151 lines
5.2 KiB
Python
151 lines
5.2 KiB
Python
import sys
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sys.path.insert(0, '../../')
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# from optimizers.darts.operations import *
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from sota.cnn.operations import *
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#from optimizers.darts.utils import drop_path
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def drop_path(x, drop_prob):
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if drop_prob > 0.:
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keep_prob = 1.-drop_prob
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mask = Variable(torch.cuda.FloatTensor(x.size(0), 1, 1, 1).bernoulli_(keep_prob))
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x.div_(keep_prob)
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x.mul_(mask)
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return x
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class Cell(nn.Module):
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def __init__(self, genotype, C_prev_prev, C_prev, C, reduction, reduction_prev):
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super(Cell, self).__init__()
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print(C_prev_prev, C_prev, C)
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if reduction_prev:
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self.preprocess0 = FactorizedReduce(C_prev_prev, C)
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else:
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self.preprocess0 = ReLUConvBN(C_prev_prev, C, 1, 1, 0)
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self.preprocess1 = ReLUConvBN(C_prev, C, 1, 1, 0)
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if reduction:
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op_names, indices = zip(*genotype.reduce)
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concat = genotype.reduce_concat
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else:
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op_names, indices = zip(*genotype.normal)
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concat = genotype.normal_concat
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self._compile(C, op_names, indices, concat, reduction)
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def _compile(self, C, op_names, indices, concat, reduction):
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assert len(op_names) == len(indices)
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self._steps = len(op_names) // 2
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self._concat = concat
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self.multiplier = len(concat)
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self._ops = nn.ModuleList()
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for name, index in zip(op_names, indices):
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stride = 2 if reduction and index < 2 else 1
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op = OPS[name](C, stride, True)
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self._ops += [op]
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self._indices = indices
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def forward(self, s0, s1, drop_prob):
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s0 = self.preprocess0(s0)
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s1 = self.preprocess1(s1)
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states = [s0, s1]
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for i in range(self._steps):
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h1 = states[self._indices[2 * i]]
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h2 = states[self._indices[2 * i + 1]]
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op1 = self._ops[2 * i]
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op2 = self._ops[2 * i + 1]
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h1 = op1(h1)
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h2 = op2(h2)
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if self.training and drop_prob > 0.:
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if not isinstance(op1, Identity):
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h1 = drop_path(h1, drop_prob)
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if not isinstance(op2, Identity):
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h2 = drop_path(h2, drop_prob)
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s = h1 + h2
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states += [s]
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return torch.cat([states[i] for i in self._concat], dim=1)
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class AuxiliaryHeadImageNet(nn.Module):
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def __init__(self, C, num_classes):
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"""assuming input size 14x14"""
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super(AuxiliaryHeadImageNet, self).__init__()
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self.features = nn.Sequential(
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nn.ReLU(inplace=True),
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nn.AvgPool2d(5, stride=2, padding=0, count_include_pad=False),
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nn.Conv2d(C, 128, 1, bias=False),
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nn.BatchNorm2d(128),
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nn.ReLU(inplace=True),
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nn.Conv2d(128, 768, 2, bias=False),
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# NOTE: This batchnorm was omitted in my earlier implementation due to a typo.
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# Commenting it out for consistency with the experiments in the paper.
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# nn.BatchNorm2d(768),
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nn.ReLU(inplace=True)
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)
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self.classifier = nn.Linear(768, num_classes)
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def forward(self, x):
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x = self.features(x)
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x = self.classifier(x.view(x.size(0), -1))
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return x
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class NetworkImageNet(nn.Module):
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def __init__(self, C, num_classes, layers, auxiliary, genotype):
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super(NetworkImageNet, self).__init__()
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self._layers = layers
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self._auxiliary = auxiliary
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self.drop_path_prob = 0.0
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self.stem0 = nn.Sequential(
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nn.Conv2d(3, C // 2, kernel_size=3, stride=2, padding=1, bias=False),
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nn.BatchNorm2d(C // 2),
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nn.ReLU(inplace=True),
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nn.Conv2d(C // 2, C, 3, stride=2, padding=1, bias=False),
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nn.BatchNorm2d(C),
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)
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self.stem1 = nn.Sequential(
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nn.ReLU(inplace=True),
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nn.Conv2d(C, C, 3, stride=2, padding=1, bias=False),
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nn.BatchNorm2d(C),
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)
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C_prev_prev, C_prev, C_curr = C, C, C
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self.cells = nn.ModuleList()
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reduction_prev = True
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for i in range(layers):
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if i in [layers // 3, 2 * layers // 3]:
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C_curr *= 2
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reduction = True
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else:
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reduction = False
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cell = Cell(genotype, C_prev_prev, C_prev, C_curr, reduction, reduction_prev)
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reduction_prev = reduction
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self.cells += [cell]
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C_prev_prev, C_prev = C_prev, cell.multiplier * C_curr
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if i == 2 * layers // 3:
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C_to_auxiliary = C_prev
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if auxiliary:
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self.auxiliary_head = AuxiliaryHeadImageNet(C_to_auxiliary, num_classes)
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self.global_pooling = nn.AvgPool2d(7)
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self.classifier = nn.Linear(C_prev, num_classes)
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def forward(self, input):
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logits_aux = None
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s0 = self.stem0(input)
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s1 = self.stem1(s0)
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for i, cell in enumerate(self.cells):
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s0, s1 = s1, cell(s0, s1, self.drop_path_prob)
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if i == 2 * self._layers // 3:
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if self._auxiliary and self.training:
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logits_aux = self.auxiliary_head(s1)
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out = self.global_pooling(s1)
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logits = self.classifier(out.view(out.size(0), -1))
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return logits, logits_aux
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