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Upgrade NAS-Bench-201 to APIv1.3/FILEv1.1

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D-X-Y
2020-03-15 22:50:17 +11:00
parent c53a9ce407
commit fb76814369
20 changed files with 259 additions and 75 deletions
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21
exps/algos/R_EA.py
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@@ -33,19 +33,21 @@ class Model(object):
# This function is to mimic the training and evaluatinig procedure for a single architecture `arch`.
# The time_cost is calculated as the total training time for a few (e.g., 12 epochs) plus the evaluation time for one epoch.
# For use_converged_LR = True, the architecture is trained for 12 epochs, with LR being decaded from 0.1 to 0.
# For use_012_epoch_training = True, the architecture is trained for 12 epochs, with LR being decaded from 0.1 to 0.
# In this case, the LR schedular is converged.
# For use_converged_LR = False, the architecture is planed to be trained for 200 epochs, but we early stop its procedure.
# For use_012_epoch_training = False, the architecture is planed to be trained for 200 epochs, but we early stop its procedure.
#
def train_and_eval(arch, nas_bench, extra_info, dataname='cifar10-valid', use_converged_LR=True):
if use_converged_LR and nas_bench is not None:
def train_and_eval(arch, nas_bench, extra_info, dataname='cifar10-valid', use_012_epoch_training=True):
if use_012_epoch_training and nas_bench is not None:
arch_index = nas_bench.query_index_by_arch( arch )
assert arch_index >= 0, 'can not find this arch : {:}'.format(arch)
info = nas_bench.get_more_info(arch_index, dataname, None, True)
valid_acc, time_cost = info['valid-accuracy'], info['train-all-time'] + info['valid-per-time']
#_, valid_acc = info.get_metrics('cifar10-valid', 'x-valid' , 25, True) # use the validation accuracy after 25 training epochs
elif not use_converged_LR and nas_bench is not None:
# Please use `use_converged_LR=False` for cifar10 only.
elif not use_012_epoch_training and nas_bench is not None:
# Please contact me if you want to use the following logic, because it has some potential issues.
# Please use `use_012_epoch_training=False` for cifar10 only.
# It did return values for cifar100 and ImageNet16-120, but it has some potential issues. (Please email me for more details)
arch_index, nepoch = nas_bench.query_index_by_arch( arch ), 25
assert arch_index >= 0, 'can not find this arch : {:}'.format(arch)
@@ -64,7 +66,7 @@ def train_and_eval(arch, nas_bench, extra_info, dataname='cifar10-valid', use_co
try:
valid_acc, time_cost = info['valid-accuracy'], estimated_train_cost + estimated_valid_cost
except:
valid_acc, time_cost = info['est-valid-accuracy'], estimated_train_cost + estimated_valid_cost
valid_acc, time_cost = info['valtest-accuracy'], estimated_train_cost + estimated_valid_cost
else:
# train a model from scratch.
raise ValueError('NOT IMPLEMENT YET')
@@ -127,7 +129,7 @@ def regularized_evolution(cycles, population_size, sample_size, time_budget, ran
while len(population) < population_size:
model = Model()
model.arch = random_arch()
model.accuracy, time_cost = train_and_eval(model.arch, nas_bench, extra_info)
model.accuracy, time_cost = train_and_eval(model.arch, nas_bench, extra_info, dataname)
population.append(model)
history.append(model)
total_time_cost += time_cost
@@ -152,7 +154,7 @@ def regularized_evolution(cycles, population_size, sample_size, time_budget, ran
child = Model()
child.arch = mutate_arch(parent.arch)
total_time_cost += time.time() - start_time
child.accuracy, time_cost = train_and_eval(child.arch, nas_bench, extra_info)
child.accuracy, time_cost = train_and_eval(child.arch, nas_bench, extra_info, dataname)
if total_time_cost + time_cost > time_budget: # return
return history, total_time_cost
else:
@@ -174,7 +176,6 @@ def main(xargs, nas_bench):
prepare_seed(xargs.rand_seed)
logger = prepare_logger(args)
assert xargs.dataset == 'cifar10', 'currently only support CIFAR-10'
if xargs.dataset == 'cifar10':
dataname = 'cifar10-valid'
else: