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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ERD | ERD-main/tests/test_models/test_roi_heads/test_bbox_heads/test_scnet_bbox_head.py | import unittest
import torch
from mmdet.models.roi_heads.bbox_heads import SCNetBBoxHead
class TestSCNetBBoxHead(unittest.TestCase):
def test_forward(self):
x = torch.rand((2, 1, 16, 16))
bbox_head = SCNetBBoxHead(
num_shared_fcs=2,
in_channels=1,
roi_feat_si... | 600 | 25.130435 | 59 | py |
ERD | ERD-main/tests/test_models/test_roi_heads/test_bbox_heads/test_double_bbox_head.py | # Copyright (c) OpenMMLab. All rights reserved.
import unittest
from unittest import TestCase
import torch
from parameterized import parameterized
from mmdet.models.roi_heads.bbox_heads import DoubleConvFCBBoxHead
class TestDoubleBboxHead(TestCase):
@parameterized.expand(['cpu', 'cuda'])
def test_forward_l... | 876 | 28.233333 | 72 | py |
ERD | ERD-main/tests/test_models/test_roi_heads/test_bbox_heads/test_sabl_bbox_head.py | # Copyright (c) OpenMMLab. All rights reserved.
from unittest import TestCase
import torch
from mmengine.config import ConfigDict
from mmengine.structures import InstanceData
from mmdet.models.roi_heads.bbox_heads import SABLHead
from mmdet.models.task_modules.samplers import SamplingResult
class TestSABLBboxHead(T... | 5,052 | 35.615942 | 78 | py |
ERD | ERD-main/tests/test_models/test_roi_heads/test_bbox_heads/test_bbox_head.py | # Copyright (c) OpenMMLab. All rights reserved.
from unittest import TestCase
import torch
from mmengine.config import ConfigDict
from mmengine.structures import InstanceData
from mmdet.models.roi_heads.bbox_heads import (BBoxHead, Shared2FCBBoxHead,
Shared4Conv1FCBBoxHe... | 5,287 | 36.503546 | 78 | py |
ERD | ERD-main/tests/test_models/test_roi_heads/test_bbox_heads/test_multi_instance_bbox_head.py | # Copyright (c) OpenMMLab. All rights reserved.
from unittest import TestCase
import torch
from mmengine.config import ConfigDict
from mmengine.structures import InstanceData
from mmdet.models.roi_heads.bbox_heads import MultiInstanceBBoxHead
class TestMultiInstanceBBoxHead(TestCase):
def test_init(self):
... | 4,203 | 34.327731 | 74 | py |
ERD | ERD-main/tests/test_models/test_roi_heads/test_mask_heads/test_fused_semantic_head.py | # Copyright (c) OpenMMLab. All rights reserved.
import unittest
from unittest import TestCase
import torch
from parameterized import parameterized
from torch import Tensor
from mmdet.models.roi_heads.mask_heads import FusedSemanticHead
class TestFusedSemanticHead(TestCase):
@parameterized.expand(['cpu', 'cuda'... | 1,013 | 28.823529 | 72 | py |
ERD | ERD-main/tests/test_models/test_roi_heads/test_mask_heads/test_maskiou_head.py | # Copyright (c) OpenMMLab. All rights reserved.
import unittest
from unittest import TestCase
import torch
from mmengine.config import ConfigDict
from mmengine.structures import InstanceData
from parameterized import parameterized
from mmdet.models.roi_heads.mask_heads import MaskIoUHead
from mmdet.models.utils impor... | 4,094 | 39.544554 | 79 | py |
ERD | ERD-main/tests/test_models/test_roi_heads/test_mask_heads/test_scnet_mask_head.py | # Copyright (c) OpenMMLab. All rights reserved.
import unittest
from unittest import TestCase
import torch
from parameterized import parameterized
from torch import Tensor
from mmdet.models.roi_heads.mask_heads import SCNetMaskHead
class TestSCNetMaskHead(TestCase):
@parameterized.expand(['cpu', 'cuda'])
d... | 828 | 26.633333 | 72 | py |
ERD | ERD-main/tests/test_models/test_roi_heads/test_mask_heads/test_global_context_head.py | # Copyright (c) OpenMMLab. All rights reserved.
import unittest
from unittest import TestCase
import torch
from parameterized import parameterized
from torch import Tensor
from mmdet.models.roi_heads.mask_heads import GlobalContextHead
class TestGlobalContextHead(TestCase):
@parameterized.expand(['cpu', 'cuda'... | 917 | 28.612903 | 76 | py |
ERD | ERD-main/tests/test_models/test_roi_heads/test_mask_heads/test_scnet_semantic_head.py | # Copyright (c) OpenMMLab. All rights reserved.
import unittest
from unittest import TestCase
import torch
from parameterized import parameterized
from torch import Tensor
from mmdet.models.roi_heads.mask_heads import SCNetSemanticHead
class TestSCNetSemanticHead(TestCase):
@parameterized.expand(['cpu', 'cuda'... | 1,013 | 28.823529 | 72 | py |
ERD | ERD-main/tests/test_models/test_roi_heads/test_mask_heads/test_htc_mask_head.py | # Copyright (c) OpenMMLab. All rights reserved.
import unittest
from unittest import TestCase
import torch
from parameterized import parameterized
from torch import Tensor
from mmdet.models.roi_heads.mask_heads import HTCMaskHead
class TestHTCMaskHead(TestCase):
@parameterized.expand(['cpu', 'cuda'])
def t... | 1,504 | 31.021277 | 72 | py |
ERD | ERD-main/tests/test_models/test_roi_heads/test_mask_heads/test_fcn_mask_head.py | # Copyright (c) OpenMMLab. All rights reserved.
import unittest
from unittest import TestCase
import torch
from mmengine.config import ConfigDict
from mmengine.structures import InstanceData
from parameterized import parameterized
from mmdet.models.roi_heads.mask_heads import FCNMaskHead
class TestFCNMaskHead(TestC... | 3,432 | 37.573034 | 79 | py |
ERD | ERD-main/tests/test_models/test_roi_heads/test_mask_heads/test_feature_relay_head.py | # Copyright (c) OpenMMLab. All rights reserved.
import unittest
from unittest import TestCase
import torch
from parameterized import parameterized
from torch import Tensor
from mmdet.models.roi_heads.mask_heads import FeatureRelayHead
class TestFeatureRelayHead(TestCase):
@parameterized.expand(['cpu', 'cuda'])... | 795 | 28.481481 | 74 | py |
ERD | ERD-main/tests/test_models/test_roi_heads/test_mask_heads/test_grid_head.py | # Copyright (c) OpenMMLab. All rights reserved.
import unittest
from unittest import TestCase
import torch
from mmengine.config import ConfigDict
from mmengine.structures import InstanceData
from parameterized import parameterized
from mmdet.models.roi_heads.mask_heads import GridHead
from mmdet.models.utils import u... | 3,034 | 34.290698 | 77 | py |
ERD | ERD-main/tests/test_models/test_roi_heads/test_mask_heads/test_coarse_mask_head.py | import unittest
import torch
from parameterized import parameterized
from mmdet.models.roi_heads.mask_heads import CoarseMaskHead
class TestCoarseMaskHead(unittest.TestCase):
def test_init(self):
with self.assertRaises(AssertionError):
CoarseMaskHead(num_fcs=0)
with self.assertRais... | 1,229 | 28.285714 | 72 | py |
ERD | ERD-main/tests/test_models/test_roi_heads/test_roi_extractors/test_generic_roi_extractor.py | import unittest
import torch
from mmdet.models.roi_heads.roi_extractors import GenericRoIExtractor
class TestGenericRoIExtractor(unittest.TestCase):
def test_init(self):
with self.assertRaises(AssertionError):
GenericRoIExtractor(
aggregation='other',
roi_lay... | 3,384 | 32.186275 | 78 | py |
ERD | ERD-main/tests/test_models/test_roi_heads/test_roi_extractors/test_single_level_roi_extractor.py | import unittest
import torch
from mmdet.models.roi_heads.roi_extractors import SingleRoIExtractor
class TestSingleRoIExtractor(unittest.TestCase):
def test_forward(self):
cfg = dict(
roi_layer=dict(type='RoIAlign', output_size=7, sampling_ratio=2),
out_channels=16,
f... | 1,246 | 30.175 | 78 | py |
ERD | ERD-main/tests/test_datasets/test_objects365.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... | 3,362 | 41.0375 | 73 | py |
ERD | ERD-main/tests/test_datasets/test_crowdhuman.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... | 521 | 29.705882 | 67 | py |
ERD | ERD-main/tests/test_datasets/test_coco.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(
... | 1,771 | 35.163265 | 71 | py |
ERD | ERD-main/tests/test_datasets/test_wider_face.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... | 880 | 29.37931 | 107 | py |
ERD | ERD-main/tests/test_datasets/test_tta.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... | 7,531 | 41.314607 | 79 | py |
ERD | ERD-main/tests/test_datasets/test_pascal_voc.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',
... | 1,367 | 34.076923 | 69 | py |
ERD | ERD-main/tests/test_datasets/test_cityscapes.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... | 6,498 | 30.396135 | 79 | py |
ERD | ERD-main/tests/test_datasets/test_coco_panoptic.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... | 8,201 | 31.808 | 79 | py |
ERD | ERD-main/tests/test_datasets/test_lvis.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 = {
... | 7,429 | 31.025862 | 79 | py |
ERD | ERD-main/tests/test_datasets/test_coco_api_wrapper.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()
... | 1,647 | 23.235294 | 73 | py |
ERD | ERD-main/tests/test_datasets/test_openimages.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=... | 1,439 | 37.918919 | 77 | py |
ERD | ERD-main/tests/test_datasets/test_samplers/test_multi_source_sampler.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 __... | 3,732 | 33.564815 | 79 | py |
ERD | ERD-main/tests/test_datasets/test_samplers/test_batch_sampler.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_... | 2,989 | 35.91358 | 78 | py |
ERD | ERD-main/tests/test_datasets/test_transforms/test_instaboost.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... | 1,782 | 29.220339 | 77 | py |
ERD | ERD-main/tests/test_datasets/test_transforms/test_formatting.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, PixelData
from mmdet.datasets.transforms import PackDetInputs
from mmdet.structures import DetDataSample
from mmdet.structures.mask import Bit... | 4,415 | 42.294118 | 78 | py |
ERD | ERD-main/tests/test_datasets/test_transforms/test_wrappers.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... | 6,834 | 38.281609 | 79 | py |
ERD | ERD-main/tests/test_datasets/test_transforms/utils.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.... | 3,135 | 39.727273 | 78 | py |
ERD | ERD-main/tests/test_datasets/test_transforms/test_loading.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... | 17,973 | 36.84 | 78 | py |
ERD | ERD-main/tests/test_datasets/test_transforms/test_colorspace.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,
... | 14,352 | 38.215847 | 79 | py |
ERD | ERD-main/tests/test_datasets/test_transforms/test_augment_wrappers.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,
... | 10,790 | 42.164 | 79 | py |
ERD | ERD-main/tests/test_datasets/test_transforms/__init__.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']
| 206 | 40.4 | 78 | py |
ERD | ERD-main/tests/test_datasets/test_transforms/test_transforms.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,
FixShapeRe... | 72,142 | 41.139603 | 79 | py |
ERD | ERD-main/tests/test_datasets/test_transforms/test_geometric.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... | 34,115 | 44.306773 | 79 | py |
ERD | ERD-main/tests/test_apis/test_inference.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... | 2,737 | 34.558442 | 78 | py |
ERD | ERD-main/tests/test_apis/test_det_inferencer.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... | 6,856 | 40.307229 | 79 | py |
ERD | ERD-main/tests/test_utils/test_setup_env.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... | 1,426 | 35.589744 | 76 | py |
ERD | ERD-main/tests/test_utils/test_memory.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... | 4,261 | 42.050505 | 75 | py |
ERD | ERD-main/tests/test_utils/test_replace_cfg_vals.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... | 2,985 | 34.547619 | 77 | py |
ERD | ERD-main/tests/test_utils/test_benchmark.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... | 11,696 | 36.732258 | 79 | py |
ERD | ERD-main/tests/test_visualization/test_palette.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, ... | 2,013 | 33.135593 | 88 | py |
ERD | ERD-main/tests/test_visualization/test_local_visualizer.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
def _rand_bboxes(num_boxes, h, w):
... | 4,149 | 33.297521 | 78 | py |
ERD | ERD-main/demo/video_demo.py | # Copyright (c) OpenMMLab. All rights reserved.
import argparse
import cv2
import mmcv
from mmcv.transforms import Compose
from mmengine.utils import track_iter_progress
from mmdet.apis import inference_detector, init_detector
from mmdet.registry import VISUALIZERS
def parse_args():
parser = argparse.ArgumentPa... | 2,771 | 32 | 79 | py |
ERD | ERD-main/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
from mmengine.utils import scandir
try:
import imageio
except ImportError:
imageio = None
# TODO verify after r... | 5,011 | 29.192771 | 79 | py |
ERD | ERD-main/demo/webcam_demo.py | # Copyright (c) OpenMMLab. All rights reserved.
import argparse
import cv2
import mmcv
import torch
from mmdet.apis import inference_detector, init_detector
from mmdet.registry import VISUALIZERS
def parse_args():
parser = argparse.ArgumentParser(description='MMDetection webcam demo')
parser.add_argument('c... | 1,930 | 28.257576 | 78 | py |
ERD | ERD-main/demo/image_demo.py | # Copyright (c) OpenMMLab. All rights reserved.
"""Image Demo.
This script adopts a new infenence class, currently supports image path,
np.array and folder input formats, and will support video and webcam
in the future.
Example:
Save visualizations and predictions results::
python demo/image_demo.py demo... | 3,648 | 31.008772 | 78 | py |
ERD | ERD-main/demo/video_gpuaccel_demo.py | # Copyright (c) OpenMMLab. All rights reserved.
import argparse
from typing import Tuple
import cv2
import mmcv
import numpy as np
import torch
import torch.nn as nn
from mmcv.transforms import Compose
from mmengine.utils import track_iter_progress
from mmdet.apis import init_detector
from mmdet.registry import VISUA... | 4,725 | 32.048951 | 77 | py |
ERD | ERD-main/configs/conditional_detr/conditional-detr_r50_8xb2-50e_coco.py | _base_ = ['../detr/detr_r50_8xb2-150e_coco.py']
model = dict(
type='ConditionalDETR',
num_queries=300,
decoder=dict(
num_layers=6,
layer_cfg=dict(
self_attn_cfg=dict(
_delete_=True,
embed_dims=256,
num_heads=8,
attn_... | 1,321 | 29.744186 | 75 | py |
ERD | ERD-main/configs/ghm/retinanet_r50_fpn_ghm-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,... | 509 | 25.842105 | 62 | py |
ERD | ERD-main/configs/ghm/retinanet_x101-32x4d_fpn_ghm-1x_coco.py | _base_ = './retinanet_r50_fpn_ghm-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 |
ERD | ERD-main/configs/ghm/retinanet_x101-64x4d_fpn_ghm-1x_coco.py | _base_ = './retinanet_r50_fpn_ghm-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 |
ERD | ERD-main/configs/ghm/retinanet_r101_fpn_ghm-1x_coco.py | _base_ = './retinanet_r50_fpn_ghm-1x_coco.py'
model = dict(
backbone=dict(
depth=101,
init_cfg=dict(type='Pretrained',
checkpoint='torchvision://resnet101')))
| 201 | 27.857143 | 61 | py |
ERD | ERD-main/configs/dcn/faster-rcnn_x101-32x4d-dconv-c3-c5_fpn_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 |
ERD | ERD-main/configs/dcn/mask-rcnn_r50-dconv-c3-c5_fpn_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 |
ERD | ERD-main/configs/dcn/cascade-mask-rcnn_r101-dconv-c3-c5_fpn_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 |
ERD | ERD-main/configs/dcn/cascade-rcnn_r101-dconv-c3-c5_fpn_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 |
ERD | ERD-main/configs/dcn/faster-rcnn_r101-dconv-c3-c5_fpn_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 |
ERD | ERD-main/configs/dcn/cascade-mask-rcnn_x101-32x4d-dconv-c3-c5_fpn_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 |
ERD | ERD-main/configs/dcn/faster-rcnn_r50-dconv-c3-c5_fpn_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 |
ERD | ERD-main/configs/dcn/mask-rcnn_r50-dconv-c3-c5_fpn_amp-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)))
# MMEngine support the following two ways, users can choose
# according to convenience
# optim_wrapper = dict... | 391 | 34.636364 | 72 | py |
ERD | ERD-main/configs/dcn/mask-rcnn_r101-dconv-c3-c5_fpn_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 |
ERD | ERD-main/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 |
ERD | ERD-main/configs/dcn/cascade-mask-rcnn_r50-dconv-c3-c5_fpn_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 |
ERD | ERD-main/configs/dcn/cascade-rcnn_r50-dconv-c3-c5_fpn_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 |
ERD | ERD-main/configs/htc/htc_x101-64x4d-dconv-c3-c5_fpn_ms-400-1400-16xb1-20e_coco.py | _base_ = './htc_x101-64x4d_fpn_16xb1-20e_coco.py'
model = dict(
backbone=dict(
dcn=dict(type='DCN', deform_groups=1, fallback_on_stride=False),
stage_with_dcn=(False, True, True, True)))
# dataset settings
train_pipeline = [
dict(type='LoadImageFromFile'),
dict(
type='LoadAnnotatio... | 616 | 28.380952 | 79 | py |
ERD | ERD-main/configs/htc/htc_r50_fpn_20e_coco.py | _base_ = './htc_r50_fpn_1x_coco.py'
# learning policy
max_epochs = 20
param_scheduler = [
dict(
type='LinearLR', start_factor=0.001, by_epoch=False, begin=0, end=500),
dict(
type='MultiStepLR',
begin=0,
end=max_epochs,
by_epoch=True,
milestones=[16, 19],
... | 373 | 21 | 79 | py |
ERD | ERD-main/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',
data_preprocessor=dict(
type='DetDataPreprocessor',
mean=[123.675, 116.28, 103.53],
std=[58.395, 57.12... | 7,857 | 34.080357 | 79 | py |
ERD | ERD-main/configs/htc/htc_x101-32x4d_fpn_16xb1-20e_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,
... | 828 | 24.121212 | 79 | py |
ERD | ERD-main/configs/htc/htc_r50_fpn_1x_coco.py | _base_ = './htc-without-semantic_r50_fpn_1x_coco.py'
model = dict(
data_preprocessor=dict(pad_seg=True),
roi_head=dict(
semantic_roi_extractor=dict(
type='SingleRoIExtractor',
roi_layer=dict(type='RoIAlign', output_size=14, sampling_ratio=0),
out_channels=256,
... | 1,195 | 34.176471 | 79 | py |
ERD | ERD-main/configs/htc/htc_x101-64x4d_fpn_16xb1-20e_coco.py | _base_ = './htc_x101-32x4d_fpn_16xb1-20e_coco.py'
model = dict(
backbone=dict(
type='ResNeXt',
groups=64,
init_cfg=dict(
type='Pretrained', checkpoint='open-mmlab://resnext101_64x4d')))
| 226 | 27.375 | 76 | py |
ERD | ERD-main/configs/htc/htc_r101_fpn_20e_coco.py | _base_ = './htc_r50_fpn_20e_coco.py'
model = dict(
backbone=dict(
depth=101,
init_cfg=dict(type='Pretrained',
checkpoint='torchvision://resnet101')))
| 192 | 26.571429 | 61 | py |
ERD | ERD-main/configs/dino/dino-4scale_r50_8xb2-12e_coco.py | _base_ = [
'../_base_/datasets/coco_detection.py', '../_base_/default_runtime.py'
]
model = dict(
type='DINO',
num_queries=900, # num_matching_queries
with_box_refine=True,
as_two_stage=True,
data_preprocessor=dict(
type='DetDataPreprocessor',
mean=[123.675, 116.28, 103.53],
... | 5,783 | 34.268293 | 85 | py |
ERD | ERD-main/configs/dino/dino-5scale_swin-l_8xb2-36e_coco.py | _base_ = './dino-5scale_swin-l_8xb2-12e_coco.py'
max_epochs = 36
train_cfg = dict(
type='EpochBasedTrainLoop', max_epochs=max_epochs, val_interval=1)
param_scheduler = [
dict(
type='MultiStepLR',
begin=0,
end=max_epochs,
by_epoch=True,
milestones=[27, 33],
gamma=0... | 326 | 22.357143 | 70 | py |
ERD | ERD-main/configs/dino/dino-4scale_r50_8xb2-36e_coco.py | _base_ = './dino-4scale_r50_8xb2-12e_coco.py'
max_epochs = 36
train_cfg = dict(
type='EpochBasedTrainLoop', max_epochs=max_epochs, val_interval=1)
param_scheduler = [
dict(
type='MultiStepLR',
begin=0,
end=max_epochs,
by_epoch=True,
milestones=[30],
gamma=0.1)
]
| 319 | 21.857143 | 70 | py |
ERD | ERD-main/configs/dino/dino-5scale_swin-l_8xb2-12e_coco.py | _base_ = './dino-4scale_r50_8xb2-12e_coco.py'
fp16 = dict(loss_scale=512.)
pretrained = 'https://github.com/SwinTransformer/storage/releases/download/v1.0.0/swin_large_patch4_window12_384_22k.pth' # noqa
num_levels = 5
model = dict(
num_feature_levels=num_levels,
backbone=dict(
_delete_=True,
... | 1,148 | 34.90625 | 129 | py |
ERD | ERD-main/configs/dino/dino-4scale_r50_8xb2-24e_coco.py | _base_ = './dino-4scale_r50_8xb2-12e_coco.py'
max_epochs = 24
train_cfg = dict(
type='EpochBasedTrainLoop', max_epochs=max_epochs, val_interval=1)
param_scheduler = [
dict(
type='MultiStepLR',
begin=0,
end=max_epochs,
by_epoch=True,
milestones=[20],
gamma=0.1)
]
| 319 | 21.857143 | 70 | py |
ERD | ERD-main/configs/strong_baselines/mask-rcnn_r50-caffe_fpn_rpn-2conv_4conv1fc_syncbn-all_amp-lsj-100e_coco.py | _base_ = 'mask-rcnn_r50-caffe_fpn_rpn-2conv_4conv1fc_syncbn-all_lsj-100e_coco.py' # noqa
# Enable automatic-mixed-precision training with AmpOptimWrapper.
optim_wrapper = dict(type='AmpOptimWrapper')
| 202 | 39.6 | 89 | py |
ERD | ERD-main/configs/strong_baselines/mask-rcnn_r50_fpn_rpn-2conv_4conv1fc_syncbn-all_lsj-100e_coco.py | _base_ = [
'../_base_/models/mask-rcnn_r50_fpn.py',
'../common/lsj-100e_coco-instance.py'
]
image_size = (1024, 1024)
batch_augments = [
dict(type='BatchFixedSizePad', size=image_size, pad_mask=True)
]
norm_cfg = dict(type='SyncBN', requires_grad=True)
# Use MMSyncBN that handles empty tensor in head. It c... | 1,123 | 35.258065 | 77 | py |
ERD | ERD-main/configs/strong_baselines/mask-rcnn_r50-caffe_fpn_rpn-2conv_4conv1fc_syncbn-all_lsj-100e_coco.py | _base_ = [
'../_base_/models/mask-rcnn_r50_fpn.py',
'../common/lsj-100e_coco-instance.py'
]
image_size = (1024, 1024)
batch_augments = [
dict(type='BatchFixedSizePad', size=image_size, pad_mask=True)
]
norm_cfg = dict(type='SyncBN', requires_grad=True)
# Use MMSyncBN that handles empty tensor in head. It ca... | 2,276 | 32 | 76 | py |
ERD | ERD-main/configs/strong_baselines/mask-rcnn_r50-caffe_fpn_rpn-2conv_4conv1fc_syncbn-all_lsj-400e_coco.py | _base_ = './mask-rcnn_r50-caffe_fpn_rpn-2conv_4conv1fc_syncbn-all_lsj-100e_coco.py' # noqa
# Use RepeatDataset to speed up training
# change repeat time from 4 (for 100 epochs) to 16 (for 400 epochs)
train_dataloader = dict(dataset=dict(times=4 * 4))
param_scheduler = [
dict(
type='LinearLR',
star... | 543 | 24.904762 | 91 | py |
ERD | ERD-main/configs/strong_baselines/mask-rcnn_r50_fpn_rpn-2conv_4conv1fc_syncbn-all_amp-lsj-100e_coco.py | _base_ = 'mask-rcnn_r50_fpn_rpn-2conv_4conv1fc_syncbn-all_lsj-100e_coco.py'
# Enable automatic-mixed-precision training with AmpOptimWrapper.
optim_wrapper = dict(type='AmpOptimWrapper')
| 188 | 36.8 | 75 | py |
ERD | ERD-main/configs/strong_baselines/mask-rcnn_r50_fpn_rpn-2conv_4conv1fc_syncbn-all_lsj-50e_coco.py | _base_ = 'mask-rcnn_r50_fpn_rpn-2conv_4conv1fc_syncbn-all_lsj-100e_coco.py'
# Use RepeatDataset to speed up training
# change repeat time from 4 (for 100 epochs) to 2 (for 50 epochs)
train_dataloader = dict(dataset=dict(times=2))
| 231 | 37.666667 | 75 | py |
ERD | ERD-main/configs/reppoints/reppoints-moment_x101-dconv-c3-c5_fpn-gn_head-gn_2x_coco.py | _base_ = './reppoints-moment_r50_fpn-gn_head-gn_2x_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),
... | 560 | 32 | 76 | py |
ERD | ERD-main/configs/reppoints/reppoints-moment_r50_fpn-gn_head-gn_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))
| 189 | 46.5 | 77 | py |
ERD | ERD-main/configs/reppoints/reppoints-minmax_r50_fpn-gn_head-gn_1x_coco.py | _base_ = './reppoints-moment_r50_fpn-gn_head-gn_1x_coco.py'
model = dict(bbox_head=dict(transform_method='minmax'))
| 116 | 38 | 59 | py |
ERD | ERD-main/configs/reppoints/reppoints-bbox_r50_fpn-gn_head-gn-grid_1x_coco.py | _base_ = './reppoints-moment_r50_fpn-gn_head-gn_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',
... | 450 | 31.214286 | 68 | py |
ERD | ERD-main/configs/reppoints/reppoints-moment_r101_fpn-gn_head-gn_2x_coco.py | _base_ = './reppoints-moment_r50_fpn-gn_head-gn_2x_coco.py'
model = dict(
backbone=dict(
depth=101,
init_cfg=dict(type='Pretrained',
checkpoint='torchvision://resnet101')))
| 215 | 29.857143 | 61 | py |
ERD | ERD-main/configs/reppoints/reppoints-bbox_r50-center_fpn-gn_head-gn-grid_1x_coco.py | _base_ = './reppoints-moment_r50_fpn-gn_head-gn_1x_coco.py'
model = dict(bbox_head=dict(transform_method='minmax', use_grid_points=True))
| 138 | 45.333333 | 77 | py |
ERD | ERD-main/configs/reppoints/reppoints-partial-minmax_r50_fpn-gn_head-gn_1x_coco.py | _base_ = './reppoints-moment_r50_fpn-gn_head-gn_1x_coco.py'
model = dict(bbox_head=dict(transform_method='partial_minmax'))
| 124 | 40.666667 | 63 | py |
ERD | ERD-main/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',
data_preprocessor=dict(
type='DetDataPreprocessor',
mean=[123.675, 116.28, 103.53],
std=[58.395, 57.12, 57.375],
... | 2,282 | 29.44 | 79 | py |
ERD | ERD-main/configs/reppoints/reppoints-moment_r50_fpn-gn_head-gn_2x_coco.py | _base_ = './reppoints-moment_r50_fpn-gn_head-gn_1x_coco.py'
max_epochs = 24
train_cfg = dict(
type='EpochBasedTrainLoop', max_epochs=max_epochs, val_interval=1)
param_scheduler = [
dict(
type='LinearLR', start_factor=0.001, by_epoch=False, begin=0, end=500),
dict(
type='MultiStepLR',
... | 429 | 22.888889 | 79 | py |
ERD | ERD-main/configs/reppoints/reppoints-moment_r101-dconv-c3-c5_fpn-gn_head-gn_2x_coco.py | _base_ = './reppoints-moment_r50_fpn-gn_head-gn_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='torchvisio... | 338 | 36.666667 | 72 | py |
ERD | ERD-main/configs/gfl/gfl_x101-32x4d-dconv-c4-c5_fpn_ms-2x_coco.py | _base_ = './gfl_r50_fpn_ms-2x_coco.py'
model = dict(
type='GFL',
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),
dcn=d... | 580 | 29.578947 | 76 | py |
ERD | ERD-main/configs/gfl/gfl_r101_fpn_ms-2x_coco.py | _base_ = './gfl_r50_fpn_ms-2x_coco.py'
model = dict(
backbone=dict(
type='ResNet',
depth=101,
num_stages=4,
out_indices=(0, 1, 2, 3),
frozen_stages=1,
norm_cfg=dict(type='BN', requires_grad=True),
norm_eval=True,
style='pytorch',
init_cfg=dict(... | 401 | 27.714286 | 61 | py |
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