repo stringclasses 454
values | file_path stringlengths 5 201 | extension stringclasses 1
value | content stringlengths 8 509k | num_lines int64 3 16.9k | size_bytes int64 8 511k |
|---|---|---|---|---|---|
mmdetection | docs/zh_cn/conf.py | .py | # Configuration file for the Sphinx documentation builder.
#
# This file only contains a selection of the most common options. For a full
# list see the documentation:
# https://www.sphinx-doc.org/en/master/usage/configuration.html
# -- Path setup --------------------------------------------------------------
# If ex... | 119 | 3,459 |
mmdetection | tests/test_apis/test_inference.py | .py | import os
from pathlib import Path
import numpy as np
import pytest
import torch
from mmdet.apis import inference_detector, init_detector
from mmdet.structures import DetDataSample
from mmdet.utils import register_all_modules
# TODO: Waiting to fix multiple call error bug
register_all_modules()
@pytest.mark.parame... | 77 | 2,733 |
mmdetection | tests/test_apis/test_det_inferencer.py | .py | # Copyright (c) OpenMMLab. All rights reserved.
import os.path as osp
import tempfile
from unittest import TestCase, mock
from unittest.mock import Mock, patch
import mmcv
import mmengine
import numpy as np
import torch
from mmengine.structures import InstanceData
from mmengine.utils import is_list_of
from parameteriz... | 166 | 6,858 |
mmdetection | tests/test_datasets/test_objects365.py | .py | # Copyright (c) OpenMMLab. All rights reserved.
import unittest
from mmdet.datasets import Objects365V1Dataset, Objects365V2Dataset
class TestObjects365V1Dataset(unittest.TestCase):
def test_obj365v1_dataset(self):
# test Objects365V1Dataset
metainfo = dict(classes=('bus', 'car'), task_name='new... | 80 | 3,362 |
mmdetection | tests/test_datasets/test_cityscapes.py | .py | # Copyright (c) OpenMMLab. All rights reserved.
import os
import unittest
from mmengine.fileio import dump
from mmdet.datasets import CityscapesDataset
class TestCityscapesDataset(unittest.TestCase):
def setUp(self) -> None:
image1 = {
'file_name': 'munster/munster_000102_000019_leftImg8bit... | 207 | 6,498 |
mmdetection | tests/test_datasets/test_youtube_vis_dataset.py | .py | # Copyright (c) OpenMMLab. All rights reserved.
from unittest import TestCase
from mmdet.datasets import YouTubeVISDataset
class TestYouTubeVISDataset(TestCase):
@classmethod
def setUpClass(cls):
cls.dataset = YouTubeVISDataset(
ann_file='tests/data/vis_sample.json', dataset_version='20... | 18 | 480 |
mmdetection | tests/test_datasets/test_lvis.py | .py | # Copyright (c) OpenMMLab. All rights reserved.
import os
import unittest
from mmengine.fileio import dump
from mmdet.datasets import LVISV1Dataset, LVISV05Dataset
try:
import lvis
except ImportError:
lvis = None
class TestLVISDataset(unittest.TestCase):
def setUp(self) -> None:
image1 = {
... | 232 | 7,429 |
mmdetection | tests/test_datasets/test_dsdldet.py | .py | # Copyright (c) OpenMMLab. All rights reserved.
import unittest
from mmdet.datasets import DSDLDetDataset
try:
from dsdl.dataset import DSDLDataset
except ImportError:
DSDLDataset = None
class TestDSDLDetDataset(unittest.TestCase):
def test_dsdldet_init(self):
if DSDLDataset is not None:
... | 26 | 734 |
mmdetection | tests/test_datasets/test_coco.py | .py | # Copyright (c) OpenMMLab. All rights reserved.
import unittest
from mmdet.datasets import CocoDataset
class TestCocoDataset(unittest.TestCase):
def test_coco_dataset(self):
# test CocoDataset
metainfo = dict(classes=('bus', 'car'), task_name='new_task')
dataset = CocoDataset(
... | 49 | 1,771 |
mmdetection | tests/test_datasets/test_coco_panoptic.py | .py | # Copyright (c) OpenMMLab. All rights reserved.
import os
import unittest
from mmengine.fileio import dump
from mmdet.datasets import CocoPanopticDataset
class TestCocoPanopticDataset(unittest.TestCase):
def setUp(self):
image1 = {
'id': 0,
'width': 640,
'height': 64... | 250 | 8,201 |
mmdetection | tests/test_datasets/test_coco_api_wrapper.py | .py | import os.path as osp
import tempfile
import unittest
from mmengine.fileio import dump
from mmdet.datasets.api_wrappers import COCOPanoptic
class TestCOCOPanoptic(unittest.TestCase):
def setUp(self):
self.tmp_dir = tempfile.TemporaryDirectory()
def tearDown(self):
self.tmp_dir.cleanup()
... | 68 | 1,647 |
mmdetection | tests/test_datasets/test_crowdhuman.py | .py | # Copyright (c) OpenMMLab. All rights reserved.
import unittest
from mmdet.datasets import CrowdHumanDataset
class TestCrowdHumanDataset(unittest.TestCase):
def test_crowdhuman_init(self):
dataset = CrowdHumanDataset(
data_root='tests/data/crowdhuman_dataset/',
ann_file='test_ann... | 17 | 521 |
mmdetection | tests/test_datasets/test_wider_face.py | .py | # Copyright (c) OpenMMLab. All rights reserved.
import unittest
import cv2
import numpy as np
from mmdet.datasets import WIDERFaceDataset
class TestWIDERFaceDataset(unittest.TestCase):
def setUp(self) -> None:
img_path = 'tests/data/WIDERFace/WIDER_train/0--Parade/0_Parade_marchingband_1_5.jpg' # noqa... | 29 | 880 |
mmdetection | tests/test_datasets/test_tta.py | .py | # Copyright (c) OpenMMLab. All rights reserved.
import os.path as osp
from unittest import TestCase
import mmcv
import pytest
from mmdet.datasets.transforms import * # noqa
from mmdet.registry import TRANSFORMS
class TestMuitiScaleFlipAug(TestCase):
def test_exception(self):
with pytest.raises(TypeErr... | 178 | 7,531 |
mmdetection | tests/test_datasets/test_reid_dataset.py | .py | # Copyright (c) OpenMMLab. All rights reserved.
import os.path as osp
from unittest import TestCase
from mmdet.datasets import ReIDDataset
PREFIX = osp.join(osp.dirname(__file__), '../data')
# This is a demo annotation file for ReIDDataset.
REID_ANN_FILE = f'{PREFIX}/demo_reid_data/mot17_reid/ann.txt'
class TestReI... | 65 | 2,231 |
mmdetection | tests/test_datasets/test_openimages.py | .py | # Copyright (c) OpenMMLab. All rights reserved.
import unittest
from mmdet.datasets import OpenImagesChallengeDataset, OpenImagesDataset
class TestOpenImagesDataset(unittest.TestCase):
def test_init(self):
dataset = OpenImagesDataset(
data_root='tests/data/OpenImages/',
ann_file=... | 37 | 1,439 |
mmdetection | tests/test_datasets/test_pascal_voc.py | .py | # Copyright (c) OpenMMLab. All rights reserved.
import unittest
from mmdet.datasets import VOCDataset
class TestVOCDataset(unittest.TestCase):
def test_voc2007_init(self):
dataset = VOCDataset(
data_root='tests/data/VOCdevkit/',
ann_file='VOC2007/ImageSets/Main/trainval.txt',
... | 39 | 1,367 |
mmdetection | tests/test_datasets/test_mot_challenge_dataset.py | .py | # Copyright (c) OpenMMLab. All rights reserved.
import unittest
from mmdet.datasets import MOTChallengeDataset
class TestMOTChallengeDataset(unittest.TestCase):
def test_mot_challenge_dataset(self):
# test CocoDataset
metainfo = dict(classes=('pedestrian'), task_name='new_task')
dataset ... | 38 | 1,553 |
mmdetection | tests/test_datasets/test_samplers/test_track_img_sampler.py | .py | # Copyright (c) OpenMMLab. All rights reserved.
from collections.abc import Iterable
from copy import deepcopy
from unittest import TestCase
from mmengine.dataset import ClassBalancedDataset, ConcatDataset
from mmdet.datasets import MOTChallengeDataset, TrackImgSampler
class TestTrackImgSampler(TestCase):
def ... | 93 | 3,601 |
mmdetection | tests/test_datasets/test_samplers/test_batch_sampler.py | .py | # Copyright (c) OpenMMLab. All rights reserved.
from unittest import TestCase
from unittest.mock import patch
import numpy as np
from mmengine.dataset import DefaultSampler
from torch.utils.data import Dataset
from mmdet.datasets.samplers import AspectRatioBatchSampler
class DummyDataset(Dataset):
def __init_... | 81 | 2,989 |
mmdetection | tests/test_datasets/test_samplers/test_multi_source_sampler.py | .py | # Copyright (c) OpenMMLab. All rights reserved.
import bisect
from unittest import TestCase
from unittest.mock import patch
import numpy as np
from torch.utils.data import ConcatDataset, Dataset
from mmdet.datasets.samplers import GroupMultiSourceSampler, MultiSourceSampler
class DummyDataset(Dataset):
def __... | 108 | 3,732 |
mmdetection | tests/test_datasets/test_transforms/test_geometric.py | .py | # Copyright (c) OpenMMLab. All rights reserved.
import copy
import unittest
import numpy as np
from mmdet.datasets.transforms import (GeomTransform, Rotate, ShearX, ShearY,
TranslateX, TranslateY)
from mmdet.structures.bbox import HorizontalBoxes
from mmdet.structures.mask impor... | 753 | 34,115 |
mmdetection | tests/test_datasets/test_transforms/test_instaboost.py | .py | import os.path as osp
import unittest
import numpy as np
from mmdet.registry import TRANSFORMS
from mmdet.utils import register_all_modules
register_all_modules()
class TestInstaboost(unittest.TestCase):
def setUp(self):
"""Setup the model and optimizer which are used in every test method.
Te... | 59 | 1,782 |
mmdetection | tests/test_datasets/test_transforms/utils.py | .py | # Copyright (c) OpenMMLab. All rights reserved.
import numpy as np
from mmengine.testing import assert_allclose
from mmdet.structures.bbox import BaseBoxes, HorizontalBoxes
from mmdet.structures.mask import BitmapMasks, PolygonMasks
def create_random_bboxes(num_bboxes, img_w, img_h):
bboxes_left_top = np.random.... | 77 | 3,135 |
mmdetection | tests/test_datasets/test_transforms/test_transforms.py | .py | # Copyright (c) OpenMMLab. All rights reserved.
import copy
import os.path as osp
import unittest
import mmcv
import numpy as np
import torch
from mmcv.transforms import LoadImageFromFile
# yapf:disable
from mmdet.datasets.transforms import (CopyPaste, CutOut, Expand,
FixScaleRe... | 1,791 | 75,490 |
mmdetection | tests/test_datasets/test_transforms/test_wrappers.py | .py | import copy
import os.path as osp
import unittest
from mmcv.transforms import Compose
from mmdet.datasets.transforms import MultiBranch, RandomOrder
from mmdet.utils import register_all_modules
from .utils import construct_toy_data
register_all_modules()
class TestMultiBranch(unittest.TestCase):
def setUp(sel... | 174 | 6,834 |
mmdetection | tests/test_datasets/test_transforms/test_augment_wrappers.py | .py | # Copyright (c) OpenMMLab. All rights reserved.
import copy
import unittest
from mmdet.datasets.transforms import (AutoAugment, AutoContrast, Brightness,
Color, Contrast, Equalize, Invert,
Posterize, RandAugment, Rotate,
... | 250 | 10,790 |
mmdetection | tests/test_datasets/test_transforms/__init__.py | .py | # Copyright (c) OpenMMLab. All rights reserved.
from .utils import construct_toy_data, create_full_masks, create_random_bboxes
__all__ = ['create_random_bboxes', 'create_full_masks', 'construct_toy_data']
| 5 | 206 |
mmdetection | tests/test_datasets/test_transforms/test_colorspace.py | .py | # Copyright (c) OpenMMLab. All rights reserved.
import copy
import unittest
from mmdet.datasets.transforms import (AutoContrast, Brightness, Color,
ColorTransform, Contrast, Equalize,
Invert, Posterize, Sharpness, Solarize,
... | 366 | 14,352 |
mmdetection | tests/test_datasets/test_transforms/test_formatting.py | .py | # Copyright (c) OpenMMLab. All rights reserved.
import copy
import os.path as osp
import unittest
import numpy as np
import torch
from mmengine.structures import InstanceData, LabelData, PixelData
from mmdet.datasets.transforms import (PackDetInputs, PackReIDInputs,
PackTrackInp... | 247 | 10,369 |
mmdetection | tests/test_datasets/test_transforms/test_frame_sampling.py | .py | import unittest
import numpy as np
from mmdet.datasets.transforms import BaseFrameSample, UniformRefFrameSample
class TestFrameSample(unittest.TestCase):
def setUp(self):
"""Setup the model and optimizer which are used in every test method.
TestCase calls functions in this order: setUp() -> te... | 120 | 4,798 |
mmdetection | tests/test_datasets/test_transforms/test_loading.py | .py | # Copyright (c) OpenMMLab. All rights reserved.
import copy
import os
import os.path as osp
import sys
import unittest
from unittest.mock import MagicMock, Mock, patch
import mmcv
import numpy as np
from mmdet.datasets.transforms import (FilterAnnotations, LoadAnnotations,
LoadE... | 540 | 20,474 |
mmdetection | tests/test_visualization/test_local_visualizer.py | .py | import os
from unittest import TestCase
import cv2
import numpy as np
import torch
from mmengine.structures import InstanceData, PixelData
from mmdet.evaluation import INSTANCE_OFFSET
from mmdet.structures import DetDataSample
from mmdet.visualization import DetLocalVisualizer, TrackLocalVisualizer
def _rand_bboxes... | 200 | 7,076 |
mmdetection | tests/test_visualization/test_palette.py | .py | # Copyright (c) OpenMMLab. All rights reserved.
import numpy as np
from mmdet.datasets import CocoDataset
from mmdet.visualization import get_palette, jitter_color, palette_val
def test_palette():
assert palette_val([(1, 2, 3)])[0] == (1 / 255, 2 / 255, 3 / 255)
# test list
palette = [(1, 0, 0), (0, 1, ... | 59 | 2,013 |
mmdetection | tests/test_utils/test_setup_env.py | .py | import datetime
import sys
from unittest import TestCase
from mmengine import DefaultScope
from mmdet.utils import register_all_modules
class TestSetupEnv(TestCase):
def test_register_all_modules(self):
from mmdet.registry import DATASETS
# not init default scope
sys.modules.pop('mmdet... | 39 | 1,426 |
mmdetection | tests/test_utils/test_replace_cfg_vals.py | .py | import os.path as osp
import tempfile
from copy import deepcopy
import pytest
from mmengine.config import Config
from mmdet.utils import replace_cfg_vals
def test_replace_cfg_vals():
temp_file = tempfile.NamedTemporaryFile()
cfg_path = f'{temp_file.name}.py'
with open(cfg_path, 'w') as f:
f.writ... | 84 | 2,985 |
mmdetection | tests/test_utils/test_benchmark.py | .py | import copy
import os
import tempfile
import unittest
import torch
from mmengine import Config, MMLogger
from mmengine.dataset import Compose
from mmengine.model import BaseModel
from torch.utils.data import Dataset
from mmdet.registry import DATASETS, MODELS
from mmdet.utils import register_all_modules
from mmdet.ut... | 310 | 11,696 |
mmdetection | tests/test_utils/test_memory.py | .py | import numpy as np
import pytest
import torch
from mmdet.utils import AvoidOOM
from mmdet.utils.memory import cast_tensor_type
def test_avoidoom():
tensor = torch.from_numpy(np.random.random((20, 20)))
if torch.cuda.is_available():
tensor = tensor.cuda()
# get default result
default_r... | 99 | 4,261 |
mmdetection | tests/test_engine/test_hooks/test_num_class_check_hook.py | .py | # Copyright (c) OpenMMLab. All rights reserved.
from copy import deepcopy
from unittest import TestCase
from unittest.mock import Mock
from mmcv.cnn import VGG
from mmengine.dataset import BaseDataset
from torch import nn
from mmdet.engine.hooks import NumClassCheckHook
from mmdet.models.roi_heads.mask_heads import F... | 107 | 3,988 |
mmdetection | tests/test_engine/test_hooks/test_pipeline_switch_hook.py | .py | # Copyright (c) OpenMMLab. All rights reserved.
from unittest import TestCase
from unittest.mock import Mock
from mmdet.engine.hooks import PipelineSwitchHook
class TestPipelineSwitchHook(TestCase):
def test_persistent_workers_on(self):
runner = Mock()
runner.model = Mock()
runner.model.... | 85 | 3,469 |
mmdetection | tests/test_engine/test_hooks/test_visualization_hook.py | .py | # Copyright (c) OpenMMLab. All rights reserved.
import os.path as osp
import shutil
import time
from unittest import TestCase
from unittest.mock import Mock
import torch
from mmengine.structures import InstanceData
from mmdet.engine.hooks import DetVisualizationHook, TrackVisualizationHook
from mmdet.structures impor... | 121 | 4,533 |
mmdetection | tests/test_engine/test_hooks/test_mean_teacher_hook.py | .py | # Copyright (c) OpenMMLab. All rights reserved.
import os.path as osp
import tempfile
from unittest import TestCase
import torch
import torch.nn as nn
from mmengine.evaluator import BaseMetric
from mmengine.model import BaseModel
from mmengine.optim import OptimWrapper
from mmengine.registry import MODEL_WRAPPERS
from... | 176 | 5,299 |
mmdetection | tests/test_engine/test_hooks/test_sync_norm_hook.py | .py | # Copyright (c) OpenMMLab. All rights reserved.
from unittest import TestCase
from unittest.mock import Mock, patch
import torch.nn as nn
from mmdet.engine.hooks import SyncNormHook
class TestSyncNormHook(TestCase):
@patch(
'mmdet.engine.hooks.sync_norm_hook.get_dist_info', return_value=(0, 1))
def... | 42 | 1,374 |
mmdetection | tests/test_engine/test_hooks/test_memory_profiler_hook.py | .py | # Copyright (c) OpenMMLab. All rights reserved.
from unittest import TestCase
from unittest.mock import Mock
from mmdet.engine.hooks import MemoryProfilerHook
class TestMemoryProfilerHook(TestCase):
def test_after_train_iter(self):
hook = MemoryProfilerHook(2)
runner = Mock()
runner.logg... | 39 | 1,222 |
mmdetection | tests/test_engine/test_hooks/test_yolox_mode_switch_hook.py | .py | # Copyright (c) OpenMMLab. All rights reserved.
from unittest import TestCase
from unittest.mock import Mock, patch
from mmdet.engine.hooks import YOLOXModeSwitchHook
class TestYOLOXModeSwitchHook(TestCase):
@patch('mmdet.engine.hooks.yolox_mode_switch_hook.is_model_wrapper')
def test_is_model_wrapper_and_p... | 75 | 3,085 |
mmdetection | tests/test_engine/test_hooks/test_checkloss_hook.py | .py | # Copyright (c) OpenMMLab. All rights reserved.
from unittest import TestCase
from unittest.mock import Mock
import torch
from mmdet.engine.hooks import CheckInvalidLossHook
class TestCheckInvalidLossHook(TestCase):
def test_after_train_iter(self):
n = 50
hook = CheckInvalidLossHook(n)
... | 38 | 1,372 |
mmdetection | tests/test_engine/test_optimizers/test_layer_decay_optimizer_constructor.py | .py | # Copyright (c) OpenMMLab. All rights reserved.
import torch
import torch.nn as nn
from mmcv.cnn import ConvModule
from mmdet.engine import LearningRateDecayOptimizerConstructor
base_lr = 1
decay_rate = 2
base_wd = 0.05
weight_decay = 0.05
expected_stage_wise_lr_wd_convnext = [{
'weight_decay': 0.0,
'lr_scal... | 169 | 4,532 |
mmdetection | tests/test_engine/test_runner/test_loops.py | .py | # Copyright (c) OpenMMLab. All rights reserved.
import tempfile
from unittest import TestCase
from unittest.mock import Mock
import torch
import torch.nn as nn
from mmengine.evaluator import Evaluator
from mmengine.model import BaseModel
from mmengine.optim import OptimWrapper
from mmengine.runner import Runner
from t... | 114 | 3,296 |
mmdetection | tests/test_engine/test_schedulers/test_quadratic_warmup.py | .py | # Copyright (c) OpenMMLab. All rights reserved.
from unittest import TestCase
import torch
import torch.nn.functional as F
import torch.optim as optim
from mmengine.optim.scheduler import _ParamScheduler
from mmengine.testing import assert_allclose
from mmdet.engine.schedulers import (QuadraticWarmupLR,
... | 109 | 4,323 |
mmdetection | tests/data/dsdl_det/config.py | .py | local = dict(
type='LocalFileReader',
working_dir='local path',
)
| 5 | 74 |
mmdetection | tests/data/configs_mmtrack/tracktor_faster-rcnn_r50_fpn_4e.py | .py | _base_ = [
'./faster-rcnn_r50_fpn.py', './mot_challenge.py',
'../../../configs/_base_/default_runtime.py'
]
model = dict(
type='Tracktor',
pretrains=dict(
detector= # noqa: E251
'https://download.openmmlab.com/mmtracking/mot/faster_rcnn/faster-rcnn_r50_fpn_4e_mot17-half-64ee2ed4.pth', ... | 71 | 2,309 |
mmdetection | tests/data/configs_mmtrack/faster_rcnn_r50_fpn.py | .py | model = dict(
detector=dict(
type='FasterRCNN',
backbone=dict(
type='ResNet',
depth=18,
base_channels=2,
num_stages=4,
out_indices=(0, 1, 2, 3),
frozen_stages=1,
norm_cfg=dict(type='BN', requires_grad=True),
... | 110 | 4,013 |
mmdetection | tests/data/configs_mmtrack/faster_rcnn_r50_dc5.py | .py | model = dict(
detector=dict(
type='FasterRCNN',
backbone=dict(
type='ResNet',
depth=18,
base_channels=2,
num_stages=4,
out_indices=(3, ),
strides=(1, 2, 2, 1),
dilations=(1, 1, 1, 2),
frozen_stages=1,
... | 114 | 4,091 |
mmdetection | tests/data/configs_mmtrack/mot_challenge.py | .py | # dataset settings
dataset_type = 'MOTChallengeDataset'
img_norm_cfg = dict(
mean=[123.675, 116.28, 103.53], std=[58.395, 57.12, 57.375], to_rgb=True)
train_pipeline = [
dict(type='LoadMultiImagesFromFile', to_float32=True),
dict(type='SeqLoadAnnotations', with_bbox=True, with_track=True),
dict(
... | 75 | 2,465 |
mmdetection | tests/data/configs_mmtrack/selsa_faster_rcnn_r101_dc5_1x.py | .py | _base_ = [
'./faster_rcnn_r50_dc5.py', './mot_challenge.py',
'../../../configs/_base_/default_runtime.py'
]
model = dict(
type='SELSA',
pretrains=None,
detector=dict(
backbone=dict(depth=18, base_channels=2),
roi_head=dict(
type='SelsaRoIHead',
bbox_head=dict(... | 49 | 1,351 |
mmdetection | tests/test_evaluation/test_metrics/test_coco_video_metric.py | .py | # Copyright (c) OpenMMLab. All rights reserved.
import os.path as osp
import tempfile
from unittest import TestCase
import numpy as np
import pycocotools.mask as mask_util
import torch
from mmengine.fileio import dump
from mmengine.structures import BaseDataElement, InstanceData
from mmdet.evaluation import CocoVideo... | 414 | 16,028 |
mmdetection | tests/test_evaluation/test_metrics/test_coco_metric.py | .py | import os.path as osp
import tempfile
from unittest import TestCase
import numpy as np
import pycocotools.mask as mask_util
import torch
from mmengine.fileio import dump
from mmdet.evaluation import CocoMetric
class TestCocoMetric(TestCase):
def _create_dummy_coco_json(self, json_name):
dummy_mask = np... | 402 | 14,349 |
mmdetection | tests/test_evaluation/test_metrics/test_coco_occluded_metric.py | .py | # Copyright (c) OpenMMLab. All rights reserved.
import os.path as osp
from tempfile import TemporaryDirectory
import mmengine
import numpy as np
from mmdet.datasets import CocoDataset
from mmdet.evaluation import CocoOccludedSeparatedMetric
def test_coco_occluded_separated_metric():
ann = [[
'fake1.jpg'... | 47 | 1,470 |
mmdetection | tests/test_evaluation/test_metrics/test_coco_panoptic_metric.py | .py | import os
import os.path as osp
import tempfile
import unittest
from copy import deepcopy
import mmcv
import numpy as np
import torch
from mmengine.fileio import dump
from mmdet.evaluation import INSTANCE_OFFSET, CocoPanopticMetric
try:
import panopticapi
except ImportError:
panopticapi = None
class TestCo... | 286 | 9,287 |
mmdetection | tests/test_evaluation/test_metrics/test_crowdhuman_metric.py | .py | import os.path as osp
import tempfile
from unittest import TestCase
import numpy as np
import torch
from mmdet.evaluation import CrowdHumanMetric
class TestCrowdHumanMetric(TestCase):
def _create_dummy_results(self):
bboxes = np.array([[1330, 317, 418, 1338], [792, 24, 723, 2017],
... | 55 | 1,834 |
mmdetection | tests/test_evaluation/test_metrics/test_cityscapes_metric.py | .py | import os
import os.path as osp
import tempfile
import unittest
import numpy as np
import torch
from PIL import Image
from mmdet.evaluation import CityScapesMetric
try:
import cityscapesscripts
except ImportError:
cityscapesscripts = None
class TestCityScapesMetric(unittest.TestCase):
def setUp(self):... | 103 | 3,528 |
mmdetection | tests/test_evaluation/test_metrics/test_lvis_metric.py | .py | import os.path as osp
import tempfile
import unittest
import numpy as np
import pycocotools.mask as mask_util
import torch
from mmdet.evaluation.metrics import LVISMetric
try:
import lvis
except ImportError:
lvis = None
from mmengine.fileio import dump
class TestLVISMetric(unittest.TestCase):
def _cr... | 364 | 13,025 |
mmdetection | tests/test_evaluation/test_metrics/test_youtube_vis_metric.py | .py | # Copyright (c) OpenMMLab. All rights reserved.
import os
import tempfile
from unittest import TestCase
import numpy as np
import pycocotools.mask as mask_util
import torch
from mmengine.registry import init_default_scope
from mmengine.structures import BaseDataElement, InstanceData
from mmdet.registry import METRICS... | 172 | 6,308 |
mmdetection | tests/test_evaluation/test_metrics/test_reid_metric.py | .py | # Copyright (c) OpenMMLab. All rights reserved.
from unittest import TestCase
import torch
from mmengine.registry import init_default_scope
from mmdet.registry import METRICS
from mmdet.structures import ReIDDataSample
class TestReIDMetrics(TestCase):
@classmethod
def setUpClass(cls):
init_default_... | 56 | 2,016 |
mmdetection | tests/test_evaluation/test_metrics/test_openimages_metric.py | .py | # Copyright (c) OpenMMLab. All rights reserved.
import unittest
import numpy as np
import torch
from mmdet.datasets import OpenImagesDataset
from mmdet.evaluation import OpenImagesMetric
from mmdet.utils import register_all_modules
class TestOpenImagesMetric(unittest.TestCase):
def _create_dummy_results(self):... | 73 | 2,768 |
mmdetection | tests/test_evaluation/test_metrics/test_mot_challenge_metrics.py | .py | # Copyright (c) OpenMMLab. All rights reserved.
import copy
import os
import tempfile
from unittest import TestCase
import torch
from mmengine.structures import BaseDataElement, InstanceData
from mmdet.evaluation import MOTChallengeMetric
from mmdet.structures import DetDataSample, TrackDataSample
class TestMOTChal... | 117 | 4,061 |
mmdetection | tests/test_evaluation/test_metrics/test_dump_det_results.py | .py | # Copyright (c) OpenMMLab. All rights reserved.
import os.path as osp
import tempfile
from unittest import TestCase
import torch
from mmengine.fileio import load
from torch import Tensor
from mmdet.evaluation import DumpDetResults
from mmdet.structures.mask import encode_mask_results
class TestDumpResults(TestCase)... | 54 | 1,963 |
mmdetection | tests/test_models/test_backbones/test_renext.py | .py | # Copyright (c) OpenMMLab. All rights reserved.
import pytest
import torch
from mmdet.models.backbones import ResNeXt
from mmdet.models.backbones.resnext import Bottleneck as BottleneckX
from .utils import is_block
def test_renext_bottleneck():
with pytest.raises(AssertionError):
# Style must be in ['pyt... | 106 | 3,528 |
mmdetection | tests/test_models/test_backbones/test_regnet.py | .py | # Copyright (c) OpenMMLab. All rights reserved.
import pytest
import torch
from mmdet.models.backbones import RegNet
regnet_test_data = [
('regnetx_400mf',
dict(w0=24, wa=24.48, wm=2.54, group_w=16, depth=22,
bot_mul=1.0), [32, 64, 160, 384]),
('regnetx_800mf',
dict(w0=56, wa=35.73, wm=2.2... | 59 | 2,177 |
mmdetection | tests/test_models/test_backbones/test_detectors_resnet.py | .py | # Copyright (c) OpenMMLab. All rights reserved.
import pytest
from mmdet.models.backbones import DetectoRS_ResNet
def test_detectorrs_resnet_backbone():
detectorrs_cfg = dict(
depth=50,
num_stages=4,
out_indices=(0, 1, 2, 3),
frozen_stages=1,
norm_cfg=dict(type='BN', requi... | 48 | 1,611 |
mmdetection | tests/test_models/test_backbones/test_mobilenet_v2.py | .py | # Copyright (c) OpenMMLab. All rights reserved.
import pytest
import torch
from torch.nn.modules import GroupNorm
from torch.nn.modules.batchnorm import _BatchNorm
from mmdet.models.backbones.mobilenet_v2 import MobileNetV2
from .utils import check_norm_state, is_block, is_norm
def test_mobilenetv2_backbone():
w... | 174 | 6,546 |
mmdetection | tests/test_models/test_backbones/utils.py | .py | # Copyright (c) OpenMMLab. All rights reserved.
from torch.nn.modules import GroupNorm
from torch.nn.modules.batchnorm import _BatchNorm
from mmdet.models.backbones.res2net import Bottle2neck
from mmdet.models.backbones.resnet import BasicBlock, Bottleneck
from mmdet.models.backbones.resnext import Bottleneck as Bottl... | 33 | 1,027 |
mmdetection | tests/test_models/test_backbones/test_efficientnet.py | .py | import pytest
import torch
from mmdet.models.backbones import EfficientNet
def test_efficientnet_backbone():
"""Test EfficientNet backbone."""
with pytest.raises(AssertionError):
# EfficientNet arch should be a key in EfficientNet.arch_settings
EfficientNet(arch='c3')
model = EfficientNe... | 26 | 859 |
mmdetection | tests/test_models/test_backbones/test_trident_resnet.py | .py | # Copyright (c) OpenMMLab. All rights reserved.
import pytest
import torch
from mmdet.models.backbones import TridentResNet
from mmdet.models.backbones.trident_resnet import TridentBottleneck
def test_trident_resnet_bottleneck():
trident_dilations = (1, 2, 3)
test_branch_idx = 1
concat_output = True
... | 181 | 6,372 |
mmdetection | tests/test_models/test_backbones/__init__.py | .py | # Copyright (c) OpenMMLab. All rights reserved.
from .utils import check_norm_state, is_block, is_norm
__all__ = ['is_block', 'is_norm', 'check_norm_state']
| 5 | 158 |
mmdetection | tests/test_models/test_backbones/test_swin.py | .py | import pytest
import torch
from mmdet.models.backbones.swin import SwinBlock, SwinTransformer
def test_swin_block():
# test SwinBlock structure and forward
block = SwinBlock(embed_dims=64, num_heads=4, feedforward_channels=256)
assert block.ffn.embed_dims == 64
assert block.attn.w_msa.num_heads == 4
... | 83 | 2,648 |
mmdetection | tests/test_models/test_backbones/test_hourglass.py | .py | # Copyright (c) OpenMMLab. All rights reserved.
import pytest
import torch
from mmdet.models.backbones.hourglass import HourglassNet
def test_hourglass_backbone():
with pytest.raises(AssertionError):
# HourglassNet's num_stacks should larger than 0
HourglassNet(num_stacks=0)
with pytest.rais... | 50 | 1,464 |
mmdetection | tests/test_models/test_backbones/test_pvt.py | .py | import pytest
import torch
from mmdet.models.backbones.pvt import (PVTEncoderLayer,
PyramidVisionTransformer,
PyramidVisionTransformerV2)
def test_pvt_block():
# test PVT structure and forward
block = PVTEncoderLayer(
emb... | 104 | 3,332 |
mmdetection | tests/test_models/test_backbones/test_csp_darknet.py | .py | # Copyright (c) OpenMMLab. All rights reserved.
import pytest
import torch
from torch.nn.modules.batchnorm import _BatchNorm
from mmdet.models.backbones.csp_darknet import CSPDarknet
from .utils import check_norm_state, is_norm
def test_csp_darknet_backbone():
with pytest.raises(ValueError):
# frozen_sta... | 117 | 4,117 |
mmdetection | tests/test_models/test_backbones/test_res2net.py | .py | # Copyright (c) OpenMMLab. All rights reserved.
import pytest
import torch
from mmdet.models.backbones import Res2Net
from mmdet.models.backbones.res2net import Bottle2neck
from .utils import is_block
def test_res2net_bottle2neck():
with pytest.raises(AssertionError):
# Style must be in ['pytorch', 'caff... | 63 | 1,976 |
mmdetection | tests/test_models/test_backbones/test_hrnet.py | .py | # Copyright (c) OpenMMLab. All rights reserved.
import pytest
import torch
from mmdet.models.backbones.hrnet import HRModule, HRNet
from mmdet.models.backbones.resnet import BasicBlock, Bottleneck
@pytest.mark.parametrize('block', [BasicBlock, Bottleneck])
def test_hrmodule(block):
# Test multiscale forward
... | 112 | 3,089 |
mmdetection | tests/test_models/test_backbones/test_resnest.py | .py | # Copyright (c) OpenMMLab. All rights reserved.
import pytest
import torch
from mmdet.models.backbones import ResNeSt
from mmdet.models.backbones.resnest import Bottleneck as BottleneckS
def test_resnest_bottleneck():
with pytest.raises(AssertionError):
# Style must be in ['pytorch', 'caffe']
Bot... | 48 | 1,473 |
mmdetection | tests/test_models/test_backbones/test_resnet.py | .py | # Copyright (c) OpenMMLab. All rights reserved.
import pytest
import torch
from mmcv.ops import DeformConv2dPack
from torch.nn.modules import AvgPool2d, GroupNorm
from torch.nn.modules.batchnorm import _BatchNorm
from mmdet.models.backbones import ResNet, ResNetV1d
from mmdet.models.backbones.resnet import BasicBlock,... | 655 | 23,003 |
mmdetection | tests/test_models/test_vis/test_masktrack_rcnn.py | .py | # Copyright (c) OpenMMLab. All rights reserved.
import time
import unittest
from unittest import TestCase
import torch
from mmengine.logging import MessageHub
from mmengine.registry import init_default_scope
from parameterized import parameterized
from mmdet.registry import MODELS
from mmdet.testing import demo_track... | 100 | 3,513 |
mmdetection | tests/test_models/test_vis/test_mask2former.py | .py | # Copyright (c) OpenMMLab. All rights reserved.
import time
import unittest
from unittest import TestCase
import torch
from mmengine.logging import MessageHub
from mmengine.registry import init_default_scope
from parameterized import parameterized
from mmdet.registry import MODELS
from mmdet.testing import demo_track... | 97 | 3,400 |
mmdetection | tests/test_models/test_seg_heads/test_heuristic_fusion_head.py | .py | import unittest
import torch
from mmengine.config import Config
from mmengine.structures import InstanceData
from mmengine.testing import assert_allclose
from mmdet.evaluation import INSTANCE_OFFSET
from mmdet.models.seg_heads.panoptic_fusion_heads import HeuristicFusionHead
class TestHeuristicFusionHead(unittest.T... | 60 | 2,440 |
mmdetection | tests/test_models/test_seg_heads/test_maskformer_fusion_head.py | .py | # Copyright (c) OpenMMLab. All rights reserved.
import unittest
import torch
from mmengine.config import Config
from mmdet.models.seg_heads.panoptic_fusion_heads import MaskFormerFusionHead
from mmdet.structures import DetDataSample
class TestMaskFormerFusionHead(unittest.TestCase):
def test_loss(self):
... | 109 | 3,827 |
mmdetection | tests/test_models/test_seg_heads/test_panoptic_fpn_head.py | .py | import unittest
import torch
from mmengine.structures import PixelData
from mmengine.testing import assert_allclose
from mmdet.models.seg_heads import PanopticFPNHead
from mmdet.structures import DetDataSample
class TestPanopticFPNHead(unittest.TestCase):
def test_init_weights(self):
head = PanopticFPN... | 71 | 2,446 |
mmdetection | tests/test_models/test_layers/test_conv_upsample.py | .py | # Copyright (c) OpenMMLab. All rights reserved.
import pytest
import torch
from mmdet.models.layers import ConvUpsample
@pytest.mark.parametrize('num_layers', [0, 1, 2])
def test_conv_upsample(num_layers):
num_upsample = num_layers if num_layers > 0 else 0
num_layers = num_layers if num_layers > 0 else 1
... | 25 | 629 |
mmdetection | tests/test_models/test_layers/test_brick_wrappers.py | .py | from unittest.mock import patch
import torch
import torch.nn as nn
import torch.nn.functional as F
from mmdet.models.layers import AdaptiveAvgPool2d, adaptive_avg_pool2d
if torch.__version__ != 'parrots':
torch_version = '1.7'
else:
torch_version = 'parrots'
@patch('torch.__version__', torch_version)
def t... | 94 | 2,932 |
mmdetection | tests/test_models/test_layers/test_position_encoding.py | .py | # Copyright (c) OpenMMLab. All rights reserved.
import pytest
import torch
from mmdet.models.layers import (LearnedPositionalEncoding,
SinePositionalEncoding)
def test_sine_positional_encoding(num_feats=16, batch_size=2):
# test invalid type of scale
with pytest.raises(Assert... | 40 | 1,439 |
mmdetection | tests/test_models/test_layers/test_se_layer.py | .py | # Copyright (c) OpenMMLab. All rights reserved.
import pytest
import torch
import torch.nn.functional as F
from mmengine.model import constant_init
from mmdet.models.layers import DyReLU, SELayer
def test_se_layer():
with pytest.raises(AssertionError):
# act_cfg sequence length must equal to 2
SE... | 55 | 1,623 |
mmdetection | tests/test_models/test_layers/test_inverted_residual.py | .py | # Copyright (c) OpenMMLab. All rights reserved.
import pytest
import torch
from mmcv.cnn import is_norm
from torch.nn.modules import GroupNorm
from mmdet.models.layers import InvertedResidual, SELayer
def test_inverted_residual():
with pytest.raises(AssertionError):
# stride must be in [1, 2]
In... | 77 | 2,636 |
mmdetection | tests/test_models/test_layers/test_transformer.py | .py | # Copyright (c) OpenMMLab. All rights reserved.
import pytest
import torch
from mmengine.config import ConfigDict
from mmdet.models.layers.transformer import (AdaptivePadding,
DDQTransformerDecoder,
DetrTransformerDecoder,
... | 525 | 15,404 |
mmdetection | tests/test_models/test_layers/test_ema.py | .py | # Copyright (c) OpenMMLab. All rights reserved.
import itertools
import math
from unittest import TestCase
import torch
import torch.nn as nn
from mmengine.testing import assert_allclose
from mmdet.models.layers import ExpMomentumEMA
class TestEMA(TestCase):
def test_exp_momentum_ema(self):
model = nn.... | 95 | 3,633 |
mmdetection | tests/test_models/test_layers/test_plugins.py | .py | # Copyright (c) OpenMMLab. All rights reserved.
import unittest
import pytest
import torch
from mmengine.config import ConfigDict
from mmdet.models.layers import DropBlock
from mmdet.registry import MODELS
from mmdet.utils import register_all_modules
register_all_modules()
def test_dropblock():
feat = torch.ra... | 172 | 6,408 |
mmdetection | tests/test_models/test_losses/test_loss.py | .py | # Copyright (c) OpenMMLab. All rights reserved.
import json
import pytest
import torch
import torch.nn.functional as F
from mmengine.utils import digit_version
from mmdet.models.losses import (BalancedL1Loss, CrossEntropyLoss, DDQAuxLoss,
DiceLoss, DistributionFocalLoss, EQLV2Loss,
... | 347 | 12,737 |
mmdetection | tests/test_models/test_losses/test_multi_pos_cross_entropy_loss.py | .py | # Copyright (c) OpenMMLab. All rights reserved.
from unittest import TestCase
import torch
from mmdet.models.losses import MultiPosCrossEntropyLoss
class TestMultiPosCrossEntropyLoss(TestCase):
def test_mpce_loss(self):
costs = torch.tensor([[1, 0], [0, 1]])
labels = torch.tensor([[1, 1], [0, 0... | 21 | 620 |
mmdetection | tests/test_models/test_losses/test_gaussian_focal_loss.py | .py | import unittest
import torch
from mmdet.models.losses import GaussianFocalLoss
class TestGaussianFocalLoss(unittest.TestCase):
def test_forward(self):
pred = torch.rand((10, 4))
target = torch.rand((10, 4))
gaussian_focal_loss = GaussianFocalLoss()
loss1 = gaussian_focal_loss(pr... | 39 | 1,303 |
mmdetection | tests/test_models/test_losses/test_triplet_loss.py | .py | # Copyright (c) OpenMMLab. All rights reserved.
from unittest import TestCase
import torch
from mmdet.models import TripletLoss
class TestTripletLoss(TestCase):
def test_triplet_loss(self):
feature = torch.Tensor([[1, 1], [1, 1], [0, 0], [0, 0]])
label = torch.Tensor([1, 1, 0, 0])
loss... | 20 | 552 |
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