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import os.path as osp
import tempfile
import numpy as np
import pytest
import torch
from mmdet.core.bbox import distance2bbox
from mmdet.core.mask.structures import BitmapMasks, PolygonMasks
from mmdet.core.utils import (center_of_mass, filter_scores_and_topk,
flip_tensor, mask2ndarray, select_single_mlvl)
from mmdet.utils import find_latest_checkpoint
def dummy_raw_polygon_masks(size):
"""
Args:
size (tuple): expected shape of dummy masks, (N, H, W)
Return:
list[list[ndarray]]: dummy mask
"""
num_obj, height, width = size
polygons = []
for _ in range(num_obj):
num_points = np.random.randint(5) * 2 + 6
polygons.append([np.random.uniform(0, min(height, width), num_points)])
return polygons
def test_mask2ndarray():
raw_masks = np.ones((3, 28, 28))
bitmap_mask = BitmapMasks(raw_masks, 28, 28)
output_mask = mask2ndarray(bitmap_mask)
assert np.allclose(raw_masks, output_mask)
raw_masks = dummy_raw_polygon_masks((3, 28, 28))
polygon_masks = PolygonMasks(raw_masks, 28, 28)
output_mask = mask2ndarray(polygon_masks)
assert output_mask.shape == (3, 28, 28)
raw_masks = np.ones((3, 28, 28))
output_mask = mask2ndarray(raw_masks)
assert np.allclose(raw_masks, output_mask)
raw_masks = torch.ones((3, 28, 28))
output_mask = mask2ndarray(raw_masks)
assert np.allclose(raw_masks, output_mask)
# test unsupported type
raw_masks = []
with pytest.raises(TypeError):
output_mask = mask2ndarray(raw_masks)
def test_distance2bbox():
point = torch.Tensor([[74., 61.], [-29., 106.], [138., 61.], [29., 170.]])
distance = torch.Tensor([[0., 0, 1., 1.], [1., 2., 10., 6.],
[22., -29., 138., 61.], [54., -29., 170., 61.]])
expected_decode_bboxes = torch.Tensor([[74., 61., 75., 62.],
[0., 104., 0., 112.],
[100., 90., 100., 120.],
[0., 120., 100., 120.]])
out_bbox = distance2bbox(point, distance, max_shape=(120, 100))
assert expected_decode_bboxes.allclose(out_bbox)
out = distance2bbox(point, distance, max_shape=torch.Tensor((120, 100)))
assert expected_decode_bboxes.allclose(out)
batch_point = point.unsqueeze(0).repeat(2, 1, 1)
batch_distance = distance.unsqueeze(0).repeat(2, 1, 1)
batch_out = distance2bbox(
batch_point, batch_distance, max_shape=(120, 100))[0]
assert out.allclose(batch_out)
batch_out = distance2bbox(
batch_point, batch_distance, max_shape=[(120, 100), (120, 100)])[0]
assert out.allclose(batch_out)
batch_out = distance2bbox(point, batch_distance, max_shape=(120, 100))[0]
assert out.allclose(batch_out)
# test max_shape is not equal to batch
with pytest.raises(AssertionError):
distance2bbox(
batch_point,
batch_distance,
max_shape=[(120, 100), (120, 100), (32, 32)])
rois = torch.zeros((0, 4))
deltas = torch.zeros((0, 4))
out = distance2bbox(rois, deltas, max_shape=(120, 100))
assert rois.shape == out.shape
rois = torch.zeros((2, 0, 4))
deltas = torch.zeros((2, 0, 4))
out = distance2bbox(rois, deltas, max_shape=(120, 100))
assert rois.shape == out.shape
@pytest.mark.parametrize('mask', [
torch.ones((28, 28)),
torch.zeros((28, 28)),
torch.rand(28, 28) > 0.5,
torch.tensor([[0, 0, 0, 0], [0, 1, 1, 0], [0, 1, 1, 0], [0, 0, 0, 0]])
])
def test_center_of_mass(mask):
center_h, center_w = center_of_mass(mask)
if mask.shape[0] == 4:
assert center_h == 1.5
assert center_w == 1.5
assert isinstance(center_h, torch.Tensor) \
and isinstance(center_w, torch.Tensor)
assert 0 <= center_h <= 28 \
and 0 <= center_w <= 28
def test_flip_tensor():
img = np.random.random((1, 3, 10, 10))
src_tensor = torch.from_numpy(img)
# test flip_direction parameter error
with pytest.raises(AssertionError):
flip_tensor(src_tensor, 'flip')
# test tensor dimension
with pytest.raises(AssertionError):
flip_tensor(src_tensor[0], 'vertical')
hfilp_tensor = flip_tensor(src_tensor, 'horizontal')
expected_hflip_tensor = torch.from_numpy(img[..., ::-1, :].copy())
expected_hflip_tensor.allclose(hfilp_tensor)
vfilp_tensor = flip_tensor(src_tensor, 'vertical')
expected_vflip_tensor = torch.from_numpy(img[..., ::-1].copy())
expected_vflip_tensor.allclose(vfilp_tensor)
diag_filp_tensor = flip_tensor(src_tensor, 'diagonal')
expected_diag_filp_tensor = torch.from_numpy(img[..., ::-1, ::-1].copy())
expected_diag_filp_tensor.allclose(diag_filp_tensor)
def test_select_single_mlvl():
mlvl_tensors = [torch.rand(2, 1, 10, 10)] * 5
mlvl_tensor_list = select_single_mlvl(mlvl_tensors, 1)
assert len(mlvl_tensor_list) == 5 and mlvl_tensor_list[0].ndim == 3
def test_filter_scores_and_topk():
score = torch.tensor([[0.1, 0.3, 0.2], [0.12, 0.7, 0.9], [0.02, 0.8, 0.08],
[0.4, 0.1, 0.08]])
bbox_pred = torch.tensor([[0.2, 0.3], [0.4, 0.7], [0.1, 0.1], [0.5, 0.1]])
score_thr = 0.15
nms_pre = 4
# test results type error
with pytest.raises(NotImplementedError):
filter_scores_and_topk(score, score_thr, nms_pre, (score, ))
filtered_results = filter_scores_and_topk(
score, score_thr, nms_pre, results=dict(bbox_pred=bbox_pred))
filtered_score, labels, keep_idxs, results = filtered_results
assert filtered_score.allclose(torch.tensor([0.9, 0.8, 0.7, 0.4]))
assert labels.allclose(torch.tensor([2, 1, 1, 0]))
assert keep_idxs.allclose(torch.tensor([1, 2, 1, 3]))
assert results['bbox_pred'].allclose(
torch.tensor([[0.4, 0.7], [0.1, 0.1], [0.4, 0.7], [0.5, 0.1]]))
def test_find_latest_checkpoint():
with tempfile.TemporaryDirectory() as tmpdir:
path = tmpdir
latest = find_latest_checkpoint(path)
# There are no checkpoints in the path.
assert latest is None
path = osp.join(tmpdir, 'none')
latest = find_latest_checkpoint(path)
# The path does not exist.
assert latest is None
with tempfile.TemporaryDirectory() as tmpdir:
with open(osp.join(tmpdir, 'latest.pth'), 'w') as f:
f.write('latest')
path = tmpdir
latest = find_latest_checkpoint(path)
assert latest == osp.join(tmpdir, 'latest.pth')
with tempfile.TemporaryDirectory() as tmpdir:
with open(osp.join(tmpdir, 'iter_4000.pth'), 'w') as f:
f.write('iter_4000')
with open(osp.join(tmpdir, 'iter_8000.pth'), 'w') as f:
f.write('iter_8000')
path = tmpdir
latest = find_latest_checkpoint(path)
assert latest == osp.join(tmpdir, 'iter_8000.pth')
with tempfile.TemporaryDirectory() as tmpdir:
with open(osp.join(tmpdir, 'epoch_1.pth'), 'w') as f:
f.write('epoch_1')
with open(osp.join(tmpdir, 'epoch_2.pth'), 'w') as f:
f.write('epoch_2')
path = tmpdir
latest = find_latest_checkpoint(path)
assert latest == osp.join(tmpdir, 'epoch_2.pth')
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