repo stringlengths 2 99 | file stringlengths 13 225 | code stringlengths 0 18.3M | file_length int64 0 18.3M | avg_line_length float64 0 1.36M | max_line_length int64 0 4.26M | extension_type stringclasses 1
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DSLA-DSLA | DSLA-DSLA/tests/test_models/test_dense_heads/test_tood_head.py | # Copyright (c) OpenMMLab. All rights reserved.
import mmcv
import torch
from mmdet.models.dense_heads import TOODHead
def test_paa_head_loss():
"""Tests paa head loss when truth is empty and non-empty."""
s = 256
img_metas = [{
'img_shape': (s, s, 3),
'scale_factor': 1,
'pad_sha... | 4,941 | 37.310078 | 79 | py |
DSLA-DSLA | DSLA-DSLA/tests/test_models/test_dense_heads/test_solo_head.py | import pytest
import torch
from mmdet.models.dense_heads import (DecoupledSOLOHead,
DecoupledSOLOLightHead, SOLOHead)
def test_solo_head_loss():
"""Tests solo head loss when truth is empty and non-empty."""
s = 256
img_metas = [{
'img_shape': (s, s, 3),
... | 9,519 | 32.403509 | 79 | py |
DSLA-DSLA | DSLA-DSLA/tests/test_models/test_dense_heads/test_yolox_head.py | # Copyright (c) OpenMMLab. All rights reserved.
import mmcv
import torch
from mmcv.cnn import ConvModule, DepthwiseSeparableConvModule
from mmdet.models.dense_heads import YOLOXHead
def test_yolox_head_loss():
"""Tests yolox head loss when truth is empty and non-empty."""
s = 256
img_metas = [{
'... | 3,809 | 41.808989 | 79 | py |
DSLA-DSLA | DSLA-DSLA/tests/test_models/test_dense_heads/test_autoassign_head.py | # Copyright (c) OpenMMLab. All rights reserved.
import mmcv
import torch
from mmdet.models.dense_heads.autoassign_head import AutoAssignHead
from mmdet.models.dense_heads.paa_head import levels_to_images
def test_autoassign_head_loss():
"""Tests autoassign head loss when truth is empty and non-empty."""
s =... | 3,580 | 37.923913 | 79 | py |
DSLA-DSLA | DSLA-DSLA/tests/test_models/test_dense_heads/test_ld_head.py | # Copyright (c) OpenMMLab. All rights reserved.
import mmcv
import torch
from mmdet.models.dense_heads import GFLHead, LDHead
def test_ld_head_loss():
"""Tests vfnet head loss when truth is empty and non-empty."""
s = 256
img_metas = [{
'img_shape': (s, s, 3),
'scale_factor': 1,
'... | 4,605 | 36.754098 | 79 | py |
DSLA-DSLA | DSLA-DSLA/tests/test_models/test_dense_heads/test_paa_head.py | # Copyright (c) OpenMMLab. All rights reserved.
import mmcv
import numpy as np
import torch
from mmdet.models.dense_heads import PAAHead, paa_head
from mmdet.models.dense_heads.paa_head import levels_to_images
def test_paa_head_loss():
"""Tests paa head loss when truth is empty and non-empty."""
class mock_... | 4,800 | 34.301471 | 79 | py |
DSLA-DSLA | DSLA-DSLA/tests/test_models/test_dense_heads/test_detr_head.py | # Copyright (c) OpenMMLab. All rights reserved.
import torch
from mmcv import ConfigDict
from mmdet.models.dense_heads import DETRHead
def test_detr_head_loss():
"""Tests transformer head loss when truth is empty and non-empty."""
s = 256
img_metas = [{
'img_shape': (s, s, 3),
'scale_fact... | 4,130 | 38.342857 | 79 | py |
DSLA-DSLA | DSLA-DSLA/tests/test_models/test_dense_heads/test_fcos_head.py | # Copyright (c) OpenMMLab. All rights reserved.
import mmcv
import torch
from mmdet.models.dense_heads import FCOSHead
def test_fcos_head_loss():
"""Tests fcos head loss when truth is empty and non-empty."""
s = 256
img_metas = [{
'img_shape': (s, s, 3),
'scale_factor': 1,
'pad_sh... | 2,406 | 36.030769 | 79 | py |
DSLA-DSLA | DSLA-DSLA/tests/test_models/test_dense_heads/test_yolact_head.py | # Copyright (c) OpenMMLab. All rights reserved.
import mmcv
import torch
from mmdet.models.dense_heads import YOLACTHead, YOLACTProtonet, YOLACTSegmHead
def test_yolact_head_loss():
"""Tests yolact head losses when truth is empty and non-empty."""
s = 550
img_metas = [{
'img_shape': (s, s, 3),
... | 5,247 | 37.028986 | 79 | py |
DSLA-DSLA | DSLA-DSLA/tests/test_models/test_dense_heads/test_sabl_retina_head.py | # Copyright (c) OpenMMLab. All rights reserved.
import mmcv
import torch
from mmdet.models.dense_heads import SABLRetinaHead
def test_sabl_retina_head_loss():
"""Tests anchor head loss when truth is empty and non-empty."""
s = 256
img_metas = [{
'img_shape': (s, s, 3),
'scale_factor': 1,
... | 3,080 | 39.012987 | 79 | py |
DSLA-DSLA | DSLA-DSLA/tests/test_models/test_dense_heads/test_atss_head.py | # Copyright (c) OpenMMLab. All rights reserved.
import mmcv
import torch
from mmdet.models.dense_heads import ATSSHead
def test_atss_head_loss():
"""Tests atss head loss when truth is empty and non-empty."""
s = 256
img_metas = [{
'img_shape': (s, s, 3),
'scale_factor': 1,
'pad_sh... | 2,949 | 36.820513 | 79 | py |
DSLA-DSLA | DSLA-DSLA/tests/test_models/test_dense_heads/test_gfl_head.py | # Copyright (c) OpenMMLab. All rights reserved.
import mmcv
import torch
from mmdet.models.dense_heads import GFLHead
def test_gfl_head_loss():
"""Tests gfl head loss when truth is empty and non-empty."""
s = 256
img_metas = [{
'img_shape': (s, s, 3),
'scale_factor': 1,
'pad_shape... | 2,786 | 36.16 | 79 | py |
DSLA-DSLA | DSLA-DSLA/tests/test_models/test_roi_heads/test_roi_extractor.py | # Copyright (c) OpenMMLab. All rights reserved.
import pytest
import torch
from mmdet.models.roi_heads.roi_extractors import GenericRoIExtractor
def test_groie():
# test with pre/post
cfg = dict(
roi_layer=dict(type='RoIAlign', output_size=7, sampling_ratio=2),
out_channels=256,
featm... | 3,257 | 27.330435 | 77 | py |
DSLA-DSLA | DSLA-DSLA/tests/test_models/test_roi_heads/utils.py | # Copyright (c) OpenMMLab. All rights reserved.
import torch
from mmdet.core import build_assigner, build_sampler
def _dummy_bbox_sampling(proposal_list, gt_bboxes, gt_labels):
"""Create sample results that can be passed to BBoxHead.get_targets."""
num_imgs = 1
feat = torch.rand(1, 1, 3, 3)
assign_co... | 1,249 | 31.051282 | 79 | py |
DSLA-DSLA | DSLA-DSLA/tests/test_models/test_roi_heads/test_mask_head.py | # Copyright (c) OpenMMLab. All rights reserved.
import mmcv
import torch
from mmdet.models.roi_heads.mask_heads import (DynamicMaskHead, FCNMaskHead,
MaskIoUHead)
from .utils import _dummy_bbox_sampling
def test_mask_head_loss():
"""Test mask head loss when mask tar... | 3,430 | 34.371134 | 79 | py |
DSLA-DSLA | DSLA-DSLA/tests/test_models/test_roi_heads/__init__.py | # Copyright (c) OpenMMLab. All rights reserved.
from .utils import _dummy_bbox_sampling
__all__ = ['_dummy_bbox_sampling']
| 124 | 24 | 47 | py |
DSLA-DSLA | DSLA-DSLA/tests/test_models/test_roi_heads/test_sabl_bbox_head.py | # Copyright (c) OpenMMLab. All rights reserved.
import mmcv
import torch
from mmdet.core import bbox2roi
from mmdet.models.roi_heads.bbox_heads import SABLHead
from .utils import _dummy_bbox_sampling
def test_sabl_bbox_head_loss():
"""Tests bbox head loss when truth is empty and non-empty."""
self = SABLHead... | 2,979 | 37.205128 | 75 | py |
DSLA-DSLA | DSLA-DSLA/tests/test_models/test_roi_heads/test_bbox_head.py | # Copyright (c) OpenMMLab. All rights reserved.
import mmcv
import numpy as np
import pytest
import torch
from mmdet.core import bbox2roi
from mmdet.models.roi_heads.bbox_heads import BBoxHead
from .utils import _dummy_bbox_sampling
def test_bbox_head_loss():
"""Tests bbox head loss when truth is empty and non-e... | 7,949 | 30.547619 | 78 | py |
DSLA-DSLA | DSLA-DSLA/tests/test_onnx/utils.py | # Copyright (c) OpenMMLab. All rights reserved.
import os
import os.path as osp
import warnings
import numpy as np
import onnx
import onnxruntime as ort
import torch
import torch.nn as nn
ort_custom_op_path = ''
try:
from mmcv.ops import get_onnxruntime_op_path
ort_custom_op_path = get_onnxruntime_op_path()
e... | 4,141 | 29.014493 | 79 | py |
DSLA-DSLA | DSLA-DSLA/tests/test_onnx/test_neck.py | # Copyright (c) OpenMMLab. All rights reserved.
import os.path as osp
import mmcv
import pytest
import torch
from mmdet import digit_version
from mmdet.models.necks import FPN, YOLOV3Neck
from .utils import ort_validate
if digit_version(torch.__version__) <= digit_version('1.5.0'):
pytest.skip(
'ort back... | 4,808 | 28.323171 | 77 | py |
DSLA-DSLA | DSLA-DSLA/tests/test_onnx/__init__.py | # Copyright (c) OpenMMLab. All rights reserved.
from .utils import ort_validate
__all__ = ['ort_validate']
| 108 | 20.8 | 47 | py |
DSLA-DSLA | DSLA-DSLA/tests/test_onnx/test_head.py | # Copyright (c) OpenMMLab. All rights reserved.
import os.path as osp
from functools import partial
import mmcv
import numpy as np
import pytest
import torch
from mmcv.cnn import Scale
from mmdet import digit_version
from mmdet.models import build_detector
from mmdet.models.dense_heads import (FCOSHead, FSAFHead, Ret... | 14,104 | 30.068282 | 78 | py |
DSLA-DSLA | DSLA-DSLA/tests/test_data/test_utils.py | # Copyright (c) OpenMMLab. All rights reserved.
import pytest
from mmdet.datasets import get_loading_pipeline, replace_ImageToTensor
def test_replace_ImageToTensor():
# with MultiScaleFlipAug
pipelines = [
dict(type='LoadImageFromFile'),
dict(
type='MultiScaleFlipAug',
... | 2,721 | 32.604938 | 70 | py |
DSLA-DSLA | DSLA-DSLA/tests/test_data/test_datasets/test_xml_dataset.py | # Copyright (c) OpenMMLab. All rights reserved.
import pytest
from mmdet.datasets import DATASETS
def test_xml_dataset():
dataconfig = {
'ann_file': 'data/VOCdevkit/VOC2007/ImageSets/Main/test.txt',
'img_prefix': 'data/VOCdevkit/VOC2007/',
'pipeline': [{
'type': 'LoadImageFrom... | 641 | 25.75 | 69 | py |
DSLA-DSLA | DSLA-DSLA/tests/test_data/test_datasets/test_common.py | # Copyright (c) OpenMMLab. All rights reserved.
import copy
import logging
import os
import os.path as osp
import tempfile
from unittest.mock import MagicMock, patch
import mmcv
import numpy as np
import pytest
import torch
import torch.nn as nn
from mmcv.runner import EpochBasedRunner
from torch.utils.data import Dat... | 12,280 | 31.749333 | 79 | py |
DSLA-DSLA | DSLA-DSLA/tests/test_data/test_datasets/test_coco_dataset.py | # Copyright (c) OpenMMLab. All rights reserved.
import os.path as osp
import tempfile
import mmcv
import pytest
from mmdet.datasets import CocoDataset
def _create_ids_error_coco_json(json_name):
image = {
'id': 0,
'width': 640,
'height': 640,
'file_name': 'fake_name.jpg',
}
... | 1,293 | 20.932203 | 76 | py |
DSLA-DSLA | DSLA-DSLA/tests/test_data/test_datasets/test_dataset_wrapper.py | # Copyright (c) OpenMMLab. All rights reserved.
import bisect
import math
from collections import defaultdict
from unittest.mock import MagicMock
import numpy as np
import pytest
from mmdet.datasets import (ClassBalancedDataset, ConcatDataset, CustomDataset,
MultiImageMixDataset, RepeatDat... | 7,107 | 38.054945 | 79 | py |
DSLA-DSLA | DSLA-DSLA/tests/test_data/test_datasets/test_panoptic_dataset.py | # Copyright (c) OpenMMLab. All rights reserved.
import os.path as osp
import tempfile
import mmcv
import numpy as np
from mmdet.datasets.api_wrappers import pq_compute_single_core
from mmdet.datasets.coco_panoptic import INSTANCE_OFFSET, CocoPanopticDataset
try:
from panopticapi.utils import id2rgb
except Import... | 10,453 | 29.747059 | 79 | py |
DSLA-DSLA | DSLA-DSLA/tests/test_data/test_datasets/test_custom_dataset.py | # Copyright (c) OpenMMLab. All rights reserved.
import os
import unittest
from unittest.mock import MagicMock, patch
import pytest
from mmdet.datasets import DATASETS
@patch('mmdet.datasets.CocoDataset.load_annotations', MagicMock())
@patch('mmdet.datasets.CustomDataset.load_annotations', MagicMock())
@patch('mmdet... | 4,775 | 33.114286 | 79 | py |
DSLA-DSLA | DSLA-DSLA/tests/test_data/test_pipelines/test_formatting.py | # Copyright (c) OpenMMLab. All rights reserved.
import os.path as osp
from mmcv.utils import build_from_cfg
from mmdet.datasets.builder import PIPELINES
def test_default_format_bundle():
results = dict(
img_prefix=osp.join(osp.dirname(__file__), '../../data'),
img_info=dict(filename='color.jpg')... | 786 | 30.48 | 65 | py |
DSLA-DSLA | DSLA-DSLA/tests/test_data/test_pipelines/test_sampler.py | # Copyright (c) OpenMMLab. All rights reserved.
import torch
from mmdet.core.bbox.assigners import MaxIoUAssigner
from mmdet.core.bbox.samplers import (OHEMSampler, RandomSampler,
ScoreHLRSampler)
def test_random_sampler():
assigner = MaxIoUAssigner(
pos_iou_thr=0.5,... | 9,735 | 28.50303 | 79 | py |
DSLA-DSLA | DSLA-DSLA/tests/test_data/test_pipelines/test_loading.py | # Copyright (c) OpenMMLab. All rights reserved.
import copy
import os.path as osp
import mmcv
import numpy as np
from mmdet.datasets.pipelines import (LoadImageFromFile, LoadImageFromWebcam,
LoadMultiChannelImageFromFiles)
class TestLoading:
@classmethod
def setup_clas... | 3,773 | 40.021739 | 77 | py |
DSLA-DSLA | DSLA-DSLA/tests/test_data/test_pipelines/test_transform/test_transform.py | # Copyright (c) OpenMMLab. All rights reserved.
import copy
import os.path as osp
import mmcv
import numpy as np
import pytest
import torch
from mmcv.utils import build_from_cfg
from mmdet.core.evaluation.bbox_overlaps import bbox_overlaps
from mmdet.datasets.builder import PIPELINES
from .utils import create_random_... | 37,401 | 36.552209 | 79 | py |
DSLA-DSLA | DSLA-DSLA/tests/test_data/test_pipelines/test_transform/test_rotate.py | # Copyright (c) OpenMMLab. All rights reserved.
import copy
import numpy as np
import pytest
from mmcv.utils import build_from_cfg
from mmdet.core.mask import BitmapMasks, PolygonMasks
from mmdet.datasets.builder import PIPELINES
from .utils import check_result_same, construct_toy_data
def test_rotate():
# test... | 6,600 | 37.156069 | 79 | py |
DSLA-DSLA | DSLA-DSLA/tests/test_data/test_pipelines/test_transform/utils.py | # Copyright (c) OpenMMLab. All rights reserved.
import numpy as np
from mmdet.core.mask import BitmapMasks, PolygonMasks
def _check_fields(results, pipeline_results, keys):
"""Check data in fields from two results are same."""
for key in keys:
if isinstance(results[key], (BitmapMasks, PolygonMasks)):... | 3,054 | 37.670886 | 78 | py |
DSLA-DSLA | DSLA-DSLA/tests/test_data/test_pipelines/test_transform/test_img_augment.py | # Copyright (c) OpenMMLab. All rights reserved.
import copy
import mmcv
import numpy as np
from mmcv.utils import build_from_cfg
from numpy.testing import assert_array_equal
from mmdet.datasets.builder import PIPELINES
from .utils import construct_toy_data
def test_adjust_color():
results = construct_toy_data()... | 6,903 | 38.451429 | 79 | py |
DSLA-DSLA | DSLA-DSLA/tests/test_data/test_pipelines/test_transform/__init__.py | # Copyright (c) OpenMMLab. All rights reserved.
from .utils import check_result_same, construct_toy_data, create_random_bboxes
__all__ = ['create_random_bboxes', 'construct_toy_data', 'check_result_same']
| 206 | 40.4 | 78 | py |
DSLA-DSLA | DSLA-DSLA/tests/test_data/test_pipelines/test_transform/test_models_aug_test.py | # Copyright (c) OpenMMLab. All rights reserved.
import os.path as osp
import mmcv
import torch
from mmcv.parallel import collate
from mmcv.utils import build_from_cfg
from mmdet.datasets.builder import PIPELINES
from mmdet.models import build_detector
def model_aug_test_template(cfg_file):
# get config
cfg ... | 4,314 | 31.689394 | 79 | py |
DSLA-DSLA | DSLA-DSLA/tests/test_data/test_pipelines/test_transform/test_translate.py | # Copyright (c) OpenMMLab. All rights reserved.
import copy
import numpy as np
import pycocotools.mask as maskUtils
import pytest
from mmcv.utils import build_from_cfg
from mmdet.core.mask import BitmapMasks, PolygonMasks
from mmdet.datasets.builder import PIPELINES
def _check_keys(results, results_translated):
... | 19,985 | 37.65764 | 79 | py |
DSLA-DSLA | DSLA-DSLA/tests/test_data/test_pipelines/test_transform/test_shear.py | # Copyright (c) OpenMMLab. All rights reserved.
import copy
import numpy as np
import pytest
from mmcv.utils import build_from_cfg
from mmdet.core.mask import BitmapMasks, PolygonMasks
from mmdet.datasets.builder import PIPELINES
from .utils import check_result_same, construct_toy_data
def test_shear():
# test ... | 6,481 | 38.284848 | 79 | py |
DSLA-DSLA | DSLA-DSLA/tests/test_utils/test_anchor.py | # Copyright (c) OpenMMLab. All rights reserved.
"""
CommandLine:
pytest tests/test_utils/test_anchor.py
xdoctest tests/test_utils/test_anchor.py zero
"""
import pytest
import torch
def test_standard_points_generator():
from mmdet.core.anchor import build_prior_generator
# teat init
anchor_generat... | 33,155 | 42.171875 | 79 | py |
DSLA-DSLA | DSLA-DSLA/tests/test_utils/test_visualization.py | # Copyright (c) OpenMMLab. All rights reserved.
import os
import os.path as osp
import tempfile
import mmcv
import numpy as np
import pytest
import torch
from mmdet.core import visualization as vis
def test_color():
assert vis.color_val_matplotlib(mmcv.Color.blue) == (0., 0., 1.)
assert vis.color_val_matplo... | 4,430 | 33.617188 | 78 | py |
DSLA-DSLA | DSLA-DSLA/tests/test_utils/test_general_data.py | import copy
import numpy as np
import pytest
import torch
from mmdet.core import GeneralData, InstanceData
def _equal(a, b):
if isinstance(a, (torch.Tensor, np.ndarray)):
return (a == b).all()
else:
return a == b
def test_general_data():
# test init
meta_info = dict(
img_s... | 21,205 | 34.820946 | 78 | py |
DSLA-DSLA | DSLA-DSLA/tests/test_utils/test_nms.py | import pytest
import torch
from mmdet.core.post_processing import mask_matrix_nms
def _create_mask(N, h, w):
masks = torch.rand((N, h, w)) > 0.5
labels = torch.rand(N)
scores = torch.rand(N)
return masks, labels, scores
def test_nms_input_errors():
with pytest.raises(AssertionError):
ma... | 2,528 | 32.276316 | 69 | py |
DSLA-DSLA | DSLA-DSLA/tests/test_utils/test_coder.py | # Copyright (c) OpenMMLab. All rights reserved.
import pytest
import torch
from mmdet.core.bbox.coder import (DeltaXYWHBBoxCoder, DistancePointBBoxCoder,
TBLRBBoxCoder, YOLOBBoxCoder)
def test_yolo_bbox_coder():
coder = YOLOBBoxCoder()
bboxes = torch.Tensor([[-42., -29., 74... | 5,779 | 44.15625 | 79 | py |
DSLA-DSLA | DSLA-DSLA/tests/test_utils/test_misc.py | # Copyright (c) OpenMMLab. All rights reserved.
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,
f... | 7,267 | 34.627451 | 79 | py |
DSLA-DSLA | DSLA-DSLA/tests/test_utils/test_version.py | # Copyright (c) OpenMMLab. All rights reserved.
from mmdet import digit_version
def test_version_check():
assert digit_version('1.0.5') > digit_version('1.0.5rc0')
assert digit_version('1.0.5') > digit_version('1.0.4rc0')
assert digit_version('1.0.5') > digit_version('1.0rc0')
assert digit_version('1.... | 798 | 46 | 64 | py |
DSLA-DSLA | DSLA-DSLA/tests/test_utils/test_masks.py | # Copyright (c) OpenMMLab. All rights reserved.
import numpy as np
import pytest
import torch
from mmdet.core import BitmapMasks, PolygonMasks
def dummy_raw_bitmap_masks(size):
"""
Args:
size (tuple): expected shape of dummy masks, (H, W) or (N, H, W)
Return:
ndarray: dummy mask
"""
... | 27,461 | 38.8 | 79 | py |
DSLA-DSLA | DSLA-DSLA/tests/test_utils/test_hook.py | # Copyright (c) OpenMMLab. All rights reserved.
import logging
import shutil
import sys
import tempfile
from unittest.mock import MagicMock, call
import numpy as np
import pytest
import torch
import torch.nn as nn
from mmcv.runner import (CheckpointHook, IterTimerHook, PaviLoggerHook,
build_ru... | 11,278 | 30.771831 | 79 | py |
DSLA-DSLA | DSLA-DSLA/tests/test_utils/test_assigner.py | # Copyright (c) OpenMMLab. All rights reserved.
"""Tests the Assigner objects.
CommandLine:
pytest tests/test_utils/test_assigner.py
xdoctest tests/test_utils/test_assigner.py zero
"""
import pytest
import torch
from mmdet.core.bbox.assigners import (ApproxMaxIoUAssigner,
... | 17,434 | 31.167897 | 79 | py |
DSLA-DSLA | DSLA-DSLA/tests/test_downstream/test_mmtrack.py | # Copyright (c) OpenMMLab. All rights reserved.
import copy
from collections import defaultdict
import numpy as np
import pytest
import torch
from mmcv import Config
@pytest.mark.parametrize(
'cfg_file',
['./tests/data/configs_mmtrack/selsa_faster_rcnn_r101_dc5_1x.py'])
def test_vid_fgfa_style_forward(cfg_fi... | 7,592 | 31.87013 | 77 | py |
DSLA-DSLA | DSLA-DSLA/tests/test_metrics/test_recall.py | import numpy as np
from mmdet.core.evaluation.recall import eval_recalls
det_bboxes = np.array([
[0, 0, 10, 10],
[10, 10, 20, 20],
[32, 32, 38, 42],
])
gt_bboxes = np.array([[0, 0, 10, 20], [0, 10, 10, 19], [10, 10, 20, 20]])
gt_ignore = np.array([[5, 5, 10, 20], [6, 10, 10, 19]])
def test_eval_recalls(... | 1,377 | 28.319149 | 73 | py |
DSLA-DSLA | DSLA-DSLA/tests/test_metrics/test_mean_ap.py | import numpy as np
from mmdet.core.evaluation.mean_ap import eval_map, tpfp_default, tpfp_imagenet
det_bboxes = np.array([
[0, 0, 10, 10],
[10, 10, 20, 20],
[32, 32, 38, 42],
])
gt_bboxes = np.array([[0, 0, 10, 20], [0, 10, 10, 19], [10, 10, 20, 20]])
gt_ignore = np.array([[5, 5, 10, 20], [6, 10, 10, 19]]... | 2,327 | 25.454545 | 79 | py |
DSLA-DSLA | DSLA-DSLA/tests/test_metrics/test_losses.py | # Copyright (c) OpenMMLab. All rights reserved.
import pytest
import torch
from mmdet.models import Accuracy, build_loss
def test_ce_loss():
# use_mask and use_sigmoid cannot be true at the same time
with pytest.raises(AssertionError):
loss_cfg = dict(
type='CrossEntropyLoss',
... | 8,694 | 34.929752 | 78 | py |
DSLA-DSLA | DSLA-DSLA/tests/test_metrics/test_box_overlap.py | # Copyright (c) OpenMMLab. All rights reserved.
import numpy as np
import pytest
import torch
from mmdet.core import BboxOverlaps2D, bbox_overlaps
from mmdet.core.evaluation.bbox_overlaps import \
bbox_overlaps as recall_overlaps
def test_bbox_overlaps_2d(eps=1e-7):
def _construct_bbox(num_bbox=None):
... | 5,316 | 38.385185 | 79 | py |
DSLA-DSLA | DSLA-DSLA/demo/video_demo.py | # Copyright (c) OpenMMLab. All rights reserved.
import argparse
import cv2
import mmcv
from mmdet.apis import inference_detector, init_detector
def parse_args():
parser = argparse.ArgumentParser(description='MMDetection video demo')
parser.add_argument('video', help='Video file')
parser.add_argument('co... | 1,974 | 30.854839 | 76 | py |
DSLA-DSLA | DSLA-DSLA/demo/create_result_gif.py | # Copyright (c) OpenMMLab. All rights reserved.
import argparse
import os
import os.path as osp
import matplotlib.patches as mpatches
import matplotlib.pyplot as plt
import mmcv
import numpy as np
try:
import imageio
except ImportError:
imageio = None
def parse_args():
parser = argparse.ArgumentParser(d... | 4,930 | 29.067073 | 79 | py |
DSLA-DSLA | DSLA-DSLA/demo/webcam_demo.py | # Copyright (c) OpenMMLab. All rights reserved.
import argparse
import cv2
import torch
from mmdet.apis import inference_detector, init_detector
def parse_args():
parser = argparse.ArgumentParser(description='MMDetection webcam demo')
parser.add_argument('config', help='test config file path')
parser.ad... | 1,308 | 26.270833 | 78 | py |
DSLA-DSLA | DSLA-DSLA/demo/image_demo.py | # Copyright (c) OpenMMLab. All rights reserved.
import asyncio
from argparse import ArgumentParser
from mmdet.apis import (async_inference_detector, inference_detector,
init_detector, show_result_pyplot)
def parse_args():
parser = ArgumentParser()
parser.add_argument('img', help='Imag... | 1,719 | 32.72549 | 76 | py |
DSLA-DSLA | DSLA-DSLA/configs/ghm/retinanet_ghm_x101_32x4d_fpn_1x_coco.py | _base_ = './retinanet_ghm_r50_fpn_1x_coco.py'
model = dict(
backbone=dict(
type='ResNeXt',
depth=101,
groups=32,
base_width=4,
num_stages=4,
out_indices=(0, 1, 2, 3),
frozen_stages=1,
norm_cfg=dict(type='BN', requires_grad=True),
style='pytorch... | 423 | 27.266667 | 76 | py |
DSLA-DSLA | DSLA-DSLA/configs/ghm/retinanet_ghm_r101_fpn_1x_coco.py | _base_ = './retinanet_ghm_r50_fpn_1x_coco.py'
model = dict(
backbone=dict(
depth=101,
init_cfg=dict(type='Pretrained',
checkpoint='torchvision://resnet101')))
| 201 | 27.857143 | 61 | py |
DSLA-DSLA | DSLA-DSLA/configs/ghm/retinanet_ghm_r50_fpn_1x_coco.py | _base_ = '../retinanet/retinanet_r50_fpn_1x_coco.py'
model = dict(
bbox_head=dict(
loss_cls=dict(
_delete_=True,
type='GHMC',
bins=30,
momentum=0.75,
use_sigmoid=True,
loss_weight=1.0),
loss_bbox=dict(
_delete_=True,... | 532 | 25.65 | 60 | py |
DSLA-DSLA | DSLA-DSLA/configs/ghm/retinanet_ghm_x101_64x4d_fpn_1x_coco.py | _base_ = './retinanet_ghm_r50_fpn_1x_coco.py'
model = dict(
backbone=dict(
type='ResNeXt',
depth=101,
groups=64,
base_width=4,
num_stages=4,
out_indices=(0, 1, 2, 3),
frozen_stages=1,
norm_cfg=dict(type='BN', requires_grad=True),
style='pytorch... | 423 | 27.266667 | 76 | py |
DSLA-DSLA | DSLA-DSLA/configs/dcn/mask_rcnn_r50_fpn_fp16_mdconv_c3-c5_1x_coco.py | _base_ = '../mask_rcnn/mask_rcnn_r50_fpn_1x_coco.py'
model = dict(
backbone=dict(
dcn=dict(type='DCNv2', deform_groups=1, fallback_on_stride=False),
stage_with_dcn=(False, True, True, True)))
fp16 = dict(loss_scale=512.)
| 242 | 29.375 | 74 | py |
DSLA-DSLA | DSLA-DSLA/configs/dcn/cascade_rcnn_r50_fpn_dconv_c3-c5_1x_coco.py | _base_ = '../cascade_rcnn/cascade_rcnn_r50_fpn_1x_coco.py'
model = dict(
backbone=dict(
dcn=dict(type='DCN', deform_groups=1, fallback_on_stride=False),
stage_with_dcn=(False, True, True, True)))
| 216 | 35.166667 | 72 | py |
DSLA-DSLA | DSLA-DSLA/configs/dcn/mask_rcnn_r50_fpn_mdconv_c3-c5_1x_coco.py | _base_ = '../mask_rcnn/mask_rcnn_r50_fpn_1x_coco.py'
model = dict(
backbone=dict(
dcn=dict(type='DCNv2', deform_groups=1, fallback_on_stride=False),
stage_with_dcn=(False, True, True, True)))
| 212 | 34.5 | 74 | py |
DSLA-DSLA | DSLA-DSLA/configs/dcn/cascade_rcnn_r101_fpn_dconv_c3-c5_1x_coco.py | _base_ = '../cascade_rcnn/cascade_rcnn_r101_fpn_1x_coco.py'
model = dict(
backbone=dict(
dcn=dict(type='DCN', deform_groups=1, fallback_on_stride=False),
stage_with_dcn=(False, True, True, True)))
| 217 | 35.333333 | 72 | py |
DSLA-DSLA | DSLA-DSLA/configs/dcn/faster_rcnn_r50_fpn_mdconv_c3-c5_1x_coco.py | _base_ = '../faster_rcnn/faster_rcnn_r50_fpn_1x_coco.py'
model = dict(
backbone=dict(
dcn=dict(type='DCNv2', deform_groups=1, fallback_on_stride=False),
stage_with_dcn=(False, True, True, True)))
| 216 | 35.166667 | 74 | py |
DSLA-DSLA | DSLA-DSLA/configs/dcn/faster_rcnn_r50_fpn_dpool_1x_coco.py | _base_ = '../faster_rcnn/faster_rcnn_r50_fpn_1x_coco.py'
model = dict(
roi_head=dict(
bbox_roi_extractor=dict(
type='SingleRoIExtractor',
roi_layer=dict(
_delete_=True,
type='DeformRoIPoolPack',
output_size=7,
output_cha... | 408 | 30.461538 | 56 | py |
DSLA-DSLA | DSLA-DSLA/configs/dcn/mask_rcnn_r50_fpn_dconv_c3-c5_1x_coco.py | _base_ = '../mask_rcnn/mask_rcnn_r50_fpn_1x_coco.py'
model = dict(
backbone=dict(
dcn=dict(type='DCN', deform_groups=1, fallback_on_stride=False),
stage_with_dcn=(False, True, True, True)))
| 210 | 34.166667 | 72 | py |
DSLA-DSLA | DSLA-DSLA/configs/dcn/cascade_mask_rcnn_x101_32x4d_fpn_dconv_c3-c5_1x_coco.py | _base_ = '../cascade_rcnn/cascade_mask_rcnn_x101_32x4d_fpn_1x_coco.py'
model = dict(
backbone=dict(
dcn=dict(type='DCN', deform_groups=1, fallback_on_stride=False),
stage_with_dcn=(False, True, True, True)))
| 228 | 37.166667 | 72 | py |
DSLA-DSLA | DSLA-DSLA/configs/dcn/mask_rcnn_r50_fpn_fp16_dconv_c3-c5_1x_coco.py | _base_ = '../mask_rcnn/mask_rcnn_r50_fpn_1x_coco.py'
model = dict(
backbone=dict(
dcn=dict(type='DCN', deform_groups=1, fallback_on_stride=False),
stage_with_dcn=(False, True, True, True)))
fp16 = dict(loss_scale=512.)
| 240 | 29.125 | 72 | py |
DSLA-DSLA | DSLA-DSLA/configs/dcn/faster_rcnn_x101_32x4d_fpn_dconv_c3-c5_1x_coco.py | _base_ = '../faster_rcnn/faster_rcnn_r50_fpn_1x_coco.py'
model = dict(
backbone=dict(
type='ResNeXt',
depth=101,
groups=32,
base_width=4,
num_stages=4,
out_indices=(0, 1, 2, 3),
frozen_stages=1,
norm_cfg=dict(type='BN', requires_grad=True),
sty... | 557 | 31.823529 | 76 | py |
DSLA-DSLA | DSLA-DSLA/configs/dcn/cascade_mask_rcnn_r50_fpn_dconv_c3-c5_1x_coco.py | _base_ = '../cascade_rcnn/cascade_mask_rcnn_r50_fpn_1x_coco.py'
model = dict(
backbone=dict(
dcn=dict(type='DCN', deform_groups=1, fallback_on_stride=False),
stage_with_dcn=(False, True, True, True)))
| 221 | 36 | 72 | py |
DSLA-DSLA | DSLA-DSLA/configs/dcn/cascade_mask_rcnn_r101_fpn_dconv_c3-c5_1x_coco.py | _base_ = '../cascade_rcnn/cascade_mask_rcnn_r101_fpn_1x_coco.py'
model = dict(
backbone=dict(
dcn=dict(type='DCN', deform_groups=1, fallback_on_stride=False),
stage_with_dcn=(False, True, True, True)))
| 222 | 36.166667 | 72 | py |
DSLA-DSLA | DSLA-DSLA/configs/dcn/faster_rcnn_r50_fpn_dconv_c3-c5_1x_coco.py | _base_ = '../faster_rcnn/faster_rcnn_r50_fpn_1x_coco.py'
model = dict(
backbone=dict(
dcn=dict(type='DCN', deform_groups=1, fallback_on_stride=False),
stage_with_dcn=(False, True, True, True)))
| 214 | 34.833333 | 72 | py |
DSLA-DSLA | DSLA-DSLA/configs/dcn/faster_rcnn_r50_fpn_mdconv_c3-c5_group4_1x_coco.py | _base_ = '../faster_rcnn/faster_rcnn_r50_fpn_1x_coco.py'
model = dict(
backbone=dict(
dcn=dict(type='DCNv2', deform_groups=4, fallback_on_stride=False),
stage_with_dcn=(False, True, True, True)))
| 216 | 35.166667 | 74 | py |
DSLA-DSLA | DSLA-DSLA/configs/dcn/faster_rcnn_r101_fpn_dconv_c3-c5_1x_coco.py | _base_ = '../faster_rcnn/faster_rcnn_r101_fpn_1x_coco.py'
model = dict(
backbone=dict(
dcn=dict(type='DCN', deform_groups=1, fallback_on_stride=False),
stage_with_dcn=(False, True, True, True)))
| 215 | 35 | 72 | py |
DSLA-DSLA | DSLA-DSLA/configs/dcn/mask_rcnn_r101_fpn_dconv_c3-c5_1x_coco.py | _base_ = '../mask_rcnn/mask_rcnn_r101_fpn_1x_coco.py'
model = dict(
backbone=dict(
dcn=dict(type='DCN', deform_groups=1, fallback_on_stride=False),
stage_with_dcn=(False, True, True, True)))
| 211 | 34.333333 | 72 | py |
DSLA-DSLA | DSLA-DSLA/configs/dcn/faster_rcnn_r50_fpn_mdpool_1x_coco.py | _base_ = '../faster_rcnn/faster_rcnn_r50_fpn_1x_coco.py'
model = dict(
roi_head=dict(
bbox_roi_extractor=dict(
type='SingleRoIExtractor',
roi_layer=dict(
_delete_=True,
type='ModulatedDeformRoIPoolPack',
output_size=7,
o... | 417 | 31.153846 | 56 | py |
DSLA-DSLA | DSLA-DSLA/configs/htc/htc_x101_64x4d_fpn_16x1_20e_coco.py | _base_ = './htc_r50_fpn_1x_coco.py'
model = dict(
backbone=dict(
type='ResNeXt',
depth=101,
groups=64,
base_width=4,
num_stages=4,
out_indices=(0, 1, 2, 3),
frozen_stages=1,
norm_cfg=dict(type='BN', requires_grad=True),
norm_eval=True,
... | 591 | 28.6 | 76 | py |
DSLA-DSLA | DSLA-DSLA/configs/htc/htc_r50_fpn_20e_coco.py | _base_ = './htc_r50_fpn_1x_coco.py'
# learning policy
lr_config = dict(step=[16, 19])
runner = dict(type='EpochBasedRunner', max_epochs=20)
| 140 | 27.2 | 53 | py |
DSLA-DSLA | DSLA-DSLA/configs/htc/htc_without_semantic_r50_fpn_1x_coco.py | _base_ = [
'../_base_/datasets/coco_instance.py',
'../_base_/schedules/schedule_1x.py', '../_base_/default_runtime.py'
]
# model settings
model = dict(
type='HybridTaskCascade',
backbone=dict(
type='ResNet',
depth=50,
num_stages=4,
out_indices=(0, 1, 2, 3),
frozen... | 8,333 | 34.164557 | 79 | py |
DSLA-DSLA | DSLA-DSLA/configs/htc/htc_x101_32x4d_fpn_16x1_20e_coco.py | _base_ = './htc_r50_fpn_1x_coco.py'
model = dict(
backbone=dict(
type='ResNeXt',
depth=101,
groups=32,
base_width=4,
num_stages=4,
out_indices=(0, 1, 2, 3),
frozen_stages=1,
norm_cfg=dict(type='BN', requires_grad=True),
norm_eval=True,
... | 591 | 28.6 | 76 | py |
DSLA-DSLA | DSLA-DSLA/configs/htc/htc_x101_64x4d_fpn_dconv_c3-c5_mstrain_400_1400_16x1_20e_coco.py | _base_ = './htc_r50_fpn_1x_coco.py'
model = dict(
backbone=dict(
type='ResNeXt',
depth=101,
groups=64,
base_width=4,
num_stages=4,
out_indices=(0, 1, 2, 3),
frozen_stages=1,
norm_cfg=dict(type='BN', requires_grad=True),
norm_eval=True,
... | 1,489 | 32.863636 | 79 | py |
DSLA-DSLA | DSLA-DSLA/configs/htc/htc_r50_fpn_1x_coco.py | _base_ = './htc_without_semantic_r50_fpn_1x_coco.py'
model = dict(
roi_head=dict(
semantic_roi_extractor=dict(
type='SingleRoIExtractor',
roi_layer=dict(type='RoIAlign', output_size=14, sampling_ratio=0),
out_channels=256,
featmap_strides=[8]),
semanti... | 1,998 | 34.070175 | 79 | py |
DSLA-DSLA | DSLA-DSLA/configs/htc/htc_r101_fpn_20e_coco.py | _base_ = './htc_r50_fpn_1x_coco.py'
model = dict(
backbone=dict(
depth=101,
init_cfg=dict(type='Pretrained',
checkpoint='torchvision://resnet101')))
# learning policy
lr_config = dict(step=[16, 19])
runner = dict(type='EpochBasedRunner', max_epochs=20)
| 295 | 28.6 | 61 | py |
DSLA-DSLA | DSLA-DSLA/configs/strong_baselines/mask_rcnn_r50_caffe_fpn_syncbn-all_rpn-2conv_lsj_400e_coco.py | _base_ = './mask_rcnn_r50_caffe_fpn_syncbn-all_rpn-2conv_lsj_100e_coco.py'
# Use RepeatDataset to speed up training
# change repeat time from 4 (for 100 epochs) to 16 (for 400 epochs)
data = dict(train=dict(times=4 * 4))
lr_config = dict(warmup_iters=500 * 4)
| 261 | 36.428571 | 74 | py |
DSLA-DSLA | DSLA-DSLA/configs/strong_baselines/mask_rcnn_r50_fpn_syncbn-all_rpn-2conv_lsj_50e_coco.py | _base_ = 'mask_rcnn_r50_fpn_syncbn-all_rpn-2conv_lsj_100e_coco.py'
# Use RepeatDataset to speed up training
# change repeat time from 4 (for 100 epochs) to 2 (for 50 epochs)
data = dict(train=dict(times=2))
| 208 | 33.833333 | 66 | py |
DSLA-DSLA | DSLA-DSLA/configs/strong_baselines/mask_rcnn_r50_fpn_syncbn-all_rpn-2conv_lsj_100e_coco.py | _base_ = [
'../_base_/models/mask_rcnn_r50_fpn.py',
'../common/lsj_100e_coco_instance.py'
]
norm_cfg = dict(type='SyncBN', requires_grad=True)
# Use MMSyncBN that handles empty tensor in head. It can be changed to
# SyncBN after https://github.com/pytorch/pytorch/issues/36530 is fixed
# Requires MMCV-full afte... | 893 | 37.869565 | 77 | py |
DSLA-DSLA | DSLA-DSLA/configs/strong_baselines/mask_rcnn_r50_fpn_syncbn-all_rpn-2conv_lsj_100e_fp16_coco.py | _base_ = 'mask_rcnn_r50_fpn_syncbn-all_rpn-2conv_lsj_100e_coco.py'
# use FP16
fp16 = dict(loss_scale=512.)
| 107 | 26 | 66 | py |
DSLA-DSLA | DSLA-DSLA/configs/strong_baselines/mask_rcnn_r50_caffe_fpn_syncbn-all_rpn-2conv_lsj_100e_fp16_coco.py | _base_ = 'mask_rcnn_r50_caffe_fpn_syncbn-all_rpn-2conv_lsj_100e_coco.py'
fp16 = dict(loss_scale=512.)
| 102 | 33.333333 | 72 | py |
DSLA-DSLA | DSLA-DSLA/configs/strong_baselines/mask_rcnn_r50_caffe_fpn_syncbn-all_rpn-2conv_lsj_100e_coco.py | _base_ = [
'../_base_/models/mask_rcnn_r50_fpn.py',
'../common/lsj_100e_coco_instance.py'
]
norm_cfg = dict(type='SyncBN', requires_grad=True)
# Use MMSyncBN that handles empty tensor in head. It can be changed to
# SyncBN after https://github.com/pytorch/pytorch/issues/36530 is fixed
# Requires MMCV-full afte... | 2,703 | 32.382716 | 77 | py |
DSLA-DSLA | DSLA-DSLA/configs/reppoints/reppoints_moment_r101_fpn_dconv_c3-c5_gn-neck+head_2x_coco.py | _base_ = './reppoints_moment_r50_fpn_gn-neck+head_2x_coco.py'
model = dict(
backbone=dict(
depth=101,
dcn=dict(type='DCN', deform_groups=1, fallback_on_stride=False),
stage_with_dcn=(False, True, True, True),
init_cfg=dict(type='Pretrained',
checkpoint='torchvis... | 340 | 36.888889 | 72 | py |
DSLA-DSLA | DSLA-DSLA/configs/reppoints/reppoints_moment_r101_fpn_gn-neck+head_2x_coco.py | _base_ = './reppoints_moment_r50_fpn_gn-neck+head_2x_coco.py'
model = dict(
backbone=dict(
depth=101,
init_cfg=dict(type='Pretrained',
checkpoint='torchvision://resnet101')))
| 217 | 30.142857 | 61 | py |
DSLA-DSLA | DSLA-DSLA/configs/reppoints/reppoints_moment_r50_fpn_1x_coco.py | _base_ = [
'../_base_/datasets/coco_detection.py',
'../_base_/schedules/schedule_1x.py', '../_base_/default_runtime.py'
]
model = dict(
type='RepPointsDetector',
backbone=dict(
type='ResNet',
depth=50,
num_stages=4,
out_indices=(0, 1, 2, 3),
frozen_stages=1,
... | 2,065 | 29.382353 | 79 | py |
DSLA-DSLA | DSLA-DSLA/configs/reppoints/reppoints_partial_minmax_r50_fpn_gn-neck+head_1x_coco.py | _base_ = './reppoints_moment_r50_fpn_gn-neck+head_1x_coco.py'
model = dict(bbox_head=dict(transform_method='partial_minmax'))
| 126 | 41.333333 | 63 | py |
DSLA-DSLA | DSLA-DSLA/configs/reppoints/reppoints_moment_r50_fpn_gn-neck+head_1x_coco.py | _base_ = './reppoints_moment_r50_fpn_1x_coco.py'
norm_cfg = dict(type='GN', num_groups=32, requires_grad=True)
model = dict(neck=dict(norm_cfg=norm_cfg), bbox_head=dict(norm_cfg=norm_cfg))
optimizer = dict(lr=0.01)
| 215 | 42.2 | 77 | py |
DSLA-DSLA | DSLA-DSLA/configs/reppoints/bbox_r50_grid_fpn_gn-neck+head_1x_coco.py | _base_ = './reppoints_moment_r50_fpn_gn-neck+head_1x_coco.py'
model = dict(
bbox_head=dict(transform_method='minmax', use_grid_points=True),
# training and testing settings
train_cfg=dict(
init=dict(
assigner=dict(
_delete_=True,
type='MaxIoUAssigner',
... | 452 | 31.357143 | 68 | py |
DSLA-DSLA | DSLA-DSLA/configs/reppoints/reppoints_moment_r50_fpn_gn-neck+head_2x_coco.py | _base_ = './reppoints_moment_r50_fpn_gn-neck+head_1x_coco.py'
lr_config = dict(step=[16, 22])
runner = dict(type='EpochBasedRunner', max_epochs=24)
| 148 | 36.25 | 61 | py |
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