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 |
|---|---|---|---|---|---|---|
LSMOL | LSMOL-main/tools/ssl_methods/lost.py | 0 | 0 | 0 | py | |
LSMOL | LSMOL-main/tools/prepare_data/waymo2range.py | import os
import math
import numpy as np
from PIL import Image
import argparse
import multiprocessing
import tensorflow.compat.v1 as tf
from waymo_open_dataset.utils import frame_utils
from waymo_open_dataset import dataset_pb2 as open_dataset
from waymo_open_dataset import dataset_pb2
tf.enable_eager_execution()
de... | 5,419 | 39.447761 | 126 | py |
LSMOL | LSMOL-main/tools/prepare_data/waymo2box_gt.py | import tensorflow.compat.v1 as tf
import matplotlib
matplotlib.use('Agg')
import multiprocessing
from waymo_open_dataset import dataset_pb2 as open_dataset
import argparse
from pathlib import Path
import cv2
import json
from glob import glob
import os
WAYMO_CLASSES = ['unknown', 'vehicle', 'pedestrian', 'sign', 'cycli... | 4,232 | 39.314286 | 154 | py |
LSMOL | LSMOL-main/tools/prepare_data/waymo2sceneflow.py | import os
import tensorflow.compat.v1 as tf
import numpy as np
import argparse
import multiprocessing
tf.enable_eager_execution()
from waymo_open_dataset.utils import frame_utils
from waymo_open_dataset import dataset_pb2 as open_dataset
from waymo_open_dataset import dataset_pb2
from waymo2point_noground import filt... | 5,069 | 47.75 | 157 | py |
LSMOL | LSMOL-main/tools/prepare_data/waymo2coco_class.py | import tensorflow.compat.v1 as tf
from waymo_open_dataset import dataset_pb2 as open_dataset
import argparse
from pathlib import Path
import cv2
import json
from glob import glob
import os
import argparse
WAYMO_CLASSES = ['unknown', 'vehicle', 'pedestrian', 'sign', 'cyclist']
def get_camera_labels(frame):
if fram... | 5,302 | 47.209091 | 160 | py |
LSMOL | LSMOL-main/tools/prepare_data/waymo2coco.py | import tensorflow.compat.v1 as tf
from waymo_open_dataset import dataset_pb2 as open_dataset
import argparse
from pathlib import Path
import cv2
import json
from glob import glob
import os
import argparse
WAYMO_CLASSES = ['unknown', 'vehicle', 'pedestrian', 'sign', 'cyclist']
def get_camera_labels(frame):
if fram... | 4,552 | 44.079208 | 160 | py |
LSMOL | LSMOL-main/tools/prepare_data/waymo2point_noground.py | import os
import tensorflow.compat.v1 as tf
import numpy as np
import multiprocessing
import argparse
tf.enable_eager_execution()
from waymo_open_dataset.utils import range_image_utils
from waymo_open_dataset.utils import transform_utils
from waymo_open_dataset.utils import frame_utils
from waymo_open_dataset import ... | 6,092 | 42.212766 | 140 | py |
LSMOL | LSMOL-main/tools/generate_annos/generate_2d_anno.py | import os
import numpy as np
import json
import argparse
WAYMO_CLASSES = ['unknown', 'object']
def main(root,box_name,min_objects,save_json_path):
segs = sorted(os.listdir(root))
global_id = 0
object_id = 0
images = []
annotations = []
categories = [{'id': i, 'name': n} for i, n in enumerate... | 2,892 | 44.920635 | 151 | py |
LSMOL | LSMOL-main/tools/initial_seg/proposals2mask.py | 0 | 0 | 0 | py | |
LSMOL | LSMOL-main/tools/initial_seg/range2proposals.py | import sys
sys.path.append('../../third_party/depth_clustering/lib')
import segment
from PIL import Image
import numpy as np
import os
import argparse
def read_data(calib_path,range_path,img_path,imgs,ranges,idx):
with open(calib_path, "r") as f:
data = f.read()
depth_path = os.path.join(range_path,r... | 1,817 | 35.36 | 107 | py |
LSMOL | LSMOL-main/tools/initial_seg/proposals2bbox_pred_flow.py | 0 | 0 | 0 | py | |
LSMOL | LSMOL-main/tools/initial_seg/proposals2bbox_gt_flow.py | import os
import numpy as np
import json
from sklearn import preprocessing
from scipy import stats
import hdbscan
import multiprocessing
import argparse
def generate_bbox(point_obj,enlarge_pixel = 2):
x_min,x_max = max(0,int(point_obj[:,0].min())-enlarge_pixel),min(int(point_obj[:,0].max())+enlarge_pixel,1919)
... | 7,724 | 42.156425 | 166 | py |
LSMOL | LSMOL-main/evaluation/generate_3d_instance_gt.py | import torch
import pickle
import os
import numpy as np
from PIL import Image
from mmdet3d.core.bbox import CameraInstance3DBoxes,get_box_type
def main():
# change the path to your own path
pickle_data_path = '/data/waymo_root/kitti_format/waymo_infos_val.pkl'
root = '/data/waymo_root/kitti_format'
f... | 2,020 | 30.578125 | 111 | py |
LSMOL | LSMOL-main/evaluation/eval_3d_instance.py | import torch
import pickle
import os
import cv2
import numpy as np
from PIL import Image
from mmdet3d.core.bbox import CameraInstance3DBoxes,get_box_type
from matplotlib import pyplot as plt
import matplotlib.patches as patches
from scipy import stats
def calculate_PR(class_tp, class_fp, class_score, class_gt_num):
... | 7,685 | 31.987124 | 123 | py |
LSMOL | LSMOL-main/third_party/nspf/optimization.py | """optimize over a network structure."""
import argparse
import logging
import os
import copy
import matplotlib.pyplot as plt
import numpy as np
import open3d as o3d
import pandas as pd
import torch
from torch.utils.data import DataLoader
from tqdm import tqdm
from model import Neural_Prior
import config
from data i... | 10,110 | 33.158784 | 122 | py |
LSMOL | LSMOL-main/third_party/nspf/loss.py | # code mostly copied from pytorch3d
from typing import Union
import torch
import torch.nn.functional as F
from pytorch3d.ops.knn import knn_gather, knn_points
from pytorch3d.structures.pointclouds import Pointclouds
def _validate_chamfer_reduction_inputs(
batch_reduction: Union[str, None], point_reduction: str
)... | 8,316 | 36.295964 | 88 | py |
LSMOL | LSMOL-main/third_party/nspf/utils.py | from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
from __future__ import unicode_literals
import torch
import time
from collections import defaultdict
# ANCHOR: metrics computation, follow FlowNet3D metrics....
def scene_flow_metrics(pred, labels):
l2_no... | 4,540 | 29.273333 | 94 | py |
LSMOL | LSMOL-main/third_party/nspf/model.py | import torch
class Neural_Prior(torch.nn.Module):
def __init__(self, dim_x=3, filter_size=128, act_fn='relu', layer_size=8):
super().__init__()
self.layer_size = layer_size
self.nn_layers = torch.nn.ModuleList([])
# input layer (default: xyz -> 128)
if layer_si... | 1,322 | 36.8 | 101 | py |
LSMOL | LSMOL-main/third_party/nspf/data.py | #!/usr/bin/env python
# -*- coding: utf-8 -*-
import glob
import numpy as np
from torch.utils.data import Dataset
class FlyingThings3D(Dataset):
def __init__(self, options, partition='test'):
self.options = options
self.partition = partition
if self.partition == 'train':
self.... | 16,165 | 39.720403 | 123 | py |
LSMOL | LSMOL-main/third_party/nspf/config.py | import argparse
import os
import numpy as np
import torch
def str2bool(v):
if v.lower() in ('yes', 'true', 't', 'y', '1'):
return True
elif v.lower() in ('no', 'false', 'f', 'n', '0'):
return False
else:
raise argparse.ArgumentTypeError('Boolean value expected.')
def init_dirs(o... | 4,058 | 60.5 | 129 | py |
LSMOL | LSMOL-main/third_party/nspf/visualize.py | import matplotlib.pyplot as plt
import numpy as np
import open3d as o3d
# NOTE: need to comment this line if do not have GUI.
# from mayavi import mlab
from collections import namedtuple
from itertools import accumulate
from typing import Optional, Tuple
from matplotlib.ticker import AutoMinorLocator
DEFAULT_TRANSITI... | 12,264 | 41.003425 | 183 | py |
LSMOL | LSMOL-main/third_party/nspf/process/preprocess_sf_nuscenes.py | import os
from dataclasses import dataclass
from pathlib import Path
import numpy as np
import open3d as o3d
from argoverse.utils.cuboid_interior import (filter_point_cloud_to_bbox_3D_vectorized)
from argoverse.utils.se3 import SE3
from argoverse.utils.transform import quat2rotmat
from torch.utils.data import Dataset
... | 22,943 | 38.020408 | 141 | py |
LSMOL | LSMOL-main/third_party/nspf/process/preprocess_sf_waymo.py | import numpy as np
import os
import argparse
def main(waymo_root):
segs = sorted(os.listdir(waymo_root))
for seg in segs:
path = os.path.join(waymo_root,seg,'PC_ng')
files = sorted(os.listdir(path))
output_dir = os.path.join(waymo_root,seg,'point')
os.makedirs(output_dir,exist_... | 1,610 | 38.292683 | 126 | py |
LSMOL | LSMOL-main/third_party/nspf/process/preprocess_sf_argoverse.py | import copy
import glob
from dataclasses import dataclass
from pathlib import Path
from typing import List, Optional
import argoverse.data_loading.object_label_record as object_label
import numpy as np
import open3d as o3d
from argoverse.map_representation.map_api import ArgoverseMap
from argoverse.utils.cuboid_interi... | 11,330 | 40.811808 | 160 | py |
LSMOL | LSMOL-main/third_party/nspf/scripts/train_all.py | import os
waymo_lsmol_root = '/data/yuqi_wang/waymo_v1.2/waymo_lsmol'
segs = sorted(os.listdir(waymo_lsmol_root))
for i in range(len(segs)):
name = segs[i]
runfile = os.path.join(waymo_lsmol_root,name,'sceneflow_nsfp')
if os.path.exists(runfile):
print('skip')
continue
else:
os.... | 400 | 32.416667 | 91 | py |
LSMOL | LSMOL-main/model_training/train_det_2d_class.py | import logging
import os
from collections import OrderedDict
import torch
import detectron2.utils.comm as comm
from detectron2.checkpoint import DetectionCheckpointer
from detectron2.config import get_cfg
from detectron2.data import MetadataCatalog
from detectron2.data import DatasetCatalog
from detectron2.data.datase... | 5,795 | 35.917197 | 154 | py |
LSMOL | LSMOL-main/model_training/train_det_2d.py | import logging
import os
from collections import OrderedDict
import torch
import detectron2.utils.comm as comm
from detectron2.checkpoint import DetectionCheckpointer
from detectron2.config import get_cfg
from detectron2.data import MetadataCatalog
from detectron2.data import DatasetCatalog
from detectron2.data.datase... | 5,880 | 35.987421 | 152 | py |
LSMOL | LSMOL-main/model_training/train_cluster_3d.py | 0 | 0 | 0 | py | |
SSKD | SSKD-master/teacher.py | import os
import os.path as osp
import argparse
import time
import numpy as np
import torch
import torch.nn as nn
import torch.nn.functional as F
import torch.optim as optim
from torch.optim.lr_scheduler import MultiStepLR
from torch.utils.data import DataLoader
import torchvision.transforms as transforms
from torchv... | 5,112 | 34.020548 | 110 | py |
SSKD | SSKD-master/student.py | import os
import os.path as osp
import argparse
import time
import numpy as np
import torch
import torch.nn as nn
import torch.nn.functional as F
import torch.optim as optim
from torch.optim.lr_scheduler import MultiStepLR
from torch.utils.data import DataLoader
import torchvision.transforms as transforms
from tensor... | 14,139 | 38.830986 | 123 | py |
SSKD | SSKD-master/wrapper.py | import torch
import torch.nn as nn
import torch.nn.functional as F
class wrapper(nn.Module):
def __init__(self, module):
super(wrapper, self).__init__()
self.backbone = module
feat_dim = list(module.children())[-1].in_features
self.proj_head = nn.Sequential(
nn.Linear... | 702 | 24.107143 | 58 | py |
SSKD | SSKD-master/utils.py | import os
import logging
import numpy as np
import torch
from torch.nn import init
class AverageMeter(object):
"""Computes and stores the average and current value"""
def __init__(self):
self.reset()
def reset(self):
self.count = 0
self.sum = 0.0
self.val = 0.0
sel... | 1,010 | 21.977273 | 69 | py |
SSKD | SSKD-master/cifar.py | from __future__ import print_function
from PIL import Image
import os
import os.path
import numpy as np
import sys
import pickle
import torch
import torch.utils.data as data
from itertools import permutations
class VisionDataset(data.Dataset):
_repr_indent = 4
def __init__(self, root, transforms=None, trans... | 7,139 | 31.752294 | 86 | py |
SSKD | SSKD-master/models/resnet.py | from __future__ import absolute_import
'''Resnet for cifar dataset.
Ported form
https://github.com/facebook/fb.resnet.torch
and
https://github.com/pytorch/vision/blob/master/torchvision/models/resnet.py
(c) YANG, Wei
'''
import torch.nn as nn
import torch.nn.functional as F
import math
__all__ = ['resnet']
def con... | 8,021 | 29.044944 | 116 | py |
SSKD | SSKD-master/models/mobilenetv2.py | """
MobileNetV2 implementation used in
<Knowledge Distillation via Route Constrained Optimization>
"""
import torch
import torch.nn as nn
import math
__all__ = ['mobilenetv2_T_w', 'mobile_half']
BN = None
def conv_bn(inp, oup, stride):
return nn.Sequential(
nn.Conv2d(inp, oup, 3, stride, 1, bias=False)... | 5,777 | 27.323529 | 115 | py |
SSKD | SSKD-master/models/vgg.py | '''VGG for CIFAR10. FC layers are removed.
(c) YANG, Wei
'''
import torch.nn as nn
import torch.nn.functional as F
import math
__all__ = [
'VGG', 'vgg11', 'vgg11_bn', 'vgg13', 'vgg13_bn', 'vgg16', 'vgg16_bn',
'vgg19_bn', 'vgg19',
]
model_urls = {
'vgg11': 'https://download.pytorch.org/models/vgg11-bbd30... | 6,971 | 28.417722 | 98 | py |
SSKD | SSKD-master/models/classifier.py | from __future__ import print_function
import torch.nn as nn
#########################################
# ===== Classifiers ===== #
#########################################
class LinearClassifier(nn.Module):
def __init__(self, dim_in, n_label=10):
super(LinearClassifier, self).__init__()
self.n... | 819 | 21.777778 | 51 | py |
SSKD | SSKD-master/models/resnetv2.py | '''ResNet in PyTorch.
For Pre-activation ResNet, see 'preact_resnet.py'.
Reference:
[1] Kaiming He, Xiangyu Zhang, Shaoqing Ren, Jian Sun
Deep Residual Learning for Image Recognition. arXiv:1512.03385
'''
import torch
import torch.nn as nn
import torch.nn.functional as F
class BasicBlock(nn.Module):
expansion... | 6,915 | 33.753769 | 106 | py |
SSKD | SSKD-master/models/ShuffleNetv1.py | '''ShuffleNet in PyTorch.
See the paper "ShuffleNet: An Extremely Efficient Convolutional Neural Network for Mobile Devices" for more details.
'''
import torch
import torch.nn as nn
import torch.nn.functional as F
class ShuffleBlock(nn.Module):
def __init__(self, groups):
super(ShuffleBlock, self).__init_... | 4,732 | 33.05036 | 126 | py |
SSKD | SSKD-master/models/util.py | from __future__ import print_function
import torch.nn as nn
import math
class Paraphraser(nn.Module):
"""Paraphrasing Complex Network: Network Compression via Factor Transfer"""
def __init__(self, t_shape, k=0.5, use_bn=False):
super(Paraphraser, self).__init__()
in_channel = t_shape[1]
... | 9,622 | 32.068729 | 107 | py |
SSKD | SSKD-master/models/ShuffleNetv2.py | '''ShuffleNetV2 in PyTorch.
See the paper "ShuffleNet V2: Practical Guidelines for Efficient CNN Architecture Design" for more details.
'''
import torch
import torch.nn as nn
import torch.nn.functional as F
class ShuffleBlock(nn.Module):
def __init__(self, groups=2):
super(ShuffleBlock, self).__init__()
... | 7,074 | 32.530806 | 107 | py |
SSKD | SSKD-master/models/__init__.py | from .resnet import resnet8, resnet14, resnet20, resnet32, resnet44, resnet56, resnet110, resnet8x4, resnet32x4, resnet14x05, resnet20x05, resnet20x0375
from .resnetv2 import ResNet50
from .wrn import wrn_16_1, wrn_16_2, wrn_40_1, wrn_40_2
from .vgg import vgg19_bn, vgg16_bn, vgg13_bn, vgg11_bn, vgg8_bn
from .mobilenet... | 1,103 | 29.666667 | 152 | py |
SSKD | SSKD-master/models/wrn.py | import math
import torch
import torch.nn as nn
import torch.nn.functional as F
"""
Original Author: Wei Yang
"""
__all__ = ['wrn']
class BasicBlock(nn.Module):
def __init__(self, in_planes, out_planes, stride, dropRate=0.0):
super(BasicBlock, self).__init__()
self.bn1 = nn.BatchNorm2d(in_planes)... | 5,519 | 31.280702 | 116 | py |
cltl-backend-naoqi | cltl-backend-naoqi-main/setup.py | from setuptools import setup, find_packages
with open("README.md", "r") as fh:
long_description = fh.read()
with open("VERSION", "r") as fh:
version = fh.read().strip()
setup(
name='cltl.backend-naoqi',
version=version,
package_dir={'': 'src'},
packages=find_packages(where='src'),
data_fi... | 1,036 | 27.805556 | 60 | py |
cltl-backend-naoqi | cltl-backend-naoqi-main/src/cltl/__init__.py | 0 | 0 | 0 | py | |
cltl-backend-naoqi | cltl-backend-naoqi-main/src/cltl/naoqi/__main__.py | from cltl.naoqi import app
if __name__ == '__main__':
app.main()
| 70 | 13.2 | 26 | py |
cltl-backend-naoqi | cltl-backend-naoqi-main/src/cltl/naoqi/tts_output.py | from __future__ import unicode_literals
import logging
from cltl.naoqi.spi.text import TextOutput
logger = logging.getLogger(__name__)
class NAOqiTextToSpeech(TextOutput):
"""
NAOqi Text to Speech
Parameters
----------
session: qi.Session
Qi Application Session.
speed: int
... | 1,108 | 23.644444 | 111 | py |
cltl-backend-naoqi | cltl-backend-naoqi-main/src/cltl/naoqi/audio_source.py | import enum
import logging
import uuid
import numpy as np
from Queue import Queue, Full, Empty
from cltl.naoqi.spi.audio import AudioSource
logger = logging.getLogger(__name__)
def reframe(queue, frame_size, frame_duration):
buffer = []
buffered = 0
while True:
try:
chunk = queue.ge... | 6,169 | 24.8159 | 119 | py |
cltl-backend-naoqi | cltl-backend-naoqi-main/src/cltl/naoqi/image_source.py | import enum
import logging
import uuid
import numpy as np
import qi
import vision_definitions
from cltl.naoqi.api.camera import Image, Bounds, CameraResolution
from cltl.naoqi.spi.image import ImageSource
logger = logging.getLogger(__name__)
class NAOqiCameraIndex(enum.IntEnum):
"""
See also:
http://do... | 5,254 | 32.050314 | 111 | py |
cltl-backend-naoqi | cltl-backend-naoqi-main/src/cltl/naoqi/app.py | import argparse
import enum
import logging
import os
import qi
from cltl.naoqi.server import BackendServer
from cltl.naoqi.api.camera import CameraResolution
from cltl.naoqi.audio_source import NAOqiMicrophoneIndex
logger = logging.getLogger(__name__)
class Env(enum.Enum):
CLTL_MIC_IDX = 0
CLTL_MIC_BUFFER ... | 4,414 | 48.606742 | 148 | py |
cltl-backend-naoqi | cltl-backend-naoqi-main/src/cltl/naoqi/server.py | import base64
import logging
import flask
import numpy as np
from flask import Flask, Response, stream_with_context, jsonify, request
from flask import g as app_context
from flask.json import JSONEncoder
from cltl.naoqi.api.camera import Image, Bounds
from cltl.naoqi.audio_source import NAOqiMicrophone
from cltl.naoq... | 5,441 | 33.443038 | 112 | py |
cltl-backend-naoqi | cltl-backend-naoqi-main/src/cltl/naoqi/behaviour.py | import logging
from cltl.naoqi.spi.behaviour import BehaviourController, Behaviour
logger = logging.getLogger(__name__)
_NAOQI_BEHAVIOR = {
Behaviour.AUTONOMOUS_VISUAL_FEEDBACK: "AutonomousBlinking"
}
class NAOqiBehaviourController(BehaviourController):
"""
NAOqi Behavior.
Parameters
--------... | 845 | 23.882353 | 84 | py |
cltl-backend-naoqi | cltl-backend-naoqi-main/src/cltl/naoqi/__init__.py | 0 | 0 | 0 | py | |
cltl-backend-naoqi | cltl-backend-naoqi-main/src/cltl/naoqi/app_tablet.py | import argparse
import logging
import os
import enum
import qi
logger = logging.getLogger(__name__)
class Env(enum.Enum):
CLTL_NAOQI_IP = 6
CLTL_NAOQI_PORT = 7
CLTL_PORT = 8
CLTL_LOG_LEVEL = 9
CLTL_SERVER = 9
def env_or_default(env_var, default):
var = env_var.name
return os.environ[v... | 1,887 | 29.95082 | 112 | py |
cltl-backend-naoqi | cltl-backend-naoqi-main/src/cltl/naoqi/spi/image.py | class ImageSource:
def __enter__(self):
return self
def __exit__(self, exc_type, exc_val, exc_tb):
pass
@property
def resolution(self):
raise NotImplementedError()
def capture(self):
raise NotImplementedError()
| 266 | 18.071429 | 50 | py |
cltl-backend-naoqi | cltl-backend-naoqi-main/src/cltl/naoqi/spi/audio.py | class AudioSource:
def __enter__(self):
return self
def __exit__(self, exc_type, exc_val, exc_tb):
pass
def __iter__(self):
return iter(self.audio)
@property
def audio(self):
raise NotImplementedError()
@property
def rate(self):
raise NotImplemente... | 552 | 18.068966 | 50 | py |
cltl-backend-naoqi | cltl-backend-naoqi-main/src/cltl/naoqi/spi/behaviour.py | import enum
import logging
logger = logging.getLogger(__name__)
class Behaviour(enum.Enum):
# TO BE EXTENDED
AUTONOMOUS_VISUAL_FEEDBACK = 1
class BehaviourController:
def start(self, behaviour):
raise NotImplementedError()
def stop(self, behaviour):
raise NotImplementedError()
| 316 | 16.611111 | 36 | py |
cltl-backend-naoqi | cltl-backend-naoqi-main/src/cltl/naoqi/spi/text.py | class TextOutput:
def consume(self, text, language=None):
raise NotImplementedError()
def consume_stream(self, texts, language=None):
for text in texts:
self.consume(text)
class TextSource:
def __enter__(self):
return self
def __exit__(self, exc_type, exc_val, exc... | 525 | 18.481481 | 51 | py |
cltl-backend-naoqi | cltl-backend-naoqi-main/src/cltl/naoqi/spi/__init__.py | 0 | 0 | 0 | py | |
cltl-backend-naoqi | cltl-backend-naoqi-main/src/cltl/naoqi/api/camera.py | import collections
import enum
import logging
import numpy as np
logger = logging.getLogger(__name__)
class CameraResolution(enum.Enum):
"""
Image height and width.
See also vision_definitions.kXXX constants in the Qi Framework.
"""
NATIVE = -1, -1
QQQQVGA = 30, 40
QQQVGA = 60, 80
Q... | 1,236 | 19.616667 | 70 | py |
cltl-backend-naoqi | cltl-backend-naoqi-main/src/cltl/naoqi/api/__init__.py | 0 | 0 | 0 | py | |
cltl-backend-naoqi | cltl-backend-naoqi-main/src/cltl/naoqi/api/microphone.py | import collections
import logging
logger = logging.getLogger(__name__)
AUDIO_RESOURCE_NAME = "cltl.backend.api.audio"
"""Resource name to be shared with the speaker to mute the microphone when the speaker is active.
The AbstractMicrophone holds a reader-lock on this resource.
"""
MIC_RESOURCE_NAME = "cltl.backend.ap... | 722 | 33.428571 | 121 | py |
cltl-backend-naoqi | cltl-backend-naoqi-main/tests/test_naoqi_audio_source.py | import time
import unittest
from threading import Thread, Event
import numpy as np
from Queue import Queue
from cltl.naoqi.audio_source import reframe, NAOqiMicrophone
class DummySession:
def service(self, *args):
return DummyService()
def registerService(self, *args):
pass
class DummySer... | 3,008 | 28.792079 | 91 | py |
cltl-backend-naoqi | cltl-backend-naoqi-main/tests/test_naoqi_image_source.py | import unittest
import numpy as np
from cltl.naoqi.image_source import NAOqiCameraIndex, CameraResolution, NAOqiCamera
class DummySession:
def service(self, name):
return DummyService()
def registerService(self, name, cls):
pass
class DummyService:
def hasDepthCamera(self):
re... | 1,365 | 25.269231 | 112 | py |
cltl-backend-naoqi | cltl-backend-naoqi-main/tests/test_server.py | import base64
import json
import unittest
import numpy as np
from Queue import Queue
from cltl.naoqi.api.camera import CameraResolution, Image, Bounds
from cltl.naoqi.server import BackendServer
from cltl.naoqi.spi.audio import AudioSource
from cltl.naoqi.spi.image import ImageSource
from cltl.naoqi.spi.text import T... | 3,508 | 25.583333 | 132 | py |
cltl-backend-naoqi | cltl-backend-naoqi-main/tests/__init__.py | 0 | 0 | 0 | py | |
scirepeval | scirepeval-main/mdcr.py | import json
from typing import Union, Dict
import logging
import os
import datasets
import numpy as np
from tqdm import tqdm
from evaluation.embeddings_generator import EmbeddingsGenerator
from evaluation.encoders import Model
from evaluation.eval_datasets import SimpleDataset
from evaluation.evaluator import IREvalu... | 2,446 | 40.474576 | 117 | py |
scirepeval | scirepeval-main/scirepeval.py | import argparse
import json
from typing import List, Union
from evaluation.encoders import Model
from evaluation.evaluator import IREvaluator, SupervisedEvaluator, SupervisedTask
from evaluation.few_shot_evaluator import FewShotEvaluator
from evaluation.gpt3_encoder import GPT3Model
from evaluation.instructor import I... | 7,865 | 52.510204 | 121 | py |
scirepeval | scirepeval-main/reviewer_matching.py | from typing import Union, Dict
import logging
import os
import datasets
import numpy as np
from tqdm import tqdm
from evaluation.embeddings_generator import EmbeddingsGenerator
from evaluation.encoders import Model
from evaluation.eval_datasets import SimpleDataset
from evaluation.evaluator import IREvaluator
from sk... | 3,607 | 53.666667 | 120 | py |
scirepeval | scirepeval-main/s2and_embeddings.py | import pickle
import numpy as np
from evaluation.encoders import Model
from evaluation.eval_datasets import SimpleDataset
from evaluation.evaluator import Evaluator
import argparse
from tqdm import tqdm
import json
def read_data(file_path):
task_data = json.load(open(file_path, "r"))
task_data = list(task_... | 2,568 | 44.070175 | 120 | py |
scirepeval | scirepeval-main/adapter_fusion.py | from typing import List, Optional, Union, Dict
from transformers.adapters import PfeifferConfig
from transformers.adapters import AutoAdapterModel
from transformers.adapters.composition import Fuse
from abc import ABC, abstractmethod
import torch
import os
class AdapterFactory:
@staticmethod
def get_adapter(c... | 4,905 | 44.850467 | 121 | py |
scirepeval | scirepeval-main/bert_pals.py | # coding=utf-8
# Copyright 2018 The Google AI Language Team Authors and The HugginFace Inc. team.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENS... | 40,279 | 45.728538 | 120 | py |
scirepeval | scirepeval-main/evaluation/instructor.py | from InstructorEmbedding import INSTRUCTOR
from transformers import AutoTokenizer
instr_format = "Represent the Scientific documents for "
class InstructorModel:
def __init__(self, embed_model: str):
self.encoder = INSTRUCTOR(embed_model)
self.task_id = None
self.instruction_map = {"[CLF]... | 1,330 | 50.192308 | 117 | py |
scirepeval | scirepeval-main/evaluation/few_shot_evaluator.py | import math
from typing import Union
import numpy as np
from sklearn.model_selection import StratifiedKFold
import evaluation.evaluator
from evaluation.encoders import Model
from evaluation.evaluator import SupervisedEvaluator, SupervisedTask
from tqdm import tqdm
class FewShotEvaluator(SupervisedEvaluator):
de... | 2,522 | 41.762712 | 114 | py |
scirepeval | scirepeval-main/evaluation/evaluator.py | from typing import Union, Dict, Tuple
import numpy as np
from lightning.classification import LinearSVC
from lightning.regression import LinearSVR
from sklearn.metrics import f1_score, accuracy_score, precision_score, recall_score, mean_squared_error, r2_score
from scipy.stats import kendalltau, pearsonr
from sklearn.... | 10,751 | 45.951965 | 119 | py |
scirepeval | scirepeval-main/evaluation/embeddings_generator.py | from typing import Dict, List, Union
from evaluation.encoders import Model
from tqdm import tqdm
import numpy as np
import json
import pathlib
import logging
logger = logging.getLogger(__name__)
class EmbeddingsGenerator:
def __init__(self, datasets, models: Union[Model, List[Model]]):
self.datasets = d... | 2,202 | 39.796296 | 106 | py |
scirepeval | scirepeval-main/evaluation/gpt3_encoder.py | import os
import openai
import torch
from transformers import GPT2TokenizerFast
class GPT3Model:
def __init__(self, embed_model: str):
openai.api_key = os.getenv("OPENAI_API_KEY")
self.embed_model = embed_model
self.tokenizer = GPT2TokenizerFast.from_pretrained("gpt2")
def __call__(se... | 1,019 | 31.903226 | 66 | py |
scirepeval | scirepeval-main/evaluation/encoders.py | from typing import Dict, Union, List
from transformers import AutoModel, AutoTokenizer
import os
from bert_pals import BertPalsEncoder, BertPalConfig, BertModel
from adapter_fusion import AdapterEncoder, AdapterFusion
import torch
import logging
logger = logging.getLogger(__name__)
class EncoderFactory:
def __i... | 6,499 | 48.618321 | 119 | py |
scirepeval | scirepeval-main/evaluation/eval_datasets.py | import logging
import os
from typing import Union, List
import datasets
logger = logging.getLogger(__name__)
class SimpleDataset:
def __init__(self, data_path: Union[str, tuple], sep_token: str, batch_size=32,
fields: List = None, key: str = None, processing_fn=None):
self.batch_size =... | 3,071 | 33.909091 | 103 | py |
scirepeval | scirepeval-main/examples/classification.py | import sys
sys.path.append('../')
from evaluation.encoders import Model
from evaluation.evaluator import SupervisedEvaluator, SupervisedTask
from adapter_fusion import AdapterEncoder
# default no control codes
# model = Model(base_checkpoint="allenai/specter")
# default control codes
# model = Model(base_checkpoint... | 955 | 37.24 | 130 | py |
scirepeval | scirepeval-main/examples/fewshot_classification.py | import sys
sys.path.append('../')
from evaluation.encoders import Model
from evaluation.few_shot_evaluator import FewShotEvaluator, SupervisedTask
# default no control codes
model = Model(base_checkpoint="allenai/specter")
# default control codes
# model = Model(base_checkpoint="../lightning_logs/full_run/scincl_ct... | 920 | 37.375 | 124 | py |
scirepeval | scirepeval-main/examples/retrieval.py | import sys
sys.path.append('../')
from evaluation.evaluator import IREvaluator
from evaluation.encoders import Model
from adapter_fusion import AdapterEncoder
from reviewer_matching import ReviewerMatchingEvaluator
# default no control codes
# model = Model(base_checkpoint="allenai/specter")
# default control codes
... | 1,773 | 43.35 | 125 | py |
scirepeval | scirepeval-main/examples/regression.py | import sys
sys.path.append('../')
from evaluation.encoders import Model
from evaluation.evaluator import SupervisedEvaluator, SupervisedTask
#default no control codes
model = Model(base_checkpoint="allenai/specter")
#default control codes
# model = Model(base_checkpoint="../lightning_logs/full_run/scincl_ctrl/check... | 884 | 35.875 | 124 | py |
scirepeval | scirepeval-main/training/pl_training.py | import json
import sys
# setting path
sys.path.append('../')
import argparse
from typing import Dict, Optional, Any
import datasets
import pytorch_lightning as pl
import torch
import torch.nn
from pytorch_lightning.callbacks import ModelCheckpoint
from pytorch_lightning.loggers import TensorBoardLogger
from pytorch_l... | 16,448 | 49.612308 | 141 | py |
scirepeval | scirepeval-main/training/strategies.py | import enum
from abc import ABC, abstractmethod
from typing import Iterable
from torch.utils.data import Dataset
import random
class BatchWrapper(ABC):
@abstractmethod
def get_batch_iter(self, datasets: Iterable[Dataset], batch_size: int):
pass
class SequentialBatching(BatchWrapper):
def get_b... | 2,200 | 26.860759 | 75 | py |
scirepeval | scirepeval-main/training/mtl_datasets.py | import decimal
from typing import Iterator, Tuple, List, Dict, Union, Any, Iterable
import torch
from torch.utils.data import IterableDataset, DataLoader, ChainDataset, get_worker_info
from torch.utils.data.dataset import T_co, Dataset
from transformers import PreTrainedTokenizer, BatchEncoding, AutoTokenizer
import da... | 15,001 | 47.083333 | 145 | py |
scirepeval | scirepeval-main/training/schedulers.py | from dataclasses import dataclass, field
import torch.optim.optimizer
from fairseq.dataclass import FairseqDataclass
from omegaconf import II
from torch.optim.optimizer import Optimizer
"""Code picked up from fairseq implementation and adapted to suit pytorch lightning"""
@dataclass
class InverseSquareRootScheduleC... | 2,683 | 36.802817 | 87 | py |
scirepeval | scirepeval-main/training/tasks.py | from typing import Dict
from torch import nn
import torch
import torch.nn.functional as F
import json
class TaskFamily:
def __init__(self, name, loss, type, dataset=None, data_files=None, multi_label=False, input_fields=None,
labels_field=None, labels=None, ctrl_token=None, head=None, contrastiv... | 6,393 | 41.065789 | 152 | py |
vqg-unknown | vqg-unknown-master/test.py | from __future__ import division
from __future__ import print_function
from __future__ import absolute_import
import matplotlib
matplotlib.use('Agg')
import matplotlib.pyplot as plt
from utils.region_extract import *
from utils.utils import *
from src.net import ResNet
from src.net import ImageCaption
import chainer
... | 4,368 | 33.952 | 115 | py |
vqg-unknown | vqg-unknown-master/src/q_test.py | import numpy as np
import chainer
from chainer import cuda, Variable, serializers, functions as F
from net import ImageCaption
import argparse
import pickle
import json
from tqdm import tqdm
def create_data(all_data, all_features, split_type):
features = []
qa_ids = []
target_vecs = []
for each_quest... | 4,795 | 33.753623 | 124 | py |
vqg-unknown | vqg-unknown-master/src/feature_extract.py | import pickle
from skimage import io
from scipy.misc import imresize
import chainer
from chainer import Variable, cuda
from chainer.links.model.vision import resnet
import numpy as np
import argparse
from net import ResNet
xp = cuda.cupy
def region_resize(image, region):
x, y, w, h = region
aspect_list = np.... | 3,779 | 33.363636 | 110 | py |
vqg-unknown | vqg-unknown-master/src/net.py | import chainer
import chainer.functions as F
import chainer.links as L
from chainer.links.model.vision import resnet
class ResNet(chainer.Chain):
def __init__(self, path, layer):
super(ResNet, self).__init__()
with self.init_scope():
self.resnet = resnet.ResNetLayers(path, layer)
... | 1,403 | 33.243902 | 78 | py |
vqg-unknown | vqg-unknown-master/src/train.py | import numpy as np
import chainer
from chainer import cuda, Variable, optimizers, serializers, functions as F
import pickle
import argparse
import os
from net import ImageCaption
from tqdm import tqdm
def forward(target_vec, image_features, questions, model, eos):
sentence_length = questions.shape[-1]
model.... | 6,451 | 34.450549 | 212 | py |
vqg-unknown | vqg-unknown-master/src/preprocess.py | import pickle
import argparse
def main(args):
all_data_path = args.ALL_DATA_PATH
word_emb_path = args.WORD_EMB_PATH
word2id_path = args.WORD2ID_PATH
with open(all_data_path, 'rb') as f:
all_data = pickle.load(f)
with open(word2id_path, 'rb') as f:
word2id = pickle.load(f)
wi... | 1,573 | 30.48 | 97 | py |
vqg-unknown | vqg-unknown-master/utils/utils.py | import chainer
from chainer import cuda
import chainer.functions as F
from chainer.links.model.vision import resnet
import numpy as np
xp = cuda.cupy
def extract_feature(image, model):
x = resnet.prepare(image, size=None)
x = chainer.Variable(xp.asarray([x], dtype=xp.float32))
with chainer.using_config('... | 2,178 | 31.044118 | 124 | py |
vqg-unknown | vqg-unknown-master/utils/__init__.py | 0 | 0 | 0 | py | |
vqg-unknown | vqg-unknown-master/utils/region_extract.py | import itertools
import numpy as np
from skimage import io
import cv2
import chainer
from chainer import cuda, Variable
import chainer.functions as F
from chainer.links.model.vision import resnet
import selectivesearch
import pyimgsaliency as psal
xp = cuda.cupy
def selective_regions(image, saliency_image):
# pe... | 4,647 | 32.2 | 97 | py |
Chinese-Word-Vectors | Chinese-Word-Vectors-master/evaluation/ana_eval_dense.py | # We reuse a fraction of code in http://bitbucket.org/omerlevy/hyperwords.
# Using the numpy and similarity matrix largely speed up the evaluation process,
# compared with evaluation scripts in word2vec and GloVe
import numpy as np
import argparse
import random
def read_vectors(path, topn): # read top n word vector... | 6,472 | 38.469512 | 122 | py |
Chinese-Word-Vectors | Chinese-Word-Vectors-master/evaluation/ana_eval_sparse.py | # We reuse a fraction of code in http://bitbucket.org/omerlevy/hyperwords.
# Using the numpy and similarity matrix largely speed up the evaluation process,
# compared with evaluation scripts in word2vec and GloVe
import numpy as np
import argparse
import random
from scipy.sparse import dok_matrix, csr_matrix
def loa... | 6,483 | 37.141176 | 122 | py |
pip | pip-main/setup.py | import os
import sys
from setuptools import find_packages, setup
def read(rel_path: str) -> str:
here = os.path.abspath(os.path.dirname(__file__))
# intentionally *not* adding an encoding option to open, See:
# https://github.com/pypa/virtualenv/issues/201#issuecomment-3145690
with open(os.path.joi... | 3,055 | 33.337079 | 81 | py |
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