init

lib/nas/operations.py

@@ -0,0 +1,122 @@
+import torch
+import torch.nn as nn
+
+OPS = {
+  'none'         : lambda C, stride, affine: Zero(stride),
+  'avg_pool_3x3' : lambda C, stride, affine: nn.Sequential(
+                                               nn.AvgPool2d(3, stride=stride, padding=1, count_include_pad=False),
+                                               nn.BatchNorm2d(C, affine=False) ),
+  'max_pool_3x3' : lambda C, stride, affine: nn.Sequential(
+                                               nn.MaxPool2d(3, stride=stride, padding=1),
+                                               nn.BatchNorm2d(C, affine=False) ),
+  'skip_connect' : lambda C, stride, affine: Identity() if stride == 1 else FactorizedReduce(C, C, affine=affine),
+  'sep_conv_3x3' : lambda C, stride, affine: SepConv(C, C, 3, stride, 1, affine=affine),
+  'sep_conv_5x5' : lambda C, stride, affine: SepConv(C, C, 5, stride, 2, affine=affine),
+  'sep_conv_7x7' : lambda C, stride, affine: SepConv(C, C, 7, stride, 3, affine=affine),
+  'dil_conv_3x3' : lambda C, stride, affine: DilConv(C, C, 3, stride, 2, 2, affine=affine),
+  'dil_conv_5x5' : lambda C, stride, affine: DilConv(C, C, 5, stride, 4, 2, affine=affine),
+  'conv_7x1_1x7' : lambda C, stride, affine: Conv717(C, C, stride, affine),
+}
+
+class Conv717(nn.Module):
+
+  def __init__(self, C_in, C_out, stride, affine):
+    super(Conv717, self).__init__()
+    self.op = nn.Sequential(
+      nn.ReLU(inplace=False),
+      nn.Conv2d(C_in , C_out, (1,7), stride=(1, stride), padding=(0, 3), bias=False),
+      nn.Conv2d(C_out, C_out, (7,1), stride=(stride, 1), padding=(3, 0), bias=False),
+      nn.BatchNorm2d(C_out, affine=affine)
+    )
+
+  def forward(self, x):
+    return self.op(x)
+
+
+class ReLUConvBN(nn.Module):
+
+  def __init__(self, C_in, C_out, kernel_size, stride, padding, affine=True):
+    super(ReLUConvBN, self).__init__()
+    self.op = nn.Sequential(
+      nn.ReLU(inplace=False),
+      nn.Conv2d(C_in, C_out, kernel_size, stride=stride, padding=padding, bias=False),
+      nn.BatchNorm2d(C_out, affine=affine)
+    )
+
+  def forward(self, x):
+    return self.op(x)
+
+
+class DilConv(nn.Module):
+    
+  def __init__(self, C_in, C_out, kernel_size, stride, padding, dilation, affine=True):
+    super(DilConv, self).__init__()
+    self.op = nn.Sequential(
+      nn.ReLU(inplace=False),
+      nn.Conv2d(C_in, C_in, kernel_size=kernel_size, stride=stride, padding=padding, dilation=dilation, groups=C_in, bias=False),
+      nn.Conv2d(C_in, C_out, kernel_size=1, padding=0, bias=False),
+      nn.BatchNorm2d(C_out, affine=affine),
+      )
+
+  def forward(self, x):
+    return self.op(x)
+
+
+class SepConv(nn.Module):
+    
+  def __init__(self, C_in, C_out, kernel_size, stride, padding, affine=True):
+    super(SepConv, self).__init__()
+    self.op = nn.Sequential(
+      nn.ReLU(inplace=False),
+      nn.Conv2d(C_in, C_in, kernel_size=kernel_size, stride=stride, padding=padding, groups=C_in, bias=False),
+      nn.Conv2d(C_in, C_in, kernel_size=1, padding=0, bias=False),
+      nn.BatchNorm2d(C_in, affine=affine),
+      nn.ReLU(inplace=False),
+      nn.Conv2d(C_in, C_in, kernel_size=kernel_size, stride=1, padding=padding, groups=C_in, bias=False),
+      nn.Conv2d(C_in, C_out, kernel_size=1, padding=0, bias=False),
+      nn.BatchNorm2d(C_out, affine=affine),
+      )
+
+  def forward(self, x):
+    return self.op(x)
+
+
+class Identity(nn.Module):
+
+  def __init__(self):
+    super(Identity, self).__init__()
+
+  def forward(self, x):
+    return x
+
+
+class Zero(nn.Module):
+
+  def __init__(self, stride):
+    super(Zero, self).__init__()
+    self.stride = stride
+
+  def forward(self, x):
+    if self.stride == 1:
+      return x.mul(0.)
+    return x[:,:,::self.stride,::self.stride].mul(0.)
+
+
+class FactorizedReduce(nn.Module):
+
+  def __init__(self, C_in, C_out, affine=True):
+    super(FactorizedReduce, self).__init__()
+    assert C_out % 2 == 0
+    self.relu = nn.ReLU(inplace=False)
+    self.conv_1 = nn.Conv2d(C_in, C_out // 2, 1, stride=2, padding=0, bias=False)
+    self.conv_2 = nn.Conv2d(C_in, C_out // 2, 1, stride=2, padding=0, bias=False) 
+    self.bn = nn.BatchNorm2d(C_out, affine=affine)
+    self.pad = nn.ConstantPad2d((0, 1, 0, 1), 0)
+
+
+  def forward(self, x):
+    x = self.relu(x)
+    y = self.pad(x)
+    out = torch.cat([self.conv_1(x), self.conv_2(y[:,:,1:,1:])], dim=1)
+    out = self.bn(out)
+    return out