netron/source/hailo-metadata.json

[
  {
    "name": "input_layer",
    "description": "Represents an input of the model",
    "attributes": [
      {
        "name": "original_names",
        "type": "string[]",
        "description": "Name of this layer in the original framework, such as Pytorch or Tensorflow",
        "visible": false
      }
    ]
  },
  {
    "name": "output_layer",
    "description": "Represents an output of the model",
    "attributes": [
      {
        "name": "original_names",
        "type": "string[]",
        "description": "Name of this layer in the original framework, such as Pytorch or Tensorflow",
        "visible": false
      }]
  },
  {
    "name": "postprocess",
    "description": "Represents a whole post-processing function of some meta-architecture",
    "attributes": [
      {
        "name": "original_names",
        "type": "string[]",
        "description": "Name of this layer in the original framework, such as Pytorch or Tensorflow",
        "visible": false
      },
      {
        "name": "max_proposals_per_class",
        "type": "int64",
        "description": "Maximum number of proposals per class",
        "visible": false
      },
      {
        "name": "iou_th",
        "type": "float32",
        "visible": false,
        "description": "Intersection over union overlap threshold, used in the NMS iterative elimination process where potential duplicates of detected items are ignored"
      },
      {
        "name": "meta_arch",
        "type": "string",
        "visible": false,
        "description": "Postprocessing meta-architecture name"
      },
      {
        "name": "max_total_output_proposals",
        "type": "int64",
        "visible": false,
        "description": "Maximum number of bounding box proposals"
      },
      {
        "name": "postprocess_type",
        "type": "string",
        "visible": false,
        "description": "Postprocessing type name"
      }
    ]
  },
  {
    "name": "conv",
    "category": "Layer",
    "description": "Convolution layer",
    "attributes": [
      {
        "name": "original_names",
        "type": "string[]",
        "description": "Name of this layer in the original framework, such as Pytorch or Tensorflow",
        "visible": false
      },
      {
        "name": "kernel_shape",
        "type": "int64[]",
        "label": "kernel",
        "description": "Shape of the kernel in Tensorflow convention (kernel height, kernel width, features in, features out)",
        "visible": true
      },
      {
        "name": "strides",
        "type": "int64[]",
        "description": "Stride along each axis (batch, height, width, features)"
      },
      {
        "name": "dilations",
        "type": "int64[]",
        "description": "Dilation value along each axis (batch, height, width, features)"
      },
      {
        "name": "padding",
        "type": "string",
        "description": "Padding mode, either VALID, SAME (symmetric, Caffe-like), SAME_TENSORFLOW, or DECONV"
      },
      {
        "name": "groups",
        "type": "int64",
        "description": "Number of groups input channels and output channels are divided into"
      },
      {
        "name": "batch_norm",
        "type": "boolean",
        "description": "Whether batch normalization is folded into the layer"
      },
      {
        "name": "elementwise_add",
        "type": "boolean",
        "description": "Whether elementwise addition is folded into the layer",
        "visible": false
      },
      {
        "name": "activation",
        "type": "string",
        "description": "Activation function name",
        "visible": false
      },
      {
        "name": "pre_layer_batch_norm",
        "type": "boolean",
        "description": "Whether batch normalization is folded into the layer, before the operation itself",
        "visible": false
      },
      {
        "name": "transpose_output_width_features",
        "type": "boolean",
        "description": "Whether to transpose the width and the features axes of the layer's output tensor",
        "visible": false
      },
      {
        "name": "spatial_flatten_output",
        "type": "boolean",
        "description": "Whether to flatten the layer's output to one row",
        "visible": false
      }
    ]
  },
  {
    "name": "relu",
    "category": "Activation"
  },
  {
    "name": "delta",
    "category": "Activation"
  },
  {
    "name": "activation",
    "category": "Activation",
    "description": "Activation function",
    "attributes": [
      {
        "name": "original_names",
        "type": "string[]",
        "description": "Name of this layer in the original framework, such as Pytorch or Tensorflow",
        "visible": false
      },
      {
        "name": "batch_norm",
        "type": "boolean",
        "description": "Whether batch normalization is folded into the layer",
        "visible": false
      },
      {
        "name": "elementwise_add",
        "type": "boolean",
        "description": "Whether elementwise addition is folded into the layer",
        "visible": false
      },
      {
        "name": "activation",
        "type": "string",
        "description": "Activation function name",
        "visible": false
      }
    ]
  },
  {
    "name": "argmax",
    "description": "Argmax layer",
    "attributes": [
      {
        "name": "original_names",
        "type": "string[]",
        "description": "Name of this layer in the original framework, such as Pytorch or Tensorflow",
        "visible": false
      }]
  },
  {
    "name": "avgpool",
    "category": "Pool",
    "description": "Average pooling layer",
    "attributes": [
      {
        "name": "original_names",
        "type": "string[]",
        "description": "Name of this layer in the original framework, such as Pytorch or Tensorflow",
        "visible": false
      },
      {
        "name": "kernel_shape",
        "type": "int64[]",
        "label": "kernel",
        "description": "Shape of the kernel in Tensorflow convention (kernel height, kernel width, features in, features out)",
        "visible": true
      },
      {
        "name": "strides",
        "type": "int64[]",
        "description": "Stride along each axis (batch, height, width, features)",
        "visible": false
      },
      {
        "name": "padding",
        "type": "string",
        "description": "Padding mode, either VALID, SAME (symmetric, Caffe-like), SAME_TENSORFLOW, or DECONV",
        "visible": false
      },
      {
        "name": "activation",
        "type": "string",
        "description": "Activation function name",
        "visible": false
      }
    ]
  },
  {
    "name": "batch_norm",
    "category": "Normalization",
    "description": "Batch normalization layer",
    "attributes": [
      {
        "name": "original_names",
        "type": "string[]",
        "description": "Name of this layer in the original framework, such as Pytorch or Tensorflow",
        "visible": false
      },
      {
        "name": "elementwise_add",
        "type": "boolean",
        "description": "Whether elementwise addition is folded into the layer",
        "visible": false
      },
      {
        "name": "activation",
        "type": "string",
        "description": "Activation function name",
        "visible": false
      }
    ]
  },
  {
    "name": "bbox_decoder",
    "description": "Bounding box decoding layer",
    "attributes": [
      {
        "name": "original_names",
        "type": "string[]",
        "description": "Name of this layer in the original framework, such as Pytorch or Tensorflow",
        "visible": false
      }
    ]
  },
  {
    "name": "deconv",
    "category": "Layer",
    "description": "Deconvolution (transposed convolution) layer",
    "attributes": [
      {
        "name": "original_names",
        "type": "string[]",
        "description": "Name of this layer in the original framework, such as Pytorch or Tensorflow",
        "visible": false
      },
      {
        "name": "kernel_shape",
        "type": "int64[]",
        "label": "kernel",
        "description": "Shape of the kernel in Tensorflow convention (kernel height, kernel width, features in, features out)",
        "visible": true
      },
      {
        "name": "strides",
        "type": "int64[]",
        "description": "Stride along each axis (batch, height, width, features)",
        "visible": false
      },
      {
        "name": "dilations",
        "type": "int64[]",
        "description": "Dilation value along each axis (batch, height, width, features)",
        "visible": false
      },
      {
        "name": "padding",
        "type": "string",
        "description": "Padding mode, either VALID, SAME (symmetric, Caffe-like), SAME_TENSORFLOW, or DECONV",
        "visible": false
      },
      {
        "name": "groups",
        "type": "int64",
        "description": "Number of groups input channels and output channels are divided into",
        "visible": false
      },
      {
        "name": "batch_norm",
        "type": "boolean",
        "description": "Whether batch normalization is folded into the layer",
        "visible": false
      },
      {
        "name": "elementwise_add",
        "type": "boolean",
        "description": "Whether elementwise addition is folded into the layer",
        "visible": false
      },
      {
        "name": "activation",
        "type": "string",
        "description": "Activation function name",
        "visible": false
      }
    ]
  },
  {
    "name": "dense",
    "category": "Layer",
    "description": "Dense (fully connected) layer",
    "attributes": [
      {
        "name": "original_names",
        "type": "string[]",
        "description": "Name of this layer in the original framework, such as Pytorch or Tensorflow",
        "visible": false
      },
      {
        "name": "kernel_shape",
        "type": "int64[]",
        "label": "kernel",
        "description": "Shape of the kernel in Tensorflow convention (kernel height, kernel width, features in, features out)",
        "visible": true
      },
      {
        "name": "batch_norm",
        "type": "boolean",
        "description": "Whether batch normalization is folded into the layer",
        "visible": false
      }
    ]
  },
  {
    "name": "depth_to_space",
    "description": "Depth to space layer",
    "attributes": [
      {
        "name": "original_names",
        "type": "string[]",
        "description": "Name of this layer in the original framework, such as Pytorch or Tensorflow",
        "visible": false
      },
      {
        "name": "block_sizes",
        "type": "int64[]",
        "description": "Block size along each spatial axis",
        "visible": false
      },
      {
        "name": "depth_to_space_type",
        "type": "string",
        "description": "Depth to space variant, either dcr (depth-column-row) or crd (column-row-depth)",
        "visible": false
      }
    ]
  },
  {
    "name": "dw",
    "description": "Depthwise convolution layer",
    "attributes": [
      {
        "name": "original_names",
        "type": "string[]",
        "description": "Name of this layer in the original framework, such as Pytorch or Tensorflow",
        "visible": false
      },
      {
        "name": "kernel_shape",
        "type": "int64[]",
        "label": "kernel",
        "description": "Shape of the kernel in Tensorflow convention (kernel height, kernel width, features in, features out)",
        "visible": true
      },
      {
        "name": "strides",
        "type": "int64[]",
        "description": "Stride along each axis (batch, height, width, features)",
        "visible": false
      },
      {
        "name": "dilations",
        "type": "int64[]",
        "description": "Dilation value along each axis (batch, height, width, features)",
        "visible": false
      },
      {
        "name": "padding",
        "type": "string",
        "description": "Padding mode, either VALID, SAME (symmetric, Caffe-like), SAME_TENSORFLOW, or DECONV",
        "visible": false
      },
      {
        "name": "groups",
        "type": "int64",
        "description": "Number of groups input channels and output channels are divided into",
        "visible": false
      },
      {
        "name": "batch_norm",
        "type": "boolean",
        "description": "Whether batch normalization is folded into the layer",
        "visible": false
      },
      {
        "name": "elementwise_add",
        "type": "boolean",
        "description": "Whether elementwise addition is folded into the layer",
        "visible": false
      },
      {
        "name": "activation",
        "type": "string",
        "description": "Activation function name",
        "visible": false
      },
      {
        "name": "transpose_output_width_features",
        "type": "string",
        "description": "Whether to transpose the width and the features axes of the layer's output tensor",
        "visible": false
      },
      {
        "name": "dynamic_weights",
        "type": "boolean",
        "description": "Whether the layer's weights are data driven",
        "visible": false
      }
    ]
  },
  {
    "name": "external_pad",
    "description": "Padding layer",
    "attributes": [
      {
        "name": "original_names",
        "type": "string[]",
        "description": "Name of this layer in the original framework, such as Pytorch or Tensorflow",
        "visible": false
      },
      {
        "name": "external_pad_params",
        "type": "int64[]",
        "description": "Padding value in pixels in each edge (top, bottom, left, right)",
        "visible": false
      }
    ]
  },
  {
    "name": "feature_interleave",
    "description": "Feature interleave layer",
    "attributes": [
      {
        "name": "original_names",
        "type": "string[]",
        "description": "Name of this layer in the original framework, such as Pytorch or Tensorflow",
        "visible": false
      }]
  },
  {
    "name": "feature_multiplier",
    "description": "Elementwise feature multiplication layer",
    "attributes": [
      {
        "name": "original_names",
        "type": "string[]",
        "description": "Name of this layer in the original framework, such as Pytorch or Tensorflow",
        "visible": false
      },
      {
        "name": "feature_multiplier_type",
        "type": "string",
        "description": "Feature multiplier variant, either square (to multiply each value by itself), or user_specified",
        "visible": false
      }
    ]
  },
  {
    "name": "feature_shuffle",
    "description": "Feature shuffle layer",
    "attributes": [
      {
        "name": "original_names",
        "type": "string[]",
        "description": "Name of this layer in the original framework, such as Pytorch or Tensorflow",
        "visible": false
      }]
  },
  {
    "name": "format_conversion",
    "description": "Reshapes the input tensor between different memory layouts",
    "attributes": [
      {
        "name": "original_names",
        "type": "string[]",
        "description": "Name of this layer in the original framework, such as Pytorch or Tensorflow",
        "visible": false
      },
      {
        "name": "expand_spatial_sizes",
        "type": "int64[]",
        "description": "New output tensor dimensions after the reshape (height, width)",
        "visible": false
      },
      {
        "name": "conversion_type",
        "type": "string",
        "visible": false,
        "description": "Format conversion variant"
      }
    ]
  },
  {
    "name": "global_avg_pool",
    "category": "Pool",
    "description": "Global average pooling layer",
    "attributes": [
      {
        "name": "original_names",
        "type": "string[]",
        "description": "Name of this layer in the original framework, such as Pytorch or Tensorflow",
        "visible": false
      }]
  },
  {
    "name": "maxpool",
    "category": "Pool",
    "description": "Maximum pooling layer",
    "attributes": [
      {
        "name": "original_names",
        "type": "string[]",
        "description": "Name of this layer in the original framework, such as Pytorch or Tensorflow",
        "visible": false
      },
      {
        "name": "kernel_shape",
        "type": "int64[]",
        "label": "kernel",
        "description": "Shape of the kernel in Tensorflow convention (kernel height, kernel width, features in, features out)",
        "visible": true
      },
      {
        "name": "strides",
        "type": "int64[]",
        "description": "Stride along each axis (batch, height, width, features)",
        "visible": false
      },
      {
        "name": "padding",
        "type": "string",
        "description": "Padding mode, either VALID, SAME (symmetric, Caffe-like), SAME_TENSORFLOW, or DECONV",
        "visible": false
      },
      {
        "name": "activation",
        "type": "string",
        "description": "Activation function name",
        "visible": false
      }
    ]
  },
  {
    "name": "nms",
    "description": "Non-maximum suppression layer",
    "attributes": [
      {
        "name": "original_names",
        "type": "string[]",
        "description": "Name of this layer in the original framework, such as Pytorch or Tensorflow",
        "visible": false
      },
      {
        "name": "scores_threshold",
        "type": "float32",
        "description": "Confidence threshold for NMS filtering",
        "visible": false
      },
      {
        "name": "iou_threshold",
        "type": "float32",
        "description": "Intersection over union overlap threshold, used in the NMS iterative elimination process where potential duplicates of detected items are ignored",
        "visible": false
      },
      {
        "name": "classes",
        "type": "int64",
        "description": "Number of NMS classes",
        "visible": false
      },
      {
        "name": "max_output_size",
        "type": "int64",
        "description": "Maximum number of proposals per class",
        "visible": false
      }
    ]
  },
  {
    "name": "normalization",
    "category": "Normalization",
    "description": "Normalization layer",
    "attributes": [
      {
        "name": "original_names",
        "type": "string[]",
        "description": "Name of this layer in the original framework, such as Pytorch or Tensorflow",
        "visible": false
      },
      {
        "name": "elementwise_add",
        "type": "boolean",
        "description": "Whether elementwise addition is folded into the layer",
        "visible": false
      },
      {
        "name": "activation",
        "type": "string",
        "description": "Activation function name",
        "visible": false
      }
    ]
  },
  {
    "name": "proposal_generator",
    "description": "Proposal generator layer",
    "attributes": [
      {
        "name": "original_names",
        "type": "string[]",
        "description": "Name of this layer in the original framework, such as Pytorch or Tensorflow",
        "visible": false
      }]
  },
  {
    "name": "reduce_l2",
    "description": "Reduce layer",
    "attributes": [
      {
        "name": "original_names",
        "type": "string[]",
        "description": "Name of this layer in the original framework, such as Pytorch or Tensorflow",
        "visible": false
      }]
  },
  {
    "name": "reduce_max",
    "description": "Reduce Max layer",
    "attributes": [
      {
        "name": "original_names",
        "type": "string[]",
        "description": "Name of this layer in the original framework, such as Pytorch or Tensorflow",
        "visible": false
      },
      {
        "name": "groups",
        "type": "int64",
        "description": "Number of groups input channels and output channels are divided into",
        "visible": false
      }
    ]
  },
  {
    "name": "reduce_sum",
    "description": "Reduce Sum layer",
    "attributes": [
      {
        "name": "original_names",
        "type": "string[]",
        "description": "Name of this layer in the original framework, such as Pytorch or Tensorflow",
        "visible": false
      },
      {
        "name": "groups",
        "type": "int64",
        "description": "Number of groups input channels and output channels are divided into",
        "visible": false
      },
      {
        "name": "activation",
        "type": "string",
        "description": "Activation function name",
        "visible": false
      },
      {
        "name": "reduce_axes",
        "type": "int64[]",
        "description": "List of axes to reduce",
        "visible": false
      }
    ]
  },
  {
    "name": "resize",
    "category": "Tensor",
    "description": "Resize layer",
    "attributes": [
      {
        "name": "original_names",
        "type": "string[]",
        "description": "Name of this layer in the original framework, such as Pytorch or Tensorflow",
        "visible": false
      },
      {
        "name": "resize_h_ratio_list",
        "type": "float32[]",
        "visible": true
      },
      {
        "name": "resize_w_ratio_list",
        "type": "float32[]",
        "visible": true
      },
      {
        "name": "resize_f_ratio_list",
        "type": "float32[]",
        "visible": true
      },
      {
        "name": "method",
        "type": "string",
        "description": "Resize method, either bilinear or nearest_neighbor",
        "visible": false
      },
      {
        "name": "resize_bilinear_pixels_mode",
        "type": "string",
        "description": "Bilinear resize variant, either half_pixels, align_corners, or disabled (where both align_corners and half_pixels are false)",
        "visible": false
      }
    ]
  },
  {
    "name": "shortcut",
    "description": "Shortcut layer",
    "attributes": [
      {
        "name": "original_names",
        "type": "string[]",
        "description": "Name of this layer in the original framework, such as Pytorch or Tensorflow",
        "visible": false
      }]
  },
  {
    "name": "slice",
    "description": "Slice layer",
    "attributes": [
      {
        "name": "original_names",
        "type": "string[]",
        "description": "Name of this layer in the original framework, such as Pytorch or Tensorflow",
        "visible": false
      },
      {
        "name": "height_slice",
        "type": "int64[]",
        "visible": false,
        "description": "Slice in the height axis (start, stop, step)"
      },
      {
        "name": "width_slice",
        "type": "int64[]",
        "visible": false,
        "description": "Slice in the width axis (start, stop, step)"
      },
      {
        "name": "features_slice",
        "type": "int64[]",
        "visible": false,
        "description": "Slice in the features axis (start, stop, step)"
      }
    ]
  },
  {
    "name": "softmax",
    "category": "Activation",
    "description": "Softmax layer",
    "attributes": [
      {
        "name": "original_names",
        "type": "string[]",
        "description": "Name of this layer in the original framework, such as Pytorch or Tensorflow",
        "visible": false
      },
      {
        "name": "groups",
        "type": "int64",
        "description": "Number of groups input channels and output channels are divided into",
        "visible": false
      }
    ]
  },
  {
    "name": "space_to_depth",
    "description": "Space to depth layer",
    "attributes": [
      {
        "name": "original_names",
        "type": "string[]",
        "description": "Name of this layer in the original framework, such as Pytorch or Tensorflow",
        "visible": false
      },
      {
        "name": "block_sizes",
        "type": "int64[]",
        "description": "Block size along each spatial axis",
        "visible": false
      },
      {
        "name": "space_to_depth_type",
        "type": "string",
        "description": "Space to depth variant, either classic_dcr (depth-column-row) classic_crd (column-row-depth), serial (used by Transformers patchify function), or focus (Yolov5-like)",
        "visible": false
      },
      {
        "name": "spatial_flatten_output",
        "type": "boolean",
        "description": "Whether to flatten the layer's output to one row",
        "visible": false
      }
    ]
  },
  {
    "name": "output_mux",
    "description": "Output muxer layer",
    "attributes": [
      {
        "name": "original_names",
        "type": "string[]",
        "description": "Name of this layer in the original framework, such as Pytorch or Tensorflow",
        "visible": false
      }]
  },
  {
    "name": "concat",
    "category": "Tensor",
    "description": "Concatenation layer",
    "attributes": [
      {
        "name": "original_names",
        "type": "string[]",
        "description": "Name of this layer in the original framework, such as Pytorch or Tensorflow",
        "visible": false
      },
      {
        "name": "concat_axis",
        "type": "int64",
        "description": "Axis to concatenate along, either features or spatial_w (which means the width axis)",
        "visible": false
      },
      {
        "name": "spatial_w_concat",
        "type": "boolean",
        "description": "Whether the concat operation is in the width dimension",
        "visible": false
      }
    ]
  },
  {
    "name": "matmul",
    "description": "Matrix multiplication layer",
    "attributes": [
      {
        "name": "original_names",
        "type": "string[]",
        "description": "Name of this layer in the original framework, such as Pytorch or Tensorflow",
        "visible": false
      },
      {
        "name": "kernel_shape",
        "type": "int64[]",
        "label": "kernel",
        "description": "Shape of the kernel in Tensorflow convention (kernel height, kernel width, features in, features out)",
        "visible": true
      },
      {
        "name": "dynamic_weights",
        "type": "boolean",
        "description": "Whether the layer's weights are data driven",
        "visible": false
      },
      {
        "name": "transpose_matmul_input",
        "type": "boolean",
        "description": "Whether to transpose the width and the features axes of the layer's second input tensor",
        "visible": false
      }
    ]
  },
  {
    "name": "ew_add",
    "description": "Elementwise addition layer",
    "attributes": [
      {
        "name": "original_names",
        "type": "string[]",
        "description": "Name of this layer in the original framework, such as Pytorch or Tensorflow",
        "visible": false
      },
      {
        "name": "activation",
        "type": "string",
        "description": "Activation function name",
        "visible": false
      }
    ]
  },
  {
    "name": "ew_div",
    "description": "Elementwise division layer",
    "attributes": [
      {
        "name": "original_names",
        "type": "string[]",
        "description": "Name of this layer in the original framework, such as Pytorch or Tensorflow",
        "visible": false
      },
      {
        "name": "activation",
        "type": "string",
        "description": "Activation function name",
        "visible": false
      }
    ]
  },
  {
    "name": "ew_mult",
    "description": "Elementwise multiplication layer",
    "attributes": [
      {
        "name": "original_names",
        "type": "string[]",
        "description": "Name of this layer in the original framework, such as Pytorch or Tensorflow",
        "visible": false
      },
      {
        "name": "activation",
        "type": "string",
        "description": "Activation function name",
        "visible": false
      }
    ]
  },
  {
    "name": "ew_sub",
    "description": "Elementwise subtraction layer",
    "attributes": [
      {
        "name": "original_names",
        "type": "string[]",
        "description": "Name of this layer in the original framework, such as Pytorch or Tensorflow",
        "visible": false
      },
      {
        "name": "activation",
        "type": "string",
        "description": "Activation function name",
        "visible": false
      }
    ]
  },
  {
    "name": "demux",
    "description": "Demuxer layer",
    "attributes": [
      {
        "name": "original_names",
        "type": "string[]",
        "description": "Name of this layer in the original framework, such as Pytorch or Tensorflow",
        "visible": false
      }]
  },
  {
    "name": "row_splitter",
    "description": "Splits the input tensor along the height axis",
    "attributes": [
      {
        "name": "original_names",
        "type": "string[]",
        "description": "Name of this layer in the original framework, such as Pytorch or Tensorflow",
        "visible": false
      }]
  },
  {
    "name": "feature_splitter",
    "description": "Splits the input tensor along the features axis",
    "attributes": [
      {
        "name": "original_names",
        "type": "string[]",
        "description": "Name of this layer in the original framework, such as Pytorch or Tensorflow",
        "visible": false
      }]
  },
  {
    "name": "const_input",
    "category": "Constant",
    "description": "Constant input",
    "attributes": [
      {
        "name": "original_names",
        "type": "string[]",
        "description": "Name of this layer in the original framework, such as Pytorch or Tensorflow",
        "visible": false
      }]
  },
  {
    "name": "inv_pos",
    "category": "Activation"
  },
  {
    "name": "exp",
    "category": "Activation"
  },
  {
    "name": "silu",
    "category": "Activation"
  },
  {
    "name": "leaky",
    "category": "Activation"
  },
  {
    "name": "layer_normalization",
    "category": "Normalization"
  }
]