78 lines
2.7 KiB
Python
78 lines
2.7 KiB
Python
|
import torch
|
||
|
|
import torch.nn as nn
|
||
|
|
from .construct_utils import drop_path
|
||
|
|
from .base_cells import InferCell
|
||
|
|
from .head_utils import ImageNetHEAD, AuxiliaryHeadImageNet
|
||
|
|
|
||
|
|
|
||
|
|
class NetworkImageNet(nn.Module):
|
||
|
|
|
||
|
|
def __init__(self, C, N, auxiliary, genotype, num_classes):
|
||
|
|
super(NetworkImageNet, self).__init__()
|
||
|
|
self._C = C
|
||
|
|
self._layerN = N
|
||
|
|
layer_channels = [C ] * N + [C*2 ] + [C*2 ] * N + [C*4 ] + [C*4] * N
|
||
|
|
layer_reductions = [False] * N + [True] + [False] * N + [True] + [False] * N
|
||
|
|
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, reduction_prev = C, C, C, True
|
||
|
|
|
||
|
|
self.cells = nn.ModuleList()
|
||
|
|
self.auxiliary_index = None
|
||
|
|
for i, (C_curr, reduction) in enumerate(zip(layer_channels, layer_reductions)):
|
||
|
|
cell = InferCell(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 reduction and C_curr == C*4:
|
||
|
|
C_to_auxiliary = C_prev
|
||
|
|
self.auxiliary_index = i
|
||
|
|
|
||
|
|
self._NNN = len(self.cells)
|
||
|
|
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 extra_repr(self):
|
||
|
|
return ('{name}(C={_C}, N=[{_layerN}, {_NNN}], aux-index={auxiliary_index}, drop-path={drop_path_prob})'.format(name=self.__class__.__name__, **self.__dict__))
|
||
|
|
|
||
|
|
def get_message(self):
|
||
|
|
return self.extra_repr()
|
||
|
|
|
||
|
|
def auxiliary_param(self):
|
||
|
|
if self.auxiliary_head is None: return []
|
||
|
|
else: return list( self.auxiliary_head.parameters() )
|
||
|
|
|
||
|
|
def forward(self, inputs):
|
||
|
|
s0 = self.stem0(inputs)
|
||
|
|
s1 = self.stem1(s0)
|
||
|
|
logits_aux = None
|
||
|
|
for i, cell in enumerate(self.cells):
|
||
|
|
s0, s1 = s1, cell(s0, s1, self.drop_path_prob)
|
||
|
|
if i == self.auxiliary_index and 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 logits_aux is None: return out, logits
|
||
|
|
else : return out, [logits, logits_aux]
|