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
value |
|---|---|---|---|---|---|---|
lanro-gym | lanro-gym-main/test/pybrobot_test.py | from lanro_gym.simulation import PyBulletSimulation
from lanro_gym.robots import Panda
def test_panda_robot_state_obs():
sim = PyBulletSimulation()
panda1 = Panda(sim, full_state=False, fixed_gripper=True)
panda2 = Panda(sim, full_state=False, fixed_gripper=False)
panda3 = Panda(sim, full_state=True, ... | 1,715 | 34.75 | 62 | py |
lanro-gym | lanro-gym-main/test/nl_env_test.py | import numpy as np
import gymnasium as gym
import lanro_gym
from lanro_gym.language_utils import parse_instructions
def check_instruction(env, obs):
instruction_representation = obs['instruction']
sentence = env.decode_instruction(instruction_representation)
instruction_representation2 = env.encode_instru... | 4,105 | 39.653465 | 137 | py |
lanro-gym | lanro-gym-main/test/simulation_test.py | import numpy as np
from lanro_gym.simulation import PyBulletSimulation
def test_init_step_close():
sim = PyBulletSimulation()
sim.step()
sim.close()
def test_box_base_pos_orn():
sim = PyBulletSimulation()
body_name = "test_box"
sim.create_box(body_name, [0.5, 0.5, 0.5], 1.0, [0, 0, 0], [1, 0... | 2,265 | 29.621622 | 94 | py |
lanro-gym | lanro-gym-main/test/utils_test.py | import numpy as np
from lanro_gym.env_utils import RGBCOLORS, SHAPES, TaskObject, valid_task_object_combination, dummys_not_goal_props
from lanro_gym.env_utils.object_properties import WEIGHTS
from lanro_gym.simulation import PyBulletSimulation
from lanro_gym.utils import goal_distance, scale_rgb, get_one_hot_list, get... | 6,983 | 39.842105 | 140 | py |
lanro-gym | lanro-gym-main/test/env_utils/task_object_list.py | import numpy as np
from lanro_gym.simulation import PyBulletSimulation
from lanro_gym.env_utils import TaskObjectList, RGBCOLORS, SHAPES
def test_task_object_list_default():
sim = PyBulletSimulation()
obj_list = TaskObjectList(sim)
assert len(obj_list) == 3
assert obj_list[0].get_color() == RGBCOLORS.... | 2,970 | 33.149425 | 108 | py |
lanro-gym | lanro-gym-main/test/env_utils/task_object.py | import pytest
import numpy as np
from lanro_gym.env_utils.object_properties import WEIGHTS
from lanro_gym.simulation import PyBulletSimulation
from lanro_gym.env_utils import TaskObject, RGBCOLORS, SHAPES, SIZES, DUMMY
def test_task_object_primary():
sim = PyBulletSimulation()
task_obj = TaskObject(sim, prima... | 4,961 | 38.696 | 118 | py |
hybrid-urban-routing-tutorial-sbrc | hybrid-urban-routing-tutorial-sbrc-master/setup.py | try:
from setuptools import setup
except ImportError:
from distutils.core import setup
config = {
'name': "Minicourse SBRC'2019",
'version': '0.2',
'description': 'Exploring hybrid multi-modal urban routes collected from tweets in São Paulo.',
'author': 'Diego Oliveira and Frances Santos',
... | 622 | 33.611111 | 179 | py |
hybrid-urban-routing-tutorial-sbrc | hybrid-urban-routing-tutorial-sbrc-master/smaframework/__init__.py | 0 | 0 | 0 | py | |
hybrid-urban-routing-tutorial-sbrc | hybrid-urban-routing-tutorial-sbrc-master/smaframework/common/address_keywords_extension_map.py | import re
'''
* Parses an address string to collect the relevant keywords.
*
* @param address - The address string.
* @param mode - `extend` (to add abbreviations) or `clean` (to remove commom words).
'''
def parse_str(address, mode='clean'):
address_str = re.sub('[^a-zA-Z0-9\-]+', ' ', address).lower()
ad... | 20,680 | 19.764056 | 4,205 | py |
hybrid-urban-routing-tutorial-sbrc | hybrid-urban-routing-tutorial-sbrc-master/smaframework/common/distribution.py | import numpy as np
def next(matrix):
random = np.random.rand(*matrix.shape)
random = np.divide(random, matrix)
argmin = random.argmin()
return np.unravel_index(argmin, matrix.shape) | 186 | 25.714286 | 46 | py |
hybrid-urban-routing-tutorial-sbrc | hybrid-urban-routing-tutorial-sbrc-master/smaframework/common/hashing.py | import hashlib
def md5(string):
m = hashlib.md5()
m.update(string.encode('utf-8'))
return m.hexdigest() | 116 | 18.5 | 36 | py |
hybrid-urban-routing-tutorial-sbrc | hybrid-urban-routing-tutorial-sbrc-master/smaframework/common/__init__.py | 0 | 0 | 0 | py | |
hybrid-urban-routing-tutorial-sbrc | hybrid-urban-routing-tutorial-sbrc-master/smaframework/common/env.py | import re
"""
* Reads configuration from the .env file.
*
* @param key: the key to look for in the .env file
* @param default: the default value to return in case key is not found
"""
def env(key=None, default=None, **kwargs):
config = {"filename": '.env'}
config.update(kwargs)
with open(config[... | 783 | 25.133333 | 71 | py |
hybrid-urban-routing-tutorial-sbrc | hybrid-urban-routing-tutorial-sbrc-master/smaframework/filters/nyc_yellow_taxis.py | from haversine import haversine
import os, time, datetime as dt
import multiprocessing as mp
import uuid as IdGenerator
import pandas as pd
import numpy as np
# constant to convert miles to km
mile2km = 1.60934400
# constant to conver km to m
km2m = 1000
# constant to convert hours to seconds
h2s = 3600
# date form... | 3,229 | 40.948052 | 240 | py |
hybrid-urban-routing-tutorial-sbrc | hybrid-urban-routing-tutorial-sbrc-master/smaframework/filters/nyc_green_taxis.py | import os, time, datetime as dt
import multiprocessing as mp
import uuid as IdGenerator
import pandas as pd
import numpy as np
mile2km = 1.60934400
km2m = 1000
h2s = 3600
date_format = '%m/%d/%Y %I:%M:%S %p'
def filter(path, **kwargs):
if 'pool_size' in kwargs.keys() and int(kwargs['pool_size']) > 1:
pool... | 3,258 | 48.378788 | 232 | py |
hybrid-urban-routing-tutorial-sbrc | hybrid-urban-routing-tutorial-sbrc-master/smaframework/filters/__init__.py | 0 | 0 | 0 | py | |
hybrid-urban-routing-tutorial-sbrc | hybrid-urban-routing-tutorial-sbrc-master/smaframework/analyzer/bucketizer.py | import os
import matplotlib
import pandas as pd
from geopy import Point
import uuid as IdGenerator
from geopy import distance
import multiprocessing as mp
from math import sin, cos, atan2, floor, sqrt, radians
# @deprecated, use smaframework.analyzer.bucketwalk.filesystem
def histogram(path, layers, show=True, max_x=... | 7,443 | 34.279621 | 164 | py |
hybrid-urban-routing-tutorial-sbrc | hybrid-urban-routing-tutorial-sbrc-master/smaframework/analyzer/routematcher.py | import smaframework.analyzer.bucketwalk.filesystem as BucketWalkFS
import smaframework.analyzer.magtools.mag as Mag
from haversine import haversine
from functools import partial
import multiprocessing as mp
import pandas as pd
"""
* @param trips - list of trips retrieved from Google with smaframework.extractor.google... | 2,656 | 39.876923 | 152 | py |
hybrid-urban-routing-tutorial-sbrc | hybrid-urban-routing-tutorial-sbrc-master/smaframework/analyzer/fuzzymatcher.py | import os, json, math, re, gc, base64
import multiprocessing as mp
import pandas as pd
import numpy as np
from geopy import distance
from geopy import Point
import uuid as IdGenerator
from random import randint
import sklearn
from sklearn.cluster import DBSCAN, Birch, KMeans
import smaframework.tool.distribution as Dis... | 19,407 | 39.517745 | 267 | py |
hybrid-urban-routing-tutorial-sbrc | hybrid-urban-routing-tutorial-sbrc-master/smaframework/analyzer/simulator.py | from math import floor
from geopy import Point, distance
import smaframework.tool.mag as Mag
import smaframework.tool.paralel as Paralel
def learn(path, layer, distance_precision=100, **kwargs):
pool_size = 1 if 'pool_size' not in kwargs.keys() else kwargs['pool_size']
nodes = Mag.nodes(path, layer, **kwa... | 1,524 | 37.125 | 100 | py |
hybrid-urban-routing-tutorial-sbrc | hybrid-urban-routing-tutorial-sbrc-master/smaframework/analyzer/__init__.py | 0 | 0 | 0 | py | |
hybrid-urban-routing-tutorial-sbrc | hybrid-urban-routing-tutorial-sbrc-master/smaframework/analyzer/hybridrouter.py | 0 | 0 | 0 | py | |
hybrid-urban-routing-tutorial-sbrc | hybrid-urban-routing-tutorial-sbrc-master/smaframework/analyzer/bucketwalk/memory.py | from haversine import haversine
import itertools
def closest(index, point, dist=None, radius=1):
if dist == None:
dist = haversine
key = hash_sample(point, index['hashing_dist'], index['origin'])
cube = get_cube(index, key, radius)
min_distance = float("inf")
closest = None
for i in c... | 1,745 | 26.28125 | 90 | py |
hybrid-urban-routing-tutorial-sbrc | hybrid-urban-routing-tutorial-sbrc-master/smaframework/analyzer/bucketwalk/filesystem.py | import os
import matplotlib
import pandas as pd
from geopy import Point
import uuid as IdGenerator
from geopy import distance
import multiprocessing as mp
from math import sin, cos, atan2, floor, sqrt, radians
import smaframework.tool.paralel as Paralel
from functools import partial
import itertools, json, sys
def his... | 7,400 | 32.488688 | 164 | py |
hybrid-urban-routing-tutorial-sbrc | hybrid-urban-routing-tutorial-sbrc-master/smaframework/analyzer/bucketwalk/__init__.py | 0 | 0 | 0 | py | |
hybrid-urban-routing-tutorial-sbrc | hybrid-urban-routing-tutorial-sbrc-master/smaframework/analyzer/clustering/flow.py | import smaframework.tool.distribution as Distribution
from sklearn.neighbors import NearestNeighbors
from sklearn.cluster import DBSCAN
from hdbscan import HDBSCAN
import pandas as pd
import numpy as np
import sklearn, json
import warnings
warnings.simplefilter(action='ignore', category=FutureWarning)
def cluster_hdb... | 8,664 | 43.896373 | 169 | py |
hybrid-urban-routing-tutorial-sbrc | hybrid-urban-routing-tutorial-sbrc-master/smaframework/analyzer/clustering/__init__.py | 0 | 0 | 0 | py | |
hybrid-urban-routing-tutorial-sbrc | hybrid-urban-routing-tutorial-sbrc-master/smaframework/analyzer/magtools/heatmap.py | import os, json
import multiprocessing as mp
import pandas as pd
import numpy as np
def _heatmap_file(args):
filename, layer = args
df = pd.read_csv(filename, header=0)
df = df[df['layer'] == layer]
return df.apply(lambda r: '{location: new google.maps.LatLng(%f, %f)},' % (r['lat'], r['lon']), axis=1)
... | 2,006 | 33.603448 | 168 | py |
hybrid-urban-routing-tutorial-sbrc | hybrid-urban-routing-tutorial-sbrc-master/smaframework/analyzer/magtools/mag.py | import os, re
import pandas as pd
_mag = None
def load(path='data/mag/', **kwargs):
global _mag
if 'target' in kwargs.keys() and kwargs['target']:
_mag = kwargs['target']
else:
_mag = {}
if 'file_regex' not in kwargs.keys():
kwargs['file_regex'] = re.compile(r"^(.*)\.csv... | 2,844 | 31.701149 | 100 | py |
hybrid-urban-routing-tutorial-sbrc | hybrid-urban-routing-tutorial-sbrc-master/smaframework/analyzer/magtools/__init__.py | 0 | 0 | 0 | py | |
hybrid-urban-routing-tutorial-sbrc | hybrid-urban-routing-tutorial-sbrc-master/smaframework/analyzer/hybrid_multimodal_router/router.py | from smaframework.common.address_keywords_extension_map import parse_str as parse_address_str
import smaframework.extractor.here.traffic as HereTrafficExtractor
import smaframework.extractor.google.directions as GoogleDirectionsExtractor
import smaframework.extractor.uber as UberExtractor
import numpy as np
import netw... | 20,794 | 40.424303 | 237 | py |
hybrid-urban-routing-tutorial-sbrc | hybrid-urban-routing-tutorial-sbrc-master/smaframework/analyzer/hybrid_multimodal_router/model.py | import math
from smaframework.tool.constants import miles2km
'''
* Evaluate the perceived time of travel.
* Based on Paper: Abrantes, P. A. L., & Wardman, M. R. (2011). Meta-analysis of UK values of travel time: An update. Transportation Research Part A: Policy and Practice, 45(1), 1–17. https://doi.org/10.1016/J.TR... | 5,509 | 44.53719 | 226 | py |
hybrid-urban-routing-tutorial-sbrc | hybrid-urban-routing-tutorial-sbrc-master/smaframework/analyzer/hybrid_multimodal_router/evaluator.py | import numpy as np
import pandas as pd
from haversine import haversine
import smaframework.analyzer.hybrid_multimodal_router.model as Model
def evaluate(trips, routes, group_id, profile=1):
frames = []
for route in routes:
if not isinstance(route, dict) or len(route['options']) == 0:
conti... | 3,570 | 33.669903 | 167 | py |
hybrid-urban-routing-tutorial-sbrc | hybrid-urban-routing-tutorial-sbrc-master/smaframework/analyzer/hybrid_multimodal_router/__init__.py | 0 | 0 | 0 | py | |
hybrid-urban-routing-tutorial-sbrc | hybrid-urban-routing-tutorial-sbrc-master/smaframework/tool/constants.py | earth_radius = 6371000.7
miles2km = 1.60934
| 51 | 16.333333 | 24 | py |
hybrid-urban-routing-tutorial-sbrc | hybrid-urban-routing-tutorial-sbrc-master/smaframework/tool/distribution.py | import time, math
import numpy as np
import pandas as pd
import pylab as pl
import scipy
def get_region(df, angle_step=5, **kwargs):
df = df.copy()
min_lat = df['lat'].min()
max_lat = df['lat'].max()
min_lon = df['lon'].min()
max_lon = df['lon'].max()
origin = ((max_lat - min_lat) / 2 + min_l... | 4,186 | 33.04065 | 110 | py |
hybrid-urban-routing-tutorial-sbrc | hybrid-urban-routing-tutorial-sbrc-master/smaframework/tool/paralel.py | # import dask.dataframe as dd
import multiprocessing as mp
import numpy as np
import pandas as pd
# def prepare(df, **kwargs):
# if 'pool_size' in kwargs.keys():
# kwargs['npartitions'] = kwargs['pool_size']
# del kwargs['pool_size']
# elif 'npartitions' not in kwargs.keys() and 'chunksize' not... | 782 | 33.043478 | 83 | py |
hybrid-urban-routing-tutorial-sbrc | hybrid-urban-routing-tutorial-sbrc-master/smaframework/tool/conversor.py | import numpy as np
import smaframework.tool.constants as Constants
def kmph2mps(speed):
'''
* Coverts a speed from Km/h to m/s.
*
* @param speed the speed to convert.
* @return float the converted speed.
'''
return speed / 3.6
def deg2rad(degree):
'''
* Coverts an ... | 1,143 | 26.902439 | 116 | py |
hybrid-urban-routing-tutorial-sbrc | hybrid-urban-routing-tutorial-sbrc-master/smaframework/tool/mag.py | import os
import math
import datetime
import pandas as pd
import uuid as IdGenerator
import multiprocessing as mp
def edges(path, edge_type=None, load_nodes=False, **kwargs):
edges_path = os.path.join(path, 'edges/')
if 'pool_size' in kwargs.keys() and int(kwargs['pool_size']) > 1:
pool = mp.Pool(int(k... | 5,339 | 35.326531 | 182 | py |
hybrid-urban-routing-tutorial-sbrc | hybrid-urban-routing-tutorial-sbrc-master/smaframework/tool/__init__.py | 0 | 0 | 0 | py | |
hybrid-urban-routing-tutorial-sbrc | hybrid-urban-routing-tutorial-sbrc-master/smaframework/extractor/shapefile.py | import smaframework.analyzer.bucketwalk.memory as BucketWalk
from pyproj import Proj, transform
from haversine import haversine
import fiona, os, re
import uuid as IdGenerator
import pandas as pd
import numpy as np
import multiprocessing as mp
import json
def get_route(params):
feature, stops, inProj, outProj, kwa... | 3,961 | 33.452174 | 191 | py |
hybrid-urban-routing-tutorial-sbrc | hybrid-urban-routing-tutorial-sbrc-master/smaframework/extractor/twitterstream.py | import tweepy, time, datetime, logging, os
from tweepy import OAuthHandler
import pandas as pd
import pyproj
import shapely
import shapely.ops as ops
from shapely.geometry.polygon import Polygon as ShapelyPolygon
from functools import partial
"""
Twitter data extarctor. The available data collected by tweepy ... | 4,250 | 34.425 | 218 | py |
hybrid-urban-routing-tutorial-sbrc | hybrid-urban-routing-tutorial-sbrc-master/smaframework/extractor/openweathermap.py | import urllib, json, uuid, os, datetime, time, logging, random, math, traceback
import pandas as pd
from shapely.geometry import Point
def extract(access, region, layer='openweathermap', **kwargs):
if 'samples' not in kwargs.keys():
kwargs['samples'] = 3
if 'wait' not in kwargs.keys():
kwargs[... | 5,191 | 44.946903 | 199 | py |
hybrid-urban-routing-tutorial-sbrc | hybrid-urban-routing-tutorial-sbrc-master/smaframework/extractor/uber.py | from smaframework.tool.constants import miles2km
import urllib.request
import json, math
'''
* Estimate the duration and cost of a list of trips. Response provided using meters for distance, seconds for time and avarage cost for price.
*
* @param token - The Uber API token to be used in the request
* @param depart... | 3,506 | 39.310345 | 153 | py |
hybrid-urban-routing-tutorial-sbrc | hybrid-urban-routing-tutorial-sbrc-master/smaframework/extractor/__init__.py | 0 | 0 | 0 | py | |
hybrid-urban-routing-tutorial-sbrc | hybrid-urban-routing-tutorial-sbrc-master/smaframework/extractor/csv.py | import os
import multiprocessing as mp
import numpy as np
import pandas as pd
import time
import datetime as dt
import uuid as IdGenerator
def extract_file(args):
file, source, dest, nodes, layer, config = args
filename = os.path.join(source, file)
df = pd.read_csv(filename, header=0)
if 'id' not in ... | 2,523 | 36.117647 | 170 | py |
hybrid-urban-routing-tutorial-sbrc | hybrid-urban-routing-tutorial-sbrc-master/smaframework/extractor/google/transit.py | import os
import numpy as np
import pandas as pd
import datetime as dt
import uuid as IdGenerator
import multiprocessing as mp
import urllib
import json
import time
def extract_url(params):
(app_key, departure, arrival, date, mode, kwargs) = params
kwargs["origin"] = '%f,%f' % departure
kwargs["destinati... | 2,124 | 33.836066 | 137 | py |
hybrid-urban-routing-tutorial-sbrc | hybrid-urban-routing-tutorial-sbrc-master/smaframework/extractor/google/directions.py | import numpy as np
import pandas as pd
import datetime as dt
import uuid as IdGenerator
import multiprocessing as mp
import os, urllib, json, time, re
from smaframework.common.address_keywords_extension_map import address_keywords_extensions
from smaframework.common.address_keywords_extension_map import parse_str as pa... | 5,497 | 42.984 | 158 | py |
hybrid-urban-routing-tutorial-sbrc | hybrid-urban-routing-tutorial-sbrc-master/smaframework/extractor/google/__init__.py | 0 | 0 | 0 | py | |
hybrid-urban-routing-tutorial-sbrc | hybrid-urban-routing-tutorial-sbrc-master/smaframework/extractor/here/transit.py | import os
import numpy as np
import pandas as pd
import time
import datetime as dt
import uuid as IdGenerator
import multiprocessing as mp
import urllib.request
import json
def extract_url(params):
(app_id, app_code, departure, arrival, date, modes_str) = params
query = {
"app_id": app_id,
"a... | 1,289 | 30.463415 | 135 | py |
hybrid-urban-routing-tutorial-sbrc | hybrid-urban-routing-tutorial-sbrc-master/smaframework/extractor/here/__init__.py | 0 | 0 | 0 | py | |
hybrid-urban-routing-tutorial-sbrc | hybrid-urban-routing-tutorial-sbrc-master/smaframework/extractor/here/traffic.py | from smaframework.common.env import env
import urllib.request
import requests
import json
import math
APP_KEY = env('HERE_APP_ID')
APP_CODE = env('HERE_APP_CODE')
'''
* Run the request to collect traffic data.
*
* @param query - the params to be sent in the querystring
'''
def extract_url(query):
url = 'https... | 4,251 | 31.96124 | 162 | py |
hybrid-urban-routing-tutorial-sbrc | hybrid-urban-routing-tutorial-sbrc-master/smaframework/extractor/tomtom/router.py | import urllib.request
import xmltodict, json, sys
from urllib.parse import quote
import smaframework.tool.conversor as Conversor
def parse(response):
if 'calculateRouteResponse' not in response.keys() or 'route' not in response['calculateRouteResponse'].keys():
return None
routes = response['calculate... | 4,827 | 40.264957 | 212 | py |
hybrid-urban-routing-tutorial-sbrc | hybrid-urban-routing-tutorial-sbrc-master/smaframework/extractor/tomtom/__init__.py | 0 | 0 | 0 | py | |
hybrid-urban-routing-tutorial-sbrc | hybrid-urban-routing-tutorial-sbrc-master/smaframework/organizer/magify.py | import os
import multiprocessing as mp
import pandas as pd
import uuid as IdGenerator
def organize_file(filename, edge_type, config):
mag_location = 'data/mag/'
nodes_location = 'data/mag/nodes/'
edges_location = 'data/mag/edges/'
if not os.path.exists(mag_location):
os.makedirs(mag_location)
... | 2,469 | 29.875 | 99 | py |
hybrid-urban-routing-tutorial-sbrc | hybrid-urban-routing-tutorial-sbrc-master/smaframework/organizer/__init__.py | 0 | 0 | 0 | py | |
ERD | ERD-main/setup.py | #!/usr/bin/env python
# Copyright (c) OpenMMLab. All rights reserved.
import os
import os.path as osp
import platform
import shutil
import sys
import warnings
from setuptools import find_packages, setup
import torch
from torch.utils.cpp_extension import (BuildExtension, CppExtension,
... | 7,887 | 34.692308 | 125 | py |
ERD | ERD-main/tools/test.py | # Copyright (c) OpenMMLab. All rights reserved.
import argparse
import os
import os.path as osp
import warnings
from copy import deepcopy
from mmengine import ConfigDict
from mmengine.config import Config, DictAction
from mmengine.runner import Runner
from mmdet.engine.hooks.utils import trigger_visualization_hook
fr... | 5,594 | 36.3 | 79 | py |
ERD | ERD-main/tools/train.py | # Copyright (c) OpenMMLab. All rights reserved.
import argparse
import logging
import os
import os.path as osp
from mmengine.config import Config, DictAction
from mmengine.logging import print_log
from mmengine.registry import RUNNERS
from mmengine.runner import Runner
from mmdet.utils import setup_cache_size_limit_o... | 4,770 | 34.604478 | 79 | py |
ERD | ERD-main/tools/deployment/test_torchserver.py | import os
from argparse import ArgumentParser
import mmcv
import requests
import torch
from mmengine.structures import InstanceData
from mmdet.apis import inference_detector, init_detector
from mmdet.registry import VISUALIZERS
from mmdet.structures import DetDataSample
def parse_args():
parser = ArgumentParser... | 3,906 | 33.27193 | 77 | py |
ERD | ERD-main/tools/deployment/mmdet2torchserve.py | # Copyright (c) OpenMMLab. All rights reserved.
from argparse import ArgumentParser, Namespace
from pathlib import Path
from tempfile import TemporaryDirectory
from mmengine.config import Config
from mmengine.utils import mkdir_or_exist
try:
from model_archiver.model_packaging import package_model
from model_... | 3,748 | 32.473214 | 78 | py |
ERD | ERD-main/tools/deployment/mmdet_handler.py | # Copyright (c) OpenMMLab. All rights reserved.
import base64
import os
import mmcv
import numpy as np
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):
... | 2,620 | 34.90411 | 79 | py |
ERD | ERD-main/tools/misc/get_image_metas.py | # Copyright (c) OpenMMLab. All rights reserved.
"""Get image metas on a specific dataset.
Here is an example to run this script.
Example:
python tools/misc/get_image_metas.py ${CONFIG} \
--out ${OUTPUT FILE NAME}
"""
import argparse
import csv
import os.path as osp
from multiprocessing import Pool
import mmc... | 3,935 | 30.238095 | 78 | py |
ERD | ERD-main/tools/misc/get_crowdhuman_id_hw.py | # Copyright (c) OpenMMLab. All rights reserved.
"""Get image shape on CrowdHuman dataset.
Here is an example to run this script.
Example:
python tools/misc/get_crowdhuman_id_hw.py ${CONFIG} \
--dataset ${DATASET_TYPE}
"""
import argparse
import json
import logging
import os.path as osp
from multiprocessing im... | 2,492 | 27.329545 | 74 | py |
ERD | ERD-main/tools/misc/gen_coco_panoptic_test_info.py | import argparse
import os.path as osp
from mmengine.fileio import dump, load
def parse_args():
parser = argparse.ArgumentParser(
description='Generate COCO test image information '
'for COCO panoptic segmentation.')
parser.add_argument('data_root', help='Path to COCO annotation directory.')
... | 968 | 27.5 | 79 | py |
ERD | ERD-main/tools/misc/download_dataset.py | import argparse
import tarfile
from itertools import repeat
from multiprocessing.pool import ThreadPool
from pathlib import Path
from tarfile import TarFile
from zipfile import ZipFile
import torch
from mmengine.utils.path import mkdir_or_exist
def parse_args():
parser = argparse.ArgumentParser(
descript... | 7,177 | 35.810256 | 144 | py |
ERD | ERD-main/tools/misc/split_coco.py | # Copyright (c) OpenMMLab. All rights reserved.
import argparse
import os.path as osp
import numpy as np
from mmengine.fileio import dump, load
from mmengine.utils import mkdir_or_exist, track_parallel_progress
prog_description = '''K-Fold coco split.
To split coco data for semi-supervised object detection:
pyth... | 3,560 | 31.081081 | 78 | py |
ERD | ERD-main/tools/misc/print_config.py | # Copyright (c) OpenMMLab. All rights reserved.
import argparse
import os
from mmengine import Config, DictAction
from mmdet.utils import replace_cfg_vals, update_data_root
def parse_args():
parser = argparse.ArgumentParser(description='Print the whole config')
parser.add_argument('config', help='config fil... | 1,797 | 28.47541 | 78 | py |
ERD | ERD-main/tools/model_converters/selfsup2mmdet.py | # Copyright (c) OpenMMLab. All rights reserved.
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']
# conver... | 1,243 | 27.930233 | 74 | py |
ERD | ERD-main/tools/model_converters/publish_model.py | # Copyright (c) OpenMMLab. All rights reserved.
import argparse
import subprocess
import torch
from mmengine.logging import print_log
def parse_args():
parser = argparse.ArgumentParser(
description='Process a checkpoint to be published')
parser.add_argument('in_file', help='input checkpoint filename'... | 1,966 | 30.725806 | 78 | py |
ERD | ERD-main/tools/model_converters/regnet2mmdet.py | # Copyright (c) OpenMMLab. All rights reserved.
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] = mode... | 3,063 | 32.67033 | 77 | py |
ERD | ERD-main/tools/model_converters/upgrade_model_version.py | # Copyright (c) OpenMMLab. All rights reserved.
import argparse
import re
import tempfile
from collections import OrderedDict
import torch
from mmengine import Config
def is_head(key):
valid_head_list = [
'bbox_head', 'mask_head', 'semantic_head', 'grid_head', 'mask_iou_head'
]
return any(key.st... | 6,852 | 31.478673 | 79 | py |
ERD | ERD-main/tools/model_converters/detectron2_to_mmdet.py | # Copyright (c) OpenMMLab. All rights reserved.
import argparse
from collections import OrderedDict
import torch
from mmengine.fileio import load
from mmengine.runner import save_checkpoint
def convert(src: str, dst: str, prefix: str = 'd2_model') -> None:
"""Convert Detectron2 checkpoint to MMDetection style.
... | 1,653 | 32.755102 | 78 | py |
ERD | ERD-main/tools/model_converters/upgrade_ssd_version.py | # Copyright (c) OpenMMLab. All rights reserved.
import argparse
import tempfile
from collections import OrderedDict
import torch
from mmengine 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:
... | 1,793 | 29.40678 | 78 | py |
ERD | ERD-main/tools/model_converters/detectron2pytorch.py | # Copyright (c) OpenMMLab. All rights reserved.
import argparse
from collections import OrderedDict
import torch
from mmengine.fileio import load
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 ... | 3,594 | 41.797619 | 78 | py |
ERD | ERD-main/tools/dataset_converters/images2coco.py | # Copyright (c) OpenMMLab. All rights reserved.
import argparse
import os
from mmengine.fileio import dump, list_from_file
from mmengine.utils import mkdir_or_exist, scandir, track_iter_progress
from PIL import Image
def parse_args():
parser = argparse.ArgumentParser(
description='Convert images to coco ... | 3,193 | 30.009709 | 77 | py |
ERD | ERD-main/tools/dataset_converters/cityscapes.py | # Copyright (c) OpenMMLab. All rights reserved.
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_pr... | 5,270 | 33.227273 | 78 | py |
ERD | ERD-main/tools/dataset_converters/pascal_voc.py | # Copyright (c) OpenMMLab. All rights reserved.
import argparse
import os.path as osp
import xml.etree.ElementTree as ET
import numpy as np
from mmengine.fileio import dump, list_from_file
from mmengine.utils import mkdir_or_exist, track_progress
from mmdet.evaluation import voc_classes
label_ids = {name: i for i, n... | 7,917 | 32.129707 | 79 | py |
ERD | ERD-main/tools/analysis_tools/analyze_results.py | # Copyright (c) OpenMMLab. All rights reserved.
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 ... | 14,578 | 35.538847 | 79 | py |
ERD | ERD-main/tools/analysis_tools/eval_metric.py | # Copyright (c) OpenMMLab. All rights reserved.
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='Ev... | 1,645 | 31.27451 | 78 | py |
ERD | ERD-main/tools/analysis_tools/benchmark.py | # Copyright (c) OpenMMLab. All rights reserved.
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 (DataL... | 4,242 | 30.664179 | 79 | py |
ERD | ERD-main/tools/analysis_tools/optimize_anchors.py | # Copyright (c) OpenMMLab. All rights reserved.
"""Optimize anchor settings on a specific dataset.
This script provides two method to optimize YOLO anchors including k-means
anchor cluster and differential evolution. You can use ``--algorithm k-means``
and ``--algorithm differential_evolution`` to switch two method.
... | 13,631 | 34.592689 | 79 | py |
ERD | ERD-main/tools/analysis_tools/coco_occluded_separated_recall.py | # Copyright (c) OpenMMLab. All rights reserved.
from argparse import ArgumentParser
import mmengine
from mmengine.logging import print_log
from mmdet.datasets import CocoDataset
from mmdet.evaluation import CocoOccludedSeparatedMetric
def main():
parser = ArgumentParser(
description='Compute recall of C... | 1,748 | 34.693878 | 77 | py |
ERD | ERD-main/tools/analysis_tools/get_flops.py | # Copyright (c) OpenMMLab. All rights reserved.
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.regi... | 5,026 | 34.907143 | 78 | py |
ERD | ERD-main/tools/analysis_tools/analyze_logs.py | # Copyright (c) OpenMMLab. All rights reserved.
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 {ar... | 7,576 | 34.740566 | 79 | py |
ERD | ERD-main/tools/analysis_tools/browse_dataset.py | # Copyright (c) OpenMMLab. All rights reserved.
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... | 3,061 | 33.022222 | 78 | py |
ERD | ERD-main/tools/analysis_tools/confusion_matrix.py | 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 mmde... | 9,900 | 35.135036 | 79 | py |
ERD | ERD-main/tools/analysis_tools/test_robustness.py | # Copyright (c) OpenMMLab. All rights reserved.
import argparse
import copy
import os
import os.path as osp
from mmengine.config import Config, DictAction
from mmengine.dist import get_dist_info
from mmengine.evaluator import DumpResults
from mmengine.fileio import dump
from mmengine.runner import Runner
from mmdet.e... | 9,120 | 37.004167 | 79 | py |
ERD | ERD-main/tools/analysis_tools/coco_error_analysis.py | # Copyright (c) OpenMMLab. All rights reserved.
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):... | 12,389 | 35.441176 | 79 | py |
ERD | ERD-main/tools/analysis_tools/robustness_eval.py | # Copyright (c) OpenMMLab. All rights reserved.
import os.path as osp
from argparse import ArgumentParser
import numpy as np
from mmengine.fileio import load
def print_coco_results(results):
def _print(result, ap=1, iouThr=None, areaRng='all', maxDets=100):
titleStr = 'Average Precision' if ap == 1 else... | 8,376 | 30.731061 | 79 | py |
ERD | ERD-main/projects/Detic/demo.py | # Copyright (c) OpenMMLab. All rights reserved.
import os
import urllib
from argparse import ArgumentParser
import mmcv
import torch
from mmengine.logging import print_log
from mmengine.utils import ProgressBar, scandir
from mmdet.apis import inference_detector, init_detector
from mmdet.registry import VISUALIZERS
fr... | 4,710 | 31.944056 | 78 | py |
ERD | ERD-main/projects/Detic/detic/detic_bbox_head.py | # Copyright (c) OpenMMLab. All rights reserved.
from typing import Optional, Union
from mmengine.config import ConfigDict
from mmengine.structures import InstanceData
from torch import Tensor
from mmdet.models.layers import multiclass_nms
from mmdet.models.roi_heads.bbox_heads import Shared2FCBBoxHead
from mmdet.mode... | 4,599 | 39.707965 | 76 | py |
ERD | ERD-main/projects/Detic/detic/utils.py | # Copyright (c) OpenMMLab. All rights reserved.
import numpy as np
import torch
import torch.nn.functional as F
from mmengine.logging import print_log
from .text_encoder import CLIPTextEncoder
# download from
# https://github.com/facebookresearch/Detic/tree/main/datasets/metadata
DATASET_EMBEDDINGS = {
'lvis': 'd... | 2,864 | 35.265823 | 78 | py |
ERD | ERD-main/projects/Detic/detic/detic_roi_head.py | # Copyright (c) OpenMMLab. All rights reserved.
from typing import List, Sequence, Tuple
import torch
from mmengine.structures import InstanceData
from torch import Tensor
from mmdet.models.roi_heads import CascadeRoIHead
from mmdet.models.task_modules.samplers import SamplingResult
from mmdet.models.test_time_augs i... | 13,673 | 40.816514 | 78 | py |
ERD | ERD-main/projects/Detic/detic/zero_shot_classifier.py | # Copyright (c) Facebook, Inc. and its affiliates.
import numpy as np
import torch
from torch import nn
from torch.nn import functional as F
from mmdet.registry import MODELS
@MODELS.register_module(force=True) # avoid bug
class ZeroShotClassifier(nn.Module):
def __init__(
self,
in_features: in... | 2,324 | 30.418919 | 79 | py |
ERD | ERD-main/projects/Detic/detic/centernet_rpn_head.py | # Copyright (c) OpenMMLab. All rights reserved.
import copy
from typing import List, Sequence, Tuple
import torch
import torch.nn as nn
from mmcv.cnn import Scale
from mmengine import ConfigDict
from mmengine.structures import InstanceData
from torch import Tensor
from mmdet.models.dense_heads import CenterNetUpdateH... | 7,938 | 39.299492 | 79 | py |
ERD | ERD-main/projects/Detic/detic/__init__.py | # Copyright (c) OpenMMLab. All rights reserved.
from .centernet_rpn_head import CenterNetRPNHead
from .detic_bbox_head import DeticBBoxHead
from .detic_roi_head import DeticRoIHead
from .zero_shot_classifier import ZeroShotClassifier
__all__ = [
'CenterNetRPNHead', 'DeticBBoxHead', 'DeticRoIHead', 'ZeroShotClassif... | 327 | 31.8 | 77 | py |
ERD | ERD-main/projects/Detic/detic/text_encoder.py | # Copyright (c) OpenMMLab. All rights reserved.
from typing import List, Union
import torch
import torch.nn as nn
class CLIPTextEncoder(nn.Module):
def __init__(self, model_name='ViT-B/32'):
super().__init__()
import clip
from clip.simple_tokenizer import SimpleTokenizer
self.tok... | 1,605 | 30.490196 | 79 | py |
ERD | ERD-main/projects/Detic/configs/detic_centernet2_swin-b_fpn_4x_lvis-coco-in21k.py | _base_ = 'mmdet::common/lsj-200e_coco-detection.py'
custom_imports = dict(
imports=['projects.Detic.detic'], allow_failed_imports=False)
image_size = (1024, 1024)
batch_augments = [dict(type='BatchFixedSizePad', size=image_size)]
cls_layer = dict(
type='ZeroShotClassifier',
zs_weight_path='rand',
zs_... | 9,887 | 32.070234 | 79 | py |
ERD | ERD-main/projects/DiffusionDet/diffusiondet/loss.py | # Copyright (c) OpenMMLab. All rights reserved.
# Copyright (c) Facebook, Inc. and its affiliates. All Rights Reserved
# Modified from https://github.com/ShoufaChen/DiffusionDet/blob/main/diffusiondet/loss.py # noqa
# This work is licensed under the CC-BY-NC 4.0 License.
# Users should be careful about adopting the... | 14,481 | 41.345029 | 142 | py |
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