model.py

import torch
import torch.nn as nn
from operations import *
from torch.autograd import Variable
# from utils import drop_path


class Cell(nn.Module):

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

    if reduction_prev is None:
      self.preprocess0 = Identity()
    elif reduction_prev is True:
      self.preprocess0 = FactorizedReduce(C_prev_prev, C)
    else:
      self.preprocess0 = ReLUConvBN(C_prev_prev, C, 1, 1, 0)
    self.preprocess1 = ReLUConvBN(C_prev, C, 1, 1, 0)
    
    if reduction:
      op_names, indices = zip(*genotype.reduce)
      concat = genotype.reduce_concat
    else:
      op_names, indices = zip(*genotype.normal)
      concat = genotype.normal_concat

    assert len(op_names) == len(indices)
    self._steps = len(op_names) // 2
    self._concat = concat
    self.multiplier = len(concat)

    self._ops = nn.ModuleList()
    for name, index in zip(op_names, indices):
      stride = 2 if reduction and index < 2 else 1
      if reduction_prev is None and index == 0:
        op = OPS[name](C_prev_prev, C, stride, True)
      else:
        op = OPS[name](C, C, stride, True)
      self._ops += [op]
    self._indices = indices

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

    states = [s0, s1]
    for i in range(self._steps):
      h1 = states[self._indices[2*i]]
      h2 = states[self._indices[2*i+1]]
      op1 = self._ops[2*i]
      op2 = self._ops[2*i+1]
      h1 = op1(h1)
      h2 = op2(h2)
      # if self.training and drop_prob > 0.:
      #   if not isinstance(op1, Identity):
      #     h1 = drop_path(h1, drop_prob)
      #   if not isinstance(op2, Identity):
      #     h2 = drop_path(h2, drop_prob)
      s = h1 + h2
      states += [s]
    return torch.cat([states[i] for i in self._concat], dim=1)


class AuxiliaryHeadImageNet(nn.Module):

  def __init__(self, C, num_classes):
    """assuming input size 14x14"""
    super(AuxiliaryHeadImageNet, self).__init__()
    self.features = nn.Sequential(
      nn.ReLU(inplace=True),
      nn.AvgPool2d(5, stride=2, padding=0, count_include_pad=False),
      nn.Conv2d(C, 128, 1, bias=False),
      nn.BatchNorm2d(128),
      nn.ReLU(inplace=True),
      nn.Conv2d(128, 768, 2, bias=False),
      nn.BatchNorm2d(768),
      nn.ReLU(inplace=True)
    )
    self.classifier = nn.Linear(768, num_classes)

  def forward(self, x):
    x = self.features(x)
    x = self.classifier(x.view(x.size(0),-1))
    return x


class NetworkImageNet(nn.Module):

  def __init__(self, C, num_classes, layers, auxiliary, genotype):
    super(NetworkImageNet, self).__init__()
    self._layers = layers
    self._auxiliary = auxiliary

    self.conv0 = nn.Conv2d(3, 96, kernel_size=3, stride=2, padding=0, bias=False)
    self.conv0_bn = nn.BatchNorm2d(96, eps=1e-3)
    self.stem1 = Cell(genotype, 96, 96, C // 4, True, None)
    self.stem2 = Cell(genotype, 96, C * self.stem1.multiplier // 4, C // 2, True, True)

    C_prev_prev, C_prev, C_curr = C * self.stem1.multiplier // 4, C * self.stem2.multiplier // 2, C

    self.cells = nn.ModuleList()
    reduction_prev = True
    for i in xrange(layers):
      if i in [layers // 3, 2 * layers // 3]:
        C_curr *= 2
        reduction = True
      else:
        reduction = False
      cell = Cell(genotype, C_prev_prev, C_prev, C_curr, reduction, reduction_prev)
      reduction_prev = reduction
      self.cells += [cell]
      C_prev_prev, C_prev = C_prev, cell.multiplier * C_curr
      if i == 2 * layers // 3:
        C_to_auxiliary = C_prev

    if auxiliary:
      self.auxiliary_head = AuxiliaryHeadImageNet(C_to_auxiliary, num_classes)
    self.relu = nn.ReLU(inplace=False)
    self.global_pooling = nn.AdaptiveAvgPool2d(1)
    self.classifier = nn.Linear(C_prev, num_classes)

  def forward(self, input):
    logits_aux = None
    s0 = self.conv0(input)
    s0 = self.conv0_bn(s0)
    s1 = self.stem1(s0, s0, self.drop_path_prob)
    s0, s1 = s1, self.stem2(s0, s1, self.drop_path_prob)
    for i, cell in enumerate(self.cells):
      s0, s1 = s1, cell(s0, s1, self.drop_path_prob)
      if i == 2 * self._layers // 3:
        if self._auxiliary and self.training:
          logits_aux = self.auxiliary_head(s1)
    s1 = self.relu(s1)
    out = self.global_pooling(s1)
    logits = self.classifier(out.view(out.size(0), -1))
    return logits, logits_aux