98 lines
3.7 KiB
Python
98 lines
3.7 KiB
Python
##################################################
|
|
# Copyright (c) Xuanyi Dong [GitHub D-X-Y], 2019 #
|
|
########################################################
|
|
# DARTS: Differentiable Architecture Search, ICLR 2019 #
|
|
########################################################
|
|
import torch
|
|
import torch.nn as nn
|
|
from copy import deepcopy
|
|
from ..cell_operations import ResNetBasicblock
|
|
from .search_cells import NAS201SearchCell as SearchCell
|
|
from .genotypes import Structure
|
|
|
|
|
|
class TinyNetworkDarts(nn.Module):
|
|
|
|
def __init__(self, C, N, max_nodes, num_classes, search_space, affine, track_running_stats):
|
|
super(TinyNetworkDarts, self).__init__()
|
|
self._C = C
|
|
self._layerN = N
|
|
self.max_nodes = max_nodes
|
|
self.stem = nn.Sequential(
|
|
nn.Conv2d(3, C, kernel_size=3, padding=1, bias=False),
|
|
nn.BatchNorm2d(C))
|
|
|
|
layer_channels = [C ] * N + [C*2 ] + [C*2 ] * N + [C*4 ] + [C*4 ] * N
|
|
layer_reductions = [False] * N + [True] + [False] * N + [True] + [False] * N
|
|
|
|
C_prev, num_edge, edge2index = C, None, None
|
|
self.cells = nn.ModuleList()
|
|
for index, (C_curr, reduction) in enumerate(zip(layer_channels, layer_reductions)):
|
|
if reduction:
|
|
cell = ResNetBasicblock(C_prev, C_curr, 2)
|
|
else:
|
|
cell = SearchCell(C_prev, C_curr, 1, max_nodes, search_space, 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 = cell.out_dim
|
|
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_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_parameters]
|
|
|
|
def show_alphas(self):
|
|
with torch.no_grad():
|
|
return 'arch-parameters :\n{:}'.format( nn.functional.softmax(self.arch_parameters, dim=-1).cpu() )
|
|
|
|
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}, Max-Nodes={max_nodes}, N={_layerN}, L={_Layer})'.format(name=self.__class__.__name__, **self.__dict__))
|
|
|
|
def genotype(self):
|
|
genotypes = []
|
|
for i in range(1, self.max_nodes):
|
|
xlist = []
|
|
for j in range(i):
|
|
node_str = '{:}<-{:}'.format(i, j)
|
|
with torch.no_grad():
|
|
weights = self.arch_parameters[ self.edge2index[node_str] ]
|
|
op_name = self.op_names[ weights.argmax().item() ]
|
|
xlist.append((op_name, j))
|
|
genotypes.append( tuple(xlist) )
|
|
return Structure( genotypes )
|
|
|
|
def forward(self, inputs):
|
|
alphas = nn.functional.softmax(self.arch_parameters, dim=-1)
|
|
|
|
feature = self.stem(inputs)
|
|
for i, cell in enumerate(self.cells):
|
|
if isinstance(cell, SearchCell):
|
|
feature = cell(feature, alphas)
|
|
else:
|
|
feature = cell(feature)
|
|
|
|
out = self.lastact(feature)
|
|
out = self.global_pooling( out )
|
|
out = out.view(out.size(0), -1)
|
|
logits = self.classifier(out)
|
|
|
|
return out, logits
|