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8b4871a28d
* Use only absolute prior boxes explicit sizes. Remove scales attributes. * Simplified PriorBox layer forward pass
259 lines
9.8 KiB
Python
259 lines
9.8 KiB
Python
# This file is a part of OpenCV project.
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# It is a subject to the license terms in the LICENSE file found in the top-level directory
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# of this distribution and at http://opencv.org/license.html.
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#
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# Copyright (C) 2018, Intel Corporation, all rights reserved.
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# Third party copyrights are property of their respective owners.
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#
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# Use this script to get the text graph representation (.pbtxt) of SSD-based
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# deep learning network trained in TensorFlow Object Detection API.
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# Then you can import it with a binary frozen graph (.pb) using readNetFromTensorflow() function.
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# See details and examples on the following wiki page: https://github.com/opencv/opencv/wiki/TensorFlow-Object-Detection-API
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import tensorflow as tf
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import argparse
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from math import sqrt
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from tensorflow.core.framework.node_def_pb2 import NodeDef
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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('--min_scale', default=0.2, type=float, help='Hyper-parameter of ssd_anchor_generator from config file.')
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parser.add_argument('--max_scale', default=0.95, type=float, help='Hyper-parameter of ssd_anchor_generator from config file.')
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parser.add_argument('--num_layers', default=6, type=int, help='Hyper-parameter of ssd_anchor_generator from config file.')
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parser.add_argument('--aspect_ratios', default=[1.0, 2.0, 0.5, 3.0, 0.333], type=float, nargs='+',
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help='Hyper-parameter of ssd_anchor_generator from config file.')
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parser.add_argument('--image_width', default=300, type=int, help='Training images width.')
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parser.add_argument('--image_height', default=300, type=int, help='Training images height.')
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args = parser.parse_args()
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# Nodes that should be kept.
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keepOps = ['Conv2D', 'BiasAdd', 'Add', 'Relu6', 'Placeholder', 'FusedBatchNorm',
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'DepthwiseConv2dNative', 'ConcatV2', 'Mul', 'MaxPool', 'AvgPool']
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# Nodes attributes that could be removed because they are not used during import.
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unusedAttrs = ['T', 'data_format', 'Tshape', 'N', 'Tidx', 'Tdim', 'use_cudnn_on_gpu',
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'Index', 'Tperm', 'is_training', 'Tpaddings']
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# Node with which prefixes should be removed
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prefixesToRemove = ('MultipleGridAnchorGenerator/', 'Postprocessor/', 'Preprocessor/')
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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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def getUnconnectedNodes():
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unconnected = []
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for node in graph_def.node:
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unconnected.append(node.name)
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for inp in node.input:
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if inp in unconnected:
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unconnected.remove(inp)
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return unconnected
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removedNodes = []
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# Detect unfused batch normalization nodes and fuse them.
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def fuse_batch_normalization():
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pattern = ['Add', 'Rsqrt', 'Mul', 'Mul', 'Mul', 'Sub', 'Add']
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candidates = []
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for node in graph_def.node:
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if node.op == pattern[len(candidates)]:
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candidates.append(node)
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else:
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candidates = []
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if len(candidates) == len(pattern):
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inp = candidates[3].input[0]
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gamma = candidates[2].input[1]
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beta = candidates[5].input[0]
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moving_mean = candidates[4].input[0]
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moving_variance = candidates[0].input[0]
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name = node.name
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node.Clear()
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node.name = name
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node.op = 'FusedBatchNorm'
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node.input.append(inp)
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node.input.append(gamma)
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node.input.append(beta)
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node.input.append(moving_mean)
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node.input.append(moving_variance)
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text_format.Merge('f: 0.001', node.attr["epsilon"])
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for candidate in candidates[:-1]:
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graph_def.node.remove(candidate)
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candidates = []
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fuse_batch_normalization()
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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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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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# Remove extra nodes and attributes.
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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 (not op in keepOps) or name.startswith(prefixesToRemove):
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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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# assert(graph_def.node[1].op == 'Conv2D')
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weights = graph_def.node[1].input[0]
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for i in range(len(graph_def.node[1].input)):
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graph_def.node[1].input.pop()
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graph_def.node[1].input.append(graph_def.node[0].name)
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graph_def.node[1].input.append(weights)
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# Create SSD postprocessing head ###############################################
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# Concatenate predictions of classes, predictions of bounding boxes and proposals.
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concatAxis = NodeDef()
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concatAxis.name = 'concat/axis_flatten'
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concatAxis.op = 'Const'
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text_format.Merge(
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'tensor {'
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' dtype: DT_INT32'
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' tensor_shape { }'
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' int_val: -1'
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'}', concatAxis.attr["value"])
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graph_def.node.extend([concatAxis])
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def addConcatNode(name, inputs):
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concat = NodeDef()
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concat.name = name
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concat.op = 'ConcatV2'
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for inp in inputs:
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concat.input.append(inp)
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concat.input.append(concatAxis.name)
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graph_def.node.extend([concat])
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for label in ['ClassPredictor', 'BoxEncodingPredictor']:
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concatInputs = []
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for i in range(args.num_layers):
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# Flatten predictions
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flatten = NodeDef()
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inpName = 'BoxPredictor_%d/%s/BiasAdd' % (i, label)
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flatten.input.append(inpName)
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flatten.name = inpName + '/Flatten'
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flatten.op = 'Flatten'
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concatInputs.append(flatten.name)
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graph_def.node.extend([flatten])
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addConcatNode('%s/concat' % label, concatInputs)
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# Add layers that generate anchors (bounding boxes proposals).
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scales = [args.min_scale + (args.max_scale - args.min_scale) * i / (args.num_layers - 1)
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for i in range(args.num_layers)] + [1.0]
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def tensorMsg(values):
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msg = 'tensor { dtype: DT_FLOAT tensor_shape { dim { size: %d } }' % len(values)
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for value in values:
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msg += 'float_val: %f ' % value
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return msg + '}'
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priorBoxes = []
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for i in range(args.num_layers):
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priorBox = NodeDef()
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priorBox.name = 'PriorBox_%d' % i
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priorBox.op = 'PriorBox'
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priorBox.input.append('BoxPredictor_%d/BoxEncodingPredictor/BiasAdd' % i)
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priorBox.input.append(graph_def.node[0].name) # image_tensor
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text_format.Merge('b: false', priorBox.attr["flip"])
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text_format.Merge('b: false', priorBox.attr["clip"])
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if i == 0:
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widths = [args.min_scale * 0.5, args.min_scale * sqrt(2.0), args.min_scale * sqrt(0.5)]
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heights = [args.min_scale * 0.5, args.min_scale / sqrt(2.0), args.min_scale / sqrt(0.5)]
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else:
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widths = [scales[i] * sqrt(ar) for ar in args.aspect_ratios]
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heights = [scales[i] / sqrt(ar) for ar in args.aspect_ratios]
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widths += [sqrt(scales[i] * scales[i + 1])]
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heights += [sqrt(scales[i] * scales[i + 1])]
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widths = [w * args.image_width for w in widths]
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heights = [h * args.image_height for h in heights]
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text_format.Merge(tensorMsg(widths), priorBox.attr["width"])
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text_format.Merge(tensorMsg(heights), priorBox.attr["height"])
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text_format.Merge(tensorMsg([0.1, 0.1, 0.2, 0.2]), priorBox.attr["variance"])
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graph_def.node.extend([priorBox])
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priorBoxes.append(priorBox.name)
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addConcatNode('PriorBox/concat', priorBoxes)
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# Sigmoid for classes predictions and DetectionOutput layer
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sigmoid = NodeDef()
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sigmoid.name = 'ClassPredictor/concat/sigmoid'
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sigmoid.op = 'Sigmoid'
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sigmoid.input.append('ClassPredictor/concat')
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graph_def.node.extend([sigmoid])
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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('BoxEncodingPredictor/concat')
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detectionOut.input.append(sigmoid.name)
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detectionOut.input.append('PriorBox/concat')
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text_format.Merge('i: %d' % (args.num_classes + 1), 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: 0.6', detectionOut.attr['nms_threshold'])
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text_format.Merge('i: 100', 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('f: 0.01', detectionOut.attr['confidence_threshold'])
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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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while True:
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unconnectedNodes = getUnconnectedNodes()
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unconnectedNodes.remove(detectionOut.name)
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if not unconnectedNodes:
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break
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for name in unconnectedNodes:
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for i in range(len(graph_def.node)):
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if graph_def.node[i].name == name:
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del graph_def.node[i]
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break
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# Save as text.
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tf.train.write_graph(graph_def, "", args.output, as_text=True)
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