loc_flat"
-  bottom: "conv6_2_mbox_loc_flat"
-  bottom: "conv7_2_mbox_loc_flat"
-  bottom: "conv8_2_mbox_loc_flat"
-  bottom: "conv9_2_mbox_loc_flat"
-  top: "mbox_loc"
-  concat_param {
-    axis: 1
-  }
-}
-layer {
-  name: "mbox_conf"
-  type: "Concat"
-  bottom: "conv4_3_norm_mbox_conf_flat"
-  bottom: "fc7_mbox_conf_flat"
-  bottom: "conv6_2_mbox_conf_flat"
-  bottom: "conv7_2_mbox_conf_flat"
-  bottom: "conv8_2_mbox_conf_flat"
-  bottom: "conv9_2_mbox_conf_flat"
-  top: "mbox_conf"
-  concat_param {
-    axis: 1
-  }
-}
-layer {
-  name: "mbox_priorbox"
-  type: "Concat"
-  bottom: "conv4_3_norm_mbox_priorbox"
-  bottom: "fc7_mbox_priorbox"
-  bottom: "conv6_2_mbox_priorbox"
-  bottom: "conv7_2_mbox_priorbox"
-  bottom: "conv8_2_mbox_priorbox"
-  bottom: "conv9_2_mbox_priorbox"
-  top: "mbox_priorbox"
-  concat_param {
-    axis: 2
-  }
-}
-layer {
-  name: "mbox_conf_reshape"
-  type: "Reshape"
-  bottom: "mbox_conf"
-  top: "mbox_conf_reshape"
-  reshape_param {
-    shape {
-      dim: 0
-      dim: -1
-      dim: 21
-    }
-  }
-}
-layer {
-  name: "mbox_conf_softmax"
-  type: "Softmax"
-  bottom: "mbox_conf_reshape"
-  top: "mbox_conf_softmax"
-  softmax_param {
-    axis: 2
-  }
-}
-layer {
-  name: "mbox_conf_flatten"
-  type: "Flatten"
-  bottom: "mbox_conf_softmax"
-  top: "mbox_conf_flatten"
-  flatten_param {
-    axis: 1
-  }
-}

From ddfac12012c71e828faff9a346d559b405784dcf Mon Sep 17 00:00:00 2001
From: Daisy Deng <daisy.deng@intel.com>
Date: Tue, 9 Oct 2018 00:33:24 +0800
Subject: [PATCH 05/26] a test

---
 INSTALL.md | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/INSTALL.md b/INSTALL.md
index 05c714dbd..84c44600c 100644
--- a/INSTALL.md
+++ b/INSTALL.md
@@ -1,4 +1,4 @@
-# Installation
+#### Installation
 
 See http://caffe.berkeleyvision.org/installation.html for the latest
 installation instructions.

From 75b019613fd5b1649d04da894d2bc7a5865f6f01 Mon Sep 17 00:00:00 2001
From: Daisy Deng <daisy.deng@intel.com>
Date: Tue, 9 Oct 2018 19:44:42 +0800
Subject: [PATCH 06/26] ci test2

---
 INSTALL.md | 1 +
 1 file changed, 1 insertion(+)

diff --git a/INSTALL.md b/INSTALL.md
index 84c44600c..e1553a265 100644
--- a/INSTALL.md
+++ b/INSTALL.md
@@ -3,5 +3,6 @@
 See http://caffe.berkeleyvision.org/installation.html for the latest
 installation instructions.
 
+
 Check the users group in case you need help:
 https://groups.google.com/forum/#!forum/caffe-users

From 73ffc3565e31037947f0b486e43d32fa6c9ba695 Mon Sep 17 00:00:00 2001
From: Daisy Deng <daisy.deng@intel.com>
Date: Tue, 9 Oct 2018 19:56:28 +0800
Subject: [PATCH 07/26] ci test3

---
 INSTALL.md | 1 +
 1 file changed, 1 insertion(+)

diff --git a/INSTALL.md b/INSTALL.md
index e1553a265..0a9674265 100644
--- a/INSTALL.md
+++ b/INSTALL.md
@@ -1,5 +1,6 @@
 #### Installation
 
+
 See http://caffe.berkeleyvision.org/installation.html for the latest
 installation instructions.
 

From 54eb7f54ada244d3c9e7290aac262be0125e57d9 Mon Sep 17 00:00:00 2001
From: Haihao Shen <haihao.shen@intel.com>
Date: Wed, 10 Oct 2018 11:21:33 +0800
Subject: [PATCH 08/26] Support S8S8

---
 scripts/calibrator.py                         |  7 +++----
 src/caffe/layers/mkldnn_convolution_layer.cpp | 10 ++++++++--
 src/caffe/proto/caffe.proto                   |  1 +
 3 files changed, 12 insertions(+), 6 deletions(-)

diff --git a/scripts/calibrator.py b/scripts/calibrator.py
index 73bc72053..e954e5107 100644
--- a/scripts/calibrator.py
+++ b/scripts/calibrator.py
@@ -231,16 +231,15 @@ def transform_convolutions(model_path, compiled_model_path, top_blobs_map, botto
         conv_input_u8 = is_convolution_input_u8(l, compiled_net, index_in_compiled_net, ["Convolution"], convs_output_with_relu) # FIXME: extended_convs_output_with_relu
         for si in range(0, len(new_net.layer[index].quantization_param.scale_in)):
             if conv_input_u8:  # u8
-                if first_conv: # FIXME: remove the condition
+                if first_conv:
                     new_net.layer[index].quantization_param.scale_in[si] = s8_max / new_net.layer[index].quantization_param.scale_in[si]
-#                    new_net.layer[index].quantization_param.negative = True
+                    new_net.layer[index].quantization_param.is_negative_input = True
                     first_conv = False
                 else:
                     new_net.layer[index].quantization_param.scale_in[si] = u8_max / new_net.layer[index].quantization_param.scale_in[si]
-                    #new_net.layer[index].quantization_param.ClearField('min_in')
             else:
                 new_net.layer[index].quantization_param.scale_in[si] = s8_max / new_net.layer[index].quantization_param.scale_in[si]
-#                new_net.layer[index].quantization_param.negative = True
+                new_net.layer[index].quantization_param.is_negative_input = True
 
         for si in range(0, len(new_net.layer[index].quantization_param.scale_params)):
             if not isclose(new_net.layer[index].quantization_param.scale_params[si], 0.0):
diff --git a/src/caffe/layers/mkldnn_convolution_layer.cpp b/src/caffe/layers/mkldnn_convolution_layer.cpp
index 3e27b552b..ff5cc4a51 100644
--- a/src/caffe/layers/mkldnn_convolution_layer.cpp
+++ b/src/caffe/layers/mkldnn_convolution_layer.cpp
@@ -258,9 +258,15 @@ void MKLDNNConvolutionLayer<Dtype>::InitConvolutionFwd(const vector<Blob<Dtype>*
 
     // ---- Initialize memory descriptors (fromat = any) to create convolution descriptor -------------
     memory::data_type mpcsn = memory::data_type::f32;
-    memory::data_type bottom_dt = this->need_quantize_ ? memory::data_type::u8 : memory::data_type::f32;
-    memory::data_type top_dt = memory::data_type::f32;
+    memory::data_type bottom_dt = memory::data_type::f32;
+    if (this->need_quantize_) {
+      if (this->layer_param_.quantization_param().is_negative_input())
+        bottom_dt = memory::data_type::s8;
+      else
+        bottom_dt = memory::data_type::u8;
+    }
 
+    memory::data_type top_dt = memory::data_type::f32;
     if (this->need_quantize_) {
       if (this->bw_layer_out_ == 8) {
         if (relu) {
diff --git a/src/caffe/proto/caffe.proto b/src/caffe/proto/caffe.proto
index 09ebd330f..461fc9a60 100755
--- a/src/caffe/proto/caffe.proto
+++ b/src/caffe/proto/caffe.proto
@@ -704,6 +704,7 @@ message QuantizationParameter{
   repeated float scale_in = 20;
   repeated float scale_out = 21;
   repeated float scale_params = 22;
+  optional bool is_negative_input = 23 [default = false];
 }
 
 message MnParamGradCompressParameter {

From 016f3cf5aa6aa421fcdd24353549be6dc8d7189a Mon Sep 17 00:00:00 2001
From: "Zhang, Guoming" <guoming.zhang@intel.com>
Date: Thu, 11 Oct 2018 00:31:41 +0800
Subject: [PATCH 09/26] Enable the ResNext-50 conv/sum fusion

---
 src/caffe/net.cpp | 46 +++++++++++++++++++++++++++++++++-------------
 1 file changed, 33 insertions(+), 13 deletions(-)

diff --git a/src/caffe/net.cpp b/src/caffe/net.cpp
index 7019a1453..3f2adafdf 100644
--- a/src/caffe/net.cpp
+++ b/src/caffe/net.cpp
@@ -880,9 +880,9 @@ void Net<Dtype>::CompilationRuleConvSumFusion(const NetParameter& param,
   LayerParameter* need_to_convert_layer = NULL;
   bool has_relu_flag = true;
   bool need_fusion_flag;
+  bool switch_flag = false;
   std::set<string> invalid_fusion_blob_names;
 
-
   for (int i = 0; i < param.layer_size(); i++) {
     need_fusion_flag = true;
     LayerParameter* layer_param =
@@ -903,7 +903,6 @@ void Net<Dtype>::CompilationRuleConvSumFusion(const NetParameter& param,
                                    i + 1 < param.layer_size() ? i + 1 : i);
       if (child_layers_params.size() > 0 &&
           child_layers_params[0]->type().compare("Eltwise") == 0) {
-
         for (int k = 0; k < child_layers_params[0]->bottom_size(); k++) {
           if (invalid_fusion_blob_names.count(
                   child_layers_params[0]->bottom(k)) > 0) {
@@ -930,10 +929,25 @@ void Net<Dtype>::CompilationRuleConvSumFusion(const NetParameter& param,
           has_relu_flag = false;
         }
 
-        if (child_layers_params[0]->bottom(0) == layer_param->top(0)) {
+        if (child_layers_params[0] !=
+            (const_cast<NetParameter&>(param)).mutable_layer(i + 1)) {
+          if (child_layers_params[0]->bottom(0) == layer_param->top(0)) {
+            switch_flag = true;
+          } else {
+            switch_flag = false;
+          }
           param_compiled->add_layer()->CopyFrom(*layer_param);
           need_to_convert_layer = layer_param;
           continue;
+
+        } else {
+          if (need_to_convert_layer == NULL) {
+            if (child_layers_params[0]->bottom(1) == layer_param->top(0)) {
+              switch_flag = true;
+            } else {
+              switch_flag = false;
+            }
+          }
         }
 
         if (has_relu_flag) {
@@ -949,8 +963,13 @@ void Net<Dtype>::CompilationRuleConvSumFusion(const NetParameter& param,
               const_cast<string&>(need_to_convert_layer->top(0)));
           need_to_convert_layer = NULL;
         } else {
-          layer_param->add_bottom(
-              const_cast<string&>(child_layers_params[0]->bottom(0)));
+          if (switch_flag) {
+            layer_param->add_bottom(
+                const_cast<string&>(child_layers_params[0]->bottom(0)));
+          } else {
+            layer_param->add_bottom(
+                const_cast<string&>(child_layers_params[0]->bottom(1)));
+          }
         }
 
         if (has_relu_flag) {
@@ -960,15 +979,16 @@ void Net<Dtype>::CompilationRuleConvSumFusion(const NetParameter& param,
           i += 1;
         }
 
-        layer_param->mutable_convolution_param()->set_fusion_type(ConvolutionParameter::SUM_FUSION);
-        size_t coeff_size = child_layers_params[0]->eltwise_param().coeff_size();
-        if (coeff_size > 0)
-        {   
-          for (int i = 0; i < coeff_size; ++i) 
-          {
-            layer_param->mutable_convolution_param()->add_coeff(child_layers_params[0]->eltwise_param().coeff(i));
+        layer_param->mutable_convolution_param()->set_fusion_type(
+            ConvolutionParameter::SUM_FUSION);
+        size_t coeff_size =
+            child_layers_params[0]->eltwise_param().coeff_size();
+        if (coeff_size > 0) {
+          for (int i = 0; i < coeff_size; ++i) {
+            layer_param->mutable_convolution_param()->add_coeff(
+                child_layers_params[0]->eltwise_param().coeff(i));
           }
-        }   
+        }
       }
     }
 

From 740351e892159e6ae83d08c4cacfb7284938d6f1 Mon Sep 17 00:00:00 2001
From: Shane Li <shane.li@intel.com>
Date: Thu, 18 Oct 2018 14:59:22 +0800
Subject: [PATCH 10/26] Add multi-nodes test support. Change-Id:
 I3cbbc2cc598f5868b2ac7b6595e694fc409179e9

---
 src/caffe/solver.cpp | 42 +++++++++++++++++++++---------------------
 1 file changed, 21 insertions(+), 21 deletions(-)

diff --git a/src/caffe/solver.cpp b/src/caffe/solver.cpp
index a9892716c..a2216a1f2 100644
--- a/src/caffe/solver.cpp
+++ b/src/caffe/solver.cpp
@@ -347,8 +347,8 @@ void Solver<Dtype>::Step(int iters) {
       ApplyUpdate();
       PERFORMANCE_MEASUREMENT_END_STATIC("weights_update");
     }else{
-       //While using multinodes mode, force to print current lr to logs
-       PrintLearningRate();
+      //While using multinodes mode, force to print current lr to logs
+      PrintLearningRate();
     }
     iter_time += iter_timer.MilliSeconds();
 
@@ -358,7 +358,7 @@ void Solver<Dtype>::Step(int iters) {
     net_->ResetTimers();
 
 #ifdef USE_MLSL
-    if (mn::is_root() == true)
+    if (mn::is_root())
 #endif
     LOG(INFO) << "iter " << iter_ << ", forward_backward_update_time: "
             << iter_time << " ms";
@@ -479,7 +479,16 @@ void Solver<Dtype>::TestClassification(const int test_net_id) {
   vector<int> test_score_output_id;
   const shared_ptr<Net<Dtype> >& test_net = test_nets_[test_net_id];
   Dtype loss = 0;
-  for (int i = 0; i < param_.test_iter(test_net_id); ++i) {
+  int global_test_iter = param_.test_iter(test_net_id);
+#ifdef USE_MLSL
+  int local_test_iter = global_test_iter / mn::get_nodes_count(); 
+  int left_test_iter = global_test_iter % mn::get_nodes_count();
+  if (mn::get_node_id() < left_test_iter)
+    local_test_iter += 1;
+#else
+  int local_test_iter = global_test_iter;
+#endif
+  for (int i = 0; i < local_test_iter; ++i) {
     SolverAction::Enum request = GetRequestedAction();
     // Check to see if stoppage of testing/training has been requested.
     while (request != SolverAction::NONE) {
@@ -526,35 +535,26 @@ void Solver<Dtype>::TestClassification(const int test_net_id) {
   if (param_.test_compute_loss()) {
 #ifdef USE_MLSL
     mn::allreduce(&loss, 1);
-    loss /= (param_.test_iter(test_net_id) * mn::get_group_size());
-    if (mn::is_root() == true)
-      LOG(INFO) << "Test loss: " << loss;
-#else /* !USE_MLSL */
-    loss /= param_.test_iter(test_net_id);
+    if (mn::is_root()) {
+#endif /* USE_MLSL */
+    loss /= global_test_iter;
     LOG(INFO) << "Test loss: " << loss;
+#ifdef USE_MLSL
+    }
 #endif /* USE_MLSL */
   }
 #ifdef USE_MLSL
   mn::allreduce(test_score.data(), test_score.size());
-  if (mn::is_root() == true)
+  if (mn::is_root())
 #endif /* USE_MLSL */
   for (int i = 0; i < test_score.size(); ++i) {
     const int output_blob_index =
         test_net->output_blob_indices()[test_score_output_id[i]];
     const string& output_name = test_net->blob_names()[output_blob_index];
-    const Dtype loss_weight = test_net->blob_loss_weights()[output_blob_index]
-#ifdef USE_MLSL
-      * mn::get_distrib()->get_data_parts()
-#endif
-      ;
+    const Dtype loss_weight = test_net->blob_loss_weights()[output_blob_index];
 
     ostringstream loss_msg_stream;
-#ifdef USE_MLSL
-    const Dtype mean_score =
-      test_score[i] / (param_.test_iter(test_net_id) * mn::get_group_size());
-#else /* !USE_MLSL */
-    const Dtype mean_score = test_score[i] / param_.test_iter(test_net_id);
-#endif /* USE_MLSL */
+    const Dtype mean_score = test_score[i] / global_test_iter;
     if (loss_weight) {
       loss_msg_stream << " (* " << loss_weight
                       << " = " << loss_weight * mean_score << " loss)";

From ef4b23dddc5713c3c32006608db48da0ed34ed62 Mon Sep 17 00:00:00 2001
From: Feng Tian <feng.tian@intel.com>
Date: Mon, 22 Oct 2018 15:35:28 +0800
Subject: [PATCH 11/26] Fix SSD/ResNet50 winograd training/test
 accuracy/assertion issues.

It's mainly due to mkldnn reorder implementation, which only support nchw input
format to wino_fmt output reorder.

Original framework code path may create wino_fmt to nchw and nchw16i16o to wino_fmt
reorder primitive, but those are not supported by mkldnn and will trigger assertion.
---
 src/caffe/layers/mkldnn_convolution_layer.cpp | 2 +-
 src/caffe/mkldnn_memory.cpp                   | 8 +++++++-
 2 files changed, 8 insertions(+), 2 deletions(-)

diff --git a/src/caffe/layers/mkldnn_convolution_layer.cpp b/src/caffe/layers/mkldnn_convolution_layer.cpp
index ff5cc4a51..53cf63873 100644
--- a/src/caffe/layers/mkldnn_convolution_layer.cpp
+++ b/src/caffe/layers/mkldnn_convolution_layer.cpp
@@ -495,7 +495,7 @@ void MKLDNNConvolutionLayer<Dtype>::InitConvolutionFwd(const vector<Blob<Dtype>*
     fwd_top_data->name = "fwd_top_data      @ " + this->layer_param_.name();
     fwd_top_data_memory = fwd_top_data->create_output_memory();
 
-    bool is_wino = conv_algorithm == algorithm::convolution_winograd ? true : false; 
+    bool is_wino = (prv_fwd_weights_data_memory_pd->desc().data.format == memory::format::wino_fmt); 
     if (fwd_weights_data == NULL) {
       if (this->need_quantize_){
         int count = 1; //single channel
diff --git a/src/caffe/mkldnn_memory.cpp b/src/caffe/mkldnn_memory.cpp
index eb522b2b0..d9fe0bf62 100644
--- a/src/caffe/mkldnn_memory.cpp
+++ b/src/caffe/mkldnn_memory.cpp
@@ -153,7 +153,13 @@ template <typename Dtype, bool is_diff>
 #ifdef DEBUG
             LOG(INFO) << "Formats of blob-prv-memory-pd and this-prv-memory-pd are not equal !";
 #endif
-            this->set_extprv_memory_pd(blob_prv_mkldnn_mem_descr->prv_memory_pd(), scale, blob_prv_mkldnn_mem_descr->get_scale(), blob_prv_mkldnn_mem_descr->get_sum());
+            if (!is_wino)
+              this->set_extprv_memory_pd(blob_prv_mkldnn_mem_descr->prv_memory_pd(), scale, blob_prv_mkldnn_mem_descr->get_scale(), blob_prv_mkldnn_mem_descr->get_sum());
+            else
+              // If the blob's prv is using wino_fmt, it is only able to accept nchw to wino reorder due to mkldnn implementation. Other input formats, such as nchw16i16o
+              // to wino reorder, are not supported by mkldnn.
+              // Therefore we have to force the blob state from prv to cpu to get nchw input format at first.
+              if (is_diff) blob->mutable_cpu_diff(); else blob->mutable_cpu_data();
         }
     }
 }

From a8a5ecb53bd605a8e0dbbb023c356ac7404012bb Mon Sep 17 00:00:00 2001
From: "Gong, Jiong" <jiong.gong@intel.com>
Date: Wed, 24 Oct 2018 04:00:35 +0800
Subject: [PATCH 12/26] support first test run with an iteration offset:
 test_offset

---
 src/caffe/proto/caffe.proto | 5 ++++-
 src/caffe/solver.cpp        | 4 ++--
 2 files changed, 6 insertions(+), 3 deletions(-)

diff --git a/src/caffe/proto/caffe.proto b/src/caffe/proto/caffe.proto
index 461fc9a60..6dc0f4fa4 100755
--- a/src/caffe/proto/caffe.proto
+++ b/src/caffe/proto/caffe.proto
@@ -262,7 +262,7 @@ message SolverBatchSizePair {
 // NOTE
 // Update the next available ID when you add a new SolverParameter field.
 //
-// SolverParameter next available ID: 52 (last added: local_gw_ratio)
+// SolverParameter next available ID: 53 (last added: test_offset)
 message SolverParameter {
   //////////////////////////////////////////////////////////////////////////////
   // Specifying the train and test networks
@@ -430,6 +430,9 @@ message SolverParameter {
 
   optional bool local_lr_auto = 50 [default = false];
   optional float local_gw_ratio = 51 [default = 0.001];
+
+  // Number of iterations before starting the first test run
+  optional int32 test_offset = 52 [default = 0];
 }
 
 // A message that stores the solver snapshots
diff --git a/src/caffe/solver.cpp b/src/caffe/solver.cpp
index a2216a1f2..5f5dd8ea9 100644
--- a/src/caffe/solver.cpp
+++ b/src/caffe/solver.cpp
@@ -282,7 +282,7 @@ void Solver<Dtype>::Step(int iters) {
   smoothed_loss_ = 0;
 
   while (iter_ < stop_iter) {
-    if (param_.test_interval() && iter_ % param_.test_interval() == 0
+    if (param_.test_interval() && (iter_ - param_.test_offset()) % param_.test_interval() == 0
         && (iter_ > 0 || param_.test_initialization())
         && Caffe::root_solver()) {
       TestAll();
@@ -437,7 +437,7 @@ void Solver<Dtype>::Solve(const char* resume_file) {
   }
 
   // in multinode last test must be done after weights update
-  if (param_.test_interval() && iter_ % param_.test_interval() == 0)
+  if (param_.test_interval() && (iter_ - param_.test_offset()) % param_.test_interval() == 0)
     TestAll();
 
   LOG(INFO) << "Optimization Done.";

From 9fc3fd4f5e024157302a2781d5f2402aa8ff1fe4 Mon Sep 17 00:00:00 2001
From: Shane Li <shane.li@intel.com>
Date: Fri, 26 Oct 2018 16:14:10 +0800
Subject: [PATCH 13/26] Fix 64+ cores platform multi-instance cores allocation
 issue.

---
 scripts/set_env.sh | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/scripts/set_env.sh b/scripts/set_env.sh
index a1b053a44..15e84a0b7 100755
--- a/scripts/set_env.sh
+++ b/scripts/set_env.sh
@@ -90,7 +90,7 @@ function init_mpi_envs
             done
             for ((mask_index=${#masks[@]}-1; mask_index>=0; mask_index--))
             do
-                mask_str+=`printf "%x" "${masks[$mask_index]}"`
+                mask_str+=`printf "%016x" "${masks[$mask_index]}"`
             done
             domain_str+="$mask_str"
             if [ $ppn_index -ne $((ppn-1)) ]; then

From 4133f8e1422a3cb0cc6444f197ac92b252362656 Mon Sep 17 00:00:00 2001
From: "Zhang, Guoming" <guoming.zhang@intel.com>
Date: Sun, 28 Oct 2018 23:00:53 +0800
Subject: [PATCH 14/26] Fix the typo for calibration algo selection

---
 scripts/calibrator.py | 4 ++--
 1 file changed, 2 insertions(+), 2 deletions(-)

diff --git a/scripts/calibrator.py b/scripts/calibrator.py
index e954e5107..e6640e4e8 100644
--- a/scripts/calibrator.py
+++ b/scripts/calibrator.py
@@ -580,7 +580,7 @@ def generate_dummy_model(model_path, dummy):
     if params.calibration_algos != 'DIRECT' and params.calibration_algos != "KL" and params.calibration_algos != "MAXP":
         user_calibration_algos = 'DIRECT'
     else:
-        user_calibration_alogs = params.calibration_algos
+        user_calibration_algos = params.calibration_algos
 
     if params.conv_algo != False and params.conv_algo != True:
         user_conv_algo = False
@@ -630,7 +630,7 @@ def generate_dummy_model(model_path, dummy):
 
     quantized_prototxt = model.rsplit('.')[0] + '_quantized.prototxt'
     print 'Sampling...'
-    (top_blobs_map, bottom_blobs_map) = generate_sample(model, user_input_weights, quantized_prototxt, user_scaling_mode, user_calibration_alogs, user_conv_algo, user_sampling_iteration, user_enable_1st_conv)
+    (top_blobs_map, bottom_blobs_map) = generate_sample(model, user_input_weights, quantized_prototxt, user_scaling_mode, user_calibration_algos, user_conv_algo, user_sampling_iteration, user_enable_1st_conv)
     print 'Sampling done'
     top_1 = None
     if not params.quantize_model:

From 298e050939dc0a37774a0122597413ded2d93ce2 Mon Sep 17 00:00:00 2001
From: Haihao Shen <haihao.shen@intel.com>
Date: Tue, 30 Oct 2018 13:49:10 +0800
Subject: [PATCH 15/26] Use the raw mobilenet-ssd

---
 .../int8/ssd_mobilenet_int8.prototxt          | 294 +++++++++++++++++-
 .../int8/ssd_mobilenet_int8_acc.prototxt      | 278 ++++++++++++++++-
 2 files changed, 544 insertions(+), 28 deletions(-)

diff --git a/models/intel_optimized_models/int8/ssd_mobilenet_int8.prototxt b/models/intel_optimized_models/int8/ssd_mobilenet_int8.prototxt
index 9a80cf463..57e29729c 100644
--- a/models/intel_optimized_models/int8/ssd_mobilenet_int8.prototxt
+++ b/models/intel_optimized_models/int8/ssd_mobilenet_int8.prototxt
@@ -15270,6 +15270,27 @@ layer {
     scale_params: 143.217559814
   }
 }
+layer {
+  name: "conv11_mbox_loc_perm"
+  type: "Permute"
+  bottom: "conv11_mbox_loc"
+  top: "conv11_mbox_loc_perm"
+  permute_param {
+    order: 0
+    order: 2
+    order: 3
+    order: 1
+  }
+}
+layer {
+  name: "conv11_mbox_loc_flat"
+  type: "Flatten"
+  bottom: "conv11_mbox_loc_perm"
+  top: "conv11_mbox_loc_flat"
+  flatten_param {
+    axis: 1
+  }
+}
 layer {
   name: "conv11_mbox_conf"
   type: "Convolution"
@@ -15365,6 +15386,27 @@ layer {
     scale_params: 38.1775665283
   }
 }
+layer {
+  name: "conv11_mbox_conf_perm"
+  type: "Permute"
+  bottom: "conv11_mbox_conf"
+  top: "conv11_mbox_conf_perm"
+  permute_param {
+    order: 0
+    order: 2
+    order: 3
+    order: 1
+  }
+}
+layer {
+  name: "conv11_mbox_conf_flat"
+  type: "Flatten"
+  bottom: "conv11_mbox_conf_perm"
+  top: "conv11_mbox_conf_flat"
+  flatten_param {
+    axis: 1
+  }
+}
 layer {
   name: "conv11_mbox_priorbox"
   type: "PriorBox"
@@ -15439,6 +15481,27 @@ layer {
     scale_params: 250.648345947
   }
 }
+layer {
+  name: "conv13_mbox_loc_perm"
+  type: "Permute"
+  bottom: "conv13_mbox_loc"
+  top: "conv13_mbox_loc_perm"
+  permute_param {
+    order: 0
+    order: 2
+    order: 3
+    order: 1
+  }
+}
+layer {
+  name: "conv13_mbox_loc_flat"
+  type: "Flatten"
+  bottom: "conv13_mbox_loc_perm"
+  top: "conv13_mbox_loc_flat"
+  flatten_param {
+    axis: 1
+  }
+}
 layer {
   name: "conv13_mbox_conf"
   type: "Convolution"
@@ -15597,6 +15660,27 @@ layer {
     scale_params: 25.7836456299
   }
 }
+layer {
+  name: "conv13_mbox_conf_perm"
+  type: "Permute"
+  bottom: "conv13_mbox_conf"
+  top: "conv13_mbox_conf_perm"
+  permute_param {
+    order: 0
+    order: 2
+    order: 3
+    order: 1
+  }
+}
+layer {
+  name: "conv13_mbox_conf_flat"
+  type: "Flatten"
+  bottom: "conv13_mbox_conf_perm"
+  top: "conv13_mbox_conf_flat"
+  flatten_param {
+    axis: 1
+  }
+}
 layer {
   name: "conv13_mbox_priorbox"
   type: "PriorBox"
@@ -15673,6 +15757,27 @@ layer {
     scale_params: 226.610321045
   }
 }
+layer {
+  name: "conv14_2_mbox_loc_perm"
+  type: "Permute"
+  bottom: "conv14_2_mbox_loc"
+  top: "conv14_2_mbox_loc_perm"
+  permute_param {
+    order: 0
+    order: 2
+    order: 3
+    order: 1
+  }
+}
+layer {
+  name: "conv14_2_mbox_loc_flat"
+  type: "Flatten"
+  bottom: "conv14_2_mbox_loc_perm"
+  top: "conv14_2_mbox_loc_flat"
+  flatten_param {
+    axis: 1
+  }
+}
 layer {
   name: "conv14_2_mbox_conf"
   type: "Convolution"
@@ -15831,6 +15936,27 @@ layer {
     scale_params: 33.4680595398
   }
 }
+layer {
+  name: "conv14_2_mbox_conf_perm"
+  type: "Permute"
+  bottom: "conv14_2_mbox_conf"
+  top: "conv14_2_mbox_conf_perm"
+  permute_param {
+    order: 0
+    order: 2
+    order: 3
+    order: 1
+  }
+}
+layer {
+  name: "conv14_2_mbox_conf_flat"
+  type: "Flatten"
+  bottom: "conv14_2_mbox_conf_perm"
+  top: "conv14_2_mbox_conf_flat"
+  flatten_param {
+    axis: 1
+  }
+}
 layer {
   name: "conv14_2_mbox_priorbox"
   type: "PriorBox"
@@ -15907,6 +16033,27 @@ layer {
     scale_params: 227.242248535
   }
 }
+layer {
+  name: "conv15_2_mbox_loc_perm"
+  type: "Permute"
+  bottom: "conv15_2_mbox_loc"
+  top: "conv15_2_mbox_loc_perm"
+  permute_param {
+    order: 0
+    order: 2
+    order: 3
+    order: 1
+  }
+}
+layer {
+  name: "conv15_2_mbox_loc_flat"
+  type: "Flatten"
+  bottom: "conv15_2_mbox_loc_perm"
+  top: "conv15_2_mbox_loc_flat"
+  flatten_param {
+    axis: 1
+  }
+}
 layer {
   name: "conv15_2_mbox_conf"
   type: "Convolution"
@@ -16065,6 +16212,27 @@ layer {
     scale_params: 33.6692047119
   }
 }
+layer {
+  name: "conv15_2_mbox_conf_perm"
+  type: "Permute"
+  bottom: "conv15_2_mbox_conf"
+  top: "conv15_2_mbox_conf_perm"
+  permute_param {
+    order: 0
+    order: 2
+    order: 3
+    order: 1
+  }
+}
+layer {
+  name: "conv15_2_mbox_conf_flat"
+  type: "Flatten"
+  bottom: "conv15_2_mbox_conf_perm"
+  top: "conv15_2_mbox_conf_flat"
+  flatten_param {
+    axis: 1
+  }
+}
 layer {
   name: "conv15_2_mbox_priorbox"
   type: "PriorBox"
@@ -16141,6 +16309,27 @@ layer {
     scale_params: 372.020050049
   }
 }
+layer {
+  name: "conv16_2_mbox_loc_perm"
+  type: "Permute"
+  bottom: "conv16_2_mbox_loc"
+  top: "conv16_2_mbox_loc_perm"
+  permute_param {
+    order: 0
+    order: 2
+    order: 3
+    order: 1
+  }
+}
+layer {
+  name: "conv16_2_mbox_loc_flat"
+  type: "Flatten"
+  bottom: "conv16_2_mbox_loc_perm"
+  top: "conv16_2_mbox_loc_flat"
+  flatten_param {
+    axis: 1
+  }
+}
 layer {
   name: "conv16_2_mbox_conf"
   type: "Convolution"
@@ -16299,6 +16488,27 @@ layer {
     scale_params: 37.764213562
   }
 }
+layer {
+  name: "conv16_2_mbox_conf_perm"
+  type: "Permute"
+  bottom: "conv16_2_mbox_conf"
+  top: "conv16_2_mbox_conf_perm"
+  permute_param {
+    order: 0
+    order: 2
+    order: 3
+    order: 1
+  }
+}
+layer {
+  name: "conv16_2_mbox_conf_flat"
+  type: "Flatten"
+  bottom: "conv16_2_mbox_conf_perm"
+  top: "conv16_2_mbox_conf_flat"
+  flatten_param {
+    axis: 1
+  }
+}
 layer {
   name: "conv16_2_mbox_priorbox"
   type: "PriorBox"
@@ -16375,6 +16585,27 @@ layer {
     scale_params: 717.409729004
   }
 }
+layer {
+  name: "conv17_2_mbox_loc_perm"
+  type: "Permute"
+  bottom: "conv17_2_mbox_loc"
+  top: "conv17_2_mbox_loc_perm"
+  permute_param {
+    order: 0
+    order: 2
+    order: 3
+    order: 1
+  }
+}
+layer {
+  name: "conv17_2_mbox_loc_flat"
+  type: "Flatten"
+  bottom: "conv17_2_mbox_loc_perm"
+  top: "conv17_2_mbox_loc_flat"
+  flatten_param {
+    axis: 1
+  }
+}
 layer {
   name: "conv17_2_mbox_conf"
   type: "Convolution"
@@ -16533,6 +16764,27 @@ layer {
     scale_params: 63.7748031616
   }
 }
+layer {
+  name: "conv17_2_mbox_conf_perm"
+  type: "Permute"
+  bottom: "conv17_2_mbox_conf"
+  top: "conv17_2_mbox_conf_perm"
+  permute_param {
+    order: 0
+    order: 2
+    order: 3
+    order: 1
+  }
+}
+layer {
+  name: "conv17_2_mbox_conf_flat"
+  type: "Flatten"
+  bottom: "conv17_2_mbox_conf_perm"
+  top: "conv17_2_mbox_conf_flat"
+  flatten_param {
+    axis: 1
+  }
+}
 layer {
   name: "conv17_2_mbox_priorbox"
   type: "PriorBox"
@@ -16556,31 +16808,29 @@ layer {
 layer {
   name: "mbox_loc"
   type: "Concat"
-  bottom: "conv11_mbox_loc"
-  bottom: "conv13_mbox_loc"
-  bottom: "conv14_2_mbox_loc"
-  bottom: "conv15_2_mbox_loc"
-  bottom: "conv16_2_mbox_loc"
-  bottom: "conv17_2_mbox_loc"
+  bottom: "conv11_mbox_loc_flat"
+  bottom: "conv13_mbox_loc_flat"
+  bottom: "conv14_2_mbox_loc_flat"
+  bottom: "conv15_2_mbox_loc_flat"
+  bottom: "conv16_2_mbox_loc_flat"
+  bottom: "conv17_2_mbox_loc_flat"
   top: "mbox_loc"
   concat_param {
     axis: 1
-    per_fla_fuse : true
   }
 }
 layer {
   name: "mbox_conf"
   type: "Concat"
-  bottom: "conv11_mbox_conf"
-  bottom: "conv13_mbox_conf"
-  bottom: "conv14_2_mbox_conf"
-  bottom: "conv15_2_mbox_conf"
-  bottom: "conv16_2_mbox_conf"
-  bottom: "conv17_2_mbox_conf"
+  bottom: "conv11_mbox_conf_flat"
+  bottom: "conv13_mbox_conf_flat"
+  bottom: "conv14_2_mbox_conf_flat"
+  bottom: "conv15_2_mbox_conf_flat"
+  bottom: "conv16_2_mbox_conf_flat"
+  bottom: "conv17_2_mbox_conf_flat"
   top: "mbox_conf"
   concat_param {
     axis: 1
-    per_fla_fuse : true
   }
 }
 layer {
@@ -16651,3 +16901,19 @@ layer {
     confidence_threshold: 0.00999999977648
   }
 }
+layer {
+  name: "detection_eval"
+  type: "DetectionEvaluate"
+  bottom: "detection_out"
+  bottom: "label"
+  top: "detection_eval"
+  include {
+    phase: TEST
+  }
+  detection_evaluate_param {
+    num_classes: 21
+    background_label_id: 0
+    overlap_threshold: 0.5
+    evaluate_difficult_gt: false
+  }
+}
diff --git a/models/intel_optimized_models/int8/ssd_mobilenet_int8_acc.prototxt b/models/intel_optimized_models/int8/ssd_mobilenet_int8_acc.prototxt
index 87947abf5..ccf1db2e9 100644
--- a/models/intel_optimized_models/int8/ssd_mobilenet_int8_acc.prototxt
+++ b/models/intel_optimized_models/int8/ssd_mobilenet_int8_acc.prototxt
@@ -15268,6 +15268,27 @@ layer {
     scale_params: 143.217559814
   }
 }
+layer {
+  name: "conv11_mbox_loc_perm"
+  type: "Permute"
+  bottom: "conv11_mbox_loc"
+  top: "conv11_mbox_loc_perm"
+  permute_param {
+    order: 0
+    order: 2
+    order: 3
+    order: 1
+  }
+}
+layer {
+  name: "conv11_mbox_loc_flat"
+  type: "Flatten"
+  bottom: "conv11_mbox_loc_perm"
+  top: "conv11_mbox_loc_flat"
+  flatten_param {
+    axis: 1
+  }
+}
 layer {
   name: "conv11_mbox_conf"
   type: "Convolution"
@@ -15363,6 +15384,27 @@ layer {
     scale_params: 38.1775665283
   }
 }
+layer {
+  name: "conv11_mbox_conf_perm"
+  type: "Permute"
+  bottom: "conv11_mbox_conf"
+  top: "conv11_mbox_conf_perm"
+  permute_param {
+    order: 0
+    order: 2
+    order: 3
+    order: 1
+  }
+}
+layer {
+  name: "conv11_mbox_conf_flat"
+  type: "Flatten"
+  bottom: "conv11_mbox_conf_perm"
+  top: "conv11_mbox_conf_flat"
+  flatten_param {
+    axis: 1
+  }
+}
 layer {
   name: "conv11_mbox_priorbox"
   type: "PriorBox"
@@ -15437,6 +15479,27 @@ layer {
     scale_params: 250.648345947
   }
 }
+layer {
+  name: "conv13_mbox_loc_perm"
+  type: "Permute"
+  bottom: "conv13_mbox_loc"
+  top: "conv13_mbox_loc_perm"
+  permute_param {
+    order: 0
+    order: 2
+    order: 3
+    order: 1
+  }
+}
+layer {
+  name: "conv13_mbox_loc_flat"
+  type: "Flatten"
+  bottom: "conv13_mbox_loc_perm"
+  top: "conv13_mbox_loc_flat"
+  flatten_param {
+    axis: 1
+  }
+}
 layer {
   name: "conv13_mbox_conf"
   type: "Convolution"
@@ -15595,6 +15658,27 @@ layer {
     scale_params: 25.7836456299
   }
 }
+layer {
+  name: "conv13_mbox_conf_perm"
+  type: "Permute"
+  bottom: "conv13_mbox_conf"
+  top: "conv13_mbox_conf_perm"
+  permute_param {
+    order: 0
+    order: 2
+    order: 3
+    order: 1
+  }
+}
+layer {
+  name: "conv13_mbox_conf_flat"
+  type: "Flatten"
+  bottom: "conv13_mbox_conf_perm"
+  top: "conv13_mbox_conf_flat"
+  flatten_param {
+    axis: 1
+  }
+}
 layer {
   name: "conv13_mbox_priorbox"
   type: "PriorBox"
@@ -15671,6 +15755,27 @@ layer {
     scale_params: 226.610321045
   }
 }
+layer {
+  name: "conv14_2_mbox_loc_perm"
+  type: "Permute"
+  bottom: "conv14_2_mbox_loc"
+  top: "conv14_2_mbox_loc_perm"
+  permute_param {
+    order: 0
+    order: 2
+    order: 3
+    order: 1
+  }
+}
+layer {
+  name: "conv14_2_mbox_loc_flat"
+  type: "Flatten"
+  bottom: "conv14_2_mbox_loc_perm"
+  top: "conv14_2_mbox_loc_flat"
+  flatten_param {
+    axis: 1
+  }
+}
 layer {
   name: "conv14_2_mbox_conf"
   type: "Convolution"
@@ -15829,6 +15934,27 @@ layer {
     scale_params: 33.4680595398
   }
 }
+layer {
+  name: "conv14_2_mbox_conf_perm"
+  type: "Permute"
+  bottom: "conv14_2_mbox_conf"
+  top: "conv14_2_mbox_conf_perm"
+  permute_param {
+    order: 0
+    order: 2
+    order: 3
+    order: 1
+  }
+}
+layer {
+  name: "conv14_2_mbox_conf_flat"
+  type: "Flatten"
+  bottom: "conv14_2_mbox_conf_perm"
+  top: "conv14_2_mbox_conf_flat"
+  flatten_param {
+    axis: 1
+  }
+}
 layer {
   name: "conv14_2_mbox_priorbox"
   type: "PriorBox"
@@ -15905,6 +16031,27 @@ layer {
     scale_params: 227.242248535
   }
 }
+layer {
+  name: "conv15_2_mbox_loc_perm"
+  type: "Permute"
+  bottom: "conv15_2_mbox_loc"
+  top: "conv15_2_mbox_loc_perm"
+  permute_param {
+    order: 0
+    order: 2
+    order: 3
+    order: 1
+  }
+}
+layer {
+  name: "conv15_2_mbox_loc_flat"
+  type: "Flatten"
+  bottom: "conv15_2_mbox_loc_perm"
+  top: "conv15_2_mbox_loc_flat"
+  flatten_param {
+    axis: 1
+  }
+}
 layer {
   name: "conv15_2_mbox_conf"
   type: "Convolution"
@@ -16063,6 +16210,27 @@ layer {
     scale_params: 33.6692047119
   }
 }
+layer {
+  name: "conv15_2_mbox_conf_perm"
+  type: "Permute"
+  bottom: "conv15_2_mbox_conf"
+  top: "conv15_2_mbox_conf_perm"
+  permute_param {
+    order: 0
+    order: 2
+    order: 3
+    order: 1
+  }
+}
+layer {
+  name: "conv15_2_mbox_conf_flat"
+  type: "Flatten"
+  bottom: "conv15_2_mbox_conf_perm"
+  top: "conv15_2_mbox_conf_flat"
+  flatten_param {
+    axis: 1
+  }
+}
 layer {
   name: "conv15_2_mbox_priorbox"
   type: "PriorBox"
@@ -16139,6 +16307,27 @@ layer {
     scale_params: 372.020050049
   }
 }
+layer {
+  name: "conv16_2_mbox_loc_perm"
+  type: "Permute"
+  bottom: "conv16_2_mbox_loc"
+  top: "conv16_2_mbox_loc_perm"
+  permute_param {
+    order: 0
+    order: 2
+    order: 3
+    order: 1
+  }
+}
+layer {
+  name: "conv16_2_mbox_loc_flat"
+  type: "Flatten"
+  bottom: "conv16_2_mbox_loc_perm"
+  top: "conv16_2_mbox_loc_flat"
+  flatten_param {
+    axis: 1
+  }
+}
 layer {
   name: "conv16_2_mbox_conf"
   type: "Convolution"
@@ -16297,6 +16486,27 @@ layer {
     scale_params: 37.764213562
   }
 }
+layer {
+  name: "conv16_2_mbox_conf_perm"
+  type: "Permute"
+  bottom: "conv16_2_mbox_conf"
+  top: "conv16_2_mbox_conf_perm"
+  permute_param {
+    order: 0
+    order: 2
+    order: 3
+    order: 1
+  }
+}
+layer {
+  name: "conv16_2_mbox_conf_flat"
+  type: "Flatten"
+  bottom: "conv16_2_mbox_conf_perm"
+  top: "conv16_2_mbox_conf_flat"
+  flatten_param {
+    axis: 1
+  }
+}
 layer {
   name: "conv16_2_mbox_priorbox"
   type: "PriorBox"
@@ -16373,6 +16583,27 @@ layer {
     scale_params: 717.409729004
   }
 }
+layer {
+  name: "conv17_2_mbox_loc_perm"
+  type: "Permute"
+  bottom: "conv17_2_mbox_loc"
+  top: "conv17_2_mbox_loc_perm"
+  permute_param {
+    order: 0
+    order: 2
+    order: 3
+    order: 1
+  }
+}
+layer {
+  name: "conv17_2_mbox_loc_flat"
+  type: "Flatten"
+  bottom: "conv17_2_mbox_loc_perm"
+  top: "conv17_2_mbox_loc_flat"
+  flatten_param {
+    axis: 1
+  }
+}
 layer {
   name: "conv17_2_mbox_conf"
   type: "Convolution"
@@ -16531,6 +16762,27 @@ layer {
     scale_params: 63.7748031616
   }
 }
+layer {
+  name: "conv17_2_mbox_conf_perm"
+  type: "Permute"
+  bottom: "conv17_2_mbox_conf"
+  top: "conv17_2_mbox_conf_perm"
+  permute_param {
+    order: 0
+    order: 2
+    order: 3
+    order: 1
+  }
+}
+layer {
+  name: "conv17_2_mbox_conf_flat"
+  type: "Flatten"
+  bottom: "conv17_2_mbox_conf_perm"
+  top: "conv17_2_mbox_conf_flat"
+  flatten_param {
+    axis: 1
+  }
+}
 layer {
   name: "conv17_2_mbox_priorbox"
   type: "PriorBox"
@@ -16554,31 +16806,29 @@ layer {
 layer {
   name: "mbox_loc"
   type: "Concat"
-  bottom: "conv11_mbox_loc"
-  bottom: "conv13_mbox_loc"
-  bottom: "conv14_2_mbox_loc"
-  bottom: "conv15_2_mbox_loc"
-  bottom: "conv16_2_mbox_loc"
-  bottom: "conv17_2_mbox_loc"
+  bottom: "conv11_mbox_loc_flat"
+  bottom: "conv13_mbox_loc_flat"
+  bottom: "conv14_2_mbox_loc_flat"
+  bottom: "conv15_2_mbox_loc_flat"
+  bottom: "conv16_2_mbox_loc_flat"
+  bottom: "conv17_2_mbox_loc_flat"
   top: "mbox_loc"
   concat_param {
     axis: 1
-    per_fla_fuse : true
   }
 }
 layer {
   name: "mbox_conf"
   type: "Concat"
-  bottom: "conv11_mbox_conf"
-  bottom: "conv13_mbox_conf"
-  bottom: "conv14_2_mbox_conf"
-  bottom: "conv15_2_mbox_conf"
-  bottom: "conv16_2_mbox_conf"
-  bottom: "conv17_2_mbox_conf"
+  bottom: "conv11_mbox_conf_flat"
+  bottom: "conv13_mbox_conf_flat"
+  bottom: "conv14_2_mbox_conf_flat"
+  bottom: "conv15_2_mbox_conf_flat"
+  bottom: "conv16_2_mbox_conf_flat"
+  bottom: "conv17_2_mbox_conf_flat"
   top: "mbox_conf"
   concat_param {
     axis: 1
-    per_fla_fuse : true
   }
 }
 layer {

From 212991fc305446af2ca9916a7e64c36e44c0996e Mon Sep 17 00:00:00 2001
From: Haihao Shen <haihao.shen@intel.com>
Date: Wed, 31 Oct 2018 11:30:09 +0800
Subject: [PATCH 16/26] Fix accuracy issue in ssd-mobilenet

---
 scripts/calibrator.py | 6 +++++-
 1 file changed, 5 insertions(+), 1 deletion(-)

diff --git a/scripts/calibrator.py b/scripts/calibrator.py
index e6640e4e8..2b9c94353 100644
--- a/scripts/calibrator.py
+++ b/scripts/calibrator.py
@@ -45,6 +45,7 @@
 from caffe.proto import caffe_pb2
 import google.protobuf.text_format as txtf
 import sampling
+import numpy as np
 
 int8_layers = ["Convolution", "ReLU", "Split", "Concat", "Pooling", "Eltwise"]
 
@@ -243,7 +244,10 @@ def transform_convolutions(model_path, compiled_model_path, top_blobs_map, botto
 
         for si in range(0, len(new_net.layer[index].quantization_param.scale_params)):
             if not isclose(new_net.layer[index].quantization_param.scale_params[si], 0.0):
-                new_net.layer[index].quantization_param.scale_params[si] = s8_max / new_net.layer[index].quantization_param.scale_params[si]
+                new_scale_param = s8_max / new_net.layer[index].quantization_param.scale_params[si]
+                if np.isinf(new_scale_param):
+                    new_scale_param = 0.0
+                new_net.layer[index].quantization_param.scale_params[si] = new_scale_param
             else:
                 new_net.layer[index].quantization_param.scale_params[si] = 0.0
 

From c76aed11062e0265cd14911ea0edb55cbbaed5b1 Mon Sep 17 00:00:00 2001
From: Haihao Shen <haihao.shen@intel.com>
Date: Thu, 15 Nov 2018 15:33:00 +0800
Subject: [PATCH 17/26] Update the ssd_mobilenet model with KL iteration 2

---
 .../int8/ssd_mobilenet_int8.prototxt          | 26988 +++++++--------
 .../int8/ssd_mobilenet_int8_acc.prototxt      | 26992 ++++++++--------
 2 files changed, 26990 insertions(+), 26990 deletions(-)

diff --git a/models/intel_optimized_models/int8/ssd_mobilenet_int8.prototxt b/models/intel_optimized_models/int8/ssd_mobilenet_int8.prototxt
index 57e29729c..26f7586f2 100644
--- a/models/intel_optimized_models/int8/ssd_mobilenet_int8.prototxt
+++ b/models/intel_optimized_models/int8/ssd_mobilenet_int8.prototxt
@@ -123,40 +123,40 @@ layer {
     bw_layer_in: 8
     bw_layer_out: 8
     bw_params: 8
-    scale_in: 18.1787223816
-    scale_out: 11.1095628738
-    scale_params: 42.0692214966
+    scale_in: 18.1995803433
+    scale_out: 11.1108459641
+    scale_params: 42.0692211633
     scale_params: 0.0
     scale_params: 0.0
     scale_params: 0.0
     scale_params: 0.0
-    scale_params: 35.9848022461
-    scale_params: 96.1156158447
+    scale_params: 35.9848012764
+    scale_params: 96.1156195195
     scale_params: 0.0
     scale_params: 0.0
     scale_params: 0.0
-    scale_params: 48.4883956909
-    scale_params: 35.077381134
-    scale_params: 35.6163787842
+    scale_params: 48.4883970917
+    scale_params: 35.0773802664
+    scale_params: 35.6163791723
     scale_params: 0.0
-    scale_params: 52.9423866272
-    scale_params: 75.9592132568
-    scale_params: 37.7726249695
+    scale_params: 52.9423880619
+    scale_params: 75.9592155111
+    scale_params: 37.7726240618
     scale_params: 0.0
-    scale_params: 23.2944393158
-    scale_params: 41.6536369324
-    scale_params: 74.3933258057
-    scale_params: 114.592155457
+    scale_params: 23.2944396919
+    scale_params: 41.6536357863
+    scale_params: 74.3933222112
+    scale_params: 114.592158772
     scale_params: 0.0
     scale_params: 0.0
-    scale_params: 58.0522079468
-    scale_params: 32.906917572
+    scale_params: 58.0522076502
+    scale_params: 32.90691715
     scale_params: 0.0
-    scale_params: 36.6667823792
-    scale_params: 76.1828308105
+    scale_params: 36.6667824012
+    scale_params: 76.182831952
     scale_params: 0.0
     scale_params: 0.0
-    scale_params: 23.9832286835
+    scale_params: 23.9832280222
   }
 }
 layer {
@@ -227,72 +227,72 @@ layer {
     bw_layer_in: 8
     bw_layer_out: 8
     bw_params: 8
-    scale_in: 11.1095628738
-    scale_out: 3.00517463684
-    scale_params: 62.2967948914
-    scale_params: 340.009216309
-    scale_params: 244.764648438
-    scale_params: 114.332649231
-    scale_params: 111.084205627
-    scale_params: 48.3250808716
-    scale_params: 276.6902771
-    scale_params: 649.946838379
-    scale_params: 136.880599976
-    scale_params: 230.259521484
-    scale_params: 226.491470337
-    scale_params: 199.632385254
-    scale_params: 58.7150344849
-    scale_params: 312.996856689
-    scale_params: 215.028823853
-    scale_params: 233.231369019
-    scale_params: 37.1700286865
-    scale_params: 236.888717651
-    scale_params: 602.868347168
-    scale_params: 111.41103363
-    scale_params: 297.324462891
-    scale_params: 157.185012817
-    scale_params: 21.9425697327
-    scale_params: 251.113037109
-    scale_params: 250.054946899
-    scale_params: 95.9932327271
-    scale_params: 101.70880127
-    scale_params: 257.062194824
-    scale_params: 371.846466064
-    scale_params: 742.45123291
-    scale_params: 202.487487793
-    scale_params: 32.1840057373
-    scale_params: 173.093582153
-    scale_params: 148.452148438
-    scale_params: 244.760742188
-    scale_params: 65.3044586182
-    scale_params: 516.906677246
-    scale_params: 326.260314941
-    scale_params: 81.105796814
-    scale_params: 232.79234314
-    scale_params: 95.4689483643
-    scale_params: 168.81300354
-    scale_params: 176.632965088
-    scale_params: 138.763305664
-    scale_params: 48.4995956421
-    scale_params: 5453.74658203
-    scale_params: 234.621231079
-    scale_params: 105.889183044
-    scale_params: 263.1277771
-    scale_params: 186.405563354
-    scale_params: 159.839416504
-    scale_params: 44.4354171753
+    scale_in: 11.1108459641
+    scale_out: 2.99188791222
+    scale_params: 62.2967965152
+    scale_params: 340.009203057
+    scale_params: 244.764655479
+    scale_params: 114.332648138
+    scale_params: 111.084208843
+    scale_params: 48.3250825088
+    scale_params: 276.69029078
+    scale_params: 649.946841604
+    scale_params: 136.880601057
+    scale_params: 230.259516075
+    scale_params: 226.491462774
+    scale_params: 199.632388939
+    scale_params: 58.7150363718
+    scale_params: 312.996863871
+    scale_params: 215.028824695
+    scale_params: 233.23137299
+    scale_params: 37.1700305265
+    scale_params: 236.888713672
+    scale_params: 602.868343112
+    scale_params: 111.411036201
+    scale_params: 297.324455257
+    scale_params: 157.185007161
+    scale_params: 21.9425703075
+    scale_params: 251.113038004
+    scale_params: 250.054944793
+    scale_params: 95.9932299007
+    scale_params: 101.708801615
+    scale_params: 257.062182994
+    scale_params: 371.846460179
+    scale_params: 742.451241001
+    scale_params: 202.487492655
+    scale_params: 32.1840056171
+    scale_params: 173.093588456
+    scale_params: 148.452152665
+    scale_params: 244.760747235
+    scale_params: 65.3044576142
+    scale_params: 516.906658139
+    scale_params: 326.260313049
+    scale_params: 81.1057987635
+    scale_params: 232.792345091
+    scale_params: 95.4689520961
+    scale_params: 168.813005162
+    scale_params: 176.63295936
+    scale_params: 138.763308036
+    scale_params: 48.499597473
+    scale_params: 5453.74677392
+    scale_params: 234.621237326
+    scale_params: 105.889185788
+    scale_params: 263.127766996
+    scale_params: 186.405568897
+    scale_params: 159.839417795
+    scale_params: 44.4354154552
     scale_params: 0.0
-    scale_params: 37.8183860779
-    scale_params: 52.843044281
-    scale_params: 129.145263672
-    scale_params: 115.619125366
-    scale_params: 283.588409424
-    scale_params: 512.381225586
-    scale_params: 323.917602539
-    scale_params: 30.7325191498
-    scale_params: 231.785415649
-    scale_params: 38.860786438
-    scale_params: 120.725563049
+    scale_params: 37.8183852566
+    scale_params: 52.8430457899
+    scale_params: 129.145267956
+    scale_params: 115.619122989
+    scale_params: 283.588420438
+    scale_params: 512.381219903
+    scale_params: 323.917591369
+    scale_params: 30.7325188791
+    scale_params: 231.785414805
+    scale_params: 38.8607860452
+    scale_params: 120.725560981
   }
 }
 layer {
@@ -366,72 +366,72 @@ layer {
     bw_layer_in: 8
     bw_layer_out: 8
     bw_params: 8
-    scale_in: 3.00517463684
-    scale_out: 16.3315830231
-    scale_params: 439.582366943
-    scale_params: 60.190322876
-    scale_params: 83.5455932617
-    scale_params: 485.688720703
-    scale_params: 369.882568359
-    scale_params: 995.153137207
-    scale_params: 77.8068389893
-    scale_params: 19.6928310394
-    scale_params: 453.722686768
-    scale_params: 76.307472229
-    scale_params: 401.455871582
-    scale_params: 94.9558563232
-    scale_params: 244.565155029
-    scale_params: 226.900314331
-    scale_params: 375.830871582
-    scale_params: 169.798782349
-    scale_params: 1268.0333252
-    scale_params: 266.880340576
-    scale_params: 28.2426528931
-    scale_params: 508.968933105
-    scale_params: 344.379272461
-    scale_params: 101.716270447
-    scale_params: 460.506439209
-    scale_params: 253.092453003
-    scale_params: 323.436859131
-    scale_params: 305.576904297
-    scale_params: 578.445739746
-    scale_params: 285.327636719
-    scale_params: 273.306732178
-    scale_params: 177.767288208
-    scale_params: 227.428817749
-    scale_params: 1689.76745605
-    scale_params: 384.924804688
-    scale_params: 437.611877441
-    scale_params: 243.390029907
-    scale_params: 815.499389648
-    scale_params: 127.469459534
-    scale_params: 384.258728027
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-    scale_params: 412.198699951
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-    scale_params: 325.13684082
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-    scale_params: 211.27532959
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-    scale_params: 236.323394775
-    scale_params: 181.688278198
-    scale_params: 403.467315674
-    scale_params: 643.55291748
+    scale_in: 2.99188791222
+    scale_out: 18.8665239088
+    scale_params: 439.582352019
+    scale_params: 60.1903245822
+    scale_params: 83.5455955173
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+    scale_params: 127.469458722
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 }
 layer {
@@ -16798,10 +16798,10 @@ layer {
     aspect_ratio: 3.0
     flip: true
     clip: false
-    variance: 0.10000000149
-    variance: 0.10000000149
-    variance: 0.20000000298
-    variance: 0.20000000298
+    variance: 0.1
+    variance: 0.1
+    variance: 0.2
+    variance: 0.2
     offset: 0.5
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 }
@@ -16893,12 +16893,12 @@ layer {
     share_location: true
     background_label_id: 0
     nms_param {
-      nms_threshold: 0.449999988079
+      nms_threshold: 0.45
       top_k: 400
     }
     code_type: CENTER_SIZE
     keep_top_k: 200
-    confidence_threshold: 0.00999999977648
+    confidence_threshold: 0.01
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 }
 layer {
diff --git a/models/intel_optimized_models/int8/ssd_mobilenet_int8_acc.prototxt b/models/intel_optimized_models/int8/ssd_mobilenet_int8_acc.prototxt
index ccf1db2e9..a6ec4df7f 100644
--- a/models/intel_optimized_models/int8/ssd_mobilenet_int8_acc.prototxt
+++ b/models/intel_optimized_models/int8/ssd_mobilenet_int8_acc.prototxt
@@ -8,7 +8,7 @@ layer {
     phase: TEST
   }
   transform_param {
-    scale: 0.007842999883
+    scale: 0.007843
     mean_value: 127.5
     mean_value: 127.5
     mean_value: 127.5
@@ -22,7 +22,7 @@ layer {
   }
   data_param {
     source: "/lustre/dataset/lmdb/voc/VOC0712_test_lmdb"
-    batch_size: 8
+    batch_size: 1 
     backend: LMDB
   }
   annotated_data_param {
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     bw_layer_out: 8
     bw_params: 8
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-    scale_out: 11.1095628738
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 }
 layer {
@@ -225,72 +225,72 @@ layer {
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@@ -500,136 +500,136 @@ layer {
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-    scale_params: 79.7694396973
-    scale_params: 60.7543792725
-    scale_params: 49.5355453491
-    scale_params: 201.317092896
-    scale_params: 55.9771995544
-    scale_params: 57.3199386597
-    scale_params: 59.6002120972
-    scale_params: 61.6781539917
-    scale_params: 89.8544921875
-    scale_params: 48.1922302246
-    scale_params: 48.4535369873
-    scale_params: 63.121307373
-    scale_params: 72.9025192261
-    scale_params: 70.0940933228
-    scale_params: 50.7963600159
-    scale_params: 64.3435134888
-    scale_params: 59.0513954163
-    scale_params: 64.4275970459
-    scale_params: 51.9593391418
-    scale_params: 79.2750473022
-    scale_params: 71.1660003662
-    scale_params: 60.4471244812
-    scale_params: 61.9578590393
-    scale_params: 66.2605056763
-    scale_params: 213.248321533
-    scale_params: 54.2597694397
-    scale_params: 52.130241394
-    scale_params: 51.3404388428
-    scale_params: 47.2602920532
-    scale_params: 47.9160079956
-    scale_params: 54.977596283
-    scale_params: 64.6997451782
-    scale_params: 59.1252937317
-    scale_params: 61.1987075806
-    scale_params: 61.2039108276
-    scale_params: 60.7045097351
-    scale_params: 59.9957122803
-    scale_params: 52.5722198486
-    scale_params: 59.3648986816
-    scale_params: 82.5999450684
-    scale_params: 50.5126228333
-    scale_params: 55.8984947205
-    scale_params: 68.3333129883
-    scale_params: 49.5446052551
-    scale_params: 63.7748031616
+    scale_params: 92.4733600971
+    scale_params: 70.2865788964
+    scale_params: 74.4145440931
+    scale_params: 85.9849246169
+    scale_params: 65.5593305072
+    scale_params: 60.4277990519
+    scale_params: 45.054694589
+    scale_params: 93.1596703924
+    scale_params: 69.0841842732
+    scale_params: 110.154878729
+    scale_params: 60.6069020948
+    scale_params: 61.0965690483
+    scale_params: 85.1604763535
+    scale_params: 47.8332789307
+    scale_params: 75.473997804
+    scale_params: 96.2246484469
+    scale_params: 72.106676543
+    scale_params: 54.6109201417
+    scale_params: 85.2344009759
+    scale_params: 71.0934657872
+    scale_params: 55.4594631512
+    scale_params: 103.131527416
+    scale_params: 69.0607625914
+    scale_params: 79.2966815332
+    scale_params: 84.1527248665
+    scale_params: 71.08201506
+    scale_params: 58.9769935338
+    scale_params: 49.0877550115
+    scale_params: 93.880262249
+    scale_params: 64.9568506969
+    scale_params: 114.674431086
+    scale_params: 60.0448037095
+    scale_params: 67.7668276036
+    scale_params: 86.4984419205
+    scale_params: 49.114585478
+    scale_params: 80.9898879831
+    scale_params: 101.845801133
+    scale_params: 71.5070114047
+    scale_params: 55.7170316569
+    scale_params: 90.192793318
+    scale_params: 72.2287469748
+    scale_params: 59.0154989238
+    scale_params: 136.068056429
+    scale_params: 69.3410563421
+    scale_params: 72.3881414131
+    scale_params: 67.0826647862
+    scale_params: 64.3849993164
+    scale_params: 57.3543843684
+    scale_params: 49.2114875946
+    scale_params: 83.0051340935
+    scale_params: 65.0285921681
+    scale_params: 90.3491380029
+    scale_params: 41.0664206633
+    scale_params: 58.6782340678
+    scale_params: 82.9395697119
+    scale_params: 45.7886797884
+    scale_params: 68.5243284269
+    scale_params: 82.7248574965
+    scale_params: 55.0535199109
+    scale_params: 52.2984450961
+    scale_params: 78.5684555699
+    scale_params: 61.0679983674
+    scale_params: 56.3336119997
+    scale_params: 134.302310961
+    scale_params: 62.5350192116
+    scale_params: 62.8504278194
+    scale_params: 72.5374576688
+    scale_params: 60.3954674313
+    scale_params: 52.980580228
+    scale_params: 43.6660830058
+    scale_params: 80.8061197509
+    scale_params: 69.3463733328
+    scale_params: 91.602225961
+    scale_params: 50.5833875796
+    scale_params: 46.7098541777
+    scale_params: 79.6673250316
+    scale_params: 48.873381738
+    scale_params: 68.4527543452
+    scale_params: 100.201598594
+    scale_params: 63.9753685815
+    scale_params: 51.9779627959
+    scale_params: 79.7694428587
+    scale_params: 60.7543805923
+    scale_params: 49.5355439413
+    scale_params: 201.317087297
+    scale_params: 55.9771978793
+    scale_params: 57.3199398002
+    scale_params: 59.6002132588
+    scale_params: 61.6781558034
+    scale_params: 89.8544912393
+    scale_params: 48.1922287591
+    scale_params: 48.4535355489
+    scale_params: 63.1213071318
+    scale_params: 72.9025177859
+    scale_params: 70.0940907443
+    scale_params: 50.7963619168
+    scale_params: 64.3435115983
+    scale_params: 59.051396704
+    scale_params: 64.4276003414
+    scale_params: 51.9593402015
+    scale_params: 79.2750439477
+    scale_params: 71.1659983401
+    scale_params: 60.4471227292
+    scale_params: 61.9578575018
+    scale_params: 66.2605082778
+    scale_params: 213.248321702
+    scale_params: 54.2597681039
+    scale_params: 52.1302414871
+    scale_params: 51.3404382932
+    scale_params: 47.2602916976
+    scale_params: 47.9160081037
+    scale_params: 54.9775959812
+    scale_params: 64.6997470139
+    scale_params: 59.1252921847
+    scale_params: 61.1987072684
+    scale_params: 61.2039106961
+    scale_params: 60.704509562
+    scale_params: 59.995711926
+    scale_params: 52.5722197062
+    scale_params: 59.3648989705
+    scale_params: 82.5999467967
+    scale_params: 50.512621598
+    scale_params: 55.8984955436
+    scale_params: 68.3333108412
+    scale_params: 49.5446065673
+    scale_params: 63.7748017291
   }
 }
 layer {
@@ -16796,10 +16796,10 @@ layer {
     aspect_ratio: 3.0
     flip: true
     clip: false
-    variance: 0.10000000149
-    variance: 0.10000000149
-    variance: 0.20000000298
-    variance: 0.20000000298
+    variance: 0.1
+    variance: 0.1
+    variance: 0.2
+    variance: 0.2
     offset: 0.5
   }
 }
@@ -16891,12 +16891,12 @@ layer {
     share_location: true
     background_label_id: 0
     nms_param {
-      nms_threshold: 0.449999988079
+      nms_threshold: 0.45
       top_k: 400
     }
     code_type: CENTER_SIZE
     keep_top_k: 200
-    confidence_threshold: 0.00999999977648
+    confidence_threshold: 0.01
   }
 }
 layer {

From ab324577462fb2c6502901f793d767733cc64dd2 Mon Sep 17 00:00:00 2001
From: Daisy Deng <daisy.deng@intel.com>
Date: Sat, 3 Nov 2018 03:51:28 +0800
Subject: [PATCH 18/26] update for latest MKLDNN

---
 external/mkl/prepare_mkl.sh | 10 +++++-----
 mkldnn.commit               |  2 +-
 2 files changed, 6 insertions(+), 6 deletions(-)

diff --git a/external/mkl/prepare_mkl.sh b/external/mkl/prepare_mkl.sh
index 18bed0f82..650e81b12 100755
--- a/external/mkl/prepare_mkl.sh
+++ b/external/mkl/prepare_mkl.sh
@@ -34,8 +34,8 @@
 # SERVICES; LOSS OF USE, DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER
 # CAUSED AND ON ANY THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY,
 # OR TORT (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE
-# OF THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE.
-#
+# OF THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE.  
+# 
 FindLibrary()
 {
 # Find all the instances of the MKL libraries present in Caffe
@@ -74,11 +74,11 @@ echo $VERSION_LINE  # Return Version Line
 # MKL
 DST=`dirname $0`
 OMP=0 
-VERSION_MATCH=20180710
-ARCHIVE_BASENAME=mklml_lnx_2019.0.20180710.tgz
+VERSION_MATCH=20180928
+ARCHIVE_BASENAME=mklml_lnx_2019.0.1.20180928.tgz
 MKL_CONTENT_DIR=`echo $ARCHIVE_BASENAME | rev | cut -d "." -f 2- | rev`
 
-MKLURL="https://github.com/intel/mkl-dnn/releases/download/v0.16/$ARCHIVE_BASENAME"
+MKLURL="https://github.com/intel/mkl-dnn/releases/download/v0.17-rc/$ARCHIVE_BASENAME"
 # there are diffrent MKL lib to be used for GCC and for ICC
 reg='^[0-9]+$'
 VERSION_LINE=`GetVersionName $MKLROOT`
diff --git a/mkldnn.commit b/mkldnn.commit
index 48f2006cd..e4958aca5 100644
--- a/mkldnn.commit
+++ b/mkldnn.commit
@@ -1 +1 @@
-4e333787e0d66a1dca1218e99a891d493dbc8ef1
+21fb5f2af1dd14e132af4f1b79160977ee487818

From aeef2052ba912e2fc360bfbb1df3e4b4396af543 Mon Sep 17 00:00:00 2001
From: Haihao Shen <haihao.shen@intel.com>
Date: Tue, 20 Nov 2018 19:20:45 +0800
Subject: [PATCH 19/26] Update SSD-MobileNet INT8 model

---
 .../int8/ssd_mobilenet_int8.prototxt          | 160 ++++++++---------
 .../int8/ssd_mobilenet_int8_acc.prototxt      | 164 +++++++++---------
 2 files changed, 162 insertions(+), 162 deletions(-)

diff --git a/models/intel_optimized_models/int8/ssd_mobilenet_int8.prototxt b/models/intel_optimized_models/int8/ssd_mobilenet_int8.prototxt
index 26f7586f2..df7dd0a0a 100644
--- a/models/intel_optimized_models/int8/ssd_mobilenet_int8.prototxt
+++ b/models/intel_optimized_models/int8/ssd_mobilenet_int8.prototxt
@@ -123,8 +123,8 @@ layer {
     bw_layer_in: 8
     bw_layer_out: 8
     bw_params: 8
-    scale_in: 18.1995803433
-    scale_out: 11.1108459641
+    scale_in: 16.3629863631
+    scale_out: 11.0183343057
     scale_params: 42.0692211633
     scale_params: 0.0
     scale_params: 0.0
@@ -227,8 +227,8 @@ layer {
     bw_layer_in: 8
     bw_layer_out: 8
     bw_params: 8
-    scale_in: 11.1108459641
-    scale_out: 2.99188791222
+    scale_in: 11.0183343057
+    scale_out: 2.81492000139
     scale_params: 62.2967965152
     scale_params: 340.009203057
     scale_params: 244.764655479
@@ -366,8 +366,8 @@ layer {
     bw_layer_in: 8
     bw_layer_out: 8
     bw_params: 8
-    scale_in: 2.99188791222
-    scale_out: 18.8665239088
+    scale_in: 2.81492000139
+    scale_out: 20.3329952882
     scale_params: 439.582352019
     scale_params: 60.1903245822
     scale_params: 83.5455955173
@@ -502,8 +502,8 @@ layer {
     bw_layer_in: 8
     bw_layer_out: 8
     bw_params: 8
-    scale_in: 18.8665239088
-    scale_out: 7.3125210462
+    scale_in: 20.3329952882
+    scale_out: 8.11085874067
     scale_params: 96.4670113878
     scale_params: 105.040459312
     scale_params: 145.78903343
@@ -704,8 +704,8 @@ layer {
     bw_layer_in: 8
     bw_layer_out: 8
     bw_params: 8
-    scale_in: 7.3125210462
-    scale_out: 9.48529430215
+    scale_in: 8.11085874067
+    scale_out: 8.87112080922
     scale_params: 90.7487428862
     scale_params: 158.827164186
     scale_params: 184.419026599
@@ -904,8 +904,8 @@ layer {
     bw_layer_in: 8
     bw_layer_out: 8
     bw_params: 8
-    scale_in: 9.48529430215
-    scale_out: 6.94529293891
+    scale_in: 8.87112080922
+    scale_out: 6.64312457387
     scale_params: 452.390858833
     scale_params: 405.671893974
     scale_params: 362.732861681
@@ -1107,8 +1107,8 @@ layer {
     bw_layer_in: 8
     bw_layer_out: 8
     bw_params: 8
-    scale_in: 6.94529293891
-    scale_out: 31.1033959569
+    scale_in: 6.64312457387
+    scale_out: 30.4137944462
     scale_params: 124.404236002
     scale_params: 153.723402458
     scale_params: 361.835854519
@@ -1307,8 +1307,8 @@ layer {
     bw_layer_in: 8
     bw_layer_out: 8
     bw_params: 8
-    scale_in: 31.1033959569
-    scale_out: 20.3665262471
+    scale_in: 30.4137944462
+    scale_out: 18.5620589902
     scale_params: 380.739712827
     scale_params: 473.192102044
     scale_params: 514.319143394
@@ -1637,8 +1637,8 @@ layer {
     bw_layer_in: 8
     bw_layer_out: 8
     bw_params: 8
-    scale_in: 20.3665262471
-    scale_out: 19.2426682474
+    scale_in: 18.5620589902
+    scale_out: 21.6248082011
     scale_params: 150.735510383
     scale_params: 99.049285552
     scale_params: 122.309771883
@@ -1965,8 +1965,8 @@ layer {
     bw_layer_in: 8
     bw_layer_out: 8
     bw_params: 8
-    scale_in: 19.2426682474
-    scale_out: 24.3586756171
+    scale_in: 21.6248082011
+    scale_out: 22.0816237647
     scale_params: 117.769234681
     scale_params: 248.940592792
     scale_params: 498.781943385
@@ -2296,8 +2296,8 @@ layer {
     bw_layer_in: 8
     bw_layer_out: 8
     bw_params: 8
-    scale_in: 24.3586756171
-    scale_out: 21.4319907586
+    scale_in: 22.0816237647
+    scale_out: 22.6878830616
     scale_params: 591.711436085
     scale_params: 452.804450038
     scale_params: 218.752813147
@@ -2624,8 +2624,8 @@ layer {
     bw_layer_in: 8
     bw_layer_out: 8
     bw_params: 8
-    scale_in: 21.4319907586
-    scale_out: 20.2237444349
+    scale_in: 22.6878830616
+    scale_out: 20.3544176256
     scale_params: 363.853652618
     scale_params: 180.594099311
     scale_params: 488.041045073
@@ -3210,8 +3210,8 @@ layer {
     bw_layer_in: 8
     bw_layer_out: 8
     bw_params: 8
-    scale_in: 20.2237444349
-    scale_out: 24.2006882985
+    scale_in: 20.3544176256
+    scale_out: 27.1497306299
     scale_params: 120.819441738
     scale_params: 283.288593717
     scale_params: 136.337804477
@@ -3794,8 +3794,8 @@ layer {
     bw_layer_in: 8
     bw_layer_out: 8
     bw_params: 8
-    scale_in: 24.2006882985
-    scale_out: 26.443396234
+    scale_in: 27.1497306299
+    scale_out: 25.6277540528
     scale_params: 396.036971642
     scale_params: 577.528519602
     scale_params: 714.287451475
@@ -4380,8 +4380,8 @@ layer {
     bw_layer_in: 8
     bw_layer_out: 8
     bw_params: 8
-    scale_in: 26.443396234
-    scale_out: 20.900680869
+    scale_in: 25.6277540528
+    scale_out: 19.4719487108
     scale_params: 458.376227929
     scale_params: 306.768493285
     scale_params: 300.723286508
@@ -4964,8 +4964,8 @@ layer {
     bw_layer_in: 8
     bw_layer_out: 8
     bw_params: 8
-    scale_in: 20.900680869
-    scale_out: 19.2998822199
+    scale_in: 19.4719487108
+    scale_out: 28.6338511023
     scale_params: 97.4303572814
     scale_params: 718.388589164
     scale_params: 439.239541293
@@ -5550,8 +5550,8 @@ layer {
     bw_layer_in: 8
     bw_layer_out: 8
     bw_params: 8
-    scale_in: 19.2998822199
-    scale_out: 31.3888673433
+    scale_in: 28.6338511023
+    scale_out: 31.8839781818
     scale_params: 222.692495588
     scale_params: 156.284762344
     scale_params: 158.938009962
@@ -6134,8 +6134,8 @@ layer {
     bw_layer_in: 8
     bw_layer_out: 8
     bw_params: 8
-    scale_in: 31.3888673433
-    scale_out: 12.3728072204
+    scale_in: 31.8839781818
+    scale_out: 14.715356973
     scale_params: 221.635065397
     scale_params: 1048.27214553
     scale_params: 434.104941553
@@ -6720,8 +6720,8 @@ layer {
     bw_layer_in: 8
     bw_layer_out: 8
     bw_params: 8
-    scale_in: 12.3728072204
-    scale_out: 29.8407049159
+    scale_in: 14.715356973
+    scale_out: 26.9842217461
     scale_params: 83.630270149
     scale_params: 222.000294235
     scale_params: 305.299631692
@@ -7304,8 +7304,8 @@ layer {
     bw_layer_in: 8
     bw_layer_out: 8
     bw_params: 8
-    scale_in: 29.8407049159
-    scale_out: 20.9318143514
+    scale_in: 26.9842217461
+    scale_out: 21.5972212606
     scale_params: 580.58155311
     scale_params: 213.486643273
     scale_params: 354.166094371
@@ -7890,8 +7890,8 @@ layer {
     bw_layer_in: 8
     bw_layer_out: 8
     bw_params: 8
-    scale_in: 20.9318143514
-    scale_out: 22.2470898642
+    scale_in: 21.5972212606
+    scale_out: 22.0371267076
     scale_params: 316.137978595
     scale_params: 497.317925169
     scale_params: 350.254714645
@@ -8474,8 +8474,8 @@ layer {
     bw_layer_in: 8
     bw_layer_out: 8
     bw_params: 8
-    scale_in: 22.2470898642
-    scale_out: 43.5443062043
+    scale_in: 22.0371267076
+    scale_out: 34.3504724372
     scale_params: 877.021417543
     scale_params: 2297.00871442
     scale_params: 404.76338444
@@ -9061,8 +9061,8 @@ layer {
     bw_layer_in: 8
     bw_layer_out: 8
     bw_params: 8
-    scale_in: 43.5443062043
-    scale_out: 23.476256768
+    scale_in: 34.3504724372
+    scale_out: 24.2417450691
     scale_params: 98.0879843847
     scale_params: 48.9490028603
     scale_params: 107.771918143
@@ -9645,8 +9645,8 @@ layer {
     bw_layer_in: 8
     bw_layer_out: 8
     bw_params: 8
-    scale_in: 23.476256768
-    scale_out: 31.786998084
+    scale_in: 24.2417450691
+    scale_out: 35.2398209635
     scale_params: 537.708009053
     scale_params: 666.963757998
     scale_params: 378.308927093
@@ -10743,8 +10743,8 @@ layer {
     bw_layer_in: 8
     bw_layer_out: 8
     bw_params: 8
-    scale_in: 31.786998084
-    scale_out: 14.710661434
+    scale_in: 35.2398209635
+    scale_out: 14.7019739536
     scale_params: 239.088170047
     scale_params: 290.498184681
     scale_params: 296.310479377
@@ -11839,8 +11839,8 @@ layer {
     bw_layer_in: 8
     bw_layer_out: 8
     bw_params: 8
-    scale_in: 14.710661434
-    scale_out: 10.004126865
+    scale_in: 14.7019739536
+    scale_out: 11.0559801648
     scale_params: 406.604586582
     scale_params: 492.239615802
     scale_params: 896.658092112
@@ -12935,8 +12935,8 @@ layer {
     bw_layer_in: 8
     bw_layer_out: 8
     bw_params: 8
-    scale_in: 10.004126865
-    scale_out: 46.1180604226
+    scale_in: 11.0559801648
+    scale_out: 52.2254057353
     scale_params: 858.395550877
     scale_params: 1030.42060506
     scale_params: 626.699329132
@@ -13265,8 +13265,8 @@ layer {
     bw_layer_in: 8
     bw_layer_out: 8
     bw_params: 8
-    scale_in: 46.1180604226
-    scale_out: 75.4528201809
+    scale_in: 52.2254057353
+    scale_out: 76.1881268169
     scale_params: 788.734180964
     scale_params: 1976.90908317
     scale_params: 1398.87500576
@@ -13849,8 +13849,8 @@ layer {
     bw_layer_in: 8
     bw_layer_out: 8
     bw_params: 8
-    scale_in: 75.4528201809
-    scale_out: 108.401902203
+    scale_in: 76.1881268169
+    scale_out: 90.8427433396
     scale_params: 909.051473761
     scale_params: 630.321714557
     scale_params: 1244.30241182
@@ -14051,8 +14051,8 @@ layer {
     bw_layer_in: 8
     bw_layer_out: 8
     bw_params: 8
-    scale_in: 108.401902203
-    scale_out: 79.6549028385
+    scale_in: 90.8427433396
+    scale_out: 65.7778143021
     scale_params: 573.440716537
     scale_params: 514.081074589
     scale_params: 674.295419112
@@ -14379,8 +14379,8 @@ layer {
     bw_layer_in: 8
     bw_layer_out: 8
     bw_params: 8
-    scale_in: 79.6549028385
-    scale_out: 118.865796029
+    scale_in: 65.7778143021
+    scale_out: 122.277858874
     scale_params: 2003.33843161
     scale_params: 1453.31353958
     scale_params: 778.296749809
@@ -14581,8 +14581,8 @@ layer {
     bw_layer_in: 8
     bw_layer_out: 8
     bw_params: 8
-    scale_in: 118.865796029
-    scale_out: 81.2545526295
+    scale_in: 122.277858874
+    scale_out: 78.8967340238
     scale_params: 566.496332852
     scale_params: 816.078866064
     scale_params: 763.692214005
@@ -14909,8 +14909,8 @@ layer {
     bw_layer_in: 8
     bw_layer_out: 8
     bw_params: 8
-    scale_in: 81.2545526295
-    scale_out: 122.925564448
+    scale_in: 78.8967340238
+    scale_out: 71.8783177979
     scale_params: 1218.4545423
     scale_params: 1160.65851361
     scale_params: 1511.43382868
@@ -15047,8 +15047,8 @@ layer {
     bw_layer_in: 8
     bw_layer_out: 8
     bw_params: 8
-    scale_in: 122.925564448
-    scale_out: 61.6728323871
+    scale_in: 71.8783177979
+    scale_out: 37.2974508202
     scale_params: 182.802937192
     scale_params: 424.943159162
     scale_params: 455.110576456
@@ -15254,7 +15254,7 @@ layer {
     bw_layer_in: 8
     bw_layer_out: 32
     bw_params: 8
-    scale_in: 43.5443062043
+    scale_in: 34.3504724372
     scale_out: 1.0
     scale_params: 208.038950726
     scale_params: 132.439600185
@@ -15319,7 +15319,7 @@ layer {
     bw_layer_in: 8
     bw_layer_out: 32
     bw_params: 8
-    scale_in: 43.5443062043
+    scale_in: 34.3504724372
     scale_out: 1.0
     scale_params: 170.42409308
     scale_params: 36.6293474677
@@ -15453,7 +15453,7 @@ layer {
     bw_layer_in: 8
     bw_layer_out: 32
     bw_params: 8
-    scale_in: 10.004126865
+    scale_in: 11.0559801648
     scale_out: 1.0
     scale_params: 203.896971813
     scale_params: 186.637345296
@@ -15530,7 +15530,7 @@ layer {
     bw_layer_in: 8
     bw_layer_out: 32
     bw_params: 8
-    scale_in: 10.004126865
+    scale_in: 11.0559801648
     scale_out: 1.0
     scale_params: 182.71914736
     scale_params: 28.1624516652
@@ -15729,7 +15729,7 @@ layer {
     bw_layer_in: 8
     bw_layer_out: 32
     bw_params: 8
-    scale_in: 75.4528201809
+    scale_in: 76.1881268169
     scale_out: 1.0
     scale_params: 277.951771789
     scale_params: 150.5504775
@@ -15806,7 +15806,7 @@ layer {
     bw_layer_in: 8
     bw_layer_out: 32
     bw_params: 8
-    scale_in: 75.4528201809
+    scale_in: 76.1881268169
     scale_out: 1.0
     scale_params: 208.421917386
     scale_params: 37.625236051
@@ -16005,7 +16005,7 @@ layer {
     bw_layer_in: 8
     bw_layer_out: 32
     bw_params: 8
-    scale_in: 79.6549028385
+    scale_in: 65.7778143021
     scale_out: 1.0
     scale_params: 396.499978878
     scale_params: 284.448993644
@@ -16082,7 +16082,7 @@ layer {
     bw_layer_in: 8
     bw_layer_out: 32
     bw_params: 8
-    scale_in: 79.6549028385
+    scale_in: 65.7778143021
     scale_out: 1.0
     scale_params: 195.801013398
     scale_params: 34.8167821514
@@ -16281,7 +16281,7 @@ layer {
     bw_layer_in: 8
     bw_layer_out: 32
     bw_params: 8
-    scale_in: 81.2545526295
+    scale_in: 78.8967340238
     scale_out: 1.0
     scale_params: 232.427685722
     scale_params: 228.63315322
@@ -16358,7 +16358,7 @@ layer {
     bw_layer_in: 8
     bw_layer_out: 32
     bw_params: 8
-    scale_in: 81.2545526295
+    scale_in: 78.8967340238
     scale_out: 1.0
     scale_params: 237.135009066
     scale_params: 29.3873323395
@@ -16557,7 +16557,7 @@ layer {
     bw_layer_in: 8
     bw_layer_out: 32
     bw_params: 8
-    scale_in: 61.6728323871
+    scale_in: 37.2974508202
     scale_out: 1.0
     scale_params: 262.461097438
     scale_params: 304.732962155
@@ -16634,7 +16634,7 @@ layer {
     bw_layer_in: 8
     bw_layer_out: 32
     bw_params: 8
-    scale_in: 61.6728323871
+    scale_in: 37.2974508202
     scale_out: 1.0
     scale_params: 92.4733600971
     scale_params: 70.2865788964
diff --git a/models/intel_optimized_models/int8/ssd_mobilenet_int8_acc.prototxt b/models/intel_optimized_models/int8/ssd_mobilenet_int8_acc.prototxt
index a6ec4df7f..79a5f4ebb 100644
--- a/models/intel_optimized_models/int8/ssd_mobilenet_int8_acc.prototxt
+++ b/models/intel_optimized_models/int8/ssd_mobilenet_int8_acc.prototxt
@@ -21,8 +21,8 @@ layer {
     }
   }
   data_param {
-    source: "/lustre/dataset/lmdb/voc/VOC0712_test_lmdb"
-    batch_size: 1 
+    source: "/home/hshen14/voc/VOC0712_test_lmdb"
+    batch_size: 8
     backend: LMDB
   }
   annotated_data_param {
@@ -121,8 +121,8 @@ layer {
     bw_layer_in: 8
     bw_layer_out: 8
     bw_params: 8
-    scale_in: 18.1995803433
-    scale_out: 11.1108459641
+    scale_in: 16.3629863631
+    scale_out: 11.0183343057
     scale_params: 42.0692211633
     scale_params: 0.0
     scale_params: 0.0
@@ -225,8 +225,8 @@ layer {
     bw_layer_in: 8
     bw_layer_out: 8
     bw_params: 8
-    scale_in: 11.1108459641
-    scale_out: 2.99188791222
+    scale_in: 11.0183343057
+    scale_out: 2.81492000139
     scale_params: 62.2967965152
     scale_params: 340.009203057
     scale_params: 244.764655479
@@ -364,8 +364,8 @@ layer {
     bw_layer_in: 8
     bw_layer_out: 8
     bw_params: 8
-    scale_in: 2.99188791222
-    scale_out: 18.8665239088
+    scale_in: 2.81492000139
+    scale_out: 20.3329952882
     scale_params: 439.582352019
     scale_params: 60.1903245822
     scale_params: 83.5455955173
@@ -500,8 +500,8 @@ layer {
     bw_layer_in: 8
     bw_layer_out: 8
     bw_params: 8
-    scale_in: 18.8665239088
-    scale_out: 7.3125210462
+    scale_in: 20.3329952882
+    scale_out: 8.11085874067
     scale_params: 96.4670113878
     scale_params: 105.040459312
     scale_params: 145.78903343
@@ -702,8 +702,8 @@ layer {
     bw_layer_in: 8
     bw_layer_out: 8
     bw_params: 8
-    scale_in: 7.3125210462
-    scale_out: 9.48529430215
+    scale_in: 8.11085874067
+    scale_out: 8.87112080922
     scale_params: 90.7487428862
     scale_params: 158.827164186
     scale_params: 184.419026599
@@ -902,8 +902,8 @@ layer {
     bw_layer_in: 8
     bw_layer_out: 8
     bw_params: 8
-    scale_in: 9.48529430215
-    scale_out: 6.94529293891
+    scale_in: 8.87112080922
+    scale_out: 6.64312457387
     scale_params: 452.390858833
     scale_params: 405.671893974
     scale_params: 362.732861681
@@ -1105,8 +1105,8 @@ layer {
     bw_layer_in: 8
     bw_layer_out: 8
     bw_params: 8
-    scale_in: 6.94529293891
-    scale_out: 31.1033959569
+    scale_in: 6.64312457387
+    scale_out: 30.4137944462
     scale_params: 124.404236002
     scale_params: 153.723402458
     scale_params: 361.835854519
@@ -1305,8 +1305,8 @@ layer {
     bw_layer_in: 8
     bw_layer_out: 8
     bw_params: 8
-    scale_in: 31.1033959569
-    scale_out: 20.3665262471
+    scale_in: 30.4137944462
+    scale_out: 18.5620589902
     scale_params: 380.739712827
     scale_params: 473.192102044
     scale_params: 514.319143394
@@ -1635,8 +1635,8 @@ layer {
     bw_layer_in: 8
     bw_layer_out: 8
     bw_params: 8
-    scale_in: 20.3665262471
-    scale_out: 19.2426682474
+    scale_in: 18.5620589902
+    scale_out: 21.6248082011
     scale_params: 150.735510383
     scale_params: 99.049285552
     scale_params: 122.309771883
@@ -1963,8 +1963,8 @@ layer {
     bw_layer_in: 8
     bw_layer_out: 8
     bw_params: 8
-    scale_in: 19.2426682474
-    scale_out: 24.3586756171
+    scale_in: 21.6248082011
+    scale_out: 22.0816237647
     scale_params: 117.769234681
     scale_params: 248.940592792
     scale_params: 498.781943385
@@ -2294,8 +2294,8 @@ layer {
     bw_layer_in: 8
     bw_layer_out: 8
     bw_params: 8
-    scale_in: 24.3586756171
-    scale_out: 21.4319907586
+    scale_in: 22.0816237647
+    scale_out: 22.6878830616
     scale_params: 591.711436085
     scale_params: 452.804450038
     scale_params: 218.752813147
@@ -2622,8 +2622,8 @@ layer {
     bw_layer_in: 8
     bw_layer_out: 8
     bw_params: 8
-    scale_in: 21.4319907586
-    scale_out: 20.2237444349
+    scale_in: 22.6878830616
+    scale_out: 20.3544176256
     scale_params: 363.853652618
     scale_params: 180.594099311
     scale_params: 488.041045073
@@ -3208,8 +3208,8 @@ layer {
     bw_layer_in: 8
     bw_layer_out: 8
     bw_params: 8
-    scale_in: 20.2237444349
-    scale_out: 24.2006882985
+    scale_in: 20.3544176256
+    scale_out: 27.1497306299
     scale_params: 120.819441738
     scale_params: 283.288593717
     scale_params: 136.337804477
@@ -3792,8 +3792,8 @@ layer {
     bw_layer_in: 8
     bw_layer_out: 8
     bw_params: 8
-    scale_in: 24.2006882985
-    scale_out: 26.443396234
+    scale_in: 27.1497306299
+    scale_out: 25.6277540528
     scale_params: 396.036971642
     scale_params: 577.528519602
     scale_params: 714.287451475
@@ -4378,8 +4378,8 @@ layer {
     bw_layer_in: 8
     bw_layer_out: 8
     bw_params: 8
-    scale_in: 26.443396234
-    scale_out: 20.900680869
+    scale_in: 25.6277540528
+    scale_out: 19.4719487108
     scale_params: 458.376227929
     scale_params: 306.768493285
     scale_params: 300.723286508
@@ -4962,8 +4962,8 @@ layer {
     bw_layer_in: 8
     bw_layer_out: 8
     bw_params: 8
-    scale_in: 20.900680869
-    scale_out: 19.2998822199
+    scale_in: 19.4719487108
+    scale_out: 28.6338511023
     scale_params: 97.4303572814
     scale_params: 718.388589164
     scale_params: 439.239541293
@@ -5548,8 +5548,8 @@ layer {
     bw_layer_in: 8
     bw_layer_out: 8
     bw_params: 8
-    scale_in: 19.2998822199
-    scale_out: 31.3888673433
+    scale_in: 28.6338511023
+    scale_out: 31.8839781818
     scale_params: 222.692495588
     scale_params: 156.284762344
     scale_params: 158.938009962
@@ -6132,8 +6132,8 @@ layer {
     bw_layer_in: 8
     bw_layer_out: 8
     bw_params: 8
-    scale_in: 31.3888673433
-    scale_out: 12.3728072204
+    scale_in: 31.8839781818
+    scale_out: 14.715356973
     scale_params: 221.635065397
     scale_params: 1048.27214553
     scale_params: 434.104941553
@@ -6718,8 +6718,8 @@ layer {
     bw_layer_in: 8
     bw_layer_out: 8
     bw_params: 8
-    scale_in: 12.3728072204
-    scale_out: 29.8407049159
+    scale_in: 14.715356973
+    scale_out: 26.9842217461
     scale_params: 83.630270149
     scale_params: 222.000294235
     scale_params: 305.299631692
@@ -7302,8 +7302,8 @@ layer {
     bw_layer_in: 8
     bw_layer_out: 8
     bw_params: 8
-    scale_in: 29.8407049159
-    scale_out: 20.9318143514
+    scale_in: 26.9842217461
+    scale_out: 21.5972212606
     scale_params: 580.58155311
     scale_params: 213.486643273
     scale_params: 354.166094371
@@ -7888,8 +7888,8 @@ layer {
     bw_layer_in: 8
     bw_layer_out: 8
     bw_params: 8
-    scale_in: 20.9318143514
-    scale_out: 22.2470898642
+    scale_in: 21.5972212606
+    scale_out: 22.0371267076
     scale_params: 316.137978595
     scale_params: 497.317925169
     scale_params: 350.254714645
@@ -8472,8 +8472,8 @@ layer {
     bw_layer_in: 8
     bw_layer_out: 8
     bw_params: 8
-    scale_in: 22.2470898642
-    scale_out: 43.5443062043
+    scale_in: 22.0371267076
+    scale_out: 34.3504724372
     scale_params: 877.021417543
     scale_params: 2297.00871442
     scale_params: 404.76338444
@@ -9059,8 +9059,8 @@ layer {
     bw_layer_in: 8
     bw_layer_out: 8
     bw_params: 8
-    scale_in: 43.5443062043
-    scale_out: 23.476256768
+    scale_in: 34.3504724372
+    scale_out: 24.2417450691
     scale_params: 98.0879843847
     scale_params: 48.9490028603
     scale_params: 107.771918143
@@ -9643,8 +9643,8 @@ layer {
     bw_layer_in: 8
     bw_layer_out: 8
     bw_params: 8
-    scale_in: 23.476256768
-    scale_out: 31.786998084
+    scale_in: 24.2417450691
+    scale_out: 35.2398209635
     scale_params: 537.708009053
     scale_params: 666.963757998
     scale_params: 378.308927093
@@ -10741,8 +10741,8 @@ layer {
     bw_layer_in: 8
     bw_layer_out: 8
     bw_params: 8
-    scale_in: 31.786998084
-    scale_out: 14.710661434
+    scale_in: 35.2398209635
+    scale_out: 14.7019739536
     scale_params: 239.088170047
     scale_params: 290.498184681
     scale_params: 296.310479377
@@ -11837,8 +11837,8 @@ layer {
     bw_layer_in: 8
     bw_layer_out: 8
     bw_params: 8
-    scale_in: 14.710661434
-    scale_out: 10.004126865
+    scale_in: 14.7019739536
+    scale_out: 11.0559801648
     scale_params: 406.604586582
     scale_params: 492.239615802
     scale_params: 896.658092112
@@ -12933,8 +12933,8 @@ layer {
     bw_layer_in: 8
     bw_layer_out: 8
     bw_params: 8
-    scale_in: 10.004126865
-    scale_out: 46.1180604226
+    scale_in: 11.0559801648
+    scale_out: 52.2254057353
     scale_params: 858.395550877
     scale_params: 1030.42060506
     scale_params: 626.699329132
@@ -13263,8 +13263,8 @@ layer {
     bw_layer_in: 8
     bw_layer_out: 8
     bw_params: 8
-    scale_in: 46.1180604226
-    scale_out: 75.4528201809
+    scale_in: 52.2254057353
+    scale_out: 76.1881268169
     scale_params: 788.734180964
     scale_params: 1976.90908317
     scale_params: 1398.87500576
@@ -13847,8 +13847,8 @@ layer {
     bw_layer_in: 8
     bw_layer_out: 8
     bw_params: 8
-    scale_in: 75.4528201809
-    scale_out: 108.401902203
+    scale_in: 76.1881268169
+    scale_out: 90.8427433396
     scale_params: 909.051473761
     scale_params: 630.321714557
     scale_params: 1244.30241182
@@ -14049,8 +14049,8 @@ layer {
     bw_layer_in: 8
     bw_layer_out: 8
     bw_params: 8
-    scale_in: 108.401902203
-    scale_out: 79.6549028385
+    scale_in: 90.8427433396
+    scale_out: 65.7778143021
     scale_params: 573.440716537
     scale_params: 514.081074589
     scale_params: 674.295419112
@@ -14377,8 +14377,8 @@ layer {
     bw_layer_in: 8
     bw_layer_out: 8
     bw_params: 8
-    scale_in: 79.6549028385
-    scale_out: 118.865796029
+    scale_in: 65.7778143021
+    scale_out: 122.277858874
     scale_params: 2003.33843161
     scale_params: 1453.31353958
     scale_params: 778.296749809
@@ -14579,8 +14579,8 @@ layer {
     bw_layer_in: 8
     bw_layer_out: 8
     bw_params: 8
-    scale_in: 118.865796029
-    scale_out: 81.2545526295
+    scale_in: 122.277858874
+    scale_out: 78.8967340238
     scale_params: 566.496332852
     scale_params: 816.078866064
     scale_params: 763.692214005
@@ -14907,8 +14907,8 @@ layer {
     bw_layer_in: 8
     bw_layer_out: 8
     bw_params: 8
-    scale_in: 81.2545526295
-    scale_out: 122.925564448
+    scale_in: 78.8967340238
+    scale_out: 71.8783177979
     scale_params: 1218.4545423
     scale_params: 1160.65851361
     scale_params: 1511.43382868
@@ -15045,8 +15045,8 @@ layer {
     bw_layer_in: 8
     bw_layer_out: 8
     bw_params: 8
-    scale_in: 122.925564448
-    scale_out: 61.6728323871
+    scale_in: 71.8783177979
+    scale_out: 37.2974508202
     scale_params: 182.802937192
     scale_params: 424.943159162
     scale_params: 455.110576456
@@ -15252,7 +15252,7 @@ layer {
     bw_layer_in: 8
     bw_layer_out: 32
     bw_params: 8
-    scale_in: 43.5443062043
+    scale_in: 34.3504724372
     scale_out: 1.0
     scale_params: 208.038950726
     scale_params: 132.439600185
@@ -15317,7 +15317,7 @@ layer {
     bw_layer_in: 8
     bw_layer_out: 32
     bw_params: 8
-    scale_in: 43.5443062043
+    scale_in: 34.3504724372
     scale_out: 1.0
     scale_params: 170.42409308
     scale_params: 36.6293474677
@@ -15451,7 +15451,7 @@ layer {
     bw_layer_in: 8
     bw_layer_out: 32
     bw_params: 8
-    scale_in: 10.004126865
+    scale_in: 11.0559801648
     scale_out: 1.0
     scale_params: 203.896971813
     scale_params: 186.637345296
@@ -15528,7 +15528,7 @@ layer {
     bw_layer_in: 8
     bw_layer_out: 32
     bw_params: 8
-    scale_in: 10.004126865
+    scale_in: 11.0559801648
     scale_out: 1.0
     scale_params: 182.71914736
     scale_params: 28.1624516652
@@ -15727,7 +15727,7 @@ layer {
     bw_layer_in: 8
     bw_layer_out: 32
     bw_params: 8
-    scale_in: 75.4528201809
+    scale_in: 76.1881268169
     scale_out: 1.0
     scale_params: 277.951771789
     scale_params: 150.5504775
@@ -15804,7 +15804,7 @@ layer {
     bw_layer_in: 8
     bw_layer_out: 32
     bw_params: 8
-    scale_in: 75.4528201809
+    scale_in: 76.1881268169
     scale_out: 1.0
     scale_params: 208.421917386
     scale_params: 37.625236051
@@ -16003,7 +16003,7 @@ layer {
     bw_layer_in: 8
     bw_layer_out: 32
     bw_params: 8
-    scale_in: 79.6549028385
+    scale_in: 65.7778143021
     scale_out: 1.0
     scale_params: 396.499978878
     scale_params: 284.448993644
@@ -16080,7 +16080,7 @@ layer {
     bw_layer_in: 8
     bw_layer_out: 32
     bw_params: 8
-    scale_in: 79.6549028385
+    scale_in: 65.7778143021
     scale_out: 1.0
     scale_params: 195.801013398
     scale_params: 34.8167821514
@@ -16279,7 +16279,7 @@ layer {
     bw_layer_in: 8
     bw_layer_out: 32
     bw_params: 8
-    scale_in: 81.2545526295
+    scale_in: 78.8967340238
     scale_out: 1.0
     scale_params: 232.427685722
     scale_params: 228.63315322
@@ -16356,7 +16356,7 @@ layer {
     bw_layer_in: 8
     bw_layer_out: 32
     bw_params: 8
-    scale_in: 81.2545526295
+    scale_in: 78.8967340238
     scale_out: 1.0
     scale_params: 237.135009066
     scale_params: 29.3873323395
@@ -16555,7 +16555,7 @@ layer {
     bw_layer_in: 8
     bw_layer_out: 32
     bw_params: 8
-    scale_in: 61.6728323871
+    scale_in: 37.2974508202
     scale_out: 1.0
     scale_params: 262.461097438
     scale_params: 304.732962155
@@ -16632,7 +16632,7 @@ layer {
     bw_layer_in: 8
     bw_layer_out: 32
     bw_params: 8
-    scale_in: 61.6728323871
+    scale_in: 37.2974508202
     scale_out: 1.0
     scale_params: 92.4733600971
     scale_params: 70.2865788964

From 86fd6c95b0029771a4bcca7952f4afda95232523 Mon Sep 17 00:00:00 2001
From: xiaolil1 <xiaoli.liu@intel.com>
Date: Thu, 22 Nov 2018 10:11:50 +0800
Subject: [PATCH 20/26] fix crash issue due to mkldnn weight reorder
 modification

---
 include/caffe/mkldnn_memory.hpp               | 17 ++++++++++-------
 src/caffe/layers/mkldnn_convolution_layer.cpp |  2 +-
 src/caffe/mkldnn_memory.cpp                   | 15 +++++++++------
 3 files changed, 20 insertions(+), 14 deletions(-)

diff --git a/include/caffe/mkldnn_memory.hpp b/include/caffe/mkldnn_memory.hpp
index 671961d1f..77b95f4a7 100644
--- a/include/caffe/mkldnn_memory.hpp
+++ b/include/caffe/mkldnn_memory.hpp
@@ -64,7 +64,8 @@ class MKLDNNMemoryDescriptorBase : public PrvMemDescr
                                 , std::vector<float>scale=std::vector<float>(1,1.)
                                 , int mask=0
                                 , bool is_sum=false
-                                , bool is_wino=false);
+                                , bool is_wino=false
+                                , bool is_weight=false);
 
 
     ~MKLDNNMemoryDescriptorBase() {}
@@ -142,12 +143,12 @@ class MKLDNNMemoryDescriptorBase : public PrvMemDescr
           // TODO: may need initialize memory by 0
         }
     }
-    void set_prv_memory_pd(shared_ptr<memory::primitive_desc> memory_pd, std::vector<float> scale, int mask, bool is_wino)  {
+    void set_prv_memory_pd(shared_ptr<memory::primitive_desc> memory_pd, std::vector<float> scale, int mask, bool is_wino, bool is_weight)  {
         _prv_memory_pd = memory_pd;
         if (_prv_memory_pd && _usr_memory_pd) {
             check_usr_with_prv_descriptors();
             std::vector<float>scale_ext = std::vector<float>(1,1.);            
-            this->create_reorder_descriptors(scale, mask, scale_ext, false, is_wino);
+            this->create_reorder_descriptors(scale, mask, scale_ext, false, is_wino, is_weight);
         }
     }
 
@@ -163,7 +164,7 @@ class MKLDNNMemoryDescriptorBase : public PrvMemDescr
         _usr_memory_pd = memory_pd;
     }
 
-    void create_reorder_descriptors(std::vector<float> scale, int mask=0, std::vector<float>scale_ext=std::vector<float>(1,1.), bool is_sum=false, bool is_wino=false);
+    void create_reorder_descriptors(std::vector<float> scale, int mask=0, std::vector<float>scale_ext=std::vector<float>(1,1.), bool is_sum=false, bool is_wino=false, bool is_weight=false);
 
     shared_ptr<memory::primitive_desc> _usr_memory_pd;
     shared_ptr<memory::primitive_desc> _prv_memory_pd;
@@ -205,7 +206,8 @@ class MKLDNNMemoryDescriptor : public MKLDNNMemoryDescriptorBase<Dtype> {
                         , std::vector<float> scale=std::vector<float>(1,1.)
                         , int mask=0
                         , bool is_sum=false
-                        , bool is_wino=false);
+                        , bool is_wino=false
+                        , bool is_weight=false);
 
     virtual void convert_from_prv(void* cpu_ptr);
     virtual void convert_to_prv(void* cpu_ptr);
@@ -253,8 +255,9 @@ class MKLDNNData : public MKLDNNMemoryDescriptor<Dtype, false>
                 , std::vector<float> scale=std::vector<float>(1,1.)
                 , int mask=0
                 , bool is_sum=false
-                , bool is_wino=false)
-        : MKLDNNMemoryDescriptor<Dtype, false>(usr_memory_pd, prv_memory_pd, blob, mkldnn_layer, scale, mask, is_sum, is_wino) {}
+                , bool is_wino=false
+                , bool is_weight=false)
+        : MKLDNNMemoryDescriptor<Dtype, false>(usr_memory_pd, prv_memory_pd, blob, mkldnn_layer, scale, mask, is_sum, is_wino, is_weight) {}
 };
 
 template <typename Dtype>
diff --git a/src/caffe/layers/mkldnn_convolution_layer.cpp b/src/caffe/layers/mkldnn_convolution_layer.cpp
index 53cf63873..cec3964d4 100644
--- a/src/caffe/layers/mkldnn_convolution_layer.cpp
+++ b/src/caffe/layers/mkldnn_convolution_layer.cpp
@@ -509,7 +509,7 @@ void MKLDNNConvolutionLayer<Dtype>::InitConvolutionFwd(const vector<Blob<Dtype>*
         for(int i=0; i<count; i++){
           scale_weight[i] = this->scale_params_[i];
         }
-        fwd_weights_data.reset(new MKLDNNData<Dtype>(usr_weights_data_memory_pd, prv_fwd_weights_data_memory_pd, this->blobs_[0].get(), this, scale_weight, reorder_mask));
+        fwd_weights_data.reset(new MKLDNNData<Dtype>(usr_weights_data_memory_pd, prv_fwd_weights_data_memory_pd, this->blobs_[0].get(), this, scale_weight, reorder_mask, false, false, true));
       } else{
         fwd_weights_data.reset(new MKLDNNData<Dtype>(usr_weights_data_memory_pd, prv_fwd_weights_data_memory_pd, this->blobs_[0].get(), this, {1.}, 0,  is_sum, is_wino));
       }
diff --git a/src/caffe/mkldnn_memory.cpp b/src/caffe/mkldnn_memory.cpp
index d9fe0bf62..961df00b0 100644
--- a/src/caffe/mkldnn_memory.cpp
+++ b/src/caffe/mkldnn_memory.cpp
@@ -50,14 +50,15 @@ MKLDNNMemoryDescriptorBase<Dtype>::MKLDNNMemoryDescriptorBase(shared_ptr<memory:
                                                             , std::vector<float> scale
                                                             , int mask
                                                             , bool is_sum
-                                                            , bool is_wino)
+                                                            , bool is_wino
+                                                            , bool is_weight)
                                     : name("MKLDNNMemoryDescriptorBase")
                                     , _reorder_usr2prv_pd(), _reorder_prv2usr_pd(), _reorder_extprv2prv_pd()
                                     ,_prv_memory(), _internal_ptr(NULL), _usr_memory(), _cpu_ptr(NULL)
                                     , _mkldnn_layer(NULL)
 {
     set_usr_memory_pd(usr_memory_pd, scale);
-    set_prv_memory_pd(prv_memory_pd, scale, mask, is_wino);
+    set_prv_memory_pd(prv_memory_pd, scale, mask, is_wino, is_weight);
     set_mkldnn_layer(mkldnn_layer);
     this->set_scale(scale);
     this->set_sum(is_sum);
@@ -80,7 +81,7 @@ void MKLDNNMemoryDescriptorBase<Dtype>::check_usr_with_prv_descriptors()
 }
 
 template <typename Dtype>
-void MKLDNNMemoryDescriptorBase<Dtype>::create_reorder_descriptors(std::vector<float> scale, int mask, std::vector<float> scale_ext, bool is_sum, bool is_wino)
+void MKLDNNMemoryDescriptorBase<Dtype>::create_reorder_descriptors(std::vector<float> scale, int mask, std::vector<float> scale_ext, bool is_sum, bool is_wino, bool is_weight)
 {
     CHECK(_usr_memory_pd);
     CHECK(_prv_memory_pd);
@@ -105,9 +106,10 @@ void MKLDNNMemoryDescriptorBase<Dtype>::create_reorder_descriptors(std::vector<f
         }
         attri.set_output_scales(mask, scales_p2u); 
         attri.set_int_output_round_mode(round_nearest);
-        if(!is_wino)
+        if(!is_wino && !is_weight){
             _reorder_prv2usr_pd = shared_ptr<reorder::primitive_desc>(
                     new reorder::primitive_desc(*_prv_memory_pd, *_usr_memory_pd, attri));
+        }
     }
     if ( _extprv_memory_pd && (*_prv_memory_pd != *_extprv_memory_pd || scale != scale_ext)) {
         if(is_sum == true && scale == scale_ext && _extprv_memory_pd->desc().data.data_type == memory::data_type::s8 && _prv_memory_pd->desc().data.data_type == memory::data_type::u8){
@@ -138,8 +140,9 @@ template <typename Dtype, bool is_diff>
                         , std::vector<float> scale
                         , int mask
                         , bool is_sum
-                        , bool is_wino)
-        : MKLDNNMemoryDescriptorBase<Dtype>(usr_memory_pd, prv_memory_pd, blob, mkldnn_layer, scale, mask, is_sum, is_wino)
+                        , bool is_wino
+                        , bool is_weight)
+        : MKLDNNMemoryDescriptorBase<Dtype>(usr_memory_pd, prv_memory_pd, blob, mkldnn_layer, scale, mask, is_sum, is_wino, is_weight)
 {
     const Dtype* prv_ptr = is_diff ?  blob->prv_diff() : blob->prv_data();
 

From b1394d40e2ec5d9cf2834eaf6a4b68b164574e48 Mon Sep 17 00:00:00 2001
From: Shane Li <shane.li@intel.com>
Date: Fri, 23 Nov 2018 08:45:17 +0800
Subject: [PATCH 21/26] Add ResNext-50.

---
 .../resnext_50/ResNeXt-50-deploy.prototxt     | 2475 +++++++++++++++++
 1 file changed, 2475 insertions(+)
 create mode 100644 models/intel_optimized_models/resnext_50/ResNeXt-50-deploy.prototxt

diff --git a/models/intel_optimized_models/resnext_50/ResNeXt-50-deploy.prototxt b/models/intel_optimized_models/resnext_50/ResNeXt-50-deploy.prototxt
new file mode 100644
index 000000000..9be711c09
--- /dev/null
+++ b/models/intel_optimized_models/resnext_50/ResNeXt-50-deploy.prototxt
@@ -0,0 +1,2475 @@
+name: "ResNeXt-50"
+
+layer { 
+  name: "data"
+  type: "Input"
+  top: "data"
+  input_param { shape: { dim: 1 dim: 3 dim: 224 dim: 224 } }
+}
+
+layer {
+  name: "bn_data"
+  type: "BatchNorm"
+  bottom: "data"
+  top: "data"
+  batch_norm_param {
+    use_global_stats: true
+    eps: 2e-5
+  }
+}
+
+layer {
+  name: "scale_bn_data"
+  bottom: "data"
+  top: "data"
+  type: "Scale"
+  scale_param {
+        bias_term: true
+  }
+}
+
+layer {
+  name: "conv0"
+  type: "Convolution"
+  bottom: "data"
+  top: "conv0"
+  convolution_param { 
+     num_output: 64
+     kernel_size: 7
+     stride: 2
+     pad: 3
+     bias_term: false
+  }
+}
+
+layer {
+  name: "bn0"
+  type: "BatchNorm"
+  bottom: "conv0"
+  top: "conv0"
+  batch_norm_param {
+    use_global_stats: true
+    eps: 2e-5
+  }
+}
+
+layer {
+  name: "scale_bn0"
+  bottom: "conv0"
+  top: "conv0"
+  type: "Scale"
+  scale_param {
+        bias_term: true
+  }
+}
+
+layer {
+  name: "relu0"
+  type: "ReLU"
+  bottom: "conv0"
+  top: "conv0"
+}
+
+layer {
+  name: "pooling0"
+  type: "Pooling"
+  bottom: "conv0"
+  top: "pooling0"
+  pooling_param {
+     pool: MAX
+     kernel_size: 3
+     stride: 2
+  }
+}
+
+layer {
+  name: "stage1_unit1_conv1"
+  type: "Convolution"
+  bottom: "pooling0"
+  top: "stage1_unit1_conv1"
+  convolution_param { 
+     num_output: 128
+     kernel_size: 1
+     stride: 1
+     pad: 0
+     bias_term: false
+  }
+}
+
+layer {
+  name: "stage1_unit1_bn1"
+  type: "BatchNorm"
+  bottom: "stage1_unit1_conv1"
+  top: "stage1_unit1_conv1"
+  batch_norm_param {
+    use_global_stats: true
+    eps: 2e-5
+  }
+}
+
+layer {
+  name: "scale_stage1_unit1_bn1"
+  bottom: "stage1_unit1_conv1"
+  top: "stage1_unit1_conv1"
+  type: "Scale"
+  scale_param {
+        bias_term: true
+  }
+}
+
+layer {
+  name: "stage1_unit1_relu1"
+  type: "ReLU"
+  bottom: "stage1_unit1_conv1"
+  top: "stage1_unit1_conv1"
+}
+
+layer {
+  name: "stage1_unit1_conv2"
+  type: "Convolution"
+  bottom: "stage1_unit1_conv1"
+  top: "stage1_unit1_conv2"
+  convolution_param { 
+     num_output: 128
+     kernel_size: 3
+     stride: 1
+     group: 32
+     pad: 1
+     bias_term: false
+  }
+}
+
+layer {
+  name: "stage1_unit1_bn2"
+  type: "BatchNorm"
+  bottom: "stage1_unit1_conv2"
+  top: "stage1_unit1_conv2"
+  batch_norm_param {
+    use_global_stats: true
+    eps: 2e-5
+  }
+}
+
+layer {
+  name: "scale_stage1_unit1_bn2"
+  bottom: "stage1_unit1_conv2"
+  top: "stage1_unit1_conv2"
+  type: "Scale"
+  scale_param {
+        bias_term: true
+  }
+}
+
+layer {
+  name: "stage1_unit1_relu2"
+  type: "ReLU"
+  bottom: "stage1_unit1_conv2"
+  top: "stage1_unit1_conv2"
+}
+
+layer {
+  name: "stage1_unit1_conv3"
+  type: "Convolution"
+  bottom: "stage1_unit1_conv2"
+  top: "stage1_unit1_conv3"
+  convolution_param { 
+     num_output: 256
+     kernel_size: 1
+     stride: 1
+     pad: 0
+     bias_term: false
+  }
+}
+
+layer {
+  name: "stage1_unit1_bn3"
+  type: "BatchNorm"
+  bottom: "stage1_unit1_conv3"
+  top: "stage1_unit1_conv3"
+  batch_norm_param {
+    use_global_stats: true
+    eps: 2e-5
+  }
+}
+
+layer {
+  name: "scale_stage1_unit1_bn3"
+  bottom: "stage1_unit1_conv3"
+  top: "stage1_unit1_conv3"
+  type: "Scale"
+  scale_param {
+        bias_term: true
+  }
+}
+
+layer {
+  name: "stage1_unit1_sc"
+  type: "Convolution"
+  bottom: "pooling0"
+  top: "stage1_unit1_sc"
+  convolution_param { 
+     num_output: 256
+     kernel_size: 1
+     stride: 1
+     pad: 0
+     bias_term: false
+  }
+}
+
+layer {
+  name: "stage1_unit1_sc_bn"
+  type: "BatchNorm"
+  bottom: "stage1_unit1_sc"
+  top: "stage1_unit1_sc"
+  batch_norm_param {
+    use_global_stats: true
+    eps: 2e-5
+  }
+}
+
+layer {
+  name: "scale_stage1_unit1_sc_bn"
+  bottom: "stage1_unit1_sc"
+  top: "stage1_unit1_sc"
+  type: "Scale"
+  scale_param {
+        bias_term: true
+  }
+}
+
+layer {
+  name: "stage1_unit1_plus"
+  type: "Eltwise"
+  bottom: "stage1_unit1_sc"
+  bottom: "stage1_unit1_conv3"
+  top: "stage1_unit1_plus"
+  eltwise_param {
+     operation: SUM
+  }
+}
+
+layer {
+  name: "stage1_unit1_relu"
+  type: "ReLU"
+  bottom: "stage1_unit1_plus"
+  top: "stage1_unit1_plus"
+}
+
+layer {
+  name: "stage1_unit2_conv1"
+  type: "Convolution"
+  bottom: "stage1_unit1_plus"
+  top: "stage1_unit2_conv1"
+  convolution_param { 
+     num_output: 128
+     kernel_size: 1
+     stride: 1
+     pad: 0
+     bias_term: false
+  }
+}
+
+layer {
+  name: "stage1_unit2_bn1"
+  type: "BatchNorm"
+  bottom: "stage1_unit2_conv1"
+  top: "stage1_unit2_conv1"
+  batch_norm_param {
+    use_global_stats: true
+    eps: 2e-5
+  }
+}
+
+layer {
+  name: "scale_stage1_unit2_bn1"
+  bottom: "stage1_unit2_conv1"
+  top: "stage1_unit2_conv1"
+  type: "Scale"
+  scale_param {
+        bias_term: true
+  }
+}
+
+layer {
+  name: "stage1_unit2_relu1"
+  type: "ReLU"
+  bottom: "stage1_unit2_conv1"
+  top: "stage1_unit2_conv1"
+}
+
+layer {
+  name: "stage1_unit2_conv2"
+  type: "Convolution"
+  bottom: "stage1_unit2_conv1"
+  top: "stage1_unit2_conv2"
+  convolution_param { 
+     num_output: 128
+     kernel_size: 3
+     stride: 1
+     group: 32
+     pad: 1
+     bias_term: false
+  }
+}
+
+layer {
+  name: "stage1_unit2_bn2"
+  type: "BatchNorm"
+  bottom: "stage1_unit2_conv2"
+  top: "stage1_unit2_conv2"
+  batch_norm_param {
+    use_global_stats: true
+    eps: 2e-5
+  }
+}
+
+layer {
+  name: "scale_stage1_unit2_bn2"
+  bottom: "stage1_unit2_conv2"
+  top: "stage1_unit2_conv2"
+  type: "Scale"
+  scale_param {
+        bias_term: true
+  }
+}
+
+layer {
+  name: "stage1_unit2_relu2"
+  type: "ReLU"
+  bottom: "stage1_unit2_conv2"
+  top: "stage1_unit2_conv2"
+}
+
+layer {
+  name: "stage1_unit2_conv3"
+  type: "Convolution"
+  bottom: "stage1_unit2_conv2"
+  top: "stage1_unit2_conv3"
+  convolution_param { 
+     num_output: 256
+     kernel_size: 1
+     stride: 1
+     pad: 0
+     bias_term: false
+  }
+}
+
+layer {
+  name: "stage1_unit2_bn3"
+  type: "BatchNorm"
+  bottom: "stage1_unit2_conv3"
+  top: "stage1_unit2_conv3"
+  batch_norm_param {
+    use_global_stats: true
+    eps: 2e-5
+  }
+}
+
+layer {
+  name: "scale_stage1_unit2_bn3"
+  bottom: "stage1_unit2_conv3"
+  top: "stage1_unit2_conv3"
+  type: "Scale"
+  scale_param {
+        bias_term: true
+  }
+}
+
+layer {
+  name: "stage1_unit2_plus"
+  type: "Eltwise"
+  bottom: "stage1_unit1_plus"
+  bottom: "stage1_unit2_conv3"
+  top: "stage1_unit2_plus"
+  eltwise_param {
+     operation: SUM
+  }
+}
+
+layer {
+  name: "stage1_unit2_relu"
+  type: "ReLU"
+  bottom: "stage1_unit2_plus"
+  top: "stage1_unit2_plus"
+}
+
+layer {
+  name: "stage1_unit3_conv1"
+  type: "Convolution"
+  bottom: "stage1_unit2_plus"
+  top: "stage1_unit3_conv1"
+  convolution_param { 
+     num_output: 128
+     kernel_size: 1
+     stride: 1
+     pad: 0
+     bias_term: false
+  }
+}
+
+layer {
+  name: "stage1_unit3_bn1"
+  type: "BatchNorm"
+  bottom: "stage1_unit3_conv1"
+  top: "stage1_unit3_conv1"
+  batch_norm_param {
+    use_global_stats: true
+    eps: 2e-5
+  }
+}
+
+layer {
+  name: "scale_stage1_unit3_bn1"
+  bottom: "stage1_unit3_conv1"
+  top: "stage1_unit3_conv1"
+  type: "Scale"
+  scale_param {
+        bias_term: true
+  }
+}
+
+layer {
+  name: "stage1_unit3_relu1"
+  type: "ReLU"
+  bottom: "stage1_unit3_conv1"
+  top: "stage1_unit3_conv1"
+}
+
+layer {
+  name: "stage1_unit3_conv2"
+  type: "Convolution"
+  bottom: "stage1_unit3_conv1"
+  top: "stage1_unit3_conv2"
+  convolution_param { 
+     num_output: 128
+     kernel_size: 3
+     stride: 1
+     group: 32
+     pad: 1
+     bias_term: false
+  }
+}
+
+layer {
+  name: "stage1_unit3_bn2"
+  type: "BatchNorm"
+  bottom: "stage1_unit3_conv2"
+  top: "stage1_unit3_conv2"
+  batch_norm_param {
+    use_global_stats: true
+    eps: 2e-5
+  }
+}
+
+layer {
+  name: "scale_stage1_unit3_bn2"
+  bottom: "stage1_unit3_conv2"
+  top: "stage1_unit3_conv2"
+  type: "Scale"
+  scale_param {
+        bias_term: true
+  }
+}
+
+layer {
+  name: "stage1_unit3_relu2"
+  type: "ReLU"
+  bottom: "stage1_unit3_conv2"
+  top: "stage1_unit3_conv2"
+}
+
+layer {
+  name: "stage1_unit3_conv3"
+  type: "Convolution"
+  bottom: "stage1_unit3_conv2"
+  top: "stage1_unit3_conv3"
+  convolution_param { 
+     num_output: 256
+     kernel_size: 1
+     stride: 1
+     pad: 0
+     bias_term: false
+  }
+}
+
+layer {
+  name: "stage1_unit3_bn3"
+  type: "BatchNorm"
+  bottom: "stage1_unit3_conv3"
+  top: "stage1_unit3_conv3"
+  batch_norm_param {
+    use_global_stats: true
+    eps: 2e-5
+  }
+}
+
+layer {
+  name: "scale_stage1_unit3_bn3"
+  bottom: "stage1_unit3_conv3"
+  top: "stage1_unit3_conv3"
+  type: "Scale"
+  scale_param {
+        bias_term: true
+  }
+}
+
+layer {
+  name: "stage1_unit3_plus"
+  type: "Eltwise"
+  bottom: "stage1_unit2_plus"
+  bottom: "stage1_unit3_conv3"
+  top: "stage1_unit3_plus"
+  eltwise_param {
+     operation: SUM
+  }
+}
+
+layer {
+  name: "stage1_unit3_relu"
+  type: "ReLU"
+  bottom: "stage1_unit3_plus"
+  top: "stage1_unit3_plus"
+}
+
+layer {
+  name: "stage2_unit1_conv1"
+  type: "Convolution"
+  bottom: "stage1_unit3_plus"
+  top: "stage2_unit1_conv1"
+  convolution_param { 
+     num_output: 256
+     kernel_size: 1
+     stride: 1
+     pad: 0
+     bias_term: false
+  }
+}
+
+layer {
+  name: "stage2_unit1_bn1"
+  type: "BatchNorm"
+  bottom: "stage2_unit1_conv1"
+  top: "stage2_unit1_conv1"
+  batch_norm_param {
+    use_global_stats: true
+    eps: 2e-5
+  }
+}
+
+layer {
+  name: "scale_stage2_unit1_bn1"
+  bottom: "stage2_unit1_conv1"
+  top: "stage2_unit1_conv1"
+  type: "Scale"
+  scale_param {
+        bias_term: true
+  }
+}
+
+layer {
+  name: "stage2_unit1_relu1"
+  type: "ReLU"
+  bottom: "stage2_unit1_conv1"
+  top: "stage2_unit1_conv1"
+}
+
+layer {
+  name: "stage2_unit1_conv2"
+  type: "Convolution"
+  bottom: "stage2_unit1_conv1"
+  top: "stage2_unit1_conv2"
+  convolution_param { 
+     num_output: 256
+     kernel_size: 3
+     stride: 2
+     group: 32
+     pad: 1
+     bias_term: false
+  }
+}
+
+layer {
+  name: "stage2_unit1_bn2"
+  type: "BatchNorm"
+  bottom: "stage2_unit1_conv2"
+  top: "stage2_unit1_conv2"
+  batch_norm_param {
+    use_global_stats: true
+    eps: 2e-5
+  }
+}
+
+layer {
+  name: "scale_stage2_unit1_bn2"
+  bottom: "stage2_unit1_conv2"
+  top: "stage2_unit1_conv2"
+  type: "Scale"
+  scale_param {
+        bias_term: true
+  }
+}
+
+layer {
+  name: "stage2_unit1_relu2"
+  type: "ReLU"
+  bottom: "stage2_unit1_conv2"
+  top: "stage2_unit1_conv2"
+}
+
+layer {
+  name: "stage2_unit1_conv3"
+  type: "Convolution"
+  bottom: "stage2_unit1_conv2"
+  top: "stage2_unit1_conv3"
+  convolution_param { 
+     num_output: 512
+     kernel_size: 1
+     stride: 1
+     pad: 0
+     bias_term: false
+  }
+}
+
+layer {
+  name: "stage2_unit1_bn3"
+  type: "BatchNorm"
+  bottom: "stage2_unit1_conv3"
+  top: "stage2_unit1_conv3"
+  batch_norm_param {
+    use_global_stats: true
+    eps: 2e-5
+  }
+}
+
+layer {
+  name: "scale_stage2_unit1_bn3"
+  bottom: "stage2_unit1_conv3"
+  top: "stage2_unit1_conv3"
+  type: "Scale"
+  scale_param {
+        bias_term: true
+  }
+}
+
+layer {
+  name: "stage2_unit1_sc"
+  type: "Convolution"
+  bottom: "stage1_unit3_plus"
+  top: "stage2_unit1_sc"
+  convolution_param { 
+     num_output: 512
+     kernel_size: 1
+     stride: 2
+     pad: 0
+     bias_term: false
+  }
+}
+
+layer {
+  name: "stage2_unit1_sc_bn"
+  type: "BatchNorm"
+  bottom: "stage2_unit1_sc"
+  top: "stage2_unit1_sc"
+  batch_norm_param {
+    use_global_stats: true
+    eps: 2e-5
+  }
+}
+
+layer {
+  name: "scale_stage2_unit1_sc_bn"
+  bottom: "stage2_unit1_sc"
+  top: "stage2_unit1_sc"
+  type: "Scale"
+  scale_param {
+        bias_term: true
+  }
+}
+
+layer {
+  name: "stage2_unit1_plus"
+  type: "Eltwise"
+  bottom: "stage2_unit1_sc"
+  bottom: "stage2_unit1_conv3"
+  top: "stage2_unit1_plus"
+  eltwise_param {
+     operation: SUM
+  }
+}
+
+layer {
+  name: "stage2_unit1_relu"
+  type: "ReLU"
+  bottom: "stage2_unit1_plus"
+  top: "stage2_unit1_plus"
+}
+
+layer {
+  name: "stage2_unit2_conv1"
+  type: "Convolution"
+  bottom: "stage2_unit1_plus"
+  top: "stage2_unit2_conv1"
+  convolution_param { 
+     num_output: 256
+     kernel_size: 1
+     stride: 1
+     pad: 0
+     bias_term: false
+  }
+}
+
+layer {
+  name: "stage2_unit2_bn1"
+  type: "BatchNorm"
+  bottom: "stage2_unit2_conv1"
+  top: "stage2_unit2_conv1"
+  batch_norm_param {
+    use_global_stats: true
+    eps: 2e-5
+  }
+}
+
+layer {
+  name: "scale_stage2_unit2_bn1"
+  bottom: "stage2_unit2_conv1"
+  top: "stage2_unit2_conv1"
+  type: "Scale"
+  scale_param {
+        bias_term: true
+  }
+}
+
+layer {
+  name: "stage2_unit2_relu1"
+  type: "ReLU"
+  bottom: "stage2_unit2_conv1"
+  top: "stage2_unit2_conv1"
+}
+
+layer {
+  name: "stage2_unit2_conv2"
+  type: "Convolution"
+  bottom: "stage2_unit2_conv1"
+  top: "stage2_unit2_conv2"
+  convolution_param { 
+     num_output: 256
+     kernel_size: 3
+     stride: 1
+     group: 32
+     pad: 1
+     bias_term: false
+  }
+}
+
+layer {
+  name: "stage2_unit2_bn2"
+  type: "BatchNorm"
+  bottom: "stage2_unit2_conv2"
+  top: "stage2_unit2_conv2"
+  batch_norm_param {
+    use_global_stats: true
+    eps: 2e-5
+  }
+}
+
+layer {
+  name: "scale_stage2_unit2_bn2"
+  bottom: "stage2_unit2_conv2"
+  top: "stage2_unit2_conv2"
+  type: "Scale"
+  scale_param {
+        bias_term: true
+  }
+}
+
+layer {
+  name: "stage2_unit2_relu2"
+  type: "ReLU"
+  bottom: "stage2_unit2_conv2"
+  top: "stage2_unit2_conv2"
+}
+
+layer {
+  name: "stage2_unit2_conv3"
+  type: "Convolution"
+  bottom: "stage2_unit2_conv2"
+  top: "stage2_unit2_conv3"
+  convolution_param { 
+     num_output: 512
+     kernel_size: 1
+     stride: 1
+     pad: 0
+     bias_term: false
+  }
+}
+
+layer {
+  name: "stage2_unit2_bn3"
+  type: "BatchNorm"
+  bottom: "stage2_unit2_conv3"
+  top: "stage2_unit2_conv3"
+  batch_norm_param {
+    use_global_stats: true
+    eps: 2e-5
+  }
+}
+
+layer {
+  name: "scale_stage2_unit2_bn3"
+  bottom: "stage2_unit2_conv3"
+  top: "stage2_unit2_conv3"
+  type: "Scale"
+  scale_param {
+        bias_term: true
+  }
+}
+
+layer {
+  name: "stage2_unit2_plus"
+  type: "Eltwise"
+  bottom: "stage2_unit1_plus"
+  bottom: "stage2_unit2_conv3"
+  top: "stage2_unit2_plus"
+  eltwise_param {
+     operation: SUM
+  }
+}
+
+layer {
+  name: "stage2_unit2_relu"
+  type: "ReLU"
+  bottom: "stage2_unit2_plus"
+  top: "stage2_unit2_plus"
+}
+
+layer {
+  name: "stage2_unit3_conv1"
+  type: "Convolution"
+  bottom: "stage2_unit2_plus"
+  top: "stage2_unit3_conv1"
+  convolution_param { 
+     num_output: 256
+     kernel_size: 1
+     stride: 1
+     pad: 0
+     bias_term: false
+  }
+}
+
+layer {
+  name: "stage2_unit3_bn1"
+  type: "BatchNorm"
+  bottom: "stage2_unit3_conv1"
+  top: "stage2_unit3_conv1"
+  batch_norm_param {
+    use_global_stats: true
+    eps: 2e-5
+  }
+}
+
+layer {
+  name: "scale_stage2_unit3_bn1"
+  bottom: "stage2_unit3_conv1"
+  top: "stage2_unit3_conv1"
+  type: "Scale"
+  scale_param {
+        bias_term: true
+  }
+}
+
+layer {
+  name: "stage2_unit3_relu1"
+  type: "ReLU"
+  bottom: "stage2_unit3_conv1"
+  top: "stage2_unit3_conv1"
+}
+
+layer {
+  name: "stage2_unit3_conv2"
+  type: "Convolution"
+  bottom: "stage2_unit3_conv1"
+  top: "stage2_unit3_conv2"
+  convolution_param { 
+     num_output: 256
+     kernel_size: 3
+     stride: 1
+     group: 32
+     pad: 1
+     bias_term: false
+  }
+}
+
+layer {
+  name: "stage2_unit3_bn2"
+  type: "BatchNorm"
+  bottom: "stage2_unit3_conv2"
+  top: "stage2_unit3_conv2"
+  batch_norm_param {
+    use_global_stats: true
+    eps: 2e-5
+  }
+}
+
+layer {
+  name: "scale_stage2_unit3_bn2"
+  bottom: "stage2_unit3_conv2"
+  top: "stage2_unit3_conv2"
+  type: "Scale"
+  scale_param {
+        bias_term: true
+  }
+}
+
+layer {
+  name: "stage2_unit3_relu2"
+  type: "ReLU"
+  bottom: "stage2_unit3_conv2"
+  top: "stage2_unit3_conv2"
+}
+
+layer {
+  name: "stage2_unit3_conv3"
+  type: "Convolution"
+  bottom: "stage2_unit3_conv2"
+  top: "stage2_unit3_conv3"
+  convolution_param { 
+     num_output: 512
+     kernel_size: 1
+     stride: 1
+     pad: 0
+     bias_term: false
+  }
+}
+
+layer {
+  name: "stage2_unit3_bn3"
+  type: "BatchNorm"
+  bottom: "stage2_unit3_conv3"
+  top: "stage2_unit3_conv3"
+  batch_norm_param {
+    use_global_stats: true
+    eps: 2e-5
+  }
+}
+
+layer {
+  name: "scale_stage2_unit3_bn3"
+  bottom: "stage2_unit3_conv3"
+  top: "stage2_unit3_conv3"
+  type: "Scale"
+  scale_param {
+        bias_term: true
+  }
+}
+
+layer {
+  name: "stage2_unit3_plus"
+  type: "Eltwise"
+  bottom: "stage2_unit2_plus"
+  bottom: "stage2_unit3_conv3"
+  top: "stage2_unit3_plus"
+  eltwise_param {
+     operation: SUM
+  }
+}
+
+layer {
+  name: "stage2_unit3_relu"
+  type: "ReLU"
+  bottom: "stage2_unit3_plus"
+  top: "stage2_unit3_plus"
+}
+
+layer {
+  name: "stage2_unit4_conv1"
+  type: "Convolution"
+  bottom: "stage2_unit3_plus"
+  top: "stage2_unit4_conv1"
+  convolution_param { 
+     num_output: 256
+     kernel_size: 1
+     stride: 1
+     pad: 0
+     bias_term: false
+  }
+}
+
+layer {
+  name: "stage2_unit4_bn1"
+  type: "BatchNorm"
+  bottom: "stage2_unit4_conv1"
+  top: "stage2_unit4_conv1"
+  batch_norm_param {
+    use_global_stats: true
+    eps: 2e-5
+  }
+}
+
+layer {
+  name: "scale_stage2_unit4_bn1"
+  bottom: "stage2_unit4_conv1"
+  top: "stage2_unit4_conv1"
+  type: "Scale"
+  scale_param {
+        bias_term: true
+  }
+}
+
+layer {
+  name: "stage2_unit4_relu1"
+  type: "ReLU"
+  bottom: "stage2_unit4_conv1"
+  top: "stage2_unit4_conv1"
+}
+
+layer {
+  name: "stage2_unit4_conv2"
+  type: "Convolution"
+  bottom: "stage2_unit4_conv1"
+  top: "stage2_unit4_conv2"
+  convolution_param { 
+     num_output: 256
+     kernel_size: 3
+     stride: 1
+     group: 32
+     pad: 1
+     bias_term: false
+  }
+}
+
+layer {
+  name: "stage2_unit4_bn2"
+  type: "BatchNorm"
+  bottom: "stage2_unit4_conv2"
+  top: "stage2_unit4_conv2"
+  batch_norm_param {
+    use_global_stats: true
+    eps: 2e-5
+  }
+}
+
+layer {
+  name: "scale_stage2_unit4_bn2"
+  bottom: "stage2_unit4_conv2"
+  top: "stage2_unit4_conv2"
+  type: "Scale"
+  scale_param {
+        bias_term: true
+  }
+}
+
+layer {
+  name: "stage2_unit4_relu2"
+  type: "ReLU"
+  bottom: "stage2_unit4_conv2"
+  top: "stage2_unit4_conv2"
+}
+
+layer {
+  name: "stage2_unit4_conv3"
+  type: "Convolution"
+  bottom: "stage2_unit4_conv2"
+  top: "stage2_unit4_conv3"
+  convolution_param { 
+     num_output: 512
+     kernel_size: 1
+     stride: 1
+     pad: 0
+     bias_term: false
+  }
+}
+
+layer {
+  name: "stage2_unit4_bn3"
+  type: "BatchNorm"
+  bottom: "stage2_unit4_conv3"
+  top: "stage2_unit4_conv3"
+  batch_norm_param {
+    use_global_stats: true
+    eps: 2e-5
+  }
+}
+
+layer {
+  name: "scale_stage2_unit4_bn3"
+  bottom: "stage2_unit4_conv3"
+  top: "stage2_unit4_conv3"
+  type: "Scale"
+  scale_param {
+        bias_term: true
+  }
+}
+
+layer {
+  name: "stage2_unit4_plus"
+  type: "Eltwise"
+  bottom: "stage2_unit3_plus"
+  bottom: "stage2_unit4_conv3"
+  top: "stage2_unit4_plus"
+  eltwise_param {
+     operation: SUM
+  }
+}
+
+layer {
+  name: "stage2_unit4_relu"
+  type: "ReLU"
+  bottom: "stage2_unit4_plus"
+  top: "stage2_unit4_plus"
+}
+
+layer {
+  name: "stage3_unit1_conv1"
+  type: "Convolution"
+  bottom: "stage2_unit4_plus"
+  top: "stage3_unit1_conv1"
+  convolution_param { 
+     num_output: 512
+     kernel_size: 1
+     stride: 1
+     pad: 0
+     bias_term: false
+  }
+}
+
+layer {
+  name: "stage3_unit1_bn1"
+  type: "BatchNorm"
+  bottom: "stage3_unit1_conv1"
+  top: "stage3_unit1_conv1"
+  batch_norm_param {
+    use_global_stats: true
+    eps: 2e-5
+  }
+}
+
+layer {
+  name: "scale_stage3_unit1_bn1"
+  bottom: "stage3_unit1_conv1"
+  top: "stage3_unit1_conv1"
+  type: "Scale"
+  scale_param {
+        bias_term: true
+  }
+}
+
+layer {
+  name: "stage3_unit1_relu1"
+  type: "ReLU"
+  bottom: "stage3_unit1_conv1"
+  top: "stage3_unit1_conv1"
+}
+
+layer {
+  name: "stage3_unit1_conv2"
+  type: "Convolution"
+  bottom: "stage3_unit1_conv1"
+  top: "stage3_unit1_conv2"
+  convolution_param { 
+     num_output: 512
+     kernel_size: 3
+     stride: 2
+     group: 32
+     pad: 1
+     bias_term: false
+  }
+}
+
+layer {
+  name: "stage3_unit1_bn2"
+  type: "BatchNorm"
+  bottom: "stage3_unit1_conv2"
+  top: "stage3_unit1_conv2"
+  batch_norm_param {
+    use_global_stats: true
+    eps: 2e-5
+  }
+}
+
+layer {
+  name: "scale_stage3_unit1_bn2"
+  bottom: "stage3_unit1_conv2"
+  top: "stage3_unit1_conv2"
+  type: "Scale"
+  scale_param {
+        bias_term: true
+  }
+}
+
+layer {
+  name: "stage3_unit1_relu2"
+  type: "ReLU"
+  bottom: "stage3_unit1_conv2"
+  top: "stage3_unit1_conv2"
+}
+
+layer {
+  name: "stage3_unit1_conv3"
+  type: "Convolution"
+  bottom: "stage3_unit1_conv2"
+  top: "stage3_unit1_conv3"
+  convolution_param { 
+     num_output: 1024
+     kernel_size: 1
+     stride: 1
+     pad: 0
+     bias_term: false
+  }
+}
+
+layer {
+  name: "stage3_unit1_bn3"
+  type: "BatchNorm"
+  bottom: "stage3_unit1_conv3"
+  top: "stage3_unit1_conv3"
+  batch_norm_param {
+    use_global_stats: true
+    eps: 2e-5
+  }
+}
+
+layer {
+  name: "scale_stage3_unit1_bn3"
+  bottom: "stage3_unit1_conv3"
+  top: "stage3_unit1_conv3"
+  type: "Scale"
+  scale_param {
+        bias_term: true
+  }
+}
+
+layer {
+  name: "stage3_unit1_sc"
+  type: "Convolution"
+  bottom: "stage2_unit4_plus"
+  top: "stage3_unit1_sc"
+  convolution_param { 
+     num_output: 1024
+     kernel_size: 1
+     stride: 2
+     pad: 0
+     bias_term: false
+  }
+}
+
+layer {
+  name: "stage3_unit1_sc_bn"
+  type: "BatchNorm"
+  bottom: "stage3_unit1_sc"
+  top: "stage3_unit1_sc"
+  batch_norm_param {
+    use_global_stats: true
+    eps: 2e-5
+  }
+}
+
+layer {
+  name: "scale_stage3_unit1_sc_bn"
+  bottom: "stage3_unit1_sc"
+  top: "stage3_unit1_sc"
+  type: "Scale"
+  scale_param {
+        bias_term: true
+  }
+}
+
+layer {
+  name: "stage3_unit1_plus"
+  type: "Eltwise"
+  bottom: "stage3_unit1_sc"
+  bottom: "stage3_unit1_conv3"
+  top: "stage3_unit1_plus"
+  eltwise_param {
+     operation: SUM
+  }
+}
+
+layer {
+  name: "stage3_unit1_relu"
+  type: "ReLU"
+  bottom: "stage3_unit1_plus"
+  top: "stage3_unit1_plus"
+}
+
+layer {
+  name: "stage3_unit2_conv1"
+  type: "Convolution"
+  bottom: "stage3_unit1_plus"
+  top: "stage3_unit2_conv1"
+  convolution_param { 
+     num_output: 512
+     kernel_size: 1
+     stride: 1
+     pad: 0
+     bias_term: false
+  }
+}
+
+layer {
+  name: "stage3_unit2_bn1"
+  type: "BatchNorm"
+  bottom: "stage3_unit2_conv1"
+  top: "stage3_unit2_conv1"
+  batch_norm_param {
+    use_global_stats: true
+    eps: 2e-5
+  }
+}
+
+layer {
+  name: "scale_stage3_unit2_bn1"
+  bottom: "stage3_unit2_conv1"
+  top: "stage3_unit2_conv1"
+  type: "Scale"
+  scale_param {
+        bias_term: true
+  }
+}
+
+layer {
+  name: "stage3_unit2_relu1"
+  type: "ReLU"
+  bottom: "stage3_unit2_conv1"
+  top: "stage3_unit2_conv1"
+}
+
+layer {
+  name: "stage3_unit2_conv2"
+  type: "Convolution"
+  bottom: "stage3_unit2_conv1"
+  top: "stage3_unit2_conv2"
+  convolution_param { 
+     num_output: 512
+     kernel_size: 3
+     stride: 1
+     group: 32
+     pad: 1
+     bias_term: false
+  }
+}
+
+layer {
+  name: "stage3_unit2_bn2"
+  type: "BatchNorm"
+  bottom: "stage3_unit2_conv2"
+  top: "stage3_unit2_conv2"
+  batch_norm_param {
+    use_global_stats: true
+    eps: 2e-5
+  }
+}
+
+layer {
+  name: "scale_stage3_unit2_bn2"
+  bottom: "stage3_unit2_conv2"
+  top: "stage3_unit2_conv2"
+  type: "Scale"
+  scale_param {
+        bias_term: true
+  }
+}
+
+layer {
+  name: "stage3_unit2_relu2"
+  type: "ReLU"
+  bottom: "stage3_unit2_conv2"
+  top: "stage3_unit2_conv2"
+}
+
+layer {
+  name: "stage3_unit2_conv3"
+  type: "Convolution"
+  bottom: "stage3_unit2_conv2"
+  top: "stage3_unit2_conv3"
+  convolution_param { 
+     num_output: 1024
+     kernel_size: 1
+     stride: 1
+     pad: 0
+     bias_term: false
+  }
+}
+
+layer {
+  name: "stage3_unit2_bn3"
+  type: "BatchNorm"
+  bottom: "stage3_unit2_conv3"
+  top: "stage3_unit2_conv3"
+  batch_norm_param {
+    use_global_stats: true
+    eps: 2e-5
+  }
+}
+
+layer {
+  name: "scale_stage3_unit2_bn3"
+  bottom: "stage3_unit2_conv3"
+  top: "stage3_unit2_conv3"
+  type: "Scale"
+  scale_param {
+        bias_term: true
+  }
+}
+
+layer {
+  name: "stage3_unit2_plus"
+  type: "Eltwise"
+  bottom: "stage3_unit1_plus"
+  bottom: "stage3_unit2_conv3"
+  top: "stage3_unit2_plus"
+  eltwise_param {
+     operation: SUM
+  }
+}
+
+layer {
+  name: "stage3_unit2_relu"
+  type: "ReLU"
+  bottom: "stage3_unit2_plus"
+  top: "stage3_unit2_plus"
+}
+
+layer {
+  name: "stage3_unit3_conv1"
+  type: "Convolution"
+  bottom: "stage3_unit2_plus"
+  top: "stage3_unit3_conv1"
+  convolution_param { 
+     num_output: 512
+     kernel_size: 1
+     stride: 1
+     pad: 0
+     bias_term: false
+  }
+}
+
+layer {
+  name: "stage3_unit3_bn1"
+  type: "BatchNorm"
+  bottom: "stage3_unit3_conv1"
+  top: "stage3_unit3_conv1"
+  batch_norm_param {
+    use_global_stats: true
+    eps: 2e-5
+  }
+}
+
+layer {
+  name: "scale_stage3_unit3_bn1"
+  bottom: "stage3_unit3_conv1"
+  top: "stage3_unit3_conv1"
+  type: "Scale"
+  scale_param {
+        bias_term: true
+  }
+}
+
+layer {
+  name: "stage3_unit3_relu1"
+  type: "ReLU"
+  bottom: "stage3_unit3_conv1"
+  top: "stage3_unit3_conv1"
+}
+
+layer {
+  name: "stage3_unit3_conv2"
+  type: "Convolution"
+  bottom: "stage3_unit3_conv1"
+  top: "stage3_unit3_conv2"
+  convolution_param { 
+     num_output: 512
+     kernel_size: 3
+     stride: 1
+     group: 32
+     pad: 1
+     bias_term: false
+  }
+}
+
+layer {
+  name: "stage3_unit3_bn2"
+  type: "BatchNorm"
+  bottom: "stage3_unit3_conv2"
+  top: "stage3_unit3_conv2"
+  batch_norm_param {
+    use_global_stats: true
+    eps: 2e-5
+  }
+}
+
+layer {
+  name: "scale_stage3_unit3_bn2"
+  bottom: "stage3_unit3_conv2"
+  top: "stage3_unit3_conv2"
+  type: "Scale"
+  scale_param {
+        bias_term: true
+  }
+}
+
+layer {
+  name: "stage3_unit3_relu2"
+  type: "ReLU"
+  bottom: "stage3_unit3_conv2"
+  top: "stage3_unit3_conv2"
+}
+
+layer {
+  name: "stage3_unit3_conv3"
+  type: "Convolution"
+  bottom: "stage3_unit3_conv2"
+  top: "stage3_unit3_conv3"
+  convolution_param { 
+     num_output: 1024
+     kernel_size: 1
+     stride: 1
+     pad: 0
+     bias_term: false
+  }
+}
+
+layer {
+  name: "stage3_unit3_bn3"
+  type: "BatchNorm"
+  bottom: "stage3_unit3_conv3"
+  top: "stage3_unit3_conv3"
+  batch_norm_param {
+    use_global_stats: true
+    eps: 2e-5
+  }
+}
+
+layer {
+  name: "scale_stage3_unit3_bn3"
+  bottom: "stage3_unit3_conv3"
+  top: "stage3_unit3_conv3"
+  type: "Scale"
+  scale_param {
+        bias_term: true
+  }
+}
+
+layer {
+  name: "stage3_unit3_plus"
+  type: "Eltwise"
+  bottom: "stage3_unit2_plus"
+  bottom: "stage3_unit3_conv3"
+  top: "stage3_unit3_plus"
+  eltwise_param {
+     operation: SUM
+  }
+}
+
+layer {
+  name: "stage3_unit3_relu"
+  type: "ReLU"
+  bottom: "stage3_unit3_plus"
+  top: "stage3_unit3_plus"
+}
+
+layer {
+  name: "stage3_unit4_conv1"
+  type: "Convolution"
+  bottom: "stage3_unit3_plus"
+  top: "stage3_unit4_conv1"
+  convolution_param { 
+     num_output: 512
+     kernel_size: 1
+     stride: 1
+     pad: 0
+     bias_term: false
+  }
+}
+
+layer {
+  name: "stage3_unit4_bn1"
+  type: "BatchNorm"
+  bottom: "stage3_unit4_conv1"
+  top: "stage3_unit4_conv1"
+  batch_norm_param {
+    use_global_stats: true
+    eps: 2e-5
+  }
+}
+
+layer {
+  name: "scale_stage3_unit4_bn1"
+  bottom: "stage3_unit4_conv1"
+  top: "stage3_unit4_conv1"
+  type: "Scale"
+  scale_param {
+        bias_term: true
+  }
+}
+
+layer {
+  name: "stage3_unit4_relu1"
+  type: "ReLU"
+  bottom: "stage3_unit4_conv1"
+  top: "stage3_unit4_conv1"
+}
+
+layer {
+  name: "stage3_unit4_conv2"
+  type: "Convolution"
+  bottom: "stage3_unit4_conv1"
+  top: "stage3_unit4_conv2"
+  convolution_param { 
+     num_output: 512
+     kernel_size: 3
+     stride: 1
+     group: 32
+     pad: 1
+     bias_term: false
+  }
+}
+
+layer {
+  name: "stage3_unit4_bn2"
+  type: "BatchNorm"
+  bottom: "stage3_unit4_conv2"
+  top: "stage3_unit4_conv2"
+  batch_norm_param {
+    use_global_stats: true
+    eps: 2e-5
+  }
+}
+
+layer {
+  name: "scale_stage3_unit4_bn2"
+  bottom: "stage3_unit4_conv2"
+  top: "stage3_unit4_conv2"
+  type: "Scale"
+  scale_param {
+        bias_term: true
+  }
+}
+
+layer {
+  name: "stage3_unit4_relu2"
+  type: "ReLU"
+  bottom: "stage3_unit4_conv2"
+  top: "stage3_unit4_conv2"
+}
+
+layer {
+  name: "stage3_unit4_conv3"
+  type: "Convolution"
+  bottom: "stage3_unit4_conv2"
+  top: "stage3_unit4_conv3"
+  convolution_param { 
+     num_output: 1024
+     kernel_size: 1
+     stride: 1
+     pad: 0
+     bias_term: false
+  }
+}
+
+layer {
+  name: "stage3_unit4_bn3"
+  type: "BatchNorm"
+  bottom: "stage3_unit4_conv3"
+  top: "stage3_unit4_conv3"
+  batch_norm_param {
+    use_global_stats: true
+    eps: 2e-5
+  }
+}
+
+layer {
+  name: "scale_stage3_unit4_bn3"
+  bottom: "stage3_unit4_conv3"
+  top: "stage3_unit4_conv3"
+  type: "Scale"
+  scale_param {
+        bias_term: true
+  }
+}
+
+layer {
+  name: "stage3_unit4_plus"
+  type: "Eltwise"
+  bottom: "stage3_unit3_plus"
+  bottom: "stage3_unit4_conv3"
+  top: "stage3_unit4_plus"
+  eltwise_param {
+     operation: SUM
+  }
+}
+
+layer {
+  name: "stage3_unit4_relu"
+  type: "ReLU"
+  bottom: "stage3_unit4_plus"
+  top: "stage3_unit4_plus"
+}
+
+layer {
+  name: "stage3_unit5_conv1"
+  type: "Convolution"
+  bottom: "stage3_unit4_plus"
+  top: "stage3_unit5_conv1"
+  convolution_param { 
+     num_output: 512
+     kernel_size: 1
+     stride: 1
+     pad: 0
+     bias_term: false
+  }
+}
+
+layer {
+  name: "stage3_unit5_bn1"
+  type: "BatchNorm"
+  bottom: "stage3_unit5_conv1"
+  top: "stage3_unit5_conv1"
+  batch_norm_param {
+    use_global_stats: true
+    eps: 2e-5
+  }
+}
+
+layer {
+  name: "scale_stage3_unit5_bn1"
+  bottom: "stage3_unit5_conv1"
+  top: "stage3_unit5_conv1"
+  type: "Scale"
+  scale_param {
+        bias_term: true
+  }
+}
+
+layer {
+  name: "stage3_unit5_relu1"
+  type: "ReLU"
+  bottom: "stage3_unit5_conv1"
+  top: "stage3_unit5_conv1"
+}
+
+layer {
+  name: "stage3_unit5_conv2"
+  type: "Convolution"
+  bottom: "stage3_unit5_conv1"
+  top: "stage3_unit5_conv2"
+  convolution_param { 
+     num_output: 512
+     kernel_size: 3
+     stride: 1
+     group: 32
+     pad: 1
+     bias_term: false
+  }
+}
+
+layer {
+  name: "stage3_unit5_bn2"
+  type: "BatchNorm"
+  bottom: "stage3_unit5_conv2"
+  top: "stage3_unit5_conv2"
+  batch_norm_param {
+    use_global_stats: true
+    eps: 2e-5
+  }
+}
+
+layer {
+  name: "scale_stage3_unit5_bn2"
+  bottom: "stage3_unit5_conv2"
+  top: "stage3_unit5_conv2"
+  type: "Scale"
+  scale_param {
+        bias_term: true
+  }
+}
+
+layer {
+  name: "stage3_unit5_relu2"
+  type: "ReLU"
+  bottom: "stage3_unit5_conv2"
+  top: "stage3_unit5_conv2"
+}
+
+layer {
+  name: "stage3_unit5_conv3"
+  type: "Convolution"
+  bottom: "stage3_unit5_conv2"
+  top: "stage3_unit5_conv3"
+  convolution_param { 
+     num_output: 1024
+     kernel_size: 1
+     stride: 1
+     pad: 0
+     bias_term: false
+  }
+}
+
+layer {
+  name: "stage3_unit5_bn3"
+  type: "BatchNorm"
+  bottom: "stage3_unit5_conv3"
+  top: "stage3_unit5_conv3"
+  batch_norm_param {
+    use_global_stats: true
+    eps: 2e-5
+  }
+}
+
+layer {
+  name: "scale_stage3_unit5_bn3"
+  bottom: "stage3_unit5_conv3"
+  top: "stage3_unit5_conv3"
+  type: "Scale"
+  scale_param {
+        bias_term: true
+  }
+}
+
+layer {
+  name: "stage3_unit5_plus"
+  type: "Eltwise"
+  bottom: "stage3_unit4_plus"
+  bottom: "stage3_unit5_conv3"
+  top: "stage3_unit5_plus"
+  eltwise_param {
+     operation: SUM
+  }
+}
+
+layer {
+  name: "stage3_unit5_relu"
+  type: "ReLU"
+  bottom: "stage3_unit5_plus"
+  top: "stage3_unit5_plus"
+}
+
+layer {
+  name: "stage3_unit6_conv1"
+  type: "Convolution"
+  bottom: "stage3_unit5_plus"
+  top: "stage3_unit6_conv1"
+  convolution_param { 
+     num_output: 512
+     kernel_size: 1
+     stride: 1
+     pad: 0
+     bias_term: false
+  }
+}
+
+layer {
+  name: "stage3_unit6_bn1"
+  type: "BatchNorm"
+  bottom: "stage3_unit6_conv1"
+  top: "stage3_unit6_conv1"
+  batch_norm_param {
+    use_global_stats: true
+    eps: 2e-5
+  }
+}
+
+layer {
+  name: "scale_stage3_unit6_bn1"
+  bottom: "stage3_unit6_conv1"
+  top: "stage3_unit6_conv1"
+  type: "Scale"
+  scale_param {
+        bias_term: true
+  }
+}
+
+layer {
+  name: "stage3_unit6_relu1"
+  type: "ReLU"
+  bottom: "stage3_unit6_conv1"
+  top: "stage3_unit6_conv1"
+}
+
+layer {
+  name: "stage3_unit6_conv2"
+  type: "Convolution"
+  bottom: "stage3_unit6_conv1"
+  top: "stage3_unit6_conv2"
+  convolution_param { 
+     num_output: 512
+     kernel_size: 3
+     stride: 1
+     group: 32
+     pad: 1
+     bias_term: false
+  }
+}
+
+layer {
+  name: "stage3_unit6_bn2"
+  type: "BatchNorm"
+  bottom: "stage3_unit6_conv2"
+  top: "stage3_unit6_conv2"
+  batch_norm_param {
+    use_global_stats: true
+    eps: 2e-5
+  }
+}
+
+layer {
+  name: "scale_stage3_unit6_bn2"
+  bottom: "stage3_unit6_conv2"
+  top: "stage3_unit6_conv2"
+  type: "Scale"
+  scale_param {
+        bias_term: true
+  }
+}
+
+layer {
+  name: "stage3_unit6_relu2"
+  type: "ReLU"
+  bottom: "stage3_unit6_conv2"
+  top: "stage3_unit6_conv2"
+}
+
+layer {
+  name: "stage3_unit6_conv3"
+  type: "Convolution"
+  bottom: "stage3_unit6_conv2"
+  top: "stage3_unit6_conv3"
+  convolution_param { 
+     num_output: 1024
+     kernel_size: 1
+     stride: 1
+     pad: 0
+     bias_term: false
+  }
+}
+
+layer {
+  name: "stage3_unit6_bn3"
+  type: "BatchNorm"
+  bottom: "stage3_unit6_conv3"
+  top: "stage3_unit6_conv3"
+  batch_norm_param {
+    use_global_stats: true
+    eps: 2e-5
+  }
+}
+
+layer {
+  name: "scale_stage3_unit6_bn3"
+  bottom: "stage3_unit6_conv3"
+  top: "stage3_unit6_conv3"
+  type: "Scale"
+  scale_param {
+        bias_term: true
+  }
+}
+
+layer {
+  name: "stage3_unit6_plus"
+  type: "Eltwise"
+  bottom: "stage3_unit5_plus"
+  bottom: "stage3_unit6_conv3"
+  top: "stage3_unit6_plus"
+  eltwise_param {
+     operation: SUM
+  }
+}
+
+layer {
+  name: "stage3_unit6_relu"
+  type: "ReLU"
+  bottom: "stage3_unit6_plus"
+  top: "stage3_unit6_plus"
+}
+
+layer {
+  name: "stage4_unit1_conv1"
+  type: "Convolution"
+  bottom: "stage3_unit6_plus"
+  top: "stage4_unit1_conv1"
+  convolution_param { 
+     num_output: 1024
+     kernel_size: 1
+     stride: 1
+     pad: 0
+     bias_term: false
+  }
+}
+
+layer {
+  name: "stage4_unit1_bn1"
+  type: "BatchNorm"
+  bottom: "stage4_unit1_conv1"
+  top: "stage4_unit1_conv1"
+  batch_norm_param {
+    use_global_stats: true
+    eps: 2e-5
+  }
+}
+
+layer {
+  name: "scale_stage4_unit1_bn1"
+  bottom: "stage4_unit1_conv1"
+  top: "stage4_unit1_conv1"
+  type: "Scale"
+  scale_param {
+        bias_term: true
+  }
+}
+
+layer {
+  name: "stage4_unit1_relu1"
+  type: "ReLU"
+  bottom: "stage4_unit1_conv1"
+  top: "stage4_unit1_conv1"
+}
+
+layer {
+  name: "stage4_unit1_conv2"
+  type: "Convolution"
+  bottom: "stage4_unit1_conv1"
+  top: "stage4_unit1_conv2"
+  convolution_param { 
+     num_output: 1024
+     kernel_size: 3
+     stride: 2
+     group: 32
+     pad: 1
+     bias_term: false
+  }
+}
+
+layer {
+  name: "stage4_unit1_bn2"
+  type: "BatchNorm"
+  bottom: "stage4_unit1_conv2"
+  top: "stage4_unit1_conv2"
+  batch_norm_param {
+    use_global_stats: true
+    eps: 2e-5
+  }
+}
+
+layer {
+  name: "scale_stage4_unit1_bn2"
+  bottom: "stage4_unit1_conv2"
+  top: "stage4_unit1_conv2"
+  type: "Scale"
+  scale_param {
+        bias_term: true
+  }
+}
+
+layer {
+  name: "stage4_unit1_relu2"
+  type: "ReLU"
+  bottom: "stage4_unit1_conv2"
+  top: "stage4_unit1_conv2"
+}
+
+layer {
+  name: "stage4_unit1_conv3"
+  type: "Convolution"
+  bottom: "stage4_unit1_conv2"
+  top: "stage4_unit1_conv3"
+  convolution_param { 
+     num_output: 2048
+     kernel_size: 1
+     stride: 1
+     pad: 0
+     bias_term: false
+  }
+}
+
+layer {
+  name: "stage4_unit1_bn3"
+  type: "BatchNorm"
+  bottom: "stage4_unit1_conv3"
+  top: "stage4_unit1_conv3"
+  batch_norm_param {
+    use_global_stats: true
+    eps: 2e-5
+  }
+}
+
+layer {
+  name: "scale_stage4_unit1_bn3"
+  bottom: "stage4_unit1_conv3"
+  top: "stage4_unit1_conv3"
+  type: "Scale"
+  scale_param {
+        bias_term: true
+  }
+}
+
+layer {
+  name: "stage4_unit1_sc"
+  type: "Convolution"
+  bottom: "stage3_unit6_plus"
+  top: "stage4_unit1_sc"
+  convolution_param { 
+     num_output: 2048
+     kernel_size: 1
+     stride: 2
+     pad: 0
+     bias_term: false
+  }
+}
+
+layer {
+  name: "stage4_unit1_sc_bn"
+  type: "BatchNorm"
+  bottom: "stage4_unit1_sc"
+  top: "stage4_unit1_sc"
+  batch_norm_param {
+    use_global_stats: true
+    eps: 2e-5
+  }
+}
+
+layer {
+  name: "scale_stage4_unit1_sc_bn"
+  bottom: "stage4_unit1_sc"
+  top: "stage4_unit1_sc"
+  type: "Scale"
+  scale_param {
+        bias_term: true
+  }
+}
+
+layer {
+  name: "stage4_unit1_plus"
+  type: "Eltwise"
+  bottom: "stage4_unit1_sc"
+  bottom: "stage4_unit1_conv3"
+  top: "stage4_unit1_plus"
+  eltwise_param {
+     operation: SUM
+  }
+}
+
+layer {
+  name: "stage4_unit1_relu"
+  type: "ReLU"
+  bottom: "stage4_unit1_plus"
+  top: "stage4_unit1_plus"
+}
+
+layer {
+  name: "stage4_unit2_conv1"
+  type: "Convolution"
+  bottom: "stage4_unit1_plus"
+  top: "stage4_unit2_conv1"
+  convolution_param { 
+     num_output: 1024
+     kernel_size: 1
+     stride: 1
+     pad: 0
+     bias_term: false
+  }
+}
+
+layer {
+  name: "stage4_unit2_bn1"
+  type: "BatchNorm"
+  bottom: "stage4_unit2_conv1"
+  top: "stage4_unit2_conv1"
+  batch_norm_param {
+    use_global_stats: true
+    eps: 2e-5
+  }
+}
+
+layer {
+  name: "scale_stage4_unit2_bn1"
+  bottom: "stage4_unit2_conv1"
+  top: "stage4_unit2_conv1"
+  type: "Scale"
+  scale_param {
+        bias_term: true
+  }
+}
+
+layer {
+  name: "stage4_unit2_relu1"
+  type: "ReLU"
+  bottom: "stage4_unit2_conv1"
+  top: "stage4_unit2_conv1"
+}
+
+layer {
+  name: "stage4_unit2_conv2"
+  type: "Convolution"
+  bottom: "stage4_unit2_conv1"
+  top: "stage4_unit2_conv2"
+  convolution_param { 
+     num_output: 1024
+     kernel_size: 3
+     stride: 1
+     group: 32
+     pad: 1
+     bias_term: false
+  }
+}
+
+layer {
+  name: "stage4_unit2_bn2"
+  type: "BatchNorm"
+  bottom: "stage4_unit2_conv2"
+  top: "stage4_unit2_conv2"
+  batch_norm_param {
+    use_global_stats: true
+    eps: 2e-5
+  }
+}
+
+layer {
+  name: "scale_stage4_unit2_bn2"
+  bottom: "stage4_unit2_conv2"
+  top: "stage4_unit2_conv2"
+  type: "Scale"
+  scale_param {
+        bias_term: true
+  }
+}
+
+layer {
+  name: "stage4_unit2_relu2"
+  type: "ReLU"
+  bottom: "stage4_unit2_conv2"
+  top: "stage4_unit2_conv2"
+}
+
+layer {
+  name: "stage4_unit2_conv3"
+  type: "Convolution"
+  bottom: "stage4_unit2_conv2"
+  top: "stage4_unit2_conv3"
+  convolution_param { 
+     num_output: 2048
+     kernel_size: 1
+     stride: 1
+     pad: 0
+     bias_term: false
+  }
+}
+
+layer {
+  name: "stage4_unit2_bn3"
+  type: "BatchNorm"
+  bottom: "stage4_unit2_conv3"
+  top: "stage4_unit2_conv3"
+  batch_norm_param {
+    use_global_stats: true
+    eps: 2e-5
+  }
+}
+
+layer {
+  name: "scale_stage4_unit2_bn3"
+  bottom: "stage4_unit2_conv3"
+  top: "stage4_unit2_conv3"
+  type: "Scale"
+  scale_param {
+        bias_term: true
+  }
+}
+
+layer {
+  name: "stage4_unit2_plus"
+  type: "Eltwise"
+  bottom: "stage4_unit1_plus"
+  bottom: "stage4_unit2_conv3"
+  top: "stage4_unit2_plus"
+  eltwise_param {
+     operation: SUM
+  }
+}
+
+layer {
+  name: "stage4_unit2_relu"
+  type: "ReLU"
+  bottom: "stage4_unit2_plus"
+  top: "stage4_unit2_plus"
+}
+
+layer {
+  name: "stage4_unit3_conv1"
+  type: "Convolution"
+  bottom: "stage4_unit2_plus"
+  top: "stage4_unit3_conv1"
+  convolution_param { 
+     num_output: 1024
+     kernel_size: 1
+     stride: 1
+     pad: 0
+     bias_term: false
+  }
+}
+
+layer {
+  name: "stage4_unit3_bn1"
+  type: "BatchNorm"
+  bottom: "stage4_unit3_conv1"
+  top: "stage4_unit3_conv1"
+  batch_norm_param {
+    use_global_stats: true
+    eps: 2e-5
+  }
+}
+
+layer {
+  name: "scale_stage4_unit3_bn1"
+  bottom: "stage4_unit3_conv1"
+  top: "stage4_unit3_conv1"
+  type: "Scale"
+  scale_param {
+        bias_term: true
+  }
+}
+
+layer {
+  name: "stage4_unit3_relu1"
+  type: "ReLU"
+  bottom: "stage4_unit3_conv1"
+  top: "stage4_unit3_conv1"
+}
+
+layer {
+  name: "stage4_unit3_conv2"
+  type: "Convolution"
+  bottom: "stage4_unit3_conv1"
+  top: "stage4_unit3_conv2"
+  convolution_param { 
+     num_output: 1024
+     kernel_size: 3
+     stride: 1
+     group: 32
+     pad: 1
+     bias_term: false
+  }
+}
+
+layer {
+  name: "stage4_unit3_bn2"
+  type: "BatchNorm"
+  bottom: "stage4_unit3_conv2"
+  top: "stage4_unit3_conv2"
+  batch_norm_param {
+    use_global_stats: true
+    eps: 2e-5
+  }
+}
+
+layer {
+  name: "scale_stage4_unit3_bn2"
+  bottom: "stage4_unit3_conv2"
+  top: "stage4_unit3_conv2"
+  type: "Scale"
+  scale_param {
+        bias_term: true
+  }
+}
+
+layer {
+  name: "stage4_unit3_relu2"
+  type: "ReLU"
+  bottom: "stage4_unit3_conv2"
+  top: "stage4_unit3_conv2"
+}
+
+layer {
+  name: "stage4_unit3_conv3"
+  type: "Convolution"
+  bottom: "stage4_unit3_conv2"
+  top: "stage4_unit3_conv3"
+  convolution_param { 
+     num_output: 2048
+     kernel_size: 1
+     stride: 1
+     pad: 0
+     bias_term: false
+  }
+}
+
+layer {
+  name: "stage4_unit3_bn3"
+  type: "BatchNorm"
+  bottom: "stage4_unit3_conv3"
+  top: "stage4_unit3_conv3"
+  batch_norm_param {
+    use_global_stats: true
+    eps: 2e-5
+  }
+}
+
+layer {
+  name: "scale_stage4_unit3_bn3"
+  bottom: "stage4_unit3_conv3"
+  top: "stage4_unit3_conv3"
+  type: "Scale"
+  scale_param {
+        bias_term: true
+  }
+}
+
+layer {
+  name: "stage4_unit3_plus"
+  type: "Eltwise"
+  bottom: "stage4_unit2_plus"
+  bottom: "stage4_unit3_conv3"
+  top: "stage4_unit3_plus"
+  eltwise_param {
+     operation: SUM
+  }
+}
+
+layer {
+  name: "stage4_unit3_relu"
+  type: "ReLU"
+  bottom: "stage4_unit3_plus"
+  top: "stage4_unit3_plus"
+}
+
+layer {
+  name: "pool1"
+  type: "Pooling"
+  bottom: "stage4_unit3_plus"
+  top: "pool1"
+  pooling_param {
+     global_pooling : true
+     pool: AVE
+  }
+}
+
+layer {
+  name: "fc1"
+  type: "InnerProduct"
+  bottom: "pool1"
+  top: "fc1"
+  param {
+    lr_mult: 1
+    decay_mult: 1
+  }
+  param {
+    lr_mult: 2
+    decay_mult: 0
+  }
+  inner_product_param {
+    num_output: 1000
+    weight_filler {
+      type: "xavier"
+    }
+    bias_filler {
+      type: "constant"
+      value: 0
+    }
+  }
+}
+
+layer {
+  name: "prob"
+  type: "Softmax"
+  bottom: "fc1"
+  top: "prob"
+}
+

From 5342d4d6f1706ff1361160acbe32e7b581953dd5 Mon Sep 17 00:00:00 2001
From: Daisy Deng <daisy.deng@intel.com>
Date: Fri, 23 Nov 2018 17:24:00 +0800
Subject: [PATCH 22/26] move to mkldnn v0.17 formal release version

---
 mkldnn.commit | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/mkldnn.commit b/mkldnn.commit
index e4958aca5..a6ef2b9e7 100644
--- a/mkldnn.commit
+++ b/mkldnn.commit
@@ -1 +1 @@
-21fb5f2af1dd14e132af4f1b79160977ee487818
+830a10059a018cd2634d94195140cf2d8790a75a

From 2cbb0b57a102e29af81b97451dca6264e7bed085 Mon Sep 17 00:00:00 2001
From: Feng Tian <feng.tian@intel.com>
Date: Wed, 28 Nov 2018 11:24:00 +0800
Subject: [PATCH 23/26] fix build failure caused by use_opencv flag being off

---
 src/caffe/data_transformer.cpp | 4 ++++
 1 file changed, 4 insertions(+)

diff --git a/src/caffe/data_transformer.cpp b/src/caffe/data_transformer.cpp
index e11a10487..b3e22d4e1 100644
--- a/src/caffe/data_transformer.cpp
+++ b/src/caffe/data_transformer.cpp
@@ -76,6 +76,7 @@ void DataTransformer<Dtype>::Transform(const Datum& datum,
   CHECK(datum.encoded()) << "For box data, datum must be encoded";
   CHECK(!(param_.force_color() && param_.force_gray()))
     << "cannot set both force_color and force_gray";
+#ifdef USE_OPENCV
   cv::Mat cv_img;
   if (param_.force_color() || param_.force_gray()) {
   // If force_color then decode in color otherwise decode in gray.
@@ -150,6 +151,9 @@ void DataTransformer<Dtype>::Transform(const Datum& datum,
   cv::resize(cv_rand_img, cv_rand_img, cv::Size(img_width, img_height));
   // Transform the cv::image into blob.
   Transform(cv_rand_img, transformed_blob);
+#else
+  LOG(FATAL) << "Image transformer requires OpenCV; compile with USE_OPENCV.";
+#endif
   return;
 }
 

From fd692c4dac9c30c1eb6c090e94732b242587f000 Mon Sep 17 00:00:00 2001
From: Feng Tian <feng.tian@intel.com>
Date: Wed, 28 Nov 2018 13:17:13 +0800
Subject: [PATCH 24/26] fix build error caused by USE_OPENMP being OFF

---
 src/caffe/data_transformer.cpp                | 10 ++++++++++
 src/caffe/layers/mkldnn_convolution_layer.cpp |  6 ++++++
 src/caffe/layers/permute_layer.cpp            |  2 ++
 src/caffe/layers/prelu_layer.cpp              |  4 ++++
 src/caffe/mkldnn_memory.cpp                   |  6 ++++++
 src/caffe/multinode/mn_activation_layer.cpp   |  4 ++++
 6 files changed, 32 insertions(+)

diff --git a/src/caffe/data_transformer.cpp b/src/caffe/data_transformer.cpp
index b3e22d4e1..c92efb581 100644
--- a/src/caffe/data_transformer.cpp
+++ b/src/caffe/data_transformer.cpp
@@ -217,7 +217,9 @@ void DataTransformer<Dtype>::Transform(const Datum& datum, Dtype* transformed_da
     mean = data_mean_.mutable_cpu_data();
   }
   if (has_mean_values) {
+   #ifdef _OPENMP
    #pragma omp critical
+   #endif
     {
       CHECK(mean_values_.size() == 1 || mean_values_.size() == datum_channels) <<
        "Specify either 1 mean_value or as many as channels: " << datum_channels;
@@ -417,7 +419,9 @@ void DataTransformer<Dtype>::Transform(const Datum& datum_in,
     mean = data_mean_.mutable_cpu_data();
   }
   if (has_mean_values) {
+   #ifdef _OPENMP
    #pragma omp critical
+   #endif
     {
       CHECK(mean_values_.size() == 1 || mean_values_.size() == datum_channels) <<
        "Specify either 1 mean_value or as many as channels: " << datum_channels;
@@ -1023,7 +1027,9 @@ void DataTransformer<Dtype>::Transform(const cv::Mat& cv_img_in,
     mean = data_mean_.mutable_cpu_data();
   }
   if (has_mean_values) {
+   #ifdef _OPENMP
    #pragma omp critical
+   #endif
     {
       CHECK(mean_values_.size() == 1 || mean_values_.size() == img_channels) <<
           "Specify either 1 mean_value or as many as channels: " << img_channels;
@@ -1126,7 +1132,9 @@ void DataTransformer<Dtype>::TransformInv(const Dtype* data, cv::Mat* cv_img,
     mean = data_mean_.mutable_cpu_data();
   }
   if (has_mean_values) {
+   #ifdef _OPENMP
    #pragma omp critical
+   #endif
     {
       CHECK(mean_values_.size() == 1 || mean_values_.size() == channels) <<
           "Specify either 1 mean_value or as many as channels: " << channels;
@@ -1261,7 +1269,9 @@ void DataTransformer<Dtype>::ExpandImage(const cv::Mat& img,
     }
   }
   if (has_mean_values) {
+   #ifdef _OPENMP
    #pragma omp critical
+   #endif
     {
       CHECK(mean_values_.size() == 1 || mean_values_.size() == img_channels) <<
           "Specify either 1 mean_value or as many as channels: " << img_channels;
diff --git a/src/caffe/layers/mkldnn_convolution_layer.cpp b/src/caffe/layers/mkldnn_convolution_layer.cpp
index cec3964d4..23873c197 100644
--- a/src/caffe/layers/mkldnn_convolution_layer.cpp
+++ b/src/caffe/layers/mkldnn_convolution_layer.cpp
@@ -350,7 +350,9 @@ void MKLDNNConvolutionLayer<Dtype>::InitConvolutionFwd(const vector<Blob<Dtype>*
       }
       std::vector<float> scales(count);
       float scale;
+      #ifdef _OPENMP
       #pragma omp parallel for if (count > 1)
+      #endif
       for(int i=0; i<count; i++){
         if (this->scale_params_[i] == 0.0)
             scale = this->scale_out_[0] * coeff1;
@@ -505,7 +507,9 @@ void MKLDNNConvolutionLayer<Dtype>::InitConvolutionFwd(const vector<Blob<Dtype>*
             reorder_mask = (g!= 1) ? (1<<1)+(1<<0) : 1<<0;
         }
         std::vector<float> scale_weight(count);
+        #ifdef _OPENMP
         #pragma omp parallel for if (count > 1)
+        #endif
         for(int i=0; i<count; i++){
           scale_weight[i] = this->scale_params_[i];
         }
@@ -528,7 +532,9 @@ void MKLDNNConvolutionLayer<Dtype>::InitConvolutionFwd(const vector<Blob<Dtype>*
                 reorder_mask = 1<<0;
             }
             std::vector<float> scale_bias(count);
+            #ifdef _OPENMP
             #pragma omp parallel for if (count > 1)
+            #endif
             for(int i=0; i<count; i++){
               if (this->scale_params_[i] == 0.0)
                   scale_bias[i] = 1.0;
diff --git a/src/caffe/layers/permute_layer.cpp b/src/caffe/layers/permute_layer.cpp
index 0dc793351..5a6c25685 100644
--- a/src/caffe/layers/permute_layer.cpp
+++ b/src/caffe/layers/permute_layer.cpp
@@ -46,7 +46,9 @@ template <typename Dtype>
 void Permute(const int count, Dtype* bottom_data, const bool forward,
     const int* permute_order, const int* old_steps, const int* new_steps,
     const int num_axes, Dtype* top_data) {
+#ifdef _OPENMP
 #pragma omp parallel for
+#endif
     for (int i = 0; i < count; ++i) {
       int old_idx = 0;
       int idx = i;
diff --git a/src/caffe/layers/prelu_layer.cpp b/src/caffe/layers/prelu_layer.cpp
index ddeed2fd7..269d5a9d3 100644
--- a/src/caffe/layers/prelu_layer.cpp
+++ b/src/caffe/layers/prelu_layer.cpp
@@ -118,7 +118,9 @@ void PReLULayer<Dtype>::Forward_cpu(const vector<Blob<Dtype>*>& bottom,
   // if channel_shared, channel index in the following computation becomes
   // always zero.
   const int div_factor = channel_shared_ ? channels : 1;
+#ifdef _OPENMP
 #pragma omp parallel for
+#endif
   for (long i = 0; i < count; ++i) {
     long c = (i / dim) % channels / div_factor;
     top_data[i] = std::max(bottom_data[i], Dtype(0))
@@ -160,7 +162,9 @@ void PReLULayer<Dtype>::Backward_cpu(const vector<Blob<Dtype>*>& top,
   // Propagate to bottom
   if (propagate_down[0]) {
     Dtype* bottom_diff = bottom[0]->mutable_cpu_diff();
+#ifdef _OPENMP
 #pragma omp parallel for
+#endif
     for (long i = 0; i < count; ++i) {
       long c = (i / dim) % channels / div_factor;
       bottom_diff[i] = top_diff[i] * ((bottom_data[i] > 0)
diff --git a/src/caffe/mkldnn_memory.cpp b/src/caffe/mkldnn_memory.cpp
index 961df00b0..398d4094c 100644
--- a/src/caffe/mkldnn_memory.cpp
+++ b/src/caffe/mkldnn_memory.cpp
@@ -90,7 +90,9 @@ void MKLDNNMemoryDescriptorBase<Dtype>::create_reorder_descriptors(std::vector<f
     int count = scale.size();
     if ( *_usr_memory_pd != *_prv_memory_pd) {
         std::vector<float> scales_u2p(count);
+        #ifdef _OPENMP
         #pragma omp parallel for if (count > 1)
+        #endif
         for(int i=0; i < count; i++){
             scales_u2p[i] = scale[i];
         }
@@ -100,7 +102,9 @@ void MKLDNNMemoryDescriptorBase<Dtype>::create_reorder_descriptors(std::vector<f
                 new reorder::primitive_desc(*_usr_memory_pd, *_prv_memory_pd, attri));
 
         std::vector<float> scales_p2u(count);
+        #ifdef _OPENMP
         #pragma omp parallel for if (count > 1)
+        #endif
         for(int i=0; i < count; i++){
             scales_p2u[i] = (1. / scale[i]);
         }
@@ -120,7 +124,9 @@ void MKLDNNMemoryDescriptorBase<Dtype>::create_reorder_descriptors(std::vector<f
         }else{
             std::vector<float> scales_e2p(count);
             float shift_scale;
+            #ifdef _OPENMP
             #pragma omp parallel for if (count > 1)
+            #endif
             for(int i=0; i < count; i++){
                 shift_scale = scale[i] / scale_ext[i]; //fp32->int8 blob_prv_mkldnn_mem_descr->get_scale() will always be 0 ?
                 scales_e2p[i] = shift_scale;
diff --git a/src/caffe/multinode/mn_activation_layer.cpp b/src/caffe/multinode/mn_activation_layer.cpp
index 45ed667a6..fe4ca5681 100644
--- a/src/caffe/multinode/mn_activation_layer.cpp
+++ b/src/caffe/multinode/mn_activation_layer.cpp
@@ -206,7 +206,9 @@ void MnActivationLayer<Dtype>::Backward_cpu(const vector<Blob<Dtype>*>& top,
 template <typename Dtype>
 void MnActivationLayer<Dtype>::Unpack(const Dtype *src, int N, int C, int HW, int numC, Dtype *dst) {
   int dstC = numC * C;
+#ifdef _OPENMP
 #pragma omp parallel for collapse (2)
+#endif
   for (int iN = 0; iN < N; iN++) {
     for (int iC = 0; iC < dstC; iC++) {
       int iSrc =  iC / C;
@@ -223,7 +225,9 @@ template <typename Dtype>
 void MnActivationLayer<Dtype>::Pack(const Dtype *src, Dtype *dst, int N, int C, int HW, int numC) {
   int srcC = numC * C;
   for (int iDst = 0; iDst < numC; iDst++) {
+#ifdef _OPENMP
 #pragma omp parallel for collapse (2)
+#endif
     for (int iN = 0; iN < N; iN++) {
       for (int iC = 0; iC < C; iC++) {
         int iSrcC = iDst * C + iC;

From 44c1a5a7cfcb1f7d74febdebf63cd62d933cf046 Mon Sep 17 00:00:00 2001
From: Daisy Deng <daisy.deng@intel.com>
Date: Tue, 11 Dec 2018 21:23:38 +0800
Subject: [PATCH 25/26] add license file for rfcn, faster-rcnn, int8

---
 examples/faster-rcnn/license.txt |  8 ++++++++
 examples/rfcn/license.txt        |  8 ++++++++
 src/caffe/layers/license.txt     | 10 ++++++++++
 src/caffe/quant/license.txt      | 14 ++++++++++++++
 src/caffe/solvers/license.txt    |  9 +++++++++
 5 files changed, 49 insertions(+)
 create mode 100644 examples/faster-rcnn/license.txt
 create mode 100644 examples/rfcn/license.txt
 create mode 100644 src/caffe/layers/license.txt
 create mode 100644 src/caffe/quant/license.txt
 create mode 100644 src/caffe/solvers/license.txt

diff --git a/examples/faster-rcnn/license.txt b/examples/faster-rcnn/license.txt
new file mode 100644
index 000000000..617c1e140
--- /dev/null
+++ b/examples/faster-rcnn/license.txt
@@ -0,0 +1,8 @@
+The MIT License
+Copyright (c) <year> <copyright holders>
+
+Permission is hereby granted, free of charge, to any person obtaining a copy of this software and associated documentation files (the "Software"), to deal in the Software without restriction, including without limitation the rights to use, copy, modify, merge, publish, distribute, sublicense, and/or sell copies of the Software, and to permit persons to whom the Software is furnished to do so, subject to the following conditions:
+
+ The above copyright notice and this permission notice shall be included in all copies or substantial portions of the Software.
+
+THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE SOFTWARE.
diff --git a/examples/rfcn/license.txt b/examples/rfcn/license.txt
new file mode 100644
index 000000000..617c1e140
--- /dev/null
+++ b/examples/rfcn/license.txt
@@ -0,0 +1,8 @@
+The MIT License
+Copyright (c) <year> <copyright holders>
+
+Permission is hereby granted, free of charge, to any person obtaining a copy of this software and associated documentation files (the "Software"), to deal in the Software without restriction, including without limitation the rights to use, copy, modify, merge, publish, distribute, sublicense, and/or sell copies of the Software, and to permit persons to whom the Software is furnished to do so, subject to the following conditions:
+
+ The above copyright notice and this permission notice shall be included in all copies or substantial portions of the Software.
+
+THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE SOFTWARE.
diff --git a/src/caffe/layers/license.txt b/src/caffe/layers/license.txt
new file mode 100644
index 000000000..6071decca
--- /dev/null
+++ b/src/caffe/layers/license.txt
@@ -0,0 +1,10 @@
+psroi_pooling_layer.cpp:
+
+BSD Two Clause License
+ 
+Redistribution and use in source and binary forms, with or without modification, are permitted provided that the following conditions are met:
+1. Redistributions of source code must retain the above copyright notice, this list of conditions and the following disclaimer.
+2. Redistributions in binary form must reproduce the above copyright notice, this list of conditions and the following disclaimer in the documentation and/or other materials provided with the distribution.
+
+THIS SOFTWARE IS PROVIDED BY THE AUTHOR "AS IS" AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE ARE DISCLAIMED. IN NO EVENT SHALL THE AUTHOR BE LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES; LOSS OF USE, DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND ON ANY THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE OF THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE. 
+   
diff --git a/src/caffe/quant/license.txt b/src/caffe/quant/license.txt
new file mode 100644
index 000000000..02bc0c9f0
--- /dev/null
+++ b/src/caffe/quant/license.txt
@@ -0,0 +1,14 @@
+./tools/sample.cpp
+./src/caffe/quant/quantization.cpp
+./src/caffe/quant/layers/base_quant_layer.cpp
+./include/caffe/quant/quantization.hpp
+./include/caffe/quant/base_quant_layer.hpp
+
+BSD Two Clause License
+ 
+Redistribution and use in source and binary forms, with or without modification, are permitted provided that the following conditions are met:
+1. Redistributions of source code must retain the above copyright notice, this list of conditions and the following disclaimer.
+2. Redistributions in binary form must reproduce the above copyright notice, this list of conditions and the following disclaimer in the documentation and/or other materials provided with the distribution.
+
+THIS SOFTWARE IS PROVIDED BY THE AUTHOR "AS IS" AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE ARE DISCLAIMED. IN NO EVENT SHALL THE AUTHOR BE LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES; LOSS OF USE, DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND ON ANY THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE OF THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE. 
+   
diff --git a/src/caffe/solvers/license.txt b/src/caffe/solvers/license.txt
new file mode 100644
index 000000000..fb8b4a042
--- /dev/null
+++ b/src/caffe/solvers/license.txt
@@ -0,0 +1,9 @@
+sgd_solver.cpp
+BSD Two Clause License
+ 
+Redistribution and use in source and binary forms, with or without modification, are permitted provided that the following conditions are met:
+1. Redistributions of source code must retain the above copyright notice, this list of conditions and the following disclaimer.
+2. Redistributions in binary form must reproduce the above copyright notice, this list of conditions and the following disclaimer in the documentation and/or other materials provided with the distribution.
+
+THIS SOFTWARE IS PROVIDED BY THE AUTHOR "AS IS" AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE ARE DISCLAIMED. IN NO EVENT SHALL THE AUTHOR BE LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES; LOSS OF USE, DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND ON ANY THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE OF THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE. 
+

From 9d10d6f5c76c04d230a7fa59877b148bce937993 Mon Sep 17 00:00:00 2001
From: Daisy Deng <daisy.deng@intel.com>
Date: Tue, 11 Dec 2018 21:47:08 +0800
Subject: [PATCH 26/26] update version number to 1.1.3

---
 CMakeLists.txt   | 4 ++--
 Makefile         | 2 +-
 docker/README.md | 2 +-
 3 files changed, 4 insertions(+), 4 deletions(-)

diff --git a/CMakeLists.txt b/CMakeLists.txt
index c22bf983f..4e20812ef 100644
--- a/CMakeLists.txt
+++ b/CMakeLists.txt
@@ -10,8 +10,8 @@ endif()
 project(Caffe C CXX)
 
 # ---[ Caffe version
-set(CAFFE_TARGET_VERSION "1.1.2" CACHE STRING "Caffe logical version")
-set(CAFFE_TARGET_SOVERSION "1.1.2" CACHE STRING "Caffe soname version")
+set(CAFFE_TARGET_VERSION "1.1.3" CACHE STRING "Caffe logical version")
+set(CAFFE_TARGET_SOVERSION "1.1.3" CACHE STRING "Caffe soname version")
 add_definitions(-DCAFFE_VERSION=${CAFFE_TARGET_VERSION})
 
 # ---[ Using cmake scripts and modules
diff --git a/Makefile b/Makefile
index 5d3bc5654..2f2416d36 100644
--- a/Makefile
+++ b/Makefile
@@ -102,7 +102,7 @@ LIB_BUILD_DIR := $(BUILD_DIR)/lib
 STATIC_NAME := $(LIB_BUILD_DIR)/lib$(LIBRARY_NAME).a
 DYNAMIC_VERSION_MAJOR 		:= 1
 DYNAMIC_VERSION_MINOR 		:= 1
-DYNAMIC_VERSION_REVISION 	:= 2
+DYNAMIC_VERSION_REVISION 	:= 3
 DYNAMIC_NAME_SHORT := lib$(LIBRARY_NAME).so
 #DYNAMIC_SONAME_SHORT := $(DYNAMIC_NAME_SHORT).$(DYNAMIC_VERSION_MAJOR)
 DYNAMIC_VERSIONED_NAME_SHORT := $(DYNAMIC_NAME_SHORT).$(DYNAMIC_VERSION_MAJOR).$(DYNAMIC_VERSION_MINOR).$(DYNAMIC_VERSION_REVISION)
diff --git a/docker/README.md b/docker/README.md
index d61cb6e58..97dcbb26a 100644
--- a/docker/README.md
+++ b/docker/README.md
@@ -17,7 +17,7 @@ docker run -ti caffe:cpu caffe --version
 ```
 which should show a message like:
 ```
-caffe version 1.1.2
+caffe version 1.1.3
 ```
 
 One can also build and run the Caffe tests in the image using: