update CVPR-2019-GDAS re-train NASNet-search-space searched models

lib/models/cell_infers/cells.py

@@ -2,6 +2,7 @@
 # Copyright (c) Xuanyi Dong [GitHub D-X-Y], 2019.01 #
 #####################################################
 
+import torch
 import torch.nn as nn
 from copy import deepcopy
 from ..cell_operations import OPS
@@ -50,3 +51,70 @@ class InferCell(nn.Module):
       node_feature = sum( self.layers[_il](nodes[_ii]) for _il, _ii in zip(node_layers, node_innods) )
       nodes.append( node_feature )
     return nodes[-1]
+
+
+
+# Learning Transferable Architectures for Scalable Image Recognition, CVPR 2018
+class NASNetInferCell(nn.Module):
+
+  def __init__(self, genotype, C_prev_prev, C_prev, C, reduction, reduction_prev, affine, track_running_stats):
+    super(NASNetInferCell, self).__init__()
+    self.reduction = reduction
+    if reduction_prev: self.preprocess0 = OPS['skip_connect'](C_prev_prev, C, 2, affine, track_running_stats)
+    else             : self.preprocess0 = OPS['nor_conv_1x1'](C_prev_prev, C, 1, affine, track_running_stats)
+    self.preprocess1 = OPS['nor_conv_1x1'](C_prev, C, 1, affine, track_running_stats)
+
+    if not reduction:
+      nodes, concats = genotype['normal'], genotype['normal_concat']
+    else:
+      nodes, concats = genotype['reduce'], genotype['reduce_concat']
+    self._multiplier = len(concats)
+    self._concats = concats
+    self._steps = len(nodes)
+    self._nodes = nodes
+    self.edges = nn.ModuleDict()
+    for i, node in enumerate(nodes):
+      for in_node in node:
+        name, j = in_node[0], in_node[1]
+        stride = 2 if reduction and j < 2 else 1
+        node_str = '{:}<-{:}'.format(i+2, j)
+        self.edges[node_str] = OPS[name](C, C, stride, affine, track_running_stats)
+
+  # [TODO] to support drop_prob in this function..
+  def forward(self, s0, s1, unused_drop_prob):
+    s0 = self.preprocess0(s0)
+    s1 = self.preprocess1(s1)
+
+    states = [s0, s1]
+    for i, node in enumerate(self._nodes):
+      clist = []
+      for in_node in node:
+        name, j = in_node[0], in_node[1]
+        node_str = '{:}<-{:}'.format(i+2, j)
+        op = self.edges[ node_str ]
+        clist.append( op(states[j]) )
+      states.append( sum(clist) )
+    return torch.cat([states[x] for x in self._concats], dim=1)
+
+
+class AuxiliaryHeadCIFAR(nn.Module):
+
+  def __init__(self, C, num_classes):
+    """assuming input size 8x8"""
+    super(AuxiliaryHeadCIFAR, self).__init__()
+    self.features = nn.Sequential(
+      nn.ReLU(inplace=True),
+      nn.AvgPool2d(5, stride=3, padding=0, count_include_pad=False), # image size = 2 x 2
+      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