298 lines
11 KiB
Python
298 lines
11 KiB
Python
import argparse
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import numpy as np
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from tf_text_graph_common import *
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parser = argparse.ArgumentParser(description='Run this script to get a text graph of '
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'Mask-RCNN 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('--config', required=True, help='Path to a *.config file is used for training.')
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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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'Preprocessor/sub',
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'Preprocessor/mul',
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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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'Conv/required_space_to_batch_paddings')
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# Load a config file.
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config = readTextMessage(args.config)
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config = config['model'][0]['faster_rcnn'][0]
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num_classes = int(config['num_classes'][0])
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grid_anchor_generator = config['first_stage_anchor_generator'][0]['grid_anchor_generator'][0]
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scales = [float(s) for s in grid_anchor_generator['scales']]
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aspect_ratios = [float(ar) for ar in grid_anchor_generator['aspect_ratios']]
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width_stride = float(grid_anchor_generator['width_stride'][0])
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height_stride = float(grid_anchor_generator['height_stride'][0])
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features_stride = float(config['feature_extractor'][0]['first_stage_features_stride'][0])
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first_stage_nms_iou_threshold = float(config['first_stage_nms_iou_threshold'][0])
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first_stage_max_proposals = int(config['first_stage_max_proposals'][0])
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print('Number of classes: %d' % num_classes)
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print('Scales: %s' % str(scales))
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print('Aspect ratios: %s' % str(aspect_ratios))
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print('Width stride: %f' % width_stride)
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print('Height stride: %f' % height_stride)
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print('Features stride: %f' % features_stride)
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# Read the graph.
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writeTextGraph(args.input, args.output, ['num_detections', 'detection_scores', 'detection_boxes', 'detection_classes', 'detection_masks'])
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graph_def = parseTextGraph(args.output)
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removeIdentity(graph_def)
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nodesToKeep = []
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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 name.startswith(scopesToIgnore) or not name.startswith(scopesToKeep) or \
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(name.startswith('CropAndResize') and op != 'CropAndResize')
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# Fuse atrous convolutions (with dilations).
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nodesMap = {node.name: node for node in graph_def.node}
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for node in reversed(graph_def.node):
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if node.op == 'BatchToSpaceND':
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del node.input[2]
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conv = nodesMap[node.input[0]]
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spaceToBatchND = nodesMap[conv.input[0]]
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paddingsNode = NodeDef()
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paddingsNode.name = conv.name + '/paddings'
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paddingsNode.op = 'Const'
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paddingsNode.addAttr('value', [2, 2, 2, 2])
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graph_def.node.insert(graph_def.node.index(spaceToBatchND), paddingsNode)
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nodesToKeep.append(paddingsNode.name)
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spaceToBatchND.input[2] = paddingsNode.name
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removeUnusedNodesAndAttrs(to_remove, graph_def)
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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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numCropAndResize = 0
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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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numCropAndResize += 1
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if numCropAndResize == 2:
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break
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addReshape('FirstStageBoxPredictor/ClassPredictor/BiasAdd',
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'FirstStageBoxPredictor/ClassPredictor/reshape_1', [0, -1, 2], graph_def)
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addSoftMax('FirstStageBoxPredictor/ClassPredictor/reshape_1',
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'FirstStageBoxPredictor/ClassPredictor/softmax', graph_def) # Compare with Reshape_4
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addFlatten('FirstStageBoxPredictor/ClassPredictor/softmax',
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'FirstStageBoxPredictor/ClassPredictor/softmax/flatten', graph_def)
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# Compare with FirstStageBoxPredictor/BoxEncodingPredictor/BiasAdd
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addFlatten('FirstStageBoxPredictor/BoxEncodingPredictor/BiasAdd',
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'FirstStageBoxPredictor/BoxEncodingPredictor/flatten', graph_def)
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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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proposals.addAttr('flip', False)
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proposals.addAttr('clip', True)
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proposals.addAttr('step', features_stride)
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proposals.addAttr('offset', 0.0)
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proposals.addAttr('variance', [0.1, 0.1, 0.2, 0.2])
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widths = []
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heights = []
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for a in aspect_ratios:
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for s in scales:
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ar = np.sqrt(a)
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heights.append((height_stride**2) * s / ar)
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widths.append((width_stride**2) * s * ar)
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proposals.addAttr('width', widths)
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proposals.addAttr('height', heights)
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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/flatten')
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detectionOut.input.append('proposals')
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detectionOut.addAttr('num_classes', 2)
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detectionOut.addAttr('share_location', True)
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detectionOut.addAttr('background_label_id', 0)
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detectionOut.addAttr('nms_threshold', first_stage_nms_iou_threshold)
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detectionOut.addAttr('top_k', 6000)
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detectionOut.addAttr('code_type', "CENTER_SIZE")
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detectionOut.addAttr('keep_top_k', first_stage_max_proposals)
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detectionOut.addAttr('clip', True)
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graph_def.node.extend([detectionOut])
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# Save as text.
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cropAndResizeNodesNames = []
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for node in reversed(topNodes):
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if node.op != 'CropAndResize':
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graph_def.node.extend([node])
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topNodes.pop()
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else:
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cropAndResizeNodesNames.append(node.name)
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if numCropAndResize == 1:
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break
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else:
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graph_def.node.extend([node])
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topNodes.pop()
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numCropAndResize -= 1
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addSoftMax('SecondStageBoxPredictor/Reshape_1', 'SecondStageBoxPredictor/Reshape_1/softmax', graph_def)
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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], graph_def)
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addReshape('SecondStageBoxPredictor/Reshape_1/slice',
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'SecondStageBoxPredictor/Reshape_1/Reshape', [1, -1], graph_def)
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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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addConstNode('SecondStageBoxPredictor/Reshape/shape2', [1, -1, 4], graph_def)
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graph_def.node[i].input.pop()
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graph_def.node[i].input.append('SecondStageBoxPredictor/Reshape/shape2')
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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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'SecondStageBoxPredictor/Flatten_1/flatten/Shape',
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'SecondStageBoxPredictor/Flatten_1/flatten/strided_slice',
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'SecondStageBoxPredictor/Flatten_1/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' or \
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node.name == 'SecondStageBoxPredictor/Flatten_1/flatten/Reshape':
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node.op = 'Flatten'
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node.input.pop()
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if node.name in ['FirstStageBoxPredictor/BoxEncodingPredictor/Conv2D',
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'SecondStageBoxPredictor/BoxEncodingPredictor/MatMul']:
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node.addAttr('loc_pred_transposed', True)
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if node.name.startswith('MaxPool2D'):
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assert(node.op == 'MaxPool')
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assert(len(cropAndResizeNodesNames) == 2)
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node.input = [cropAndResizeNodesNames[0]]
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del cropAndResizeNodesNames[0]
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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], graph_def)
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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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variance.addAttr('value', [0.1, 0.1, 0.2, 0.2])
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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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varianceEncoder.addAttr('axis', 2)
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graph_def.node.extend([varianceEncoder])
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addReshape('detection_out/slice', 'detection_out/slice/reshape', [1, 1, -1], graph_def)
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addFlatten('variance_encoded', 'variance_encoded/flatten', graph_def)
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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/flatten')
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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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detectionOut.addAttr('num_classes', num_classes)
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detectionOut.addAttr('share_location', False)
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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('code_type', "CENTER_SIZE")
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detectionOut.addAttr('keep_top_k',100)
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detectionOut.addAttr('clip', True)
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detectionOut.addAttr('variance_encoded_in_target', True)
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detectionOut.addAttr('confidence_threshold', 0.3)
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detectionOut.addAttr('group_by_classes', False)
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graph_def.node.extend([detectionOut])
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for node in reversed(topNodes):
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graph_def.node.extend([node])
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if node.name.startswith('MaxPool2D'):
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assert(node.op == 'MaxPool')
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assert(len(cropAndResizeNodesNames) == 1)
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node.input = [cropAndResizeNodesNames[0]]
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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_final')
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break
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graph_def.node[-1].name = 'detection_masks'
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graph_def.node[-1].op = 'Sigmoid'
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graph_def.node[-1].input.pop()
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def getUnconnectedNodes():
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unconnected = [node.name for node in graph_def.node]
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for node in graph_def.node:
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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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while True:
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unconnectedNodes = getUnconnectedNodes()
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unconnectedNodes.remove(graph_def.node[-1].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(args.output)
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