Move to xautodl

xautodl/nas_infer_model/DXYs/CifarNet.py

@@ -0,0 +1,76 @@
+import torch
+import torch.nn as nn
+from .construct_utils import drop_path
+from .head_utils      import CifarHEAD, AuxiliaryHeadCIFAR
+from .base_cells      import InferCell
+
+
+class NetworkCIFAR(nn.Module):
+
+  def __init__(self, C, N, stem_multiplier, auxiliary, genotype, num_classes):
+    super(NetworkCIFAR, self).__init__()
+    self._C               = C
+    self._layerN          = N
+    self._stem_multiplier = stem_multiplier
+
+    C_curr = self._stem_multiplier * C
+    self.stem = CifarHEAD(C_curr)
+  
+    layer_channels   = [C    ] * N + [C*2 ] + [C*2  ] * N + [C*4 ] + [C*4  ] * N    
+    layer_reductions = [False] * N + [True] + [False] * N + [True] + [False] * N
+    block_indexs     = [0    ] * N + [-1  ] + [1    ] * N + [-1  ] + [2    ] * N
+    block2index      = {0:[], 1:[], 2:[]}
+
+    C_prev_prev, C_prev, C_curr = C_curr, C_curr, C
+    reduction_prev, spatial, dims = False, 1, []
+    self.auxiliary_index = None
+    self.auxiliary_head  = None
+    self.cells = nn.ModuleList()
+    for index, (C_curr, reduction) in enumerate(zip(layer_channels, layer_reductions)):
+      cell = InferCell(genotype, C_prev_prev, C_prev, C_curr, reduction, reduction_prev)
+      reduction_prev = reduction
+      self.cells.append( cell )
+      C_prev_prev, C_prev = C_prev, cell._multiplier*C_curr
+      if reduction and C_curr == C*4:
+        if auxiliary:
+          self.auxiliary_head = AuxiliaryHeadCIFAR(C_prev, num_classes)
+          self.auxiliary_index = index
+
+      if reduction: spatial *= 2
+      dims.append( (C_prev, spatial) )
+      
+    self._Layer= len(self.cells)
+
+
+    self.global_pooling = nn.AdaptiveAvgPool2d(1)
+    self.classifier = nn.Linear(C_prev, num_classes)
+    self.drop_path_prob = -1
+
+  def update_drop_path(self, drop_path_prob):
+    self.drop_path_prob = drop_path_prob
+
+  def auxiliary_param(self):
+    if self.auxiliary_head is None: return []
+    else: return list( self.auxiliary_head.parameters() )
+
+  def get_message(self):
+    return self.extra_repr()
+
+  def extra_repr(self):
+    return ('{name}(C={_C}, N={_layerN}, L={_Layer}, stem={_stem_multiplier}, drop-path={drop_path_prob})'.format(name=self.__class__.__name__, **self.__dict__))
+
+  def forward(self, inputs):
+    stem_feature, logits_aux = self.stem(inputs), None
+    cell_results = [stem_feature, stem_feature]
+    for i, cell in enumerate(self.cells):
+      cell_feature = cell(cell_results[-2], cell_results[-1], self.drop_path_prob)
+      cell_results.append( cell_feature )
+
+      if self.auxiliary_index is not None and i == self.auxiliary_index and self.training:
+        logits_aux = self.auxiliary_head( cell_results[-1] )
+    out = self.global_pooling( cell_results[-1] )
+    out = out.view(out.size(0), -1)
+    logits = self.classifier(out)
+
+    if logits_aux is None: return out, logits
+    else                 : return out, [logits, logits_aux]