update GDAS and SETN

lib/models/cell_searchs/cells.py

@@ -2,7 +2,7 @@ import math, torch
 import torch.nn as nn
 import torch.nn.functional as F
 from copy import deepcopy
-from .operations import OPS, ReLUConvBN
+from ..cell_operations import OPS
 
 
 class SearchCell(nn.Module):
@@ -113,84 +113,3 @@ class SearchCell(nn.Module):
         inter_nodes.append( self.edges[node_str][op_index]( nodes[j] ) )
       nodes.append( sum(inter_nodes) )
     return nodes[-1]
-
-
-class InferCell(nn.Module):
-
-  def __init__(self, genotype, C_in, C_out, stride):
-    super(InferCell, self).__init__()
-
-    self.layers  = nn.ModuleList()
-    self.node_IN = []
-    self.node_IX = []
-    self.genotype = deepcopy(genotype)
-    for i in range(1, len(genotype)):
-      node_info = genotype[i-1]
-      cur_index = []
-      cur_innod = []
-      for (op_name, op_in) in node_info:
-        if op_in == 0:
-          layer = OPS[op_name](C_in , C_out, stride)
-        else:
-          layer = OPS[op_name](C_out, C_out,      1)
-        cur_index.append( len(self.layers) )
-        cur_innod.append( op_in )
-        self.layers.append( layer )
-      self.node_IX.append( cur_index )
-      self.node_IN.append( cur_innod )
-    self.nodes   = len(genotype)
-    self.in_dim  = C_in
-    self.out_dim = C_out
-
-  def extra_repr(self):
-    string = 'info :: nodes={nodes}, inC={in_dim}, outC={out_dim}'.format(**self.__dict__)
-    laystr = []
-    for i, (node_layers, node_innods) in enumerate(zip(self.node_IX,self.node_IN)):
-      y = ['I{:}-L{:}'.format(_ii, _il) for _il, _ii in zip(node_layers, node_innods)]
-      x = '{:}<-({:})'.format(i+1, ','.join(y))
-      laystr.append( x )
-    return string + ', [{:}]'.format( ' | '.join(laystr) ) + ', {:}'.format(self.genotype.tostr())
-
-  def forward(self, inputs):
-    nodes = [inputs]
-    for i, (node_layers, node_innods) in enumerate(zip(self.node_IX,self.node_IN)):
-      node_feature = sum( self.layers[_il](nodes[_ii]) for _il, _ii in zip(node_layers, node_innods) )
-      nodes.append( node_feature )
-    return nodes[-1]
-
-
-
-class ResNetBasicblock(nn.Module):
-
-  def __init__(self, inplanes, planes, stride):
-    super(ResNetBasicblock, self).__init__()
-    assert stride == 1 or stride == 2, 'invalid stride {:}'.format(stride)
-    self.conv_a = ReLUConvBN(inplanes, planes, 3, stride, 1, 1)
-    self.conv_b = ReLUConvBN(  planes, planes, 3,      1, 1, 1)
-    if stride == 2:
-      self.downsample = nn.Sequential(
-                           nn.AvgPool2d(kernel_size=2, stride=2, padding=0),
-                           nn.Conv2d(inplanes, planes, kernel_size=1, stride=1, padding=0, bias=False))
-    elif inplanes != planes:
-      self.downsample = ReLUConvBN(inplanes, planes, 1, 1, 0, 1)
-    else:
-      self.downsample = None
-    self.in_dim  = inplanes
-    self.out_dim = planes
-    self.stride  = stride
-    self.num_conv = 2
-
-  def extra_repr(self):
-    string = '{name}(inC={in_dim}, outC={out_dim}, stride={stride})'.format(name=self.__class__.__name__, **self.__dict__)
-    return string
-
-  def forward(self, inputs):
-
-    basicblock = self.conv_a(inputs)
-    basicblock = self.conv_b(basicblock)
-
-    if self.downsample is not None:
-      residual = self.downsample(inputs)
-    else:
-      residual = inputs
-    return residual + basicblock