id int64 0 190k | prompt stringlengths 21 13.4M | docstring stringlengths 1 12k ⌀ |
|---|---|---|
26,493 | import argparse
import torch
from mmcv.runner import save_checkpoint
from torch import nn as nn
from mmdet3d.apis import init_model
def parse_args():
parser = argparse.ArgumentParser(
description='fuse Conv and BN layers in a model')
parser.add_argument('config', help='config file path')
parser.add... | null |
26,495 | from argparse import ArgumentParser, Namespace
from pathlib import Path
from tempfile import TemporaryDirectory
import mmcv
The provided code snippet includes necessary dependencies for implementing the `mmdet3d2torchserve` function. Write a Python function `def mmdet3d2torchserve( config_file: str, checkpoint... | Converts MMDetection3D model (config + checkpoint) to TorchServe `.mar`. Args: config_file (str): In MMDetection3D config format. The contents vary for each task repository. checkpoint_file (str): In MMDetection3D checkpoint format. The contents vary for each task repository. output_folder (str): Folder where `{model_n... |
26,496 | from argparse import ArgumentParser, Namespace
from pathlib import Path
from tempfile import TemporaryDirectory
import mmcv
def parse_args():
parser = ArgumentParser(
description='Convert MMDetection models to TorchServe `.mar` format.')
parser.add_argument('config', type=str, help='config file path')
... | null |
26,497 | import argparse
import time
from os import path as osp
import mmcv
import numpy as np
from mmdet3d.core.bbox import limit_period
def update_sunrgbd_infos(root_dir, out_dir, pkl_files):
print(f'{pkl_files} will be modified because '
f'of the refactor of the Depth coordinate system.')
if root_dir == ou... | null |
26,498 | import argparse
import time
from os import path as osp
import mmcv
import numpy as np
from mmdet3d.core.bbox import limit_period
def update_outdoor_dbinfos(root_dir, out_dir, pkl_files):
print(f'{pkl_files} will be modified because '
f'of the refactor of the LIDAR coordinate system.')
if root_dir == ... | null |
26,499 | import argparse
import time
from os import path as osp
import mmcv
import numpy as np
from mmdet3d.core.bbox import limit_period
def update_nuscenes_or_lyft_infos(root_dir, out_dir, pkl_files):
print(f'{pkl_files} will be modified because '
f'of the refactor of the LIDAR coordinate system.')
if root... | null |
26,500 | import numpy as np
from mmdet.datasets.builder import PIPELINES
from shapely.geometry import LineString
def evaluate_line(polyline):
edge = np.linalg.norm(polyline[1:] - polyline[:-1], axis=-1)
start_end_weight = edge[(0, -1), ].copy()
mid_weight = (edge[:-1] + edge[1:]) * .5
pts_weight = np.concate... | null |
26,501 | import numpy as np
from mmdet.datasets.builder import PIPELINES
from shapely.geometry import LineString
The provided code snippet includes necessary dependencies for implementing the `quantize_verts` function. Write a Python function `def quantize_verts(verts, canvas_size, coord_dim)` to solve the following problem:
C... | Convert vertices from its original range ([-1,1]) to discrete values in [0, n_bits**2 - 1]. Args: verts (array): vertices coordinates, shape (seqlen, coords_dim) canvas_size (tuple): bev feature size coord_dim (int): dimension of point coordinates Returns: quantized_verts (array): quantized vertices, shape (seqlen, coo... |
26,502 | import numpy as np
from mmdet.datasets.builder import PIPELINES
from shapely.geometry import LineString
The provided code snippet includes necessary dependencies for implementing the `get_bbox` function. Write a Python function `def get_bbox(polyline, threshold)` to solve the following problem:
Convert vertices from i... | Convert vertices from its original range ([-1,1]) to discrete values in [0, n_bits**2 - 1]. Args: polyline (array): point coordinates, shape (seqlen, 2) threshold (float): threshold for minimum bbox size Returns: bbox (array): bounding box in xyxy format, shape (2, 2) |
26,503 | import numpy as np
from .distance import chamfer_distance, frechet_distance
from typing import List, Tuple, Union
from numpy.typing import NDArray
The provided code snippet includes necessary dependencies for implementing the `average_precision` function. Write a Python function `def average_precision(recalls, precisi... | Calculate average precision. Args: recalls (ndarray): shape (num_dets, ) precisions (ndarray): shape (num_dets, ) mode (str): 'area' or '11points', 'area' means calculating the area under precision-recall curve, '11points' means calculating the average precision of recalls at [0, 0.1, ..., 1] Returns: float: calculated... |
26,504 | import numpy as np
from .distance import chamfer_distance, frechet_distance
from typing import List, Tuple, Union
from numpy.typing import NDArray
def chamfer_distance(line1: NDArray, line2: NDArray) -> float:
''' Calculate chamfer distance between two lines. Make sure the
lines are interpolated.
Args:
... | Compute whether detected lines are true positive or false positive. Args: pred_lines (List): Detected lines of a sample, each line has shape (INTERP_NUM, 2 or 3). scores (array): Confidence score of each line, of shape (M, ). gt_lines (List): GT lines of a sample, each line has shape (INTERP_NUM, 2 or 3). thresholds (l... |
26,505 | import torch
from torch import nn as nn
from torch.nn import functional as F
from mmdet.models.losses import l1_loss
from mmdet.models.losses.utils import weighted_loss
import mmcv
from mmdet.models.builder import LOSSES
The provided code snippet includes necessary dependencies for implementing the `smooth_l1_loss` fu... | Smooth L1 loss. Args: pred (torch.Tensor): The prediction. target (torch.Tensor): The learning target of the prediction. beta (float, optional): The threshold in the piecewise function. Defaults to 1.0. Returns: torch.Tensor: Calculated loss |
26,506 | import torch
from torch import nn as nn
from torch.nn import functional as F
from mmdet.models.losses import l1_loss
from mmdet.models.losses.utils import weighted_loss
import mmcv
from mmdet.models.builder import LOSSES
The provided code snippet includes necessary dependencies for implementing the `bce` function. Wri... | pred: B,nquery,npts label: B,nquery,npts |
26,507 | import torch
from torch import nn as nn
from torch.nn import functional as F
from mmdet.models.losses import l1_loss
from mmdet.models.losses.utils import weighted_loss
import mmcv
from mmdet.models.builder import LOSSES
The provided code snippet includes necessary dependencies for implementing the `ce` function. Writ... | pred: B*nquery,npts label: B*nquery, |
26,508 | import mmcv
import numpy as np
import torch
import torch.nn as nn
import torch.nn.functional as F
from torch.nn.utils.rnn import pad_sequence
from torchvision.models.resnet import resnet18, resnet50
from mmdet3d.models.builder import (build_backbone, build_head,
build_neck)
from .bas... | null |
26,509 | import mmcv
import numpy as np
import torch
import torch.nn as nn
import torch.nn.functional as F
from torch.nn.utils.rnn import pad_sequence
from torchvision.models.resnet import resnet18, resnet50
from mmdet3d.models.builder import (build_backbone, build_head,
build_neck)
from .bas... | null |
26,510 | import copy
import math
import numpy as np
import torch
import torch.nn as nn
import torch.nn.functional as F
from mmdet3d.models.builder import BACKBONES
from mmdet.models import build_backbone, build_neck
The provided code snippet includes necessary dependencies for implementing the `construct_plane_grid` function. ... | Returns: plane: H, W, 3 |
26,511 | import copy
import math
import numpy as np
import torch
import torch.nn as nn
import torch.nn.functional as F
from mmdet3d.models.builder import BACKBONES
from mmdet.models import build_backbone, build_neck
The provided code snippet includes necessary dependencies for implementing the `get_campos` function. Write a Py... | Find the each refence point's corresponding pixel in each camera Args: reference_points: [B, num_query, 3] ego2cam: (B, num_cam, 4, 4) Outs: reference_points_cam: (B*num_cam, num_query, 2) mask: (B, num_cam, num_query) num_query == W*H |
26,512 | import copy
import math
import numpy as np
import torch
import torch.nn as nn
import torch.nn.functional as F
from mmdet3d.models.builder import BACKBONES
from mmdet.models import build_backbone, build_neck
def _test():
pass | null |
26,513 | import torch
import torch.nn as nn
from collections import OrderedDict
import torch.utils.checkpoint as checkpoint
from timm.models.layers import trunc_normal_, DropPath
from mmcv.runner import _load_checkpoint
from mmcv.cnn import constant_init, trunc_normal_init
from mmseg.utils import get_root_logger
from ops_dcnv3 ... | null |
26,514 | import torch
import torch.nn as nn
from collections import OrderedDict
import torch.utils.checkpoint as checkpoint
from timm.models.layers import trunc_normal_, DropPath
from mmcv.runner import _load_checkpoint
from mmcv.cnn import constant_init, trunc_normal_init
from mmseg.utils import get_root_logger
from ops_dcnv3 ... | null |
26,515 | import torch
from mmdet.core.bbox.match_costs.builder import MATCH_COST
from mmdet.core.bbox.match_costs import build_match_cost
from mmdet.core.bbox.iou_calculators import bbox_overlaps
from mmdet.core.bbox.transforms import bbox_cxcywh_to_xyxy
The provided code snippet includes necessary dependencies for implementin... | Args: pred: [num_points, 2] gt: [num_gt, 2] Out: torch.FloatTensor of shape (1, ) |
26,516 | import copy
import torch
import torch.nn as nn
import torch.nn.functional as F
from mmcv.cnn import Conv2d, Linear
from mmcv.runner import force_fp32
from torch.distributions.categorical import Categorical
from mmdet.core import multi_apply, reduce_mean
from mmdet.models import HEADS
from .detr_head import DETRMapFixed... | null |
26,517 | import torch
import torch.nn.functional as F
from torch import Tensor
The provided code snippet includes necessary dependencies for implementing the `generate_square_subsequent_mask` function. Write a Python function `def generate_square_subsequent_mask(sz: int, condition_len: int = 1, bool_out=False, device: str = "c... | Generate the attention mask for causal decoding |
26,518 | import torch
import torch.nn.functional as F
from torch import Tensor
The provided code snippet includes necessary dependencies for implementing the `dequantize_verts` function. Write a Python function `def dequantize_verts(verts, canvas_size: Tensor, add_noise=False)` to solve the following problem:
Quantizes vertice... | Quantizes vertices and outputs integers with specified n_bits. |
26,519 | import torch
import torch.nn.functional as F
from torch import Tensor
The provided code snippet includes necessary dependencies for implementing the `quantize_verts` function. Write a Python function `def quantize_verts( verts, canvas_size: Tensor)` to solve the following problem:
Convert vertices from... | Convert vertices from its original range ([-1,1]) to discrete values in [0, n_bits**2 - 1]. Args: verts: seqlen, 2 |
26,520 | import torch
import torch.nn.functional as F
from torch import Tensor
The provided code snippet includes necessary dependencies for implementing the `top_k_logits` function. Write a Python function `def top_k_logits(logits, k)` to solve the following problem:
Masks logits such that logits not in top-k are small.
Here... | Masks logits such that logits not in top-k are small. |
26,521 | import torch
import torch.nn.functional as F
from torch import Tensor
The provided code snippet includes necessary dependencies for implementing the `top_p_logits` function. Write a Python function `def top_p_logits(logits, p)` to solve the following problem:
Masks logits using nucleus (top-p) sampling.
Here is the f... | Masks logits using nucleus (top-p) sampling. |
26,522 | import torch
import torch.nn as nn
from typing import Optional
from torch import Tensor
from mmcv.cnn.bricks.registry import ATTENTION
from mmcv.utils import build_from_cfg
The provided code snippet includes necessary dependencies for implementing the `build_attention` function. Write a Python function `def build_atte... | Builder for attention. |
26,523 | import torch
import torch.nn as nn
from typing import Optional
from torch import Tensor
from mmcv.cnn.bricks.registry import ATTENTION
from mmcv.utils import build_from_cfg
The provided code snippet includes necessary dependencies for implementing the `generate_square_subsequent_mask` function. Write a Python function... | Generate the attention mask for causal decoding |
26,524 | import copy
import torch
import torch.nn as nn
from mmcv.cnn import Linear, bias_init_with_prob, build_activation_layer
from mmcv.cnn.bricks.transformer import build_positional_encoding
from mmcv.runner import force_fp32
from mmdet.models import HEADS, build_head, build_loss
from mmdet.models.utils import build_transfo... | null |
26,525 | import math
import torch
import torch.nn as nn
import torch.nn.functional as F
from torch.distributions.categorical import Categorical
from mmdet.models import HEADS
from .detgen_utils.causal_trans import (CausalTransformerDecoder,
CausalTransformerDecoderLayer)
from .detgen_utils.utils impor... | null |
26,526 | import math
import torch
import torch.nn as nn
import torch.nn.functional as F
from torch.distributions.categorical import Categorical
from mmdet.models import HEADS
from .detgen_utils.causal_trans import (CausalTransformerDecoder,
CausalTransformerDecoderLayer)
from .detgen_utils.utils impor... | null |
26,527 | import math
import torch
import torch.nn as nn
import torch.nn.functional as F
from torch.distributions.categorical import Categorical
from mmdet.models import HEADS
from .detgen_utils.causal_trans import (CausalTransformerDecoder,
CausalTransformerDecoderLayer)
from .detgen_utils.utils impor... | null |
26,528 | from turtle import forward
import warnings
from mmcv.runner import force_fp32, auto_fp16
from mmcv.cnn.bricks.registry import ATTENTION
from mmcv.runner.base_module import BaseModule, ModuleList, Sequential
from mmcv.cnn.bricks.transformer import build_attention
import math
import warnings
import torch
import torch.nn ... | CPU version of multi-scale deformable attention. Args: value (Tensor): The value has shape (bs, num_keys, mum_heads, embed_dims//num_heads) value_spatial_shapes (Tensor): Spatial shape of each feature map, has shape (num_levels, 2), last dimension 2 represent (h, w) sampling_locations (Tensor): The location of sampling... |
26,529 | import math
import warnings
import torch
import torch.nn as nn
from mmcv.cnn import build_activation_layer, build_norm_layer, xavier_init
from mmcv.cnn.bricks.registry import (TRANSFORMER_LAYER,
TRANSFORMER_LAYER_SEQUENCE)
from mmcv.cnn.bricks.transformer import (BaseTransformerLay... | Inverse function of sigmoid. Args: x (Tensor): The tensor to do the inverse. eps (float): EPS avoid numerical overflow. Defaults 1e-5. Returns: Tensor: The x has passed the inverse function of sigmoid, has same shape with input. |
26,530 | from __future__ import division
import argparse
import copy
import mmcv
import os
import time
import torch
import warnings
from mmcv import Config, DictAction
from mmcv.runner import get_dist_info, init_dist
from os import path as osp
from mmdet import __version__ as mmdet_version
from mmdet3d import __version__ as mmd... | null |
26,531 | import random
import warnings
import numpy as np
import torch
from mmcv.parallel import MMDataParallel, MMDistributedDataParallel
from mmcv.runner import (HOOKS, DistSamplerSeedHook, EpochBasedRunner,
Fp16OptimizerHook, OptimizerHook, build_optimizer,
build_runner)
from... | Set random seed. Args: seed (int): Seed to be used. deterministic (bool): Whether to set the deterministic option for CUDNN backend, i.e., set `torch.backends.cudnn.deterministic` to True and `torch.backends.cudnn.benchmark` to False. Default: False. |
26,532 | import random
import warnings
import numpy as np
import torch
from mmcv.parallel import MMDataParallel, MMDistributedDataParallel
from mmcv.runner import (HOOKS, DistSamplerSeedHook, EpochBasedRunner,
Fp16OptimizerHook, OptimizerHook, build_optimizer,
build_runner)
from... | null |
26,533 | import argparse
import mmcv
from mmcv import Config
import os
from renderer import Renderer
def parse_args():
parser = argparse.ArgumentParser(
description='Visualize groundtruth and results')
parser.add_argument('log_id', type=str,
help='log_id of data to visualize')
parser.add_argume... | null |
26,534 | import argparse
import mmcv
from mmcv import Config
import os
from renderer import Renderer
The provided code snippet includes necessary dependencies for implementing the `import_plugin` function. Write a Python function `def import_plugin(cfg)` to solve the following problem:
import modules, registry will be update
... | import modules, registry will be update |
26,535 | import os.path as osp
import os
import numpy as np
import copy
import cv2
import matplotlib.pyplot as plt
from PIL import Image
from shapely.geometry import LineString
def points_ego2img(pts_ego, extrinsics, intrinsics):
pts_ego_4d = np.concatenate([pts_ego, np.ones([len(pts_ego), 1])], axis=-1)
pts_cam_4d = ex... | null |
26,536 | import sys
import os
from src.datasets.evaluation.vector_eval import VectorEvaluate
import argparse
def parse_args():
parser = argparse.ArgumentParser(
description='Evaluate a submission file')
parser.add_argument('submission',
help='submission file in pickle or json format to be eval... | null |
26,537 | import torch
def normalize_bbox(bboxes, pc_range):
cx = bboxes[..., 0:1]
cy = bboxes[..., 1:2]
cz = bboxes[..., 2:3]
w = bboxes[..., 3:4].log()
l = bboxes[..., 4:5].log()
h = bboxes[..., 5:6].log()
rot = bboxes[..., 6:7]
if bboxes.size(-1) > 7:
vx = bboxes[..., 7:8]
v... | null |
26,539 | import bisect
import os.path as osp
import mmcv
import torch.distributed as dist
from mmcv.runner import DistEvalHook as BaseDistEvalHook
from mmcv.runner import EvalHook as BaseEvalHook
from torch.nn.modules.batchnorm import _BatchNorm
from mmdet.core.evaluation.eval_hooks import DistEvalHook
def _calc_dynamic_interv... | null |
26,540 | from mmcv.ops.multi_scale_deform_attn import multi_scale_deformable_attn_pytorch
import mmcv
import cv2 as cv
import copy
import warnings
from matplotlib import pyplot as plt
import numpy as np
import torch
import torch.nn as nn
import torch.nn.functional as F
from mmcv.cnn import xavier_init, constant_init
from mmcv.c... | Inverse function of sigmoid. Args: x (Tensor): The tensor to do the inverse. eps (float): EPS avoid numerical overflow. Defaults 1e-5. Returns: Tensor: The x has passed the inverse function of sigmoid, has same shape with input. |
26,541 | from .mmdet_train import custom_train_detector
from mmseg.apis import train_segmentor
from mmdet.apis import train_detector
def custom_train_detector(model,
dataset,
cfg,
distributed=False,
validate=False,
timestamp=None,
... | A function wrapper for launching model training according to cfg. Because we need different eval_hook in runner. Should be deprecated in the future. |
26,542 | from .mmdet_train import custom_train_detector
from mmseg.apis import train_segmentor
from mmdet.apis import train_detector
The provided code snippet includes necessary dependencies for implementing the `train_model` function. Write a Python function `def train_model(model, dataset, cfg... | A function wrapper for launching model training according to cfg. Because we need different eval_hook in runner. Should be deprecated in the future. |
26,545 | import json
from mmcv.runner import OPTIMIZER_BUILDERS, DefaultOptimizerConstructor
from mmcv.runner import get_dist_info
from mmdet.utils import get_root_logger
def get_num_layer_for_swin(var_name, num_max_layer, depths):
if var_name.startswith("img_backbone.patch_embed"):
return 0
elif "level_embeds"... | null |
26,546 | from __future__ import absolute_import
from __future__ import print_function
from __future__ import division
import warnings
import torch
from torch import nn
import torch.nn.functional as F
from torch.nn.init import xavier_uniform_, constant_
from ..functions import DCNv3Function, dcnv3_core_pytorch
class to_channels_... | null |
26,550 | from __future__ import absolute_import
from __future__ import print_function
from __future__ import division
import torch
import torch.nn.functional as F
from torch.autograd import Function
from torch.autograd.function import once_differentiable
from torch.cuda.amp import custom_bwd, custom_fwd
import DCNv3
def _get_re... | null |
26,551 | import numpy as np
import os
from pathlib import Path
from tqdm import tqdm
import pickle as pkl
import argparse
import time
import torch
import sys, platform
from sklearn.neighbors import KDTree
from termcolor import colored
from pathlib import Path
from copy import deepcopy
from functools import reduce
The provided ... | Produces a colored string for printing Parameters ---------- string : str String that will be colored color : str Color to use on_color : str Background color to use attrs : list of str Different attributes for the string Returns ------- string: str Colored string |
26,552 | import numpy as np
import os
from pathlib import Path
from tqdm import tqdm
import pickle as pkl
import argparse
import time
import torch
import sys, platform
from sklearn.neighbors import KDTree
from termcolor import colored
from pathlib import Path
from copy import deepcopy
from functools import reduce
np.seterr(divi... | null |
26,553 | import numpy as np
import os
from pathlib import Path
from tqdm import tqdm
import pickle as pkl
import argparse
import time
import torch
import sys, platform
from sklearn.neighbors import KDTree
from termcolor import colored
from pathlib import Path
from copy import deepcopy
from functools import reduce
np.seterr(divi... | null |
26,554 | import argparse
import copy
import json
import os
import time
from typing import Tuple, Dict, Any
import torch
import numpy as np
from nuscenes import NuScenes
from nuscenes.eval.common.config import config_factory
from nuscenes.eval.common.data_classes import EvalBoxes
from nuscenes.eval.detection.data_classes import ... | Plot the true positive curve for the specified class. :param md_list: DetectionMetricDataList instance. :param metrics: DetectionMetrics instance. :param detection_name: :param min_recall: Minimum recall value. :param dist_th_tp: The distance threshold used to determine matches. :param savepath: If given, saves the the... |
26,555 | import argparse
import copy
import json
import os
import time
from typing import Tuple, Dict, Any
import torch
import numpy as np
from nuscenes import NuScenes
from nuscenes.eval.common.config import config_factory
from nuscenes.eval.common.data_classes import EvalBoxes
from nuscenes.eval.detection.data_classes import ... | Check if a box is visible in images but not all corners in image . :param box: The box to be checked. :param intrinsic: <float: 3, 3>. Intrinsic camera matrix. :param imsize: (width, height). :param vis_level: One of the enumerations of <BoxVisibility>. :return True if visibility condition is satisfied. |
26,556 | import argparse
import copy
import json
import os
import time
from typing import Tuple, Dict, Any
import torch
import numpy as np
from nuscenes import NuScenes
from nuscenes.eval.common.config import config_factory
from nuscenes.eval.common.data_classes import EvalBoxes
from nuscenes.eval.detection.data_classes import ... | Loads ground truth boxes from DB. :param nusc: A NuScenes instance. :param eval_split: The evaluation split for which we load GT boxes. :param box_cls: Type of box to load, e.g. DetectionBox or TrackingBox. :param verbose: Whether to print messages to stdout. :return: The GT boxes. |
26,557 | import argparse
import copy
import json
import os
import time
from typing import Tuple, Dict, Any
import torch
import numpy as np
from nuscenes import NuScenes
from nuscenes.eval.common.config import config_factory
from nuscenes.eval.common.data_classes import EvalBoxes
from nuscenes.eval.detection.data_classes import ... | Applies filtering to boxes. Distance, bike-racks and points per box. :param nusc: An instance of the NuScenes class. :param eval_boxes: An instance of the EvalBoxes class. :param is: the anns token set that used to keep bboxes. :param verbose: Whether to print to stdout. |
26,558 | import argparse
import copy
import json
import os
import time
from typing import Tuple, Dict, Any
import torch
import numpy as np
from nuscenes import NuScenes
from nuscenes.eval.common.config import config_factory
from nuscenes.eval.common.data_classes import EvalBoxes
from nuscenes.eval.detection.data_classes import ... | Applies filtering to boxes. Distance, bike-racks and points per box. :param nusc: An instance of the NuScenes class. :param eval_boxes: An instance of the EvalBoxes class. :param is: the anns token set that used to keep bboxes. :param verbose: Whether to print to stdout. |
26,559 | import argparse
import copy
import json
import os
import time
from typing import Tuple, Dict, Any
import torch
import numpy as np
from nuscenes import NuScenes
from nuscenes.eval.common.config import config_factory
from nuscenes.eval.common.data_classes import EvalBoxes
from nuscenes.eval.detection.data_classes import ... | null |
26,560 | import argparse
import copy
import json
import os
import time
from typing import Tuple, Dict, Any
import torch
import numpy as np
from nuscenes import NuScenes
from nuscenes.eval.common.config import config_factory
from nuscenes.eval.common.data_classes import EvalBoxes
from nuscenes.eval.detection.data_classes import ... | Applies filtering to boxes. basedon overlap . :param nusc: An instance of the NuScenes class. :param eval_boxes: An instance of the EvalBoxes class. :param verbose: Whether to print to stdout. |
26,561 | from collections import OrderedDict
from mmcv.runner import BaseModule
from mmdet.models.builder import BACKBONES
import torch
import torch.nn as nn
import torch.nn.functional as F
from torch.nn.modules.batchnorm import _BatchNorm
The provided code snippet includes necessary dependencies for implementing the `dw_conv3... | 3x3 convolution with padding |
26,562 | from collections import OrderedDict
from mmcv.runner import BaseModule
from mmdet.models.builder import BACKBONES
import torch
import torch.nn as nn
import torch.nn.functional as F
from torch.nn.modules.batchnorm import _BatchNorm
The provided code snippet includes necessary dependencies for implementing the `conv3x3`... | 3x3 convolution with padding |
26,563 | from collections import OrderedDict
from mmcv.runner import BaseModule
from mmdet.models.builder import BACKBONES
import torch
import torch.nn as nn
import torch.nn.functional as F
from torch.nn.modules.batchnorm import _BatchNorm
The provided code snippet includes necessary dependencies for implementing the `conv1x1`... | 1x1 convolution with padding |
26,564 | import torch
from torchvision.utils import make_grid
import torchvision
import matplotlib.pyplot as plt
import cv2
def convert_color(img_path):
plt.figure()
img = cv2.imread(img_path, cv2.IMREAD_GRAYSCALE)
plt.imsave(img_path, img, cmap=plt.get_cmap('viridis'))
plt.close()
def save_tensor(tensor, path,... | null |
26,565 | import functools
import time
from collections import defaultdict
import torch
time_maps = defaultdict(lambda :0.)
count_maps = defaultdict(lambda :0.)
def run_time(name):
def middle(fn):
def wrapper(*args, **kwargs):
torch.cuda.synchronize()
start = time.time()
res = fn(... | null |
26,566 | from __future__ import division
import argparse
import copy
import mmcv
import os
import time
import torch
import warnings
from mmcv import Config, DictAction
from mmcv.runner import get_dist_info, init_dist
from os import path as osp
from mmdet import __version__ as mmdet_version
from mmdet3d import __version__ as mmd... | null |
26,567 | from data_converter.create_gt_database import create_groundtruth_database
from data_converter import nuscenes_converter as nuscenes_converter
from data_converter import nuscenes_occ_converter as occ_converter
import argparse
from os import path as osp
import sys
The provided code snippet includes necessary dependencie... | Prepare data related to nuScenes dataset. Related data consists of '.pkl' files recording basic infos, 2D annotations and groundtruth database. Args: root_path (str): Path of dataset root. info_prefix (str): The prefix of info filenames. version (str): Dataset version. dataset_name (str): The dataset class name. out_di... |
26,568 | from data_converter.create_gt_database import create_groundtruth_database
from data_converter import nuscenes_converter as nuscenes_converter
from data_converter import nuscenes_occ_converter as occ_converter
import argparse
from os import path as osp
import sys
The provided code snippet includes necessary dependencie... | Prepare occ data related to nuScenes dataset. Related data consists of '.pkl' files recording basic infos, 2D annotations and groundtruth database. Args: root_path (str): Path of dataset root. info_prefix (str): The prefix of info filenames. version (str): Dataset version. dataset_name (str): The dataset class name. ou... |
26,569 | import mmcv
from nuscenes.nuscenes import NuScenes
from PIL import Image
from nuscenes.utils.geometry_utils import view_points, box_in_image, BoxVisibility, transform_matrix
from typing import Tuple, List, Iterable
import matplotlib.pyplot as plt
import numpy as np
from PIL import Image
from matplotlib import rcParams
... | Render selected annotation. :param anntoken: Sample_annotation token. :param margin: How many meters in each direction to include in LIDAR view. :param view: LIDAR view point. :param box_vis_level: If sample_data is an image, this sets required visibility for boxes. :param out_path: Optional path to save the rendered f... |
26,570 | import mmcv
from nuscenes.nuscenes import NuScenes
from PIL import Image
from nuscenes.utils.geometry_utils import view_points, box_in_image, BoxVisibility, transform_matrix
from typing import Tuple, List, Iterable
import matplotlib.pyplot as plt
import numpy as np
from PIL import Image
from matplotlib import rcParams
... | Render sample data onto axis. :param sample_data_token: Sample_data token. :param with_anns: Whether to draw box annotations. :param box_vis_level: If sample_data is an image, this sets required visibility for boxes. :param axes_limit: Axes limit for lidar and radar (measured in meters). :param ax: Axes onto which to r... |
26,571 | import argparse
import json
import numpy as np
import seaborn as sns
from collections import defaultdict
from matplotlib import pyplot as plt
def cal_train_time(log_dicts, args):
for i, log_dict in enumerate(log_dicts):
print(f'{"-" * 5}Analyze train time of {args.json_logs[i]}{"-" * 5}')
all_times... | null |
26,572 | import argparse
import json
import numpy as np
import seaborn as sns
from collections import defaultdict
from matplotlib import pyplot as plt
def plot_curve(log_dicts, args):
if args.backend is not None:
plt.switch_backend(args.backend)
sns.set_style(args.style)
# if legend is None, use {filename}_... | null |
26,573 | import argparse
import json
import numpy as np
import seaborn as sns
from collections import defaultdict
from matplotlib import pyplot as plt
def add_plot_parser(subparsers):
def add_time_parser(subparsers):
def parse_args():
parser = argparse.ArgumentParser(description='Analyze Json Log')
# currently only sup... | null |
26,574 | import argparse
import json
import numpy as np
import seaborn as sns
from collections import defaultdict
from matplotlib import pyplot as plt
def load_json_logs(json_logs):
# load and convert json_logs to log_dict, key is epoch, value is a sub dict
# keys of sub dict is different metrics, e.g. memory, bbox_mAP... | null |
26,575 | import argparse
import time
import torch
from mmcv import Config
from mmcv.parallel import MMDataParallel
from mmcv.runner import load_checkpoint, wrap_fp16_model
import sys
from projects.mmdet3d_plugin.datasets.builder import build_dataloader
from projects.mmdet3d_plugin.datasets import custom_build_dataset
from mmdet... | null |
26,578 | import argparse
import torch
from collections import OrderedDict
def convert_stem(model_key, model_weight, state_dict, converted_names):
new_key = model_key.replace('stem.conv', 'conv1')
new_key = new_key.replace('stem.bn', 'bn1')
state_dict[new_key] = model_weight
converted_names.add(model_key)
pri... | Convert keys in pycls pretrained RegNet models to mmdet style. |
26,581 | import mmcv
import numpy as np
import os
from collections import OrderedDict
from nuscenes.nuscenes import NuScenes
from nuscenes.utils.geometry_utils import view_points
from os import path as osp
from pyquaternion import Quaternion
from shapely.geometry import MultiPoint, box
from typing import List, Tuple, Union
from... | Create info file of nuscene dataset. Given the raw data, generate its related info file in pkl format. Args: root_path (str): Path of the data root. info_prefix (str): Prefix of the info file to be generated. version (str): Version of the data. Default: 'v1.0-trainval' max_sweeps (int): Max number of sweeps. Default: 1... |
26,582 | import mmcv
import numpy as np
import os
from collections import OrderedDict
from nuscenes.nuscenes import NuScenes
from nuscenes.utils.geometry_utils import view_points
from os import path as osp
from pyquaternion import Quaternion
from shapely.geometry import MultiPoint, box
from typing import List, Tuple, Union
from... | Export 2d annotation from the info file and raw data. Args: root_path (str): Root path of the raw data. info_path (str): Path of the info file. version (str): Dataset version. mono3d (bool): Whether to export mono3d annotation. Default: True. |
26,583 | import mmcv
import numpy as np
import pickle
from mmcv import track_iter_progress
from mmcv.ops import roi_align
from os import path as osp
from pycocotools import mask as maskUtils
from pycocotools.coco import COCO
from mmdet3d.core.bbox import box_np_ops as box_np_ops
from mmdet3d.datasets import build_dataset
from m... | null |
26,584 | import mmcv
import numpy as np
import pickle
from mmcv import track_iter_progress
from mmcv.ops import roi_align
from os import path as osp
from pycocotools import mask as maskUtils
from pycocotools.coco import COCO
from mmdet3d.core.bbox import box_np_ops as box_np_ops
from mmdet3d.datasets import build_dataset
from m... | Given the raw data, generate the ground truth database. Args: dataset_class_name （str): Name of the input dataset. data_path (str): Path of the data. info_prefix (str): Prefix of the info file. info_path (str): Path of the info file. Default: None. mask_anno_path (str): Path of the mask_anno. Default: None. used_classe... |
26,585 | from __future__ import division
import argparse
import copy
import mmcv
import os
import time
import torch
import warnings
from mmcv import Config, DictAction
from mmcv.runner import get_dist_info, init_dist, wrap_fp16_model
from os import path as osp
from mmdet import __version__ as mmdet_version
from mmdet3d import _... | null |
26,586 | import argparse
import numpy as np
import warnings
from mmcv import Config, DictAction, mkdir_or_exist, track_iter_progress
from os import path as osp
from mmdet3d.core.bbox import (Box3DMode, CameraInstance3DBoxes, Coord3DMode,
DepthInstance3DBoxes, LiDARInstance3DBoxes)
from mmdet3d.cor... | null |
26,587 | import argparse
import numpy as np
import warnings
from mmcv import Config, DictAction, mkdir_or_exist, track_iter_progress
from os import path as osp
from mmdet3d.core.bbox import (Box3DMode, CameraInstance3DBoxes, Coord3DMode,
DepthInstance3DBoxes, LiDARInstance3DBoxes)
from mmdet3d.cor... | Build data config for loading visualization data. |
26,588 | import argparse
import numpy as np
import warnings
from mmcv import Config, DictAction, mkdir_or_exist, track_iter_progress
from os import path as osp
from mmdet3d.core.bbox import (Box3DMode, CameraInstance3DBoxes, Coord3DMode,
DepthInstance3DBoxes, LiDARInstance3DBoxes)
from mmdet3d.cor... | Visualize 3D point cloud and 3D bboxes. |
26,589 | import argparse
import numpy as np
import warnings
from mmcv import Config, DictAction, mkdir_or_exist, track_iter_progress
from os import path as osp
from mmdet3d.core.bbox import (Box3DMode, CameraInstance3DBoxes, Coord3DMode,
DepthInstance3DBoxes, LiDARInstance3DBoxes)
from mmdet3d.cor... | Visualize 3D point cloud and segmentation mask. |
26,590 | import argparse
import numpy as np
import warnings
from mmcv import Config, DictAction, mkdir_or_exist, track_iter_progress
from os import path as osp
from mmdet3d.core.bbox import (Box3DMode, CameraInstance3DBoxes, Coord3DMode,
DepthInstance3DBoxes, LiDARInstance3DBoxes)
from mmdet3d.cor... | Visualize 3D bboxes on 2D image by projection. |
26,592 | import argparse
import torch
from mmcv.runner import save_checkpoint
from torch import nn as nn
from mmdet.apis import init_model
def fuse_conv_bn(conv, bn):
def fuse_module(m):
last_conv = None
last_conv_name = None
for name, child in m.named_children():
if isinstance(child, (nn.BatchNorm2d, nn.S... | null |
26,593 | import argparse
import torch
from mmcv.runner import save_checkpoint
from torch import nn as nn
from mmdet.apis import init_model
def parse_args():
parser = argparse.ArgumentParser(
description='fuse Conv and BN layers in a model')
parser.add_argument('config', help='config file path')
parser.add_a... | null |
26,595 | import open3d as o3d
import pickle
import numpy as np
import torch
import math
from pathlib import Path
import os
from glob import glob
The provided code snippet includes necessary dependencies for implementing the `rotz` function. Write a Python function `def rotz(t)` to solve the following problem:
Rotation about th... | Rotation about the z-axis. |
26,596 | import open3d as o3d
import pickle
import numpy as np
import torch
import math
from pathlib import Path
import os
from glob import glob
color = colors_map[:, :3] / 255
def voxel2points(voxel, voxelSize, range=[-40.0, -40.0, -1.0, 40.0, 40.0, 5.4], ignore_labels=[17, 255]):
if isinstance(voxel, np.ndarray): voxel = ... | null |
26,597 | import argparse
import copy
import os
import os.path as osp
import time
import warnings
import mmcv
import torch
import torch.distributed as dist
from mmcv.cnn.utils import revert_sync_batchnorm
from mmcv.runner import get_dist_info, init_dist
from mmcv.utils import Config, DictAction, get_git_hash
from mmseg import __... | null |
26,598 | from argparse import ArgumentParser
import mmcv
import mmcv_custom
import mmseg_custom
from mmseg.apis import inference_segmentor, init_segmentor, show_result_pyplot
from mmseg.core.evaluation import get_palette
from mmcv.runner import load_checkpoint
from mmseg.core import get_classes
import cv2
import os.path as ... | null |
26,599 | import argparse
import numpy as np
import torch
from mmcv import Config, DictAction
from mmseg.models import build_segmentor
import mmcv_custom
import mmseg_custom
def parse_args():
parser = argparse.ArgumentParser(description='Train a detector')
parser.add_argument('config', help='train config file path')
... | null |
26,600 | import argparse
import numpy as np
import torch
from mmcv import Config, DictAction
from mmseg.models import build_segmentor
import mmcv_custom
import mmseg_custom
def dcnv3_flops(n, k, c):
return 5 * n * k * c
if __name__ == '__main__':
args = parse_args()
if len(args.shape) == 1:
h = w = args.... | null |
26,601 | import argparse
import logging
import os
import os.path as osp
from functools import partial
import mmcv
import torch.multiprocessing as mp
from torch.multiprocessing import Process, set_start_method
from mmdeploy.apis import (create_calib_input_data, extract_model,
get_predefined_partition_c... | null |
26,602 | import argparse
import logging
import os
import os.path as osp
from functools import partial
import mmcv
import torch.multiprocessing as mp
from torch.multiprocessing import Process, set_start_method
from mmdeploy.apis import (create_calib_input_data, extract_model,
get_predefined_partition_c... | null |
26,603 | import argparse
import logging
import os
import os.path as osp
from functools import partial
import mmcv
import torch.multiprocessing as mp
from torch.multiprocessing import Process, set_start_method
from mmdeploy.apis import (create_calib_input_data, extract_model,
get_predefined_partition_c... | Return the conversion function from torch to the intermediate representation. Args: ir_type (IR): The type of the intermediate representation. |
26,604 | from __future__ import absolute_import
from __future__ import print_function
from __future__ import division
import warnings
import torch
from torch import nn
import torch.nn.functional as F
from torch.nn.init import xavier_uniform_, constant_
from ..functions import DCNv3Function, dcnv3_core_pytorch
import math
class ... | null |
26,605 | from __future__ import absolute_import
from __future__ import print_function
from __future__ import division
import warnings
import torch
from torch import nn
import torch.nn.functional as F
from torch.nn.init import xavier_uniform_, constant_
from ..functions import DCNv3Function, dcnv3_core_pytorch
import math
def b... | null |
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