105 lines
3.7 KiB
Python
105 lines
3.7 KiB
Python
# Copyright (c) OpenMMLab. All rights reserved.
|
|
import os.path as osp
|
|
import shutil
|
|
from unittest import TestCase
|
|
|
|
import numpy as np
|
|
import torch
|
|
from mmengine.structures import PixelData
|
|
|
|
from mmseg.evaluation import IoUMetric
|
|
from mmseg.structures import SegDataSample
|
|
|
|
|
|
class TestIoUMetric(TestCase):
|
|
|
|
def _demo_mm_inputs(self,
|
|
batch_size=2,
|
|
image_shapes=(3, 64, 64),
|
|
num_classes=5):
|
|
"""Create a superset of inputs needed to run test or train batches.
|
|
|
|
Args:
|
|
batch_size (int): batch size. Default to 2.
|
|
image_shapes (List[tuple], Optional): image shape.
|
|
Default to (3, 64, 64)
|
|
num_classes (int): number of different classes.
|
|
Default to 5.
|
|
"""
|
|
if isinstance(image_shapes, list):
|
|
assert len(image_shapes) == batch_size
|
|
else:
|
|
image_shapes = [image_shapes] * batch_size
|
|
|
|
data_samples = []
|
|
for idx in range(batch_size):
|
|
image_shape = image_shapes[idx]
|
|
_, h, w = image_shape
|
|
|
|
data_sample = SegDataSample()
|
|
gt_semantic_seg = np.random.randint(
|
|
0, num_classes, (1, h, w), dtype=np.uint8)
|
|
gt_semantic_seg = torch.LongTensor(gt_semantic_seg)
|
|
gt_sem_seg_data = dict(data=gt_semantic_seg)
|
|
data_sample.gt_sem_seg = PixelData(**gt_sem_seg_data)
|
|
|
|
data_samples.append(data_sample.to_dict())
|
|
|
|
return data_samples
|
|
|
|
def _demo_mm_model_output(self,
|
|
data_samples,
|
|
batch_size=2,
|
|
image_shapes=(3, 64, 64),
|
|
num_classes=5):
|
|
|
|
_, h, w = image_shapes
|
|
|
|
for data_sample in data_samples:
|
|
data_sample['seg_logits'] = dict(
|
|
data=torch.randn(num_classes, h, w))
|
|
data_sample['pred_sem_seg'] = dict(
|
|
data=torch.randint(0, num_classes, (1, h, w)))
|
|
data_sample[
|
|
'img_path'] = 'tests/data/pseudo_dataset/imgs/00000_img.jpg'
|
|
return data_samples
|
|
|
|
def test_evaluate(self):
|
|
"""Test using the metric in the same way as Evalutor."""
|
|
|
|
data_samples = self._demo_mm_inputs()
|
|
data_samples = self._demo_mm_model_output(data_samples)
|
|
|
|
iou_metric = IoUMetric(iou_metrics=['mIoU'])
|
|
iou_metric.dataset_meta = dict(
|
|
classes=['wall', 'building', 'sky', 'floor', 'tree'],
|
|
label_map=dict(),
|
|
reduce_zero_label=False)
|
|
iou_metric.process([0] * len(data_samples), data_samples)
|
|
res = iou_metric.evaluate(2)
|
|
self.assertIsInstance(res, dict)
|
|
|
|
# test save segment file in output_dir
|
|
iou_metric = IoUMetric(iou_metrics=['mIoU'], output_dir='tmp')
|
|
iou_metric.dataset_meta = dict(
|
|
classes=['wall', 'building', 'sky', 'floor', 'tree'],
|
|
label_map=dict(),
|
|
reduce_zero_label=False)
|
|
iou_metric.process([0] * len(data_samples), data_samples)
|
|
assert osp.exists('tmp')
|
|
assert osp.isfile('tmp/00000_img.png')
|
|
shutil.rmtree('tmp')
|
|
|
|
# test format_only
|
|
iou_metric = IoUMetric(
|
|
iou_metrics=['mIoU'], output_dir='tmp', format_only=True)
|
|
iou_metric.dataset_meta = dict(
|
|
classes=['wall', 'building', 'sky', 'floor', 'tree'],
|
|
label_map=dict(),
|
|
reduce_zero_label=False)
|
|
iou_metric.process([0] * len(data_samples), data_samples)
|
|
assert iou_metric.results == []
|
|
assert osp.exists('tmp')
|
|
assert osp.isfile('tmp/00000_img.png')
|
|
shutil.rmtree('tmp')
|