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sota/cnn/model.py

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+import torch
+import torch.nn as nn
+
+from sota.cnn.operations import *
+import sys
+sys.path.insert(0, '../../')
+from nasbench201.utils import drop_path
+
+
+class Cell(nn.Module):
+
+    def __init__(self, genotype, C_prev_prev, C_prev, C, reduction, reduction_prev):
+        super(Cell, self).__init__()
+        if reduction_prev:
+            self.preprocess0 = FactorizedReduce(C_prev_prev, C)
+        else:
+            self.preprocess0 = ReLUConvBN(C_prev_prev, C, 1, 1, 0)
+        self.preprocess1 = ReLUConvBN(C_prev, C, 1, 1, 0)
+
+
+        if reduction:
+            op_names, indices = zip(*genotype.reduce)
+            concat = genotype.reduce_concat
+        else:
+            op_names, indices = zip(*genotype.normal)
+            concat = genotype.normal_concat
+        self._compile(C, op_names, indices, concat, reduction)
+
+    def _compile(self, C, op_names, indices, concat, reduction):
+        assert len(op_names) == len(indices)
+        self._steps = len(op_names) // 2
+        self._concat = concat
+        self.multiplier = len(concat)
+
+        self._ops = nn.ModuleList()
+        for name, index in zip(op_names, indices):
+            stride = 2 if reduction and index < 2 else 1
+            op = OPS[name](C, stride, True)
+            self._ops += [op]
+        self._indices = indices
+
+    def forward(self, s0, s1, drop_prob):
+        s0 = self.preprocess0(s0)
+        s1 = self.preprocess1(s1)
+
+        states = [s0, s1]
+        for i in range(self._steps):
+            h1 = states[self._indices[2*i]]
+            h2 = states[self._indices[2*i+1]]
+            op1 = self._ops[2*i]
+            op2 = self._ops[2*i+1]
+            h1 = op1(h1)
+            h2 = op2(h2)
+            if self.training and drop_prob > 0.:
+                if not isinstance(op1, Identity):
+                    h1 = drop_path(h1, drop_prob)
+                if not isinstance(op2, Identity):
+                    h2 = drop_path(h2, drop_prob)
+            s = h1 + h2
+            states += [s]
+        return torch.cat([states[i] for i in self._concat], dim=1)
+
+
+class AuxiliaryHead(nn.Module):
+
+    def __init__(self, C, num_classes):
+        """assuming input size 8x8"""
+        super(AuxiliaryHead, self).__init__()
+        self.features = nn.Sequential(
+            nn.ReLU(inplace=True),
+            # image size = 2 x 2
+            nn.AvgPool2d(5, stride=3, padding=0, count_include_pad=False),
+            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
+
+
+class Network(nn.Module):
+
+    def __init__(self, C, num_classes, layers, auxiliary, genotype):
+        super(Network, self).__init__()
+        self._layers = layers
+        self._auxiliary = auxiliary
+
+        stem_multiplier = 3
+        C_curr = stem_multiplier*C
+        self.stem = nn.Sequential(
+            nn.Conv2d(3, C_curr, 3, padding=1, bias=False),
+            nn.BatchNorm2d(C_curr)
+        )
+
+        C_prev_prev, C_prev, C_curr = C_curr, C_curr, C
+        self.cells = nn.ModuleList()
+        reduction_prev = False
+        for i in range(layers):
+            if i in [layers//3, 2*layers//3]:
+                C_curr *= 2
+                reduction = True
+            else:
+                reduction = False
+            cell = Cell(genotype, C_prev_prev, C_prev, C_curr, reduction, reduction_prev)
+            reduction_prev = reduction
+            self.cells += [cell]
+            C_prev_prev, C_prev = C_prev, cell.multiplier*C_curr
+            if i == 2*layers//3:
+                C_to_auxiliary = C_prev
+
+        if auxiliary:
+            self.auxiliary_head = AuxiliaryHead(C_to_auxiliary, num_classes)
+        self.global_pooling = nn.AdaptiveAvgPool2d(1)
+        self.classifier = nn.Linear(C_prev, num_classes)
+
+    def forward(self, input):
+        logits_aux = None
+        s0 = s1 = self.stem(input)
+        for i, cell in enumerate(self.cells):
+            s0, s1 = s1, cell(s0, s1, self.drop_path_prob)
+            if i == 2*self._layers//3:
+                if self._auxiliary and self.training:
+                    logits_aux = self.auxiliary_head(s1)
+        out = self.global_pooling(s1)
+        logits = self.classifier(out.view(out.size(0), -1))
+        return logits, logits_aux