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update DARTS-V1

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This commit is contained in:
D-X-Y 2020-01-18 22:47:10 +11:00
parent 28d354880c
commit 1ce3249a5a
2 changed files with 46 additions and 3 deletions
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7
exps/algos/DARTS-V1.py
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@ -20,7 +20,7 @@ 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, logger, gradient_clip):
data_time, batch_time = AverageMeter(), AverageMeter()
base_losses, base_top1, base_top5 = AverageMeter(), AverageMeter(), AverageMeter()
arch_losses, arch_top1, arch_top5 = AverageMeter(), AverageMeter(), AverageMeter()
@ -38,7 +38,7 @@ def search_func(xloader, network, criterion, scheduler, w_optimizer, a_optimizer
_, logits = network(base_inputs)
base_loss = criterion(logits, base_targets)
base_loss.backward()
torch.nn.utils.clip_grad_norm_(network.parameters(), 5)
if gradient_clip > 0: torch.nn.utils.clip_grad_norm_(network.parameters(), gradient_clip)
w_optimizer.step()
# record
base_prec1, base_prec5 = obtain_accuracy(logits.data, base_targets.data, topk=(1, 5))
@ -165,7 +165,7 @@ 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())))
search_w_loss, search_w_top1, search_w_top5 = search_func(search_loader, network, criterion, w_scheduler, w_optimizer, a_optimizer, epoch_str, xargs.print_freq, logger)
search_w_loss, search_w_top1, search_w_top5 = search_func(search_loader, network, criterion, w_scheduler, w_optimizer, a_optimizer, epoch_str, xargs.print_freq, logger, xargs.gradient_clip)
search_time.update(time.time() - start_time)
logger.log('[{:}] searching : 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))
valid_a_loss , valid_a_top1 , valid_a_top5 = valid_func(valid_loader, network, criterion)
@ -225,6 +225,7 @@ if __name__ == '__main__':
parser.add_argument('--track_running_stats',type=int, choices=[0,1],help='Whether use track_running_stats or not in the BN layer.')
parser.add_argument('--config_path', type=str, help='The config path.')
parser.add_argument('--model_config', type=str, help='The path of the model configuration. When this arg is set, it will cover max_nodes / channels / num_cells.')
parser.add_argument('--gradient_clip', type=float, default=5, help='')
# architecture leraning rate
parser.add_argument('--arch_learning_rate', type=float, default=3e-4, help='learning rate for arch encoding')
parser.add_argument('--arch_weight_decay', type=float, default=1e-3, help='weight decay for arch encoding')

42
scripts-search/algos/DARTS-test-Gradient.sh Normal file
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@ -0,0 +1,42 @@
#!/bin/bash
# bash ./scripts-search/algos/DARTS-test-Gradient.sh cifar10 0 5
echo script name: $0
echo $# arguments
if [ "$#" -ne 3 ] ;then
echo "Input illegal number of parameters " $#
echo "Need 3 parameters for dataset, tracking_status, and gradient_clip"
exit 1
fi
if [ "$TORCH_HOME" = "" ]; then
echo "Must set TORCH_HOME envoriment variable for data dir saving"
exit 1
else
echo "TORCH_HOME : $TORCH_HOME"
fi
dataset=$1
BN=$2
gradient_clip=$3
seed=-1
channel=16
num_cells=5
max_nodes=4
space=nas-bench-201
if [ "$dataset" == "cifar10" ] || [ "$dataset" == "cifar100" ]; then
data_path="$TORCH_HOME/cifar.python"
else
data_path="$TORCH_HOME/cifar.python/ImageNet16"
fi
save_dir=./output/search-cell-${space}/DARTS-V1-${dataset}-BN${BN}-Gradient${gradient_clip}
OMP_NUM_THREADS=4 python ./exps/algos/DARTS-V1.py \
--save_dir ${save_dir} --max_nodes ${max_nodes} --channel ${channel} --num_cells ${num_cells} \
--dataset ${dataset} --data_path ${data_path} \
--search_space_name ${space} \
--config_path configs/nas-benchmark/algos/DARTS.config \
--arch_nas_dataset ${TORCH_HOME}/NAS-Bench-201-v1_0-e61699.pth \
--track_running_stats ${BN} --gradient_clip ${gradient_clip} \
--arch_learning_rate 0.0003 --arch_weight_decay 0.001 \
--workers 4 --print_freq 200 --rand_seed ${seed}