autodl-projects/xautodl/nas_infer_model/DXYs/CifarNet.py

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2019-09-28 10:24:47 +02:00
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]