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Prototype generic nas model (cont.).

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D-X-Y 2020-07-18 22:49:35 +00:00
parent 68f9d037eb
commit 7ca2ca70b4
3 changed files with 115 additions and 52 deletions
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118
exps/algos-v2/search-cell.py
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@ -4,6 +4,14 @@
# python ./exps/algos-v2/search-cell.py --dataset cifar10 --data_path $TORCH_HOME/cifar.python --algo darts-v1 --rand_seed 1
# python ./exps/algos-v2/search-cell.py --dataset cifar100 --data_path $TORCH_HOME/cifar.python --algo darts-v1
# python ./exps/algos-v2/search-cell.py --dataset ImageNet16-120 --data_path $TORCH_HOME/cifar.python/ImageNet16 --algo darts-v1
####
# python ./exps/algos-v2/search-cell.py --dataset cifar10 --data_path $TORCH_HOME/cifar.python --algo darts-v2 --rand_seed 1
# python ./exps/algos-v2/search-cell.py --dataset cifar100 --data_path $TORCH_HOME/cifar.python --algo darts-v2
# python ./exps/algos-v2/search-cell.py --dataset ImageNet16-120 --data_path $TORCH_HOME/cifar.python/ImageNet16 --algo darts-v2
####
# python ./exps/algos-v2/search-cell.py --dataset cifar10 --data_path $TORCH_HOME/cifar.python --algo gdas --rand_seed 1
# python ./exps/algos-v2/search-cell.py --dataset cifar100 --data_path $TORCH_HOME/cifar.python --algo gdas
# python ./exps/algos-v2/search-cell.py --dataset ImageNet16-120 --data_path $TORCH_HOME/cifar.python/ImageNet16 --algo gdas
######################################################################################
import os, sys, time, random, argparse
import numpy as np
@ -22,7 +30,7 @@ from models import get_cell_based_tiny_net, get_search_spaces
from nas_201_api import NASBench201API as API
def search_func(xloader, network, criterion, scheduler, w_optimizer, a_optimizer, epoch_str, print_freq, logger):
def search_func(xloader, network, criterion, scheduler, w_optimizer, a_optimizer, epoch_str, print_freq, algo, logger):
data_time, batch_time = AverageMeter(), AverageMeter()
base_losses, base_top1, base_top5 = AverageMeter(), AverageMeter(), AverageMeter()
arch_losses, arch_top1, arch_top5 = AverageMeter(), AverageMeter(), AverageMeter()
@ -30,15 +38,26 @@ def search_func(xloader, network, criterion, scheduler, w_optimizer, a_optimizer
network.train()
for step, (base_inputs, base_targets, arch_inputs, arch_targets) in enumerate(xloader):
scheduler.update(None, 1.0 * step / len(xloader))
base_inputs = base_inputs.cuda(non_blocking=True)
arch_inputs = arch_inputs.cuda(non_blocking=True)
base_targets = base_targets.cuda(non_blocking=True)
arch_targets = arch_targets.cuda(non_blocking=True)
# measure data loading time
data_time.update(time.time() - end)
# update the weights
sampled_arch = network.module.dync_genotype(True)
network.module.set_cal_mode('dynamic', sampled_arch)
#network.module.set_cal_mode( 'urs' )
# Update the weights
if algo == 'setn':
sampled_arch = network.dync_genotype(True)
network.set_cal_mode('dynamic', sampled_arch)
elif algo == 'gdas':
network.set_cal_mode('gdas', None)
elif algo.startswith('darts'):
network.set_cal_mode('joint', None)
elif algo == 'random':
network.set_cal_mode('urs', None)
else:
raise ValueError('Invalid algo name : {:}'.format(algo))
network.zero_grad()
_, logits = network(base_inputs)
base_loss = criterion(logits, base_targets)
@ -51,7 +70,16 @@ def search_func(xloader, network, criterion, scheduler, w_optimizer, a_optimizer
base_top5.update (base_prec5.item(), base_inputs.size(0))
# update the architecture-weight
network.module.set_cal_mode( 'joint' )
if algo == 'setn':
network.set_cal_mode('joint')
elif algo == 'gdas':
network.set_cal_mode('gdas', None)
elif algo.startswith('darts'):
network.set_cal_mode('joint', None)
elif algo == 'random':
network.set_cal_mode('urs', None)
else:
raise ValueError('Invalid algo name : {:}'.format(algo))
network.zero_grad()
_, logits = network(arch_inputs)
arch_loss = criterion(logits, arch_targets)
@ -73,36 +101,38 @@ def search_func(xloader, network, criterion, scheduler, w_optimizer, a_optimizer
Wstr = 'Base [Loss {loss.val:.3f} ({loss.avg:.3f}) Prec@1 {top1.val:.2f} ({top1.avg:.2f}) Prec@5 {top5.val:.2f} ({top5.avg:.2f})]'.format(loss=base_losses, top1=base_top1, top5=base_top5)
Astr = 'Arch [Loss {loss.val:.3f} ({loss.avg:.3f}) Prec@1 {top1.val:.2f} ({top1.avg:.2f}) Prec@5 {top5.val:.2f} ({top5.avg:.2f})]'.format(loss=arch_losses, top1=arch_top1, top5=arch_top5)
logger.log(Sstr + ' ' + Tstr + ' ' + Wstr + ' ' + Astr)
#print (nn.functional.softmax(network.module.arch_parameters, dim=-1))
#print (network.module.arch_parameters)
return base_losses.avg, base_top1.avg, base_top5.avg, arch_losses.avg, arch_top1.avg, arch_top5.avg
def get_best_arch(xloader, network, n_samples):
def get_best_arch(xloader, network, n_samples, algo):
with torch.no_grad():
network.eval()
archs, valid_accs = network.module.return_topK(n_samples), []
#print ('obtain the top-{:} architectures'.format(n_samples))
if algo == 'random':
archs, valid_accs = network.return_topK(n_samples, True), []
elif algo == 'setn':
archs, valid_accs = network.return_topK(n_samples, False), []
elif algo.startswith('darts') or algo == 'gdas':
arch = network.genotype
archs, valid_accs = [arch], []
else:
raise ValueError('Invalid algorithm name : {:}'.format(algo))
loader_iter = iter(xloader)
for i, sampled_arch in enumerate(archs):
network.module.set_cal_mode('dynamic', sampled_arch)
network.set_cal_mode('dynamic', sampled_arch)
try:
inputs, targets = next(loader_iter)
except:
loader_iter = iter(xloader)
inputs, targets = next(loader_iter)
_, logits = network(inputs)
_, logits = network(inputs.cuda(non_blocking=True))
val_top1, val_top5 = obtain_accuracy(logits.cpu().data, targets.data, topk=(1, 5))
valid_accs.append(val_top1.item())
best_idx = np.argmax(valid_accs)
best_arch, best_valid_acc = archs[best_idx], valid_accs[best_idx]
return best_arch, best_valid_acc
def valid_func(xloader, network, criterion):
def valid_func(xloader, network, criterion, algo, logger):
data_time, batch_time = AverageMeter(), AverageMeter()
arch_losses, arch_top1, arch_top5 = AverageMeter(), AverageMeter(), AverageMeter()
end = time.time()
@ -113,7 +143,7 @@ def valid_func(xloader, network, criterion):
# measure data loading time
data_time.update(time.time() - end)
# prediction
_, logits = network(arch_inputs)
_, logits = network(arch_inputs.cuda(non_blocking=True))
arch_loss = criterion(logits, arch_targets)
# record
arch_prec1, arch_prec5 = obtain_accuracy(logits.data, arch_targets.data, topk=(1, 5))
@ -166,7 +196,6 @@ def main(xargs):
logger.log('{:} create API = {:} done'.format(time_string(), api))
last_info, model_base_path, model_best_path = logger.path('info'), logger.path('model'), logger.path('best')
# network, criterion = torch.nn.DataParallel(search_model).cuda(), criterion.cuda()
network, criterion = search_model.cuda(), criterion.cuda() # use a single GPU
last_info, model_base_path, model_best_path = logger.path('info'), logger.path('model'), logger.path('best')
@ -185,7 +214,7 @@ def main(xargs):
logger.log("=> loading checkpoint of the last-info '{:}' start with {:}-th epoch.".format(last_info, start_epoch))
else:
logger.log("=> do not find the last-info file : {:}".format(last_info))
start_epoch, valid_accuracies, genotypes = 0, {'best': -1}, {}
start_epoch, valid_accuracies, genotypes = 0, {'best': -1}, {-1: network.return_topK(1, True)[0]}
# start training
start_time, search_time, epoch_time, total_epoch = time.time(), AverageMeter(), AverageMeter(), config.epochs + config.warmup
@ -195,28 +224,25 @@ def main(xargs):
epoch_str = '{:03d}-{:03d}'.format(epoch, total_epoch)
logger.log('\n[Search the {:}-th epoch] {:}, LR={:}'.format(epoch_str, need_time, min(w_scheduler.get_lr())))
import pdb; pdb.set_trace()
search_w_loss, search_w_top1, search_w_top5, search_a_loss, search_a_top1, search_a_top5 \
= search_func(search_loader, network, criterion, w_scheduler, w_optimizer, a_optimizer, epoch_str, xargs.print_freq, logger)
= search_func(search_loader, network, criterion, w_scheduler, w_optimizer, a_optimizer, epoch_str, xargs.print_freq, xargs.algo, logger)
search_time.update(time.time() - start_time)
logger.log('[{:}] search [base] : loss={:.2f}, accuracy@1={:.2f}%, accuracy@5={:.2f}%, time-cost={:.1f} s'.format(epoch_str, search_w_loss, search_w_top1, search_w_top5, search_time.sum))
logger.log('[{:}] search [arch] : loss={:.2f}, accuracy@1={:.2f}%, accuracy@5={:.2f}%'.format(epoch_str, search_a_loss, search_a_top1, search_a_top5))
genotype, temp_accuracy = get_best_arch(valid_loader, network, xargs.select_num)
network.module.set_cal_mode('dynamic', genotype)
valid_a_loss , valid_a_top1 , valid_a_top5 = valid_func(valid_loader, network, criterion)
genotype, temp_accuracy = get_best_arch(valid_loader, network, xargs.eval_candidate_num, xargs.algo)
if xargs.algo == 'setn':
network.set_cal_mode('dynamic', genotype)
elif xargs.algo == 'gdas':
network.set_cal_mode('gdas', None)
elif xargs.algo.startswith('darts'):
network.set_cal_mode('joint', None)
elif xargs.algo == 'random':
network.set_cal_mode('urs', None)
else:
raise ValueError('Invalid algorithm name : {:}'.format(xargs.algo))
valid_a_loss , valid_a_top1 , valid_a_top5 = valid_func(valid_loader, network, criterion, xargs.algo, logger)
logger.log('[{:}] evaluate : loss={:.2f}, accuracy@1={:.2f}%, accuracy@5={:.2f}% | {:}'.format(epoch_str, valid_a_loss, valid_a_top1, valid_a_top5, genotype))
#search_model.set_cal_mode('urs')
#valid_a_loss , valid_a_top1 , valid_a_top5 = valid_func(valid_loader, network, criterion)
#logger.log('[{:}] URS---evaluate : loss={:.2f}, accuracy@1={:.2f}%, accuracy@5={:.2f}%'.format(epoch_str, valid_a_loss, valid_a_top1, valid_a_top5))
#search_model.set_cal_mode('joint')
#valid_a_loss , valid_a_top1 , valid_a_top5 = valid_func(valid_loader, network, criterion)
#logger.log('[{:}] JOINT-evaluate : loss={:.2f}, accuracy@1={:.2f}%, accuracy@5={:.2f}%'.format(epoch_str, valid_a_loss, valid_a_top1, valid_a_top5))
#search_model.set_cal_mode('select')
#valid_a_loss , valid_a_top1 , valid_a_top5 = valid_func(valid_loader, network, criterion)
#logger.log('[{:}] Selec-evaluate : loss={:.2f}, accuracy@1={:.2f}%, accuracy@5={:.2f}%'.format(epoch_str, valid_a_loss, valid_a_top1, valid_a_top5))
# check the best accuracy
valid_accuracies[epoch] = valid_a_top1
genotypes[epoch] = genotype
@ -245,15 +271,25 @@ def main(xargs):
# the final post procedure : count the time
start_time = time.time()
genotype, temp_accuracy = get_best_arch(valid_loader, network, xargs.select_num)
genotype, temp_accuracy = get_best_arch(valid_loader, network, xargs.eval_candidate_num, xargs.algo)
if xargs.algo == 'setn':
network.set_cal_mode('dynamic', genotype)
elif xargs.algo == 'gdas':
network.set_cal_mode('gdas', None)
elif xargs.algo.startswith('darts'):
network.set_cal_mode('joint', None)
elif xargs.algo == 'random':
network.set_cal_mode('urs', None)
else:
raise ValueError('Invalid algorithm name : {:}'.format(xargs.algo))
search_time.update(time.time() - start_time)
network.module.set_cal_mode('dynamic', genotype)
valid_a_loss , valid_a_top1 , valid_a_top5 = valid_func(valid_loader, network, criterion)
valid_a_loss , valid_a_top1 , valid_a_top5 = valid_func(valid_loader, network, criterion, xargs.algo, logger)
logger.log('Last : the gentotype is : {:}, with the validation accuracy of {:.3f}%.'.format(genotype, valid_a_top1))
logger.log('\n' + '-'*100)
# check the performance from the architecture dataset
logger.log('SETN : run {:} epochs, cost {:.1f} s, last-geno is {:}.'.format(total_epoch, search_time.sum, genotype))
logger.log('[{:}] run {:} epochs, cost {:.1f} s, last-geno is {:}.'.format(xargs.algo, total_epoch, search_time.sum, genotype))
if api is not None: logger.log('{:}'.format(api.query_by_arch(genotype, '200') ))
logger.close()
@ -281,7 +317,7 @@ if __name__ == '__main__':
# log
parser.add_argument('--workers', type=int, default=2, help='number of data loading workers (default: 2)')
parser.add_argument('--save_dir', type=str, default='./output/search', help='Folder to save checkpoints and log.')
parser.add_argument('--print_freq', type=int, help='print frequency (default: 200)')
parser.add_argument('--print_freq', type=int, default=200, help='print frequency (default: 200)')
parser.add_argument('--rand_seed', type=int, help='manual seed')
args = parser.parse_args()
if args.rand_seed is None or args.rand_seed < 0: args.rand_seed = random.randint(1, 100000)

10
lib/models/cell_operations.py
View File

@ -242,6 +242,16 @@ class PartAwareOp(nn.Module):
return outputs
def drop_path(x, drop_prob):
if drop_prob > 0.:
keep_prob = 1. - drop_prob
mask = x.new_zeros(x.size(0), 1, 1, 1)
mask = mask.bernoulli_(keep_prob)
x = torch.div(x, keep_prob)
x.mul_(mask)
return x
# Searching for A Robust Neural Architecture in Four GPU Hours
class GDAS_Reduction_Cell(nn.Module):

39
lib/models/cell_searchs/generic_model.py
View File

@ -6,7 +6,7 @@ import torch.nn as nn
from copy import deepcopy
from typing import Text
from ..cell_operations import ResNetBasicblock
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
@ -48,6 +48,7 @@ class GenericNAS201Model(nn.Module):
self.dynamic_cell = None
self._tau = None
self._algo = None
self._drop_path = None
def set_algo(self, algo: Text):
# used for searching
@ -62,7 +63,7 @@ class GenericNAS201Model(nn.Module):
def set_cal_mode(self, mode, dynamic_cell=None):
assert mode in ['gdas', 'enas', 'urs', 'joint', 'select', 'dynamic']
self.mode = mode
self._mode = mode
if mode == 'dynamic': self.dynamic_cell = deepcopy(dynamic_cell)
else : self.dynamic_cell = None
@ -70,6 +71,10 @@ class GenericNAS201Model(nn.Module):
def mode(self):
return self._mode
@property
def drop_path(self):
return self._drop_path
@property
def weights(self):
xlist = list(self._stem.parameters())
@ -100,6 +105,15 @@ class GenericNAS201Model(nn.Module):
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__))
@ -112,7 +126,7 @@ class GenericNAS201Model(nn.Module):
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() ]
op_name = self._op_names[ weights.argmax().item() ]
xlist.append((op_name, j))
genotypes.append(tuple(xlist))
return Structure(genotypes)
@ -126,11 +140,11 @@ class GenericNAS201Model(nn.Module):
for j in range(i):
node_str = '{:}<-{:}'.format(i, j)
if use_random:
op_name = random.choice(self.op_names)
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 ]
op_name = self._op_names[ op_index ]
xlist.append((op_name, j))
genotypes.append(tuple(xlist))
return Structure(genotypes)
@ -142,17 +156,20 @@ class GenericNAS201Model(nn.Module):
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)
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):
archs = Structure.gen_all(self.op_names, self._max_nodes, False)
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)
sorted_pairs = sorted(pairs, key=lambda x: -x[0])
return_pairs = [sorted_pairs[_][1] for _ in range(K)]
return return_pairs
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':