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OpenCV face detection network in TensorFlow
This commit is contained in:
Dmitry Kurtaev
parent
53305d4a7e
commit
eab556e1e0
195
modules/dnn/misc/face_detector_accuracy.py
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195
modules/dnn/misc/face_detector_accuracy.py
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# This script is used to estimate an accuracy of different face detection models.
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# COCO evaluation tool is used to compute an accuracy metrics (Average Precision).
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# Script works with different face detection datasets.
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import os
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import json
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from fnmatch import fnmatch
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from math import pi
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import cv2 as cv
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import argparse
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import os
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import sys
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from pycocotools.coco import COCO
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from pycocotools.cocoeval import COCOeval
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parser = argparse.ArgumentParser(
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description='Evaluate OpenCV face detection algorithms '
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'using COCO evaluation tool, http://cocodataset.org/#detections-eval')
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parser.add_argument('--proto', help='Path to .prototxt of Caffe model or .pbtxt of TensorFlow graph')
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parser.add_argument('--model', help='Path to .caffemodel trained in Caffe or .pb from TensorFlow')
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parser.add_argument('--caffe', help='Indicate that tested model is from Caffe. Otherwise model from TensorFlow is expected.', action='store_true')
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parser.add_argument('--cascade', help='Optional path to trained Haar cascade as '
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'an additional model for evaluation')
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parser.add_argument('--ann', help='Path to text file with ground truth annotations')
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parser.add_argument('--pics', help='Path to images root directory')
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parser.add_argument('--fddb', help='Evaluate FDDB dataset, http://vis-www.cs.umass.edu/fddb/', action='store_true')
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parser.add_argument('--wider', help='Evaluate WIDER FACE dataset, http://mmlab.ie.cuhk.edu.hk/projects/WIDERFace/', action='store_true')
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args = parser.parse_args()
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dataset = {}
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dataset['images'] = []
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dataset['categories'] = [{ 'id': 0, 'name': 'face' }]
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dataset['annotations'] = []
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def ellipse2Rect(params):
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rad_x = params[0]
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rad_y = params[1]
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angle = params[2] * 180.0 / pi
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center_x = params[3]
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center_y = params[4]
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pts = cv.ellipse2Poly((int(center_x), int(center_y)), (int(rad_x), int(rad_y)),
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int(angle), 0, 360, 10)
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rect = cv.boundingRect(pts)
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left = rect[0]
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top = rect[1]
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right = rect[0] + rect[2]
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bottom = rect[1] + rect[3]
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return left, top, right, bottom
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def addImage(imagePath):
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assert('images' in dataset)
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imageId = len(dataset['images'])
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dataset['images'].append({
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'id': int(imageId),
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'file_name': imagePath
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})
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return imageId
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def addBBox(imageId, left, top, width, height):
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assert('annotations' in dataset)
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dataset['annotations'].append({
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'id': len(dataset['annotations']),
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'image_id': int(imageId),
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'category_id': 0, # Face
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'bbox': [int(left), int(top), int(width), int(height)],
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'iscrowd': 0,
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'area': float(width * height)
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})
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def addDetection(detections, imageId, left, top, width, height, score):
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detections.append({
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'image_id': int(imageId),
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'category_id': 0, # Face
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'bbox': [int(left), int(top), int(width), int(height)],
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'score': float(score)
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})
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def fddb_dataset(annotations, images):
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for d in os.listdir(annotations):
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if fnmatch(d, 'FDDB-fold-*-ellipseList.txt'):
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with open(os.path.join(annotations, d), 'rt') as f:
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lines = [line.rstrip('\n') for line in f]
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lineId = 0
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while lineId < len(lines):
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# Image
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imgPath = lines[lineId]
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lineId += 1
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imageId = addImage(os.path.join(images, imgPath) + '.jpg')
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img = cv.imread(os.path.join(images, imgPath) + '.jpg')
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# Faces
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numFaces = int(lines[lineId])
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lineId += 1
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for i in range(numFaces):
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params = [float(v) for v in lines[lineId].split()]
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lineId += 1
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left, top, right, bottom = ellipse2Rect(params)
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addBBox(imageId, left, top, width=right - left + 1,
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height=bottom - top + 1)
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def wider_dataset(annotations, images):
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with open(annotations, 'rt') as f:
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lines = [line.rstrip('\n') for line in f]
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lineId = 0
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while lineId < len(lines):
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# Image
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imgPath = lines[lineId]
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lineId += 1
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imageId = addImage(os.path.join(images, imgPath))
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# Faces
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numFaces = int(lines[lineId])
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lineId += 1
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for i in range(numFaces):
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params = [int(v) for v in lines[lineId].split()]
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lineId += 1
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left, top, width, height = params[0], params[1], params[2], params[3]
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addBBox(imageId, left, top, width, height)
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def evaluate():
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cocoGt = COCO('annotations.json')
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cocoDt = cocoGt.loadRes('detections.json')
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cocoEval = COCOeval(cocoGt, cocoDt, 'bbox')
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cocoEval.evaluate()
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cocoEval.accumulate()
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cocoEval.summarize()
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### Convert to COCO annotations format #########################################
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assert(args.fddb or args.wider)
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if args.fddb:
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fddb_dataset(args.ann, args.pics)
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elif args.wider:
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wider_dataset(args.ann, args.pics)
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with open('annotations.json', 'wt') as f:
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json.dump(dataset, f)
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### Obtain detections ##########################################################
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detections = []
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if args.proto and args.model:
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if args.caffe:
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net = cv.dnn.readNetFromCaffe(args.proto, args.model)
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else:
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net = cv.dnn.readNetFromTensorflow(args.model, args.proto)
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def detect(img, imageId):
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imgWidth = img.shape[1]
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imgHeight = img.shape[0]
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net.setInput(cv.dnn.blobFromImage(img, 1.0, (300, 300), (104., 177., 123.), False, False))
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out = net.forward()
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for i in range(out.shape[2]):
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confidence = out[0, 0, i, 2]
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left = int(out[0, 0, i, 3] * img.shape[1])
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top = int(out[0, 0, i, 4] * img.shape[0])
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right = int(out[0, 0, i, 5] * img.shape[1])
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bottom = int(out[0, 0, i, 6] * img.shape[0])
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addDetection(detections, imageId, left, top, width=right - left + 1,
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height=bottom - top + 1, score=confidence)
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elif args.cascade:
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cascade = cv.CascadeClassifier(args.cascade)
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def detect(img, imageId):
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srcImgGray = cv.cvtColor(img, cv.COLOR_BGR2GRAY)
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faces = cascade.detectMultiScale(srcImgGray)
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for rect in faces:
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left, top, width, height = rect[0], rect[1], rect[2], rect[3]
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addDetection(detections, imageId, left, top, width, height, score=1.0)
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for i in range(len(dataset['images'])):
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sys.stdout.write('\r%d / %d' % (i + 1, len(dataset['images'])))
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sys.stdout.flush()
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img = cv.imread(dataset['images'][i]['file_name'])
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imageId = int(dataset['images'][i]['id'])
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detect(img, imageId)
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with open('detections.json', 'wt') as f:
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json.dump(detections, f)
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evaluate()
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def rm(f):
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if os.path.exists(f):
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os.remove(f)
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rm('annotations.json')
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rm('detections.json')
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348
modules/dnn/misc/quantize_face_detector.py
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modules/dnn/misc/quantize_face_detector.py
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import argparse
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import cv2 as cv
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import tensorflow as tf
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import numpy as np
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import struct
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from tensorflow.python.tools import optimize_for_inference_lib
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from tensorflow.tools.graph_transforms import TransformGraph
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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="Use this script to create TensorFlow graph "
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"with weights from OpenCV's face detection network. "
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"Only backbone part of SSD model is converted this way. "
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"Look for .pbtxt configuration file at "
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"https://github.com/opencv/opencv_extra/tree/master/testdata/dnn/opencv_face_detector.pbtxt")
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parser.add_argument('--model', help='Path to .caffemodel weights', required=True)
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parser.add_argument('--proto', help='Path to .prototxt Caffe model definition', required=True)
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parser.add_argument('--pb', help='Path to output .pb TensorFlow model', required=True)
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parser.add_argument('--pbtxt', help='Path to output .pbxt TensorFlow graph', required=True)
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parser.add_argument('--quantize', help='Quantize weights to uint8', action='store_true')
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parser.add_argument('--fp16', help='Convert weights to half precision floats', action='store_true')
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args = parser.parse_args()
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assert(not args.quantize or not args.fp16)
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dtype = tf.float16 if args.fp16 else tf.float32
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################################################################################
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cvNet = cv.dnn.readNetFromCaffe(args.proto, args.model)
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def dnnLayer(name):
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return cvNet.getLayer(long(cvNet.getLayerId(name)))
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def scale(x, name):
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with tf.variable_scope(name):
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layer = dnnLayer(name)
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w = tf.Variable(layer.blobs[0].flatten(), dtype=dtype, name='mul')
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if len(layer.blobs) > 1:
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b = tf.Variable(layer.blobs[1].flatten(), dtype=dtype, name='add')
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return tf.nn.bias_add(tf.multiply(x, w), b)
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else:
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return tf.multiply(x, w, name)
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def conv(x, name, stride=1, pad='SAME', dilation=1, activ=None):
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with tf.variable_scope(name):
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layer = dnnLayer(name)
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w = tf.Variable(layer.blobs[0].transpose(2, 3, 1, 0), dtype=dtype, name='weights')
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if dilation == 1:
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conv = tf.nn.conv2d(x, filter=w, strides=(1, stride, stride, 1), padding=pad)
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else:
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assert(stride == 1)
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conv = tf.nn.atrous_conv2d(x, w, rate=dilation, padding=pad)
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if len(layer.blobs) > 1:
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b = tf.Variable(layer.blobs[1].flatten(), dtype=dtype, name='bias')
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conv = tf.nn.bias_add(conv, b)
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return activ(conv) if activ else conv
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def batch_norm(x, name):
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with tf.variable_scope(name):
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# Unfortunately, TensorFlow's batch normalization layer doesn't work with fp16 input.
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# Here we do a cast to fp32 but remove it in the frozen graph.
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if x.dtype != tf.float32:
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x = tf.cast(x, tf.float32)
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layer = dnnLayer(name)
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assert(len(layer.blobs) >= 3)
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mean = layer.blobs[0].flatten()
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std = layer.blobs[1].flatten()
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scale = layer.blobs[2].flatten()
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eps = 1e-5
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hasBias = len(layer.blobs) > 3
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hasWeights = scale.shape != (1,)
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if not hasWeights and not hasBias:
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mean /= scale[0]
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std /= scale[0]
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mean = tf.Variable(mean, dtype=tf.float32, name='mean')
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std = tf.Variable(std, dtype=tf.float32, name='std')
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gamma = tf.Variable(scale if hasWeights else np.ones(mean.shape), dtype=tf.float32, name='gamma')
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beta = tf.Variable(layer.blobs[3].flatten() if hasBias else np.zeros(mean.shape), dtype=tf.float32, name='beta')
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bn = tf.nn.fused_batch_norm(x, gamma, beta, mean, std, eps,
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is_training=False)[0]
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if bn.dtype != dtype:
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bn = tf.cast(bn, dtype)
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return bn
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def l2norm(x, name):
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with tf.variable_scope(name):
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layer = dnnLayer(name)
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w = tf.Variable(layer.blobs[0].flatten(), dtype=dtype, name='mul')
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return tf.nn.l2_normalize(x, 3, epsilon=1e-10) * w
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### Graph definition ###########################################################
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inp = tf.placeholder(dtype, [1, 300, 300, 3], 'data')
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data_bn = batch_norm(inp, 'data_bn')
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data_scale = scale(data_bn, 'data_scale')
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data_scale = tf.pad(data_scale, [[0, 0], [3, 3], [3, 3], [0, 0]])
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conv1_h = conv(data_scale, stride=2, pad='VALID', name='conv1_h')
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conv1_bn_h = batch_norm(conv1_h, 'conv1_bn_h')
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conv1_scale_h = scale(conv1_bn_h, 'conv1_scale_h')
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conv1_relu = tf.nn.relu(conv1_scale_h)
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conv1_pool = tf.layers.max_pooling2d(conv1_relu, pool_size=(3, 3), strides=(2, 2),
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padding='SAME', name='conv1_pool')
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layer_64_1_conv1_h = conv(conv1_pool, 'layer_64_1_conv1_h')
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layer_64_1_bn2_h = batch_norm(layer_64_1_conv1_h, 'layer_64_1_bn2_h')
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layer_64_1_scale2_h = scale(layer_64_1_bn2_h, 'layer_64_1_scale2_h')
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layer_64_1_relu2 = tf.nn.relu(layer_64_1_scale2_h)
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layer_64_1_conv2_h = conv(layer_64_1_relu2, 'layer_64_1_conv2_h')
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layer_64_1_sum = layer_64_1_conv2_h + conv1_pool
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layer_128_1_bn1_h = batch_norm(layer_64_1_sum, 'layer_128_1_bn1_h')
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layer_128_1_scale1_h = scale(layer_128_1_bn1_h, 'layer_128_1_scale1_h')
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layer_128_1_relu1 = tf.nn.relu(layer_128_1_scale1_h)
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layer_128_1_conv1_h = conv(layer_128_1_relu1, stride=2, name='layer_128_1_conv1_h')
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layer_128_1_bn2 = batch_norm(layer_128_1_conv1_h, 'layer_128_1_bn2')
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layer_128_1_scale2 = scale(layer_128_1_bn2, 'layer_128_1_scale2')
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layer_128_1_relu2 = tf.nn.relu(layer_128_1_scale2)
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layer_128_1_conv2 = conv(layer_128_1_relu2, 'layer_128_1_conv2')
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layer_128_1_conv_expand_h = conv(layer_128_1_relu1, stride=2, name='layer_128_1_conv_expand_h')
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layer_128_1_sum = layer_128_1_conv2 + layer_128_1_conv_expand_h
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layer_256_1_bn1 = batch_norm(layer_128_1_sum, 'layer_256_1_bn1')
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layer_256_1_scale1 = scale(layer_256_1_bn1, 'layer_256_1_scale1')
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layer_256_1_relu1 = tf.nn.relu(layer_256_1_scale1)
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layer_256_1_conv1 = tf.pad(layer_256_1_relu1, [[0, 0], [1, 1], [1, 1], [0, 0]])
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layer_256_1_conv1 = conv(layer_256_1_conv1, stride=2, pad='VALID', name='layer_256_1_conv1')
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layer_256_1_bn2 = batch_norm(layer_256_1_conv1, 'layer_256_1_bn2')
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layer_256_1_scale2 = scale(layer_256_1_bn2, 'layer_256_1_scale2')
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layer_256_1_relu2 = tf.nn.relu(layer_256_1_scale2)
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layer_256_1_conv2 = conv(layer_256_1_relu2, 'layer_256_1_conv2')
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layer_256_1_conv_expand = conv(layer_256_1_relu1, stride=2, name='layer_256_1_conv_expand')
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layer_256_1_sum = layer_256_1_conv2 + layer_256_1_conv_expand
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layer_512_1_bn1 = batch_norm(layer_256_1_sum, 'layer_512_1_bn1')
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layer_512_1_scale1 = scale(layer_512_1_bn1, 'layer_512_1_scale1')
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layer_512_1_relu1 = tf.nn.relu(layer_512_1_scale1)
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layer_512_1_conv1_h = conv(layer_512_1_relu1, 'layer_512_1_conv1_h')
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layer_512_1_bn2_h = batch_norm(layer_512_1_conv1_h, 'layer_512_1_bn2_h')
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layer_512_1_scale2_h = scale(layer_512_1_bn2_h, 'layer_512_1_scale2_h')
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layer_512_1_relu2 = tf.nn.relu(layer_512_1_scale2_h)
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layer_512_1_conv2_h = conv(layer_512_1_relu2, dilation=2, name='layer_512_1_conv2_h')
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layer_512_1_conv_expand_h = conv(layer_512_1_relu1, 'layer_512_1_conv_expand_h')
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layer_512_1_sum = layer_512_1_conv2_h + layer_512_1_conv_expand_h
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last_bn_h = batch_norm(layer_512_1_sum, 'last_bn_h')
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last_scale_h = scale(last_bn_h, 'last_scale_h')
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fc7 = tf.nn.relu(last_scale_h, name='last_relu')
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conv6_1_h = conv(fc7, 'conv6_1_h', activ=tf.nn.relu)
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conv6_2_h = conv(conv6_1_h, stride=2, name='conv6_2_h', activ=tf.nn.relu)
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conv7_1_h = conv(conv6_2_h, 'conv7_1_h', activ=tf.nn.relu)
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conv7_2_h = tf.pad(conv7_1_h, [[0, 0], [1, 1], [1, 1], [0, 0]])
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conv7_2_h = conv(conv7_2_h, stride=2, pad='VALID', name='conv7_2_h', activ=tf.nn.relu)
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conv8_1_h = conv(conv7_2_h, pad='SAME', name='conv8_1_h', activ=tf.nn.relu)
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conv8_2_h = conv(conv8_1_h, pad='SAME', name='conv8_2_h', activ=tf.nn.relu)
|
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conv9_1_h = conv(conv8_2_h, 'conv9_1_h', activ=tf.nn.relu)
|
||||
conv9_2_h = conv(conv9_1_h, pad='SAME', name='conv9_2_h', activ=tf.nn.relu)
|
||||
|
||||
conv4_3_norm = l2norm(layer_256_1_relu1, 'conv4_3_norm')
|
||||
|
||||
### Locations and confidences ##################################################
|
||||
locations = []
|
||||
confidences = []
|
||||
flattenLayersNames = [] # Collect all reshape layers names that should be replaced to flattens.
|
||||
for top, suffix in zip([locations, confidences], ['_mbox_loc', '_mbox_conf']):
|
||||
for bottom, name in zip([conv4_3_norm, fc7, conv6_2_h, conv7_2_h, conv8_2_h, conv9_2_h],
|
||||
['conv4_3_norm', 'fc7', 'conv6_2', 'conv7_2', 'conv8_2', 'conv9_2']):
|
||||
name += suffix
|
||||
flat = tf.layers.flatten(conv(bottom, name))
|
||||
flattenLayersNames.append(flat.name[:flat.name.find(':')])
|
||||
top.append(flat)
|
||||
|
||||
mbox_loc = tf.concat(locations, axis=-1, name='mbox_loc')
|
||||
mbox_conf = tf.concat(confidences, axis=-1, name='mbox_conf')
|
||||
|
||||
total = int(np.prod(mbox_conf.shape[1:]))
|
||||
mbox_conf_reshape = tf.reshape(mbox_conf, [-1, 2], name='mbox_conf_reshape')
|
||||
mbox_conf_softmax = tf.nn.softmax(mbox_conf_reshape, name='mbox_conf_softmax')
|
||||
mbox_conf_flatten = tf.reshape(mbox_conf_softmax, [-1, total], name='mbox_conf_flatten')
|
||||
flattenLayersNames.append('mbox_conf_flatten')
|
||||
|
||||
with tf.Session() as sess:
|
||||
sess.run(tf.global_variables_initializer())
|
||||
|
||||
### Check correctness ######################################################
|
||||
out_nodes = ['mbox_loc', 'mbox_conf_flatten']
|
||||
inp_nodes = [inp.name[:inp.name.find(':')]]
|
||||
|
||||
np.random.seed(2701)
|
||||
inputData = np.random.standard_normal([1, 3, 300, 300]).astype(np.float32)
|
||||
|
||||
cvNet.setInput(inputData)
|
||||
outDNN = cvNet.forward(out_nodes)
|
||||
|
||||
outTF = sess.run([mbox_loc, mbox_conf_flatten], feed_dict={inp: inputData.transpose(0, 2, 3, 1)})
|
||||
print 'Max diff @ locations: %e' % np.max(np.abs(outDNN[0] - outTF[0]))
|
||||
print 'Max diff @ confidence: %e' % np.max(np.abs(outDNN[1] - outTF[1]))
|
||||
|
||||
# Save a graph
|
||||
graph_def = sess.graph.as_graph_def()
|
||||
|
||||
# Freeze graph. Replaces variables to constants.
|
||||
graph_def = tf.graph_util.convert_variables_to_constants(sess, graph_def, out_nodes)
|
||||
# Optimize graph. Removes training-only ops, unused nodes.
|
||||
graph_def = optimize_for_inference_lib.optimize_for_inference(graph_def, inp_nodes, out_nodes, dtype.as_datatype_enum)
|
||||
# Fuse constant operations.
|
||||
transforms = ["fold_constants(ignore_errors=True)"]
|
||||
if args.quantize:
|
||||
transforms += ["quantize_weights(minimum_size=0)"]
|
||||
transforms += ["sort_by_execution_order"]
|
||||
graph_def = TransformGraph(graph_def, inp_nodes, out_nodes, transforms)
|
||||
|
||||
# By default, float16 weights are stored in repeated tensor's field called
|
||||
# `half_val`. It has type int32 with leading zeros for unused bytes.
|
||||
# This type is encoded by Varint that means only 7 bits are used for value
|
||||
# representation but the last one is indicated the end of encoding. This way
|
||||
# float16 might takes 1 or 2 or 3 bytes depends on value. To impove compression,
|
||||
# we replace all `half_val` values to `tensor_content` using only 2 bytes for everyone.
|
||||
for node in graph_def.node:
|
||||
if 'value' in node.attr:
|
||||
halfs = node.attr["value"].tensor.half_val
|
||||
if not node.attr["value"].tensor.tensor_content and halfs:
|
||||
node.attr["value"].tensor.tensor_content = struct.pack('H' * len(halfs), *halfs)
|
||||
node.attr["value"].tensor.ClearField('half_val')
|
||||
|
||||
# Serialize
|
||||
with tf.gfile.FastGFile(args.pb, 'wb') as f:
|
||||
f.write(graph_def.SerializeToString())
|
||||
|
||||
|
||||
################################################################################
|
||||
# Write a text graph representation
|
||||
################################################################################
|
||||
def tensorMsg(values):
|
||||
msg = 'tensor { dtype: DT_FLOAT tensor_shape { dim { size: %d } }' % len(values)
|
||||
for value in values:
|
||||
msg += 'float_val: %f ' % value
|
||||
return msg + '}'
|
||||
|
||||
# Remove Const nodes and unused attributes.
|
||||
for i in reversed(range(len(graph_def.node))):
|
||||
if graph_def.node[i].op in ['Const', 'Dequantize']:
|
||||
del graph_def.node[i]
|
||||
for attr in ['T', 'data_format', 'Tshape', 'N', 'Tidx', 'Tdim',
|
||||
'use_cudnn_on_gpu', 'Index', 'Tperm', 'is_training',
|
||||
'Tpaddings']:
|
||||
if attr in graph_def.node[i].attr:
|
||||
del graph_def.node[i].attr[attr]
|
||||
|
||||
# Append prior box generators
|
||||
min_sizes = [30, 60, 111, 162, 213, 264]
|
||||
max_sizes = [60, 111, 162, 213, 264, 315]
|
||||
steps = [8, 16, 32, 64, 100, 300]
|
||||
aspect_ratios = [[2], [2, 3], [2, 3], [2, 3], [2], [2]]
|
||||
layers = [conv4_3_norm, fc7, conv6_2_h, conv7_2_h, conv8_2_h, conv9_2_h]
|
||||
for i in range(6):
|
||||
priorBox = NodeDef()
|
||||
priorBox.name = 'PriorBox_%d' % i
|
||||
priorBox.op = 'PriorBox'
|
||||
priorBox.input.append(layers[i].name[:layers[i].name.find(':')])
|
||||
priorBox.input.append(inp_nodes[0]) # data
|
||||
|
||||
text_format.Merge('i: %d' % min_sizes[i], priorBox.attr["min_size"])
|
||||
text_format.Merge('i: %d' % max_sizes[i], priorBox.attr["max_size"])
|
||||
text_format.Merge('b: true', priorBox.attr["flip"])
|
||||
text_format.Merge('b: false', priorBox.attr["clip"])
|
||||
text_format.Merge(tensorMsg(aspect_ratios[i]), priorBox.attr["aspect_ratio"])
|
||||
text_format.Merge(tensorMsg([0.1, 0.1, 0.2, 0.2]), priorBox.attr["variance"])
|
||||
text_format.Merge('f: %f' % steps[i], priorBox.attr["step"])
|
||||
text_format.Merge('f: 0.5', priorBox.attr["offset"])
|
||||
graph_def.node.extend([priorBox])
|
||||
|
||||
# Concatenate prior boxes
|
||||
concat = NodeDef()
|
||||
concat.name = 'mbox_priorbox'
|
||||
concat.op = 'ConcatV2'
|
||||
for i in range(6):
|
||||
concat.input.append('PriorBox_%d' % i)
|
||||
concat.input.append('mbox_loc/axis')
|
||||
graph_def.node.extend([concat])
|
||||
|
||||
# DetectionOutput layer
|
||||
detectionOut = NodeDef()
|
||||
detectionOut.name = 'detection_out'
|
||||
detectionOut.op = 'DetectionOutput'
|
||||
|
||||
detectionOut.input.append('mbox_loc')
|
||||
detectionOut.input.append('mbox_conf_flatten')
|
||||
detectionOut.input.append('mbox_priorbox')
|
||||
|
||||
text_format.Merge('i: 2', detectionOut.attr['num_classes'])
|
||||
text_format.Merge('b: true', detectionOut.attr['share_location'])
|
||||
text_format.Merge('i: 0', detectionOut.attr['background_label_id'])
|
||||
text_format.Merge('f: 0.45', detectionOut.attr['nms_threshold'])
|
||||
text_format.Merge('i: 400', detectionOut.attr['top_k'])
|
||||
text_format.Merge('s: "CENTER_SIZE"', detectionOut.attr['code_type'])
|
||||
text_format.Merge('i: 200', detectionOut.attr['keep_top_k'])
|
||||
text_format.Merge('f: 0.01', detectionOut.attr['confidence_threshold'])
|
||||
|
||||
graph_def.node.extend([detectionOut])
|
||||
|
||||
# Replace L2Normalization subgraph onto a single node.
|
||||
for i in reversed(range(len(graph_def.node))):
|
||||
if graph_def.node[i].name in ['conv4_3_norm/l2_normalize/Square',
|
||||
'conv4_3_norm/l2_normalize/Sum',
|
||||
'conv4_3_norm/l2_normalize/Maximum',
|
||||
'conv4_3_norm/l2_normalize/Rsqrt']:
|
||||
del graph_def.node[i]
|
||||
for node in graph_def.node:
|
||||
if node.name == 'conv4_3_norm/l2_normalize':
|
||||
node.op = 'L2Normalize'
|
||||
node.input.pop()
|
||||
node.input.pop()
|
||||
node.input.append(layer_256_1_relu1.name)
|
||||
break
|
||||
|
||||
softmaxShape = NodeDef()
|
||||
softmaxShape.name = 'reshape_before_softmax'
|
||||
softmaxShape.op = 'Const'
|
||||
text_format.Merge(
|
||||
'tensor {'
|
||||
' dtype: DT_INT32'
|
||||
' tensor_shape { dim { size: 3 } }'
|
||||
' int_val: 0'
|
||||
' int_val: -1'
|
||||
' int_val: 2'
|
||||
'}', softmaxShape.attr["value"])
|
||||
graph_def.node.extend([softmaxShape])
|
||||
|
||||
for node in graph_def.node:
|
||||
if node.name == 'mbox_conf_reshape':
|
||||
node.input[1] = softmaxShape.name
|
||||
elif node.name == 'mbox_conf_softmax':
|
||||
text_format.Merge('i: 2', node.attr['axis'])
|
||||
elif node.name in flattenLayersNames:
|
||||
node.op = 'Flatten'
|
||||
inpName = node.input[0]
|
||||
node.input.pop()
|
||||
node.input.pop()
|
||||
node.input.append(inpName)
|
||||
|
||||
tf.train.write_graph(graph_def, "", args.pbtxt, as_text=True)
|
||||
@ -651,7 +651,8 @@ static void addConstNodes(tensorflow::GraphDef& net, std::map<String, int>& cons
|
||||
tensor->set_dtype(tensorflow::DT_FLOAT);
|
||||
tensor->set_tensor_content(content.data, content.total() * content.elemSize1());
|
||||
|
||||
ExcludeLayer(net, li, 0, false);
|
||||
net.mutable_node(tensorId)->set_name(name);
|
||||
CV_Assert(const_layers.insert(std::make_pair(name, tensorId)).second);
|
||||
layers_to_ignore.insert(name);
|
||||
continue;
|
||||
}
|
||||
@ -1477,6 +1478,17 @@ void TFImporter::populateNet(Net dstNet)
|
||||
|
||||
connect(layer_id, dstNet, parsePin(layer.input(0)), id, 0);
|
||||
}
|
||||
else if (type == "L2Normalize")
|
||||
{
|
||||
// op: "L2Normalize"
|
||||
// input: "input"
|
||||
CV_Assert(layer.input_size() == 1);
|
||||
layerParams.set("across_spatial", false);
|
||||
layerParams.set("channel_shared", false);
|
||||
int id = dstNet.addLayer(name, "Normalize", layerParams);
|
||||
layer_id[name] = id;
|
||||
connect(layer_id, dstNet, parsePin(layer.input(0)), id, 0);
|
||||
}
|
||||
else if (type == "PriorBox")
|
||||
{
|
||||
if (hasLayerAttr(layer, "min_size"))
|
||||
@ -1489,6 +1501,8 @@ void TFImporter::populateNet(Net dstNet)
|
||||
layerParams.set("clip", getLayerAttr(layer, "clip").b());
|
||||
if (hasLayerAttr(layer, "offset"))
|
||||
layerParams.set("offset", getLayerAttr(layer, "offset").f());
|
||||
if (hasLayerAttr(layer, "step"))
|
||||
layerParams.set("step", getLayerAttr(layer, "step").f());
|
||||
|
||||
const std::string paramNames[] = {"variance", "aspect_ratio", "scales",
|
||||
"width", "height"};
|
||||
@ -1538,8 +1552,17 @@ void TFImporter::populateNet(Net dstNet)
|
||||
connect(layer_id, dstNet, parsePin(layer.input(i)), id, i);
|
||||
data_layouts[name] = DATA_LAYOUT_UNKNOWN;
|
||||
}
|
||||
else if (type == "Softmax")
|
||||
{
|
||||
if (hasLayerAttr(layer, "axis"))
|
||||
layerParams.set("axis", getLayerAttr(layer, "axis").i());
|
||||
|
||||
int id = dstNet.addLayer(name, "Softmax", layerParams);
|
||||
layer_id[name] = id;
|
||||
connectToAllBlobs(layer_id, dstNet, parsePin(layer.input(0)), id, layer.input_size());
|
||||
}
|
||||
else if (type == "Abs" || type == "Tanh" || type == "Sigmoid" ||
|
||||
type == "Relu" || type == "Elu" || type == "Softmax" ||
|
||||
type == "Relu" || type == "Elu" ||
|
||||
type == "Identity" || type == "Relu6")
|
||||
{
|
||||
std::string dnnType = type;
|
||||
|
||||
@ -353,4 +353,28 @@ TEST(Test_TensorFlow, memory_read)
|
||||
runTensorFlowNet("batch_norm_text", DNN_TARGET_CPU, true, l1, lInf, true);
|
||||
}
|
||||
|
||||
TEST(Test_TensorFlow, opencv_face_detector_uint8)
|
||||
{
|
||||
std::string proto = findDataFile("dnn/opencv_face_detector.pbtxt", false);
|
||||
std::string model = findDataFile("dnn/opencv_face_detector_uint8.pb", false);
|
||||
|
||||
Net net = readNetFromTensorflow(model, proto);
|
||||
Mat img = imread(findDataFile("gpu/lbpcascade/er.png", false));
|
||||
Mat blob = blobFromImage(img, 1.0, Size(), Scalar(104.0, 177.0, 123.0), false, false);
|
||||
|
||||
net.setInput(blob);
|
||||
// Output has shape 1x1xNx7 where N - number of detections.
|
||||
// An every detection is a vector of values [id, classId, confidence, left, top, right, bottom]
|
||||
Mat out = net.forward();
|
||||
|
||||
// References are from test for Caffe model.
|
||||
Mat ref = (Mat_<float>(6, 5) << 0.99520785, 0.80997437, 0.16379407, 0.87996572, 0.26685631,
|
||||
0.9934696, 0.2831718, 0.50738752, 0.345781, 0.5985168,
|
||||
0.99096733, 0.13629119, 0.24892329, 0.19756334, 0.3310290,
|
||||
0.98977017, 0.23901358, 0.09084064, 0.29902688, 0.1769477,
|
||||
0.97203469, 0.67965847, 0.06876482, 0.73999709, 0.1513494,
|
||||
0.95097077, 0.51901293, 0.45863652, 0.5777427, 0.5347801);
|
||||
normAssert(out.reshape(1, out.total() / 7).rowRange(0, 6).colRange(2, 7), ref, "", 2.8e-4, 3.4e-3);
|
||||
}
|
||||
|
||||
}
|
||||
|
||||
Loading…
Reference in New Issue
Block a user