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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SleePyCo | SleePyCo-main/train_crl.py | import os
import json
import argparse
import warnings
import torch
import torch.optim as optim
from torch.utils.data import DataLoader
from utils import *
from loss import SupConLoss
from loader import EEGDataLoader
from models.main_model import MainModel
class OneFoldTrainer:
def __init__(self, args, fold, con... | 5,454 | 36.62069 | 169 | py |
SleePyCo | SleePyCo-main/transform.py | import torch
import random
import numpy as np
from scipy import signal
from scipy.ndimage.interpolation import shift
class TwoTransform:
def __init__(self, transform):
self.transform = transform
def __call__(self, x):
return [self.transform(x), self.transform(x)]
class Compose:
de... | 4,204 | 26.48366 | 144 | py |
SleePyCo | SleePyCo-main/loss.py | import torch
import torch.nn as nn
class SupConLoss(nn.Module):
"""Supervised Contrastive Learning: https://arxiv.org/pdf/2004.11362.pdf.
It also supports the unsupervised contrastive loss in SimCLR"""
def __init__(self, temperature=0.07, contrast_mode='all',
base_temperature=0.07):
... | 3,650 | 38.684783 | 80 | py |
SleePyCo | SleePyCo-main/utils.py | import os
import sys
import math
import time
import torch
import random
import numpy as np
import sklearn.metrics as skmet
from terminaltables import SingleTable
from termcolor import colored
_, term_width = os.popen('stty size', 'r').read().split()
term_width = int(term_width)
TOTAL_BAR_LENGTH = 25.
last_time = tim... | 8,844 | 34.239044 | 129 | py |
SleePyCo | SleePyCo-main/loader.py | import os
import glob
import torch
import numpy as np
from transform import *
from torch.utils.data import Dataset
class EEGDataLoader(Dataset):
def __init__(self, config, fold, set='train'):
self.set = set
self.fold = fold
self.sr = 100
self.dset_cfg = config['dataset']... | 4,913 | 39.278689 | 117 | py |
SleePyCo | SleePyCo-main/models/iitnet.py | import torch.nn as nn
def conv3(in_planes, out_planes, stride=1):
return nn.Conv1d(in_planes, out_planes, kernel_size=3, stride=stride, padding=1, bias=False)
class Bottleneck(nn.Module):
expansion = 4
def __init__(self, inplanes, planes, stride=1, downsample=None):
super(Bottleneck, self).__in... | 4,111 | 31.896 | 96 | py |
SleePyCo | SleePyCo-main/models/xsleepnet.py | import torch.nn as nn
class XSleepNetFeature(nn.Module):
def __init__(self, config):
super(XSleepNetFeature, self).__init__()
self.training_mode = config['training_params']['mode']
# architecture
self.conv1 = self.make_layers(1, 16)
self.conv2 = self.make_layers(1... | 2,631 | 32.74359 | 86 | py |
SleePyCo | SleePyCo-main/models/utils.py | import torch.utils.data
from torch.nn import functional as F
import math
import torch
import torch.nn as nn
from torch.nn.parameter import Parameter
from torch.nn.functional import pad
from torch.nn.modules import Module
from torch.nn.modules.utils import _single, _pair, _triple
class _ConvNd(Module):
def __ini... | 7,340 | 37.434555 | 128 | py |
SleePyCo | SleePyCo-main/models/main_model.py | import torch.nn as nn
import torch.nn.functional as F
from .sleepyco import SleePyCoBackbone
from .xsleepnet import XSleepNetFeature
from .iitnet import IITNetBackbone
from .utime import UTimeEncoder
from .deepsleepnet import DeepSleepNetFeature
from .classifiers import get_classifier
last_chn_dict = {
'SleePyC... | 3,898 | 36.490385 | 133 | py |
SleePyCo | SleePyCo-main/models/classifiers.py | import math
import torch
import torch.nn as nn
feature_len_dict = {
'SleePyCo': [
[5, 24, 120],
[10, 48, 240],
[15, 72, 360],
[20, 96, 480],
[24, 120, 600],
[29, 144, 720],
[34, 168, 840],
[39, 192, 960],
[44, 216, 1080],
[48, 240, 12... | 8,588 | 30.811111 | 146 | py |
SleePyCo | SleePyCo-main/models/sleepyco.py | import torch
import torch.nn as nn
import torch.nn.functional as F
class SleePyCoBackbone(nn.Module):
def __init__(self, config):
super(SleePyCoBackbone, self).__init__()
self.training_mode = config['training_params']['mode']
# architecture
self.init_layer = self.make_layers... | 6,271 | 37.012121 | 154 | py |
SleePyCo | SleePyCo-main/models/deepsleepnet.py | import torch
import torch.nn as nn
from .utils import Conv1d, MaxPool1d
class DeepSleepNetFeature(nn.Module):
def __init__(self, config):
super(DeepSleepNetFeature, self).__init__()
self.chn = 64
# architecture
self.dropout = nn.Dropout(p=0.5)
self.path1 = nn.Sequential(C... | 3,855 | 44.904762 | 100 | py |
SleePyCo | SleePyCo-main/models/utime.py | import torch
import torch.nn as nn
from .utils import Conv1d
class ConvUnit(nn.Module):
def __init__(self, in_channels, out_channels, kernel_size, stride, padding, dilation):
super(ConvUnit, self).__init__()
self.conv = Conv1d(
in_channels=in_channels,
out_channels... | 4,199 | 37.181818 | 119 | py |
SASA | SASA-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 |
SASA | SASA-main/tools/test.py | import argparse
import datetime
import glob
import os
import re
import time
from pathlib import Path
import numpy as np
import torch
from tensorboardX import SummaryWriter
from eval_utils import eval_utils
from pcdet.config import cfg, cfg_from_list, cfg_from_yaml_file, log_config_to_file
from pcdet.datasets import b... | 9,291 | 40.855856 | 120 | py |
SASA | SASA-main/tools/demo.py | import argparse
import glob
from pathlib import Path
import mayavi.mlab as mlab
import numpy as np
import torch
from pcdet.config import cfg, cfg_from_yaml_file
from pcdet.datasets import DatasetTemplate
from pcdet.models import build_network, load_data_to_gpu
from pcdet.utils import common_utils
from visual_utils im... | 3,575 | 33.384615 | 118 | py |
SASA | SASA-main/tools/train.py | import argparse
import datetime
import glob
import os
from pathlib import Path
from test import repeat_eval_ckpt
import torch
import torch.distributed as dist
import torch.nn as nn
from tensorboardX import SummaryWriter
from pcdet.config import cfg, cfg_from_list, cfg_from_yaml_file, log_config_to_file
from pcdet.dat... | 8,839 | 42.762376 | 118 | py |
SASA | SASA-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 key in metric.keys():
if key in ret_dict:
metric[key] += ret_dict[key]
min_thres... | 4,772 | 34.887218 | 131 | py |
SASA | SASA-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... | 5,667 | 37.297297 | 117 | py |
SASA | SASA-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)... | 10,477 | 38.992366 | 117 | py |
SASA | SASA-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 |
SASA | SASA-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 == 'sgd':
optim... | 2,289 | 35.935484 | 113 | py |
SASA | SASA-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 |
SASA | SASA-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 ... | 1,074 | 25.219512 | 77 | py |
SASA | SASA-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
class Detector3DTemplate(nn.Module):
def... | 17,009 | 44.119363 | 111 | py |
SASA | SASA-main/pcdet/models/detectors/point_3dssd.py | import torch
from .detector3d_template import Detector3DTemplate
from ...ops.iou3d_nms import iou3d_nms_utils
from ...ops.roiaware_pool3d import roiaware_pool3d_utils
class Point3DSSD(Detector3DTemplate):
def __init__(self, model_cfg, num_class, dataset):
super().__init__(model_cfg=model_cfg, num_class=n... | 10,022 | 47.892683 | 134 | py |
SASA | SASA-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 |
SASA | SASA-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 |
SASA | SASA-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... | 15,273 | 41.077135 | 119 | py |
SASA | SASA-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... | 9,638 | 39.1625 | 121 | py |
SASA | SASA-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 |
SASA | SASA-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_featu... | 1,038 | 31.46875 | 99 | py |
SASA | SASA-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
class PFNLayer(nn.Module):
def __init__(self,
in_channels,
out_channels,
use_norm=True,
last_layer=False):
super().__init__()
... | 5,089 | 40.048387 | 137 | py |
SASA | SASA-main/pcdet/models/dense_heads/anchor_head_single.py | import numpy as np
import torch.nn as nn
from .anchor_head_template import AnchorHeadTemplate
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):
super... | 2,928 | 37.539474 | 136 | py |
SASA | SASA-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
... | 9,475 | 45 | 119 | py |
SASA | SASA-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... | 12,364 | 43.800725 | 118 | py |
SASA | SASA-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 |
SASA | SASA-main/pcdet/models/dense_heads/point_head_box.py | import torch
from ...utils import box_coder_utils, box_utils
from ...utils.loss_utils import PointSASALoss
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... | 6,616 | 41.146497 | 106 | py |
SASA | SASA-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 |
SASA | SASA-main/pcdet/models/dense_heads/point_head_vote.py | import torch
import torch.nn as nn
import torch.nn.functional as F
import numpy as np
from ...ops.iou3d_nms import iou3d_nms_utils
from ...ops.roiaware_pool3d import roiaware_pool3d_utils
from ...ops.pointnet2.pointnet2_batch import pointnet2_modules
from ...utils import box_coder_utils, box_utils, common_utils, loss_... | 37,917 | 45.754624 | 127 | py |
SASA | SASA-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 |
SASA | SASA-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 |
SASA | SASA-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... | 9,874 | 45.14486 | 118 | py |
SASA | SASA-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 |
SASA | SASA-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):
... | 11,451 | 43.216216 | 128 | py |
SASA | SASA-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 |
SASA | SASA-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__(... | 7,628 | 40.688525 | 116 | py |
SASA | SASA-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... | 7,866 | 42.705556 | 116 | py |
SASA | SASA-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:
... | 9,946 | 42.436681 | 117 | py |
SASA | SASA-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 = ... | 2,419 | 35.666667 | 116 | py |
SASA | SASA-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 |
SASA | SASA-main/pcdet/models/backbones_2d/map_to_bev/pointpillar_scatter.py | import torch
import torch.nn as nn
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
assert se... | 1,545 | 39.684211 | 123 | py |
SASA | SASA-main/pcdet/models/backbones_2d/map_to_bev/height_compression.py | import torch.nn as nn
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:
batch_dict:
... | 870 | 31.259259 | 90 | py |
SASA | SASA-main/pcdet/datasets/dataset.py | from collections import defaultdict
from pathlib import Path
import numpy as np
import torch.utils.data as torch_data
from ..utils import common_utils
from .augmentor.data_augmentor import DataAugmentor
from .processor.data_processor import DataProcessor
from .processor.point_feature_encoder import PointFeatureEncode... | 6,966 | 37.071038 | 118 | py |
SASA | SASA-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 .nuscenes.nuscenes_dataset import NuScenesDataset
from .waymo.waymo_d... | 2,440 | 32.438356 | 101 | py |
SASA | SASA-main/pcdet/datasets/waymo/waymo_dataset.py | # OpenPCDet PyTorch Dataloader and Evaluation Tools for Waymo Open Dataset
# Reference https://github.com/open-mmlab/OpenPCDet
# Written by Shaoshuai Shi, Chaoxu Guo
# All Rights Reserved 2019-2020.
import os
import pickle
import copy
import numpy as np
import torch
import multiprocessing
from tqdm import tqdm
from pa... | 15,837 | 41.461126 | 127 | py |
SASA | SASA-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 |
SASA | SASA-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
from ..dataset import DatasetTemplate
class NuScenesDataset(DatasetTemplate):
def __init__(self, dataset_cfg, class_names, traini... | 15,322 | 39.861333 | 120 | py |
SASA | SASA-main/pcdet/datasets/kitti/kitti_dataset.py | import copy
import pickle
import numpy as np
from skimage import io
from ...ops.roiaware_pool3d import roiaware_pool3d_utils
from ...utils import box_utils, calibration_kitti, common_utils, object3d_kitti
from ..dataset import DatasetTemplate
class KittiDataset(DatasetTemplate):
def __init__(self, dataset_cfg, ... | 19,046 | 41.995485 | 140 | py |
SASA | SASA-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... | 10,568 | 34.466443 | 118 | py |
SASA | SASA-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
from ..ops.roiaware_pool3d import roiaware_pool3d_utils
class SigmoidFocalClassificationLoss(nn.Module):
"""
Sigmoid focal cross entropy loss.
"""
def __init__(self, gamma: float = 2.0, alph... | 14,516 | 35.938931 | 105 | py |
SASA | SASA-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.... | 13,258 | 35.226776 | 107 | py |
SASA | SASA-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
return x, False
... | 5,750 | 28.341837 | 97 | py |
SASA | SASA-main/pcdet/utils/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 |
SASA | SASA-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 |
SASA | SASA-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 |
SASA | SASA-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 |
SASA | SASA-main/pcdet/ops/pointnet2/pointnet2_batch/pointnet2_utils.py | from typing import List, Tuple
import torch
import torch.nn as nn
import torch.nn.functional as F
from torch.autograd import Function, Variable
from . import pointnet2_batch_cuda as pointnet2
@torch.no_grad()
def calc_dist_matrix_for_sampling(xyz: torch.Tensor, features: torch.Tensor = None,
... | 14,049 | 35.588542 | 116 | py |
SASA | SASA-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... | 18,910 | 41.688488 | 119 | py |
SASA | SASA-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
def boxes_bev_iou_cpu(boxes_a, boxes_b):
"""
Args:
boxes_a: (N, 7) [x, y, z, dx, dy, dz, heading]
boxes_b: (N, 7) [x, ... | 3,650 | 30.205128 | 109 | py |
SASA | SASA-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... | 4,688 | 34.522727 | 120 | py |
chainer | chainer-master/setup.py | #!/usr/bin/env python
import os
import pkg_resources
import sys
from setuptools import setup
import chainerx_build_helper
if sys.version_info[:3] == (3, 5, 0):
if not int(os.getenv('CHAINER_PYTHON_350_FORCE', '0')):
msg = """
Chainer does not work with Python 3.5.0.
We strongly recommend to use anothe... | 6,477 | 28.990741 | 78 | py |
chainer | chainer-master/examples/caffe_export/export.py | import argparse
import os
import numpy as np
import chainer
from chainer.exporters import caffe
from chainer.links.model.vision import googlenet
from chainer.links.model.vision import resnet
from chainer.links.model.vision import vgg
archs = {
'googlenet': googlenet.GoogLeNet,
'resnet50': resnet.ResNet50Laye... | 1,953 | 27.318841 | 71 | py |
chainer | chainer-master/examples/chainermn/cifar/models/VGG.py | from chainer.utils import argument
import chainer
import chainer.functions as F
import chainer.links as L
import warnings
class Block(chainer.Chain):
"""A convolution, batch norm, ReLU block.
A block in a feedforward network that performs a
convolution followed by batch normalization followed
by a ... | 4,303 | 30.647059 | 76 | py |
chainer | chainer-master/examples/text_classification/text_datasets.py | import csv
import glob
import io
import os
import shutil
import sys
import tarfile
import tempfile
import numpy
import chainer
from nlp_utils import make_vocab
from nlp_utils import normalize_text
from nlp_utils import split_text
from nlp_utils import transform_to_array
URL_DBPEDIA = 'https://github.com/le-scientif... | 5,533 | 30.988439 | 102 | py |
chainer | chainer-master/examples/cifar/models/VGG.py | import chainer
import chainer.functions as F
import chainer.links as L
class Block(chainer.Chain):
"""A convolution, batch norm, ReLU block.
A block in a feedforward network that performs a
convolution followed by batch normalization followed
by a ReLU activation.
For the convolution operation,... | 3,799 | 29.894309 | 76 | py |
chainer | chainer-master/examples/modelzoo/download_model.py | #!/usr/bin/env python
import argparse
import zipfile
import six
parser = argparse.ArgumentParser(
description='Download a Caffe reference model')
parser.add_argument('model_type',
choices=('alexnet', 'caffenet', 'googlenet', 'resnet'),
help='Model type (alexnet, caffenet, ... | 1,438 | 33.261905 | 76 | py |
chainer | chainer-master/examples/modelzoo/download_mean_file.py | #!/usr/bin/env python
import six
print('Downloading ILSVRC12 mean file for NumPy...')
six.moves.urllib.request.urlretrieve(
'https://github.com/BVLC/caffe/raw/master/python/caffe/imagenet/'
'ilsvrc_2012_mean.npy',
'ilsvrc_2012_mean.npy')
print('Done')
| 266 | 23.272727 | 69 | py |
chainer | chainer-master/examples/modelzoo/evaluate_caffe_net.py | #!/usr/bin/env python
"""Example code of evaluating a Caffe reference model for ILSVRC2012 task.
Prerequisite: To run this example, crop the center of ILSVRC2012 validation
images and scale them to 256x256, and make a list of space-separated CSV each
column of which contains a full path to an image at the fist column ... | 4,652 | 31.767606 | 79 | py |
chainer | chainer-master/examples/static_graph_optimizations/cifar/models/VGG.py | import chainer
import chainer.functions as F
import chainer.links as L
from chainer import static_graph
class Block(chainer.Chain):
"""A convolution, batch norm, ReLU block.
A block in a feedforward network that performs a
convolution followed by batch normalization followed
by a ReLU activation.
... | 3,852 | 29.824 | 76 | py |
chainer | chainer-master/onnx_chainer/testing/test_mxnet.py | import collections
import os
import warnings
import chainer
import numpy as np
from onnx_chainer.testing.test_onnxruntime import load_test_data
try:
import mxnet
MXNET_AVAILABLE = True
except ImportError:
warnings.warn(
'MXNet is not installed. Please install mxnet to use '
'testing utili... | 2,113 | 36.087719 | 77 | py |
chainer | chainer-master/tests/chainer_tests/exporters_tests/test_caffe.py | import os
import unittest
import warnings
import numpy
import chainer
import chainer.functions as F
import chainer.links as L
from chainer import testing
# The caffe submodule relies on protobuf which under protobuf==3.7.0 and
# Python 3.7 raises a DeprecationWarning from the collections module.
with warnings.catch... | 4,411 | 28.218543 | 78 | py |
chainer | chainer-master/tests/chainer_tests/links_tests/caffe_tests/test_caffe_function.py | import os
import tempfile
import unittest
import warnings
import mock
import numpy
import six
import chainer
from chainer import links
from chainer import testing
# The caffe submodule relies on protobuf which under protobuf==3.7.0 and
# Python 3.7 raises a DeprecationWarning from the collections module.
with warni... | 35,121 | 25.151899 | 79 | py |
chainer | chainer-master/tests/onnx_chainer_tests/conftest.py | import chainer
import onnx
import pytest
import onnx_chainer
def pytest_addoption(parser):
parser.addoption(
'--value-check-runtime',
dest='value-check-runtime', default='onnxruntime',
choices=['skip', 'onnxruntime', 'mxnet'], help='select test runtime')
parser.addoption(
'--o... | 2,049 | 31.03125 | 84 | py |
chainer | chainer-master/docs/source/conf.py | # -*- coding: utf-8 -*-
#
# Chainer documentation build configuration file, created by
# sphinx-quickstart on Sun May 10 12:22:10 2015.
#
# This file is execfile()d with the current directory set to its
# containing dir.
#
# Note that not all possible configuration values are present in this
# autogenerated file.
#
# A... | 14,917 | 30.209205 | 80 | py |
chainer | chainer-master/chainer/initializer.py | import typing as tp # NOQA
from chainer import types # NOQA
from chainer import utils
class Initializer(object):
"""Initializes array.
It initializes the given array.
Attributes:
dtype: Data type specifier. It is for type check in ``__call__``
function.
"""
def __init__(... | 1,502 | 27.358491 | 75 | py |
chainer | chainer-master/chainer/functions/array/as_strided.py | import numpy as np
import six
from chainer import backend
from chainer.backends import cuda
from chainer import function_node
from chainer.utils import type_check
index_dtype = {t().itemsize: t for t in np.sctypes['int']}
def _byte2step(iterable, itemsize):
for i in iterable:
assert i % itemsize == 0
... | 14,427 | 35.994872 | 79 | py |
chainer | chainer-master/chainer/links/caffe/caffe_function.py | import warnings
import numpy
import six
from chainer import configuration
from chainer import functions
from chainer import initializer
from chainer import link
from chainer.links.caffe.protobuf3 import caffe_pb2 as caffe_pb
from chainer.links.connection import convolution_2d
from chainer.links.connection import deco... | 22,528 | 31.841108 | 79 | py |
chainer | chainer-master/chainer/links/caffe/__init__.py | from chainer.links.caffe.caffe_function import CaffeFunction # NOQA
| 69 | 34 | 68 | py |
chainer | chainer-master/chainer/links/caffe/protobuf3/caffe_pb2.py | # Generated by the protocol buffer compiler. DO NOT EDIT!
# source: caffe.proto
import sys
_b=sys.version_info[0]<3 and (lambda x:x) or (lambda x:x.encode('latin1'))
from google.protobuf.internal import enum_type_wrapper
from google.protobuf import descriptor as _descriptor
from google.protobuf import message as _mes... | 242,354 | 41.963127 | 27,796 | py |
chainer | chainer-master/chainer/links/model/vision/resnet.py | import collections
import os
import sys
import warnings
import numpy
try:
from PIL import Image
available = True
except ImportError as e:
available = False
_import_error = e
import chainer
from chainer.dataset.convert import concat_examples
from chainer.dataset import download
from chainer import func... | 33,339 | 41.74359 | 94 | py |
chainer | chainer-master/chainer/links/model/vision/vgg.py | import collections
import os
import sys
import numpy
try:
from PIL import Image
available = True
except ImportError as e:
available = False
_import_error = e
import chainer
from chainer.dataset.convert import concat_examples
from chainer.dataset import download
from chainer import function
from chaine... | 20,475 | 39.546535 | 79 | py |
chainer | chainer-master/chainer/links/model/vision/googlenet.py | import collections
import os
import sys
import numpy
try:
from PIL import Image
available = True
except ImportError as e:
available = False
_import_error = e
import chainer
from chainer.dataset.convert import concat_examples
from chainer.dataset import download
from chainer import function
from chaine... | 18,874 | 40.032609 | 80 | py |
chainer | chainer-master/chainer/initializers/uniform.py | import numpy
from chainer import backend
from chainer import initializer
from chainer.utils import argument
# Original code forked from MIT licensed keras project
# https://github.com/fchollet/keras/blob/master/keras/initializations.py
class Uniform(initializer.Initializer):
"""Initializes array with a scaled ... | 5,202 | 33.230263 | 76 | py |
chainer | chainer-master/chainer/initializers/normal.py | import numpy
from chainer import backend
from chainer.backends import cuda
from chainer import initializer
from chainer.utils import argument
# Original code forked from MIT licensed keras project
# https://github.com/fchollet/keras/blob/master/keras/initializations.py
class Normal(initializer.Initializer):
""... | 6,197 | 34.016949 | 78 | py |
chainer | chainer-master/chainer/initializers/orthogonal.py | import numpy
from chainer import backend
from chainer import initializer
from chainer import utils
from chainer.utils import argument
_orthogonal_constraints = { # (assert emb., assert proj.)
'auto': (False, False),
'projection': (False, True),
'embedding': (True, False),
'basis': (True, True),
}
... | 4,361 | 38.297297 | 78 | py |
chainer | chainer-master/chainer/exporters/__init__.py | from chainer.exporters import caffe # NOQA
| 44 | 21.5 | 43 | py |
chainer | chainer-master/chainer/exporters/caffe.py | import collections
import heapq
import os
import numpy
import six
import chainer
from chainer import function
from chainer import function_node
from chainer.links.caffe.protobuf3 import caffe_pb2 as caffe_pb
from chainer import variable
_function_types = (function.Function, function_node.FunctionNode)
def _add_bl... | 18,050 | 35.763747 | 79 | py |
DeSpaWN | DeSpaWN-main/Script_DeSpaWN.py | # -*- coding: utf-8 -*-
"""
Title: Fully Learnable Deep Wavelet Transform for Unsupervised Monitoring of High-Frequency Time Series
------ (DeSpaWN)
Description:
--------------
Toy script to showcase the deep neural network DeSpaWN.
Please cite the corresponding paper:
Michau, G., Frusque, G., & Fin... | 5,817 | 38.849315 | 195 | py |
DeSpaWN | DeSpaWN-main/lib/despawnLayers.py | # -*- coding: utf-8 -*-
"""
Title: Fully Learnable Deep Wavelet Transform for Unsupervised Monitoring of High-Frequency Time Series
------ (DeSpaWN)
Description:
--------------
Function to generate the layers used in DeSpaWN TF model.
Please cite the corresponding paper:
Michau, G., Frusque, G., & Fink, O. (... | 6,326 | 40.084416 | 111 | py |
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