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# Copyright (c) Xuanyi Dong [GitHub D-X-Y], 2019 #
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# DARTS: Differentiable Architecture Search, ICLR 2019 #
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import torch
import torch.nn as nn
from copy import deepcopy
from ..cell_operations import ResNetBasicblock
from .search_cells     import SearchCell
from .genotypes        import Structure


class TinyNetworkDartsV1(nn.Module):

  def __init__(self, C, N, max_nodes, num_classes, search_space):
    super(TinyNetworkDartsV1, 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)
        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.arch_parameters = nn.Parameter( 1*torch.randn(num_edge, len(search_space)))    
    self.arch_parameters = nn.Parameter( 1e-3*torch.randn(num_edge, len(search_space)))
    #self.arch_parameters = nn.Parameter( torch.ones(num_edge, len(search_space)) )
    #self.arch_parameters = nn.Parameter( 1e-2*torch.randn(num_edge, len(search_space)))    
    #self.arch_parameters = nn.Parameter( 1e-1*torch.randn(num_edge, len(search_space)))        
    #self.arch_parameters = nn.Parameter( 1e-4*torch.randn(num_edge, len(search_space)))    
    #self.arch_parameters = nn.Parameter( 2e-4*torch.randn(num_edge, len(search_space)))       
    #self.arch_parameters = nn.Parameter( 4e-4*torch.randn(num_edge, len(search_space)))      
    #self.arch_parameters = nn.Parameter( 6e-4*torch.randn(num_edge, len(search_space)))     
    #self.arch_parameters = nn.Parameter( 1e-5*torch.randn(num_edge, len(search_space)))       
    #self.arch_parameters = nn.Parameter( 1e-7*torch.randn(num_edge, len(search_space)))    
    #self.arch_parameters = nn.Parameter( torch.randn(num_edge, len(search_space)))            
    #self.arch_parameters.data = nn.functional.softmax(self.arch_parameters, dim=-1).data
    
    

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

  def get_alphas(self):
    return [self.arch_parameters]
    
 # def get_alphas_softmax(self):
    #return [nn.functional.softmax(self.arch_parameters, dim=-1)]    
    

  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}, Max-Nodes={max_nodes}, N={_layerN}, L={_Layer})'.format(name=self.__class__.__name__, **self.__dict__))

  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 genotype_prune(self, initial_metric):
    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 = initial_metric[ 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 forward(self, inputs):
    alphas  = nn.functional.softmax(self.arch_parameters, dim=-1)
    
    #alphas  = self.arch_parameters
    feature = self.stem(inputs)
    for i, cell in enumerate(self.cells):
      if isinstance(cell, SearchCell):
        feature = cell(feature, alphas)
      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


  def forward_new(self, inputs, operation_position_1, operation_position_2):
    alphas  = nn.functional.softmax(self.arch_parameters, dim=-1)
    
    alphas.data[operation_position_1,operation_position_2]=float(0)
    
    #alphas  = self.arch_parameters
    feature = self.stem(inputs)
    for i, cell in enumerate(self.cells):
      if isinstance(cell, SearchCell):
        feature = cell(feature, alphas)
      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


