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refactor eval_semantic_segmentation #875

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yuyu2172 merged 3 commits into from
May 20, 2019
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refactor eval_semantic_segmentation #875

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0557b8a
refactor eval_semantic_segmentation
knorth55 May 18, 2019
c08eee7
use batchsize in eval_semantic_segmentation.py
knorth55 May 19, 2019
4843cd8
use batchsize in eval_semantic_segmentation_multi.py
knorth55 May 19, 2019
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110 changes: 55 additions & 55 deletions examples/semantic_segmentation/eval_semantic_segmentation.py
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Original file line number Diff line number Diff line change
Expand Up @@ -21,8 +21,16 @@
from chainercv.utils import ProgressHook


def get_dataset_and_model(dataset_name, model_name, pretrained_model,
input_size):
models = {
'pspnet_resnet50': (PSPNetResNet50, {}, 1),
'pspnet_resnet101': (PSPNetResNet101, {}, 1),
'segnet': (SegNetBasic, {}, 1),
'deeplab_v3plus_xception65': (DeepLabV3plusXception65, {}, 1),
}


def setup(dataset, model, pretrained_model, batchsize, input_size):
dataset_name = dataset
if dataset_name == 'cityscapes':
dataset = CityscapesSemanticSegmentationDataset(
split='val', label_resolution='fine')
Expand All @@ -37,81 +45,73 @@ def get_dataset_and_model(dataset_name, model_name, pretrained_model,
dataset = VOCSemanticSegmentationDataset(split='val')
label_names = voc_semantic_segmentation_label_names

n_class = len(label_names)

if pretrained_model:
pretrained_model = pretrained_model
def eval_(out_values, rest_values):
pred_labels, = out_values
gt_labels, = rest_values

result = eval_semantic_segmentation(pred_labels, gt_labels)

for iu, label_name in zip(result['iou'], label_names):
print('{:>23} : {:.4f}'.format(label_name, iu))
print('=' * 34)
print('{:>23} : {:.4f}'.format('mean IoU', result['miou']))
print('{:>23} : {:.4f}'.format(
'Class average accuracy', result['mean_class_accuracy']))
print('{:>23} : {:.4f}'.format(
'Global average accuracy', result['pixel_accuracy']))

cls, pretrained_models, default_batchsize = models[model]
if pretrained_model is None:
pretrained_model = pretrained_models.get(dataset_name, dataset_name)
if input_size is None:
input_size = None
else:
pretrained_model = dataset_name
if model_name == 'pspnet_resnet101':
model = PSPNetResNet101(
n_class=n_class,
pretrained_model=pretrained_model,
input_size=input_size
)
elif model_name == 'pspnet_resnet50':
model = PSPNetResNet50(
n_class=n_class,
pretrained_model=pretrained_model,
input_size=input_size
)
elif model_name == 'segnet':
model = SegNetBasic(
n_class=n_class, pretrained_model=pretrained_model)
elif model_name == 'deeplab_v3plus_xception65':
model = DeepLabV3plusXception65(
n_class=n_class,
pretrained_model=pretrained_model,
min_input_size=input_size)

return dataset, label_names, model
input_size = (input_size, input_size)

kwargs = {
'n_class': len(label_names),
'pretrained_model': pretrained_model,
}
if model in ['pspnet_resnet50', 'pspnet_resnet101']:
kwargs.update({'input_size': input_size})
elif model == 'deeplab_v3plus_xception65':
kwargs.update({'min_input_size': input_size})
model = cls(**kwargs)

if batchsize is None:
batchsize = default_batchsize

return dataset, eval_, model, batchsize


def main():
parser = argparse.ArgumentParser()
parser.add_argument(
'--dataset', choices=('cityscapes', 'ade20k', 'camvid', 'voc'))
parser.add_argument(
'--model', choices=(
'pspnet_resnet101', 'segnet', 'deeplab_v3plus_xception65'))
parser.add_argument('--model', choices=sorted(models.keys()))
parser.add_argument('--gpu', type=int, default=-1)
parser.add_argument('--pretrained-model')
parser.add_argument('--batchsize', type=int)
parser.add_argument('--input-size', type=int, default=None)
args = parser.parse_args()

if args.input_size is None:
input_size = None
else:
input_size = (args.input_size, args.input_size)

dataset, label_names, model = get_dataset_and_model(
dataset, eval_, model, batchsize = setup(
args.dataset, args.model, args.pretrained_model,
input_size)
args.batchsize, args.input_size)

if args.gpu >= 0:
chainer.cuda.get_device_from_id(args.gpu).use()
model.to_gpu()

it = iterators.SerialIterator(
dataset, 1, repeat=False, shuffle=False)
iterator = iterators.SerialIterator(
dataset, batchsize, repeat=False, shuffle=False)

in_values, out_values, rest_values = apply_to_iterator(
model.predict, it, hook=ProgressHook(len(dataset)))
model.predict, iterator, hook=ProgressHook(len(dataset)))
# Delete an iterator of images to save memory usage.
del in_values
pred_labels, = out_values
gt_labels, = rest_values

result = eval_semantic_segmentation(pred_labels, gt_labels)

for iu, label_name in zip(result['iou'], label_names):
print('{:>23} : {:.4f}'.format(label_name, iu))
print('=' * 34)
print('{:>23} : {:.4f}'.format('mean IoU', result['miou']))
print('{:>23} : {:.4f}'.format(
'Class average accuracy', result['mean_class_accuracy']))
print('{:>23} : {:.4f}'.format(
'Global average accuracy', result['pixel_accuracy']))

eval_(out_values, rest_values)


if __name__ == '__main__':
Expand Down
33 changes: 8 additions & 25 deletions examples/semantic_segmentation/eval_semantic_segmentation_multi.py
View file
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Original file line number Diff line number Diff line change
Expand Up @@ -5,35 +5,29 @@

import chainermn

from chainercv.evaluations import eval_semantic_segmentation
from chainercv.utils import apply_to_iterator
from chainercv.utils import ProgressHook

from eval_semantic_segmentation import get_dataset_and_model
from eval_semantic_segmentation import models
from eval_semantic_segmentation import setup


def main():
parser = argparse.ArgumentParser()
parser.add_argument(
'--dataset', choices=('cityscapes', 'ade20k', 'camvid', 'voc'))
parser.add_argument(
'--model', choices=(
'pspnet_resnet101', 'segnet', 'deeplab_v3plus_xception65'))
parser.add_argument('--model', choices=sorted(models.keys()))
parser.add_argument('--pretrained-model')
parser.add_argument('--batchsize', type=int)
parser.add_argument('--input-size', type=int, default=None)
args = parser.parse_args()

comm = chainermn.create_communicator('pure_nccl')
device = comm.intra_rank

if args.input_size is None:
input_size = None
else:
input_size = (args.input_size, args.input_size)

dataset, label_names, model = get_dataset_and_model(
dataset, eval_, model, batchsize = setup(
args.dataset, args.model, args.pretrained_model,
input_size)
args.batchsize, args.input_size)

chainer.cuda.get_device_from_id(device).use()
model.to_gpu()
Expand All @@ -43,25 +37,14 @@ def main():
return

it = iterators.MultithreadIterator(
dataset, comm.size, repeat=False, shuffle=False)
dataset, batchsize * comm.size, repeat=False, shuffle=False)

in_values, out_values, rest_values = apply_to_iterator(
model.predict, it, hook=ProgressHook(len(dataset)), comm=comm)
# Delete an iterator of images to save memory usage.
del in_values
pred_labels, = out_values
gt_labels, = rest_values

result = eval_semantic_segmentation(pred_labels, gt_labels)

for iu, label_name in zip(result['iou'], label_names):
print('{:>23} : {:.4f}'.format(label_name, iu))
print('=' * 34)
print('{:>23} : {:.4f}'.format('mean IoU', result['miou']))
print('{:>23} : {:.4f}'.format(
'Class average accuracy', result['mean_class_accuracy']))
print('{:>23} : {:.4f}'.format(
'Global average accuracy', result['pixel_accuracy']))
eval_(out_values, rest_values)


if __name__ == '__main__':
Expand Down