repo stringlengths 1 99 | file stringlengths 13 215 | code stringlengths 12 59.2M | file_length int64 12 59.2M | avg_line_length float64 3.82 1.48M | max_line_length int64 12 2.51M | extension_type stringclasses 1
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AdvBCT | AdvBCT-main/loss/reid/triplet_loss.py | import torch
from torch import nn
def normalize(x, axis=-1):
"""Normalizing to unit length along the specified dimension.
Args:
x: pytorch Variable
Returns:
x: pytorch Variable, same shape as input
"""
x = 1. * x / (torch.norm(x, 2, axis, keepdim=True).expand_as(x) + 1e-12)
return ... | 4,747 | 33.656934 | 82 | py |
AdvBCT | AdvBCT-main/loss/reid/arcface.py | import torch
import torch.nn as nn
import torch.nn.functional as F
from torch.nn import Parameter
import math
class ArcFace(nn.Module):
def __init__(self, in_features, out_features, s=30.0, m=0.50, bias=False):
super(ArcFace, self).__init__()
self.in_features = in_features
self.out_feature... | 2,975 | 36.2 | 105 | py |
AdvBCT | AdvBCT-main/loss/reid/center_loss.py | from __future__ import absolute_import
import torch
from torch import nn
class CenterLoss(nn.Module):
"""Center loss.
Reference:
Wen et al. A Discriminative Feature Learning Approach for Deep Face Recognition. ECCV 2016.
Args:
num_classes (int): number of classes.
feat_dim (int): fe... | 2,335 | 33.352941 | 110 | py |
AdvBCT | AdvBCT-main/loss/reid/make_loss.py | # encoding: utf-8
"""
@author: liaoxingyu
@contact: sherlockliao01@gmail.com
"""
import torch.nn.functional as F
from .softmax_loss import CrossEntropyLabelSmooth, LabelSmoothingCrossEntropy
from .triplet_loss import TripletLoss
from .center_loss import CenterLoss
def make_loss(cfg, num_classes,feat_dim=256): # ... | 3,674 | 42.75 | 106 | py |
AdvBCT | AdvBCT-main/evaluate/reranking.py | #!/usr/bin/env python3
# -*- coding: utf-8 -*-
"""
Created on Fri, 25 May 2018 20:29:09
"""
"""
CVPR2017 paper:Zhong Z, Zheng L, Cao D, et al. Re-ranking Person Re-identification with k-reciprocal Encoding[J]. 2017.
url:http://openaccess.thecvf.com/content_cvpr_2017/papers/Zhong_Re-Ranking_Person_Re-Identification_CVP... | 4,392 | 44.28866 | 119 | py |
AdvBCT | AdvBCT-main/evaluate/eval.py | import torch
import numpy as np
import os
import os.path as osp
import torch.distributed as dist
# import mkl
# mkl.get_max_threads()
import faiss, time
from evaluate.roxford_rparis_metrics import calculate_mAP_roxford_rparis
from evaluate.metric import calculate_mAP_gldv2,calculate_mAP_ijb_c
from utils.util import Ave... | 10,259 | 42.659574 | 116 | py |
AdvBCT | AdvBCT-main/evaluate/eval_hot_refresh.py | import torch
import numpy as np
import os
import os.path as osp
import torch.distributed as dist
import faiss,time
from evaluate.roxford_rparis_metrics import calculate_mAP_roxford_rparis
from evaluate.metric import calculate_mAP_gldv2,calculate_mAP_ijb_c
from utils.util import AverageMeter
from tqdm import tqdm
import... | 9,723 | 44.018519 | 120 | py |
AdvBCT | AdvBCT-main/evaluate/roxford_rparis_metrics.py | """
Modified from https://github.com/filipradenovic/cnnimageretrieval-pytorch/blob/master/cirtorch/utils/evaluate.py
"""
import numpy as np
def compute_ap(ranks, nres):
"""
Computes average precision for given ranked indexes.
Arguments
---------
ranks : zerro-based ranks of positive images
n... | 4,597 | 29.052288 | 113 | py |
AdvBCT | AdvBCT-main/evaluate/metric.py | import numpy as np
import torch
from evaluate.reranking import re_ranking
def calculate_mAP_gldv2(ranked_gallery_indices, query_gts, topk):
num_q = ranked_gallery_indices.shape[0]
average_precision = np.zeros(num_q, dtype=float)
for i in range(num_q):
retrieved_indices = np.where(np.in1d(ranked_gall... | 6,639 | 37.16092 | 112 | py |
AdvBCT | AdvBCT-main/utils/util.py | import argparse
import collections
import json
import os
import random
import warnings
from collections import OrderedDict
from itertools import repeat
from pathlib import Path
import numpy as np
import pandas as pd
import torch
from torch.optim.lr_scheduler import _LRScheduler
from torch.optim.lr_scheduler import Red... | 10,179 | 38.305019 | 116 | py |
AdvBCT | AdvBCT-main/utils/process_dataset.py | import os
from pathlib import Path
import csv
import argparse
import random
import numpy as np
import os.path as osp
import mxnet as mx
from tqdm import tqdm
import numbers
from _collections import defaultdict
import glob
import re
random.seed(666)
np.random.seed(666)
def gen_ms1m_total_list(root):
path_imgrec = ... | 20,479 | 39.715706 | 124 | py |
AdvBCT | AdvBCT-main/data_loaders/__init__.py | from data_loaders.landmark.gldv2 import ROxfordParisTestDataset,Gldv2TrainDataset,Gldv2TestDataset, Gldv2TrainDataset1
import torch
from typing import Sequence
from torch.utils.data import Dataset, DataLoader
from torchvision import transforms
from timm.data.random_erasing import RandomErasing
import torch.distributed ... | 3,436 | 41.432099 | 140 | py |
AdvBCT | AdvBCT-main/data_loaders/dataset/sampler_ddp.py | from torch.utils.data.sampler import Sampler
from collections import defaultdict
import copy
import random
import numpy as np
import math
import torch.distributed as dist
_LOCAL_PROCESS_GROUP = None
import torch
import pickle
def train_collate_fn(batch):
imgs, pids, camids, viewids , _ = zip(*batch)
pids = tor... | 11,116 | 36.305369 | 167 | py |
AdvBCT | AdvBCT-main/data_loaders/reid/base.py | from PIL import Image, ImageFile
from torch.utils.data import Dataset
import os.path as osp
ImageFile.LOAD_TRUNCATED_IMAGES = True
def read_image(img_path):
"""Keep reading image until succeed.
This can avoid IOError incurred by heavy IO process."""
got_img = False
if not osp.exists(img_path):
... | 2,831 | 32.714286 | 112 | py |
AdvBCT | AdvBCT-main/data_loaders/reid/market1501.py | # encoding: utf-8
"""
@author: sherlock
@contact: sherlockliao01@gmail.com
"""
import glob
import re
import os.path as osp
import torch.distributed as dist
from data_loaders.reid.base import BaseImageDataset
class Market1501(BaseImageDataset):
"""
Market1501
Reference:
Zheng et al. Scalable Person R... | 4,151 | 40.52 | 138 | py |
AdvBCT | AdvBCT-main/data_loaders/reid/ms1m.py | from torch.utils.data import Dataset
import os.path as osp
import mxnet as mx
import numbers
import torch
import numpy as np
from PIL import Image
def files_reader(path,root):
flist = []
with open(path) as f:
for line in f.readlines():
[imid,label]=line.split()
flist.append([osp.... | 3,168 | 34.606742 | 101 | py |
AdvBCT | AdvBCT-main/data_loaders/landmark/base_dataset.py | from torch.utils.data import Dataset
from PIL import Image
import os.path as osp
class TrainDataset(Dataset):
def __int__(self,flist,transform):
self.imlist = flist
self.transform = transform
def __getitem__(self, index):
path, label = self.imlist[index]
img = self._reader(path)... | 937 | 25.055556 | 66 | py |
astrospice | astrospice-main/docs/conf.py | # -*- coding: utf-8 -*-
#
# Configuration file for the Sphinx documentation builder.
#
# This file does only contain a selection of the most common options. For a
# full list see the documentation:
# http://www.sphinx-doc.org/en/master/config
# -- Project information --------------------------------------------------... | 2,604 | 30.385542 | 86 | py |
LiDAR-Distillation | LiDAR-Distillation-main/setup.py | import os
import subprocess
from setuptools import find_packages, setup
from torch.utils.cpp_extension import BuildExtension, CUDAExtension
def get_git_commit_number():
if not os.path.exists('.git'):
return '0000000'
cmd_out = subprocess.run(['git', 'rev-parse', 'HEAD'], stdout=subprocess.PIPE)
... | 3,616 | 31.294643 | 95 | py |
LiDAR-Distillation | LiDAR-Distillation-main/tools/test.py | import _init_path
import os
import torch
from tensorboardX import SummaryWriter
import time
import glob
import re
import datetime
import argparse
import numpy as np
from pathlib import Path
import torch.distributed as dist
from pcdet.datasets import build_dataloader
from pcdet.models import build_network
from pcdet.uti... | 9,448 | 41.183036 | 136 | py |
LiDAR-Distillation | LiDAR-Distillation-main/tools/train_mimic.py | import _init_path
import os
import torch
import torch.nn as nn
from tensorboardX import SummaryWriter
from pcdet.config import cfg, log_config_to_file, cfg_from_list, cfg_from_yaml_file
from pcdet.utils import common_utils
from pcdet.datasets import build_dataloader
from pcdet.models import build_network, model_fn_deco... | 10,784 | 42.663968 | 134 | py |
LiDAR-Distillation | LiDAR-Distillation-main/tools/train.py | import _init_path
import os
import torch
import torch.nn as nn
from tensorboardX import SummaryWriter
from pcdet.config import cfg, log_config_to_file, cfg_from_list, cfg_from_yaml_file
from pcdet.utils import common_utils
from pcdet.datasets import build_dataloader
from pcdet.models import build_network, model_fn_deco... | 9,713 | 42.954751 | 134 | py |
LiDAR-Distillation | LiDAR-Distillation-main/tools/eval_utils/model_key_mapping.py | import os, sys
import torch
src = torch.load(sys.argv[1]) # source
src = src['model_state']
def second_key_mapping():
src = torch.load(sys.argv[1])
src = src['model_state']
key_mapping = {
'rpn_net.conv': 'backbone_3d.conv',
'rpn_head.deblocks.': 'backbone_2d.deblocks.',
'rpn_h... | 2,831 | 36.76 | 93 | py |
LiDAR-Distillation | LiDAR-Distillation-main/tools/eval_utils/eval_utils.py | import pickle
import time
import numpy as np
import torch
import tqdm
from pcdet.models import load_data_to_gpu
from pcdet.utils import common_utils
def statistics_info(cfg, ret_dict, metric, disp_dict):
for cur_thresh in cfg.MODEL.POST_PROCESSING.RECALL_THRESH_LIST:
metric['recall_roi_%s' % str(cur_thr... | 4,840 | 36.527132 | 131 | py |
LiDAR-Distillation | LiDAR-Distillation-main/tools/train_utils/train_mimic_utils.py | import glob
import os
import torch
import tqdm
from torch.nn.utils import clip_grad_norm_
from pcdet.models import load_data_to_gpu
import pdb
import numpy as np
def cal_mimic_loss(batch_teacher, batch, model_cfg, mimic_mode):
teacher_features = batch_teacher['spatial_features_2d'].detach()
student_features =... | 9,171 | 39.584071 | 137 | py |
LiDAR-Distillation | LiDAR-Distillation-main/tools/train_utils/train_st_utils.py | import torch
import os
import glob
import tqdm
from torch.nn.utils import clip_grad_norm_
from pcdet.utils import common_utils
from pcdet.utils import self_training_utils
from pcdet.config import cfg
from .train_utils import save_checkpoint, checkpoint_state
def train_one_epoch_st(model, optimizer, source_reader, tar... | 8,005 | 44.488636 | 108 | py |
LiDAR-Distillation | LiDAR-Distillation-main/tools/train_utils/train_utils.py | import glob
import os
import torch
import tqdm
from torch.nn.utils import clip_grad_norm_
def train_one_epoch(model, optimizer, train_loader, model_func, lr_scheduler, accumulated_iter, optim_cfg,
rank, tbar, total_it_each_epoch, dataloader_iter, tb_log=None, leave_pbar=False):
if total_it_ea... | 5,769 | 37.724832 | 117 | py |
LiDAR-Distillation | LiDAR-Distillation-main/tools/train_utils/optimization/fastai_optim.py | # This file is modified from https://github.com/traveller59/second.pytorch
from collections import Iterable
import torch
from torch import nn
from torch._utils import _unflatten_dense_tensors
from torch.nn.utils import parameters_to_vector
bn_types = (nn.BatchNorm1d, nn.BatchNorm2d, nn.BatchNorm3d, nn.SyncBatchNorm)... | 10,335 | 38.753846 | 117 | py |
LiDAR-Distillation | LiDAR-Distillation-main/tools/train_utils/optimization/learning_schedules_fastai.py | # This file is modified from https://github.com/traveller59/second.pytorch
import math
from functools import partial
import numpy as np
import torch.optim.lr_scheduler as lr_sched
from .fastai_optim import OptimWrapper
class LRSchedulerStep(object):
def __init__(self, fai_optimizer: OptimWrapper, total_step, l... | 4,169 | 35.26087 | 118 | py |
LiDAR-Distillation | LiDAR-Distillation-main/tools/train_utils/optimization/__init__.py | from functools import partial
import torch.nn as nn
import torch.optim as optim
import torch.optim.lr_scheduler as lr_sched
from .fastai_optim import OptimWrapper
from .learning_schedules_fastai import CosineWarmupLR, OneCycle
def build_optimizer(model, optim_cfg):
if optim_cfg.OPTIMIZER == 'adam':
opti... | 2,401 | 36.53125 | 113 | py |
LiDAR-Distillation | LiDAR-Distillation-main/tools/visual_utils/visualize_utils.py | import mayavi.mlab as mlab
import numpy as np
import torch
box_colormap = [
[1, 1, 1],
[0, 1, 0],
[0, 1, 1],
[1, 1, 0],
]
def check_numpy_to_torch(x):
if isinstance(x, np.ndarray):
return torch.from_numpy(x).float(), True
return x, False
def rotate_points_along_z(points, angle):
... | 8,540 | 38.541667 | 121 | py |
LiDAR-Distillation | LiDAR-Distillation-main/pcdet/models/__init__.py | from collections import namedtuple
import numpy as np
import torch
from .detectors import build_detector
def build_network(model_cfg, num_class, dataset):
model = build_detector(
model_cfg=model_cfg, num_class=num_class, dataset=dataset
)
return model
def load_data_to_gpu(batch_dict):
for ... | 1,354 | 26.653061 | 77 | py |
LiDAR-Distillation | LiDAR-Distillation-main/pcdet/models/detectors/detector3d_template.py | import os
import torch
import torch.nn as nn
from ...ops.iou3d_nms import iou3d_nms_utils
from .. import backbones_2d, backbones_3d, dense_heads, roi_heads
from ..backbones_2d import map_to_bev
from ..backbones_3d import pfe, vfe
from ..model_utils import model_nms_utils
from pcdet.config import cfg
import pdb
clas... | 17,221 | 43.966057 | 111 | py |
LiDAR-Distillation | LiDAR-Distillation-main/pcdet/models/detectors/pointpillar_iou.py | import torch
from .detector3d_template import Detector3DTemplate
from ..model_utils.model_nms_utils import class_agnostic_nms
from ...ops.roiaware_pool3d import roiaware_pool3d_utils
class PointPillarIoU(Detector3DTemplate):
def __init__(self, model_cfg, num_class, dataset):
super().__init__(model_cfg=mode... | 7,769 | 40.774194 | 127 | py |
LiDAR-Distillation | LiDAR-Distillation-main/pcdet/models/detectors/pv_rcnn.py | from .detector3d_template import Detector3DTemplate
import torch # added by Zibu Wei in 2021/09/28
from ..model_utils.model_nms_utils import class_agnostic_nms # added by Zibu Wei in 2021/09/28
class PVRCNN(Detector3DTemplate):
def __init__(self, model_cfg, num_class, dataset):
super().__init__(model_cfg=... | 5,291 | 40.669291 | 127 | py |
LiDAR-Distillation | LiDAR-Distillation-main/pcdet/models/detectors/second_net_iou.py | import torch
from .detector3d_template import Detector3DTemplate
from ..model_utils.model_nms_utils import class_agnostic_nms
from ...ops.roiaware_pool3d import roiaware_pool3d_utils
class SECONDNetIoU(Detector3DTemplate):
def __init__(self, model_cfg, num_class, dataset):
super().__init__(model_cfg=model... | 7,767 | 40.763441 | 127 | py |
LiDAR-Distillation | LiDAR-Distillation-main/pcdet/models/backbones_3d/spconv_unet.py | from functools import partial
import spconv
import torch
import torch.nn as nn
from ...utils import common_utils
from .spconv_backbone import post_act_block
class SparseBasicBlock(spconv.SparseModule):
expansion = 1
def __init__(self, inplanes, planes, stride=1, downsample=None, indice_key=None, norm_fn=No... | 8,445 | 38.839623 | 117 | py |
LiDAR-Distillation | LiDAR-Distillation-main/pcdet/models/backbones_3d/spconv_backbone.py | from functools import partial
import spconv
import torch.nn as nn
def post_act_block(in_channels, out_channels, kernel_size, indice_key=None, stride=1, padding=0,
conv_type='subm', norm_fn=None):
if conv_type == 'subm':
conv = spconv.SubMConv3d(in_channels, out_channels, kernel_size, ... | 9,376 | 34.790076 | 118 | py |
LiDAR-Distillation | LiDAR-Distillation-main/pcdet/models/backbones_3d/pointnet2_backbone.py | import torch
import torch.nn as nn
from ...ops.pointnet2.pointnet2_batch import pointnet2_modules
from ...ops.pointnet2.pointnet2_stack import pointnet2_modules as pointnet2_modules_stack
from ...ops.pointnet2.pointnet2_stack import pointnet2_utils as pointnet2_utils_stack
class PointNet2MSG(nn.Module):
def __in... | 8,540 | 40.26087 | 132 | py |
LiDAR-Distillation | LiDAR-Distillation-main/pcdet/models/backbones_3d/pfe/voxel_set_abstraction.py | import torch
import torch.nn as nn
from ....ops.pointnet2.pointnet2_stack import pointnet2_modules as pointnet2_stack_modules
from ....ops.pointnet2.pointnet2_stack import pointnet2_utils as pointnet2_stack_utils
from ....utils import common_utils
def bilinear_interpolate_torch(im, x, y):
"""
Args:
i... | 9,731 | 39.381743 | 121 | py |
LiDAR-Distillation | LiDAR-Distillation-main/pcdet/models/backbones_3d/vfe/vfe_template.py | import torch.nn as nn
class VFETemplate(nn.Module):
def __init__(self, model_cfg, **kwargs):
super().__init__()
self.model_cfg = model_cfg
def get_output_feature_dim(self):
raise NotImplementedError
def forward(self, **kwargs):
"""
Args:
**kwargs:
... | 470 | 19.478261 | 45 | py |
LiDAR-Distillation | LiDAR-Distillation-main/pcdet/models/backbones_3d/vfe/mean_vfe.py | import torch
from .vfe_template import VFETemplate
class MeanVFE(VFETemplate):
def __init__(self, model_cfg, num_point_features, **kwargs):
super().__init__(model_cfg=model_cfg)
self.num_point_features = num_point_features
def get_output_feature_dim(self):
return self.num_point_featur... | 1,037 | 32.483871 | 99 | py |
LiDAR-Distillation | LiDAR-Distillation-main/pcdet/models/backbones_3d/vfe/pillar_vfe.py | import torch
import torch.nn as nn
import torch.nn.functional as F
from .vfe_template import VFETemplate
import pdb
class PFNLayer(nn.Module):
def __init__(self,
in_channels,
out_channels,
use_norm=True,
last_layer=False):
super().__init_... | 5,096 | 40.439024 | 137 | py |
LiDAR-Distillation | LiDAR-Distillation-main/pcdet/models/dense_heads/anchor_head_single.py | import numpy as np
import torch.nn as nn
from .anchor_head_template import AnchorHeadTemplate
import pdb
class AnchorHeadSingle(AnchorHeadTemplate):
def __init__(self, model_cfg, input_channels, num_class, class_names, grid_size, point_cloud_range,
predict_boxes_when_training=True, **kwargs):
... | 3,039 | 37.974359 | 136 | py |
LiDAR-Distillation | LiDAR-Distillation-main/pcdet/models/dense_heads/point_head_template.py | import torch
import torch.nn as nn
import torch.nn.functional as F
from ...ops.roiaware_pool3d import roiaware_pool3d_utils
from ...utils import common_utils, loss_utils
class PointHeadTemplate(nn.Module):
def __init__(self, model_cfg, num_class):
super().__init__()
self.model_cfg = model_cfg
... | 9,776 | 45.336493 | 119 | py |
LiDAR-Distillation | LiDAR-Distillation-main/pcdet/models/dense_heads/anchor_head_template.py | import numpy as np
import torch
import torch.nn as nn
from ...utils import box_coder_utils, common_utils, loss_utils
from .target_assigner.anchor_generator import AnchorGenerator
from .target_assigner.atss_target_assigner import ATSSTargetAssigner
from .target_assigner.axis_aligned_target_assigner import AxisAlignedTa... | 12,421 | 43.844765 | 118 | py |
LiDAR-Distillation | LiDAR-Distillation-main/pcdet/models/dense_heads/anchor_head_multi.py | import numpy as np
import torch
import torch.nn as nn
from ..backbones_2d import BaseBEVBackbone
from .anchor_head_template import AnchorHeadTemplate
class SingleHead(BaseBEVBackbone):
def __init__(self, model_cfg, input_channels, num_class, num_anchors_per_location, code_size, rpn_head_cfg=None,
... | 17,041 | 44.566845 | 117 | py |
LiDAR-Distillation | LiDAR-Distillation-main/pcdet/models/dense_heads/point_head_box.py | import torch
from ...utils import box_coder_utils, box_utils
from .point_head_template import PointHeadTemplate
class PointHeadBox(PointHeadTemplate):
"""
A simple point-based segmentation head, which are used for PointRCNN.
Reference Paper: https://arxiv.org/abs/1812.04244
PointRCNN: 3D Object Propo... | 4,930 | 41.508621 | 106 | py |
LiDAR-Distillation | LiDAR-Distillation-main/pcdet/models/dense_heads/point_head_simple.py | import torch
from ...utils import box_utils
from .point_head_template import PointHeadTemplate
class PointHeadSimple(PointHeadTemplate):
"""
A simple point-based segmentation head, which are used for PV-RCNN keypoint segmentaion.
Reference Paper: https://arxiv.org/abs/1912.13192
PV-RCNN: Point-Voxel ... | 3,568 | 37.793478 | 106 | py |
LiDAR-Distillation | LiDAR-Distillation-main/pcdet/models/dense_heads/point_intra_part_head.py | import torch
from ...utils import box_coder_utils, box_utils
from .point_head_template import PointHeadTemplate
class PointIntraPartOffsetHead(PointHeadTemplate):
"""
Point-based head for predicting the intra-object part locations.
Reference Paper: https://arxiv.org/abs/1907.03670
From Points to Part... | 5,568 | 42.507813 | 107 | py |
LiDAR-Distillation | LiDAR-Distillation-main/pcdet/models/dense_heads/target_assigner/anchor_generator.py | import torch
class AnchorGenerator(object):
def __init__(self, anchor_range, anchor_generator_config):
super().__init__()
self.anchor_generator_cfg = anchor_generator_config
self.anchor_range = anchor_range
self.anchor_sizes = [config['anchor_sizes'] for config in anchor_generator_... | 3,990 | 48.8875 | 122 | py |
LiDAR-Distillation | LiDAR-Distillation-main/pcdet/models/dense_heads/target_assigner/axis_aligned_target_assigner.py | import numpy as np
import torch
from ....ops.iou3d_nms import iou3d_nms_utils
from ....utils import box_utils
class AxisAlignedTargetAssigner(object):
def __init__(self, model_cfg, class_names, box_coder, match_height=False):
super().__init__()
anchor_generator_cfg = model_cfg.ANCHOR_GENERATOR_C... | 9,843 | 46.326923 | 118 | py |
LiDAR-Distillation | LiDAR-Distillation-main/pcdet/models/dense_heads/target_assigner/atss_target_assigner.py | import torch
from ....ops.iou3d_nms import iou3d_nms_utils
from ....utils import common_utils
class ATSSTargetAssigner(object):
"""
Reference: https://arxiv.org/abs/1912.02424
"""
def __init__(self, topk, box_coder, match_height=False):
self.topk = topk
self.box_coder = box_coder
... | 6,050 | 41.612676 | 117 | py |
LiDAR-Distillation | LiDAR-Distillation-main/pcdet/models/roi_heads/roi_head_template.py | import numpy as np
import torch
import torch.nn as nn
import torch.nn.functional as F
from ...utils import box_coder_utils, common_utils, loss_utils
from ..model_utils.model_nms_utils import class_agnostic_nms
from .target_assigner.proposal_target_layer import ProposalTargetLayer
class RoIHeadTemplate(nn.Module):
... | 11,475 | 43.48062 | 128 | py |
LiDAR-Distillation | LiDAR-Distillation-main/pcdet/models/roi_heads/partA2_head.py | import numpy as np
import spconv
import torch
import torch.nn as nn
from ...ops.roiaware_pool3d import roiaware_pool3d_utils
from .roi_head_template import RoIHeadTemplate
class PartA2FCHead(RoIHeadTemplate):
def __init__(self, input_channels, model_cfg, num_class=1):
super().__init__(num_class=num_class... | 10,039 | 43.622222 | 120 | py |
LiDAR-Distillation | LiDAR-Distillation-main/pcdet/models/roi_heads/pillar_head.py | import torch
import torch.nn as nn
from .roi_head_template import RoIHeadTemplate
from ...utils import common_utils, loss_utils
class PillarHead(RoIHeadTemplate):
def __init__(self, input_channels, model_cfg, num_class=1):
super().__init__(num_class=num_class, model_cfg=model_cfg, is_loss=False)
s... | 3,929 | 38.3 | 120 | py |
LiDAR-Distillation | LiDAR-Distillation-main/pcdet/models/roi_heads/pvrcnn_head.py | import torch.nn as nn
from ...ops.pointnet2.pointnet2_stack import pointnet2_modules as pointnet2_stack_modules
from ...utils import common_utils
from .roi_head_template import RoIHeadTemplate
class PVRCNNHead(RoIHeadTemplate):
def __init__(self, input_channels, model_cfg, num_class=1):
super().__init__(... | 8,801 | 41.521739 | 116 | py |
LiDAR-Distillation | LiDAR-Distillation-main/pcdet/models/roi_heads/second_head.py | import torch
import torch.nn as nn
from .roi_head_template import RoIHeadTemplate
from ...utils import common_utils, loss_utils
class SECONDHead(RoIHeadTemplate):
def __init__(self, input_channels, model_cfg, num_class=1):
super().__init__(num_class=num_class, model_cfg=model_cfg)
self.model_cfg =... | 8,011 | 41.391534 | 120 | py |
LiDAR-Distillation | LiDAR-Distillation-main/pcdet/models/roi_heads/pointrcnn_head.py | import torch
import torch.nn as nn
from ...ops.pointnet2.pointnet2_batch import pointnet2_modules
from ...ops.roipoint_pool3d import roipoint_pool3d_utils
from ...utils import common_utils
from .roi_head_template import RoIHeadTemplate
class PointRCNNHead(RoIHeadTemplate):
def __init__(self, input_channels, mode... | 7,825 | 42.477778 | 116 | py |
LiDAR-Distillation | LiDAR-Distillation-main/pcdet/models/roi_heads/target_assigner/proposal_target_layer.py | import numpy as np
import torch
import torch.nn as nn
from ....ops.iou3d_nms import iou3d_nms_utils
class ProposalTargetLayer(nn.Module):
def __init__(self, roi_sampler_cfg):
super().__init__()
self.roi_sampler_cfg = roi_sampler_cfg
def forward(self, batch_dict):
"""
Args:
... | 10,057 | 42.921397 | 117 | py |
LiDAR-Distillation | LiDAR-Distillation-main/pcdet/models/model_utils/model_nms_utils.py | import torch
from ...ops.iou3d_nms import iou3d_nms_utils
def class_agnostic_nms(box_scores, box_preds, nms_config, score_thresh=None):
src_box_scores = box_scores
if score_thresh is not None:
scores_mask = (box_scores >= score_thresh)
box_scores = box_scores[scores_mask]
box_preds = b... | 2,456 | 36.227273 | 116 | py |
LiDAR-Distillation | LiDAR-Distillation-main/pcdet/models/backbones_2d/base_bev_backbone.py | import numpy as np
import torch
import torch.nn as nn
class BaseBEVBackbone(nn.Module):
def __init__(self, model_cfg, input_channels):
super().__init__()
self.model_cfg = model_cfg
if self.model_cfg.get('LAYER_NUMS', None) is not None:
assert len(self.model_cfg.LAYER_NUMS) == ... | 4,318 | 37.221239 | 121 | py |
LiDAR-Distillation | LiDAR-Distillation-main/pcdet/models/backbones_2d/map_to_bev/pointpillar_scatter.py | import torch
import torch.nn as nn
import pdb
class PointPillarScatter(nn.Module):
def __init__(self, model_cfg, grid_size, **kwargs):
super().__init__()
self.model_cfg = model_cfg
self.num_bev_features = self.model_cfg.NUM_BEV_FEATURES
self.nx, self.ny, self.nz = grid_size
... | 1,569 | 38.25 | 123 | py |
LiDAR-Distillation | LiDAR-Distillation-main/pcdet/models/backbones_2d/map_to_bev/height_compression.py | import torch.nn as nn
import pdb
class HeightCompression(nn.Module):
def __init__(self, model_cfg, **kwargs):
super().__init__()
self.model_cfg = model_cfg
self.num_bev_features = self.model_cfg.NUM_BEV_FEATURES
def forward(self, batch_dict):
"""
Args:
batc... | 881 | 30.5 | 90 | py |
LiDAR-Distillation | LiDAR-Distillation-main/pcdet/datasets/dataset.py | import torch
import copy
from pathlib import Path
from collections import defaultdict
import numpy as np
import torch.utils.data as torch_data
from .augmentor.data_augmentor import DataAugmentor
from .processor.data_processor import DataProcessor
from .processor.point_feature_encoder import PointFeatureEncoder
from ..u... | 13,290 | 40.021605 | 201 | py |
LiDAR-Distillation | LiDAR-Distillation-main/pcdet/datasets/__init__.py | import torch
from torch.utils.data import DataLoader
from torch.utils.data import DistributedSampler as _DistributedSampler
from pcdet.utils import common_utils
from .dataset import DatasetTemplate
from .kitti.kitti_dataset import KittiDataset
from .waymo.waymo_dataset import WaymoDataset
from .nuscenes.nuscenes_datas... | 4,260 | 32.031008 | 156 | py |
LiDAR-Distillation | LiDAR-Distillation-main/pcdet/datasets/waymo/waymo_dataset.py | import os
import pickle
import copy
import numpy as np
import torch
import multiprocessing
from tqdm import tqdm
from pathlib import Path
from ...ops.roiaware_pool3d import roiaware_pool3d_utils
from ...utils import box_utils, common_utils
from ..dataset import DatasetTemplate
import time
class WaymoDataset(DatasetTe... | 17,542 | 41.997549 | 139 | py |
LiDAR-Distillation | LiDAR-Distillation-main/pcdet/datasets/lyft/lyft_dataset.py | import copy
import pickle
from pathlib import Path
import numpy as np
from tqdm import tqdm
from ...ops.roiaware_pool3d import roiaware_pool3d_utils
from ...utils import common_utils, box_utils, self_training_utils
from ..dataset import DatasetTemplate
class LyftDataset(DatasetTemplate):
def __init__(self, data... | 16,968 | 41.635678 | 115 | py |
LiDAR-Distillation | LiDAR-Distillation-main/pcdet/datasets/augmentor/augmentor_utils.py | import torch
import numpy as np
import numba
import copy
from ...utils import common_utils
from ...ops.roiaware_pool3d import roiaware_pool3d_utils
from ...ops.iou3d_nms import iou3d_nms_utils
import warnings
try:
from numba.errors import NumbaPerformanceWarning
warnings.filterwarnings("ignore", category=Numba... | 12,006 | 34.841791 | 120 | py |
LiDAR-Distillation | LiDAR-Distillation-main/pcdet/datasets/nuscenes/nuscenes_utils.py | """
The NuScenes data pre-processing and evaluation is modified from
https://github.com/traveller59/second.pytorch and https://github.com/poodarchu/Det3D
"""
import operator
from functools import reduce
from pathlib import Path
import numpy as np
import tqdm
from nuscenes.utils.data_classes import Box
from nuscenes.u... | 18,474 | 35.876248 | 111 | py |
LiDAR-Distillation | LiDAR-Distillation-main/pcdet/datasets/nuscenes/nuscenes_dataset.py | import copy
import pickle
from pathlib import Path
import numpy as np
from tqdm import tqdm
from ...ops.roiaware_pool3d import roiaware_pool3d_utils
from ...utils import common_utils, box_utils, self_training_utils
from ..dataset import DatasetTemplate
import pdb
class NuScenesDataset(DatasetTemplate):
def __in... | 21,150 | 41.643145 | 130 | py |
LiDAR-Distillation | LiDAR-Distillation-main/pcdet/datasets/kitti/kitti_dataset.py | import copy
import pickle
import numpy as np
from skimage import io
import os
from ...ops.roiaware_pool3d import roiaware_pool3d_utils
from ...utils import box_utils, calibration_kitti, common_utils, object3d_kitti, self_training_utils, downsample_utils
from ..dataset import DatasetTemplate
class KittiDataset(Datas... | 23,151 | 43.015209 | 140 | py |
LiDAR-Distillation | LiDAR-Distillation-main/pcdet/utils/downsample_utils.py | import numpy as np
from sklearn.cluster import KMeans
from sklearn import cluster
import cv2
from torch.nn import functional as F
import torch
import pdb
def compute_angles(pc_np):
tan_theta = pc_np[:, 2] / (pc_np[:, 0]**2 + pc_np[:, 1]**2)**(0.5)
theta = np.arctan(tan_theta)
theta = (theta / np.pi) * 180
... | 1,324 | 27.804348 | 70 | py |
LiDAR-Distillation | LiDAR-Distillation-main/pcdet/utils/box_utils.py | import numpy as np
import scipy
import torch
import copy
from scipy.spatial import Delaunay
from ..ops.roiaware_pool3d import roiaware_pool3d_utils
from . import common_utils
def in_hull(p, hull):
"""
:param p: (N, K) test points
:param hull: (M, K) M corners of a box
:return (N) bool
"""
try... | 10,569 | 34.351171 | 118 | py |
LiDAR-Distillation | LiDAR-Distillation-main/pcdet/utils/loss_utils.py | import numpy as np
import torch
import torch.nn as nn
import torch.nn.functional as F
from . import box_utils
class SigmoidFocalClassificationLoss(nn.Module):
"""
Sigmoid focal cross entropy loss.
"""
def __init__(self, gamma: float = 2.0, alpha: float = 0.25):
"""
Args:
g... | 8,264 | 34.625 | 95 | py |
LiDAR-Distillation | LiDAR-Distillation-main/pcdet/utils/box_coder_utils.py | import numpy as np
import torch
class ResidualCoder(object):
def __init__(self, code_size=7, encode_angle_by_sincos=False, **kwargs):
super().__init__()
self.code_size = code_size
self.encode_angle_by_sincos = encode_angle_by_sincos
if self.encode_angle_by_sincos:
self.... | 7,677 | 33.276786 | 105 | py |
LiDAR-Distillation | LiDAR-Distillation-main/pcdet/utils/common_utils.py | import logging
import os
import pickle
import random
import shutil
import subprocess
import numpy as np
import torch
import torch.distributed as dist
import torch.multiprocessing as mp
def check_numpy_to_torch(x):
if isinstance(x, np.ndarray):
return torch.from_numpy(x).float(), True
if isinstance(x,... | 7,457 | 27.574713 | 97 | py |
LiDAR-Distillation | LiDAR-Distillation-main/pcdet/utils/self_training_utils.py | import torch
import os
import glob
import tqdm
import numpy as np
import torch.distributed as dist
from pcdet.config import cfg
from pcdet.models import load_data_to_gpu
from pcdet.utils import common_utils, commu_utils, memory_ensemble_utils
import pickle as pkl
import re
PSEUDO_LABELS = {}
NEW_PSEUDO_LABELS = {}
d... | 8,549 | 35.228814 | 95 | py |
LiDAR-Distillation | LiDAR-Distillation-main/pcdet/utils/cal_quality_utils.py | import copy
import os
import torch
import argparse
import pickle
import glob
from pcdet.datasets.kitti.kitti_object_eval_python import eval as kitti_eval
import numpy as np
from pcdet.utils import common_utils, box_utils
from pcdet.ops.iou3d_nms import iou3d_nms_utils
class QualityMetric(object):
def __init__(se... | 10,198 | 35.555556 | 114 | py |
LiDAR-Distillation | LiDAR-Distillation-main/pcdet/utils/memory_ensemble_utils.py | import torch
import numpy as np
from scipy.optimize import linear_sum_assignment
from pcdet.utils import common_utils
from pcdet.ops.iou3d_nms import iou3d_nms_utils
from pcdet.models.model_utils.model_nms_utils import class_agnostic_nms
def consistency_ensemble(gt_infos_a, gt_infos_b, memory_ensemble_cfg):
"""
... | 14,715 | 41.90379 | 117 | py |
LiDAR-Distillation | LiDAR-Distillation-main/pcdet/utils/commu_utils.py | """
This file contains primitives for multi-gpu communication.
This is useful when doing distributed training.
deeply borrow from maskrcnn-benchmark
"""
import pickle
import time
import torch
import torch.distributed as dist
def get_world_size():
if not dist.is_available():
return 1
if not dist.is_... | 3,411 | 27.433333 | 84 | py |
LiDAR-Distillation | LiDAR-Distillation-main/pcdet/ops/roipoint_pool3d/roipoint_pool3d_utils.py | import torch
import torch.nn as nn
from torch.autograd import Function
from ...utils import box_utils
from . import roipoint_pool3d_cuda
class RoIPointPool3d(nn.Module):
def __init__(self, num_sampled_points=512, pool_extra_width=1.0):
super().__init__()
self.num_sampled_points = num_sampled_poin... | 2,226 | 31.75 | 112 | py |
LiDAR-Distillation | LiDAR-Distillation-main/pcdet/ops/pointnet2/pointnet2_stack/pointnet2_utils.py | import torch
import torch.nn as nn
from torch.autograd import Function, Variable
from . import pointnet2_stack_cuda as pointnet2
class BallQuery(Function):
@staticmethod
def forward(ctx, radius: float, nsample: int, xyz: torch.Tensor, xyz_batch_cnt: torch.Tensor,
new_xyz: torch.Tensor, new_x... | 9,462 | 34.441948 | 123 | py |
LiDAR-Distillation | LiDAR-Distillation-main/pcdet/ops/pointnet2/pointnet2_stack/pointnet2_modules.py | from typing import List
import torch
import torch.nn as nn
import torch.nn.functional as F
from . import pointnet2_utils
class StackSAModuleMSG(nn.Module):
def __init__(self, *, radii: List[float], nsamples: List[int], mlps: List[List[int]],
use_xyz: bool = True, pool_method='max_pool'):
... | 5,425 | 38.318841 | 113 | py |
LiDAR-Distillation | LiDAR-Distillation-main/pcdet/ops/pointnet2/pointnet2_batch/pointnet2_utils.py | from typing import Tuple
import torch
import torch.nn as nn
from torch.autograd import Function, Variable
from . import pointnet2_batch_cuda as pointnet2
class FurthestPointSampling(Function):
@staticmethod
def forward(ctx, xyz: torch.Tensor, npoint: int) -> torch.Tensor:
"""
Uses iterative ... | 9,693 | 32.312715 | 118 | py |
LiDAR-Distillation | LiDAR-Distillation-main/pcdet/ops/pointnet2/pointnet2_batch/pointnet2_modules.py | from typing import List
import torch
import torch.nn as nn
import torch.nn.functional as F
from . import pointnet2_utils
class _PointnetSAModuleBase(nn.Module):
def __init__(self):
super().__init__()
self.npoint = None
self.groupers = None
self.mlps = None
self.pool_meth... | 6,631 | 36.897143 | 119 | py |
LiDAR-Distillation | LiDAR-Distillation-main/pcdet/ops/iou3d_nms/iou3d_nms_utils.py | """
3D IoU Calculation and Rotated NMS
Written by Shaoshuai Shi
All Rights Reserved 2019-2020.
"""
import torch
from ...utils import common_utils
from . import iou3d_nms_cuda
import pdb
def boxes_bev_iou_cpu(boxes_a, boxes_b):
"""
Args:
boxes_a: (N, 7) [x, y, z, dx, dy, dz, heading]
boxes_b: ... | 3,661 | 30.033898 | 109 | py |
LiDAR-Distillation | LiDAR-Distillation-main/pcdet/ops/roiaware_pool3d/roiaware_pool3d_utils.py | import torch
import torch.nn as nn
from torch.autograd import Function
from ...utils import common_utils
from . import roiaware_pool3d_cuda
def points_in_boxes_cpu(points, boxes):
"""
Args:
points: (num_points, 3)
boxes: [x, y, z, dx, dy, dz, heading], (x, y, z) is the box center, each box DO ... | 4,074 | 35.711712 | 120 | py |
CBCE-Net | CBCE-Net-main/tensorflow_deeplab_resnet/convert.py | #!/usr/bin/env python
# This script belongs to https://github.com/ethereon/caffe-tensorflow
import os
import sys
import numpy as np
import argparse
from kaffe import KaffeError, print_stderr
from kaffe.tensorflow import TensorFlowTransformer
def fatal_error(msg):
print_stderr(msg)
exit(-1)
def validate_arg... | 2,250 | 35.306452 | 89 | py |
CBCE-Net | CBCE-Net-main/tensorflow_deeplab_resnet/deeplab_resnet/model.py | # Converted to TensorFlow .caffemodel
# with the DeepLab-ResNet configuration.
# The batch normalisation layer is provided by
# the slim library (https://github.com/tensorflow/tensorflow/tree/master/tensorflow/contrib/slim).
from ..kaffe.tensorflow import Network
import tensorflow as tf
class DeepLabResNetModel(Netwo... | 27,119 | 62.811765 | 108 | py |
CBCE-Net | CBCE-Net-main/tensorflow_deeplab_resnet/kaffe/transformers.py | '''
A collection of graph transforms.
A transformer is a callable that accepts a graph and returns a transformed version.
'''
import numpy as np
from .caffe import get_caffe_resolver, has_pycaffe
from .errors import KaffeError, print_stderr
from .layers import NodeKind
class DataInjector(object):
'''
Assoc... | 10,811 | 36.154639 | 99 | py |
CBCE-Net | CBCE-Net-main/tensorflow_deeplab_resnet/kaffe/graph.py | from google.protobuf import text_format
from .caffe import get_caffe_resolver
from .errors import KaffeError, print_stderr
from .layers import LayerAdapter, LayerType, NodeKind, NodeDispatch
from .shapes import TensorShape
class Node(object):
def __init__(self, name, kind, layer=None):
self.name = name
... | 11,653 | 37.462046 | 99 | py |
CBCE-Net | CBCE-Net-main/tensorflow_deeplab_resnet/kaffe/caffe/caffepb.py | # Generated by the protocol buffer compiler. DO NOT EDIT!
# source: caffe.proto
from google.protobuf.internal import enum_type_wrapper
from google.protobuf import descriptor as _descriptor
from google.protobuf import message as _message
from google.protobuf import reflection as _reflection
from google.protobuf import... | 237,573 | 42.35292 | 28,178 | py |
CBCE-Net | CBCE-Net-main/tensorflow_deeplab_resnet/kaffe/caffe/resolver.py | import sys
SHARED_CAFFE_RESOLVER = None
class CaffeResolver(object):
def __init__(self):
self.import_caffe()
def import_caffe(self):
self.caffe = None
try:
# Try to import PyCaffe first
import caffe
self.caffe = caffe
except ImportError:
... | 1,422 | 28.040816 | 68 | py |
CBCE-Net | CBCE-Net-main/tensorflow_deeplab_resnet/kaffe/caffe/__init__.py | from .resolver import get_caffe_resolver, has_pycaffe
| 54 | 26.5 | 53 | py |
CBCE-Net | CBCE-Net-main/tensorflow_deeplab_resnet/kaffe/tensorflow/transformer.py | import numpy as np
from ..errors import KaffeError, print_stderr
from ..graph import GraphBuilder, NodeMapper
from ..layers import NodeKind
from ..transformers import (DataInjector, DataReshaper, NodeRenamer, ReLUFuser,
BatchNormScaleBiasFuser, BatchNormPreprocessor, ParameterNamer)
from .... | 10,312 | 35.059441 | 97 | py |
Gated-LIF | Gated-LIF-master/schedulers.py | import math
from functools import wraps
import warnings
import weakref
from torch.optim.optimizer import Optimizer
EPOCH_DEPRECATION_WARNING = (
"The epoch parameter in `scheduler.step()` was not necessary and is being "
"deprecated where possible. Please use `scheduler.step()` to step the "
"scheduler. Du... | 7,949 | 39.979381 | 116 | py |
Gated-LIF | Gated-LIF-master/utils.py | import os
import re
import torch
import torch.nn as nn
import numpy as np
from collections import OrderedDict
choice_param_name = ['alpha', 'beta', 'gamma']
lifcal_param_name = ['tau', 'Vth', 'leak', 'conduct']
init_constrain = 0.2
def randomize_gate(model):
for name, module in model._modules.items():
if ha... | 5,033 | 31.901961 | 101 | py |
Gated-LIF | Gated-LIF-master/layers.py | import torch
import torch.nn as nn
import math
torch.pi = torch.acos(torch.zeros(1)).item() * 2
steps = 4
a = 0.25
Vth = 0.5 # V_threshold
aa = Vth
tau = 0.25 # exponential decay coefficient
conduct = 0.5 # time-dependent synaptic weight
linear_decay = Vth/(steps * 2) #linear decay coefficient
gamma_SG = 1.
class... | 15,948 | 48.996865 | 212 | py |
Gated-LIF | Gated-LIF-master/Regularization.py | import torch.nn as nn
import torch
class Loss(nn.Module):
def __init__(self, args=None):
super(Loss, self).__init__()
self.param_loss = nn.CrossEntropyLoss()
def forward(self, output, target):
tloss = self.param_loss(output, target)
return tloss
class CrossEntropyLabelSmooth(... | 899 | 30.034483 | 55 | py |
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