102 lines
3.1 KiB
Python
102 lines
3.1 KiB
Python
import torch
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import torch.nn as nn
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from .construct_utils import Cell, Transition
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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.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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if reduction and genotype.reduce is None:
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cell = Transition(C_prev_prev, C_prev, C_curr, reduction_prev)
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else:
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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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else:
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self.auxiliary_head = None
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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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self.drop_path_prob = -1
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def update_drop_path(self, drop_path_prob):
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self.drop_path_prob = drop_path_prob
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def auxiliary_param(self):
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if self.auxiliary_head is None: return []
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else: return list( self.auxiliary_head.parameters() )
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def forward(self, input):
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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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#print ('{:} : {:} - {:}'.format(i, s0.size(), s1.size()))
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if i == 2 * self._layers // 3:
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if self.auxiliary_head 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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if self.auxiliary_head and self.training:
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return logits, logits_aux
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else:
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return logits
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