108 lines
4.6 KiB
Python
108 lines
4.6 KiB
Python
|
####################
|
||
|
|
# DARTS, ICLR 2019 #
|
||
|
|
####################
|
||
|
|
import torch
|
||
|
|
import torch.nn as nn
|
||
|
|
from copy import deepcopy
|
||
|
|
from .search_cells import NASNetSearchCell as SearchCell
|
||
|
|
from .genotypes import Structure
|
||
|
|
|
||
|
|
|
||
|
|
# The macro structure is based on NASNet
|
||
|
|
class NASNetworkDARTS(nn.Module):
|
||
|
|
|
||
|
|
def __init__(self, C, N, steps, multiplier, stem_multiplier, num_classes, search_space, affine, track_running_stats):
|
||
|
|
super(NASNetworkDARTS, self).__init__()
|
||
|
|
self._C = C
|
||
|
|
self._layerN = N
|
||
|
|
self._steps = steps
|
||
|
|
self._multiplier = multiplier
|
||
|
|
self.stem = nn.Sequential(
|
||
|
|
nn.Conv2d(3, C*stem_multiplier, kernel_size=3, padding=1, bias=False),
|
||
|
|
nn.BatchNorm2d(C*stem_multiplier))
|
||
|
|
|
||
|
|
# config for each layer
|
||
|
|
layer_channels = [C ] * N + [C*2 ] + [C*2 ] * (N-1) + [C*4 ] + [C*4 ] * (N-1)
|
||
|
|
layer_reductions = [False] * N + [True] + [False] * (N-1) + [True] + [False] * (N-1)
|
||
|
|
|
||
|
|
num_edge, edge2index = None, None
|
||
|
|
C_prev_prev, C_prev, C_curr, reduction_prev = C*stem_multiplier, C*stem_multiplier, C, False
|
||
|
|
|
||
|
|
self.cells = nn.ModuleList()
|
||
|
|
for index, (C_curr, reduction) in enumerate(zip(layer_channels, layer_reductions)):
|
||
|
|
cell = SearchCell(search_space, steps, multiplier, C_prev_prev, C_prev, C_curr, reduction, reduction_prev, affine, track_running_stats)
|
||
|
|
if num_edge is None: num_edge, edge2index = cell.num_edges, cell.edge2index
|
||
|
|
else: assert num_edge == cell.num_edges and edge2index == cell.edge2index, 'invalid {:} vs. {:}.'.format(num_edge, cell.num_edges)
|
||
|
|
self.cells.append( cell )
|
||
|
|
C_prev_prev, C_prev, reduction_prev = C_prev, multiplier*C_curr, reduction
|
||
|
|
self.op_names = deepcopy( search_space )
|
||
|
|
self._Layer = len(self.cells)
|
||
|
|
self.edge2index = edge2index
|
||
|
|
self.lastact = nn.Sequential(nn.BatchNorm2d(C_prev), nn.ReLU(inplace=True))
|
||
|
|
self.global_pooling = nn.AdaptiveAvgPool2d(1)
|
||
|
|
self.classifier = nn.Linear(C_prev, num_classes)
|
||
|
|
self.arch_normal_parameters = nn.Parameter( 1e-3*torch.randn(num_edge, len(search_space)) )
|
||
|
|
self.arch_reduce_parameters = nn.Parameter( 1e-3*torch.randn(num_edge, len(search_space)) )
|
||
|
|
|
||
|
|
def get_weights(self):
|
||
|
|
xlist = list( self.stem.parameters() ) + list( self.cells.parameters() )
|
||
|
|
xlist+= list( self.lastact.parameters() ) + list( self.global_pooling.parameters() )
|
||
|
|
xlist+= list( self.classifier.parameters() )
|
||
|
|
return xlist
|
||
|
|
|
||
|
|
def get_alphas(self):
|
||
|
|
return [self.arch_normal_parameters, self.arch_reduce_parameters]
|
||
|
|
|
||
|
|
def show_alphas(self):
|
||
|
|
with torch.no_grad():
|
||
|
|
A = 'arch-normal-parameters :\n{:}'.format( nn.functional.softmax(self.arch_normal_parameters, dim=-1).cpu() )
|
||
|
|
B = 'arch-reduce-parameters :\n{:}'.format( nn.functional.softmax(self.arch_reduce_parameters, dim=-1).cpu() )
|
||
|
|
return '{:}\n{:}'.format(A, B)
|
||
|
|
|
||
|
|
def get_message(self):
|
||
|
|
string = self.extra_repr()
|
||
|
|
for i, cell in enumerate(self.cells):
|
||
|
|
string += '\n {:02d}/{:02d} :: {:}'.format(i, len(self.cells), cell.extra_repr())
|
||
|
|
return string
|
||
|
|
|
||
|
|
def extra_repr(self):
|
||
|
|
return ('{name}(C={_C}, N={_layerN}, steps={_steps}, multiplier={_multiplier}, L={_Layer})'.format(name=self.__class__.__name__, **self.__dict__))
|
||
|
|
|
||
|
|
def genotype(self):
|
||
|
|
def _parse(weights):
|
||
|
|
gene = []
|
||
|
|
for i in range(self._steps):
|
||
|
|
edges = []
|
||
|
|
for j in range(2+i):
|
||
|
|
node_str = '{:}<-{:}'.format(i, j)
|
||
|
|
ws = weights[ self.edge2index[node_str] ]
|
||
|
|
for k, op_name in enumerate(self.op_names):
|
||
|
|
if op_name == 'none': continue
|
||
|
|
edges.append( (op_name, j, ws[k]) )
|
||
|
|
edges = sorted(edges, key=lambda x: -x[-1])
|
||
|
|
selected_edges = edges[:2]
|
||
|
|
gene.append( tuple(selected_edges) )
|
||
|
|
return gene
|
||
|
|
with torch.no_grad():
|
||
|
|
gene_normal = _parse(torch.softmax(self.arch_normal_parameters, dim=-1).cpu().numpy())
|
||
|
|
gene_reduce = _parse(torch.softmax(self.arch_reduce_parameters, dim=-1).cpu().numpy())
|
||
|
|
return {'normal': gene_normal, 'normal_concat': list(range(2+self._steps-self._multiplier, self._steps+2)),
|
||
|
|
'reduce': gene_reduce, 'reduce_concat': list(range(2+self._steps-self._multiplier, self._steps+2))}
|
||
|
|
|
||
|
|
def forward(self, inputs):
|
||
|
|
|
||
|
|
normal_w = nn.functional.softmax(self.arch_normal_parameters, dim=1)
|
||
|
|
reduce_w = nn.functional.softmax(self.arch_reduce_parameters, dim=1)
|
||
|
|
|
||
|
|
s0 = s1 = self.stem(inputs)
|
||
|
|
for i, cell in enumerate(self.cells):
|
||
|
|
if cell.reduction: ww = reduce_w
|
||
|
|
else : ww = normal_w
|
||
|
|
s0, s1 = s1, cell.forward_darts(s0, s1, ww)
|
||
|
|
out = self.lastact(s1)
|
||
|
|
out = self.global_pooling( out )
|
||
|
|
out = out.view(out.size(0), -1)
|
||
|
|
logits = self.classifier(out)
|
||
|
|
|
||
|
|
return out, logits
|