Python genotypes.Genotype方法代码示例

# 需要导入模块: import genotypes [as 别名]
# 或者: from genotypes import Genotype [as 别名]
def genotype(self):

      def _parse(probs):
        gene = []
        start = 0
        for i in range(STEPS):
          end = start + i + 1
          W = probs[start:end].copy()
          j = sorted(range(i + 1), key=lambda x: -max(W[x][k] for k in range(len(W[x])) if k != PRIMITIVES.index('none')))[0]
          k_best = None
          for k in range(len(W[j])):
            if k != PRIMITIVES.index('none'):
              if k_best is None or W[j][k] > W[j][k_best]:
                k_best = k
          gene.append((PRIMITIVES[k_best], j))
          start = end
        return gene

      gene = _parse(F.softmax(self.weights, dim=-1).data.cpu().numpy())
      genotype = Genotype(recurrent=gene, concat=range(STEPS+1)[-CONCAT:])
      return genotype 

# 需要导入模块: import genotypes [as 别名]
# 或者: from genotypes import Genotype [as 别名]
def parse_results(results, n_nodes):
    concat = range(2, 2 + n_nodes)
    normal_gene = []
    reduction_gene = []
    for i in range(n_nodes):
        normal_node = []
        reduction_node = []
        for j in range(2 + i):
            normal_key = 'normal_n{}_p{}'.format(i + 2, j)
            reduction_key = 'reduce_n{}_p{}'.format(i + 2, j)
            normal_op = results[normal_key].cpu().numpy()
            reduction_op = results[reduction_key].cpu().numpy()
            if sum(normal_op == 1):
                normal_index = np.argmax(normal_op)
                normal_node.append((PRIMITIVES[normal_index], j))
            if sum(reduction_op == 1):
                reduction_index = np.argmax(reduction_op)
                reduction_node.append((PRIMITIVES[reduction_index], j))
        normal_gene.append(normal_node)
        reduction_gene.append(reduction_node)

    genotypes = Genotype(normal=normal_gene, normal_concat=concat,
                         reduce=reduction_gene, reduce_concat=concat)
    return genotypes 

# 需要导入模块: import genotypes [as 别名]
# 或者: from genotypes import Genotype [as 别名]
def parse_arch(arch):
    arch = list(map(int, arch.split()))
    lenth = len(arch) // 2
    recurrent = []
    concat = [i for i in range(lenth+1)]
    ACT = {
        0: 'tanh',
        1: 'relu',
        2: 'identity',
        3: 'sigmoid'
    }
    for i in range(lenth):
        pre_node = arch[2*i]
        activation = ACT[arch[2*i+1]]
        recurrent.append((activation, pre_node))
        """
        if pre_node in concat:
            concat.remove(pre_node)"""
    concat = range(1, lenth+1)
    return Genotype(recurrent=recurrent, concat=concat) 

# 需要导入模块: import genotypes [as 别名]
# 或者: from genotypes import Genotype [as 别名]
def get_genotype(self, force=False):
        if force:
          gene_normal = self.parse_gene_force(self.normal_candidate_flags,
                                              self.normal_selected_idxs,
                                              self.alphas_normal)
          gene_reduce = self.parse_gene_force(self.reduce_candidate_flags,
                                              self.reduce_selected_idxs,
                                              self.alphas_reduce)
        else:
          gene_normal = self.parse_gene(self.normal_selected_idxs)
          gene_reduce = self.parse_gene(self.reduce_selected_idxs)
        n = 2
        concat = range(n + self._steps - self._multiplier, self._steps + n)
        genotype = Genotype(
            normal=gene_normal, normal_concat=concat,
            reduce=gene_reduce, reduce_concat=concat
        )
        return genotype 

# 需要导入模块: import genotypes [as 别名]
# 或者: from genotypes import Genotype [as 别名]
def genotype(self):
        gene_normal = gt.parse(self.alpha_normal, k=2)
        gene_reduce = gt.parse(self.alpha_reduce, k=2)
        concat = range(2, 2+self.n_nodes) # concat all intermediate nodes

        return gt.Genotype(normal=gene_normal, normal_concat=concat,
                           reduce=gene_reduce, reduce_concat=concat) 

# 需要导入模块: import genotypes [as 别名]
# 或者: from genotypes import Genotype [as 别名]
def genotype(self):

    def _parse(weights):
      gene = []
      n = 2
      start = 0
      for i in range(self._steps):
        end = start + n
        W = weights[start:end].copy()
        edges = sorted(range(i + 2), key=lambda x: -max(W[x][k] for k in range(len(W[x])) if k != PRIMITIVES.index('none')))[:2]
        for j in edges:
          k_best = None
          for k in range(len(W[j])):
            if k != PRIMITIVES.index('none'):
              if k_best is None or W[j][k] > W[j][k_best]:
                k_best = k
          gene.append((PRIMITIVES[k_best], j))
        start = end
        n += 1
      return gene

    gene_normal = _parse(F.softmax(self.alphas_normal, dim=-1).data.cpu().numpy())
    gene_reduce = _parse(F.softmax(self.alphas_reduce, dim=-1).data.cpu().numpy())

    concat = range(2+self._steps-self._multiplier, self._steps+2)
    genotype = Genotype(
      normal=gene_normal, normal_concat=concat,
      reduce=gene_reduce, reduce_concat=concat
    )
    return genotype 

# 需要导入模块: import genotypes [as 别名]
# 或者: from genotypes import Genotype [as 别名]
def parse_network(switches_normal, switches_reduce):

    def _parse_switches(switches):
        n = 2
        start = 0
        gene = []
        step = 4
        for i in range(step):
            end = start + n
            for j in range(start, end):
                for k in range(len(switches[j])):
                    if switches[j][k]:
                        gene.append((PRIMITIVES[k], j - start))
            start = end
            n = n + 1
        return gene
    gene_normal = _parse_switches(switches_normal)
    gene_reduce = _parse_switches(switches_reduce)
    
    concat = range(2, 6)
    
    genotype = Genotype(
        normal=gene_normal, normal_concat=concat, 
        reduce=gene_reduce, reduce_concat=concat
    )
    
    return genotype 

# 需要导入模块: import genotypes [as 别名]
# 或者: from genotypes import Genotype [as 别名]
def get_genotype(self, force=False):
        if force:
            gene_normal = self.parse_gene_force(self.normal_candidate_flags,
                                                self.normal_selected_idxs,
                                                self.alphas_normal)
        else:
            gene_normal = self.parse_gene(self.normal_selected_idxs)
        n = 2
        concat = range(n + self._steps - self._multiplier, self._steps + n)
        genotype = Genotype(
            normal=gene_normal, normal_concat=concat
        )
        return genotype 

# 需要导入模块: import genotypes [as 别名]
# 或者: from genotypes import Genotype [as 别名]
def get_genotype(self, force=False):
        if force:
            gene_normal = self.parse_gene_force(self.normal_candidate_flags,
                                                self.normal_selected_idxs,
                                                self.alphas_normal)
        else:
            gene_normal = self.parse_gene(self.normal_selected_idxs)
        n = 2
        concat = range(n + self._steps - self._multiplier, self._steps + n)
        genotype = Genotype(normal=gene_normal, normal_concat=concat)
        return genotype 

# 需要导入模块: import genotypes [as 别名]
# 或者: from genotypes import Genotype [as 别名]
def genotype(self):

    def _parse(weights,weights2):
      gene = []
      n = 2
      start = 0
      for i in range(self._steps):
        end = start + n
        W = weights[start:end].copy()
        W2 = weights2[start:end].copy()
        for j in range(n):
          W[j,:]=W[j,:]*W2[j]
        edges = sorted(range(i + 2), key=lambda x: -max(W[x][k] for k in range(len(W[x])) if k != PRIMITIVES.index('none')))[:2]
        
        #edges = sorted(range(i + 2), key=lambda x: -W2[x])[:2]
        for j in edges:
          k_best = None
          for k in range(len(W[j])):
            if k != PRIMITIVES.index('none'):
              if k_best is None or W[j][k] > W[j][k_best]:
                k_best = k
          gene.append((PRIMITIVES[k_best], j))
        start = end
        n += 1
      return gene
    n = 3
    start = 2
    weightsr2 = F.softmax(self.betas_reduce[0:2], dim=-1)
    weightsn2 = F.softmax(self.betas_normal[0:2], dim=-1)
    for i in range(self._steps-1):
      end = start + n
      tw2 = F.softmax(self.betas_reduce[start:end], dim=-1)
      tn2 = F.softmax(self.betas_normal[start:end], dim=-1)
      start = end
      n += 1
      weightsr2 = torch.cat([weightsr2,tw2],dim=0)
      weightsn2 = torch.cat([weightsn2,tn2],dim=0)
    gene_normal = _parse(F.softmax(self.alphas_normal, dim=-1).data.cpu().numpy(),weightsn2.data.cpu().numpy())
    gene_reduce = _parse(F.softmax(self.alphas_reduce, dim=-1).data.cpu().numpy(),weightsr2.data.cpu().numpy())

    concat = range(2+self._steps-self._multiplier, self._steps+2)
    genotype = Genotype(
      normal=gene_normal, normal_concat=concat,
      reduce=gene_reduce, reduce_concat=concat
    )
    return genotype 

# 需要导入模块: import genotypes [as 别名]
# 或者: from genotypes import Genotype [as 别名]
def genotype(self):

    def _parse(weights,weights2):
      gene = []
      n = 2
      start = 0
      for i in range(self._steps):
        end = start + n
        W = weights[start:end].copy()
        W2 = weights2[start:end].copy()
        for j in range(n):
          W[j,:]=W[j,:]*W2[j]
        edges = sorted(range(i + 2), key=lambda x: -max(W[x][k] for k in range(len(W[x])) if k != PRIMITIVES.index('none')))[:2]
        
        #edges = sorted(range(i + 2), key=lambda x: -W2[x])[:2]
        for j in edges:
          k_best = None
          for k in range(len(W[j])):
            if k != PRIMITIVES.index('none'):
              if k_best is None or W[j][k] > W[j][k_best]:
                k_best = k
          gene.append((PRIMITIVES[k_best], j))
        start = end
        n += 1
      return gene
    n = 3
    start = 2
    weightsr2 = F.softmax(self.betas_reduce[0:2], dim=-1)
    weightsn2 = F.softmax(self.betas_normal[0:2], dim=-1)
    for i in range(self._steps-1):
      end = start + n
      #print(self.betas_reduce[start:end])
      tw2 = F.softmax(self.betas_reduce[start:end], dim=-1)
      tn2 = F.softmax(self.betas_normal[start:end], dim=-1)
      start = end
      n += 1
      weightsr2 = torch.cat([weightsr2,tw2],dim=0)
      weightsn2 = torch.cat([weightsn2,tn2],dim=0)
    gene_normal = _parse(F.softmax(self.alphas_normal, dim=-1).data.cpu().numpy(),weightsn2.data.cpu().numpy())
    gene_reduce = _parse(F.softmax(self.alphas_reduce, dim=-1).data.cpu().numpy(),weightsr2.data.cpu().numpy())

    concat = range(2+self._steps-self._multiplier, self._steps+2)
    genotype = Genotype(
      normal=gene_normal, normal_concat=concat,
      reduce=gene_reduce, reduce_concat=concat
    )
    return genotype