model_search_pcdarts.py

import torch
import torch.nn as nn
import torch.nn.functional as F
from operations import *
from torch.autograd import Variable
from genotypes import PRIMITIVES
from genotypes import Genotype

def channel_shuffle(x, groups):
  batchsize, num_channels, height, width = x.data.size()

  channels_per_group = num_channels // groups
    
    # reshape
  x = x.view(batchsize, groups, channels_per_group, height, width)

  x = torch.transpose(x, 1, 2).contiguous()

    # flatten
  x = x.view(batchsize, -1, height, width)

  return x


class MixedOp(nn.Module):

  def __init__(self, C, stride):
    super(MixedOp, self).__init__()
    self._ops = nn.ModuleList()
    self.mp = nn.MaxPool2d(2,2)
    self.k = 4
    for primitive in PRIMITIVES:
      op = OPS[primitive](C //self.k, stride, False)
      if 'pool' in primitive:
        op = nn.Sequential(op, nn.BatchNorm2d(C //self.k, affine=False))
      self._ops.append(op)


  def forward(self, x, weights):
    #channel proportion k=4  
    dim_2 = x.shape[1]
    xtemp = x[ : , :  dim_2//self.k, :, :]
    xtemp2 = x[ : ,  dim_2//self.k:, :, :]
    temp1 = sum(w * op(xtemp) for w, op in zip(weights, self._ops))
    #reduction cell needs pooling before concat
    if temp1.shape[2] == x.shape[2]:
      ans = torch.cat([temp1,xtemp2],dim=1)
    else:
      ans = torch.cat([temp1,self.mp(xtemp2)], dim=1)
    ans = channel_shuffle(ans,self.k)
    # ans = torch.cat([ans[ : ,  dim_2//4:, :, :],ans[ : , :  dim_2//4, :, :]],dim=1)
    #except channe shuffle, channel shift also works
    return ans


class Cell(nn.Module):

  def __init__(self, steps, multiplier, C_prev_prev, C_prev, C, reduction, reduction_prev):
    super(Cell, self).__init__()
    self.reduction = reduction

    if reduction_prev:
      self.preprocess0 = FactorizedReduce(C_prev_prev, C, affine=False)
    else:
      self.preprocess0 = ReLUConvBN(C_prev_prev, C, 1, 1, 0, affine=False)
    self.preprocess1 = ReLUConvBN(C_prev, C, 1, 1, 0, affine=False)
    self._steps = steps
    self._multiplier = multiplier

    self._ops = nn.ModuleList()
    self._bns = nn.ModuleList()
    for i in range(self._steps):
      for j in range(2+i):
        stride = 2 if reduction and j < 2 else 1
        op = MixedOp(C, stride)
        self._ops.append(op)

  def forward(self, s0, s1, weights,weights2):
    s0 = self.preprocess0(s0)
    s1 = self.preprocess1(s1)

    states = [s0, s1]
    offset = 0
    for i in range(self._steps):
      s = sum(weights2[offset+j]*self._ops[offset+j](h, weights[offset+j]) for j, h in enumerate(states))
      offset += len(states)
      states.append(s)

    return torch.cat(states[-self._multiplier:], dim=1)


class Network(nn.Module):

  def __init__(self, C, num_classes, layers, criterion, steps=4, multiplier=4, stem_multiplier=3):
    super(Network, self).__init__()
    self._C = C
    self._num_classes = num_classes
    self._layers = layers
    self.criterion = criterion
    self._steps = steps
    self._multiplier = multiplier

    C_curr = stem_multiplier*C
    self.stem = nn.Sequential(
      nn.Conv2d(3, C_curr, 3, padding=1, bias=False),
      nn.BatchNorm2d(C_curr)
    )
 
    C_prev_prev, C_prev, C_curr = C_curr, C_curr, C
    self.cells = nn.ModuleList()
    reduction_prev = False
    for i in range(layers):
      if i in [layers//3, 2*layers//3]:
        C_curr *= 2
        reduction = True
      else:
        reduction = False
      cell = Cell(steps, multiplier, C_prev_prev, C_prev, C_curr, reduction, reduction_prev)
      reduction_prev = reduction
      self.cells += [cell]
      C_prev_prev, C_prev = C_prev, multiplier*C_curr

    self.global_pooling = nn.AdaptiveAvgPool2d(1)
    self.classifier = nn.Linear(C_prev, num_classes)

    # self._initialize_alphas()

  # def new(self):
  #   model_new = Network(self._C, self._num_classes, self._layers, self._criterion).cuda()
  #   for x, y in zip(model_new.arch_parameters(), self.arch_parameters()):
  #       x.data.copy_(y.data)
  #   return model_new

  def forward(self, x, alphas):
    # s0 = s1 = self.stem(input)
    s0 = s1 = self.stem(x)
    alpha_normal, alpha_reduce, beta_normal, beta_reduce = alphas
    for i, cell in enumerate(self.cells):
      if cell.reduction:
        weights = F.softmax(alpha_reduce, dim=-1)
        n = 3
        start = 2
        weights2 = F.softmax(beta_reduce[0:2], dim=-1)
        for i in range(self._steps-1):
          end = start + n
          tw2 = F.softmax(beta_reduce[start:end], dim=-1)
          start = end
          n += 1
          weights2 = torch.cat([weights2,tw2],dim=0)
      else:
        weights = F.softmax(alpha_normal, dim=-1)
        n = 3
        start = 2
        weights2 = F.softmax(beta_normal[0:2], dim=-1)
        for i in range(self._steps-1):
          end = start + n
          tw2 = F.softmax(beta_normal[start:end], dim=-1)
          start = end
          n += 1
          weights2 = torch.cat([weights2,tw2],dim=0)
      s0, s1 = s1, cell(s0, s1, weights,weights2)
    out = self.global_pooling(s1)
    logits = self.classifier(out.view(out.size(0),-1))
    return logits

  # def _loss(self, input, target):
  #   logits = self(input)
  #   return self._criterion(logits, target) 

  def loss(self, x, alphas, target, acc=False):
    logits = self(x, alphas)
    if not acc:
      return self.criterion(logits, target)
    correct = (logits.argmax(dim=1) == target).float().sum().item()
    return self.criterion(logits, target), correct



  def genotype(self,alphas):
    alpha_normal, alpha_reduce, beta_normal, beta_reduce = alphas

    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(beta_reduce[0:2], dim=-1)
    weightsn2 = F.softmax(beta_normal[0:2], dim=-1)
    for i in range(self._steps-1):
      end = start + n
      tw2 = F.softmax(beta_reduce[start:end], dim=-1)
      tn2 = F.softmax(beta_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(alpha_normal, dim=-1).data.cpu().numpy(),weightsn2.data.cpu().numpy())
    gene_reduce = _parse(F.softmax(alpha_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




class Architecture(nn.Module):
  def __init__(self, steps):
    super(Architecture, self).__init__()

    k = sum(1 for i in range(steps) for j in range(2 + i))
    num_ops = len(PRIMITIVES)

    self.alpha_normal = nn.Parameter(torch.randn(k, num_ops))
    self.alpha_reduce = nn.Parameter(torch.randn(k, num_ops))
    self.beta_normal = nn.Parameter(torch.randn(k))
    self.beta_reduce = nn.Parameter(torch.randn(k))
    with torch.no_grad():
      # initialize to smaller value
      self.alpha_normal.mul_(1e-3)
      self.alpha_reduce.mul_(1e-3)
      self.beta_normal.mul_(1e-3)
      self.beta_reduce.mul_(1e-3)
    
    # self.alpha_normal = Variable(1e-3*torch.randn(k, num_ops).cuda(), requires_grad=True)
    # self.alpha_reduce = Variable(1e-3*torch.randn(k, num_ops).cuda(), requires_grad=True)
    # self.beta_normal = Variable(1e-3*torch.randn(k).cuda(), requires_grad=True)
    # self.beta_reduce = Variable(1e-3*torch.randn(k).cuda(), requires_grad=True)
    
            
            

  def forward(self):
   return [self.alpha_normal, self.alpha_reduce, self.beta_normal, self.beta_reduce]