77 lines
2.8 KiB
Python
77 lines
2.8 KiB
Python
import torch
|
|
import torch.nn as nn
|
|
from .construct_utils import drop_path
|
|
from .head_utils import CifarHEAD, AuxiliaryHeadCIFAR
|
|
from .base_cells import InferCell
|
|
|
|
|
|
class NetworkCIFAR(nn.Module):
|
|
|
|
def __init__(self, C, N, stem_multiplier, auxiliary, genotype, num_classes):
|
|
super(NetworkCIFAR, self).__init__()
|
|
self._C = C
|
|
self._layerN = N
|
|
self._stem_multiplier = stem_multiplier
|
|
|
|
C_curr = self._stem_multiplier * C
|
|
self.stem = CifarHEAD(C_curr)
|
|
|
|
layer_channels = [C ] * N + [C*2 ] + [C*2 ] * N + [C*4 ] + [C*4 ] * N
|
|
layer_reductions = [False] * N + [True] + [False] * N + [True] + [False] * N
|
|
block_indexs = [0 ] * N + [-1 ] + [1 ] * N + [-1 ] + [2 ] * N
|
|
block2index = {0:[], 1:[], 2:[]}
|
|
|
|
C_prev_prev, C_prev, C_curr = C_curr, C_curr, C
|
|
reduction_prev, spatial, dims = False, 1, []
|
|
self.auxiliary_index = None
|
|
self.auxiliary_head = None
|
|
self.cells = nn.ModuleList()
|
|
for index, (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.append( cell )
|
|
C_prev_prev, C_prev = C_prev, cell._multiplier*C_curr
|
|
if reduction and C_curr == C*4:
|
|
if auxiliary:
|
|
self.auxiliary_head = AuxiliaryHeadCIFAR(C_prev, num_classes)
|
|
self.auxiliary_index = index
|
|
|
|
if reduction: spatial *= 2
|
|
dims.append( (C_prev, spatial) )
|
|
|
|
self._Layer= len(self.cells)
|
|
|
|
|
|
self.global_pooling = nn.AdaptiveAvgPool2d(1)
|
|
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 auxiliary_param(self):
|
|
if self.auxiliary_head is None: return []
|
|
else: return list( self.auxiliary_head.parameters() )
|
|
|
|
def get_message(self):
|
|
return self.extra_repr()
|
|
|
|
def extra_repr(self):
|
|
return ('{name}(C={_C}, N={_layerN}, L={_Layer}, stem={_stem_multiplier}, drop-path={drop_path_prob})'.format(name=self.__class__.__name__, **self.__dict__))
|
|
|
|
def forward(self, inputs):
|
|
stem_feature, logits_aux = self.stem(inputs), None
|
|
cell_results = [stem_feature, stem_feature]
|
|
for i, cell in enumerate(self.cells):
|
|
cell_feature = cell(cell_results[-2], cell_results[-1], self.drop_path_prob)
|
|
cell_results.append( cell_feature )
|
|
|
|
if self.auxiliary_index is not None and i == self.auxiliary_index and self.training:
|
|
logits_aux = self.auxiliary_head( cell_results[-1] )
|
|
out = self.global_pooling( cell_results[-1] )
|
|
out = out.view(out.size(0), -1)
|
|
logits = self.classifier(out)
|
|
|
|
if logits_aux is None: return out, logits
|
|
else : return out, [logits, logits_aux]
|