add ImageNet V2 benchmark

ImageNetUseCaseV2/Caffe/Model/VGG_ILSVRC_19_layers_solver.prototxt

+test_iter: 1000
+test_interval: 1000
+base_lr: 0.01
+lr_policy: "step"
+gamma: 0.1
+stepsize: 100000
+display: 20
+momentum: 0.9
+weight_decay: 0.0005
+snapshot: 10000
+snapshot_after_train: true
+solver_mode: GPU
+max_iter: 10009
+net: "/gpfs16l/pwrwork/idris/sts/ssts001/ImageNetUseCaseV2/Caffe/Model/VGG_ILSVRC_19_layers_train_val.prototxt"
+snapshot_prefix: "/gpfs16l/pwrwork/idris/sts/ssts001/ImageNetUseCaseV2/Caffe/Model/Snapshots/VGG_ILSVRC_19_layers_train_"

ImageNetUseCaseV2/Caffe/Model/VGG_ILSVRC_19_layers_solver.prototxt.template

+test_iter: 1000
+test_interval: 1000
+base_lr: 0.01
+lr_policy: "step"
+gamma: 0.1
+stepsize: 100000
+display: 20
+momentum: 0.9
+weight_decay: 0.0005
+snapshot: 10000
+snapshot_after_train: true
+solver_mode: GPU
+max_iter: <num-iterations>
+net: "<path-to-net-file>"
+snapshot_prefix: "<path-to-snapshot-dir>/VGG_ILSVRC_19_layers_train_"

ImageNetUseCaseV2/Caffe/Model/VGG_ILSVRC_19_layers_train_val.prototxt

+name: "VGG_ILSVRC_19_layers"
+layers {
+  name: "data"
+  type: DATA
+  include {
+    phase: TRAIN
+  }
+# transform_param {
+#    crop_size: 224
+#    mean_value: 104
+#    mean_value: 117
+#    mean_value: 123
+#    mirror: true
+# }
+  transform_param {
+    mirror: true
+    crop_size: 224
+    mean_file: "/gpfs16l/pwrwork/idris/sts/ssts001/ImageNetUseCaseV2/Dataset/PostProcessedLMDB/train//imagenet_mean.binaryproto"
+  }
+  data_param {
+    source: "/gpfs16l/pwrwork/idris/sts/ssts001/ImageNetUseCaseV2/Dataset/PostProcessedLMDB/train/"
+    batch_size: 32
+    backend: LMDB
+  }
+  top: "data"
+  top: "label"
+}
+layers {
+  name: "data"
+  type: DATA
+  include {
+    phase: TEST
+  }
+# transform_param {
+#    crop_size: 224
+#    mean_value: 104
+#    mean_value: 117
+#    mean_value: 123
+#    mirror: false
+# }
+  transform_param {
+    mirror: false
+    crop_size: 224
+    mean_file: "/gpfs16l/pwrwork/idris/sts/ssts001/ImageNetUseCaseV2/Dataset/PostProcessedLMDB/train//imagenet_mean.binaryproto"
+  }
+ data_param {
+    source: "/gpfs16l/pwrwork/idris/sts/ssts001/ImageNetUseCaseV2/Dataset/PostProcessedLMDB/val/"
+    batch_size: 50
+    backend: LMDB
+  }
+  top: "data"
+  top: "label"
+}
+layers {
+  bottom: "data"
+  top: "conv1_1"
+  name: "conv1_1"
+  type: CONVOLUTION
+  convolution_param {
+    num_output: 64
+    pad: 1
+    kernel_size: 3
+  } 
+}
+layers {
+  bottom: "conv1_1"
+  top: "conv1_1"
+  name: "relu1_1"
+  type: RELU
+}
+layers {
+  bottom: "conv1_1"
+  top: "conv1_2"
+  name: "conv1_2"
+  type: CONVOLUTION
+  convolution_param {
+    num_output: 64
+    pad: 1
+    kernel_size: 3
+  }
+}
+layers {
+  bottom: "conv1_2"
+  top: "conv1_2"
+  name: "relu1_2"
+  type: RELU
+}
+layers {
+  bottom: "conv1_2"
+  top: "pool1"
+  name: "pool1"
+  type: POOLING
+  pooling_param {
+    pool: MAX
+    kernel_size: 2
+    stride: 2
+  }
+}
+layers {
+  bottom: "pool1"
+  top: "conv2_1"
+  name: "conv2_1"
+  type: CONVOLUTION
+  convolution_param {
+    num_output: 128
+    pad: 1
+    kernel_size: 3
+  } 
+}
+layers {
+  bottom: "conv2_1"
+  top: "conv2_1"
+  name: "relu2_1"
+  type: RELU
+}
+layers {
+  bottom: "conv2_1"
+  top: "conv2_2"
+  name: "conv2_2"
+  type: CONVOLUTION
+  convolution_param {
+    num_output: 128
+    pad: 1
+    kernel_size: 3
+  }
+}
+layers {
+  bottom: "conv2_2"
+  top: "conv2_2"
+  name: "relu2_2"
+  type: RELU
+}
+layers {
+  bottom: "conv2_2"
+  top: "pool2"
+  name: "pool2"
+  type: POOLING
+  pooling_param {
+    pool: MAX
+    kernel_size: 2
+    stride: 2
+  }
+}
+layers {
+  bottom: "pool2"
+  top: "conv3_1"
+  name: "conv3_1"
+  type: CONVOLUTION
+  convolution_param {
+    num_output: 256
+    pad: 1
+    kernel_size: 3
+  }
+}
+layers {
+  bottom: "conv3_1"
+  top: "conv3_1"
+  name: "relu3_1"
+  type: RELU
+}
+layers {
+  bottom: "conv3_1"
+  top: "conv3_2"
+  name: "conv3_2"
+  type: CONVOLUTION
+  convolution_param {
+    num_output: 256
+    pad: 1
+    kernel_size: 3
+  }
+}
+layers {
+  bottom: "conv3_2"
+  top: "conv3_2"
+  name: "relu3_2"
+  type: RELU
+}
+layers {
+  bottom: "conv3_2"
+  top: "conv3_3"
+  name: "conv3_3"
+  type: CONVOLUTION
+  convolution_param {
+    num_output: 256
+    pad: 1
+    kernel_size: 3
+  }
+}
+layers {
+  bottom: "conv3_3"
+  top: "conv3_3"
+  name: "relu3_3"
+  type: RELU
+}
+layers {
+  bottom: "conv3_3"
+  top: "conv3_4"
+  name: "conv3_4"
+  type: CONVOLUTION
+  convolution_param {
+    num_output: 256
+    pad: 1
+    kernel_size: 3
+  }
+}
+layers {
+  bottom: "conv3_4"
+  top: "conv3_4"
+  name: "relu3_4"
+  type: RELU
+}
+layers {
+  bottom: "conv3_4"
+  top: "pool3"
+  name: "pool3"
+  type: POOLING
+  pooling_param {
+    pool: MAX
+    kernel_size: 2
+    stride: 2
+  }
+}
+layers {
+  bottom: "pool3"
+  top: "conv4_1"
+  name: "conv4_1"
+  type: CONVOLUTION
+  convolution_param {
+    num_output: 512
+    pad: 1
+    kernel_size: 3
+  } 
+}
+layers {
+  bottom: "conv4_1"
+  top: "conv4_1"
+  name: "relu4_1"
+  type: RELU
+}
+layers {
+  bottom: "conv4_1"
+  top: "conv4_2"
+  name: "conv4_2"
+  type: CONVOLUTION
+  convolution_param {
+    num_output: 512
+    pad: 1
+    kernel_size: 3
+  }
+}
+layers {
+  bottom: "conv4_2"
+  top: "conv4_2"
+  name: "relu4_2"
+  type: RELU
+}
+layers {
+  bottom: "conv4_2"
+  top: "conv4_3"
+  name: "conv4_3"
+  type: CONVOLUTION
+  convolution_param {
+    num_output: 512
+    pad: 1
+    kernel_size: 3
+  } 
+}
+layers {
+  bottom: "conv4_3"
+  top: "conv4_3"
+  name: "relu4_3"
+  type: RELU
+}
+layers {
+  bottom: "conv4_3"
+  top: "conv4_4"
+  name: "conv4_4"
+  type: CONVOLUTION
+  convolution_param {
+    num_output: 512
+    pad: 1
+    kernel_size: 3
+  } 
+}
+layers {
+  bottom: "conv4_4"
+  top: "conv4_4"
+  name: "relu4_4"
+  type: RELU
+}
+layers {
+  bottom: "conv4_4"
+  top: "pool4"
+  name: "pool4"
+  type: POOLING
+  pooling_param {
+    pool: MAX
+    kernel_size: 2
+    stride: 2
+  }
+}
+layers {
+  bottom: "pool4"
+  top: "conv5_1"
+  name: "conv5_1"
+  type: CONVOLUTION
+  convolution_param {
+    num_output: 512
+    pad: 1
+    kernel_size: 3
+  }
+}
+layers {
+  bottom: "conv5_1"
+  top: "conv5_1"
+  name: "relu5_1"
+  type: RELU
+}
+layers {
+  bottom: "conv5_1"
+  top: "conv5_2"
+  name: "conv5_2"
+  type: CONVOLUTION
+  convolution_param {
+    num_output: 512
+    pad: 1
+    kernel_size: 3
+  } 
+}
+layers {
+  bottom: "conv5_2"
+  top: "conv5_2"
+  name: "relu5_2"
+  type: RELU
+}
+layers {
+  bottom: "conv5_2"
+  top: "conv5_3"
+  name: "conv5_3"
+  type: CONVOLUTION
+  convolution_param {
+    num_output: 512
+    pad: 1
+    kernel_size: 3
+  } 
+}
+layers {
+  bottom: "conv5_3"
+  top: "conv5_3"
+  name: "relu5_3"
+  type: RELU
+}
+layers {
+  bottom: "conv5_3"
+  top: "conv5_4"
+  name: "conv5_4"
+  type: CONVOLUTION
+  convolution_param {
+    num_output: 512
+    pad: 1
+    kernel_size: 3
+  } 
+}
+layers {
+  bottom: "conv5_4"
+  top: "conv5_4"
+  name: "relu5_4"
+  type: RELU
+}
+layers {
+  bottom: "conv5_4"
+  top: "pool5"
+  name: "pool5"
+  type: POOLING
+  pooling_param {
+    pool: MAX
+    kernel_size: 2
+    stride: 2
+  }
+}
+layers {
+  bottom: "pool5"
+  top: "fc6"
+  name: "fc6"
+  type: INNER_PRODUCT
+  inner_product_param {
+    num_output: 4096
+  }
+}
+layers {
+  bottom: "fc6"
+  top: "fc6"
+  name: "relu6"
+  type: RELU
+}
+layers {
+  bottom: "fc6"
+  top: "fc6"
+  name: "drop6"
+  type: DROPOUT
+  dropout_param {
+    dropout_ratio: 0.5
+  }
+}
+layers {
+  bottom: "fc6"
+  top: "fc7"
+  name: "fc7"
+  type: INNER_PRODUCT
+  inner_product_param {
+    num_output: 4096
+  }
+}
+layers {
+  bottom: "fc7"
+  top: "fc7"
+  name: "relu7"
+  type: RELU
+}
+layers {
+  bottom: "fc7"
+  top: "fc7"
+  name: "drop7"
+  type: DROPOUT
+  dropout_param {
+    dropout_ratio: 0.5
+  }
+}
+layers {
+  name: "fc8"
+  bottom: "fc7"
+  top: "fc8"
+  type: INNER_PRODUCT
+  inner_product_param {
+    num_output: 1000
+  }
+}
+layers {
+  name: "loss"
+  type: SOFTMAX_LOSS
+  bottom: "fc8"
+  bottom: "label"
+  top: "loss/loss"
+}
+layers {
+  name: "accuracy/top1"
+  type: ACCURACY
+  bottom: "fc8"
+  bottom: "label"
+  top: "accuracy@1"
+  include: { phase: TEST }
+  accuracy_param {
+    top_k: 1
+  }
+}
+layers {
+  name: "accuracy/top5"
+  type: ACCURACY
+  bottom: "fc8"
+  bottom: "label"
+  top: "accuracy@5"
+  include: { phase: TEST }
+  accuracy_param {
+    top_k: 5
+  }
+}

ImageNetUseCaseV2/Caffe/Model/VGG_ILSVRC_19_layers_train_val.prototxt.template

+name: "VGG_ILSVRC_19_layers"
+layers {
+  name: "data"
+  type: DATA
+  include {
+    phase: TRAIN
+  }
+# transform_param {
+#    crop_size: 224
+#    mean_value: 104
+#    mean_value: 117
+#    mean_value: 123
+#    mirror: true
+# }
+  transform_param {
+    mirror: true
+    crop_size: 224
+    mean_file: "<mean-file>"
+  }
+  data_param {
+    source: "<dataset-train>"
+    batch_size: <batch-size>
+    backend: LMDB
+  }
+  top: "data"
+  top: "label"
+}
+layers {
+  name: "data"
+  type: DATA
+  include {
+    phase: TEST
+  }
+# transform_param {
+#    crop_size: 224
+#    mean_value: 104
+#    mean_value: 117
+#    mean_value: 123
+#    mirror: false
+# }
+  transform_param {
+    mirror: false
+    crop_size: 224
+    mean_file: "<mean-file>"
+  }
+ data_param {
+    source: "<dataset-val>"
+    batch_size: 50
+    backend: LMDB
+  }
+  top: "data"
+  top: "label"
+}
+layers {
+  bottom: "data"
+  top: "conv1_1"
+  name: "conv1_1"
+  type: CONVOLUTION
+  convolution_param {
+    num_output: 64
+    pad: 1
+    kernel_size: 3
+  } 
+}
+layers {
+  bottom: "conv1_1"
+  top: "conv1_1"
+  name: "relu1_1"
+  type: RELU
+}
+layers {
+  bottom: "conv1_1"
+  top: "conv1_2"
+  name: "conv1_2"
+  type: CONVOLUTION
+  convolution_param {
+    num_output: 64
+    pad: 1
+    kernel_size: 3
+  }
+}
+layers {
+  bottom: "conv1_2"
+  top: "conv1_2"
+  name: "relu1_2"
+  type: RELU
+}
+layers {
+  bottom: "conv1_2"
+  top: "pool1"
+  name: "pool1"
+  type: POOLING
+  pooling_param {
+    pool: MAX
+    kernel_size: 2
+    stride: 2
+  }
+}
+layers {
+  bottom: "pool1"
+  top: "conv2_1"
+  name: "conv2_1"
+  type: CONVOLUTION
+  convolution_param {
+    num_output: 128
+    pad: 1
+    kernel_size: 3
+  } 
+}
+layers {
+  bottom: "conv2_1"
+  top: "conv2_1"
+  name: "relu2_1"