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| Original file line number | Diff line number | Diff line change |
|---|---|---|
| @@ -0,0 +1,301 @@ | ||
| import os | ||
| import subprocess | ||
| import numpy as np | ||
| import datetime | ||
| import random | ||
| import warnings | ||
| import ROOT as rt | ||
| import math | ||
| from array import array | ||
|
|
||
| class wkiter(object): | ||
| def __init__(self,data_path,data_names=['data'],label_names=['softmax_label'],batch_size=100,begin=0.0,end=1.0,rat=0.7,endcut=1,arnum=16,maxx=0.4,maxy=0.4,istrain=0, fstart=0, friend=0, zboson=0,varbs=0,w=0): | ||
| self.istrain=istrain | ||
| #if(batch_size<100): | ||
| # print("batch_size is small it might cause error") | ||
| self.friend=friend | ||
| self.zboson=zboson | ||
| self.w=w | ||
| print(self.friend,istrain) | ||
| if(friend!=0): | ||
| if(self.zboson==0): | ||
| data_path=["root/cutb/mg5_pp_qq_balanced_pt_100_500_","root/cutb/mg5_pp_gg_balanced_pt_100_500_"] | ||
| else: | ||
| data_path=["root/cut/mg5_pp_zq_passed_pt_100_500_","root/cut/mg5_pp_zg_passed_pt_100_500_"] | ||
| self.gf=0 | ||
| self.qf=0 | ||
| self.gq=int(rat*friend) | ||
| self.qg=int(rat*friend) | ||
| self.frat=int(friend*rat) | ||
| self.qfile=[] | ||
| self.gfile=[] | ||
| self.qjet=[] | ||
| self.gjet=[] | ||
| self.qim=[] | ||
| self.gim=[] | ||
| self.qlabel=[] | ||
| self.glabel=[] | ||
| self.qEntries=[] | ||
| self.gEntries=[] | ||
| self.qBegin=[] | ||
| self.gBegin=[] | ||
| self.qEnd=[] | ||
| self.gEnd=[] | ||
| self.qnum=0 | ||
| self.gnum=0 | ||
| for i in range(friend): | ||
| dataname1=data_path[0]+str(fstart+i+1)+"_img.root" | ||
| dataname2=data_path[1]+str(fstart+i+1)+"_img.root" | ||
| self.qfile.append(rt.TFile(dataname1,'read')) | ||
| self.gfile.append(rt.TFile(dataname2,'read')) | ||
| self.qjet.append(self.qfile[i].Get("image")) | ||
| self.gjet.append(self.gfile[i].Get("image")) | ||
| self.qim.append(array('B', [0]*(3*(arnum*2+1)*(arnum*2+1)))) | ||
| self.gim.append(array('B', [0]*(3*(arnum*2+1)*(arnum*2+1)))) | ||
| #self.qlabel.append(array('B', [0])) | ||
| #self.glabel.append(array('B', [0])) | ||
| self.qjet[i].SetBranchAddress("image", self.qim[i]) | ||
| self.gjet[i].SetBranchAddress("image", self.gim[i]) | ||
| #self.qjet[i].SetBranchAddress("label", self.qlabel[i]) | ||
| #self.gjet[i].SetBranchAddress("label", self.glabel[i]) | ||
| self.qEntries.append(self.qjet[i].GetEntriesFast()) | ||
| self.gEntries.append(self.gjet[i].GetEntriesFast()) | ||
| self.qBegin.append(int(begin*self.qEntries[i])) | ||
| self.gBegin.append(int(begin*self.gEntries[i])) | ||
| self.qEnd.append(int(self.qEntries[i]*end)) | ||
| self.gEnd.append(int(self.gEntries[i]*end)) | ||
| self.qnum+=self.qEnd[i]-self.qBegin[i] | ||
| self.gnum+=self.gEnd[i]-self.qBegin[i] | ||
| self.a=self.gBegin[0] | ||
| self.b=self.qBegin[0] | ||
| self.aq=self.gBegin[self.frat] | ||
| self.bg=self.qBegin[self.frat] | ||
| else: | ||
| #self.file=rt.TFile(data_path,'read') | ||
| dataname1=data_path[0] | ||
| dataname2=data_path[1] | ||
| self.qfile=rt.TFile(dataname1,'read') | ||
| self.gfile=rt.TFile(dataname2,'read') | ||
| self.qjet=self.qfile.Get("image") | ||
| self.gjet=self.gfile.Get("image") | ||
| self.qim = array('B', [0]*(3*(arnum*2+1)*(arnum*2+1))) | ||
| self.gim = array('B', [0]*(3*(arnum*2+1)*(arnum*2+1))) | ||
| self.qjet.SetBranchAddress("image", self.qim) | ||
| self.gjet.SetBranchAddress("image", self.gim) | ||
| #self.qlabel = array('B', [0]) | ||
| #self.glabel = array('B', [0]) | ||
| #self.qjet.SetBranchAddress("label", self.qlabel) | ||
| #self.gjet.SetBranchAddress("label", self.glabel) | ||
| self.qEntries=self.qjet.GetEntriesFast() | ||
| self.gEntries=self.gjet.GetEntriesFast() | ||
| self.qBegin=int(begin*self.qEntries) | ||
| self.gBegin=int(begin*self.gEntries) | ||
| self.qEnd=int(self.qEntries*end) | ||
| self.gEnd=int(self.gEntries*end) | ||
| self.a=self.gBegin | ||
| self.b=self.qBegin | ||
| self.ratt=rat | ||
| self.rat=sorted([1-rat,rat]) | ||
| self.batch_size = batch_size | ||
| if(varbs==0): | ||
| self._provide_data = zip(data_names, [(self.batch_size, 3, 33, 33)]) | ||
| else: | ||
| data_names=['images','variables'] | ||
| self._provide_data = zip(data_names, [(self.batch_size, 3, 33, 33),(self.batch_size,5)]) | ||
| self.varbs=varbs | ||
| self._provide_label = zip(label_names, [(self.batch_size,)]) | ||
| self.arnum=arnum | ||
| self.maxx=maxx | ||
| self.maxy=maxy | ||
| self.endfile=0 | ||
| self.endcut=endcut | ||
| def __iter__(self): | ||
| return self | ||
|
|
||
| def reset(self): | ||
| if(self.friend!=0): | ||
| #print("@@",self.istrain,"g",self.gf,"q",self.qf,"@@") | ||
| for i in range(self.friend): | ||
| self.qjet[i].GetEntry(self.qBegin[i]) | ||
| self.gjet[i].GetEntry(self.gBegin[i]) | ||
| self.a=self.gBegin[0] | ||
| self.b=self.qBegin[0] | ||
| self.aq=self.gBegin[self.frat] | ||
| self.bg=self.qBegin[self.frat] | ||
| self.gf=0 | ||
| self.qf=0 | ||
| self.gq=self.frat | ||
| self.qg=self.frat | ||
| self.endfile=0 | ||
| else: | ||
| self.qjet.GetEntry(self.qBegin) | ||
| self.gjet.GetEntry(self.gBegin) | ||
| self.a=self.gBegin | ||
| self.b=self.qBegin | ||
| self.endfile = 0 | ||
|
|
||
| def __next__(self): | ||
| return self.next() | ||
|
|
||
| @property | ||
| def provide_data(self): | ||
| return self._provide_data | ||
|
|
||
| @property | ||
| def provide_label(self): | ||
| return self._provide_label | ||
|
|
||
| def close(self): | ||
| self.file.Close() | ||
| def sampleallnum(self): | ||
| return self.Entries | ||
| def trainnum(self): | ||
| return self.End-self.Begin | ||
| def totalnum(self): | ||
| if(self.friend!=0): | ||
| #a=0 | ||
| #for i in range(self.friend): | ||
| # a+=self.qEnd[i]-self.qBegin[i]+self.gEnd[i]-self.qBegin[i] | ||
| return int((self.qnum+self.gnum)/self.batch_size*0.99) | ||
| else: | ||
| return int((self.qEnd-self.qBegin+self.gEnd-self.qBegin)/self.batch_size*0.99) | ||
| def next(self): | ||
| while self.endfile==0: | ||
| arnum=self.arnum | ||
| jetset=[] | ||
| variables=[] | ||
| labels=[] | ||
| rand=random.choice(self.rat) | ||
| if(self.friend!=0 and self.zboson==0 and self.istrain==1): | ||
| rand=0.4 | ||
| if(self.friend!=0 and self.zboson==0 and self.istrain==0): | ||
| rand=0.31354286 | ||
| if(self.friend!=0 and self.zboson==1): | ||
| rand=0.526 | ||
| if(self.friend!=0 and self.zboson==0 and self.istrain==1 and self.w==1): | ||
| rand=0.37 | ||
| if(self.friend!=0): | ||
| rand=self.gnum/1./(self.qnum+self.gnum) | ||
| for i in range(self.batch_size): | ||
| if(self.friend!=0): | ||
| if(self.w==0): | ||
| if(random.random()<rand): | ||
| self.gjet[self.gf].GetEntry(self.a) | ||
| self.a+=1 | ||
| jetset.append(np.array(self.gim[self.gf]).reshape((3,2*arnum+1,2*arnum+1))) | ||
| labels.append([1,0]) | ||
| if(self.varbs==1): | ||
| variables.append([self.gjet[self.gf].ptD,self.gjet[self.gf].axis1,self.gjet[self.gf].axis2,self.gjet[self.gf].nmult,self.gjet[self.gf].cmult]) | ||
| if(self.a>=self.gEnd[self.gf]): | ||
| self.gf+=1 | ||
| if(self.gf==self.friend): | ||
| self.endfile=1 | ||
| self.gf=0 | ||
| self.a=self.gBegin[self.gf] | ||
| else: | ||
| self.qjet[self.qf].GetEntry(self.b) | ||
| self.b+=1 | ||
| jetset.append(np.array(self.qim[self.qf]).reshape((3,2*arnum+1,2*arnum+1))) | ||
| labels.append([0,1]) | ||
| if(self.varbs==1): | ||
| variables.append([self.qjet[self.qf].ptD,self.qjet[self.qf].axis1,self.qjet[self.qf].axis2,self.qjet[self.qf].nmult,self.qjet[self.qf].cmult]) | ||
| if(self.b>=self.qEnd[self.qf]): | ||
| self.qf+=1 | ||
| if(self.qf==self.friend): | ||
| self.endfile=1 | ||
| self.qf=0 | ||
| self.b=self.gBegin[self.qf] | ||
| ####----------- | ||
| else: | ||
| if(random.random()<rand): | ||
| if(random.random()<self.ratt): | ||
| self.gjet[self.gf].GetEntry(self.a) | ||
| self.a+=1 | ||
| jetset.append(np.array(self.gim[self.gf]).reshape((3,2*arnum+1,2*arnum+1))) | ||
| labels.append([1,0]) | ||
| if(self.varbs==1): | ||
| variables.append([self.gjet[self.gf].ptD,self.gjet[self.gf].axis1,self.gjet[self.gf].axis2,self.gjet[self.gf].nmult,self.gjet[self.gf].cmult]) | ||
| if(self.a>=self.gEnd[self.gf]): | ||
| self.gf+=1 | ||
| if(self.gf==self.frat): | ||
| self.endfile=1 | ||
| self.gf=0 | ||
| self.a=self.gBegin[self.gf] | ||
| else: | ||
| self.qjet[self.gq].GetEntry(self.aq) | ||
| self.aq+=1 | ||
| jetset.append(np.array(self.gim[self.gq]).reshape((3,2*arnum+1,2*arnum+1))) | ||
| labels.append([1,0]) | ||
| if(self.varbs==1): | ||
| variables.append([self.qjet[self.gq].ptD,self.qjet[self.gq].axis1,self.qjet[self.gq].axis2,self.qjet[self.gq].nmult,self.qjet[self.gq].cmult]) | ||
| if(self.aq>=self.gEnd[self.gq]): | ||
| self.gq+=1 | ||
| if(self.gq==self.friend): | ||
| self.endfile=1 | ||
| self.gq=self.frat | ||
| self.aq=self.gBegin[self.gq] | ||
| else: | ||
| if(random.random()<self.ratt): | ||
| self.qjet[self.qf].GetEntry(self.b) | ||
| self.b+=1 | ||
| jetset.append(np.array(self.qim[self.qf]).reshape((3,2*arnum+1,2*arnum+1))) | ||
| labels.append([0,1]) | ||
| if(self.varbs==1): | ||
| variables.append([self.qjet[self.qf].ptD,self.qjet[self.qf].axis1,self.qjet[self.qf].axis2,self.qjet[self.qf].nmult,self.qjet[self.qf].cmult]) | ||
| if(self.b>=self.qEnd[self.qf]): | ||
| self.qf+=1 | ||
| if(self.qf==self.frat): | ||
| self.endfile=1 | ||
| self.qf=0 | ||
| self.b=self.gBegin[self.qf] | ||
| else: | ||
| self.gjet[self.qg].GetEntry(self.bg) | ||
| self.bg+=1 | ||
| jetset.append(np.array(self.qim[self.qg]).reshape((3,2*arnum+1,2*arnum+1))) | ||
| labels.append([0,1]) | ||
| if(self.varbs==1): | ||
| variables.append([self.gjet[self.qg].ptD,self.gjet[self.qg].axis1,self.gjet[self.qg].axis2,self.gjet[self.qg].nmult,self.gjet[self.qg].cmult]) | ||
| if(self.bg>=self.qEnd[self.qg]): | ||
| self.qg+=1 | ||
| if(self.qg==self.friend): | ||
| self.endfile=1 | ||
| self.qg=self.frat | ||
| self.bg=self.gBegin[self.qg] | ||
|
|
||
|
|
||
| else: | ||
| if(random.random()<0.5): | ||
| #if(random.random()<rand): | ||
| self.gjet.GetEntry(self.a) | ||
| self.a+=1 | ||
| jetset.append(np.array(self.gim).reshape((3,2*arnum+1,2*arnum+1))) | ||
| labels.append([1,0]) | ||
| if(self.a>=self.gEnd): | ||
| self.a=self.gBegin | ||
| self.endfile=1 | ||
| else: | ||
| self.qjet.GetEntry(self.b) | ||
| self.b+=1 | ||
| jetset.append(np.array(self.qim).reshape((3,2*arnum+1,2*arnum+1))) | ||
| labels.append([0,1]) | ||
| if(self.b>=self.qEnd): | ||
| self.b=self.gBegin | ||
| self.endfile=1 | ||
| #if(rand<0.5): | ||
| # labels.append(0) | ||
| #else: | ||
| # labels.append(1) | ||
| #if(self.endcut==0 and self.ent>=self.End): | ||
| #self.ent=self.Begin | ||
| #self.endfile=1 | ||
| if(self.varbs==1): | ||
| data=[np.array(jetset),np.array(variables)] | ||
| else: | ||
| data=np.array(jetset) | ||
| label=np.array(labels) | ||
| yield data, label | ||
| #else: | ||
| #if(self.istrain==1): | ||
| # print "\n",datetime.datetime.now() | ||
| #raise StopIteration | ||
|
|
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| Original file line number | Diff line number | Diff line change |
|---|---|---|
| @@ -0,0 +1,70 @@ | ||
| from keras.models import Sequential | ||
| from keras.layers.core import Flatten, Dense, Dropout | ||
| from keras.layers.convolutional import Convolution2D, MaxPooling2D, ZeroPadding2D | ||
| from keras.optimizers import SGD | ||
| import cv2, numpy as np | ||
|
|
||
| def get_symbol(input_shape,num_classes,weights_path=None): | ||
| model = Sequential() | ||
| model.add(ZeroPadding2D((1,1),input_shape=input_shape)) | ||
| model.add(Convolution2D(64, 3, 3, activation='relu')) | ||
| model.add(ZeroPadding2D((1,1))) | ||
| model.add(Convolution2D(64, 3, 3, activation='relu')) | ||
| model.add(MaxPooling2D((2,2), strides=(2,2))) | ||
|
|
||
| model.add(ZeroPadding2D((1,1))) | ||
| model.add(Convolution2D(128, 3, 3, activation='relu')) | ||
| model.add(ZeroPadding2D((1,1))) | ||
| model.add(Convolution2D(128, 3, 3, activation='relu')) | ||
| model.add(MaxPooling2D((2,2), strides=(2,2))) | ||
|
|
||
| model.add(ZeroPadding2D((1,1))) | ||
| model.add(Convolution2D(256, 3, 3, activation='relu')) | ||
| model.add(ZeroPadding2D((1,1))) | ||
| model.add(Convolution2D(256, 3, 3, activation='relu')) | ||
| model.add(ZeroPadding2D((1,1))) | ||
| model.add(Convolution2D(256, 3, 3, activation='relu')) | ||
| model.add(MaxPooling2D((2,2), strides=(2,2))) | ||
|
|
||
| model.add(ZeroPadding2D((1,1))) | ||
| model.add(Convolution2D(512, 3, 3, activation='relu')) | ||
| model.add(ZeroPadding2D((1,1))) | ||
| model.add(Convolution2D(512, 3, 3, activation='relu')) | ||
| model.add(ZeroPadding2D((1,1))) | ||
| model.add(Convolution2D(512, 3, 3, activation='relu')) | ||
| model.add(MaxPooling2D((2,2), strides=(2,2))) | ||
|
|
||
| model.add(ZeroPadding2D((1,1))) | ||
| model.add(Convolution2D(512, 3, 3, activation='relu')) | ||
| model.add(ZeroPadding2D((1,1))) | ||
| model.add(Convolution2D(512, 3, 3, activation='relu')) | ||
| model.add(ZeroPadding2D((1,1))) | ||
| model.add(Convolution2D(512, 3, 3, activation='relu')) | ||
| model.add(MaxPooling2D((2,2), strides=(2,2))) | ||
|
|
||
| model.add(Flatten()) | ||
| model.add(Dense(4096, activation='relu')) | ||
| model.add(Dropout(0.5)) | ||
| model.add(Dense(4096, activation='relu')) | ||
| model.add(Dropout(0.5)) | ||
| model.add(Dense(num_classes, activation='softmax')) | ||
|
|
||
| if weights_path: | ||
| model.load_weights(weights_path) | ||
|
|
||
| return model | ||
|
|
||
| if __name__ == "__main__": | ||
| im = cv2.resize(cv2.imread('cat.jpg'), (224, 224)).astype(np.float32) | ||
| im[:,:,0] -= 103.939 | ||
| im[:,:,1] -= 116.779 | ||
| im[:,:,2] -= 123.68 | ||
| im = im.transpose((2,0,1)) | ||
| im = np.expand_dims(im, axis=0) | ||
|
|
||
| # Test pretrained model | ||
| model = VGG_16('vgg16_weights.h5') | ||
| sgd = SGD(lr=0.1, decay=1e-6, momentum=0.9, nesterov=True) | ||
| model.compile(optimizer=sgd, loss='categorical_crossentropy') | ||
| out = model.predict(im) | ||
| print np.argmax(out) |
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