TensorFlow text graphs - alalek/opencv GitHub Wiki

To read networks from TensorFlow framework there is cv::dnn::readNetFromTensorflow method which can work with .pb files with frozen TensorFlow graph. However sometimes it is not enough to have only .pb file to import network into OpenCV.

Depends on topology, graph may contains some unfused layers which are not covered by internal subgraphs fusion procedure. In example, instead of a single Flatten node for tensors flattening (reshaping from 4D [N, C, H, W] to 2D [N, C*H*W]) graph will have a set of nodes that precompute final shape and then call Reshape operation:

If you know how to replace TensorFlow subgraph to something simpler which can be parsed by OpenCV (see tf_importer.cpp and tf_graph_simplifier.cpp) you may use optional parameter of cv::dnn::readNetFromTensorflow to use a text graph representation of a frozen .pb file.

Whet is text graph representation? TensorFlow uses Google Protobuf format to store deep learning networks. This format can be as binary as textual. Due models usually have a lot of trainable weights it is more optimal to store them as binary rather than text file.

To generate .pbtxt file for OpenCV, you need to convert a .pb model to text file. The following script can do it:

import tensorflow as tf

pb_file = 'model.pb'
graph_def = tf.compat.v1.GraphDef()

try:
    with tf.io.gfile.GFile(pb_file, 'rb') as f:
        graph_def.ParseFromString(f.read())
except:
    with tf.gfile.FastGFile(pb_file, 'rb') as f:
        graph_def.ParseFromString(f.read())

# Delete weights
for i in reversed(range(len(graph_def.node))):
    if graph_def.node[i].op == 'Const':
        del graph_def.node[i]

graph_def.library.Clear()

tf.compat.v1.train.write_graph(graph_def, "", 'model.pbtxt', as_text=True)

Then open model.pbtxt and replace a problematic subgraph

node {
  name: "flatten_1/Shape"
  op: "Shape"
  input: "max_pooling2d_2/MaxPool"
  attr {
    key: "T"
    value {
      type: DT_FLOAT
    }
  }
  attr {
    key: "out_type"
    value {
      type: DT_INT32
    }
  }
}
node {
  name: "flatten_1/strided_slice"
  op: "StridedSlice"
  input: "flatten_1/Shape"
  input: "flatten_1/strided_slice/stack"
  input: "flatten_1/strided_slice/stack_1"
  input: "flatten_1/strided_slice/stack_2"
  attr {
    key: "Index"
    value {
      type: DT_INT32
    }
  }
  attr {
    key: "T"
    value {
      type: DT_INT32
    }
  }
  attr {
    key: "begin_mask"
    value {
      i: 0
    }
  }
  attr {
    key: "ellipsis_mask"
    value {
      i: 0
    }
  }
  attr {
    key: "end_mask"
    value {
      i: 1
    }
  }
  attr {
    key: "new_axis_mask"
    value {
      i: 0
    }
  }
  attr {
    key: "shrink_axis_mask"
    value {
      i: 0
    }
  }
}
node {
  name: "flatten_1/Prod"
  op: "Prod"
  input: "flatten_1/strided_slice"
  input: "flatten_1/Const"
  attr {
    key: "T"
    value {
      type: DT_INT32
    }
  }
  attr {
    key: "Tidx"
    value {
      type: DT_INT32
    }
  }
  attr {
    key: "keep_dims"
    value {
      b: false
    }
  }
}
node {
  name: "flatten_1/stack_3862"
  op: "Pack"
  input: "flatten_1/stack_3862/0"
  input: "flatten_1/Prod"
  attr {
    key: "N"
    value {
      i: 2
    }
  }
  attr {
    key: "T"
    value {
      type: DT_INT32
    }
  }
  attr {
    key: "axis"
    value {
      i: 0
    }
  }
}
node {
  name: "flatten_1/Reshape"
  op: "Reshape"
  input: "max_pooling2d_2/MaxPool"
  input: "flatten_1/stack_3862"
  attr {
    key: "T"
    value {
      type: DT_FLOAT
    }
  }
  attr {
    key: "Tshape"
    value {
      type: DT_INT32
    }
  }
}

to a single Flatten layer which can be easily imported by OpenCV:

node {
  name: "flatten_1/Reshape"
  op: "Flatten"
  input: "max_pooling2d_2/MaxPool"
}

NOTE: check that after removing the nodes connections between nodes are not broken. That's why we use the same name for new Flatten node because there is a consumer which is connected to this specific name:

node {
  name: "dense_1/MatMul"
  op: "MatMul"
  input: "flatten_1/Reshape"
  input: "dense_1/MatMul/ReadVariableOp"
  attr {
    key: "T"
    value { type: DT_FLOAT }
  }
  attr {
    key: "transpose_a"
    value { b: false }
  }
  attr {
    key: "transpose_b"
    value { b: false }
  }
}

Finally, use both .pb and .pbtxt to import network into OpenCV:

import cv2 as cv

net = cv.dnn.readNetFromTensorflow('model.pb', 'model.pbtxt')