c1_resnet.py

from __future__ import print_function

from keras.models import Model
from keras.layers import Input
from keras.applications import resnet50

from b1_net_pair import BaseNetU
from module import cvac,ca3,ca33,cb3,cba3,dmp,uu,ct,sk,db,td,uta,tu


class NetU_ResNet(BaseNetU):
    config={
        'resnet50':(resnet50.ResNet50,[1,10,22,40,49])
    }
    def __init__(self,**kwargs):
        super(NetU_ResNet,self).__init__(**kwargs)
        self.fs=kwargs.get('filters', [64,128,256,512,768,1024])
        self.ps=kwargs.get('poolings', [2]*len(self.fs))
        self.variation=kwargs.get('variation', 'resnet50')
        self.preproc=kwargs.get('preproc', sk)
        # self.downconv=kwargs.get('downconv', ca33)
        # self.downmerge=kwargs.get('downmerge', sk) # before downsize, ->1st skip connect
        # self.downsamp=kwargs.get('downsamp', dmp)
        # self.downjoin=kwargs.get('downjoin', sk) # after downsize, ->2nd skip connect
        # self.downproc=kwargs.get('downproc', sk)
        self.upconv=kwargs.get('upconv', ca3)
        self.upjoin=kwargs.get('upjoin', ct) # before upsample, 2nd skip connect->
        self.upsamp=kwargs.get('upsamp', uu)
        self.upmerge=kwargs.get('upmerge', ct) # after upsample, 1st skip connect->
        self.upproc=kwargs.get('upproc', ca3)
        self.postproc=kwargs.get('postproc', ca3)

    def build_net(self,is_train):
        super(NetU_ResNet,self).build_net(is_train)
        locals()['in0']=Input(shape=(self.row_in,self.col_in,self.dep_in))
        locals()['pre0']=self.preproc(locals()['in0'],'pre0',0,self.fs[0],self.act)
        creater,numbers=self.config[self.variation]
        base_model=creater(input_tensor=locals()['pre0'],include_top=False,weights='imagenet' if self.pre_trained else None)
        # print(base_model.summary())
        for layer in base_model.layers: layer.trainable=True  # allow training on pre-trained weights
        locals()['dmerge0']=locals()['dconv0']=None
        locals()['dproc1']=locals()['djoin1']=locals()['dsamp1']=base_model.get_layer("activation_1").output

        locals()['dmerge1']=locals()['dconv1']=None
        locals()['dproc2']=locals()['djoin2']=locals()['dsamp2']=base_model.get_layer("max_pooling2d_1").output

        for i in range(2,5):
            key="activation_%d"%(numbers[i-1])
            locals()['dmerge%d'%i]=locals()['dconv%d'%i]=base_model.get_layer(key).output
            locals()['dproc%d'%(i+1)]=locals()['djoin%d'%(i+1)]=None
        locals()['djoin5']=locals()['uproc5']=base_model.get_layer("activation_%d"%numbers[-1]).output

        for i in range(4,-1,-1):
            locals()['uconv%d'%(i+1)]=self.upconv(locals()['uproc%d'%(i+1)],'uconv%d'%(i+1),i,self.fs[i+1],self.act)
            locals()['ujoin%d'%(i+1)]=self.upjoin(locals()['uconv%d'%(i+1)],locals()['djoin%d'%(i+1)],'ujoin%d'%(i+1),i,self.fs[i+1],self.act)
            locals()['usamp%d'%i]=self.upsamp(locals()['ujoin%d'%(i+1)],self.ps[i],'usamp%d'%i,i,self.fs[i+1],self.act)
            locals()['umerge%d'%i]=self.upmerge(locals()['usamp%d'%i],locals()['dmerge%d'%i],'umerge%d'%i,i,self.fs[i],self.act)
            locals()['uproc%d'%i]=self.upproc(locals()['umerge%d'%i],'uproc%d'%i,i,self.fs[i],self.act)

        locals()['post0']=self.postproc(locals()['uproc0'],'post0',0,self.fs[0],self.act)
        locals()['out0']=cvac(locals()['post0'],'out0',0,self.dep_out,self.out,size=1)
        self.net=Model(locals()['in0'],locals()['out0'])

    def __str__(self):
        return '_'.join([type(self).__name__+''.join(c for c in self.variation if c.isdigit()),
            "%dF%d-%dP%d-%d"%(len(self.fs),self.fs[0],self.fs[-1],self.ps[0],self.ps[-1]),
            # "%df%d-%dp%s" % (len(self.fs), self.fs[0], self.fs[-1], ''.join(self.pssize)),
            # self.cap_lim_join(10, self.preproc.__name__, self.downconv.__name__,
            #                   self.downmerge.__name__, self.downsamp.__name__,
            #                   self.downjoin.__name__, self.downproc.__name__),
            self.cap_lim_join(10,self.upconv.__name__,self.upjoin.__name__,self.upsamp.__name__,self.upmerge.__name__,self.upproc.__name__,
                self.postproc.__name__),self.cap_lim_join(4,self.feed,self.act,self.out,
                (self.loss if isinstance(self.loss,str) else self.loss.__name__).replace('_','').replace('loss',''))+str(self.dep_out)])