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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DDOD | DDOD-main/coco_cfg/ddod_r50_1x_fcos.py | fp16 = dict(loss_scale=512.)
model = dict(
type='ATSS',
backbone=dict(
type='ResNet',
depth=50,
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... | 4,374 | 30.028369 | 99 | py |
DDOD | DDOD-main/tools/test.py | import argparse
import os
import os.path as osp
import time
import warnings
import mmcv
import torch
from mmcv import Config, DictAction
from mmcv.cnn import fuse_conv_bn
from mmcv.parallel import MMDataParallel, MMDistributedDataParallel
from mmcv.runner import (get_dist_info, init_dist, load_checkpoint,
... | 9,315 | 38.142857 | 79 | py |
DDOD | DDOD-main/tools/train.py | import argparse
import copy
import os
import os.path as osp
import time
import warnings
import mmcv
import torch
from mmcv import Config, DictAction
from mmcv.runner import get_dist_info, init_dist
from mmcv.utils import get_git_hash
from mmdet import __version__
from mmdet.apis import set_random_seed, train_detector... | 6,914 | 35.587302 | 79 | py |
DDOD | DDOD-main/tools/deployment/mmdet2torchserve.py | from argparse import ArgumentParser, Namespace
from pathlib import Path
from tempfile import TemporaryDirectory
import mmcv
try:
from model_archiver.model_packaging import package_model
from model_archiver.model_packaging_utils import ModelExportUtils
except ImportError:
package_model = None
def mmdet2t... | 3,645 | 32.145455 | 78 | py |
DDOD | DDOD-main/tools/deployment/test.py | import argparse
import mmcv
from mmcv import Config, DictAction
from mmcv.parallel import MMDataParallel
from mmdet.apis import single_gpu_test
from mmdet.datasets import (build_dataloader, build_dataset,
replace_ImageToTensor)
def parse_args():
parser = argparse.ArgumentParser(
... | 5,433 | 37 | 78 | py |
DDOD | DDOD-main/tools/deployment/onnx2tensorrt.py | import argparse
import os
import os.path as osp
import warnings
import numpy as np
import onnx
import torch
from mmcv import Config
from mmcv.tensorrt import is_tensorrt_plugin_loaded, onnx2trt, save_trt_engine
from mmdet.core.export import preprocess_example_input
from mmdet.core.export.model_wrappers import (ONNXRu... | 8,467 | 32.338583 | 78 | py |
DDOD | DDOD-main/tools/deployment/mmdet_handler.py | import base64
import os
import mmcv
import torch
from ts.torch_handler.base_handler import BaseHandler
from mmdet.apis import inference_detector, init_detector
class MMdetHandler(BaseHandler):
threshold = 0.5
def initialize(self, context):
properties = context.system_properties
self.map_loc... | 2,462 | 34.185714 | 79 | py |
DDOD | DDOD-main/tools/deployment/pytorch2onnx.py | import argparse
import os.path as osp
import warnings
from functools import partial
import numpy as np
import onnx
import torch
from mmcv import Config, DictAction
from mmdet.core.export import build_model_from_cfg, preprocess_example_input
from mmdet.core.export.model_wrappers import ONNXRuntimeDetector
def pytorc... | 10,374 | 32.905229 | 79 | py |
DDOD | DDOD-main/tools/misc/print_config.py | import argparse
import warnings
from mmcv import Config, DictAction
def parse_args():
parser = argparse.ArgumentParser(description='Print the whole config')
parser.add_argument('config', help='config file path')
parser.add_argument(
'--options',
nargs='+',
action=DictAction,
... | 1,848 | 32.618182 | 78 | py |
DDOD | DDOD-main/tools/misc/browse_dataset.py | import argparse
import os
from pathlib import Path
import mmcv
from mmcv import Config, DictAction
from mmdet.core.utils import mask2ndarray
from mmdet.core.visualization import imshow_det_bboxes
from mmdet.datasets.builder import build_dataset
def parse_args():
parser = argparse.ArgumentParser(description='Bro... | 3,031 | 30.257732 | 78 | py |
DDOD | DDOD-main/tools/model_converters/selfsup2mmdet.py | import argparse
from collections import OrderedDict
import torch
def moco_convert(src, dst):
"""Convert keys in pycls pretrained moco models to mmdet style."""
# load caffe model
moco_model = torch.load(src)
blobs = moco_model['state_dict']
# convert to pytorch style
state_dict = OrderedDict(... | 1,195 | 27.47619 | 74 | py |
DDOD | DDOD-main/tools/model_converters/publish_model.py | import argparse
import subprocess
import torch
def parse_args():
parser = argparse.ArgumentParser(
description='Process a checkpoint to be published')
parser.add_argument('in_file', help='input checkpoint filename')
parser.add_argument('out_file', help='output checkpoint filename')
args = par... | 1,253 | 28.162791 | 78 | py |
DDOD | DDOD-main/tools/model_converters/regnet2mmdet.py | import argparse
from collections import OrderedDict
import torch
def convert_stem(model_key, model_weight, state_dict, converted_names):
new_key = model_key.replace('stem.conv', 'conv1')
new_key = new_key.replace('stem.bn', 'bn1')
state_dict[new_key] = model_weight
converted_names.add(model_key)
... | 3,015 | 32.511111 | 77 | py |
DDOD | DDOD-main/tools/model_converters/upgrade_model_version.py | import argparse
import re
import tempfile
from collections import OrderedDict
import torch
from mmcv import Config
def is_head(key):
valid_head_list = [
'bbox_head', 'mask_head', 'semantic_head', 'grid_head', 'mask_iou_head'
]
return any(key.startswith(h) for h in valid_head_list)
def parse_co... | 6,800 | 31.385714 | 79 | py |
DDOD | DDOD-main/tools/model_converters/upgrade_ssd_version.py | import argparse
import tempfile
from collections import OrderedDict
import torch
from mmcv import Config
def parse_config(config_strings):
temp_file = tempfile.NamedTemporaryFile()
config_path = f'{temp_file.name}.py'
with open(config_path, 'w') as f:
f.write(config_strings)
config = Config.... | 1,741 | 29.034483 | 78 | py |
DDOD | DDOD-main/tools/model_converters/detectron2pytorch.py | import argparse
from collections import OrderedDict
import mmcv
import torch
arch_settings = {50: (3, 4, 6, 3), 101: (3, 4, 23, 3)}
def convert_bn(blobs, state_dict, caffe_name, torch_name, converted_names):
# detectron replace bn with affine channel layer
state_dict[torch_name + '.bias'] = torch.from_numpy... | 3,530 | 41.542169 | 78 | py |
DDOD | DDOD-main/tools/dataset_converters/cityscapes.py | import argparse
import glob
import os.path as osp
import cityscapesscripts.helpers.labels as CSLabels
import mmcv
import numpy as np
import pycocotools.mask as maskUtils
def collect_files(img_dir, gt_dir):
suffix = 'leftImg8bit.png'
files = []
for img_file in glob.glob(osp.join(img_dir, '**/*.png')):
... | 5,124 | 32.717105 | 75 | py |
DDOD | DDOD-main/tools/dataset_converters/pascal_voc.py | import argparse
import os.path as osp
import xml.etree.ElementTree as ET
import mmcv
import numpy as np
from mmdet.core import voc_classes
label_ids = {name: i for i, name in enumerate(voc_classes())}
def parse_xml(args):
xml_path, img_path = args
tree = ET.parse(xml_path)
root = tree.getroot()
siz... | 7,793 | 31.886076 | 79 | py |
DDOD | DDOD-main/tools/analysis_tools/analyze_results.py | import argparse
import os.path as osp
import mmcv
import numpy as np
from mmcv import Config, DictAction
from mmdet.core.evaluation import eval_map
from mmdet.core.visualization import imshow_gt_det_bboxes
from mmdet.datasets import build_dataset, get_loading_pipeline
def bbox_map_eval(det_result, annotation):
... | 7,048 | 33.724138 | 78 | py |
DDOD | DDOD-main/tools/analysis_tools/eval_metric.py | import argparse
import mmcv
from mmcv import Config, DictAction
from mmdet.datasets import build_dataset
def parse_args():
parser = argparse.ArgumentParser(description='Evaluate metric of the '
'results saved in pkl format')
parser.add_argument('config', help='Config of ... | 3,070 | 35.559524 | 79 | py |
DDOD | DDOD-main/tools/analysis_tools/benchmark.py | import argparse
import os
import time
import torch
from mmcv import Config, DictAction
from mmcv.cnn import fuse_conv_bn
from mmcv.parallel import MMDistributedDataParallel
from mmcv.runner import init_dist, load_checkpoint, wrap_fp16_model
from mmdet.datasets import (build_dataloader, build_dataset,
... | 4,795 | 32.538462 | 78 | py |
DDOD | DDOD-main/tools/analysis_tools/get_flops.py | import argparse
import torch
from mmcv import Config, DictAction
from mmdet.models import build_detector
try:
from mmcv.cnn import get_model_complexity_info
except ImportError:
raise ImportError('Please upgrade mmcv to >0.6.2')
def parse_args():
parser = argparse.ArgumentParser(description='Train a det... | 2,566 | 30.304878 | 79 | py |
DDOD | DDOD-main/tools/analysis_tools/analyze_logs.py | import argparse
import json
from collections import defaultdict
import matplotlib.pyplot as plt
import numpy as np
import seaborn as sns
def cal_train_time(log_dicts, args):
for i, log_dict in enumerate(log_dicts):
print(f'{"-" * 5}Analyze train time of {args.json_logs[i]}{"-" * 5}')
all_times = ... | 6,252 | 33.738889 | 79 | py |
DDOD | DDOD-main/tools/analysis_tools/test_robustness.py | import argparse
import copy
import os
import os.path as osp
import mmcv
import torch
from mmcv import DictAction
from mmcv.parallel import MMDataParallel, MMDistributedDataParallel
from mmcv.runner import (get_dist_info, init_dist, load_checkpoint,
wrap_fp16_model)
from pycocotools.coco import... | 15,373 | 38.319693 | 79 | py |
DDOD | DDOD-main/tools/analysis_tools/coco_error_analysis.py | import copy
import os
from argparse import ArgumentParser
from multiprocessing import Pool
import matplotlib.pyplot as plt
import numpy as np
from pycocotools.coco import COCO
from pycocotools.cocoeval import COCOeval
def makeplot(rs, ps, outDir, class_name, iou_type):
cs = np.vstack([
np.ones((2, 3)),
... | 12,341 | 35.40708 | 79 | py |
DDOD | DDOD-main/tools/analysis_tools/robustness_eval.py | import os.path as osp
from argparse import ArgumentParser
import mmcv
import numpy as np
def print_coco_results(results):
def _print(result, ap=1, iouThr=None, areaRng='all', maxDets=100):
titleStr = 'Average Precision' if ap == 1 else 'Average Recall'
typeStr = '(AP)' if ap == 1 else '(AR)'
... | 8,064 | 31.131474 | 79 | py |
DDOD | DDOD-main/crowd_code/check_anno.py | import mmcv
data_path = '/mnt/cache/chenzehui/code/DDOD/data/crowdhuman/annotations/val.json'
data = mmcv.load(data_path)
print(data['annotations'][0])
print(data['images'][0])
print(data['categories']) | 204 | 24.625 | 81 | py |
DDOD | DDOD-main/crowd_code/create_crowd_anno.py | import argparse
import os
import pickle as pkl
import numpy as np
import random
from PIL import Image
import concurrent.futures
import json
import mmcv
def parse_args():
parser = argparse.ArgumentParser(description='Generate MMDetection Annotations for Crowdhuman-like dataset')
parser.add_argument('--dataset',... | 3,013 | 30.726316 | 112 | py |
DDOD | DDOD-main/crowd_code/eval_crowd_metric.py | import mmcv
import numpy as np
from pycocotools.coco import COCO
from evaluate import compute_JI, compute_APMR
import json
from utils import misc_utils
anno_path = './data/crowdhuman/annotations/annotation_val.json'
pred_path = './work_dirs/results.pkl'
anno_data = COCO(anno_path)
pred_data = mmcv.load(pred_path)
im... | 2,244 | 33.538462 | 115 | py |
DDOD | DDOD-main/crowd_code/evaluate/compute_APMR.py | import argparse
from .APMRToolkits import *
dbName = 'human'
def compute_APMR(dt_path, gt_path, target_key=None, mode=0):
database = Database(gt_path, dt_path, target_key, None, mode)
database.compare()
mAP,_ = database.eval_AP()
mMR,_ = database.eval_MR()
line = 'AP:{:.4f}, MR:{:.4f}.'.format(mAP,... | 712 | 36.526316 | 93 | py |
DDOD | DDOD-main/crowd_code/evaluate/compute_JI.py | import os
import sys
import math
import argparse
from multiprocessing import Queue, Process
from tqdm import tqdm
import numpy as np
from .JIToolkits.JI_tools import compute_matching, get_ignores
sys.path.insert(0, '../')
import utils.misc_utils as misc_utils
gtfile = '/data/annotation_val.odgt'
nr_procs = 10
def e... | 5,048 | 37.838462 | 126 | py |
DDOD | DDOD-main/crowd_code/evaluate/__init__.py | 0 | 0 | 0 | py | |
DDOD | DDOD-main/crowd_code/evaluate/APMRToolkits/image.py | import numpy as np
class Image(object):
def __init__(self, mode):
self.ID = None
self._width = None
self._height = None
self.dtboxes = None
self.gtboxes = None
self.eval_mode = mode
self._ignNum = None
self._gtNum = None
self._dtNum = None
... | 12,322 | 41.05802 | 121 | py |
DDOD | DDOD-main/crowd_code/evaluate/APMRToolkits/database.py | import os
import json
import numpy as np
from .image import *
PERSON_CLASSES = ['background', 'person']
# DBBase
class Database(object):
def __init__(self, gtpath=None, dtpath=None, body_key=None, head_key=None, mode=0):
"""
mode=0: only body; mode=1: only head
"""
self.images = dic... | 4,972 | 33.776224 | 97 | py |
DDOD | DDOD-main/crowd_code/evaluate/APMRToolkits/__init__.py | # -*- coding:utf8 -*-
__author__ = 'jyn'
__email__ = '[email protected]'
from .image import *
from .database import *
| 116 | 15.714286 | 28 | py |
DDOD | DDOD-main/crowd_code/evaluate/JIToolkits/matching.py | """Weighted maximum matching in general graphs.
The algorithm is taken from "Efficient Algorithms for Finding Maximum
Matching in Graphs" by Zvi Galil, ACM Computing Surveys, 1986.
It is based on the "blossom" method for finding augmenting paths and
the "primal-dual" method for finding a matching of maximum weight, bo... | 35,792 | 40.523202 | 150 | py |
DDOD | DDOD-main/crowd_code/evaluate/JIToolkits/JI_tools.py | #coding:utf-8
import numpy as np
from .matching import maxWeightMatching
def compute_matching(dt_boxes, gt_boxes, bm_thr):
assert dt_boxes.shape[-1] > 3 and gt_boxes.shape[-1] > 3
if dt_boxes.shape[0] < 1 or gt_boxes.shape[0] < 1:
return list()
N, K = dt_boxes.shape[0], gt_boxes.shape[0]
ious =... | 5,635 | 41.37594 | 108 | py |
DDOD | DDOD-main/crowd_code/utils/misc_utils.py | import os
import json
import numpy as np
def load_img(image_path):
import cv2
img = cv2.imread(image_path, cv2.IMREAD_COLOR)
return img
def load_json_lines(fpath):
assert os.path.exists(fpath)
with open(fpath,'r') as fid:
lines = fid.readlines()
records = [json.loads(line.strip('\n')) ... | 3,810 | 30.495868 | 117 | py |
DDOD | DDOD-main/crowd_code/utils/SGD_bias.py | import torch
from torch.optim.optimizer import Optimizer, required
class SGD(Optimizer):
"""Implements stochastic gradient descent (optionally with momentum).
Args:
params (iterable): iterable of parameters to optimize or dicts defining
parameter groups
lr (float): learning rate
... | 2,777 | 39.26087 | 88 | py |
DDOD | DDOD-main/crowd_code/utils/nms_utils.py | import numpy as np
import pdb
def set_cpu_nms(dets, thresh):
"""Pure Python NMS baseline."""
def _overlap(det_boxes, basement, others):
eps = 1e-8
x1_basement, y1_basement, x2_basement, y2_basement \
= det_boxes[basement, 0], det_boxes[basement, 1], \
det_boxes... | 3,035 | 32.362637 | 82 | py |
DDOD | DDOD-main/crowd_code/utils/visual_utils.py | import os
import json
import numpy as np
import cv2
color = {'green':(0,255,0),
'blue':(255,165,0),
'dark red':(0,0,139),
'red':(0, 0, 255),
'dark slate blue':(139,61,72),
'aqua':(255,255,0),
'brown':(42,42,165),
'deep pink':(147,20,255),
'fuchisia':(255,... | 1,161 | 32.2 | 101 | py |
DDOD | DDOD-main/.dev_scripts/convert_train_benchmark_script.py | import argparse
import os
import os.path as osp
def parse_args():
parser = argparse.ArgumentParser(
description='Convert benchmark model json to script')
parser.add_argument(
'txt_path', type=str, help='txt path output by benchmark_filter')
parser.add_argument(
'--partition',
... | 3,259 | 31.929293 | 74 | py |
DDOD | DDOD-main/.dev_scripts/gather_test_benchmark_metric.py | import argparse
import glob
import os.path as osp
import mmcv
from mmcv import Config
def parse_args():
parser = argparse.ArgumentParser(
description='Gather benchmarked models metric')
parser.add_argument('config', help='test config file path')
parser.add_argument(
'root',
type=s... | 3,868 | 39.302083 | 79 | py |
DDOD | DDOD-main/.dev_scripts/benchmark_filter.py | import argparse
import os
import os.path as osp
def parse_args():
parser = argparse.ArgumentParser(description='Filter configs to train')
parser.add_argument(
'--basic-arch',
action='store_true',
help='to train models in basic arch')
parser.add_argument(
'--datasets', actio... | 7,048 | 41.209581 | 92 | py |
DDOD | DDOD-main/.dev_scripts/gather_models.py | import argparse
import glob
import json
import os.path as osp
import shutil
import subprocess
from collections import OrderedDict
import mmcv
import torch
import yaml
def ordered_yaml_dump(data, stream=None, Dumper=yaml.SafeDumper, **kwds):
class OrderedDumper(Dumper):
pass
def _dict_representer(du... | 9,240 | 34.817829 | 79 | py |
DDOD | DDOD-main/.dev_scripts/benchmark_inference_fps.py | import argparse
import os
import os.path as osp
import mmcv
from mmcv import Config, DictAction
from mmcv.runner import init_dist
from tools.analysis_tools.benchmark import measure_inferense_speed
def parse_args():
parser = argparse.ArgumentParser(
description='MMDet benchmark a model of FPS')
parser... | 3,578 | 37.074468 | 79 | py |
DDOD | DDOD-main/.dev_scripts/gather_train_benchmark_metric.py | import argparse
import glob
import os.path as osp
import mmcv
from gather_models import get_final_results
try:
import xlrd
except ImportError:
xlrd = None
try:
import xlutils
from xlutils.copy import copy
except ImportError:
xlutils = None
def parse_args():
parser = argparse.ArgumentParser(
... | 5,795 | 37.64 | 79 | py |
DDOD | DDOD-main/.dev_scripts/batch_test_list.py | # yapf: disable
atss = dict(
config='configs/atss/atss_r50_fpn_1x_coco.py',
checkpoint='atss_r50_fpn_1x_coco_20200209-985f7bd0.pth',
eval='bbox',
metric=dict(bbox_mAP=39.4),
)
autoassign = dict(
config='configs/autoassign/autoassign_r50_fpn_8x2_1x_coco.py',
checkpoint='auto_assign_r50_fpn_1x_coc... | 12,184 | 34.318841 | 117 | py |
DDOD | DDOD-main/.dev_scripts/benchmark_test_image.py | import logging
import os.path as osp
from argparse import ArgumentParser
from mmcv import Config
from mmdet.apis import inference_detector, init_detector, show_result_pyplot
from mmdet.utils import get_root_logger
def parse_args():
parser = ArgumentParser()
parser.add_argument('config', help='test config fi... | 3,626 | 34.558824 | 77 | py |
DDOD | DDOD-main/.dev_scripts/convert_test_benchmark_script.py | import argparse
import os
import os.path as osp
from mmcv import Config
def parse_args():
parser = argparse.ArgumentParser(
description='Convert benchmark model list to script')
parser.add_argument('config', help='test config file path')
parser.add_argument('--port', type=int, default=29666, help... | 3,556 | 28.890756 | 79 | py |
DDOD | DDOD-main/tests/test_runtime/async_benchmark.py | import asyncio
import os
import shutil
import urllib
import mmcv
import torch
from mmdet.apis import (async_inference_detector, inference_detector,
init_detector)
from mmdet.utils.contextmanagers import concurrent
from mmdet.utils.profiling import profile_time
async def main():
"""Benchm... | 3,167 | 30.058824 | 77 | py |
DDOD | DDOD-main/tests/test_runtime/test_async.py | """Tests for async interface."""
import asyncio
import os
import sys
import asynctest
import mmcv
import torch
from mmdet.apis import async_inference_detector, init_detector
if sys.version_info >= (3, 7):
from mmdet.utils.contextmanagers import concurrent
class AsyncTestCase(asynctest.TestCase):
use_defau... | 2,560 | 29.855422 | 75 | py |
DDOD | DDOD-main/tests/test_runtime/test_config.py | from os.path import dirname, exists, join, relpath
from unittest.mock import Mock
import pytest
import torch
from mmcv.runner import build_optimizer
from mmdet.core import BitmapMasks, PolygonMasks
from mmdet.datasets.builder import DATASETS
from mmdet.datasets.utils import NumClassCheckHook
def _get_config_directo... | 17,127 | 39.112412 | 79 | py |
DDOD | DDOD-main/tests/test_runtime/test_eval_hook.py | import os.path as osp
import tempfile
import unittest.mock as mock
from collections import OrderedDict
from unittest.mock import MagicMock, patch
import pytest
import torch
import torch.nn as nn
from mmcv.runner import EpochBasedRunner, build_optimizer
from mmcv.utils import get_logger
from torch.utils.data import Dat... | 8,542 | 32.900794 | 79 | py |
DDOD | DDOD-main/tests/test_runtime/test_fp16.py | import numpy as np
import pytest
import torch
import torch.nn as nn
from mmcv.runner import auto_fp16, force_fp32
from mmcv.runner.fp16_utils import cast_tensor_type
def test_cast_tensor_type():
inputs = torch.FloatTensor([5.])
src_type = torch.float32
dst_type = torch.int32
outputs = cast_tensor_type... | 9,698 | 31.222591 | 75 | py |
DDOD | DDOD-main/tests/test_models/test_loss.py | import pytest
import torch
from mmdet.models.losses import (BalancedL1Loss, BoundedIoULoss, CIoULoss,
CrossEntropyLoss, DIoULoss,
DistributionFocalLoss, FocalLoss,
GaussianFocalLoss, GIoULoss, IoULoss, L1Loss,
... | 3,668 | 34.970588 | 79 | py |
DDOD | DDOD-main/tests/test_models/test_forward.py | """pytest tests/test_forward.py."""
import copy
from os.path import dirname, exists, join
import numpy as np
import pytest
import torch
def _get_config_directory():
"""Find the predefined detector config directory."""
try:
# Assume we are running in the source mmdetection repo
repo_dpath = di... | 19,749 | 30.701445 | 110 | py |
DDOD | DDOD-main/tests/test_models/test_necks.py | import pytest
import torch
from torch.nn.modules.batchnorm import _BatchNorm
from mmdet.models.necks import (FPN, ChannelMapper, CTResNetNeck,
DilatedEncoder, SSDNeck, YOLOV3Neck)
def test_fpn():
"""Tests fpn."""
s = 64
in_channels = [8, 16, 32, 64]
feat_sizes = [s // ... | 12,179 | 31.393617 | 79 | py |
DDOD | DDOD-main/tests/test_models/test_backbones/test_hourglass.py | 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.raises(AssertionError):
# len(stage_channels... | 1,301 | 27.933333 | 65 | py |
DDOD | DDOD-main/tests/test_models/test_backbones/test_res2net.py | 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', 'caffe']
Bottle2neck(64, 64, base_width=26, s... | 1,961 | 30.142857 | 72 | py |
DDOD | DDOD-main/tests/test_models/test_backbones/test_resnet.py | import pytest
import torch
from mmcv import assert_params_all_zeros
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, Bottle... | 23,501 | 34.235382 | 78 | py |
DDOD | DDOD-main/tests/test_models/test_backbones/utils.py | 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 BottleneckX
from mmdet.models.utils import Simplified... | 978 | 29.59375 | 77 | py |
DDOD | DDOD-main/tests/test_models/test_backbones/test_mobilenet_v2.py | 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():
with pytest.raises(ValueError):
# frozen_... | 6,748 | 35.879781 | 77 | py |
DDOD | DDOD-main/tests/test_models/test_backbones/test_renext.py | 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 ['pytorch', 'caffe']
BottleneckX(64, 64, grou... | 3,513 | 32.150943 | 73 | py |
DDOD | DDOD-main/tests/test_models/test_backbones/test_trident_resnet.py | 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
trident_build_config = (trident_dilations, test_... | 6,353 | 34.104972 | 79 | py |
DDOD | DDOD-main/tests/test_models/test_backbones/test_resnest.py | 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']
BottleneckS(64, 64, radix=2, reduction_factor=4, st... | 1,420 | 31.295455 | 76 | py |
DDOD | DDOD-main/tests/test_models/test_backbones/test_regnet.py | 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=2.28, group_w=16, depth=16,
bot_mul=1.0),... | 2,168 | 35.762712 | 73 | py |
DDOD | DDOD-main/tests/test_models/test_backbones/__init__.py | from .utils import check_norm_state, is_block, is_norm
__all__ = ['is_block', 'is_norm', 'check_norm_state']
| 110 | 26.75 | 54 | py |
DDOD | DDOD-main/tests/test_models/test_backbones/test_detectors_resnet.py | 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', requires_grad=True),
norm_eval=True,
... | 1,561 | 32.234043 | 77 | py |
DDOD | DDOD-main/tests/test_models/test_utils/test_position_encoding.py | import pytest
import torch
from mmdet.models.utils import (LearnedPositionalEncoding,
SinePositionalEncoding)
def test_sine_positional_encoding(num_feats=16, batch_size=2):
# test invalid type of scale
with pytest.raises(AssertionError):
module = SinePositionalEncoding... | 1,389 | 34.641026 | 79 | py |
DDOD | DDOD-main/tests/test_models/test_utils/test_se_layer.py | import pytest
import torch
from mmdet.models.utils import SELayer
def test_se_layer():
with pytest.raises(AssertionError):
# act_cfg sequence length must equal to 2
SELayer(channels=32, act_cfg=(dict(type='ReLU'), ))
with pytest.raises(AssertionError):
# act_cfg sequence must be a tu... | 626 | 25.125 | 76 | py |
DDOD | DDOD-main/tests/test_models/test_utils/test_inverted_residual.py | import pytest
import torch
from mmcv.cnn import is_norm
from torch.nn.modules import GroupNorm
from mmdet.models.utils import InvertedResidual, SELayer
def test_inverted_residual():
with pytest.raises(AssertionError):
# stride must be in [1, 2]
InvertedResidual(16, 16, 32, stride=3)
with py... | 2,587 | 33.052632 | 71 | py |
DDOD | DDOD-main/tests/test_models/test_utils/test_transformer.py | import pytest
from mmcv.utils import ConfigDict
from mmdet.models.utils.transformer import (DetrTransformerDecoder,
DetrTransformerEncoder,
Transformer)
def test_detr_transformer_dencoder_encoder_layer():
config = ConfigDict(... | 4,037 | 35.378378 | 79 | py |
DDOD | DDOD-main/tests/test_models/test_dense_heads/test_anchor_head.py | import mmcv
import torch
from mmdet.models.dense_heads import AnchorHead
def test_anchor_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,
'pad_shape': (s, s, 3)
}]
cfg = mmcv.Con... | 2,500 | 34.728571 | 79 | py |
DDOD | DDOD-main/tests/test_models/test_dense_heads/test_centernet_head.py | import numpy as np
import torch
from mmcv import ConfigDict
from mmdet.models.dense_heads import CenterNetHead
def test_center_head_loss():
"""Tests center head loss when truth is empty and non-empty."""
s = 256
img_metas = [{
'img_shape': (s, s, 3),
'scale_factor': 1,
'pad_shape'... | 4,337 | 39.542056 | 79 | py |
DDOD | DDOD-main/tests/test_models/test_dense_heads/test_dense_heads_attr.py | import warnings
from terminaltables import AsciiTable
from mmdet.models import dense_heads
from mmdet.models.dense_heads import * # noqa: F401,F403
def test_dense_heads_test_attr():
"""Tests inference methods such as simple_test and aug_test."""
# make list of dense heads
exceptions = ['FeatureAdaption... | 1,654 | 36.613636 | 77 | py |
DDOD | DDOD-main/tests/test_models/test_dense_heads/test_ga_anchor_head.py | import mmcv
import torch
from mmdet.models.dense_heads import GuidedAnchorHead
def test_ga_anchor_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,
'pad_shape': (s, s, 3)
}]
cfg =... | 3,362 | 35.956044 | 79 | py |
DDOD | DDOD-main/tests/test_models/test_dense_heads/test_yolof_head.py | import mmcv
import torch
from mmdet.models.dense_heads import YOLOFHead
def test_yolof_head_loss():
"""Tests yolof head loss when truth is empty and non-empty."""
s = 256
img_metas = [{
'img_shape': (s, s, 3),
'scale_factor': 1,
'pad_shape': (s, s, 3)
}]
train_cfg = mmcv.C... | 2,668 | 34.118421 | 79 | py |
DDOD | DDOD-main/tests/test_models/test_dense_heads/test_vfnet_head.py | import mmcv
import torch
from mmdet.models.dense_heads import VFNetHead
def test_vfnet_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,
'pad_shape': (s, s, 3)
}]
train_cfg = mmcv.C... | 2,513 | 38.904762 | 79 | py |
DDOD | DDOD-main/tests/test_models/test_dense_heads/test_pisa_head.py | import mmcv
import torch
from mmdet.models.dense_heads import PISARetinaHead, PISASSDHead
from mmdet.models.roi_heads import PISARoIHead
def test_pisa_retinanet_head_loss():
"""Tests pisa retinanet head loss when truth is empty and non-empty."""
s = 256
img_metas = [{
'img_shape': (s, s, 3),
... | 8,757 | 34.746939 | 79 | py |
DDOD | DDOD-main/tests/test_models/test_dense_heads/test_corner_head.py | import torch
from mmdet.core.evaluation.bbox_overlaps import bbox_overlaps
from mmdet.models.dense_heads import CornerHead
def test_corner_head_loss():
"""Tests corner head loss when truth is empty and non-empty."""
s = 256
img_metas = [{
'img_shape': (s, s, 3),
'scale_factor': 1,
... | 6,708 | 39.173653 | 79 | py |
DDOD | DDOD-main/tests/test_models/test_dense_heads/test_fsaf_head.py | import mmcv
import torch
from mmdet.models.dense_heads import FSAFHead
def test_fsaf_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,
'pad_shape': (s, s, 3)
}]
cfg = dict(
... | 3,049 | 36.195122 | 79 | py |
DDOD | DDOD-main/tests/test_models/test_dense_heads/test_autoassign_head.py | 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 = 256
img_metas = [{
'img_shape': (s,... | 3,430 | 36.293478 | 79 | py |
DDOD | DDOD-main/tests/test_models/test_dense_heads/test_ld_head.py | 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,
'pad_shape': (s, s, 3)
}]
train_cfg = mmc... | 4,557 | 36.669421 | 79 | py |
DDOD | DDOD-main/tests/test_models/test_dense_heads/test_paa_head.py | 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_skm:
def GaussianMixture(self, *args, *... | 4,193 | 33.097561 | 79 | py |
DDOD | DDOD-main/tests/test_models/test_dense_heads/test_detr_head.py | 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_factor': 1,
'pad_shape': (s, s, 3),
... | 4,082 | 38.259615 | 79 | py |
DDOD | DDOD-main/tests/test_models/test_dense_heads/test_fcos_head.py | 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_shape': (s, s, 3)
}]
train_cfg = mmcv.Conf... | 2,358 | 35.859375 | 79 | py |
DDOD | DDOD-main/tests/test_models/test_dense_heads/test_yolact_head.py | 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),
'scale_factor': 1,
'pad_shape': (s... | 5,199 | 36.956204 | 79 | py |
DDOD | DDOD-main/tests/test_models/test_dense_heads/test_sabl_retina_head.py | 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,
'pad_shape': (s, s, 3)
}]
cfg =... | 3,032 | 38.907895 | 79 | py |
DDOD | DDOD-main/tests/test_models/test_dense_heads/test_atss_head.py | 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_shape': (s, s, 3)
}]
train_cfg = mmcv.Conf... | 2,901 | 36.688312 | 79 | py |
DDOD | DDOD-main/tests/test_models/test_dense_heads/test_gfl_head.py | 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': (s, s, 3)
}]
train_cfg = mmcv.Config(... | 2,738 | 36.013514 | 79 | py |
DDOD | DDOD-main/tests/test_models/test_roi_heads/test_roi_extractor.py | 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,
featmap_strides=[4, 8, 16, 32],
pre_cfg=dict(... | 3,209 | 27.157895 | 77 | py |
DDOD | DDOD-main/tests/test_models/test_roi_heads/utils.py | 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_config = dict(
type='MaxIoUAssigner',
... | 1,201 | 30.631579 | 79 | py |
DDOD | DDOD-main/tests/test_models/test_roi_heads/test_mask_head.py | import mmcv
import torch
from mmdet.models.roi_heads.mask_heads import FCNMaskHead, MaskIoUHead
from .utils import _dummy_bbox_sampling
def test_mask_head_loss():
"""Test mask head loss when mask target is empty."""
self = FCNMaskHead(
num_convs=1,
roi_feat_size=6,
in_channels=8,
... | 2,440 | 33.871429 | 79 | py |
DDOD | DDOD-main/tests/test_models/test_roi_heads/__init__.py | from .utils import _dummy_bbox_sampling
__all__ = ['_dummy_bbox_sampling']
| 76 | 18.25 | 39 | py |
DDOD | DDOD-main/tests/test_models/test_roi_heads/test_sabl_bbox_head.py | 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(
num_classes=4,
cls_in_channels... | 2,931 | 37.077922 | 75 | py |
DDOD | DDOD-main/tests/test_models/test_roi_heads/test_bbox_head.py | 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-empty."""
self = BBoxHead(in_channels=8, roi_... | 7,901 | 30.482072 | 78 | py |
DDOD | DDOD-main/tests/test_onnx/utils.py | 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()
except (ImportError, ModuleNotFoundError):
wa... | 4,093 | 28.883212 | 79 | py |
DDOD | DDOD-main/tests/test_onnx/test_neck.py | 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 backend does not support version below 1.5.0',
... | 4,760 | 28.208589 | 77 | py |
DDOD | DDOD-main/tests/test_onnx/__init__.py | from .utils import ort_validate
__all__ = ['ort_validate']
| 60 | 14.25 | 31 | py |
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