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# Copyright (c) Xuanyi Dong [GitHub D-X-Y], 2020.07 #
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import torch, random
import torch.nn as nn
from copy import deepcopy
from typing import Text
from ..cell_operations import ResNetBasicblock, drop_path
from .search_cells import NAS201SearchCell as SearchCell
from .genotypes import Structure
from .search_model_enas_utils import Controller
class GenericNAS201Model(nn.Module):
def __init__(self, C, N, max_nodes, num_classes, search_space, affine, track_running_stats):
super(GenericNAS201Model, 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._num_edge = num_edge
# algorithm related
self.arch_parameters = nn.Parameter( 1e-3*torch.randn(num_edge, len(search_space)) )
self._mode = None
self.dynamic_cell = None
self._tau = None
self._algo = None
self._drop_path = None
def set_algo(self, algo: Text):
# used for searching
assert self._algo is None, 'This functioin can only be called once.'
self._algo = algo
if algo == 'enas':
self.controller = Controller(len(self.edge2index), len(self._op_names))
else:
self.arch_parameters = nn.Parameter( 1e-3*torch.randn(self._num_edge, len(self._op_names)) )
if algo == 'gdas':
self._tau = 10
def set_cal_mode(self, mode, dynamic_cell=None):
assert mode in ['gdas', 'enas', 'urs', 'joint', 'select', 'dynamic']
self._mode = mode
if mode == 'dynamic': self.dynamic_cell = deepcopy(dynamic_cell)
else : self.dynamic_cell = None
@property
def mode(self):
return self._mode
@property
def drop_path(self):
return self._drop_path
@property
def weights(self):
xlist = list(self._stem.parameters())
xlist+= list(self._cells.parameters())
xlist+= list(self.lastact.parameters())
xlist+= list(self.global_pooling.parameters())
xlist+= list(self.classifier.parameters())
return xlist
def set_tau(self, tau):
self._tau = tau
@property
def tau(self):
return self._tau
@property
def alphas(self):
if self._algo == 'enas':
return list(self.controller.parameters())
else:
return [self.arch_parameters]
@property
def 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 show_alphas(self):
with torch.no_grad():
if self._algo == 'enas':
import pdb; pdb.set_trace()
print('-')
else:
return 'arch-parameters :\n{:}'.format( nn.functional.softmax(self.arch_parameters, dim=-1).cpu() )
def extra_repr(self):
return ('{name}(C={_C}, Max-Nodes={_max_nodes}, N={_layerN}, L={_Layer}, alg={_algo})'.format(name=self.__class__.__name__, **self.__dict__))
@property
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 dync_genotype(self, use_random=False):
genotypes = []
with torch.no_grad():
alphas_cpu = nn.functional.softmax(self.arch_parameters, dim=-1)
for i in range(1, self._max_nodes):
xlist = []
for j in range(i):
node_str = '{:}<-{:}'.format(i, j)
if use_random:
op_name = random.choice(self._op_names)
else:
weights = alphas_cpu[ self.edge2index[node_str] ]
op_index = torch.multinomial(weights, 1).item()
op_name = self._op_names[ op_index ]
xlist.append((op_name, j))
genotypes.append(tuple(xlist))
return Structure(genotypes)
def get_log_prob(self, arch):
with torch.no_grad():
logits = nn.functional.log_softmax(self.arch_parameters, dim=-1)
select_logits = []
for i, node_info in enumerate(arch.nodes):
for op, xin in node_info:
node_str = '{:}<-{:}'.format(i+1, xin)
op_index = self._op_names.index(op)
select_logits.append( logits[self.edge2index[node_str], op_index] )
return sum(select_logits).item()
def return_topK(self, K, use_random=False):
archs = Structure.gen_all(self._op_names, self._max_nodes, False)
pairs = [(self.get_log_prob(arch), arch) for arch in archs]
if K < 0 or K >= len(archs): K = len(archs)
if use_random:
return random.sample(archs, K)
else:
sorted_pairs = sorted(pairs, key=lambda x: -x[0])
return_pairs = [sorted_pairs[_][1] for _ in range(K)]
return return_pairs
def normalize_archp(self):
if self.mode == 'gdas':
while True:
gumbels = -torch.empty_like(self.arch_parameters).exponential_().log()
logits = (self.arch_parameters.log_softmax(dim=1) + gumbels) / self.tau
probs = nn.functional.softmax(logits, dim=1)
index = probs.max(-1, keepdim=True)[1]
one_h = torch.zeros_like(logits).scatter_(-1, index, 1.0)
hardwts = one_h - probs.detach() + probs
if (torch.isinf(gumbels).any()) or (torch.isinf(probs).any()) or (torch.isnan(probs).any()):
continue
else: break
with torch.no_grad():
hardwts_cpu = hardwts.detach().cpu()
return hardwts, hardwts_cpu, index
else:
alphas = nn.functional.softmax(self.arch_parameters, dim=-1)
index = alphas.max(-1, keepdim=True)[1]
with torch.no_grad():
alphas_cpu = alphas.detach().cpu()
return alphas, alphas_cpu, index
def forward(self, inputs):
alphas, alphas_cpu, index = self.normalize_archp()
feature = self._stem(inputs)
for i, cell in enumerate(self._cells):
if isinstance(cell, SearchCell):
if self.mode == 'urs':
feature = cell.forward_urs(feature)
elif self.mode == 'select':
feature = cell.forward_select(feature, alphas_cpu)
elif self.mode == 'joint':
feature = cell.forward_joint(feature, alphas)
elif self.mode == 'dynamic':
feature = cell.forward_dynamic(feature, self.dynamic_cell)
elif self.mode == 'gdas':
feature = cell.forward_gdas(feature, alphas, index)
else: raise ValueError('invalid mode={:}'.format(self.mode))
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