414 lines
18 KiB
Python
414 lines
18 KiB
Python
|
# This file is a part of OpenCV project.
|
||
|
|
# It is a subject to the license terms in the LICENSE file found in the top-level directory
|
||
|
|
# of this distribution and at http://opencv.org/license.html.
|
||
|
|
#
|
||
|
|
# Copyright (C) 2018, Intel Corporation, all rights reserved.
|
||
|
|
# Third party copyrights are property of their respective owners.
|
||
|
|
#
|
||
|
|
# Use this script to get the text graph representation (.pbtxt) of SSD-based
|
||
|
|
# deep learning network trained in TensorFlow Object Detection API.
|
||
|
|
# Then you can import it with a binary frozen graph (.pb) using readNetFromTensorflow() function.
|
||
|
|
# See details and examples on the following wiki page: https://github.com/opencv/opencv/wiki/TensorFlow-Object-Detection-API
|
||
|
|
import argparse
|
||
|
|
import re
|
||
|
|
from math import sqrt
|
||
|
|
from tf_text_graph_common import *
|
||
|
|
|
||
|
|
class SSDAnchorGenerator:
|
||
|
|
def __init__(self, min_scale, max_scale, num_layers, aspect_ratios,
|
||
|
|
reduce_boxes_in_lowest_layer, image_width, image_height):
|
||
|
|
self.min_scale = min_scale
|
||
|
|
self.aspect_ratios = aspect_ratios
|
||
|
|
self.reduce_boxes_in_lowest_layer = reduce_boxes_in_lowest_layer
|
||
|
|
self.image_width = image_width
|
||
|
|
self.image_height = image_height
|
||
|
|
self.scales = [min_scale + (max_scale - min_scale) * i / (num_layers - 1)
|
||
|
|
for i in range(num_layers)] + [1.0]
|
||
|
|
|
||
|
|
def get(self, layer_id):
|
||
|
|
if layer_id == 0 and self.reduce_boxes_in_lowest_layer:
|
||
|
|
widths = [0.1, self.min_scale * sqrt(2.0), self.min_scale * sqrt(0.5)]
|
||
|
|
heights = [0.1, self.min_scale / sqrt(2.0), self.min_scale / sqrt(0.5)]
|
||
|
|
else:
|
||
|
|
widths = [self.scales[layer_id] * sqrt(ar) for ar in self.aspect_ratios]
|
||
|
|
heights = [self.scales[layer_id] / sqrt(ar) for ar in self.aspect_ratios]
|
||
|
|
|
||
|
|
widths += [sqrt(self.scales[layer_id] * self.scales[layer_id + 1])]
|
||
|
|
heights += [sqrt(self.scales[layer_id] * self.scales[layer_id + 1])]
|
||
|
|
min_size = min(self.image_width, self.image_height)
|
||
|
|
widths = [w * min_size for w in widths]
|
||
|
|
heights = [h * min_size for h in heights]
|
||
|
|
return widths, heights
|
||
|
|
|
||
|
|
|
||
|
|
class MultiscaleAnchorGenerator:
|
||
|
|
def __init__(self, min_level, aspect_ratios, scales_per_octave, anchor_scale):
|
||
|
|
self.min_level = min_level
|
||
|
|
self.aspect_ratios = aspect_ratios
|
||
|
|
self.anchor_scale = anchor_scale
|
||
|
|
self.scales = [2**(float(s) / scales_per_octave) for s in range(scales_per_octave)]
|
||
|
|
|
||
|
|
def get(self, layer_id):
|
||
|
|
widths = []
|
||
|
|
heights = []
|
||
|
|
for a in self.aspect_ratios:
|
||
|
|
for s in self.scales:
|
||
|
|
base_anchor_size = 2**(self.min_level + layer_id) * self.anchor_scale
|
||
|
|
ar = sqrt(a)
|
||
|
|
heights.append(base_anchor_size * s / ar)
|
||
|
|
widths.append(base_anchor_size * s * ar)
|
||
|
|
return widths, heights
|
||
|
|
|
||
|
|
|
||
|
|
def createSSDGraph(modelPath, configPath, outputPath):
|
||
|
|
# Nodes that should be kept.
|
||
|
|
keepOps = ['Conv2D', 'BiasAdd', 'Add', 'AddV2', 'Relu', 'Relu6', 'Placeholder', 'FusedBatchNorm',
|
||
|
|
'DepthwiseConv2dNative', 'ConcatV2', 'Mul', 'MaxPool', 'AvgPool', 'Identity',
|
||
|
|
'Sub', 'ResizeNearestNeighbor', 'Pad', 'FusedBatchNormV3', 'Mean']
|
||
|
|
|
||
|
|
# Node with which prefixes should be removed
|
||
|
|
prefixesToRemove = ('MultipleGridAnchorGenerator/', 'Concatenate/', 'Postprocessor/', 'Preprocessor/map')
|
||
|
|
|
||
|
|
# Load a config file.
|
||
|
|
config = readTextMessage(configPath)
|
||
|
|
config = config['model'][0]['ssd'][0]
|
||
|
|
num_classes = int(config['num_classes'][0])
|
||
|
|
|
||
|
|
fixed_shape_resizer = config['image_resizer'][0]['fixed_shape_resizer'][0]
|
||
|
|
image_width = int(fixed_shape_resizer['width'][0])
|
||
|
|
image_height = int(fixed_shape_resizer['height'][0])
|
||
|
|
|
||
|
|
box_predictor = 'convolutional' if 'convolutional_box_predictor' in config['box_predictor'][0] else 'weight_shared_convolutional'
|
||
|
|
|
||
|
|
anchor_generator = config['anchor_generator'][0]
|
||
|
|
if 'ssd_anchor_generator' in anchor_generator:
|
||
|
|
ssd_anchor_generator = anchor_generator['ssd_anchor_generator'][0]
|
||
|
|
min_scale = float(ssd_anchor_generator['min_scale'][0])
|
||
|
|
max_scale = float(ssd_anchor_generator['max_scale'][0])
|
||
|
|
num_layers = int(ssd_anchor_generator['num_layers'][0])
|
||
|
|
aspect_ratios = [float(ar) for ar in ssd_anchor_generator['aspect_ratios']]
|
||
|
|
reduce_boxes_in_lowest_layer = True
|
||
|
|
if 'reduce_boxes_in_lowest_layer' in ssd_anchor_generator:
|
||
|
|
reduce_boxes_in_lowest_layer = ssd_anchor_generator['reduce_boxes_in_lowest_layer'][0] == 'true'
|
||
|
|
priors_generator = SSDAnchorGenerator(min_scale, max_scale, num_layers,
|
||
|
|
aspect_ratios, reduce_boxes_in_lowest_layer,
|
||
|
|
image_width, image_height)
|
||
|
|
|
||
|
|
|
||
|
|
print('Scale: [%f-%f]' % (min_scale, max_scale))
|
||
|
|
print('Aspect ratios: %s' % str(aspect_ratios))
|
||
|
|
print('Reduce boxes in the lowest layer: %s' % str(reduce_boxes_in_lowest_layer))
|
||
|
|
elif 'multiscale_anchor_generator' in anchor_generator:
|
||
|
|
multiscale_anchor_generator = anchor_generator['multiscale_anchor_generator'][0]
|
||
|
|
min_level = int(multiscale_anchor_generator['min_level'][0])
|
||
|
|
max_level = int(multiscale_anchor_generator['max_level'][0])
|
||
|
|
anchor_scale = float(multiscale_anchor_generator['anchor_scale'][0])
|
||
|
|
aspect_ratios = [float(ar) for ar in multiscale_anchor_generator['aspect_ratios']]
|
||
|
|
scales_per_octave = int(multiscale_anchor_generator['scales_per_octave'][0])
|
||
|
|
num_layers = max_level - min_level + 1
|
||
|
|
priors_generator = MultiscaleAnchorGenerator(min_level, aspect_ratios,
|
||
|
|
scales_per_octave, anchor_scale)
|
||
|
|
print('Levels: [%d-%d]' % (min_level, max_level))
|
||
|
|
print('Anchor scale: %f' % anchor_scale)
|
||
|
|
print('Scales per octave: %d' % scales_per_octave)
|
||
|
|
print('Aspect ratios: %s' % str(aspect_ratios))
|
||
|
|
else:
|
||
|
|
print('Unknown anchor_generator')
|
||
|
|
exit(0)
|
||
|
|
|
||
|
|
print('Number of classes: %d' % num_classes)
|
||
|
|
print('Number of layers: %d' % num_layers)
|
||
|
|
print('box predictor: %s' % box_predictor)
|
||
|
|
print('Input image size: %dx%d' % (image_width, image_height))
|
||
|
|
|
||
|
|
# Read the graph.
|
||
|
|
outNames = ['num_detections', 'detection_scores', 'detection_boxes', 'detection_classes']
|
||
|
|
|
||
|
|
writeTextGraph(modelPath, outputPath, outNames)
|
||
|
|
graph_def = parseTextGraph(outputPath)
|
||
|
|
|
||
|
|
def getUnconnectedNodes():
|
||
|
|
unconnected = []
|
||
|
|
for node in graph_def.node:
|
||
|
|
unconnected.append(node.name)
|
||
|
|
for inp in node.input:
|
||
|
|
if inp in unconnected:
|
||
|
|
unconnected.remove(inp)
|
||
|
|
return unconnected
|
||
|
|
|
||
|
|
|
||
|
|
def fuse_nodes(nodesToKeep):
|
||
|
|
# Detect unfused batch normalization nodes and fuse them.
|
||
|
|
# Add_0 <-- moving_variance, add_y
|
||
|
|
# Rsqrt <-- Add_0
|
||
|
|
# Mul_0 <-- Rsqrt, gamma
|
||
|
|
# Mul_1 <-- input, Mul_0
|
||
|
|
# Mul_2 <-- moving_mean, Mul_0
|
||
|
|
# Sub_0 <-- beta, Mul_2
|
||
|
|
# Add_1 <-- Mul_1, Sub_0
|
||
|
|
nodesMap = {node.name: node for node in graph_def.node}
|
||
|
|
subgraphBatchNorm = ['Add',
|
||
|
|
['Mul', 'input', ['Mul', ['Rsqrt', ['Add', 'moving_variance', 'add_y']], 'gamma']],
|
||
|
|
['Sub', 'beta', ['Mul', 'moving_mean', 'Mul_0']]]
|
||
|
|
subgraphBatchNormV2 = ['AddV2',
|
||
|
|
['Mul', 'input', ['Mul', ['Rsqrt', ['AddV2', 'moving_variance', 'add_y']], 'gamma']],
|
||
|
|
['Sub', 'beta', ['Mul', 'moving_mean', 'Mul_0']]]
|
||
|
|
# Detect unfused nearest neighbor resize.
|
||
|
|
subgraphResizeNN = ['Reshape',
|
||
|
|
['Mul', ['Reshape', 'input', ['Pack', 'shape_1', 'shape_2', 'shape_3', 'shape_4', 'shape_5']],
|
||
|
|
'ones'],
|
||
|
|
['Pack', ['StridedSlice', ['Shape', 'input'], 'stack', 'stack_1', 'stack_2'],
|
||
|
|
'out_height', 'out_width', 'out_channels']]
|
||
|
|
def checkSubgraph(node, targetNode, inputs, fusedNodes):
|
||
|
|
op = targetNode[0]
|
||
|
|
if node.op == op and (len(node.input) >= len(targetNode) - 1):
|
||
|
|
fusedNodes.append(node)
|
||
|
|
for i, inpOp in enumerate(targetNode[1:]):
|
||
|
|
if isinstance(inpOp, list):
|
||
|
|
if not node.input[i] in nodesMap or \
|
||
|
|
not checkSubgraph(nodesMap[node.input[i]], inpOp, inputs, fusedNodes):
|
||
|
|
return False
|
||
|
|
else:
|
||
|
|
inputs[inpOp] = node.input[i]
|
||
|
|
|
||
|
|
return True
|
||
|
|
else:
|
||
|
|
return False
|
||
|
|
|
||
|
|
nodesToRemove = []
|
||
|
|
for node in graph_def.node:
|
||
|
|
inputs = {}
|
||
|
|
fusedNodes = []
|
||
|
|
if checkSubgraph(node, subgraphBatchNorm, inputs, fusedNodes) or \
|
||
|
|
checkSubgraph(node, subgraphBatchNormV2, inputs, fusedNodes):
|
||
|
|
name = node.name
|
||
|
|
node.Clear()
|
||
|
|
node.name = name
|
||
|
|
node.op = 'FusedBatchNorm'
|
||
|
|
node.input.append(inputs['input'])
|
||
|
|
node.input.append(inputs['gamma'])
|
||
|
|
node.input.append(inputs['beta'])
|
||
|
|
node.input.append(inputs['moving_mean'])
|
||
|
|
node.input.append(inputs['moving_variance'])
|
||
|
|
node.addAttr('epsilon', 0.001)
|
||
|
|
nodesToRemove += fusedNodes[1:]
|
||
|
|
|
||
|
|
inputs = {}
|
||
|
|
fusedNodes = []
|
||
|
|
if checkSubgraph(node, subgraphResizeNN, inputs, fusedNodes):
|
||
|
|
name = node.name
|
||
|
|
node.Clear()
|
||
|
|
node.name = name
|
||
|
|
node.op = 'ResizeNearestNeighbor'
|
||
|
|
node.input.append(inputs['input'])
|
||
|
|
node.input.append(name + '/output_shape')
|
||
|
|
|
||
|
|
out_height_node = nodesMap[inputs['out_height']]
|
||
|
|
out_width_node = nodesMap[inputs['out_width']]
|
||
|
|
out_height = int(out_height_node.attr['value']['tensor'][0]['int_val'][0])
|
||
|
|
out_width = int(out_width_node.attr['value']['tensor'][0]['int_val'][0])
|
||
|
|
|
||
|
|
shapeNode = NodeDef()
|
||
|
|
shapeNode.name = name + '/output_shape'
|
||
|
|
shapeNode.op = 'Const'
|
||
|
|
shapeNode.addAttr('value', [out_height, out_width])
|
||
|
|
graph_def.node.insert(graph_def.node.index(node), shapeNode)
|
||
|
|
nodesToKeep.append(shapeNode.name)
|
||
|
|
|
||
|
|
nodesToRemove += fusedNodes[1:]
|
||
|
|
for node in nodesToRemove:
|
||
|
|
graph_def.node.remove(node)
|
||
|
|
|
||
|
|
nodesToKeep = []
|
||
|
|
fuse_nodes(nodesToKeep)
|
||
|
|
|
||
|
|
removeIdentity(graph_def)
|
||
|
|
|
||
|
|
def to_remove(name, op):
|
||
|
|
return (not name in nodesToKeep) and \
|
||
|
|
(op == 'Const' or (not op in keepOps) or name.startswith(prefixesToRemove))
|
||
|
|
|
||
|
|
removeUnusedNodesAndAttrs(to_remove, graph_def)
|
||
|
|
|
||
|
|
|
||
|
|
# Connect input node to the first layer
|
||
|
|
assert(graph_def.node[0].op == 'Placeholder')
|
||
|
|
try:
|
||
|
|
input_shape = graph_def.node[0].attr['shape']['shape'][0]['dim']
|
||
|
|
input_shape[1]['size'] = image_height
|
||
|
|
input_shape[2]['size'] = image_width
|
||
|
|
except:
|
||
|
|
print("Input shapes are undefined")
|
||
|
|
# assert(graph_def.node[1].op == 'Conv2D')
|
||
|
|
weights = graph_def.node[1].input[-1]
|
||
|
|
for i in range(len(graph_def.node[1].input)):
|
||
|
|
graph_def.node[1].input.pop()
|
||
|
|
graph_def.node[1].input.append(graph_def.node[0].name)
|
||
|
|
graph_def.node[1].input.append(weights)
|
||
|
|
|
||
|
|
# check and correct the case when preprocessing block is after input
|
||
|
|
preproc_id = "Preprocessor/"
|
||
|
|
if graph_def.node[2].name.startswith(preproc_id) and \
|
||
|
|
graph_def.node[2].input[0].startswith(preproc_id):
|
||
|
|
|
||
|
|
if not any(preproc_id in inp for inp in graph_def.node[3].input):
|
||
|
|
graph_def.node[3].input.insert(0, graph_def.node[2].name)
|
||
|
|
|
||
|
|
|
||
|
|
# Create SSD postprocessing head ###############################################
|
||
|
|
|
||
|
|
# Concatenate predictions of classes, predictions of bounding boxes and proposals.
|
||
|
|
def addConcatNode(name, inputs, axisNodeName):
|
||
|
|
concat = NodeDef()
|
||
|
|
concat.name = name
|
||
|
|
concat.op = 'ConcatV2'
|
||
|
|
for inp in inputs:
|
||
|
|
concat.input.append(inp)
|
||
|
|
concat.input.append(axisNodeName)
|
||
|
|
graph_def.node.extend([concat])
|
||
|
|
|
||
|
|
addConstNode('concat/axis_flatten', [-1], graph_def)
|
||
|
|
addConstNode('PriorBox/concat/axis', [-2], graph_def)
|
||
|
|
|
||
|
|
for label in ['ClassPredictor', 'BoxEncodingPredictor' if box_predictor == 'convolutional' else 'BoxPredictor']:
|
||
|
|
concatInputs = []
|
||
|
|
for i in range(num_layers):
|
||
|
|
# Flatten predictions
|
||
|
|
flatten = NodeDef()
|
||
|
|
if box_predictor == 'convolutional':
|
||
|
|
inpName = 'BoxPredictor_%d/%s/BiasAdd' % (i, label)
|
||
|
|
else:
|
||
|
|
if i == 0:
|
||
|
|
inpName = 'WeightSharedConvolutionalBoxPredictor/%s/BiasAdd' % label
|
||
|
|
else:
|
||
|
|
inpName = 'WeightSharedConvolutionalBoxPredictor_%d/%s/BiasAdd' % (i, label)
|
||
|
|
flatten.input.append(inpName)
|
||
|
|
flatten.name = inpName + '/Flatten'
|
||
|
|
flatten.op = 'Flatten'
|
||
|
|
|
||
|
|
concatInputs.append(flatten.name)
|
||
|
|
graph_def.node.extend([flatten])
|
||
|
|
addConcatNode('%s/concat' % label, concatInputs, 'concat/axis_flatten')
|
||
|
|
|
||
|
|
num_matched_layers = 0
|
||
|
|
for node in graph_def.node:
|
||
|
|
if re.match('BoxPredictor_\d/BoxEncodingPredictor/convolution', node.name) or \
|
||
|
|
re.match('BoxPredictor_\d/BoxEncodingPredictor/Conv2D', node.name) or \
|
||
|
|
re.match('WeightSharedConvolutionalBoxPredictor(_\d)*/BoxPredictor/Conv2D', node.name):
|
||
|
|
node.addAttr('loc_pred_transposed', True)
|
||
|
|
num_matched_layers += 1
|
||
|
|
assert(num_matched_layers == num_layers)
|
||
|
|
|
||
|
|
# Add layers that generate anchors (bounding boxes proposals).
|
||
|
|
priorBoxes = []
|
||
|
|
boxCoder = config['box_coder'][0]
|
||
|
|
fasterRcnnBoxCoder = boxCoder['faster_rcnn_box_coder'][0]
|
||
|
|
boxCoderVariance = [1.0/float(fasterRcnnBoxCoder['x_scale'][0]), 1.0/float(fasterRcnnBoxCoder['y_scale'][0]), 1.0/float(fasterRcnnBoxCoder['width_scale'][0]), 1.0/float(fasterRcnnBoxCoder['height_scale'][0])]
|
||
|
|
for i in range(num_layers):
|
||
|
|
priorBox = NodeDef()
|
||
|
|
priorBox.name = 'PriorBox_%d' % i
|
||
|
|
priorBox.op = 'PriorBox'
|
||
|
|
if box_predictor == 'convolutional':
|
||
|
|
priorBox.input.append('BoxPredictor_%d/BoxEncodingPredictor/BiasAdd' % i)
|
||
|
|
else:
|
||
|
|
if i == 0:
|
||
|
|
priorBox.input.append('WeightSharedConvolutionalBoxPredictor/BoxPredictor/Conv2D')
|
||
|
|
else:
|
||
|
|
priorBox.input.append('WeightSharedConvolutionalBoxPredictor_%d/BoxPredictor/BiasAdd' % i)
|
||
|
|
priorBox.input.append(graph_def.node[0].name) # image_tensor
|
||
|
|
|
||
|
|
priorBox.addAttr('flip', False)
|
||
|
|
priorBox.addAttr('clip', False)
|
||
|
|
|
||
|
|
widths, heights = priors_generator.get(i)
|
||
|
|
|
||
|
|
priorBox.addAttr('width', widths)
|
||
|
|
priorBox.addAttr('height', heights)
|
||
|
|
priorBox.addAttr('variance', boxCoderVariance)
|
||
|
|
|
||
|
|
graph_def.node.extend([priorBox])
|
||
|
|
priorBoxes.append(priorBox.name)
|
||
|
|
|
||
|
|
# Compare this layer's output with Postprocessor/Reshape
|
||
|
|
addConcatNode('PriorBox/concat', priorBoxes, 'concat/axis_flatten')
|
||
|
|
|
||
|
|
# Sigmoid for classes predictions and DetectionOutput layer
|
||
|
|
addReshape('ClassPredictor/concat', 'ClassPredictor/concat3d', [0, -1, num_classes + 1], graph_def)
|
||
|
|
|
||
|
|
sigmoid = NodeDef()
|
||
|
|
sigmoid.name = 'ClassPredictor/concat/sigmoid'
|
||
|
|
sigmoid.op = 'Sigmoid'
|
||
|
|
sigmoid.input.append('ClassPredictor/concat3d')
|
||
|
|
graph_def.node.extend([sigmoid])
|
||
|
|
|
||
|
|
addFlatten(sigmoid.name, sigmoid.name + '/Flatten', graph_def)
|
||
|
|
|
||
|
|
detectionOut = NodeDef()
|
||
|
|
detectionOut.name = 'detection_out'
|
||
|
|
detectionOut.op = 'DetectionOutput'
|
||
|
|
|
||
|
|
if box_predictor == 'convolutional':
|
||
|
|
detectionOut.input.append('BoxEncodingPredictor/concat')
|
||
|
|
else:
|
||
|
|
detectionOut.input.append('BoxPredictor/concat')
|
||
|
|
detectionOut.input.append(sigmoid.name + '/Flatten')
|
||
|
|
detectionOut.input.append('PriorBox/concat')
|
||
|
|
|
||
|
|
detectionOut.addAttr('num_classes', num_classes + 1)
|
||
|
|
detectionOut.addAttr('share_location', True)
|
||
|
|
detectionOut.addAttr('background_label_id', 0)
|
||
|
|
|
||
|
|
postProcessing = config['post_processing'][0]
|
||
|
|
batchNMS = postProcessing['batch_non_max_suppression'][0]
|
||
|
|
|
||
|
|
if 'iou_threshold' in batchNMS:
|
||
|
|
detectionOut.addAttr('nms_threshold', float(batchNMS['iou_threshold'][0]))
|
||
|
|
else:
|
||
|
|
detectionOut.addAttr('nms_threshold', 0.6)
|
||
|
|
|
||
|
|
if 'score_threshold' in batchNMS:
|
||
|
|
detectionOut.addAttr('confidence_threshold', float(batchNMS['score_threshold'][0]))
|
||
|
|
else:
|
||
|
|
detectionOut.addAttr('confidence_threshold', 0.01)
|
||
|
|
|
||
|
|
if 'max_detections_per_class' in batchNMS:
|
||
|
|
detectionOut.addAttr('top_k', int(batchNMS['max_detections_per_class'][0]))
|
||
|
|
else:
|
||
|
|
detectionOut.addAttr('top_k', 100)
|
||
|
|
|
||
|
|
if 'max_total_detections' in batchNMS:
|
||
|
|
detectionOut.addAttr('keep_top_k', int(batchNMS['max_total_detections'][0]))
|
||
|
|
else:
|
||
|
|
detectionOut.addAttr('keep_top_k', 100)
|
||
|
|
|
||
|
|
detectionOut.addAttr('code_type', "CENTER_SIZE")
|
||
|
|
|
||
|
|
graph_def.node.extend([detectionOut])
|
||
|
|
|
||
|
|
while True:
|
||
|
|
unconnectedNodes = getUnconnectedNodes()
|
||
|
|
unconnectedNodes.remove(detectionOut.name)
|
||
|
|
if not unconnectedNodes:
|
||
|
|
break
|
||
|
|
|
||
|
|
for name in unconnectedNodes:
|
||
|
|
for i in range(len(graph_def.node)):
|
||
|
|
if graph_def.node[i].name == name:
|
||
|
|
del graph_def.node[i]
|
||
|
|
break
|
||
|
|
|
||
|
|
# Save as text.
|
||
|
|
graph_def.save(outputPath)
|
||
|
|
|
||
|
|
|
||
|
|
if __name__ == "__main__":
|
||
|
|
parser = argparse.ArgumentParser(description='Run this script to get a text graph of '
|
||
|
|
'SSD model from TensorFlow Object Detection API. '
|
||
|
|
'Then pass it with .pb file to cv::dnn::readNetFromTensorflow function.')
|
||
|
|
parser.add_argument('--input', required=True, help='Path to frozen TensorFlow graph.')
|
||
|
|
parser.add_argument('--output', required=True, help='Path to output text graph.')
|
||
|
|
parser.add_argument('--config', required=True, help='Path to a *.config file is used for training.')
|
||
|
|
args = parser.parse_args()
|
||
|
|
|
||
|
|
createSSDGraph(args.input, args.config, args.output)
|