From 736c16cb7480a99c7272918b8076cefd09dc755e Mon Sep 17 00:00:00 2001 From: lywen Date: Wed, 14 Nov 2018 00:38:32 +0800 Subject: [PATCH] =?UTF-8?q?=E4=BF=AE=E6=AD=A3=E9=83=A8=E5=88=86bug,?= =?UTF-8?q?=E6=96=B0=E5=A2=9Eocr=E8=AE=AD=E7=BB=83?= MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit --- .gitignore | 6 +- config.py | 2 + crnn/util.py | 3 +- darknet | 2 +- darknet_detect.py | 12 +- detector/detectors.py | 15 +- models/text.cfg | 785 +++++++++++++++++++++++++++++++++++++ models/text.names | 2 + opencv_dnn_detect.py | 2 +- setup-cpu.sh | 2 +- setup.sh | 2 +- test.ipynb | 155 ++++++++ train/__init__.py | 0 train/data/ocr/1 | 1 + train/ocr/__init__.py | 0 train/ocr/dataset.py | 132 +++++++ train/ocr/generic_utils.py | 444 +++++++++++++++++++++ train/ocr/train-ocr.ipynb | 540 +++++++++++++++++++++++++ 18 files changed, 2088 insertions(+), 17 deletions(-) create mode 100644 models/text.cfg create mode 100644 models/text.names create mode 100644 test.ipynb create mode 100644 train/__init__.py create mode 120000 train/data/ocr/1 create mode 100644 train/ocr/__init__.py create mode 100644 train/ocr/dataset.py create mode 100644 train/ocr/generic_utils.py create mode 100644 train/ocr/train-ocr.ipynb diff --git a/.gitignore b/.gitignore index daee7dc..a263254 100644 --- a/.gitignore +++ b/.gitignore @@ -3,9 +3,13 @@ *.so *.egg *.egg-info +*.pth +*.pb +*.pbtxt +*.weights dist buil .DS_Store* .ipynb_checkpoints __pycache__ - +darknet diff --git a/config.py b/config.py index 3710c5b..43d10cd 100644 --- a/config.py +++ b/config.py @@ -13,7 +13,9 @@ DETECTANGLE=True##是否进行文字方向检测 LSTMFLAG = True##OCR模型是否调用LSTM层 GPU = True##OCR 是否启用GPU +GPUID=0##调用GPU序号 chinsesModel = True##模型选择 True:中英文模型 False:英文模型 + if chinsesModel: if LSTMFLAG: ocrModel = os.path.join(pwd,"models","ocr-lstm.pth") diff --git a/crnn/util.py b/crnn/util.py index 7ed5239..c64d31e 100644 --- a/crnn/util.py +++ b/crnn/util.py @@ -9,7 +9,7 @@ class strLabelConverter(object): def __init__(self, alphabet): - self.alphabet = alphabet + u'-' # for `-1` index + self.alphabet = alphabet + 'ç' # for `-1` index self.dict = {} for i, char in enumerate(alphabet): # NOTE: 0 is reserved for 'blank' required by wrap_ctc @@ -19,7 +19,6 @@ def encode(self, text, depth=0): length = [] result=[] for str in text: - str = unicode(str,"utf8") length.append(len(str)) for char in str: #print(char) diff --git a/darknet b/darknet index 9e406ab..f6d8617 160000 --- a/darknet +++ b/darknet @@ -1 +1 @@ -Subproject commit 9e406ab330031882b23cfab0d3538dfdaf8ea0d3 +Subproject commit f6d861736038da22c9eb0739dca84003c5a5e275 diff --git a/darknet_detect.py b/darknet_detect.py index 5550416..2dcda9e 100644 --- a/darknet_detect.py +++ b/darknet_detect.py @@ -3,7 +3,8 @@ pwd = os.getcwd() import numpy as np from PIL import Image -from config import yoloCfg,yoloWeights,yoloData,yoloData,darknetRoot + +from config import yoloCfg,yoloWeights,yoloData,yoloData,darknetRoot,GPU,GPUID os.chdir(darknetRoot) sys.path.append('python') import darknet as dn @@ -52,13 +53,16 @@ def to_box(r): import pdb -#dn.set_gpu(0) +if GPU: + try: + dn.set_gpu(GPUID) + except: + pass net = dn.load_net(yoloCfg.encode('utf-8'), yoloWeights.encode('utf-8'), 0) meta = dn.load_meta(yoloData.encode('utf-8')) os.chdir(pwd) def text_detect(img): - inputBlob = cv2.dnn.blobFromImage(img, scalefactor=0.00390625, size=(608, 608),swapRB=True ,crop=False); - r = detect_np(net, meta, img,thresh=0.1, hier_thresh=0.5, nms=0.8) + r = detect_np(net, meta, img,thresh=0, hier_thresh=0.5, nms=None)##输出所有box,与opencv dnn统一 bboxes = to_box(r) return bboxes diff --git a/detector/detectors.py b/detector/detectors.py index f77c781..c650e66 100644 --- a/detector/detectors.py +++ b/detector/detectors.py @@ -1,19 +1,22 @@ #coding:utf-8 from detector.other import normalize import numpy as np -import numpy as np +from config import GPUID,GPU from detector.utils.cython_nms import nms as cython_nms -try: - from detector.utils.gpu_nms import gpu_nms -except: - gpu_nms =cython_nms +##优先加载编译对GPU编译的gpu_nms 如果不想调用GPU,在程序启动执行os.environ["CUDA_VISIBLE_DEVICES"] = "0" +if GPU: + try: + from detector.utils.gpu_nms import gpu_nms + except: + gpu_nms =cython_nms def nms(dets, thresh): if dets.shape[0] == 0: return [] try: - return gpu_nms(dets, thresh, device_id=0) + if GPU and GPUID is not None: + return gpu_nms(dets, thresh, device_id=GPUID) except: return cython_nms(dets, thresh) diff --git a/models/text.cfg b/models/text.cfg new file mode 100644 index 0000000..eeaf099 --- /dev/null +++ b/models/text.cfg @@ -0,0 +1,785 @@ +[net] +# Testing +# batch=1 +# subdivisions=1 +# Training +batch=32 +subdivisions=16 +width=608 +height=608 +channels=3 +momentum=0.9 +decay=0.0005 +angle=0 +saturation = 1.5 +exposure = 1.5 +hue=.1 + +learning_rate=0.0001 +burn_in=1000 +max_batches = 50200 +policy=steps +steps=40000,45000 +scales=.1,.1 + + + +[convolutional] +batch_normalize=1 +filters=32 +size=3 +stride=1 +pad=1 +activation=leaky + +# Downsample + +[convolutional] +batch_normalize=1 +filters=64 +size=3 +stride=2 +pad=1 +activation=leaky + +[convolutional] +batch_normalize=1 +filters=32 +size=1 +stride=1 +pad=1 +activation=leaky + +[convolutional] +batch_normalize=1 +filters=64 +size=3 +stride=1 +pad=1 +activation=leaky + +[shortcut] +from=-3 +activation=linear + +# Downsample + +[convolutional] +batch_normalize=1 +filters=128 +size=3 +stride=2 +pad=1 +activation=leaky + +[convolutional] +batch_normalize=1 +filters=64 +size=1 +stride=1 +pad=1 +activation=leaky + +[convolutional] +batch_normalize=1 +filters=128 +size=3 +stride=1 +pad=1 +activation=leaky + +[shortcut] +from=-3 +activation=linear + +[convolutional] +batch_normalize=1 +filters=64 +size=1 +stride=1 +pad=1 +activation=leaky + +[convolutional] +batch_normalize=1 +filters=128 +size=3 +stride=1 +pad=1 +activation=leaky + +[shortcut] +from=-3 +activation=linear + +# Downsample + +[convolutional] +batch_normalize=1 +filters=256 +size=3 +stride=2 +pad=1 +activation=leaky + +[convolutional] +batch_normalize=1 +filters=128 +size=1 +stride=1 +pad=1 +activation=leaky + +[convolutional] +batch_normalize=1 +filters=256 +size=3 +stride=1 +pad=1 +activation=leaky + +[shortcut] +from=-3 +activation=linear + +[convolutional] +batch_normalize=1 +filters=128 +size=1 +stride=1 +pad=1 +activation=leaky + +[convolutional] +batch_normalize=1 +filters=256 +size=3 +stride=1 +pad=1 +activation=leaky + +[shortcut] +from=-3 +activation=linear + +[convolutional] +batch_normalize=1 +filters=128 +size=1 +stride=1 +pad=1 +activation=leaky + +[convolutional] +batch_normalize=1 +filters=256 +size=3 +stride=1 +pad=1 +activation=leaky + +[shortcut] +from=-3 +activation=linear + +[convolutional] +batch_normalize=1 +filters=128 +size=1 +stride=1 +pad=1 +activation=leaky + +[convolutional] +batch_normalize=1 +filters=256 +size=3 +stride=1 +pad=1 +activation=leaky + +[shortcut] +from=-3 +activation=linear + + +[convolutional] +batch_normalize=1 +filters=128 +size=1 +stride=1 +pad=1 +activation=leaky + +[convolutional] +batch_normalize=1 +filters=256 +size=3 +stride=1 +pad=1 +activation=leaky + +[shortcut] +from=-3 +activation=linear + +[convolutional] +batch_normalize=1 +filters=128 +size=1 +stride=1 +pad=1 +activation=leaky + +[convolutional] +batch_normalize=1 +filters=256 +size=3 +stride=1 +pad=1 +activation=leaky + +[shortcut] +from=-3 +activation=linear + +[convolutional] +batch_normalize=1 +filters=128 +size=1 +stride=1 +pad=1 +activation=leaky + +[convolutional] +batch_normalize=1 +filters=256 +size=3 +stride=1 +pad=1 +activation=leaky + +[shortcut] +from=-3 +activation=linear + +[convolutional] +batch_normalize=1 +filters=128 +size=1 +stride=1 +pad=1 +activation=leaky + +[convolutional] +batch_normalize=1 +filters=256 +size=3 +stride=1 +pad=1 +activation=leaky + +[shortcut] +from=-3 +activation=linear + +# Downsample + +[convolutional] +batch_normalize=1 +filters=512 +size=3 +stride=2 +pad=1 +activation=leaky + +[convolutional] +batch_normalize=1 +filters=256 +size=1 +stride=1 +pad=1 +activation=leaky + +[convolutional] +batch_normalize=1 +filters=512 +size=3 +stride=1 +pad=1 +activation=leaky + +[shortcut] +from=-3 +activation=linear + + +[convolutional] +batch_normalize=1 +filters=256 +size=1 +stride=1 +pad=1 +activation=leaky + +[convolutional] +batch_normalize=1 +filters=512 +size=3 +stride=1 +pad=1 +activation=leaky + +[shortcut] +from=-3 +activation=linear + + +[convolutional] +batch_normalize=1 +filters=256 +size=1 +stride=1 +pad=1 +activation=leaky + +[convolutional] +batch_normalize=1 +filters=512 +size=3 +stride=1 +pad=1 +activation=leaky + +[shortcut] +from=-3 +activation=linear + + +[convolutional] +batch_normalize=1 +filters=256 +size=1 +stride=1 +pad=1 +activation=leaky + +[convolutional] +batch_normalize=1 +filters=512 +size=3 +stride=1 +pad=1 +activation=leaky + +[shortcut] +from=-3 +activation=linear + +[convolutional] +batch_normalize=1 +filters=256 +size=1 +stride=1 +pad=1 +activation=leaky + +[convolutional] +batch_normalize=1 +filters=512 +size=3 +stride=1 +pad=1 +activation=leaky + +[shortcut] +from=-3 +activation=linear + + +[convolutional] +batch_normalize=1 +filters=256 +size=1 +stride=1 +pad=1 +activation=leaky + +[convolutional] +batch_normalize=1 +filters=512 +size=3 +stride=1 +pad=1 +activation=leaky + +[shortcut] +from=-3 +activation=linear + + +[convolutional] +batch_normalize=1 +filters=256 +size=1 +stride=1 +pad=1 +activation=leaky + +[convolutional] +batch_normalize=1 +filters=512 +size=3 +stride=1 +pad=1 +activation=leaky + +[shortcut] +from=-3 +activation=linear + +[convolutional] +batch_normalize=1 +filters=256 +size=1 +stride=1 +pad=1 +activation=leaky + +[convolutional] +batch_normalize=1 +filters=512 +size=3 +stride=1 +pad=1 +activation=leaky + +[shortcut] +from=-3 +activation=linear + +# Downsample + +[convolutional] +batch_normalize=1 +filters=1024 +size=3 +stride=2 +pad=1 +activation=leaky + +[convolutional] +batch_normalize=1 +filters=512 +size=1 +stride=1 +pad=1 +activation=leaky + +[convolutional] +batch_normalize=1 +filters=1024 +size=3 +stride=1 +pad=1 +activation=leaky + +[shortcut] +from=-3 +activation=linear + +[convolutional] +batch_normalize=1 +filters=512 +size=1 +stride=1 +pad=1 +activation=leaky + +[convolutional] +batch_normalize=1 +filters=1024 +size=3 +stride=1 +pad=1 +activation=leaky + +[shortcut] +from=-3 +activation=linear + +[convolutional] +batch_normalize=1 +filters=512 +size=1 +stride=1 +pad=1 +activation=leaky + +[convolutional] +batch_normalize=1 +filters=1024 +size=3 +stride=1 +pad=1 +activation=leaky + +[shortcut] +from=-3 +activation=linear + +[convolutional] +batch_normalize=1 +filters=512 +size=1 +stride=1 +pad=1 +activation=leaky + +[convolutional] +batch_normalize=1 +filters=1024 +size=3 +stride=1 +pad=1 +activation=leaky + +[shortcut] +from=-3 +activation=linear + +###################### + +[convolutional] +batch_normalize=1 +filters=512 +size=1 +stride=1 +pad=1 +activation=leaky + +[convolutional] +batch_normalize=1 +size=3 +stride=1 +pad=1 +filters=1024 +activation=leaky + +[convolutional] +batch_normalize=1 +filters=512 +size=1 +stride=1 +pad=1 +activation=leaky + +[convolutional] +batch_normalize=1 +size=3 +stride=1 +pad=1 +filters=1024 +activation=leaky + +[convolutional] +batch_normalize=1 +filters=512 +size=1 +stride=1 +pad=1 +activation=leaky + +[convolutional] +batch_normalize=1 +size=3 +stride=1 +pad=1 +filters=1024 +activation=leaky + +[convolutional] +size=1 +stride=1 +pad=1 +filters=21 +activation=linear + +[yolo] +mask = 6,7,8 +anchors = 10,13, 16,30, 33,23, 30,61, 62,45, 59,119, 116,90, 156,198, 373,326 +classes=2 +num=9 +jitter=.3 +ignore_thresh = .5 +truth_thresh = 1 +random=1 + +[route] +layers = -4 + +[convolutional] +batch_normalize=1 +filters=256 +size=1 +stride=1 +pad=1 +activation=leaky + +[upsample] +stride=2 + +[route] +layers = -1, 61 + + + +[convolutional] +batch_normalize=1 +filters=256 +size=1 +stride=1 +pad=1 +activation=leaky + +[convolutional] +batch_normalize=1 +size=3 +stride=1 +pad=1 +filters=512 +activation=leaky + +[convolutional] +batch_normalize=1 +filters=256 +size=1 +stride=1 +pad=1 +activation=leaky + +[convolutional] +batch_normalize=1 +size=3 +stride=1 +pad=1 +filters=512 +activation=leaky + +[convolutional] +batch_normalize=1 +filters=256 +size=1 +stride=1 +pad=1 +activation=leaky + +[convolutional] +batch_normalize=1 +size=3 +stride=1 +pad=1 +filters=512 +activation=leaky + +[convolutional] +size=1 +stride=1 +pad=1 +filters=21 +activation=linear + +[yolo] +mask = 3,4,5 +anchors = 10,13, 16,30, 33,23, 30,61, 62,45, 59,119, 116,90, 156,198, 373,326 +classes=2 +num=9 +jitter=.3 +ignore_thresh = .5 +truth_thresh = 1 +random=1 + +[route] +layers = -4 + +[convolutional] +batch_normalize=1 +filters=128 +size=1 +stride=1 +pad=1 +activation=leaky + +[upsample] +stride=2 + +[route] +layers = -1, 36 + + + +[convolutional] +batch_normalize=1 +filters=128 +size=1 +stride=1 +pad=1 +activation=leaky + +[convolutional] +batch_normalize=1 +size=3 +stride=1 +pad=1 +filters=256 +activation=leaky + +[convolutional] +batch_normalize=1 +filters=128 +size=1 +stride=1 +pad=1 +activation=leaky + +[convolutional] +batch_normalize=1 +size=3 +stride=1 +pad=1 +filters=256 +activation=leaky + +[convolutional] +batch_normalize=1 +filters=128 +size=1 +stride=1 +pad=1 +activation=leaky + +[convolutional] +batch_normalize=1 +size=3 +stride=1 +pad=1 +filters=256 +activation=leaky + +[convolutional] +size=1 +stride=1 +pad=1 +filters=21 +activation=linear + +[yolo] +mask = 0,1,2 +anchors = 10,13, 16,30, 33,23, 30,61, 62,45, 59,119, 116,90, 156,198, 373,326 +classes=2 +num=9 +jitter=.3 +ignore_thresh = .5 +truth_thresh = 1 +random=1 + diff --git a/models/text.names b/models/text.names new file mode 100644 index 0000000..9de5aa3 --- /dev/null +++ b/models/text.names @@ -0,0 +1,2 @@ +text +none \ No newline at end of file diff --git a/opencv_dnn_detect.py b/opencv_dnn_detect.py index 7da3e96..b4f22d5 100644 --- a/opencv_dnn_detect.py +++ b/opencv_dnn_detect.py @@ -7,7 +7,7 @@ textNet = cv2.dnn.readNetFromDarknet(yoloCfg,yoloWeights) angleNet = cv2.dnn.readNetFromTensorflow(AngleModelPb,AngleModelPbtxt)##文字方向检测 def text_detect(img): - thresh=0.1 + thresh=0 h,w = img.shape[:2] inputBlob = cv2.dnn.blobFromImage(img, scalefactor=0.00390625, size=IMGSIZE,swapRB=True ,crop=False); textNet.setInput(inputBlob) diff --git a/setup-cpu.sh b/setup-cpu.sh index 285a925..98c4bea 100644 --- a/setup-cpu.sh +++ b/setup-cpu.sh @@ -3,7 +3,7 @@ conda create -n chineseocr python=3.6 pip scipy numpy jupyter ipython ##运用co source activate chineseocr git submodule init && git submodule update cd darknet/ && make && cd .. -pip install easydict opencv-contrib-python Cython h5py lmdb mahotas pandas requests -i https://pypi.tuna.tsinghua.edu.cn/simple/ +pip install easydict opencv-contrib-python==3.4.2.16 Cython h5py lmdb mahotas pandas requests -i https://pypi.tuna.tsinghua.edu.cn/simple/ pip install -U pillow -i https://pypi.tuna.tsinghua.edu.cn/simple/ pip install web.py==0.40.dev0 ## mac diff --git a/setup.sh b/setup.sh index 72d99dd..41e9cf4 100644 --- a/setup.sh +++ b/setup.sh @@ -2,7 +2,7 @@ conda create -n chineseocr python=3.6 pip scipy numpy jupyter ipython ##运用conda 创建python环境 source activate chineseocr git submodule init && git submodule update -pip install easydict opencv-contrib-python Cython h5py lmdb mahotas pandas requests -i https://pypi.tuna.tsinghua.edu.cn/simple/ +pip install easydict opencv-contrib-python==3.4.2.16 Cython h5py lmdb mahotas pandas requests -i https://pypi.tuna.tsinghua.edu.cn/simple/ pip install -U pillow -i https://pypi.tuna.tsinghua.edu.cn/simple/ pip install web.py==0.40.dev0 conda install pytorch torchvision -c pytorch diff --git a/test.ipynb b/test.ipynb new file mode 100644 index 0000000..019d324 --- /dev/null +++ b/test.ipynb @@ -0,0 +1,155 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## 加载模型\n" + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "metadata": {}, + "outputs": [], + "source": [ + "import model" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": {}, + "outputs": [], + "source": [ + "import cv2\n", + "\n", + "import numpy as np\n", + "def plot_boxes(img,angle, result,color=(0,0,0)):\n", + " tmp = np.array(img)\n", + " c = color\n", + " w,h = img.size\n", + " thick = int((h + w) / 300)\n", + " i = 0\n", + " if angle in [90,270]:\n", + " imgW,imgH = img.size[::-1]\n", + " \n", + " else:\n", + " imgW,imgH = img.size\n", + "\n", + " for line in result:\n", + " cx =line['cx']\n", + " cy = line['cy']\n", + " degree =line['degree']\n", + " w = line['w']\n", + " h = line['h']\n", + "\n", + " x1,y1,x2,y2,x3,y3,x4,y4 = model.xy_rotate_box(cx, cy, w, h, degree/180*np.pi)\n", + " \n", + " x1,y1,x2,y2,x3,y3,x4,y4 = model.box_rotate([x1,y1,x2,y2,x3,y3,x4,y4],angle=(360-angle)%360,imgH=imgH,imgW=imgW)\n", + " cx =np.mean([x1,x2,x3,x4])\n", + " cy = np.mean([y1,y2,y3,y4])\n", + " cv2.line(tmp,(int(x1),int(y1)),(int(x2),int(y2)),c,1)\n", + " cv2.line(tmp,(int(x2),int(y2)),(int(x3),int(y3)),c,1)\n", + " cv2.line(tmp,(int(x3),int(y3)),(int(x4),int(y4)),c,1)\n", + " cv2.line(tmp,(int(x4),int(y4)),(int(x1),int(y1)),c,1)\n", + " mess=str(i)\n", + " cv2.putText(tmp, mess, (int(cx), int(cy)),0, 1e-3 * h, c, thick // 2)\n", + " i+=1\n", + " return Image.fromarray(tmp)" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": {}, + "outputs": [], + "source": [ + "import time\n", + "from PIL import Image\n", + "\n", + "p = './test/card.png'\n", + "img = Image.open(p).convert(\"RGB\")\n", + "timeTake = time.time()\n", + "_,result,angle= model.model(img,\n", + " detectAngle=True,##是否进行文字方向检测\n", + " config=dict(MAX_HORIZONTAL_GAP=80,##字符之间的最大间隔,用于文本行的合并\n", + " MIN_V_OVERLAPS=0.6,\n", + " MIN_SIZE_SIM=0.6,\n", + " TEXT_PROPOSALS_MIN_SCORE=0.2,\n", + " TEXT_PROPOSALS_NMS_THRESH=0.3,\n", + " TEXT_LINE_NMS_THRESH = 0.99,##文本行之间测iou值\n", + " MIN_RATIO=1.0,\n", + " LINE_MIN_SCORE=0.2,\n", + " TEXT_PROPOSALS_WIDTH=0,\n", + " MIN_NUM_PROPOSALS=0, \n", + " ),\n", + " leftAdjust=True,##对检测的文本行进行向左延伸\n", + " rightAdjust=True,##对检测的文本行进行向右延伸\n", + " alph=0.1,##对检测的文本行进行向右、左延伸的倍数\n", + " ifadjustDegree=True\n", + " )\n", + " \n", + "timeTake = time.time()-timeTake\n" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "It take:3.4039392471313477s\n", + "姓名小静\n", + "性别女民族汉\n", + "出生1987年9月17日\n", + "往址北京市海淀区西北旺东路\n", + "100号中关村科技园\n", + "公民身份号码370782198709170246\n" + ] + }, + { + "data": { + "image/png": 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3osf9eN+OlDi515QWyJL50Ec8RpLIvIMBxJiAatbVwcSa+tT7yMfIunVT60hh5GZqfQT8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\nnh6X9Mzn6nK14vLssV6w7u7MVy6PZvXak7aaCQOg0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XIQxgKY+IOnntqlB9/zjd/0lV/z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c86DJ2AOYKcMIDXsBR7vK3hZpQveAly5mgPauhYBqQdNeSoh3beBAyBiUxY0nIZsNytHB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\n", + "text/plain": [ + "" + ] + }, + "execution_count": 4, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "print('It take:{}s'.format(timeTake))\n", + "for line in result:\n", + " print(line['text'])\n", + "plot_boxes(img,angle, result,color=(0,0,0))" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "chineseocr", + "language": "python", + "name": "chineseocr" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.6.2" + } + }, + "nbformat": 4, + "nbformat_minor": 2 +} diff --git a/train/__init__.py b/train/__init__.py new file mode 100644 index 0000000..e69de29 diff --git a/train/data/ocr/1 b/train/data/ocr/1 new file mode 120000 index 0000000..58b7b5a --- /dev/null +++ b/train/data/ocr/1 @@ -0,0 +1 @@ +/home/lywen/data/ocr \ No newline at end of file diff --git a/train/ocr/__init__.py b/train/ocr/__init__.py new file mode 100644 index 0000000..e69de29 diff --git a/train/ocr/dataset.py b/train/ocr/dataset.py new file mode 100644 index 0000000..5968359 --- /dev/null +++ b/train/ocr/dataset.py @@ -0,0 +1,132 @@ +import random +import lmdb +import six +import sys +from PIL import Image +import numpy as np +import torch +from torch.utils.data import Dataset +from torch.utils.data import sampler +import torchvision.transforms as transforms + + +class PathDataset(Dataset): + + def __init__(self, jpgPaths,alphabetChinese, transform=None, target_transform=None): + """ + 加载本地目录图片 + """ + + self.jpgPaths = jpgPaths + self.nSamples = len(self.jpgPaths) + self.alphabetChinese = alphabetChinese + self.transform = transform + self.target_transform = target_transform + + def __len__(self): + return self.nSamples + + def __getitem__(self, index): + if index >= len(self): + index=0 + + imP = self.jpgPaths[index] + txtP = imP.replace('.jpg','.txt') + im = Image.open(imP).convert('L') + with open(txtP) as f: + label = f.read().strip() + + label = ''.join([ x for x in label if x in self.alphabetChinese ]) + + if self.transform is not None: + + im = self.transform(im) + + if self.target_transform is not None: + label = self.target_transform(label) + + + return (im, label) + + +class resizeNormalize(object): + + def __init__(self, size, interpolation=Image.BILINEAR): + self.size = size + self.interpolation = interpolation + + def __call__(self, img): + size = self.size + imgW,imgH = size + scale = img.size[1]*1.0 / imgH + w = img.size[0] / scale + w = int(w) + img = img.resize((w,imgH),self.interpolation) + w,h = img.size + if w<=imgW: + newImage = np.zeros((imgH,imgW),dtype='uint8') + newImage[:] = 255 + newImage[:,:w] = np.array(img) + img = Image.fromarray(newImage) + else: + img = img.resize((imgW,imgH),self.interpolation) + #img = (np.array(img)/255.0-0.5)/0.5 + img = transforms.ToTensor()(img) + img.sub_(0.5).div_(0.5) + return img + + +class randomSequentialSampler(sampler.Sampler): + + def __init__(self, data_source, batch_size): + self.num_samples = len(data_source) + self.batch_size = batch_size + + def __iter__(self): + n_batch = len(self) // self.batch_size + tail = len(self) % self.batch_size + index = torch.LongTensor(len(self)).fill_(0) + for i in range(n_batch): + random_start = random.randint(0, len(self) - self.batch_size) + batch_index = random_start + torch.arange(0, self.batch_size ) + index[i * self.batch_size:(i + 1) * self.batch_size] = batch_index + # deal with tail + if tail: + random_start = random.randint(0, len(self) - self.batch_size) + tail_index = random_start + torch.arange(0, tail ) + index[(i + 1) * self.batch_size:] = tail_index + + return iter(index) + + def __len__(self): + return self.num_samples + + +class alignCollate(object): + + def __init__(self, imgH=32, imgW=100, keep_ratio=False, min_ratio=1): + self.imgH = imgH + self.imgW = imgW + self.keep_ratio = keep_ratio + self.min_ratio = min_ratio + + def __call__(self, batch): + images, labels = zip(*batch) + + imgH = self.imgH + imgW = self.imgW + if self.keep_ratio: + ratios = [] + for image in images: + w, h = image.size + ratios.append(w / float(h)) + ratios.sort() + max_ratio = ratios[-1] + imgW = int(np.floor(max_ratio * imgH)) + imgW = max(imgH * self.min_ratio, imgW) # assure imgH >= imgW + + transform = resizeNormalize((imgW, imgH)) + images = [transform(image) for image in images] + images = torch.cat([t.unsqueeze(0) for t in images], 0) + + return images, labels diff --git a/train/ocr/generic_utils.py b/train/ocr/generic_utils.py new file mode 100644 index 0000000..c8ed1eb --- /dev/null +++ b/train/ocr/generic_utils.py @@ -0,0 +1,444 @@ +""" +# Reference +Python utilities required by Keras. + https://github.com/keras-team/keras/blob/master/keras/utils/generic_utils.py +""" + +from __future__ import absolute_import +from __future__ import division +from __future__ import print_function + +import binascii +import numpy as np + +import time +import sys +import six +import marshal +import types as python_types +import inspect +import codecs +import collections + +_GLOBAL_CUSTOM_OBJECTS = {} + + +class CustomObjectScope(object): + """Provides a scope that changes to `_GLOBAL_CUSTOM_OBJECTS` cannot escape. + + Code within a `with` statement will be able to access custom objects + by name. Changes to global custom objects persist + within the enclosing `with` statement. At end of the `with` statement, + global custom objects are reverted to state + at beginning of the `with` statement. + + # Example + + Consider a custom object `MyObject` (e.g. a class): + + ```python + with CustomObjectScope({'MyObject':MyObject}): + layer = Dense(..., kernel_regularizer='MyObject') + # save, load, etc. will recognize custom object by name + ``` + """ + + def __init__(self, *args): + self.custom_objects = args + self.backup = None + + def __enter__(self): + self.backup = _GLOBAL_CUSTOM_OBJECTS.copy() + for objects in self.custom_objects: + _GLOBAL_CUSTOM_OBJECTS.update(objects) + return self + + def __exit__(self, *args, **kwargs): + _GLOBAL_CUSTOM_OBJECTS.clear() + _GLOBAL_CUSTOM_OBJECTS.update(self.backup) + + +def custom_object_scope(*args): + """Provides a scope that changes to `_GLOBAL_CUSTOM_OBJECTS` cannot escape. + + Convenience wrapper for `CustomObjectScope`. + Code within a `with` statement will be able to access custom objects + by name. Changes to global custom objects persist + within the enclosing `with` statement. At end of the `with` statement, + global custom objects are reverted to state + at beginning of the `with` statement. + + # Example + + Consider a custom object `MyObject` + + ```python + with custom_object_scope({'MyObject':MyObject}): + layer = Dense(..., kernel_regularizer='MyObject') + # save, load, etc. will recognize custom object by name + ``` + + # Arguments + *args: Variable length list of dictionaries of name, + class pairs to add to custom objects. + + # Returns + Object of type `CustomObjectScope`. + """ + return CustomObjectScope(*args) + + +def get_custom_objects(): + """Retrieves a live reference to the global dictionary of custom objects. + + Updating and clearing custom objects using `custom_object_scope` + is preferred, but `get_custom_objects` can + be used to directly access `_GLOBAL_CUSTOM_OBJECTS`. + + # Example + + ```python + get_custom_objects().clear() + get_custom_objects()['MyObject'] = MyObject + ``` + + # Returns + Global dictionary of names to classes (`_GLOBAL_CUSTOM_OBJECTS`). + """ + return _GLOBAL_CUSTOM_OBJECTS + + +def serialize_keras_object(instance): + if instance is None: + return None + if hasattr(instance, 'get_config'): + return { + 'class_name': instance.__class__.__name__, + 'config': instance.get_config() + } + if hasattr(instance, '__name__'): + return instance.__name__ + else: + raise ValueError('Cannot serialize', instance) + + +def deserialize_keras_object(identifier, module_objects=None, + custom_objects=None, + printable_module_name='object'): + if isinstance(identifier, dict): + # In this case we are dealing with a Keras config dictionary. + config = identifier + if 'class_name' not in config or 'config' not in config: + raise ValueError('Improper config format: ' + str(config)) + class_name = config['class_name'] + if custom_objects and class_name in custom_objects: + cls = custom_objects[class_name] + elif class_name in _GLOBAL_CUSTOM_OBJECTS: + cls = _GLOBAL_CUSTOM_OBJECTS[class_name] + else: + module_objects = module_objects or {} + cls = module_objects.get(class_name) + if cls is None: + raise ValueError('Unknown ' + printable_module_name + + ': ' + class_name) + if hasattr(cls, 'from_config'): + custom_objects = custom_objects or {} + if has_arg(cls.from_config, 'custom_objects'): + return cls.from_config(config['config'], + custom_objects=dict(list(_GLOBAL_CUSTOM_OBJECTS.items()) + + list(custom_objects.items()))) + with CustomObjectScope(custom_objects): + return cls.from_config(config['config']) + else: + # Then `cls` may be a function returning a class. + # in this case by convention `config` holds + # the kwargs of the function. + custom_objects = custom_objects or {} + with CustomObjectScope(custom_objects): + return cls(**config['config']) + elif isinstance(identifier, six.string_types): + function_name = identifier + if custom_objects and function_name in custom_objects: + fn = custom_objects.get(function_name) + elif function_name in _GLOBAL_CUSTOM_OBJECTS: + fn = _GLOBAL_CUSTOM_OBJECTS[function_name] + else: + fn = module_objects.get(function_name) + if fn is None: + raise ValueError('Unknown ' + printable_module_name + + ':' + function_name) + return fn + else: + raise ValueError('Could not interpret serialized ' + + printable_module_name + ': ' + identifier) + + +def func_dump(func): + """Serializes a user defined function. + + # Arguments + func: the function to serialize. + + # Returns + A tuple `(code, defaults, closure)`. + """ + raw_code = marshal.dumps(func.__code__) + code = codecs.encode(raw_code, 'base64').decode('ascii') + defaults = func.__defaults__ + if func.__closure__: + closure = tuple(c.cell_contents for c in func.__closure__) + else: + closure = None + return code, defaults, closure + + +def func_load(code, defaults=None, closure=None, globs=None): + """Deserializes a user defined function. + + # Arguments + code: bytecode of the function. + defaults: defaults of the function. + closure: closure of the function. + globs: dictionary of global objects. + + # Returns + A function object. + """ + if isinstance(code, (tuple, list)): # unpack previous dump + code, defaults, closure = code + if isinstance(defaults, list): + defaults = tuple(defaults) + + def ensure_value_to_cell(value): + """Ensures that a value is converted to a python cell object. + + # Arguments + value: Any value that needs to be casted to the cell type + + # Returns + A value wrapped as a cell object (see function "func_load") + + """ + def dummy_fn(): + value # just access it so it gets captured in .__closure__ + + cell_value = dummy_fn.__closure__[0] + if not isinstance(value, type(cell_value)): + return cell_value + else: + return value + + if closure is not None: + closure = tuple(ensure_value_to_cell(_) for _ in closure) + try: + raw_code = codecs.decode(code.encode('ascii'), 'base64') + code = marshal.loads(raw_code) + except (UnicodeEncodeError, binascii.Error, ValueError): + # backwards compatibility for models serialized prior to 2.1.2 + raw_code = code.encode('raw_unicode_escape') + code = marshal.loads(raw_code) + if globs is None: + globs = globals() + return python_types.FunctionType(code, globs, + name=code.co_name, + argdefs=defaults, + closure=closure) + + +def has_arg(fn, name, accept_all=False): + """Checks if a callable accepts a given keyword argument. + + For Python 2, checks if there is an argument with the given name. + + For Python 3, checks if there is an argument with the given name, and + also whether this argument can be called with a keyword (i.e. if it is + not a positional-only argument). + + # Arguments + fn: Callable to inspect. + name: Check if `fn` can be called with `name` as a keyword argument. + accept_all: What to return if there is no parameter called `name` + but the function accepts a `**kwargs` argument. + + # Returns + bool, whether `fn` accepts a `name` keyword argument. + """ + if sys.version_info < (3,): + arg_spec = inspect.getargspec(fn) + if accept_all and arg_spec.keywords is not None: + return True + return (name in arg_spec.args) + elif sys.version_info < (3, 3): + arg_spec = inspect.getfullargspec(fn) + if accept_all and arg_spec.varkw is not None: + return True + return (name in arg_spec.args or + name in arg_spec.kwonlyargs) + else: + signature = inspect.signature(fn) + parameter = signature.parameters.get(name) + if parameter is None: + if accept_all: + for param in signature.parameters.values(): + if param.kind == inspect.Parameter.VAR_KEYWORD: + return True + return False + return (parameter.kind in (inspect.Parameter.POSITIONAL_OR_KEYWORD, + inspect.Parameter.KEYWORD_ONLY)) + + +class Progbar(object): + """Displays a progress bar. + + # Arguments + target: Total number of steps expected, None if unknown. + width: Progress bar width on screen. + verbose: Verbosity mode, 0 (silent), 1 (verbose), 2 (semi-verbose) + stateful_metrics: Iterable of string names of metrics that + should *not* be averaged over time. Metrics in this list + will be displayed as-is. All others will be averaged + by the progbar before display. + interval: Minimum visual progress update interval (in seconds). + """ + + def __init__(self, target, width=30, verbose=1, interval=0.05, + stateful_metrics=None): + self.target = target + self.width = width + self.verbose = verbose + self.interval = interval + if stateful_metrics: + self.stateful_metrics = set(stateful_metrics) + else: + self.stateful_metrics = set() + + self._dynamic_display = ((hasattr(sys.stdout, 'isatty') and + sys.stdout.isatty()) or + 'ipykernel' in sys.modules) + self._total_width = 0 + self._seen_so_far = 0 + self._values = collections.OrderedDict() + self._start = time.time() + self._last_update = 0 + + def update(self, current, values=None): + """Updates the progress bar. + + # Arguments + current: Index of current step. + values: List of tuples: + `(name, value_for_last_step)`. + If `name` is in `stateful_metrics`, + `value_for_last_step` will be displayed as-is. + Else, an average of the metric over time will be displayed. + """ + values = values or [] + for k, v in values: + if k not in self.stateful_metrics: + if k not in self._values: + self._values[k] = [v * (current - self._seen_so_far), + current - self._seen_so_far] + else: + self._values[k][0] += v * (current - self._seen_so_far) + self._values[k][1] += (current - self._seen_so_far) + else: + self._values[k] = v + self._seen_so_far = current + + now = time.time() + info = ' - %.0fs' % (now - self._start) + if self.verbose == 1: + if (now - self._last_update < self.interval and + self.target is not None and current < self.target): + return + + prev_total_width = self._total_width + if self._dynamic_display: + sys.stdout.write('\b' * prev_total_width) + sys.stdout.write('\r') + else: + sys.stdout.write('\n') + + if self.target is not None: + numdigits = int(np.floor(np.log10(self.target))) + 1 + barstr = '%%%dd/%d [' % (numdigits, self.target) + bar = barstr % current + prog = float(current) / self.target + prog_width = int(self.width * prog) + if prog_width > 0: + bar += ('=' * (prog_width - 1)) + if current < self.target: + bar += '>' + else: + bar += '=' + bar += ('.' * (self.width - prog_width)) + bar += ']' + else: + bar = '%7d/Unknown' % current + + self._total_width = len(bar) + sys.stdout.write(bar) + + if current: + time_per_unit = (now - self._start) / current + else: + time_per_unit = 0 + if self.target is not None and current < self.target: + eta = time_per_unit * (self.target - current) + if eta > 3600: + eta_format = '%d:%02d:%02d' % (eta // 3600, (eta % 3600) // 60, eta % 60) + elif eta > 60: + eta_format = '%d:%02d' % (eta // 60, eta % 60) + else: + eta_format = '%ds' % eta + + info = ' - ETA: %s' % eta_format + else: + if time_per_unit >= 1: + info += ' %.0fs/step' % time_per_unit + elif time_per_unit >= 1e-3: + info += ' %.0fms/step' % (time_per_unit * 1e3) + else: + info += ' %.0fus/step' % (time_per_unit * 1e6) + + for k in self._values: + info += ' - %s:' % k + if isinstance(self._values[k], list): + avg = np.mean( + self._values[k][0] / max(1, self._values[k][1])) + if abs(avg) > 1e-3: + info += ' %.4f' % avg + else: + info += ' %.4e' % avg + else: + info += ' %s' % self._values[k] + + self._total_width += len(info) + if prev_total_width > self._total_width: + info += (' ' * (prev_total_width - self._total_width)) + + if self.target is not None and current >= self.target: + info += '\n' + + sys.stdout.write(info) + sys.stdout.flush() + + elif self.verbose == 2: + if self.target is None or current >= self.target: + for k in self._values: + info += ' - %s:' % k + avg = np.mean( + self._values[k][0] / max(1, self._values[k][1])) + if avg > 1e-3: + info += ' %.4f' % avg + else: + info += ' %.4e' % avg + info += '\n' + + sys.stdout.write(info) + sys.stdout.flush() + + self._last_update = now + + def add(self, n, values=None): + self.update(self._seen_so_far + n, values) diff --git a/train/ocr/train-ocr.ipynb b/train/ocr/train-ocr.ipynb new file mode 100644 index 0000000..ebc683a --- /dev/null +++ b/train/ocr/train-ocr.ipynb @@ -0,0 +1,540 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## crnn ocr 模型训练" + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "metadata": {}, + "outputs": [], + "source": [ + "import os\n", + "import numpy as np\n", + "import torch\n", + "from PIL import Image\n", + "import numpy as np\n", + "from torch.autograd import Variable\n", + "import torch.backends.cudnn as cudnn\n", + "import torch.optim as optim\n", + "import torch.utils.data\n", + "import numpy as np\n", + "from warpctc_pytorch import CTCLoss" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## 创建数据软连接" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "!ln -s /home/lywen/data/ocr ../data/ocr/1" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## 加载数据集" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": {}, + "outputs": [], + "source": [ + "import os\n", + "os.chdir('../../')\n", + "from train.ocr.dataset import PathDataset,randomSequentialSampler,alignCollate\n", + "from glob import glob\n", + "from sklearn.model_selection import train_test_split\n", + "\n", + "roots = glob('./train/data/ocr/*/*.jpg')\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## 训练字符集" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": {}, + "outputs": [], + "source": [ + "alphabetChinese = '1234567890abcdefghijklmnopqrstuvwxyzABCDEFGHIJKLMNOPQRSTUVWXYZ'" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": {}, + "outputs": [], + "source": [ + "trainP,testP = train_test_split(roots,test_size=0.1)##此处未考虑字符平衡划分\n", + "traindataset = PathDataset(trainP,alphabetChinese)\n", + "testdataset = PathDataset(testP,alphabetChinese)\n", + "\n", + "batchSize = 32\n", + "workers = 1\n", + "imgH = 32\n", + "imgW = 280\n", + "keep_ratio = True\n", + "cuda = True\n", + "ngpu = 1\n", + "nh =256\n", + "sampler = randomSequentialSampler(traindataset, batchSize)\n", + "train_loader = torch.utils.data.DataLoader(\n", + " traindataset, batch_size=batchSize,\n", + " shuffle=False, sampler=None,\n", + " num_workers=int(workers),\n", + " collate_fn=alignCollate(imgH=imgH, imgW=imgW, keep_ratio=keep_ratio))\n", + "\n", + "train_iter = iter(train_loader)\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## 加载预训练模型权重" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "metadata": {}, + "outputs": [], + "source": [ + "def weights_init(m):\n", + " classname = m.__class__.__name__\n", + " if classname.find('Conv') != -1:\n", + " m.weight.data.normal_(0.0, 0.02)\n", + " elif classname.find('BatchNorm') != -1:\n", + " m.weight.data.normal_(1.0, 0.02)\n", + " m.bias.data.fill_(0)\n", + " \n", + "from crnn.models.crnn import CRNN\n", + "from config import ocrModel,LSTMFLAG,GPU\n", + "model = CRNN(32, 1, len(alphabetChinese)+1, 256, 1,lstmFlag=LSTMFLAG)\n", + "model.apply(weights_init)\n", + "preWeightDict = torch.load(ocrModel,map_location=lambda storage, loc: storage)##加入项目训练的权重\n", + "\n", + "modelWeightDict = model.state_dict()\n", + "\n", + "for k, v in preWeightDict.items():\n", + " name = k.replace('module.','') # remove `module.`\n", + " if 'rnn.1.embedding' not in name:##不加载最后一层权重\n", + " modelWeightDict[name] = v\n", + " \n", + "model.load_state_dict(modelWeightDict)\n" + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "CRNN(\n", + " (cnn): Sequential(\n", + " (conv0): Conv2d(1, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n", + " (relu0): ReLU(inplace)\n", + " (pooling0): MaxPool2d(kernel_size=2, stride=2, padding=0, dilation=1, ceil_mode=False)\n", + " (conv1): Conv2d(64, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n", + " (relu1): ReLU(inplace)\n", + " (pooling1): MaxPool2d(kernel_size=2, stride=2, padding=0, dilation=1, ceil_mode=False)\n", + " (conv2): Conv2d(128, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n", + " (batchnorm2): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n", + " (relu2): ReLU(inplace)\n", + " (conv3): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n", + " (relu3): ReLU(inplace)\n", + " (pooling2): MaxPool2d(kernel_size=(2, 2), stride=(2, 1), padding=(0, 1), dilation=1, ceil_mode=False)\n", + " (conv4): Conv2d(256, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n", + " (batchnorm4): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n", + " (relu4): ReLU(inplace)\n", + " (conv5): Conv2d(512, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n", + " (relu5): ReLU(inplace)\n", + " (pooling3): MaxPool2d(kernel_size=(2, 2), stride=(2, 1), padding=(0, 1), dilation=1, ceil_mode=False)\n", + " (conv6): Conv2d(512, 512, kernel_size=(2, 2), stride=(1, 1))\n", + " (batchnorm6): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n", + " (relu6): ReLU(inplace)\n", + " )\n", + " (rnn): Sequential(\n", + " (0): BidirectionalLSTM(\n", + " (rnn): LSTM(512, 256, bidirectional=True)\n", + " (embedding): Linear(in_features=512, out_features=256, bias=True)\n", + " )\n", + " (1): BidirectionalLSTM(\n", + " (rnn): LSTM(256, 256, bidirectional=True)\n", + " (embedding): Linear(in_features=512, out_features=63, bias=True)\n", + " )\n", + " )\n", + ")" + ] + }, + "execution_count": 6, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "model" + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "metadata": {}, + "outputs": [], + "source": [ + "##优化器\n", + "from crnn.util import strLabelConverter\n", + "lr = 0.1\n", + "optimizer = optim.Adadelta(model.parameters(), lr=lr)\n", + "converter = strLabelConverter(''.join(alphabetChinese))\n", + "criterion = CTCLoss()" + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "metadata": {}, + "outputs": [], + "source": [ + "\n", + "from train.ocr.dataset import resizeNormalize\n", + "from crnn.util import loadData\n", + "image = torch.FloatTensor(batchSize, 3, imgH, imgH)\n", + "text = torch.IntTensor(batchSize * 5)\n", + "length = torch.IntTensor(batchSize)\n", + "\n", + "if torch.cuda.is_available():\n", + " model.cuda()\n", + " model = torch.nn.DataParallel(model, device_ids=[0])##转换为多GPU训练模型\n", + " image = image.cuda()\n", + " criterion = criterion.cuda()\n", + " " + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + }, + { + "cell_type": "code", + "execution_count": 9, + "metadata": {}, + "outputs": [], + "source": [ + "\n", + "def trainBatch(net, criterion, optimizer,cpu_images, cpu_texts):\n", + " #data = train_iter.next()\n", + " #cpu_images, cpu_texts = data\n", + " batch_size = cpu_images.size(0)\n", + " loadData(image, cpu_images)\n", + " t, l = converter.encode(cpu_texts)\n", + " \n", + " loadData(text, t)\n", + " loadData(length, l)\n", + " preds = net(image)\n", + " preds_size = Variable(torch.IntTensor([preds.size(0)] * batch_size))\n", + " cost = criterion(preds, text, preds_size, length) / batch_size\n", + " net.zero_grad()\n", + " cost.backward()\n", + " optimizer.step()\n", + " return cost\n", + "\n", + "\n", + "\n", + "\n", + "def predict(im):\n", + " \"\"\"\n", + " 预测\n", + " \"\"\"\n", + " image = im.convert('L')\n", + " scale = image.size[1]*1.0 / 32\n", + " w = image.size[0] / scale\n", + " w = int(w)\n", + " transformer = resizeNormalize((w, 32))\n", + " \n", + " image = transformer(image)\n", + " if torch.cuda.is_available():\n", + " image = image.cuda()\n", + " image = image.view(1, *image.size())\n", + " image = Variable(image)\n", + " preds = model(image)\n", + " _, preds = preds.max(2)\n", + " preds = preds.transpose(1, 0).contiguous().view(-1)\n", + " preds_size = Variable(torch.IntTensor([preds.size(0)]))\n", + " sim_pred = converter.decode(preds.data, preds_size.data, raw=False)\n", + " return sim_pred\n", + " \n", + " \n", + "def val(net, dataset, max_iter=100):\n", + "\n", + " for p in net.parameters():\n", + " p.requires_grad = False\n", + " net.eval()\n", + " i = 0\n", + " n_correct = 0\n", + " N = len(dataset)\n", + " \n", + " max_iter = min(max_iter, N)\n", + " for i in range(max_iter):\n", + " im,label = dataset[np.random.randint(0,N)]\n", + " if im.size[0]>1024:\n", + " continue\n", + " \n", + " pred = predict(im)\n", + " if pred.strip() ==label:\n", + " n_correct += 1\n", + "\n", + " accuracy = n_correct / float(max_iter )\n", + " return accuracy\n", + " \n", + " \n" + ] + }, + { + "cell_type": "code", + "execution_count": 10, + "metadata": {}, + "outputs": [], + "source": [ + "from train.ocr.generic_utils import Progbar\n", + "##进度条参考 https://github.com/keras-team/keras/blob/master/keras/utils/generic_utils.py\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## 模型训练" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## 冻结预训练模型层参数" + ] + }, + { + "cell_type": "code", + "execution_count": 11, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "epoch:0/10\n", + " 3/1407 [..............................] - ETA: 1:28 - loss: 20.9233 - acc: 0.0000e+00" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "Exception ignored in: >\n", + "Traceback (most recent call last):\n", + " File \"/home/lywen/anaconda3/envs/chineseocr/lib/python3.6/site-packages/torch/utils/data/dataloader.py\", line 399, in __del__\n", + " self._shutdown_workers()\n", + " File \"/home/lywen/anaconda3/envs/chineseocr/lib/python3.6/site-packages/torch/utils/data/dataloader.py\", line 378, in _shutdown_workers\n", + " self.worker_result_queue.get()\n", + " File \"/home/lywen/anaconda3/envs/chineseocr/lib/python3.6/multiprocessing/queues.py\", line 344, in get\n", + " return _ForkingPickler.loads(res)\n", + " File \"/home/lywen/anaconda3/envs/chineseocr/lib/python3.6/site-packages/torch/multiprocessing/reductions.py\", line 151, in rebuild_storage_fd\n", + " fd = df.detach()\n", + " File \"/home/lywen/anaconda3/envs/chineseocr/lib/python3.6/multiprocessing/resource_sharer.py\", line 58, in detach\n", + " return reduction.recv_handle(conn)\n", + " File \"/home/lywen/anaconda3/envs/chineseocr/lib/python3.6/multiprocessing/reduction.py\", line 182, in recv_handle\n", + " return recvfds(s, 1)[0]\n", + " File \"/home/lywen/anaconda3/envs/chineseocr/lib/python3.6/multiprocessing/reduction.py\", line 153, in recvfds\n", + " msg, ancdata, flags, addr = sock.recvmsg(1, socket.CMSG_LEN(bytes_size))\n", + "ConnectionResetError: [Errno 104] Connection reset by peer\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "1407/1407 [==============================] - 56s 40ms/step - loss: 5.2403 - acc: 0.2484\n", + "epoch:1/10\n", + "1407/1407 [==============================] - 57s 41ms/step - loss: 0.0821 - acc: 0.7834\n", + "epoch:2/10\n", + "1407/1407 [==============================] - 57s 40ms/step - loss: 0.0253 - acc: 0.9102\n", + "epoch:3/10\n", + "1407/1407 [==============================] - 58s 41ms/step - loss: 0.0173 - acc: 0.9287\n", + "epoch:4/10\n", + "1407/1407 [==============================] - 57s 40ms/step - loss: 0.0139 - acc: 0.9414\n", + "epoch:5/10\n", + "1407/1407 [==============================] - 57s 40ms/step - loss: 0.0120 - acc: 0.9478\n", + "epoch:6/10\n", + "1407/1407 [==============================] - 58s 41ms/step - loss: 0.0108 - acc: 0.9570\n", + "epoch:7/10\n", + "1407/1407 [==============================] - 58s 41ms/step - loss: 0.0100 - acc: 0.9600\n", + "epoch:8/10\n", + "1407/1407 [==============================] - 57s 40ms/step - loss: 0.0094 - acc: 0.9600\n", + "epoch:9/10\n", + "1407/1407 [==============================] - 57s 40ms/step - loss: 0.0089 - acc: 0.9600\n" + ] + } + ], + "source": [ + "nepochs = 10\n", + "acc = 0\n", + "\n", + "interval = len(train_loader)//2##评估模型\n", + "\n", + " \n", + "for i in range(nepochs):\n", + " print('epoch:{}/{}'.format(i,nepochs))\n", + " n = len(train_loader)\n", + " pbar = Progbar(target=n)\n", + " train_iter = iter(train_loader)\n", + " loss = 0\n", + " for j in range(n):\n", + " for p in model.named_parameters():\n", + " p[1].requires_grad = True\n", + " if 'rnn.1.embedding' in p[0]:\n", + " p[1].requires_grad = True\n", + " else:\n", + " p[1].requires_grad = False##冻结模型层\n", + "\n", + " model.train()\n", + " cpu_images, cpu_texts = train_iter.next()\n", + " cost = trainBatch(model, criterion, optimizer,cpu_images, cpu_texts)\n", + "\n", + " loss += cost.data.numpy()\n", + " \n", + " if (j+1)%interval==0:\n", + " curAcc = val(model, testdataset, max_iter=1024)\n", + " if curAcc>acc:\n", + " acc = curAcc\n", + " torch.save(model.state_dict(), 'train/ocr/modellstm.pth')\n", + " \n", + " \n", + " pbar.update(j+1,values=[('loss',loss/((j+1)*batchSize)),('acc',acc)])\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## 释放模型层参数" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "epoch:10/10\n", + "1407/1407 [==============================] - 151s 107ms/step - loss: 0.0061 - acc: 0.9766\n", + "epoch:11/10\n", + " 517/1407 [==========>...................] - ETA: 1:31 - loss: 0.0027 - acc: 0.9814" + ] + } + ], + "source": [ + "nepochs = 10\n", + "#acc = 0\n", + "\n", + "interval = len(train_loader)//2##评估模型\n", + "\n", + " \n", + "for i in range(10,10+nepochs):\n", + " print('epoch:{}/{}'.format(i,nepochs))\n", + " n = len(train_loader)\n", + " pbar = Progbar(target=n)\n", + " train_iter = iter(train_loader)\n", + " loss = 0\n", + " for j in range(n):\n", + " for p in model.named_parameters():\n", + " p[1].requires_grad = True\n", + "\n", + "\n", + " model.train()\n", + " cpu_images, cpu_texts = train_iter.next()\n", + " cost = trainBatch(model, criterion, optimizer,cpu_images, cpu_texts)\n", + "\n", + " loss += cost.data.numpy()\n", + " \n", + " if (j+1)%interval==0:\n", + " curAcc = val(model, testdataset, max_iter=1024)\n", + " if curAcc>acc:\n", + " acc = curAcc\n", + " torch.save(model.state_dict(), 'train/ocr/modellstm.pth')\n", + " \n", + " \n", + " pbar.update(j+1,values=[('loss',loss/((j+1)*batchSize)),('acc',acc)])\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## 预测demo" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "im,label = dataset[np.random.randint(0,N)]\n", + "pred = predict(im)\n", + "print('true:{},pred:{}'.format(label,pred))\n", + "im\n" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "chineseocr", + "language": "python", + "name": "chineseocr" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.6.2" + } + }, + "nbformat": 4, + "nbformat_minor": 2 +}