mirror of https://github.com/opencv/opencv.git
Dmitry Kurtaev
5 years ago
parent
d5e8792f55
commit
d9bada9867
5 changed files with 302 additions and 8 deletions
@ -0,0 +1,236 @@ |
||||
# 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) 2020, 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 EfficientDet |
||||
# deep learning network trained in https://github.com/google/automl. |
||||
# 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 AnchorGenerator: |
||||
def __init__(self, min_level, aspect_ratios, num_scales, anchor_scale): |
||||
self.min_level = min_level |
||||
self.aspect_ratios = aspect_ratios |
||||
self.anchor_scale = anchor_scale |
||||
self.scales = [2**(float(s) / num_scales) for s in range(num_scales)] |
||||
|
||||
def get(self, layer_id): |
||||
widths = [] |
||||
heights = [] |
||||
for s in self.scales: |
||||
for a in self.aspect_ratios: |
||||
base_anchor_size = 2**(self.min_level + layer_id) * self.anchor_scale |
||||
heights.append(base_anchor_size * s * a[1]) |
||||
widths.append(base_anchor_size * s * a[0]) |
||||
return widths, heights |
||||
|
||||
|
||||
def createGraph(modelPath, outputPath, min_level, aspect_ratios, num_scales, |
||||
anchor_scale, num_classes, image_width, image_height): |
||||
print('Min level: %d' % min_level) |
||||
print('Anchor scale: %f' % anchor_scale) |
||||
print('Num scales: %d' % num_scales) |
||||
print('Aspect ratios: %s' % str(aspect_ratios)) |
||||
print('Number of classes: %d' % num_classes) |
||||
print('Input image size: %dx%d' % (image_width, image_height)) |
||||
|
||||
# Read the graph. |
||||
_inpNames = ['image_arrays'] |
||||
outNames = ['detections'] |
||||
|
||||
writeTextGraph(modelPath, outputPath, outNames) |
||||
graph_def = parseTextGraph(outputPath) |
||||
|
||||
def getUnconnectedNodes(): |
||||
unconnected = [] |
||||
for node in graph_def.node: |
||||
if node.op == 'Const': |
||||
continue |
||||
unconnected.append(node.name) |
||||
for inp in node.input: |
||||
if inp in unconnected: |
||||
unconnected.remove(inp) |
||||
return unconnected |
||||
|
||||
|
||||
nodesToKeep = ['truediv'] # Keep preprocessing nodes |
||||
|
||||
removeIdentity(graph_def) |
||||
|
||||
scopesToKeep = ('image_arrays', 'efficientnet', 'resample_p6', 'resample_p7', |
||||
'fpn_cells', 'class_net', 'box_net', 'Reshape', 'concat') |
||||
|
||||
addConstNode('scale_w', [2.0], graph_def) |
||||
addConstNode('scale_h', [2.0], graph_def) |
||||
nodesToKeep += ['scale_w', 'scale_h'] |
||||
|
||||
for node in graph_def.node: |
||||
if re.match('efficientnet-(.*)/blocks_\d+/se/mul_1', node.name): |
||||
node.input[0], node.input[1] = node.input[1], node.input[0] |
||||
|
||||
if re.match('fpn_cells/cell_\d+/fnode\d+/resample(.*)/nearest_upsampling/Reshape_1$', node.name): |
||||
node.op = 'ResizeNearestNeighbor' |
||||
node.input[1] = 'scale_w' |
||||
node.input.append('scale_h') |
||||
|
||||
for inpNode in graph_def.node: |
||||
if inpNode.name == node.name[:node.name.rfind('_')]: |
||||
node.input[0] = inpNode.input[0] |
||||
|
||||
if re.match('box_net/box-predict(_\d)*/separable_conv2d$', node.name): |
||||
node.addAttr('loc_pred_transposed', True) |
||||
|
||||
# Replace RealDiv to Mul with inversed scale for compatibility |
||||
if node.op == 'RealDiv': |
||||
for inpNode in graph_def.node: |
||||
if inpNode.name != node.input[1] or not 'value' in inpNode.attr: |
||||
continue |
||||
|
||||
tensor = inpNode.attr['value']['tensor'][0] |
||||
if not 'float_val' in tensor: |
||||
continue |
||||
scale = float(inpNode.attr['value']['tensor'][0]['float_val'][0]) |
||||
|
||||
addConstNode(inpNode.name + '/inv', [1.0 / scale], graph_def) |
||||
nodesToKeep.append(inpNode.name + '/inv') |
||||
node.input[1] = inpNode.name + '/inv' |
||||
node.op = 'Mul' |
||||
break |
||||
|
||||
|
||||
def to_remove(name, op): |
||||
if name in nodesToKeep: |
||||
return False |
||||
return op == 'Const' or not name.startswith(scopesToKeep) |
||||
|
||||
removeUnusedNodesAndAttrs(to_remove, graph_def) |
||||
|
||||
# Attach unconnected preprocessing |
||||
assert(graph_def.node[1].name == 'truediv' and graph_def.node[1].op == 'RealDiv') |
||||
graph_def.node[1].input.insert(0, 'image_arrays') |
||||
graph_def.node[2].input.insert(0, 'truediv') |
||||
|
||||
priors_generator = AnchorGenerator(min_level, aspect_ratios, num_scales, anchor_scale) |
||||
priorBoxes = [] |
||||
for i in range(5): |
||||
inpName = '' |
||||
for node in graph_def.node: |
||||
if node.name == 'Reshape_%d' % (i * 2 + 1): |
||||
inpName = node.input[0] |
||||
break |
||||
|
||||
priorBox = NodeDef() |
||||
priorBox.name = 'PriorBox_%d' % i |
||||
priorBox.op = 'PriorBox' |
||||
priorBox.input.append(inpName) |
||||
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', [1.0, 1.0, 1.0, 1.0]) |
||||
|
||||
graph_def.node.extend([priorBox]) |
||||
priorBoxes.append(priorBox.name) |
||||
|
||||
addConstNode('concat/axis_flatten', [-1], graph_def) |
||||
|
||||
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]) |
||||
|
||||
addConcatNode('PriorBox/concat', priorBoxes, 'concat/axis_flatten') |
||||
|
||||
sigmoid = NodeDef() |
||||
sigmoid.name = 'concat/sigmoid' |
||||
sigmoid.op = 'Sigmoid' |
||||
sigmoid.input.append('concat') |
||||
graph_def.node.extend([sigmoid]) |
||||
|
||||
addFlatten(sigmoid.name, sigmoid.name + '/Flatten', graph_def) |
||||
addFlatten('concat_1', 'concat_1/Flatten', graph_def) |
||||
|
||||
detectionOut = NodeDef() |
||||
detectionOut.name = 'detection_out' |
||||
detectionOut.op = 'DetectionOutput' |
||||
|
||||
detectionOut.input.append('concat_1/Flatten') |
||||
detectionOut.input.append(sigmoid.name + '/Flatten') |
||||
detectionOut.input.append('PriorBox/concat') |
||||
|
||||
detectionOut.addAttr('num_classes', num_classes) |
||||
detectionOut.addAttr('share_location', True) |
||||
detectionOut.addAttr('background_label_id', num_classes + 1) |
||||
detectionOut.addAttr('nms_threshold', 0.6) |
||||
detectionOut.addAttr('confidence_threshold', 0.2) |
||||
detectionOut.addAttr('top_k', 100) |
||||
detectionOut.addAttr('keep_top_k', 100) |
||||
detectionOut.addAttr('code_type', "CENTER_SIZE") |
||||
graph_def.node.extend([detectionOut]) |
||||
|
||||
graph_def.node[0].attr['shape'] = { |
||||
'shape': { |
||||
'dim': [ |
||||
{'size': -1}, |
||||
{'size': image_height}, |
||||
{'size': image_width}, |
||||
{'size': 3} |
||||
] |
||||
} |
||||
} |
||||
|
||||
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('--min_level', default=3, type=int, help='Parameter from training config') |
||||
parser.add_argument('--num_scales', default=3, type=int, help='Parameter from training config') |
||||
parser.add_argument('--anchor_scale', default=4.0, type=float, help='Parameter from training config') |
||||
parser.add_argument('--aspect_ratios', default=[1.0, 1.0, 1.4, 0.7, 0.7, 1.4], |
||||
nargs='+', type=float, help='Parameter from training config') |
||||
parser.add_argument('--num_classes', default=90, type=int, help='Number of classes to detect') |
||||
parser.add_argument('--width', default=512, type=int, help='Network input width') |
||||
parser.add_argument('--height', default=512, type=int, help='Network input height') |
||||
args = parser.parse_args() |
||||
|
||||
ar = args.aspect_ratios |
||||
assert(len(ar) % 2 == 0) |
||||
ar = list(zip(ar[::2], ar[1::2])) |
||||
|
||||
createGraph(args.input, args.output, args.min_level, ar, args.num_scales, |
||||
args.anchor_scale, args.num_classes, args.width, args.height) |
||||
Loading…
Reference in new issue