id int64 0 190k | prompt stringlengths 21 13.4M | docstring stringlengths 1 12k ⌀ |
|---|---|---|
144,704 | import argparse
import os.path as osp
from collections import OrderedDict
import mmengine
import torch
from mmengine.runner import CheckpointLoader
def convert_beit(ckpt):
new_ckpt = OrderedDict()
for k, v in ckpt.items():
if k.startswith('patch_embed'):
new_key = k.replace('patch_embed.pr... | null |
144,705 | import argparse
import os.path as osp
import mmengine
import torch
from mmengine.runner import CheckpointLoader
def convert_stdc(ckpt, stdc_type):
new_state_dict = {}
if stdc_type == 'STDC1':
stage_lst = ['0', '1', '2.0', '2.1', '3.0', '3.1', '4.0', '4.1']
else:
stage_lst = [
'0... | null |
144,706 | import argparse
import os.path as osp
from collections import OrderedDict
import mmengine
import torch
from mmengine.runner import CheckpointLoader
def convert_mit(ckpt):
new_ckpt = OrderedDict()
# Process the concat between q linear weights and kv linear weights
for k, v in ckpt.items():
if k.star... | null |
144,707 | import argparse
import os.path as osp
from collections import OrderedDict
import mmengine
import torch
from mmengine.runner import CheckpointLoader
def convert_vit(ckpt):
new_ckpt = OrderedDict()
for k, v in ckpt.items():
if k.startswith('head'):
continue
if k.startswith('norm'):
... | null |
144,708 | import argparse
import os.path as osp
import mmengine
import numpy as np
import torch
def vit_jax_to_torch(jax_weights, num_layer=12):
torch_weights = dict()
# patch embedding
conv_filters = jax_weights['embedding/kernel']
conv_filters = conv_filters.permute(3, 2, 0, 1)
torch_weights['patch_embed.... | null |
144,709 | import argparse
import os.path as osp
from collections import OrderedDict
import mmengine
import torch
from mmengine.runner import CheckpointLoader
def convert_vitlayer(paras):
new_para_name = ''
if paras[0] == 'ln_1':
new_para_name = '.'.join(['ln1'] + paras[1:])
elif paras[0] == 'attn':
ne... | null |
144,710 | import argparse
import os.path as osp
from collections import OrderedDict
import mmengine
import torch
from mmengine.runner import CheckpointLoader
def convert_tensor(ckpt):
cls_token = ckpt['image_encoder.cls_token']
new_cls_token = cls_token.unsqueeze(0).unsqueeze(0)
ckpt['image_encoder.cls_token'] = new... | null |
144,711 | import argparse
import os.path as osp
from collections import OrderedDict
import mmengine
import torch
from mmengine.runner import CheckpointLoader
def convert_twins(args, ckpt):
new_ckpt = OrderedDict()
for k, v in list(ckpt.items()):
new_v = v
if k.startswith('head'):
continue
... | null |
144,712 | import argparse
import os.path as osp
from collections import OrderedDict
import mmengine
import torch
from mmengine.runner import CheckpointLoader
def convert_swin(ckpt):
new_ckpt = OrderedDict()
def correct_unfold_reduction_order(x):
out_channel, in_channel = x.shape
x = x.reshape(out_channe... | null |
144,713 | import argparse
import os.path as osp
from collections import OrderedDict
import mmengine
import torch
from mmengine.runner import CheckpointLoader
def convert_key_name(ckpt):
new_ckpt = OrderedDict()
for k, v in ckpt.items():
key_list = k.split('.')
if key_list[0] == 'clip_visual_extractor':
... | null |
144,715 | import argparse
import glob
import math
import os
import os.path as osp
import mmcv
import numpy as np
from mmengine.utils import ProgressBar
def parse_args():
parser = argparse.ArgumentParser(
description='Convert levir-cd dataset to mmsegmentation format')
parser.add_argument('--dataset_path', help='... | null |
144,716 | import argparse
import glob
import math
import os
import os.path as osp
import mmcv
import numpy as np
from mmengine.utils import ProgressBar
def clip_big_image(image_path, clip_save_dir, args, to_label=False):
image = mmcv.imread(image_path)
h, w, c = image.shape
clip_size = args.clip_size
stride_siz... | null |
144,717 | import argparse
import os.path as osp
import shutil
import tempfile
import zipfile
from mmengine.utils import mkdir_or_exist
def parse_args():
parser = argparse.ArgumentParser(
description='Convert NYU Depth dataset to mmsegmentation format')
parser.add_argument('raw_data', help='the path of raw data')... | null |
144,718 | import argparse
import os.path as osp
import shutil
import tempfile
import zipfile
from mmengine.utils import mkdir_or_exist
The provided code snippet includes necessary dependencies for implementing the `reorganize` function. Write a Python function `def reorganize(raw_data_dir: str, out_dir: str)` to solve the follo... | Reorganize NYU Depth dataset files into the required directory structure. Args: raw_data_dir (str): Path to the raw data directory. out_dir (str): Output directory for the organized dataset. |
144,719 | import argparse
import os.path as osp
from cityscapesscripts.preparation.json2labelImg import json2labelImg
from mmengine.utils import (mkdir_or_exist, scandir, track_parallel_progress,
track_progress)
def convert_json_to_label(json_file):
label_file = json_file.replace('_polygons.json'... | null |
144,720 | import argparse
import os.path as osp
from cityscapesscripts.preparation.json2labelImg import json2labelImg
from mmengine.utils import (mkdir_or_exist, scandir, track_parallel_progress,
track_progress)
def parse_args():
parser = argparse.ArgumentParser(
description='Convert City... | null |
144,721 | import argparse
import os
import os.path as osp
import tempfile
import zipfile
import mmcv
import numpy as np
from mmengine.utils import mkdir_or_exist
def parse_args():
parser = argparse.ArgumentParser(
description='Convert REFUGE dataset to mmsegmentation format')
parser.add_argument('--raw_data_root... | null |
144,722 | import argparse
import os
import os.path as osp
import tempfile
import zipfile
import mmcv
import numpy as np
from mmengine.utils import mkdir_or_exist
The provided code snippet includes necessary dependencies for implementing the `extract_img` function. Write a Python function `def extract_img(root: str, ... | _summary_ Args: Args: root (str): root where the extracted data is saved cur_dir (cur_dir): dir where the zip_file exists out_dir (str): root dir where the data is saved mode (str, optional): Defaults to 'train'. file_type (str, optional): Defaults to 'img',else to 'mask'. |
144,723 | import argparse
import os
import os.path as osp
import tempfile
import zipfile
import cv2
import mmcv
from mmengine.utils import mkdir_or_exist
def parse_args():
parser = argparse.ArgumentParser(
description='Convert DRIVE dataset to mmsegmentation format')
parser.add_argument(
'training_path',... | null |
144,724 | import argparse
import os.path as osp
from functools import partial
import numpy as np
from detail import Detail
from mmengine.utils import mkdir_or_exist, track_progress
from PIL import Image
_mapping = np.sort(
np.array([
0, 2, 259, 260, 415, 324, 9, 258, 144, 18, 19, 22, 23, 397, 25, 284,
158, 15... | null |
144,725 | import argparse
import os.path as osp
from functools import partial
import numpy as np
from detail import Detail
from mmengine.utils import mkdir_or_exist, track_progress
from PIL import Image
def parse_args():
parser = argparse.ArgumentParser(
description='Convert PASCAL VOC annotations to mmsegmentation ... | null |
144,726 | import argparse
import os.path as osp
import shutil
from functools import partial
from glob import glob
import numpy as np
from mmengine.utils import (mkdir_or_exist, track_parallel_progress,
track_progress)
from PIL import Image
clsID_to_trID = {
0: 0,
1: 1,
2: 2,
3: 3,
... | null |
144,727 | import argparse
import os.path as osp
import shutil
from functools import partial
from glob import glob
import numpy as np
from mmengine.utils import (mkdir_or_exist, track_parallel_progress,
track_progress)
from PIL import Image
def parse_args():
parser = argparse.ArgumentParser(
... | null |
144,728 | import argparse
import glob
import math
import os
import os.path as osp
import tempfile
import zipfile
import mmcv
import numpy as np
from mmengine.utils import ProgressBar, mkdir_or_exist
def parse_args():
parser = argparse.ArgumentParser(
description='Convert vaihingen dataset to mmsegmentation format')
... | null |
144,729 | import argparse
import glob
import math
import os
import os.path as osp
import tempfile
import zipfile
import mmcv
import numpy as np
from mmengine.utils import ProgressBar, mkdir_or_exist
def clip_big_image(image_path, clip_save_dir, to_label=False):
# Original image of Vaihingen dataset is very large, thus pre-p... | null |
144,730 | import argparse
import os.path as osp
import shutil
from functools import partial
import numpy as np
from mmengine.utils import (mkdir_or_exist, track_parallel_progress,
track_progress)
from PIL import Image
from scipy.io import loadmat
clsID_to_trID = {
0: 0,
1: 1,
2: 2,
3: ... | null |
144,731 | import argparse
import os.path as osp
import shutil
from functools import partial
import numpy as np
from mmengine.utils import (mkdir_or_exist, track_parallel_progress,
track_progress)
from PIL import Image
from scipy.io import loadmat
def generate_coco_list(folder):
train_list = osp.j... | null |
144,732 | import argparse
import os.path as osp
import shutil
from functools import partial
import numpy as np
from mmengine.utils import (mkdir_or_exist, track_parallel_progress,
track_progress)
from PIL import Image
from scipy.io import loadmat
def parse_args():
parser = argparse.ArgumentParser... | null |
144,733 | import argparse
import os
import os.path as osp
import tempfile
import zipfile
import mmcv
from mmengine.utils import mkdir_or_exist
def parse_args():
parser = argparse.ArgumentParser(
description='Convert CHASE_DB1 dataset to mmsegmentation format')
parser.add_argument('dataset_path', help='path of CH... | null |
144,734 | import argparse
import os.path as osp
import nibabel as nib
import numpy as np
from mmengine.utils import mkdir_or_exist
from PIL import Image
def read_files_from_txt(txt_path):
with open(txt_path) as f:
files = f.readlines()
files = [file.strip() for file in files]
return files | null |
144,735 | import argparse
import os.path as osp
import nibabel as nib
import numpy as np
from mmengine.utils import mkdir_or_exist
from PIL import Image
def read_nii_file(nii_path):
img = nib.load(nii_path).get_fdata()
return img | null |
144,736 | import argparse
import os.path as osp
import nibabel as nib
import numpy as np
from mmengine.utils import mkdir_or_exist
from PIL import Image
def split_3d_image(img):
c, _, _ = img.shape
res = []
for i in range(c):
res.append(img[i, :, :])
return res | null |
144,737 | import argparse
import os.path as osp
import nibabel as nib
import numpy as np
from mmengine.utils import mkdir_or_exist
from PIL import Image
The provided code snippet includes necessary dependencies for implementing the `label_mapping` function. Write a Python function `def label_mapping(label)` to solve the followi... | Label mapping from TransUNet paper setting. It only has 9 classes, which are 'background', 'aorta', 'gallbladder', 'left_kidney', 'right_kidney', 'liver', 'pancreas', 'spleen', 'stomach', respectively. Other foreground classes in original dataset are all set to background. More details could be found here: https://arxi... |
144,738 | import argparse
import os.path as osp
import nibabel as nib
import numpy as np
from mmengine.utils import mkdir_or_exist
from PIL import Image
def pares_args():
parser = argparse.ArgumentParser(
description='Convert synapse dataset to mmsegmentation format')
parser.add_argument(
'--dataset-path... | null |
144,739 | import argparse
import os
import os.path as osp
import tempfile
import zipfile
import mmcv
from mmengine.utils import mkdir_or_exist
def parse_args():
parser = argparse.ArgumentParser(
description='Convert HRF dataset to mmsegmentation format')
parser.add_argument('healthy_path', help='the path of heal... | null |
144,740 | import argparse
import glob
import math
import os
import os.path as osp
import tempfile
import zipfile
import mmcv
import numpy as np
from mmengine.utils import ProgressBar, mkdir_or_exist
def parse_args():
parser = argparse.ArgumentParser(
description='Convert potsdam dataset to mmsegmentation format')
... | null |
144,741 | import argparse
import glob
import math
import os
import os.path as osp
import tempfile
import zipfile
import mmcv
import numpy as np
from mmengine.utils import ProgressBar, mkdir_or_exist
def clip_big_image(image_path, clip_save_dir, args, to_label=False):
# Original image of Potsdam dataset is very large, thus p... | null |
144,742 | import argparse
import os
import os.path as osp
import shutil
import tempfile
import zipfile
from mmengine.utils import mkdir_or_exist
def parse_args():
parser = argparse.ArgumentParser(
description='Convert LoveDA dataset to mmsegmentation format')
parser.add_argument('dataset_path', help='LoveDA fold... | null |
144,743 | import argparse
import glob
import os
import os.path as osp
import shutil
import tempfile
import zipfile
import mmcv
import numpy as np
from mmengine.utils import ProgressBar, mkdir_or_exist
from PIL import Image
def slide_crop_image(src_path, out_dir, mode, patch_H, patch_W, overlap):
img = np.asarray(Image.open(... | null |
144,744 | import argparse
import glob
import os
import os.path as osp
import shutil
import tempfile
import zipfile
import mmcv
import numpy as np
from mmengine.utils import ProgressBar, mkdir_or_exist
from PIL import Image
def iSAID_convert_from_color(arr_3d, palette=iSAID_invert_palette):
def slide_crop_label(src_path, out_dir... | null |
144,745 | import argparse
import glob
import os
import os.path as osp
import shutil
import tempfile
import zipfile
import mmcv
import numpy as np
from mmengine.utils import ProgressBar, mkdir_or_exist
from PIL import Image
def parse_args():
parser = argparse.ArgumentParser(
description='Convert iSAID dataset to mmse... | null |
144,746 | import argparse
import gzip
import os
import os.path as osp
import tarfile
import tempfile
import mmcv
from mmengine.utils import mkdir_or_exist
def un_gz(src, dst):
g_file = gzip.GzipFile(src)
with open(dst, 'wb+') as f:
f.write(g_file.read())
g_file.close() | null |
144,747 | import argparse
import gzip
import os
import os.path as osp
import tarfile
import tempfile
import mmcv
from mmengine.utils import mkdir_or_exist
def parse_args():
parser = argparse.ArgumentParser(
description='Convert STARE dataset to mmsegmentation format')
parser.add_argument('image_path', help='the ... | null |
144,748 | import argparse
import os.path as osp
from functools import partial
import numpy as np
from mmengine.utils import mkdir_or_exist, scandir, track_parallel_progress
from PIL import Image
from scipy.io import loadmat
def convert_mat(mat_file, in_dir, out_dir):
data = loadmat(osp.join(in_dir, mat_file))
mask = dat... | null |
144,749 | import argparse
import os.path as osp
from functools import partial
import numpy as np
from mmengine.utils import mkdir_or_exist, scandir, track_parallel_progress
from PIL import Image
from scipy.io import loadmat
def generate_aug_list(merged_list, excluded_list):
return list(set(merged_list) - set(excluded_list)) | null |
144,750 | import argparse
import os.path as osp
from functools import partial
import numpy as np
from mmengine.utils import mkdir_or_exist, scandir, track_parallel_progress
from PIL import Image
from scipy.io import loadmat
def parse_args():
parser = argparse.ArgumentParser(
description='Convert PASCAL VOC annotatio... | null |
144,751 | import argparse
import os.path as osp
from mmengine import Config, DictAction
from mmengine.registry import init_default_scope
from mmengine.utils import ProgressBar
from mmseg.registry import DATASETS, VISUALIZERS
def parse_args():
parser = argparse.ArgumentParser(description='Browse a dataset')
parser.add_ar... | null |
144,752 | import argparse
import subprocess
from hashlib import sha256
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 ... | null |
144,753 | import argparse
import subprocess
from hashlib import sha256
import torch
def sha256sum(filename: str) -> str:
"""Compute SHA256 message digest from a file."""
hash_func = sha256()
byte_array = bytearray(BLOCK_SIZE)
memory_view = memoryview(byte_array)
with open(filename, 'rb', buffering=0) as file:... | null |
144,754 | import argparse
import warnings
from mmengine import Config, DictAction
from mmseg.apis import init_model
def parse_args():
parser = argparse.ArgumentParser(description='Print the whole config')
parser.add_argument('config', help='config file path')
parser.add_argument(
'--graph', action='store_tru... | null |
144,755 | import argparse
import numpy as np
import torch
import torch._C
import torch.serialization
from mmengine import Config
from mmengine.runner import load_checkpoint
from torch import nn
from mmseg.models import build_segmentor
torch.manual_seed(3)
def digit_version(version_str):
digit_version = []
for x in versio... | null |
144,756 | import argparse
import numpy as np
import torch
import torch._C
import torch.serialization
from mmengine import Config
from mmengine.runner import load_checkpoint
from torch import nn
from mmseg.models import build_segmentor
torch.manual_seed(3)
def _convert_batchnorm(module):
module_output = module
if isinsta... | null |
144,757 | import argparse
import numpy as np
import torch
import torch._C
import torch.serialization
from mmengine import Config
from mmengine.runner import load_checkpoint
from torch import nn
from mmseg.models import build_segmentor
torch.manual_seed(3)
def _demo_mm_inputs(input_shape, num_classes):
"""Create a superset of... | Export Pytorch model to TorchScript model and verify the outputs are same between Pytorch and TorchScript. Args: model (nn.Module): Pytorch model we want to export. input_shape (tuple): Use this input shape to construct the corresponding dummy input and execute the model. show (bool): Whether print the computation grap... |
144,758 | import argparse
import numpy as np
import torch
import torch._C
import torch.serialization
from mmengine import Config
from mmengine.runner import load_checkpoint
from torch import nn
from mmseg.models import build_segmentor
def parse_args():
parser = argparse.ArgumentParser(
description='Convert MMSeg to ... | null |
144,760 | import os
from functools import partial
from typing import Callable
import torch
from torch import nn
from torch.utils import checkpoint
from mmengine.model import BaseModule
from mmdet.registry import MODELS as MODELS_MMDET
from mmseg.registry import MODELS as MODELS_MMSEG
from typing import Union, Tuple, Any
def mmen... | null |
144,761 | import os
from functools import partial
from typing import Callable
import torch
from torch import nn
from torch.utils import checkpoint
from mmengine.model import BaseModule
from mmdet.registry import MODELS as MODELS_MMDET
from mmseg.registry import MODELS as MODELS_MMSEG
from typing import Union, Tuple, Any
def mmen... | null |
144,762 | import argparse
import os
import os.path as osp
from mmengine.config import Config, DictAction
from mmengine.registry import RUNNERS
from mmengine.runner import Runner
from mmdet.utils import setup_cache_size_limit_of_dynamo
import model
def parse_args():
parser = argparse.ArgumentParser(description='Train a detec... | null |
144,763 | import argparse
import tempfile
from functools import partial
from pathlib import Path
import numpy as np
import torch
from mmengine.config import Config, DictAction
from mmengine.logging import MMLogger
from mmengine.model import revert_sync_batchnorm
from mmengine.registry import init_default_scope
from mmengine.runn... | null |
144,764 | import argparse
import tempfile
from functools import partial
from pathlib import Path
import numpy as np
import torch
from mmengine.config import Config, DictAction
from mmengine.logging import MMLogger
from mmengine.model import revert_sync_batchnorm
from mmengine.registry import init_default_scope
from mmengine.runn... | null |
144,765 | import argparse
import os.path as osp
import numpy as np
import torch
from mmengine.config import Config
from mmengine.fileio import dump
from mmengine.logging import MMLogger
from mmengine.registry import init_default_scope
from mmengine.utils import ProgressBar
from scipy.optimize import differential_evolution
from m... | null |
144,766 | import argparse
import os
import matplotlib.pyplot as plt
import numpy as np
from matplotlib.ticker import MultipleLocator
from mmcv.ops import nms
from mmengine import Config, DictAction
from mmengine.fileio import load
from mmengine.registry import init_default_scope
from mmengine.utils import ProgressBar
from mmdet.... | null |
144,767 | import argparse
import os
import matplotlib.pyplot as plt
import numpy as np
from matplotlib.ticker import MultipleLocator
from mmcv.ops import nms
from mmengine import Config, DictAction
from mmengine.fileio import load
from mmengine.registry import init_default_scope
from mmengine.utils import ProgressBar
from mmdet.... | Calculate the confusion matrix. Args: dataset (Dataset): Test or val dataset. results (list[ndarray]): A list of detection results in each image. score_thr (float|optional): Score threshold to filter bboxes. Default: 0. nms_iou_thr (float|optional): nms IoU threshold, the detection results have done nms in the detector... |
144,768 | import argparse
import os
import matplotlib.pyplot as plt
import numpy as np
from matplotlib.ticker import MultipleLocator
from mmcv.ops import nms
from mmengine import Config, DictAction
from mmengine.fileio import load
from mmengine.registry import init_default_scope
from mmengine.utils import ProgressBar
from mmdet.... | Draw confusion matrix with matplotlib. Args: confusion_matrix (ndarray): The confusion matrix. labels (list[str]): List of class names. save_dir (str|optional): If set, save the confusion matrix plot to the given path. Default: None. show (bool): Whether to show the plot. Default: True. title (str): Title of the plot. ... |
144,771 | import argparse
import os.path as osp
from mmengine.config import Config, DictAction
from mmengine.registry import init_default_scope
from mmengine.utils import ProgressBar
from mmdet.models.utils import mask2ndarray
from mmdet.registry import DATASETS, VISUALIZERS
from mmdet.structures.bbox import BaseBoxes
def parse... | null |
144,772 | import argparse
import os
import os.path as osp
import re
import mmcv
import motmetrics as mm
import numpy as np
import pandas as pd
from mmengine import Config
from mmengine.logging import print_log
from mmengine.registry import init_default_scope
from torch.utils.data import Dataset
from mmdet.registry import DATASET... | null |
144,773 | import argparse
import os
import os.path as osp
import re
import mmcv
import motmetrics as mm
import numpy as np
import pandas as pd
from mmengine import Config
from mmengine.logging import print_log
from mmengine.registry import init_default_scope
from torch.utils.data import Dataset
from mmdet.registry import DATASET... | Evaluate the results of the video. Args: results_dir (str): the directory of the MOT results. dataset (Dataset): MOT dataset of the video to be evaluated. video_name (str): Name of the video to be evaluated. Returns: tuple: (acc, res, gt), acc contains the results of MOT metrics, res is the results of inference and gt ... |
144,774 | import argparse
import os.path as osp
import mmengine
from mmengine import Config, DictAction
from mmengine.registry import init_default_scope
from mmdet.registry import DATASETS, VISUALIZERS
def parse_args():
parser = argparse.ArgumentParser(description='Browse a dataset')
parser.add_argument('config', help='... | null |
144,775 | import argparse
import os
import os.path as osp
from itertools import product
from mmengine.config import Config, DictAction
from mmengine.dist import get_dist_info
from mmengine.logging import MMLogger, print_log
from mmengine.model import is_model_wrapper
from mmengine.registry import init_default_scope
from mmengine... | null |
144,776 | import argparse
import os
import os.path as osp
from itertools import product
from mmengine.config import Config, DictAction
from mmengine.dist import get_dist_info
from mmengine.logging import MMLogger, print_log
from mmengine.model import is_model_wrapper
from mmengine.registry import init_default_scope
from mmengine... | null |
144,777 | import argparse
from mmengine.fileio import dump, load
from mmengine.logging import print_log
from mmengine.utils import ProgressBar
from pycocotools.coco import COCO
from pycocotools.cocoeval import COCOeval
from mmdet.models.utils import weighted_boxes_fusion
def parse_args():
parser = argparse.ArgumentParser(de... | null |
144,778 | import argparse
import mmengine
from mmengine import Config, DictAction
from mmengine.evaluator import Evaluator
from mmengine.registry import init_default_scope
from mmdet.registry import DATASETS
def parse_args():
parser = argparse.ArgumentParser(description='Evaluate metric of the '
... | null |
144,784 | import argparse
import os
from mmengine import MMLogger
from mmengine.config import Config, DictAction
from mmengine.dist import init_dist
from mmengine.registry import init_default_scope
from mmengine.utils import mkdir_or_exist
from mmdet.utils.benchmark import (DataLoaderBenchmark, DatasetBenchmark,
... | null |
144,785 | import argparse
import os
from mmengine import MMLogger
from mmengine.config import Config, DictAction
from mmengine.dist import init_dist
from mmengine.registry import init_default_scope
from mmengine.utils import mkdir_or_exist
from mmdet.utils.benchmark import (DataLoaderBenchmark, DatasetBenchmark,
... | null |
144,786 | import argparse
import os
from mmengine import MMLogger
from mmengine.config import Config, DictAction
from mmengine.dist import init_dist
from mmengine.registry import init_default_scope
from mmengine.utils import mkdir_or_exist
from mmdet.utils.benchmark import (DataLoaderBenchmark, DatasetBenchmark,
... | null |
144,787 | import argparse
import os
from mmengine import MMLogger
from mmengine.config import Config, DictAction
from mmengine.dist import init_dist
from mmengine.registry import init_default_scope
from mmengine.utils import mkdir_or_exist
from mmdet.utils.benchmark import (DataLoaderBenchmark, DatasetBenchmark,
... | null |
144,788 | import argparse
import os.path as osp
from multiprocessing import Pool
import mmcv
import numpy as np
from mmengine.config import Config, DictAction
from mmengine.fileio import load
from mmengine.registry import init_default_scope
from mmengine.runner import Runner
from mmengine.structures import InstanceData, PixelDat... | Evaluate mAP of single image det result. Args: det_result (list[list]): [[cls1_det, cls2_det, ...], ...]. The outer list indicates images, and the inner list indicates per-class detected bboxes. annotation (dict): Ground truth annotations where keys of annotations are: - bboxes: numpy array of shape (n, 4) - labels: nu... |
144,789 | import argparse
import os.path as osp
from multiprocessing import Pool
import mmcv
import numpy as np
from mmengine.config import Config, DictAction
from mmengine.fileio import load
from mmengine.registry import init_default_scope
from mmengine.runner import Runner
from mmengine.structures import InstanceData, PixelDat... | null |
144,791 | import argparse
import subprocess
from collections import OrderedDict
import torch
from mmengine.runner import CheckpointLoader
def swin_converter(ckpt):
new_ckpt = OrderedDict()
def correct_unfold_reduction_order(x):
out_channel, in_channel = x.shape
x = x.reshape(out_channel, 4, in_channel ... | null |
144,792 | import argparse
import tempfile
from collections import OrderedDict
import torch
from mmengine import Config
from mmengine.utils import digit_version
def parse_config(config_strings):
temp_file = tempfile.NamedTemporaryFile()
config_path = f'{temp_file.name}.py'
with open(config_path, 'w') as f:
f.w... | null |
144,795 | import argparse
import subprocess
from collections import OrderedDict
import torch
from mmengine.runner import CheckpointLoader
def correct_unfold_reduction_order(x):
out_channel, in_channel = x.shape
x = x.reshape(out_channel, 4, in_channel // 4)
x = x[:, [0, 2, 1, 3], :].transpose(1, 2).reshape(out_channe... | null |
144,796 | import argparse
import subprocess
from collections import OrderedDict
import torch
from mmengine.runner import CheckpointLoader
convert_dict_fpn = {
'module.backbone.fpn.fpn_inner2': 'neck.lateral_convs.0.conv',
'module.backbone.fpn.fpn_inner3': 'neck.lateral_convs.1.conv',
'module.backbone.fpn.fpn_inner4':... | null |
144,799 | import argparse
import subprocess
import torch
from mmengine.logging import print_log
from mmengine.utils import digit_version
def parse_args():
parser = argparse.ArgumentParser(
description='Process a checkpoint to be published')
parser.add_argument('in_file', help='input checkpoint filename')
par... | null |
144,800 | import argparse
import subprocess
import torch
from mmengine.logging import print_log
from mmengine.utils import digit_version
def process_checkpoint(in_file, out_file, save_keys=['meta', 'state_dict']):
checkpoint = torch.load(in_file, map_location='cpu')
# only keep `meta` and `state_dict` for smaller file ... | null |
144,801 | import argparse
import subprocess
from collections import OrderedDict
import torch
from mmengine.runner import CheckpointLoader
convert_dict_fpn = {
'backbone.fpn_lateral3': 'neck.lateral_convs.0.conv',
'backbone.fpn_lateral4': 'neck.lateral_convs.1.conv',
'backbone.fpn_lateral5': 'neck.lateral_convs.2.conv... | null |
144,802 | import argparse
import os
import os.path as osp
from collections import defaultdict
import mmengine
import numpy as np
from tqdm import tqdm
def parse_args():
parser = argparse.ArgumentParser(
description='Convert MOT label and detections to COCO-VID format.')
parser.add_argument('-i', '--input', help=... | null |
144,803 | import argparse
import os
import os.path as osp
from collections import defaultdict
import mmengine
import numpy as np
from tqdm import tqdm
def parse_gts(gts, is_mot15):
outputs = defaultdict(list)
for gt in gts:
gt = gt.strip().split(',')
frame_id, ins_id = map(int, gt[:2])
bbox = lis... | null |
144,804 | import argparse
import os
import os.path as osp
from collections import defaultdict
import mmengine
import numpy as np
from tqdm import tqdm
def parse_dets(dets):
outputs = defaultdict(list)
for det in dets:
det = det.strip().split(',')
frame_id, ins_id = map(int, det[:2])
assert ins_id... | null |
144,805 | import argparse
import json
import os
from pathlib import Path
import numpy as np
import pycocotools.mask as mask_util
from mmengine.utils import ProgressBar, mkdir_or_exist
from panopticapi.utils import IdGenerator, save_json
from PIL import Image
from mmdet.datasets.ade20k import ADE20KPanopticDataset
def parse_args... | null |
144,806 | import argparse
import json
import os
from pathlib import Path
import numpy as np
import pycocotools.mask as mask_util
from mmengine.utils import ProgressBar, mkdir_or_exist
from panopticapi.utils import IdGenerator, save_json
from PIL import Image
from mmdet.datasets.ade20k import ADE20KPanopticDataset
def prepare_in... | null |
144,807 | import argparse
import json
import os
from pathlib import Path
import numpy as np
import pycocotools.mask as mask_util
from mmengine.utils import ProgressBar, mkdir_or_exist
from panopticapi.utils import IdGenerator, save_json
from PIL import Image
from mmdet.datasets.ade20k import ADE20KPanopticDataset
ORIGINAL_CATEGO... | null |
144,809 | 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
from mmengine.fileio import dump
from mmengine.utils import (Timer, mkdir_or_exist, track_parallel_progress,
track_progres... | null |
144,814 | import argparse
import os.path as osp
from functools import partial
from glob import glob
import numpy as np
from mmengine.utils import (mkdir_or_exist, track_parallel_progress,
track_progress)
from PIL import Image
clsID_to_trID = {
0: 0,
1: 1,
2: 2,
3: 3,
4: 4,
5: 5... | null |
144,815 | import argparse
import os.path as osp
from functools import partial
from glob import glob
import numpy as np
from mmengine.utils import (mkdir_or_exist, track_parallel_progress,
track_progress)
from PIL import Image
def parse_args():
parser = argparse.ArgumentParser(
description... | null |
144,819 | import argparse
import json
import os
import os.path as osp
from collections import defaultdict
import mmengine
from PIL import Image
from tqdm import tqdm
def parse_args():
parser = argparse.ArgumentParser(
description='CrowdHuman to COCO Video format')
parser.add_argument(
'-i',
'--in... | null |
144,820 | import argparse
import json
import os
import os.path as osp
from collections import defaultdict
import mmengine
from PIL import Image
from tqdm import tqdm
def load_odgt(filename):
with open(filename, 'r') as f:
lines = f.readlines()
data_infos = [json.loads(line.strip('\n')) for line in lines]
retu... | Convert CrowdHuman dataset in COCO style. Args: ann_dir (str): The path of CrowdHuman dataset. save_dir (str): The path to save annotation files. mode (str): Convert train dataset or validation dataset. Options are 'train', 'val'. Default: 'train'. |
144,821 | import argparse
import copy
import os
import os.path as osp
from collections import defaultdict
import mmengine
from tqdm import tqdm
def parse_args():
parser = argparse.ArgumentParser(
description='YouTube-VIS to COCO Video format')
parser.add_argument(
'-i',
'--input',
help='r... | null |
144,822 | import argparse
import copy
import os
import os.path as osp
from collections import defaultdict
import mmengine
from tqdm import tqdm
The provided code snippet includes necessary dependencies for implementing the `convert_vis` function. Write a Python function `def convert_vis(ann_dir, save_dir, dataset_version, mode=... | Convert YouTube-VIS dataset in COCO style. Args: ann_dir (str): The path of YouTube-VIS dataset. save_dir (str): The path to save `VIS`. dataset_version (str): The version of dataset. Options are '2019', '2021'. mode (str): Convert train dataset or validation dataset or test dataset. Options are 'train', 'valid', 'test... |
144,823 | import argparse
import functools
import json
import multiprocessing as mp
import os
import time
import numpy as np
from panopticapi.utils import rgb2id
from PIL import Image
def _process_panoptic_to_semantic(input_panoptic, output_semantic, segments,
id_map):
panoptic = np.asarray(... | Create semantic segmentation annotations from panoptic segmentation annotations, to be used by PanopticFPN. It maps all thing categories to class 0, and maps all unlabeled pixels to class 255. It maps all stuff categories to contiguous ids starting from 1. Args: panoptic_json (str): path to the panoptic json file, in C... |
144,824 | import argparse
import functools
import json
import multiprocessing as mp
import os
import time
import numpy as np
from panopticapi.utils import rgb2id
from PIL import Image
def parse_args():
parser = argparse.ArgumentParser(
description=\
'Convert COCO Stuff 164k annotations to mmdet format') # n... | null |
144,825 | import argparse
import os
import os.path as osp
import random
import mmcv
import numpy as np
from mmengine.fileio import list_from_file
from tqdm import tqdm
def parse_args():
parser = argparse.ArgumentParser(
description='Convert MOT dataset into ReID dataset.')
parser.add_argument('-i', '--input', he... | null |
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