mirror of https://github.com/opencv/opencv.git
Open Source Computer Vision Library
https://opencv.org/
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292 lines
11 KiB
292 lines
11 KiB
7 years ago
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import argparse
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import numpy as np
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import tensorflow as tf
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from tensorflow.core.framework.node_def_pb2 import NodeDef
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from tensorflow.tools.graph_transforms import TransformGraph
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from google.protobuf import text_format
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parser = argparse.ArgumentParser(description='Run this script to get a text graph of '
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'SSD model from TensorFlow Object Detection API. '
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'Then pass it with .pb file to cv::dnn::readNetFromTensorflow function.')
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parser.add_argument('--input', required=True, help='Path to frozen TensorFlow graph.')
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parser.add_argument('--output', required=True, help='Path to output text graph.')
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parser.add_argument('--num_classes', default=90, type=int, help='Number of trained classes.')
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parser.add_argument('--scales', default=[0.25, 0.5, 1.0, 2.0], type=float, nargs='+',
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help='Hyper-parameter of grid_anchor_generator from a config file.')
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parser.add_argument('--aspect_ratios', default=[0.5, 1.0, 2.0], type=float, nargs='+',
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help='Hyper-parameter of grid_anchor_generator from a config file.')
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parser.add_argument('--features_stride', default=16, type=float, nargs='+',
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help='Hyper-parameter from a config file.')
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args = parser.parse_args()
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scopesToKeep = ('FirstStageFeatureExtractor', 'Conv',
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'FirstStageBoxPredictor/BoxEncodingPredictor',
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'FirstStageBoxPredictor/ClassPredictor',
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'CropAndResize',
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'MaxPool2D',
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'SecondStageFeatureExtractor',
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'SecondStageBoxPredictor',
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'image_tensor')
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scopesToIgnore = ('FirstStageFeatureExtractor/Assert',
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'FirstStageFeatureExtractor/Shape',
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'FirstStageFeatureExtractor/strided_slice',
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'FirstStageFeatureExtractor/GreaterEqual',
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'FirstStageFeatureExtractor/LogicalAnd')
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unusedAttrs = ['T', 'Tshape', 'N', 'Tidx', 'Tdim', 'use_cudnn_on_gpu',
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'Index', 'Tperm', 'is_training', 'Tpaddings']
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# Read the graph.
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with tf.gfile.FastGFile(args.input, 'rb') as f:
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graph_def = tf.GraphDef()
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graph_def.ParseFromString(f.read())
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# Removes Identity nodes
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def removeIdentity():
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identities = {}
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for node in graph_def.node:
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if node.op == 'Identity':
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identities[node.name] = node.input[0]
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graph_def.node.remove(node)
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for node in graph_def.node:
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for i in range(len(node.input)):
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if node.input[i] in identities:
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node.input[i] = identities[node.input[i]]
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removeIdentity()
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removedNodes = []
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for i in reversed(range(len(graph_def.node))):
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op = graph_def.node[i].op
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name = graph_def.node[i].name
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if op == 'Const' or name.startswith(scopesToIgnore) or not name.startswith(scopesToKeep):
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if op != 'Const':
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removedNodes.append(name)
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del graph_def.node[i]
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else:
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for attr in unusedAttrs:
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if attr in graph_def.node[i].attr:
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del graph_def.node[i].attr[attr]
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# Remove references to removed nodes except Const nodes.
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for node in graph_def.node:
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for i in reversed(range(len(node.input))):
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if node.input[i] in removedNodes:
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del node.input[i]
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# Connect input node to the first layer
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assert(graph_def.node[0].op == 'Placeholder')
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graph_def.node[1].input.insert(0, graph_def.node[0].name)
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# Temporarily remove top nodes.
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topNodes = []
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while True:
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node = graph_def.node.pop()
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topNodes.append(node)
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if node.op == 'CropAndResize':
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break
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def tensorMsg(values):
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if all([isinstance(v, float) for v in values]):
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dtype = 'DT_FLOAT'
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field = 'float_val'
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elif all([isinstance(v, int) for v in values]):
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dtype = 'DT_INT32'
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field = 'int_val'
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else:
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raise Exception('Wrong values types')
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msg = 'tensor { dtype: ' + dtype + ' tensor_shape { dim { size: %d } }' % len(values)
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for value in values:
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msg += '%s: %s ' % (field, str(value))
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return msg + '}'
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def addSlice(inp, out, begins, sizes):
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beginsNode = NodeDef()
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beginsNode.name = out + '/begins'
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beginsNode.op = 'Const'
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text_format.Merge(tensorMsg(begins), beginsNode.attr["value"])
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graph_def.node.extend([beginsNode])
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sizesNode = NodeDef()
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sizesNode.name = out + '/sizes'
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sizesNode.op = 'Const'
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text_format.Merge(tensorMsg(sizes), sizesNode.attr["value"])
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graph_def.node.extend([sizesNode])
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sliced = NodeDef()
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sliced.name = out
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sliced.op = 'Slice'
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sliced.input.append(inp)
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sliced.input.append(beginsNode.name)
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sliced.input.append(sizesNode.name)
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graph_def.node.extend([sliced])
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def addReshape(inp, out, shape):
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shapeNode = NodeDef()
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shapeNode.name = out + '/shape'
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shapeNode.op = 'Const'
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text_format.Merge(tensorMsg(shape), shapeNode.attr["value"])
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graph_def.node.extend([shapeNode])
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reshape = NodeDef()
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reshape.name = out
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reshape.op = 'Reshape'
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reshape.input.append(inp)
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reshape.input.append(shapeNode.name)
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graph_def.node.extend([reshape])
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def addSoftMax(inp, out):
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softmax = NodeDef()
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softmax.name = out
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softmax.op = 'Softmax'
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text_format.Merge('i: -1', softmax.attr['axis'])
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softmax.input.append(inp)
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graph_def.node.extend([softmax])
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addReshape('FirstStageBoxPredictor/ClassPredictor/BiasAdd',
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'FirstStageBoxPredictor/ClassPredictor/reshape_1', [0, -1, 2])
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addSoftMax('FirstStageBoxPredictor/ClassPredictor/reshape_1',
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'FirstStageBoxPredictor/ClassPredictor/softmax') # Compare with Reshape_4
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flatten = NodeDef()
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flatten.name = 'FirstStageBoxPredictor/BoxEncodingPredictor/flatten' # Compare with FirstStageBoxPredictor/BoxEncodingPredictor/BiasAdd
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flatten.op = 'Flatten'
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flatten.input.append('FirstStageBoxPredictor/BoxEncodingPredictor/BiasAdd')
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graph_def.node.extend([flatten])
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proposals = NodeDef()
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proposals.name = 'proposals' # Compare with ClipToWindow/Gather/Gather (NOTE: normalized)
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proposals.op = 'PriorBox'
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proposals.input.append('FirstStageBoxPredictor/BoxEncodingPredictor/BiasAdd')
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proposals.input.append(graph_def.node[0].name) # image_tensor
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text_format.Merge('b: false', proposals.attr["flip"])
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text_format.Merge('b: true', proposals.attr["clip"])
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text_format.Merge('f: %f' % args.features_stride, proposals.attr["step"])
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text_format.Merge('f: 0.0', proposals.attr["offset"])
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text_format.Merge(tensorMsg([0.1, 0.1, 0.2, 0.2]), proposals.attr["variance"])
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widths = []
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heights = []
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for a in args.aspect_ratios:
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for s in args.scales:
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ar = np.sqrt(a)
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heights.append((args.features_stride**2) * s / ar)
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widths.append((args.features_stride**2) * s * ar)
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text_format.Merge(tensorMsg(widths), proposals.attr["width"])
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text_format.Merge(tensorMsg(heights), proposals.attr["height"])
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graph_def.node.extend([proposals])
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# Compare with Reshape_5
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detectionOut = NodeDef()
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detectionOut.name = 'detection_out'
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detectionOut.op = 'DetectionOutput'
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detectionOut.input.append('FirstStageBoxPredictor/BoxEncodingPredictor/flatten')
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detectionOut.input.append('FirstStageBoxPredictor/ClassPredictor/softmax')
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detectionOut.input.append('proposals')
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text_format.Merge('i: 2', detectionOut.attr['num_classes'])
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text_format.Merge('b: true', detectionOut.attr['share_location'])
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text_format.Merge('i: 0', detectionOut.attr['background_label_id'])
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text_format.Merge('f: 0.7', detectionOut.attr['nms_threshold'])
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text_format.Merge('i: 6000', detectionOut.attr['top_k'])
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text_format.Merge('s: "CENTER_SIZE"', detectionOut.attr['code_type'])
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text_format.Merge('i: 100', detectionOut.attr['keep_top_k'])
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text_format.Merge('b: true', detectionOut.attr['clip'])
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text_format.Merge('b: true', detectionOut.attr['loc_pred_transposed'])
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graph_def.node.extend([detectionOut])
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# Save as text.
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for node in reversed(topNodes):
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graph_def.node.extend([node])
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addSoftMax('SecondStageBoxPredictor/Reshape_1', 'SecondStageBoxPredictor/Reshape_1/softmax')
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addSlice('SecondStageBoxPredictor/Reshape_1/softmax',
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'SecondStageBoxPredictor/Reshape_1/slice',
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[0, 0, 1], [-1, -1, -1])
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addReshape('SecondStageBoxPredictor/Reshape_1/slice',
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'SecondStageBoxPredictor/Reshape_1/Reshape', [1, -1])
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# Replace Flatten subgraph onto a single node.
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for i in reversed(range(len(graph_def.node))):
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if graph_def.node[i].op == 'CropAndResize':
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graph_def.node[i].input.insert(1, 'detection_out')
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if graph_def.node[i].name == 'SecondStageBoxPredictor/Reshape':
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shapeNode = NodeDef()
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shapeNode.name = 'SecondStageBoxPredictor/Reshape/shape2'
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shapeNode.op = 'Const'
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text_format.Merge(tensorMsg([1, -1, 4]), shapeNode.attr["value"])
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graph_def.node.extend([shapeNode])
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graph_def.node[i].input.pop()
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graph_def.node[i].input.append(shapeNode.name)
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if graph_def.node[i].name in ['SecondStageBoxPredictor/Flatten/flatten/Shape',
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'SecondStageBoxPredictor/Flatten/flatten/strided_slice',
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'SecondStageBoxPredictor/Flatten/flatten/Reshape/shape']:
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del graph_def.node[i]
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for node in graph_def.node:
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if node.name == 'SecondStageBoxPredictor/Flatten/flatten/Reshape':
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node.op = 'Flatten'
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node.input.pop()
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break
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################################################################################
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### Postprocessing
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################################################################################
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addSlice('detection_out', 'detection_out/slice', [0, 0, 0, 3], [-1, -1, -1, 4])
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variance = NodeDef()
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variance.name = 'proposals/variance'
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variance.op = 'Const'
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text_format.Merge(tensorMsg([0.1, 0.1, 0.2, 0.2]), variance.attr["value"])
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graph_def.node.extend([variance])
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varianceEncoder = NodeDef()
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varianceEncoder.name = 'variance_encoded'
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varianceEncoder.op = 'Mul'
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varianceEncoder.input.append('SecondStageBoxPredictor/Reshape')
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varianceEncoder.input.append(variance.name)
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text_format.Merge('i: 2', varianceEncoder.attr["axis"])
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graph_def.node.extend([varianceEncoder])
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addReshape('detection_out/slice', 'detection_out/slice/reshape', [1, 1, -1])
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detectionOut = NodeDef()
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detectionOut.name = 'detection_out_final'
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detectionOut.op = 'DetectionOutput'
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detectionOut.input.append('variance_encoded')
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detectionOut.input.append('SecondStageBoxPredictor/Reshape_1/Reshape')
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detectionOut.input.append('detection_out/slice/reshape')
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text_format.Merge('i: %d' % args.num_classes, detectionOut.attr['num_classes'])
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text_format.Merge('b: false', detectionOut.attr['share_location'])
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text_format.Merge('i: %d' % (args.num_classes + 1), detectionOut.attr['background_label_id'])
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text_format.Merge('f: 0.6', detectionOut.attr['nms_threshold'])
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text_format.Merge('s: "CENTER_SIZE"', detectionOut.attr['code_type'])
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text_format.Merge('i: 100', detectionOut.attr['keep_top_k'])
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text_format.Merge('b: true', detectionOut.attr['loc_pred_transposed'])
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text_format.Merge('b: true', detectionOut.attr['clip'])
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text_format.Merge('b: true', detectionOut.attr['variance_encoded_in_target'])
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graph_def.node.extend([detectionOut])
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tf.train.write_graph(graph_def, "", args.output, as_text=True)
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