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# Copyright (c) Xuanyi Dong [GitHub D-X-Y], 2019.01 #
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# One-Shot Neural Architecture Search via Self-Evaluated Template Network, ICCV 2019 #
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import torch
import torch.nn as nn
from copy import deepcopy
from typing import List, Text, Dict
from .search_cells     import NASNetSearchCell as SearchCell


# The macro structure is based on NASNet
class NASNetworkSETN(nn.Module):

  def __init__(self, C: int, N: int, steps: int, multiplier: int, stem_multiplier: int,
               num_classes: int, search_space: List[Text], affine: bool, track_running_stats: bool):
    super(NASNetworkSETN, self).__init__()
    self._C        = C
    self._layerN   = N
    self._steps    = steps
    self._multiplier = multiplier
    self.stem = nn.Sequential(
                    nn.Conv2d(3, C*stem_multiplier, kernel_size=3, padding=1, bias=False),
                    nn.BatchNorm2d(C*stem_multiplier))
  
    # config for each layer
    layer_channels   = [C    ] * N + [C*2 ] + [C*2  ] * (N-1) + [C*4 ] + [C*4  ] * (N-1)
    layer_reductions = [False] * N + [True] + [False] * (N-1) + [True] + [False] * (N-1)

    num_edge, edge2index = None, None
    C_prev_prev, C_prev, C_curr, reduction_prev = C*stem_multiplier, C*stem_multiplier, C, False

    self.cells = nn.ModuleList()
    for index, (C_curr, reduction) in enumerate(zip(layer_channels, layer_reductions)):
      cell = SearchCell(search_space, steps, multiplier, C_prev_prev, C_prev, C_curr, reduction, reduction_prev, 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_prev, C_prev, reduction_prev = C_prev, multiplier*C_curr, reduction
    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.arch_normal_parameters = nn.Parameter( 1e-3*torch.randn(num_edge, len(search_space)) )
    self.arch_reduce_parameters = nn.Parameter( 1e-3*torch.randn(num_edge, len(search_space)) )
    self.mode = 'urs'
    self.dynamic_cell = None

  def set_cal_mode(self, mode, dynamic_cell=None):
    assert mode in ['urs', 'joint', 'select', 'dynamic']
    self.mode = mode
    if mode == 'dynamic':
      self.dynamic_cell = deepcopy(dynamic_cell)
    else:
      self.dynamic_cell = None

  def get_weights(self):
    xlist = list( self.stem.parameters() ) + list( self.cells.parameters() )
    xlist+= list( self.lastact.parameters() ) + list( self.global_pooling.parameters() )
    xlist+= list( self.classifier.parameters() )
    return xlist

  def get_alphas(self):
    return [self.arch_normal_parameters, self.arch_reduce_parameters]

  def show_alphas(self):
    with torch.no_grad():
      A = 'arch-normal-parameters :\n{:}'.format( nn.functional.softmax(self.arch_normal_parameters, dim=-1).cpu() )
      B = 'arch-reduce-parameters :\n{:}'.format( nn.functional.softmax(self.arch_reduce_parameters, dim=-1).cpu() )
    return '{:}\n{:}'.format(A, B)

  def get_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 extra_repr(self):
    return ('{name}(C={_C}, N={_layerN}, steps={_steps}, multiplier={_multiplier}, L={_Layer})'.format(name=self.__class__.__name__, **self.__dict__))

  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 genotype(self):
    def _parse(weights):
      gene = []
      for i in range(self._steps):
        edges = []
        for j in range(2+i):
          node_str = '{:}<-{:}'.format(i, j)
          ws = weights[ self.edge2index[node_str] ]
          for k, op_name in enumerate(self.op_names):
            if op_name == 'none': continue
            edges.append( (op_name, j, ws[k]) )
        edges = sorted(edges, key=lambda x: -x[-1])
        selected_edges = edges[:2]
        gene.append( tuple(selected_edges) )
      return gene
    with torch.no_grad():
      gene_normal = _parse(torch.softmax(self.arch_normal_parameters, dim=-1).cpu().numpy())
      gene_reduce = _parse(torch.softmax(self.arch_reduce_parameters, dim=-1).cpu().numpy())
    return {'normal': gene_normal, 'normal_concat': list(range(2+self._steps-self._multiplier, self._steps+2)),
            'reduce': gene_reduce, 'reduce_concat': list(range(2+self._steps-self._multiplier, self._steps+2))}

  def forward(self, inputs):
    normal_hardwts = nn.functional.softmax(self.arch_normal_parameters, dim=-1)
    reduce_hardwts = nn.functional.softmax(self.arch_reduce_parameters, dim=-1)

    s0 = s1 = self.stem(inputs)
    for i, cell in enumerate(self.cells):
      # [TODO]
      raise NotImplementedError
      if cell.reduction: hardwts, index = reduce_hardwts, reduce_index
      else             : hardwts, index = normal_hardwts, normal_index
      s0, s1 = s1, cell.forward_gdas(s0, s1, hardwts, index)
    out = self.lastact(s1)
    out = self.global_pooling( out )
    out = out.view(out.size(0), -1)
    logits = self.classifier(out)

    return out, logits
