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Open Source Computer Vision Library
https://opencv.org/
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237 lines
9.0 KiB
237 lines
9.0 KiB
5 years ago
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# 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) 2020, 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 EfficientDet
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# deep learning network trained in https://github.com/google/automl.
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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 argparse
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import re
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from math import sqrt
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from tf_text_graph_common import *
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class AnchorGenerator:
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def __init__(self, min_level, aspect_ratios, num_scales, anchor_scale):
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self.min_level = min_level
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self.aspect_ratios = aspect_ratios
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self.anchor_scale = anchor_scale
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self.scales = [2**(float(s) / num_scales) for s in range(num_scales)]
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def get(self, layer_id):
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widths = []
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heights = []
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for s in self.scales:
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for a in self.aspect_ratios:
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base_anchor_size = 2**(self.min_level + layer_id) * self.anchor_scale
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heights.append(base_anchor_size * s * a[1])
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widths.append(base_anchor_size * s * a[0])
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return widths, heights
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def createGraph(modelPath, outputPath, min_level, aspect_ratios, num_scales,
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anchor_scale, num_classes, image_width, image_height):
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print('Min level: %d' % min_level)
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print('Anchor scale: %f' % anchor_scale)
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print('Num scales: %d' % num_scales)
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print('Aspect ratios: %s' % str(aspect_ratios))
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print('Number of classes: %d' % num_classes)
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print('Input image size: %dx%d' % (image_width, image_height))
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# Read the graph.
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_inpNames = ['image_arrays']
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outNames = ['detections']
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writeTextGraph(modelPath, outputPath, outNames)
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graph_def = parseTextGraph(outputPath)
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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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if node.op == 'Const':
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continue
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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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nodesToKeep = ['truediv'] # Keep preprocessing nodes
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removeIdentity(graph_def)
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scopesToKeep = ('image_arrays', 'efficientnet', 'resample_p6', 'resample_p7',
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'fpn_cells', 'class_net', 'box_net', 'Reshape', 'concat')
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addConstNode('scale_w', [2.0], graph_def)
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addConstNode('scale_h', [2.0], graph_def)
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nodesToKeep += ['scale_w', 'scale_h']
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for node in graph_def.node:
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if re.match('efficientnet-(.*)/blocks_\d+/se/mul_1', node.name):
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node.input[0], node.input[1] = node.input[1], node.input[0]
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if re.match('fpn_cells/cell_\d+/fnode\d+/resample(.*)/nearest_upsampling/Reshape_1$', node.name):
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node.op = 'ResizeNearestNeighbor'
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node.input[1] = 'scale_w'
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node.input.append('scale_h')
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for inpNode in graph_def.node:
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if inpNode.name == node.name[:node.name.rfind('_')]:
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node.input[0] = inpNode.input[0]
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if re.match('box_net/box-predict(_\d)*/separable_conv2d$', node.name):
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node.addAttr('loc_pred_transposed', True)
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# Replace RealDiv to Mul with inversed scale for compatibility
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if node.op == 'RealDiv':
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for inpNode in graph_def.node:
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if inpNode.name != node.input[1] or not 'value' in inpNode.attr:
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continue
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tensor = inpNode.attr['value']['tensor'][0]
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if not 'float_val' in tensor:
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continue
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scale = float(inpNode.attr['value']['tensor'][0]['float_val'][0])
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addConstNode(inpNode.name + '/inv', [1.0 / scale], graph_def)
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nodesToKeep.append(inpNode.name + '/inv')
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node.input[1] = inpNode.name + '/inv'
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node.op = 'Mul'
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break
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def to_remove(name, op):
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if name in nodesToKeep:
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return False
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return op == 'Const' or not name.startswith(scopesToKeep)
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removeUnusedNodesAndAttrs(to_remove, graph_def)
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# Attach unconnected preprocessing
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assert(graph_def.node[1].name == 'truediv' and graph_def.node[1].op == 'RealDiv')
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graph_def.node[1].input.insert(0, 'image_arrays')
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graph_def.node[2].input.insert(0, 'truediv')
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priors_generator = AnchorGenerator(min_level, aspect_ratios, num_scales, anchor_scale)
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priorBoxes = []
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for i in range(5):
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inpName = ''
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for node in graph_def.node:
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if node.name == 'Reshape_%d' % (i * 2 + 1):
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inpName = node.input[0]
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break
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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(inpName)
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priorBox.input.append(graph_def.node[0].name) # image_tensor
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priorBox.addAttr('flip', False)
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priorBox.addAttr('clip', False)
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widths, heights = priors_generator.get(i)
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priorBox.addAttr('width', widths)
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priorBox.addAttr('height', heights)
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priorBox.addAttr('variance', [1.0, 1.0, 1.0, 1.0])
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graph_def.node.extend([priorBox])
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priorBoxes.append(priorBox.name)
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addConstNode('concat/axis_flatten', [-1], graph_def)
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def addConcatNode(name, inputs, axisNodeName):
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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(axisNodeName)
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graph_def.node.extend([concat])
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addConcatNode('PriorBox/concat', priorBoxes, 'concat/axis_flatten')
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sigmoid = NodeDef()
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sigmoid.name = 'concat/sigmoid'
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sigmoid.op = 'Sigmoid'
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sigmoid.input.append('concat')
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graph_def.node.extend([sigmoid])
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addFlatten(sigmoid.name, sigmoid.name + '/Flatten', graph_def)
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addFlatten('concat_1', 'concat_1/Flatten', graph_def)
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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('concat_1/Flatten')
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detectionOut.input.append(sigmoid.name + '/Flatten')
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detectionOut.input.append('PriorBox/concat')
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detectionOut.addAttr('num_classes', num_classes)
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detectionOut.addAttr('share_location', True)
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detectionOut.addAttr('background_label_id', num_classes + 1)
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detectionOut.addAttr('nms_threshold', 0.6)
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detectionOut.addAttr('confidence_threshold', 0.2)
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detectionOut.addAttr('top_k', 100)
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detectionOut.addAttr('keep_top_k', 100)
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detectionOut.addAttr('code_type', "CENTER_SIZE")
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graph_def.node.extend([detectionOut])
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graph_def.node[0].attr['shape'] = {
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'shape': {
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'dim': [
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{'size': -1},
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{'size': image_height},
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{'size': image_width},
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{'size': 3}
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]
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}
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}
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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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graph_def.save(outputPath)
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if __name__ == "__main__":
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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('--min_level', default=3, type=int, help='Parameter from training config')
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parser.add_argument('--num_scales', default=3, type=int, help='Parameter from training config')
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parser.add_argument('--anchor_scale', default=4.0, type=float, help='Parameter from training config')
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parser.add_argument('--aspect_ratios', default=[1.0, 1.0, 1.4, 0.7, 0.7, 1.4],
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nargs='+', type=float, help='Parameter from training config')
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parser.add_argument('--num_classes', default=90, type=int, help='Number of classes to detect')
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parser.add_argument('--width', default=512, type=int, help='Network input width')
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parser.add_argument('--height', default=512, type=int, help='Network input height')
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args = parser.parse_args()
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ar = args.aspect_ratios
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assert(len(ar) % 2 == 0)
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ar = list(zip(ar[::2], ar[1::2]))
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createGraph(args.input, args.output, args.min_level, ar, args.num_scales,
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args.anchor_scale, args.num_classes, args.width, args.height)
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