105 lines
3.2 KiB
Python
105 lines
3.2 KiB
Python
import torch
|
|
import torch.nn as nn
|
|
from .construct_utils import Cell, Transition
|
|
|
|
class AuxiliaryHeadImageNet(nn.Module):
|
|
|
|
def __init__(self, C, num_classes):
|
|
"""assuming input size 14x14"""
|
|
super(AuxiliaryHeadImageNet, self).__init__()
|
|
self.features = nn.Sequential(
|
|
nn.ReLU(inplace=True),
|
|
nn.AvgPool2d(5, stride=2, 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),
|
|
# NOTE: This batchnorm was omitted in my earlier implementation due to a typo.
|
|
# Commenting it out for consistency with the experiments in the paper.
|
|
# 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 NetworkImageNet(nn.Module):
|
|
|
|
def __init__(self, C, num_classes, layers, auxiliary, genotype):
|
|
super(NetworkImageNet, self).__init__()
|
|
self._layers = layers
|
|
|
|
self.stem0 = nn.Sequential(
|
|
nn.Conv2d(3, C // 2, kernel_size=3, stride=2, padding=1, bias=False),
|
|
nn.BatchNorm2d(C // 2),
|
|
nn.ReLU(inplace=True),
|
|
nn.Conv2d(C // 2, C, 3, stride=2, padding=1, bias=False),
|
|
nn.BatchNorm2d(C),
|
|
)
|
|
|
|
self.stem1 = nn.Sequential(
|
|
nn.ReLU(inplace=True),
|
|
nn.Conv2d(C, C, 3, stride=2, padding=1, bias=False),
|
|
nn.BatchNorm2d(C),
|
|
)
|
|
|
|
C_prev_prev, C_prev, C_curr = C, C, C
|
|
|
|
self.cells = nn.ModuleList()
|
|
reduction_prev = True
|
|
for i in range(layers):
|
|
if i in [layers // 3, 2 * layers // 3]:
|
|
C_curr *= 2
|
|
reduction = True
|
|
else:
|
|
reduction = False
|
|
if reduction and genotype.reduce is None:
|
|
cell = Transition(C_prev_prev, C_prev, C_curr, reduction_prev)
|
|
else:
|
|
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 = AuxiliaryHeadImageNet(C_to_auxiliary, num_classes)
|
|
else:
|
|
self.auxiliary_head = None
|
|
self.global_pooling = nn.AvgPool2d(7)
|
|
self.classifier = nn.Linear(C_prev, num_classes)
|
|
self.drop_path_prob = -1
|
|
|
|
def update_drop_path(self, drop_path_prob):
|
|
self.drop_path_prob = drop_path_prob
|
|
|
|
def get_drop_path(self):
|
|
return self.drop_path_prob
|
|
|
|
def auxiliary_param(self):
|
|
if self.auxiliary_head is None: return []
|
|
else: return list( self.auxiliary_head.parameters() )
|
|
|
|
def forward(self, input):
|
|
s0 = self.stem0(input)
|
|
s1 = self.stem1(s0)
|
|
for i, cell in enumerate(self.cells):
|
|
s0, s1 = s1, cell(s0, s1, self.drop_path_prob)
|
|
#print ('{:} : {:} - {:}'.format(i, s0.size(), s1.size()))
|
|
if i == 2 * self._layers // 3:
|
|
if self.auxiliary_head and self.training:
|
|
logits_aux = self.auxiliary_head(s1)
|
|
out = self.global_pooling(s1)
|
|
logits = self.classifier(out.view(out.size(0), -1))
|
|
if self.auxiliary_head and self.training:
|
|
return logits, logits_aux
|
|
else:
|
|
return logits
|