autodl-projects/lib/models/cell_searchs/generic_model.py

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# Copyright (c) Xuanyi Dong [GitHub D-X-Y], 2020.07 #
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import torch, random
import torch.nn as nn
from copy import deepcopy
from typing import Text

from ..cell_operations import ResNetBasicblock, drop_path
from .search_cells     import NAS201SearchCell as SearchCell
from .genotypes        import Structure
from .search_model_enas_utils import Controller


class GenericNAS201Model(nn.Module):

  def __init__(self, C, N, max_nodes, num_classes, search_space, affine, track_running_stats):
    super(GenericNAS201Model, self).__init__()
    self._C          = C
    self._layerN     = N
    self._max_nodes  = max_nodes
    self._stem       = nn.Sequential(
                         nn.Conv2d(3, C, kernel_size=3, padding=1, bias=False),
                         nn.BatchNorm2d(C))
    layer_channels   = [C    ] * N + [C*2 ] + [C*2  ] * N + [C*4 ] + [C*4  ] * N    
    layer_reductions = [False] * N + [True] + [False] * N + [True] + [False] * N
    C_prev, num_edge, edge2index = C, None, None
    self._cells      = nn.ModuleList()
    for index, (C_curr, reduction) in enumerate(zip(layer_channels, layer_reductions)):
      if reduction:
        cell = ResNetBasicblock(C_prev, C_curr, 2)
      else:
        cell = SearchCell(C_prev, C_curr, 1, max_nodes, search_space, affine, track_running_stats)
        if num_edge is None: num_edge, edge2index = cell.num_edges, cell.edge2index
        else: assert num_edge == cell.num_edges and edge2index == cell.edge2index, 'invalid {:} vs. {:}.'.format(num_edge, cell.num_edges)
      self._cells.append(cell)
      C_prev = cell.out_dim
    self._op_names   = deepcopy(search_space)
    self._Layer      = len(self._cells)
    self.edge2index  = edge2index
    self.lastact     = nn.Sequential(nn.BatchNorm2d(C_prev), nn.ReLU(inplace=True))
    self.global_pooling = nn.AdaptiveAvgPool2d(1)
    self.classifier  = nn.Linear(C_prev, num_classes)
    self._num_edge   = num_edge
    # algorithm related
    self.arch_parameters = nn.Parameter( 1e-3*torch.randn(num_edge, len(search_space)) )
    self._mode        = None
    self.dynamic_cell = None
    self._tau         = None
    self._algo        = None
    self._drop_path   = None

  def set_algo(self, algo: Text):
    # used for searching
    assert self._algo is None, 'This functioin can only be called once.'
    self._algo = algo
    if algo == 'enas':
      self.controller = Controller(len(self.edge2index), len(self._op_names))
    else:
      self.arch_parameters = nn.Parameter( 1e-3*torch.randn(self._num_edge, len(self._op_names)) )
      if algo == 'gdas':
        self._tau         = 10
    
  def set_cal_mode(self, mode, dynamic_cell=None):
    assert mode in ['gdas', 'enas', 'urs', 'joint', 'select', 'dynamic']
    self._mode = mode
    if mode == 'dynamic': self.dynamic_cell = deepcopy(dynamic_cell)
    else                : self.dynamic_cell = None

  @property
  def mode(self):
    return self._mode

  @property
  def drop_path(self):
    return self._drop_path

  @property
  def weights(self):
    xlist = list(self._stem.parameters())
    xlist+= list(self._cells.parameters())
    xlist+= list(self.lastact.parameters())
    xlist+= list(self.global_pooling.parameters())
    xlist+= list(self.classifier.parameters())
    return xlist

  def set_tau(self, tau):
    self._tau = tau

  @property
  def tau(self):
    return self._tau

  @property
  def alphas(self):
    if self._algo == 'enas':
      return list(self.controller.parameters())
    else:
      return [self.arch_parameters]

  @property
  def message(self):
    string = self.extra_repr()
    for i, cell in enumerate(self._cells):
      string += '\n {:02d}/{:02d} :: {:}'.format(i, len(self._cells), cell.extra_repr())
    return string

  def show_alphas(self):
    with torch.no_grad():
      if self._algo == 'enas':
        import pdb; pdb.set_trace()
        print('-')
      else:
        return 'arch-parameters :\n{:}'.format( nn.functional.softmax(self.arch_parameters, dim=-1).cpu() )
          

  def extra_repr(self):
    return ('{name}(C={_C}, Max-Nodes={_max_nodes}, N={_layerN}, L={_Layer}, alg={_algo})'.format(name=self.__class__.__name__, **self.__dict__))

  @property
  def genotype(self):
    genotypes = []
    for i in range(1, self._max_nodes):
      xlist = []
      for j in range(i):
        node_str = '{:}<-{:}'.format(i, j)
        with torch.no_grad():
          weights = self.arch_parameters[ self.edge2index[node_str] ]
          op_name = self._op_names[ weights.argmax().item() ]
        xlist.append((op_name, j))
      genotypes.append(tuple(xlist))
    return Structure(genotypes)

  def dync_genotype(self, use_random=False):
    genotypes = []
    with torch.no_grad():
      alphas_cpu = nn.functional.softmax(self.arch_parameters, dim=-1)
    for i in range(1, self._max_nodes):
      xlist = []
      for j in range(i):
        node_str = '{:}<-{:}'.format(i, j)
        if use_random:
          op_name  = random.choice(self._op_names)
        else:
          weights  = alphas_cpu[ self.edge2index[node_str] ]
          op_index = torch.multinomial(weights, 1).item()
          op_name  = self._op_names[ op_index ]
        xlist.append((op_name, j))
      genotypes.append(tuple(xlist))
    return Structure(genotypes)

  def get_log_prob(self, arch):
    with torch.no_grad():
      logits = nn.functional.log_softmax(self.arch_parameters, dim=-1)
    select_logits = []
    for i, node_info in enumerate(arch.nodes):
      for op, xin in node_info:
        node_str = '{:}<-{:}'.format(i+1, xin)
        op_index = self._op_names.index(op)
        select_logits.append( logits[self.edge2index[node_str], op_index] )
    return sum(select_logits).item()

  def return_topK(self, K, use_random=False):
    archs = Structure.gen_all(self._op_names, self._max_nodes, False)
    pairs = [(self.get_log_prob(arch), arch) for arch in archs]
    if K < 0 or K >= len(archs): K = len(archs)
    if use_random:
      return random.sample(archs, K)
    else:
      sorted_pairs = sorted(pairs, key=lambda x: -x[0])
      return_pairs = [sorted_pairs[_][1] for _ in range(K)]
      return return_pairs

  def normalize_archp(self):
    if self.mode == 'gdas':
      while True:
        gumbels = -torch.empty_like(self.arch_parameters).exponential_().log()
        logits  = (self.arch_parameters.log_softmax(dim=1) + gumbels) / self.tau
        probs   = nn.functional.softmax(logits, dim=1)
        index   = probs.max(-1, keepdim=True)[1]
        one_h   = torch.zeros_like(logits).scatter_(-1, index, 1.0)
        hardwts = one_h - probs.detach() + probs
        if (torch.isinf(gumbels).any()) or (torch.isinf(probs).any()) or (torch.isnan(probs).any()):
          continue
        else: break
      with torch.no_grad():
        hardwts_cpu = hardwts.detach().cpu()
      return hardwts, hardwts_cpu, index
    else:
      alphas  = nn.functional.softmax(self.arch_parameters, dim=-1)
      index   = alphas.max(-1, keepdim=True)[1]
      with torch.no_grad():
        alphas_cpu = alphas.detach().cpu()
      return alphas, alphas_cpu, index

  def forward(self, inputs):
    alphas, alphas_cpu, index = self.normalize_archp()
    feature = self._stem(inputs)
    for i, cell in enumerate(self._cells):
      if isinstance(cell, SearchCell):
        if self.mode == 'urs':
          feature = cell.forward_urs(feature)
        elif self.mode == 'select':
          feature = cell.forward_select(feature, alphas_cpu)
        elif self.mode == 'joint':
          feature = cell.forward_joint(feature, alphas)
        elif self.mode == 'dynamic':
          feature = cell.forward_dynamic(feature, self.dynamic_cell)
        elif self.mode == 'gdas':
          feature = cell.forward_gdas(feature, alphas, index)
        else: raise ValueError('invalid mode={:}'.format(self.mode))
      else: feature = cell(feature)
    out = self.lastact(feature)
    out = self.global_pooling(out)
    out = out.view(out.size(0), -1)
    logits = self.classifier(out)
    return out, logits