Python wrapper

examples/c3d_train_ucf101/conv3d_ucf101_deploy.prototxt

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+name: "deep_c3d_ucf101"
+input: "data"
+input_dim: 30
+input_dim: 3
+input_dim: 16
+input_dim: 112
+input_dim: 112
+# ----------- 1st layer group ---------------
+layers {
+  name: "conv1a"
+  type: CONVOLUTION3D
+  bottom: "data"
+  top: "conv1a"
+  blobs_lr: 1
+  blobs_lr: 2
+  weight_decay: 1
+  weight_decay: 0
+  convolution_param {
+    num_output: 64
+    kernel_size: 3
+    kernel_depth: 3
+    pad: 1
+    temporal_pad: 1
+    stride: 1
+    weight_filler {
+      type: "gaussian"
+      std: 0.01
+    }
+    bias_filler {
+      type: "constant"
+      value: 0
+    }
+  }
+}
+layers {
+  name: "relu1a"
+  type: RELU
+  bottom: "conv1a"
+  top: "conv1a"
+}
+layers {
+  name: "pool1"
+  type: POOLING3D
+  bottom: "conv1a"
+  top: "pool1"
+  pooling_param {
+    pool: MAX
+    kernel_size: 2
+    kernel_depth: 1
+    stride: 2
+    temporal_stride: 1
+  }
+}
+# ------------- 2nd layer group --------------
+layers {
+  name: "conv2a"
+  type: CONVOLUTION3D
+  bottom: "pool1"
+  top: "conv2a"
+  blobs_lr: 1
+  blobs_lr: 2
+  weight_decay: 1
+  weight_decay: 0
+  convolution_param {
+    num_output: 128
+    kernel_size: 3
+    kernel_depth: 3
+    pad: 1
+    temporal_pad: 1
+    weight_filler {
+      type: "gaussian"
+      std: 0.01
+    }
+    bias_filler {
+      type: "constant"
+      value: 1
+    }
+  }
+}
+layers {
+  name: "relu2a"
+  type: RELU
+  bottom: "conv2a"
+  top: "conv2a"
+}
+layers {
+  name: "pool2"
+  type: POOLING3D
+  bottom: "conv2a"
+  top: "pool2"
+  pooling_param {
+    pool: MAX
+    kernel_size: 2
+    kernel_depth: 2
+    stride: 2
+    temporal_stride: 2
+  }
+}
+# ----------------- 3rd layer group --------------
+layers {
+  name: "conv3a"
+  type: CONVOLUTION3D
+  bottom: "pool2"
+  top: "conv3a"
+  blobs_lr: 1
+  blobs_lr: 2
+  weight_decay: 1
+  weight_decay: 0
+  convolution_param {
+    num_output: 256
+    kernel_size: 3
+    kernel_depth: 3
+    pad: 1
+    temporal_pad: 1
+    weight_filler {
+      type: "gaussian"
+      std: 0.01
+    }
+    bias_filler {
+      type: "constant"
+      value: 1
+    }
+  }
+}
+layers {
+  name: "relu3a"
+  type: RELU
+  bottom: "conv3a"
+  top: "conv3a"
+}
+layers {
+  name: "pool3"
+  type: POOLING3D
+  bottom: "conv3a"
+  top: "pool3"
+  pooling_param {
+    pool: MAX
+    kernel_size: 2
+    kernel_depth: 2
+    stride: 2
+    temporal_stride: 2
+  }
+}
+
+# --------- 4th layer group
+layers {
+  name: "conv4a"
+  type: CONVOLUTION3D
+  bottom: "pool3"
+  top: "conv4a"
+  blobs_lr: 1
+  blobs_lr: 2
+  weight_decay: 1
+  weight_decay: 0
+  convolution_param {
+    num_output: 256
+    kernel_size: 3
+    kernel_depth: 3
+    pad: 1
+    temporal_pad: 1
+    weight_filler {
+      type: "gaussian"
+      std: 0.01
+    }
+    bias_filler {
+      type: "constant"
+      value: 1
+    }
+  }
+}
+layers {
+  name: "relu4a"
+  type: RELU
+  bottom: "conv4a"
+  top: "conv4a"
+}
+layers {
+  name: "pool4"
+  type: POOLING3D
+  bottom: "conv4a"
+  top: "pool4"
+  pooling_param {
+    pool: MAX
+    kernel_size: 2
+    kernel_depth: 2
+    stride: 2
+    temporal_stride: 2
+  }
+}
+
+# --------------- 5th layer group --------
+layers {
+  name: "conv5a"
+  type: CONVOLUTION3D
+  bottom: "pool4"
+  top: "conv5a"
+  blobs_lr: 1
+  blobs_lr: 2
+  weight_decay: 1
+  weight_decay: 0
+  convolution_param {
+    num_output: 256
+    kernel_size: 3
+    kernel_depth: 3
+    pad: 1
+    temporal_pad: 1
+    weight_filler {
+      type: "gaussian"
+      std: 0.01
+    }
+    bias_filler {
+      type: "constant"
+      value: 1
+    }
+  }
+}
+layers {
+  name: "relu5a"
+  type: RELU
+  bottom: "conv5a"
+  top: "conv5a"
+}
+layers {
+  name: "pool5"
+  type: POOLING3D
+  bottom: "conv5a"
+  top: "pool5"
+  pooling_param {
+    pool: MAX
+    kernel_size: 2
+    kernel_depth: 2
+    stride: 2
+    temporal_stride: 2
+  }
+}
+# ---------------- fc layers -------------
+layers {
+  name: "fc6"
+  type: INNER_PRODUCT
+  bottom: "pool5"
+  top: "fc6"
+  blobs_lr: 1
+  blobs_lr: 2
+  weight_decay: 1
+  weight_decay: 0
+  inner_product_param {
+    num_output: 2048
+    weight_filler {
+      type: "gaussian"
+      std: 0.005
+    }
+    bias_filler {
+      type: "constant"
+      value: 1
+    }
+  }
+}
+layers {
+  name: "relu6"
+  type: RELU
+  bottom: "fc6"
+  top: "fc6"
+}
+layers {
+  name: "drop6"
+  type: DROPOUT
+  bottom: "fc6"
+  top: "fc6"
+  dropout_param {
+    dropout_ratio: 0.5
+  }
+}
+layers {
+  name: "fc7"
+  type: INNER_PRODUCT
+  bottom: "fc6"
+  top: "fc7"
+  blobs_lr: 1
+  blobs_lr: 2
+  weight_decay: 1
+  weight_decay: 0
+  inner_product_param {
+    num_output: 2048
+    weight_filler {
+      type: "gaussian"
+      std: 0.005
+    }
+    bias_filler {
+      type: "constant"
+      value: 1
+    }
+  }
+}
+layers {
+  name: "relu7"
+  type: RELU
+  bottom: "fc7"
+  top: "fc7"
+}
+layers {
+  name: "drop7"
+  type: DROPOUT
+  bottom: "fc7"
+  top: "fc7"
+  dropout_param {
+    dropout_ratio: 0.5
+  }
+}
+layers {
+  name: "fc8"
+  type: INNER_PRODUCT
+  bottom: "fc7"
+  top: "fc8"
+  blobs_lr: 1
+  blobs_lr: 2
+  weight_decay: 1
+  weight_decay: 0
+  inner_product_param {
+    num_output: 101
+    weight_filler {
+      type: "gaussian"
+      std: 0.01
+    }
+    bias_filler {
+      type: "constant"
+      value: 0
+    }
+  }
+}
+layers {
+  name: "prob"
+  type: SOFTMAX
+  bottom: "fc8"
+  top: "prob"
+}
+#layers {
+#  top: "accuracy"
+#  name: "accuracy"
+#  type: ACCURACY
+#  bottom: "prob"
+#  bottom: "label"
+#}