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102->201 / NAS->autoDL / more configs of TAS / reorganize docs / fix bugs in NAS baselines

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This commit is contained in:
D-X-Y
2020-01-15 00:52:06 +11:00
parent 33384a78af
commit bb2f405961
62 changed files with 789 additions and 412 deletions
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30
exps/algos/R_EA.py
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@@ -1,5 +1,5 @@
##################################################
# Copyright (c) Xuanyi Dong [GitHub D-X-Y], 2019 #
# Copyright (c) Xuanyi Dong [GitHub D-X-Y], 2020 #
##################################################################
# Regularized Evolution for Image Classifier Architecture Search #
##################################################################
@@ -16,7 +16,7 @@ from datasets import get_datasets, SearchDataset
from procedures import prepare_seed, prepare_logger, save_checkpoint, copy_checkpoint, get_optim_scheduler
from utils import get_model_infos, obtain_accuracy
from log_utils import AverageMeter, time_string, convert_secs2time
from nas_102_api import NASBench102API as API
from nas_201_api import NASBench201API as API
from models import CellStructure, get_search_spaces
@@ -31,30 +31,8 @@ class Model(object):
return '{:}'.format(self.arch)
def valid_func(xloader, network, criterion):
data_time, batch_time = AverageMeter(), AverageMeter()
arch_losses, arch_top1, arch_top5 = AverageMeter(), AverageMeter(), AverageMeter()
network.train()
end = time.time()
with torch.no_grad():
for step, (arch_inputs, arch_targets) in enumerate(xloader):
arch_targets = arch_targets.cuda(non_blocking=True)
# measure data loading time
data_time.update(time.time() - end)
# prediction
_, logits = network(arch_inputs)
arch_loss = criterion(logits, arch_targets)
# record
arch_prec1, arch_prec5 = obtain_accuracy(logits.data, arch_targets.data, topk=(1, 5))
arch_losses.update(arch_loss.item(), arch_inputs.size(0))
arch_top1.update (arch_prec1.item(), arch_inputs.size(0))
arch_top5.update (arch_prec5.item(), arch_inputs.size(0))
# measure elapsed time
batch_time.update(time.time() - end)
end = time.time()
return arch_losses.avg, arch_top1.avg, arch_top5.avg
# 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.
def train_and_eval(arch, nas_bench, extra_info):
if nas_bench is not None:
arch_index = nas_bench.query_index_by_arch( arch )