Initial commit

models/shape_infers/InferTinyCellNet.py

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+#####################################################
+# Copyright (c) Xuanyi Dong [GitHub D-X-Y], 2019.01 #
+#####################################################
+from typing import List, Text, Any
+import torch.nn as nn
+from models.cell_operations import ResNetBasicblock
+from models.cell_infers.cells import InferCell
+
+
+class DynamicShapeTinyNet(nn.Module):
+
+  def __init__(self, channels: List[int], genotype: Any, num_classes: int):
+    super(DynamicShapeTinyNet, self).__init__()
+    self._channels = channels
+    if len(channels) % 3 != 2:
+      raise ValueError('invalid number of layers : {:}'.format(len(channels)))
+    self._num_stage = N = len(channels) // 3
+
+    self.stem = nn.Sequential(
+                    nn.Conv2d(3, channels[0], kernel_size=3, padding=1, bias=False),
+                    nn.BatchNorm2d(channels[0]))
+
+    # 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 = channels[0]
+    self.cells = nn.ModuleList()
+    for index, (c_curr, reduction) in enumerate(zip(channels, layer_reductions)):
+      if reduction : cell = ResNetBasicblock(c_prev, c_curr, 2, True)
+      else         : cell = InferCell(genotype, c_prev, c_curr, 1)
+      self.cells.append( cell )
+      c_prev = cell.out_dim
+    self._num_layer = len(self.cells)
+
+    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)
+
+  def get_message(self) -> Text:
+    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={_channels}, N={_num_stage}, L={_num_layer})'.format(name=self.__class__.__name__, **self.__dict__))
+
+  def forward(self, inputs):
+    feature = self.stem(inputs)
+    for i, cell in enumerate(self.cells):
+      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