upload

sota/cnn/operations.py

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+import torch
+import torch.nn as nn
+from torch.autograd import Variable
+
+OPS = {
+    'noise': lambda C, stride, affine: NoiseOp(stride, 0., 1.),
+    'none': lambda C, stride, affine: Zero(stride),
+    'avg_pool_3x3': lambda C, stride, affine: nn.AvgPool2d(3, stride=stride, padding=1, count_include_pad=False),
+    'max_pool_3x3' : lambda C, stride, affine: nn.MaxPool2d(3, stride=stride, padding=1),
+    '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: nn.Sequential(
+        nn.ReLU(inplace=False),
+        nn.Conv2d(C, C, (1, 7), stride=(1, stride), padding=(0, 3), bias=False),
+        nn.Conv2d(C, C, (7, 1), stride=(stride, 1), padding=(3, 0), bias=False),
+        nn.BatchNorm2d(C, affine=affine)
+    ),
+    'sep_conv_3x3_skip': lambda C, stride, affine: SepConvSkip(C, C, 3, stride, 1, affine=affine),
+    'sep_conv_5x5_skip': lambda C, stride, affine: SepConvSkip(C, C, 5, stride, 2, affine=affine),
+    'dil_conv_3x3_skip': lambda C, stride, affine: DilConvSkip(C, C, 3, stride, 2, 2, affine=affine),
+    'dil_conv_5x5_skip': lambda C, stride, affine: DilConvSkip(C, C, 5, stride, 4, 2, affine=affine),
+}
+
+
+class NoiseOp(nn.Module):
+    def __init__(self, stride, mean, std):
+        super(NoiseOp, self).__init__()
+        self.stride = stride
+        self.mean = mean
+        self.std = std
+
+    def forward(self, x, block_input=False):
+        if block_input:
+            x = x*0
+        if self.stride != 1:
+            x_new = x[:,:,::self.stride,::self.stride]
+        else:
+            x_new = x
+        noise = Variable(x_new.data.new(x_new.size()).normal_(self.mean, self.std))
+
+        return noise
+
+
+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, block_input=False):
+        if block_input:
+            x = x*0
+        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, block_input=False):
+        if block_input:
+            x = x*0
+        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, block_input=False):
+        if block_input:
+            x = x*0
+        return self.op(x)
+
+
+class Identity(nn.Module):
+
+    def __init__(self):
+        super(Identity, self).__init__()
+
+    def forward(self, x, block_input=False):
+        if block_input:
+            x = x*0
+        return x
+
+
+class Zero(nn.Module):
+
+    def __init__(self, stride):
+        super(Zero, self).__init__()
+        self.stride = stride
+
+    def forward(self, x, block_input=False):
+        if block_input:
+            x = x*0
+        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)
+
+    def forward(self, x, block_input=False):
+        if block_input:
+            x = x*0
+        x = self.relu(x)
+        out = torch.cat([self.conv_1(x), self.conv_2(x[:, :, 1:, 1:])], dim=1)
+        out = self.bn(out)
+        return out
+
+
+#### operations with skip
+class DilConvSkip(nn.Module):
+    def __init__(self, C_in, C_out, kernel_size, stride, padding, dilation, affine=True):
+        super(DilConvSkip, 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, block_input=False):
+        if block_input:
+            x = x*0
+        return self.op(x) + x
+
+
+class SepConvSkip(nn.Module):
+    def __init__(self, C_in, C_out, kernel_size, stride, padding, affine=True):
+        super(SepConvSkip, 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, block_input=False):
+        if block_input:
+            x = x*0
+        return self.op(x) + x